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Computational Quantitative Analytics-Finance-Risk Management Projects
Digital Transformation - Artificial Intelligence: ML-DL-NLP-RPA - Cyber-Crypto Computing - Post AI-Quantum Computing
"There are many definitions of knowledge management. It has been described as "a systematic process for capturing and communicating knowledge people can use." Others have said it is "understanding what your knowledge assets are and how to profit from them." Or the flip side of that: "to obsolete what you know before others obsolete it." (Malhotra) "
- U.S. Department of Defense, Office of the Under Secretary of Defense (Comptroller)
"KM is obsoleting what you know before others obsolete it and profit by creating the challenges and opportunities others haven't even thought about -- Dr. Yogesh Malhotra, Inc. Technology"
- U.S. Defense Information Systems Agency Interoperability Directorate
"If you spend some time at [the digital research lab] founded by Dr. Malhotra you will be blessed by some of the world's most astute thinking on the nature of knowledge and its value."
- U.S. Army Knowledge Symposium, Theme: "Knowledge Dominance: Transforming the Army...from Tooth to Tail", Department of Defense, United States Army.
"We are observing diminishing credibility of information technologists. A key reason for this is an urgent need to understand how technologies, people and processes together combine to influence enterprise performance. Today's effective CIO doesn't deliver IT. He delivers business transformation services."
- Yogesh Malhotra, Journal of Knowledge Management, 2005
- United States Air Force Research Lab CIO Col. Tom Hamilton
in presentation to the Armed Forces Communications Electronics Association titled 'Enterprise IT Solutions Are Tough But They're Tougher If You're Stupid', July 21, 2005.
"Knowledge Management refers to the critical issues of organizational adaptation, survival and competence against discontinuous environmental change. Essentially it embodies organizational processes that seek synergistic combination of data and information processing capacity of information technologies, and the creative and innovative capacity of human beings." -- Yogesh Malhotra
- United States Department of Navy
"Dr. Yogesh Malhotra, PhD, drawing upon numerous sources, proposes several theories as to how IT can be used to drive the change of organizations. As environments become more turbulent, organizations must adapt at the same rate to maintain its advantage. Among his theories are that the turbulent environments (in this case, business, but can translate to the turbulent military conflict environment) drive organizations to use IT for empowering workers at all levels, increasing span of control, and increasing lateral communications."
- United States Marine Corps, Reorganization Of The Marine Air Command And Control System To Meet 21St Century Doctrine And Technology, Thesis, September 2001.
"The self-organizing capacity of dynamically adaptive systems is amazing. They tend to eliminate redundancy, minimize connections, and establish priorities--all without outside direction. When something is organized, we tend to believe that someone organized it, some outside influence. But that's not necessarily so. Self-organization is a process in which the organization of a system occurs spontaneously based on the action of its members, without this process being controlled by an external system. The richness of possible behavior increases rapidly with the number of interconnections and the level of feedback. (Malhotra) "
- U.S. Army War College Quarterly
"Dr. Malhotra argues in Business Process Redesign that reengineering is the notion of discontinuous thinking -- recognizing and breaking away from outdated rules and fundamental assumptions. He suggests that reengineering principles are organized around outcomes, and that people who use the output should perform the process. This links parallel activities instead of integrating results, and puts the decision point where the work is performed (Malhotra, 1996). Integrating the DPW processes further into the installation staff can achieve these outcomes. Seventy percent of Business Process Redesigns (BPR) fail because of business focus on cost-cutting and narrow technical approaches (Malhotra, 1996). The installation commanders should decide how DPWs could best serve the community. They should have the opportunity to focus on efficient output and not on restructuring to cut cost. Developing the Corps as the primary service provider narrows the commander's options and does not solve the problem, merely the symptoms. The ultimate success of BPR depends on the experience of people who execute it and how well they apply their creativity to redesigning the processes."
- U.S. Army Management Staff College
"These activities are often described as "knowledge management." See Knowledge Management, in the World Wide Web Virtual Library, edited by Yogesh Malhotra. (Accessed June 16, 1998)....The terms "marshalling" and "mobilization" are intended here to represent two major activities of knowledge management in U.S. national security decisionmaking. Although others may describe and classify basic knowledge-building activities differently, "knowledge management" has been accepted as an umbrella term. See. for example, TheWorld Wide Web Virtual Library on Knowledge Management, edited by Yogesh Malhotra, (Accessed June 16, 1998)..."
- U.S. Air Force Colonel Roc A. Myers, Colonel (s), Harvard University Air Force National Defense Fellow with the Program in 1997-98. Strategic Knowledgecraft: Operational Art for the Twenty-First Century, Roc A. Myers, Prepared while an Air Force National Defense Fellow with the Program in 1997-98 (September 2000).
"Seventy percent of BPR projects fail. Three primary obstacles inhibit the success of reengineering projects: Lack of sustained management commitment and leadership -- It is critical that senior leadership not only support BPR but also be a vocal advocate. Unrealistic scope and expectations -- It is important to manage expectations. BPR is not a panacea that will cure all ills. Resistance to change -- The world is changing all the time and the pace of change continues to accelerate. It will continue to change whether we participate or not. We must change with it or be left behind. AIT provides AIS program managers the opportunity to completely reexamine and reengineer their entire business process, because it offers capabilities not previously available in terms of timeliness and accuracy of data capture. During the operational prototype, the Air Force provided an excellent example of a reengineered business process as a result of AIT. The Supply Asset Tracking System (SATS) is a front-end server that integrates AIT with the supply AIS, the Standard Base Supply System (SBSS). SATS uses linear bar codes for tracking and inventory purposes and smart cards for personal identification to verify receipt and establish personal accountability of property. (Malhotra) "
- U.S. Department of Defense Logistics Implementation Plan
"Knowledge Management caters to the critical issues of organisational adaption, survival and competence in the face of increasingly discontinuous environmental change ... Essentially, it embodies organisational process that seek synergistic combination of data and information processing capacity of information technologies and the creative and innovative capacity of human beings." - Yogesh Malhotra
- Royal Australian Air Force (RAAF) AIRCDRE John Blackburn, Director General Policy and Planning - Air Force (DGPP-AF), Royal Australian Air Force (RAAF), in Air Power Conference 2000.
"First intangible assets are defined in relation to core competencies of the firm. Each core competence is a combination of intangible assets such as knowledge and skills, standards and values, explicit know-how and technology, management processes and assets, and endowments such as image, relationships, and networks. Knowledge creation is the core competence of any firm (Malhotra, 2000)."
- Government of UK, Ministry of Defence
"Malhotra noted the importance of Information Systems for organizational learning, mentioning a series of techniques, methods and tools that can foster organizational learning at many steps of the process: knowledge acquisition, creation and distribution [Malhotra, 1996]."
- Canadian Department of National Defence, Canada, Defence R&D Canada
"Knowledge Management caters to the critical issues of organisational adaption, survival and competence in the face of increasingly discontinuous environmental change. Essentially, it embodies organisational process that seek synergistic combination of data and information processing capacity of information technologies and the creative and innovative capacity of human beings. -- Yogesh Malhotra"
- Air Force, Australia, Director General Policy and Planning
"According to Malhotra, KM ensures that right knowledge is applied at the right place and time and it is about doing the right thing instead of doing things right. Its application to R&D will avoid unnecessary duplication of research. It can help support both individual and organizational learning from past successes and failures while guiding future actions and changes."
- International Atomic Energy Agency
"The Knowledge Management (KM) area has become so diverse over the past ten years as researchers have begun to investigate not only the mechanics of knowledge creation and transfer but also of social and cultural issues that are of importance in understanding this topic. KM is the process of leveraging and utilizing the vast, untapped potential of both implied and documented knowledge to achieve optimal performance, both are equally important for improving performance. Knowledge Management enables businesses to exchange and optimize the knowledge and experience. "Knowledge Management caters for the critical issues of organisational adoption, survival and competence in face of increasingly discontinuous environmental change. Essentially, it embodies organizational processes that seek synergistic combination of data and information processing capacity of information technologies, and the creative and innovative capacity of human beings" (Dr. Yogesh Malhotra 1997)."
- IBM
"In his latest book, Knowledge Management and Virtual Organisations, KM luminary, Dr. Yogesh Malhotra, offers some cautionary advice. He exposes three myths often associated with KM solutions. The first of these is that knowledge management technologies can deliver the right information to the right person at the right time. This assumes businesses will develop incrementally in stable markets. However as Malhotra says, "the new business model in the Information Age is marked by fundamental, not incremental change. Businesses can't plan long-term; instead, they must shift to a more flexible 'anticipation of surprise' model. Thus it is impossible to build a system that predicts who the right person at the right time even is, let alone what constitutes the right information."
- Microsoft Corporation
"All can be used to further the goal of keeping the channels of communication open to allow for the exchange of issues and ideas within an organization. According to BRINT Institute chairman and CKO Dr. Yogesh Malhotra, "The key issue is not about the latest information technologies, but whether those technologies are used within, and for facilitating, a culture of information sharing, relationship building and trust." With communication and trust, set within the solid framework of a component architecture, your business can harness that elusive ability to get the right information to the right people at the right time for the right business purposes."
- Cisco Systems, Inc.
"According to Yogesh Malhotra, Knowledge Management practitioner and web author, "Knowledge Management is a brand new field emerging at the confluence of organization theory, management strategy, and management information systems." Breaking apart this definition, Knowledge Management can be defined as an internal, corporate strategy. Knowledge Management can also stand alone as a separate, Information Technology program. Malhotra is right on target when he states that Knowledge Management is a brand new field. Knowledge Management began receiving airplay in 1996. At that time, Tom Davenport wrote in CIO Magazine that a chief knowledge officer "captures and leverages structured knowledge, with information technology as a key enabler." Expanding upon Malhotra and Davenport's definitions, Knowledge Management within NCR Corporation can be defined via a business objective (strategic), a method of Knowledge Management delivery (the management information system), and a role within the organization. NCR's objective is to create, capture, and disseminate knowledge."
- NCR Corporation
"Institutionalization of 'best practices' by embedding them in information technology might facilitate efficient handling of routine, 'linear,' and predictable situations during stable or incrementally changing environments. However, when this change is discontinuous, there is a persistent need for continuous renewal of the basic premises underlying the 'best practices' stored in organizational knowledge bases. -- Yogesh Malhotra in Knowledge Management in Inquiring Organizations"
- Vice President, SAP, North America in SAP Portals ASUG Meeting
"Often used synonymously, the terms knowledge and information, are actually different. Information facilitates knowledge, and can exist without knowledge. Knowledge, however, cannot exist without information. To simplify the concept, Dr. Yogesh Malhotra, renowned scholar on Knowledge Management, defines "Knowledge" as potential for action that has an immediate link to performance. This definition suggests that a person's response or action, or contextual consideration for future action, based on information, is knowledge."
- VeriSign Inc.
"It is generally agreed that the greatest challenges to knowledge management initiatives are resistance to change in both an organization's information-sharing culture and the business processes that occur as a result. K.M. Malhotra defined the problem as follows: Culture is the most difficult component of KM to define, quantify, measure and influence. However, the success or failure of an effective KM program is almost solely dependant upon whether an organization's culture encourages or hinders sharing and transferring knowledge freely within the organization's structure. One thing is certain: an organization's cultural predisposition toward the free transfer of knowledge is largely reflective of the proactive stance demonstrated by the organization's leadership."
- Northrop Grumman
"Il Knowledge Management essenzialmente coinvolge processi organizzativi che cercano di realizzare una combinazione tra le capacità di elaborazione di dati e informazioni e le capacità creative e innovative degli esseri umani. (fonte: Yogesh Malhotra, Ph.D., Knowledge Management for the New World of Business...)"
- Microsoft, Italy
"Knowledge Management refers to the critical issues of organisational adaptation, survival and competence against discontinuous environmental change. Essentially it embodies organisational processes that seek synergistic combination of data and information processing capacity of information technologies, and the creative and innovative capacity of human beings. This definition proposed by Dr. Yogesh Malhotra summarises a key issue for e-learning strategies and the way they will impact professional training and companies' organisation policies."
- European Commission
"In the Committee's view, definitions that treat the area as a discipline rather than a mere collection of technologies best encapsulate what knowledge management means. For example, Malhotra says:, "Knowledge Management caters to the critical issue of organisational adaptation, survival and competence in the face of increasingly discontinuous environmental change..."
- Parliament of Victoria, Australia
"It is therefore impossible to typify the roles of Knowledge Management workers other than the CKO, and indeed these roles themselves are in a constant state of change. Dr. Yogesh Malhotra defines this as follows: Given the need for autonomy in learning and decision making, such knowledge workers would also need to be comfortable with self-control and self-learning."
- Government of UK
"We are facing "permanent white-waters" which demands strategies for adaptation to uncertainty in contrast to the conventional emphasis on optimisation based on prediction (Malhotra 1999). To quote a decision-maker in a large multinational firm; "The future is moving so quickly that you can't anticipate it. We have put a tremendous emphasis on quick response instead of planning. We will continue to be surprised, but we won't be surprised that we are surprised. We will anticipate the surprise." (Malhotra 1999)."
- Government of Sweden
"It is difficult, not to say impossible, to replace the significance of individual or collective face-to-face interactions in the sharing of tacit knowledge and articulating it as explicit in an organization, even if rapid development of interactive multimedia applications combining text, image and sound offers increasingly advanced communication potential. Virtual forms of working and work organization might at best supplement, but never totally replace, self-managing teams with close physical and social contacts, for instance, as a forum for learning. (Malhotra) "
- Government of Finland
"A key feature of knowledge management is the sharing of knowledge as opposed to simply the dissemination of information. Knowledge has a different quality to information. Knowledge includes human experience and the ability to make complex judgments based on past experience. Information is more about mere data whereas knowledge is 'potential for action'. (Malhotra)
- Government of Australia
"Ich glaube die Technology ist der leichtere Teil des Ganzen. Die wirkliche Herausforderung stecken doch darin wie die Geschäfts-Prozessen und die darauf aufbauenden Geschäfts- Modelle in Einklang gehalten werden mit den radikalen änderungen in der Geschäftswelt und dem Berufsbild der "Knowledge Worker."[Malhotra, 1993]."
- Government of Austria
"Knowledge management refers to the critical issues of organizational adaptation, survival and competence against discontinuous environmental change. Essentially it embodies organizational processes that seek synergistic combination of data and information processing capacity of information technologies, and the creative and innovative capacity of human beings," says Dr. Yogesh Malhotra, founding chairman and chief knowledge architect of the BRINT Institute, in an interview with Alistair Craven. Widely recognized as a knowledge management pioneer, Malhotra adds, "Knowledge management is more about the pragmatic and thoughtful application of any concept or definition, as it is not in the definition but in real world execution where opportunities and challenges lie. Any definition therefore must be understood within the specific context of expected performance outcomes and value propositions that answer the question 'Why' about relevance of KM.""
- U.S. Embassy, American Center, New Delhi, India
"Knowledge management, which is a new field emerging from the confluence of organisation theory, management strategy and management information systems, is viewed as an essential driver for innovation. According to Malhotra "Knowledge Management caters to the critical issues of organisational adaption, survival and competence in face of increasingly discontinuous change. Essentially it embodies organisational processes that seek a synergistic combination of data and information processing capacity of information technologies, and the creative and innovative capacity of human beings"."
- Government of South Africa
"Estes ativos do conhecimento aumentam com o uso e daí a importância de as empresas identificarem o que sabem e manterem todo o esforço para desenvolverem área de gestão do conhecimento. A gestão do conhecimento, segundo Malhotra é a capacidade de catalizar os aspectos críticos de adaptação, sobrevivência e competência, buscando uma combinação sinérgica da capacidade de processar informações e conhecimento com a capacidade criativa e inovativa dos seres humanos. (MALHOTRA, 1999)."
- Government of Brazil
"Esta enumeración no implica que algún factor no pueda ocupar a la vez distintas posiciones. La principal característica del nuevo entorno de las organizaciones es su alto nivel de incertidumbre. Por incertidumbre entendemos "la diferencia entre la cantidad de información requerida para realizar una tarea y la cantidad de información ya en poder de la organización" YOGESH, Malhotra.""
- Government of Argentina
"The disconnect between IT expenditures and the firms' organizational performance could be attributed to an economic transition from an era of competitive advantage based on information to one based on knowledge creation." - Yogesh Malhotra
- Government of Mauritius
"The focus of knowledge management is on 'doing the right thing' instead of doing things right’, (Yogesh Malhotra, 2001). The emphasize is that that knowledge management provides framework within which the organization views all processes of the activities to sustain the business and/or ensuring the business survival. Within the army organization, there is no difference. The army needs to keep pace with the technology advancement preparing for the increasingly dynamic and unpredictable regional and world environment."
- Royal Military Police Directorate, Army HQ, Malaysia
"Knowledge Management embodies organnizational processes that seek synergistic combinations of data and information processing capacity of information technologies, and the creative and innovative capacity of human beings." -- Yogesh Malhotra, Ph.D."
- Government of Malta
"Dr. Yogesh Malhotra, one of the experts and founder contributor in the development of concept of KM has defined the KM as under : "Knowledge Management caters to the critical issues of organizational adaptation, survival and competence in face of increasingly discontinuous environmental change. Essentially, it embodies organizational processes that seek synergistic combination of data and information processing capacity of information technologies, and the creative and innovative capacity of human beings". As it is clear from this definition that objective of Knowledge Management as a crucial management function is not only to survive under changing environment but also to make the organisation adaptable and competitive. The same is particularly applicable for Banks in India, since they are now operating under such a dynamic business environment."
- Indian Banks' Association, India
"Dr. Yogesh Malhotra, the Founder and Chief Knowledge Architect of BRINT, and a well-known expert in the field of K-economy, opines: "The challenges facing us as we enter the 21st Century are formidable. Globalization, Information Technology and Shareholders' Values are transforming the world. To meet these challenges is to become a knowledge-creating or knowledge intensive organization"."
- Indian Banks' Association, India
"Knowledge Management has structural and functional basis in the IM (Information Management or IRM. The main difference is the high degree of dynamic activity involved in the KM system. To summarize in the words of Dr. Malhotra, (10) 'use of the information and control systems and compliance with pre-defined goals, objectives and best practices may not necessarily achieve long-term organizational competence. This is the world of 're-use,' 're-engineering', 're-cycling' etc, which challenges the assumptions underlying the 'accepted way of doing things.' This world needs the capability to understand the problems afresh given the changing environmental conditions. Knowledge management focuses on 'doing the right thing' instead of 'doing things right.'"
- Indian Statistical Institute, Bangalore, India
"Knowledge Management caters to the critical issues of organizational adaption, survival and competence in face of increasingly discontinuous environmental change. Essentially, it embodies organizational processes that seek synergistic combination of data and information processing capacity of information technologies, and the creative and innovative capacity of human beings."
- National Academy of Psychology (NAOP), India
"Finally, all who are concerned with IT security issues should understand -- and appreciate -- the difference between information and knowledge. Information, writes Yogesh Malhotra, PhD, is embedded in a computer -- while knowledge is embedded in people. "Information generated by a computer is not a very rich carrier of human interpretation for potential action," he writes. "Computer are merely tools, however great their information-processing capabilities may be."
- Chairman of the Board, The Institute of Internal Auditors
"Leadership Quote of the Week: The focus of knowledge management is on doing the right thing instead of doing things right... Yogesh Malhotra"
- Chartered Management Institute, UK
"Dr. Yogesh Malhotra, founder of the Brint Institute and a pioneer in knowledge management, posits that "the basic premise is that you can predict how and what you'll need to do and that IS can simplify this and do it efficiently". However, the new business model, he says, is marked by fundamental, not incremental, change and businesses can't plan long-term. Instead, they must shift to a more flexible "anticipation of surprise" model, making it impossible to build a system that can predict what is the right information to be delivered to the right person at the right time. This is not to say that information technology has been displaced from the knowledge management equation; its place has been preserved by a growing realisation among developers that software alone cannot automatically be seen as the solution."
- National President of the Australian Computer Society, Australia
"Yogesh Malhotra, founding chairman and chief knowledge officer for the BRINT Institute in Syracuse, New York, believes that the fundamental distinction between data and knowledge plays a major role in whether a system is designed for adaptation and quick response to change. "Dynamic and radically changing environments overwhelm the deterministic logic of a structured model, resulting in a 70 percent failure rate that has characterized implementations of knowledge management models" says Malhotra. Recounting his visit to a Silicon Valley hi-tech consulting firm, Malhotra attributes most failed corporate intranet initiatives to the above fallacy... Malhotra says that once routinized for efficiency and optimization, knowledge-harvesting processes may be delegated to others. However, supply managers need to be more proactively involved in knowledge-creation and knowledge-renewal processes..."
- Institute for Supply Management (ISM)
"Yogesh Malhotra, founding Chairman and Chief Knowledge Architect of the BRINT Institute states: "Knowledge management software is not a canned solution; "Knowledge management technologies cannot always deliver the right information to the right person at the right time; "Information technologies cannot store human intelligence and experience; "Knowledge management systems do not account for renewal of existing knowledge and creation of new knowledge; "Greater incentives are needed for workers to contribute quality content to KMS." Improper use of KMS databases can waste resources if an organization does not really know what knowledge assets it possesses and fails to capitalize on potential new initiatives."
- National Association of Realtors
"Similarly, Dr. Yogesh Malhotra, the famous "Knowledge Architect", wrote a cautionary article on "When Best [Practices] Becomes Worst", Momentum: the Quality Magazine of Australasia, Quality Society of Australasia, NSW (Australia, 2002). In fact, the conditions for producing and utilizing knowledge workers are not a question of the persons concerned merely acquiring subject-matter expertise, problem-solving competency and communication skills. It is essential to provide an environment where such persons can operate and flourish. In the same vein, one of Malhotra's recent books (monograph) for UNESCO discusses knowledge work taking place in "hyper turbulent organizational environments.""
- International Labour Office (ILO)
"Knowledge Management - Discipline that seeks to improve the performance of individual organizations by maintaining and leveraging present and future value of knowledge assets, encompassing both human and automated activities. " Knowledge Management caters to the critical issues of organizational adaption, survival and competence in face of increasingly discontinuous environmental change.... Essentially, it embodies organizational processes that seek synergistic combination of data and information processing capacity of information technologies, and the creative and innovative capacity of human beings." - Dr. Yogesh Malhotra"
- U.S. Department of Health & Human Services
"The mechanistic model of information processing and control based upon compliance is not only limited to the computational machinery, but extends to specification of goals, tasks, best practices and institutionalized procedures to achieve the pre-specified outcomes." -- Yogesh Malhotra
- European Health Management Association, Ireland
"KM has become an increasingly important management discipline in recent years. Nevertheless, some say the phrase KM is unhelpful because 'knowledge is not a "thing" that can be "managed"1. They challenge the 'dominance and control model' that often underlies traditional views of knowledge and organisational management and development. They assert instead the notion that knowledge is largely cognitive, tacit and highly personal. They champion the fundamental role of people and the social interactive basis of knowledge sharing and creation. (Malhotra, Y..) "
- UK Department of Health
"Knowledge management is viewed as an essentialdriver for innovation. According to Malhotra, "Knowledge Management caters to the critical issuesof organisational adaptation, survival and competencein the face of increasingly discontinuous change.Essentially it embodies organisational processes thatseek a synergistic combination of the data andinformation processing capacity of informationtechnologies, and the creative and innovative capacityof human beings"."
- United Nations Development Program (UNDP), Geneva, Switzerland
"Adaptive Learning (See: Double Loop Learning): "Adaptive learning, or, single-loop learning, focuses on solving problems in the present without examining the appropriateness of current learning behaviors." -- Malhotra, Y., Organizational learning and learning organizations: an overview."
- World Health Organization (WHO)
"Dr. Yogesh Malhotra is regarded among the world's most influential practitioners and thought leaders on knowledge management. Widely recognized as a knowledge management pioneer, in this extensive interview read what Dr. Malhotra has to say about knowledge, information, technology and chasing success in this field."
- Emerald Group Publishing Ltd (UK)
"Dr. Yogesh Malhotra in the US is a leader in the knowledge management field. In a recent article written for the US Journal for Quality & Participation, he has pointed to a problem in relation to organisations investing heavily in information technology but not realising gains in terms of knowledge creation."
- Irish Times, Ireland
"Be that as it may, there is no doubt that domestic enterprises, faced by a complete bankruptcy of knowledge and ideas, will, some day, understand the value of the knowledge held by their employees. In the meantime, they would do well to study the writings of Dr. Yogesh Malhotra, an authority on technology and innovation management, business performance, and corporate strategy issues related to information systems, knowledge management, e-business and electronic commerce, business decision models, and new organisation forms."
- The Hindu, A Major National Daily Newspaper, India
"Professor Yogesh Malhotra of Syracuse University, New York, and expert in this field, has recently argued that one of the reasons for this failure is that more often than not knowledge management is practiced in isolation and does not take into account the dynamism of the external environment."
- Malaysian Business, Malaysia
Model Risk Management: Leading Industry Leaders & Setting the Benchmark
JP Morgan Private Bank, Goldman Sachs Alumnus' Asset Manager & Venture Capital
Econometric Modeling, Quantitative Finance, Quantitative Risk ModelingWall Street Investment Banks & Venture Capital Projects on Quantitative Finance & Risk Modeling
Technologies & Frameworks Applied
Quantitative Finance, Quantitative Analytics, Econometric Modeling, Data Science, Market Risk,
Credit Risk, Liquidity Risk, Financial Modeling, Risk Management, Stress Testing, Portfolio
Optimization, Derivatives, SAS, SQL, MATLAB, C++, Microsoft Excel, VBA, R, Python, Bloomberg,
Financial Risk, Model Risk, Portfolio Management, Hedge Funds, Financial Econometrics, Algorithms,
Machine Learning, Predictive Analytics, Statistical Modeling, Data Modeling, Software Engineering,
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Modeling, Market Microstructure, Investment Management, Asset Liability Management, Data Mining,
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Accounting, Financial Statement Auditing,Optimization.
Led & Advised: Top Wall Street Hedge Funds with $1 Trillion AUM: EDs, MDs, PMs, Quants Teams
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Credit Risk Models
Credit Default Swaps, Default Probabilities, Gaussian Copula, Nth to Default Swaps, Simulations, Large Portfolio Approximation, CreditMetrics, KMV, VaR, Expected Default Frequency (EDF), Counterparty Risk, Credit Valuation Adjustment (CVA), Stress Testing, Basel II/III, Worst Case Default Rate (WCDR), Exposure at Default (EAD), Loss Given Default (LGD), Probability of Default (PD), Risk Weighted Assets (RWA)Market Risk Models
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JP Morgan Private Bank $500-$600 Billion Multi-Asset Class Portfolio Construction & Optimization Leadership
Portfolio Construction & Optimization Framework Development for Liquidity AssessmentJP Morgan (JPM) Hands-On Team Leadership Projects, Midtown Manhattan, New York
Mentor: Dr. Georgiy Zhikharev, JPM Global Head of Quantitative Research & Analytics,
JPM US Head of Portfolio Construction. JPM Top-4 Risk Managers in Harvard Case.JP Morgan Portfolio Construction, Optimization & Stress Testing Leader
17-Asset Portfolio Liquidity Assessment & Stress Testing Research & Analysis
Technical Framework & Project Management Foundation:
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Technical Liquidity Risk Models, Methods, & Measures Research: ~5,000 documents ~ 60,000 pages
Research Presentations: Weekly: 225 slides, Final Executive Summary Overview: 5 slides.MS-Excel/VBA/MATLAB Models for 17-Asset Portfolio Liquidity Assessment & Stress Testing
~ 250 MS-Excel /VBA Linked Worksheets within Aggregate Porfolio and Specific Asset Class Workbooks.
MATLAB Code and Execution Outputs for Stress Testing Portfolio of 17 Asset Classes: 74-pages.JP Morgan Portfolio Liquidity Assessment Framework Development Leader
Portfolio Assets Modeled: 17 Asset Classes:
Hedge Funds (HF), Alternative Investments, Equities, Commodities, Fixed Income, Bonds, Currencies:
Developed Large Equity
Developed Small Equity
Emerging Equity
Unlisted Equity
Various Commodities
Government BondsInvestment Grade Bonds
Inflation-Linked Bonds
High Yield Corporate Bonds
Emerging Market Hard Currency Bonds
Emerging Market Local Currency Bonds
Major Currencies
Statistical Arbitrage Hedge Fund
Equity Hedge Hedge Fund
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Macro Hedge Fund
Relative Value Hedge FundAsset Pricing, Risk Management, Stress Testing, Liquidity Risk, Market Risk, Credit Risk, ALM Risk, Portfolio Risk,
Investment Risk, Non-Normality, Non-Linearity.
Mentor: JPM Top-4 Leadership ED in Global Financial Crisis Management, Harvard Case.
Led quantitative portfolio liquidity modeling for multiple financial asset classes.
Led literature review of all liquidity risk models, methods, and measures.
Led project management & scheduling and delivering high quality results on time.
Led interpretations of all outcomes & findings to ED team of Quants, CIO, MDs, PMs..
Assets: alternatives, HF, equities, commodities, fixed income, bonds, currencies.
Analyzed market risk, credit risk, ALM risk, portfolio risk, investment risk.
Led modeling and stress-testing for all asset classes and composite portfolio.
Led validation of all liquidity and liquidity risk models and measures.
Led verification of model performance, limiting behaviors, responses to stress.
Led modeling of pricing & risk measurement with specific focus on liquidity.
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Modeled historical simulation, parametric & modified VaR, expected shortfall.
Modeled and analyzed multi-asset volatility, variances & correlations, GARCH, MLE.
Modeled VaR, QMLE, non-normality, Cornish-Fisher, EVT stochastic models for assets.
Modeled and analyzed liquidity risk models for all assets and portfolio optimization.
Identified & defined benchmark indices & data sources for all asset classes.
Assessed soundness of liquidity & liquidity risk models for assets & portfolio.Presentation to JP Morgan MDs/EDs, JP Morgan, 270 Park Ave., New York
JP Morgan Bank Portfolio Construction & Optimization Liquidity Assessment FrameworkGuidance to JP Morgan Managing Directors/Executive Directors/Portfolio Managers
Axioms of Coherency and Convexity of Risk Measures
Exponential and Power Utility Functions for Spectral Risk Measures
Why Gaussian Risk Measures Fail and Where Regulation is Headed Next
Liquidity Measure for Illiquid Assets Solves Material Error in Liquidity Measures
Measuring Liquidity As Shadow Cost For Hedge Fund Indexes
Structuring and Pricing of Liquidity Options Hedge Funds for Price Discovery
Devising and Testing Liquidity Measures for Spreads of CDS Contracts
Liquifiability Index as What You May See in Basel Next
Modeling Measuring and Testing Liquidity Risk Across All Asset ClassesGoldman Sachs Alumnus' $400 Billion Asset Management Firm
Hedge Fund Large Scale Data High Frequency Econometric Modeling Project Leadership
High Frequency Econometric Modeling of Market Microstructure Liquidity & Price Impact
Hedge Fund Performance Analysis of 400 Trading Strategies for Alpha and Risk
Goldman Sachs Alumnus' Firm Hands-On Team Leadership Projects, Midtown Manhattan, New YorkMentor: Wall Street SVP Hedge Fund Manager with Top Wall Street Investment Banks:
Harvard Computer Scientist & Mathematician Alumnus Wall Street Hedge Fund SVP/PM.
Goldman Sachs Alumnus' $400 Billion+ Asset Management Firm
Firm: Top Wall Street Investment Bank launched by a Goldman Sachs alumnus with $400 billion to $500 billion AUM at the time of the project.
Project Management and Technical Team Leadership
SAS High Frequency Econometric Modeling of Market Microstructure of Liquidity
High Frequency Econometrics Models of Trade Price Impact & Market Microstructure.
Researched Co-Integrated Time Series for Ultra-High Frequency Tick-and-Quote (TAQ) Data.
Replicated /Analyzed Large Scale Data HF Econometrics Models of Market Microstructure.
Taught VARMAX Models of Co-Integrated Time Series for High Frequency Econometrics.
Analysis of 400 SSA Quarterly Scan Trading Strategies for Alpha and Hedging
Hedge Fund Performance Analysis Quantitative Finance & Quantitative Risk Modeling Research
Analyzed 400 State Street Associates Quarterly Scan Alpha Trading Strategies.
Critical Review of State Street Associates Quarterly Scan Trading Strategies.
Analysis: Why Existing `Alpha´ Research Is Insufficient for Profitable Hedge Fund Asset Management.
Sample of Quantitative Risk Modeling, Quantitative Finance & Econometric Modeling Research
Other Quantitative Risk Modeling, Quantitative Finance & Econometric Modeling Projects
Sample of SSA Quarterly Scan Finance and Economics Studies Reviewed for Goldman Sachs Alumnus' Asset Manager Project.
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AFAForeign Exchange Risk Premia and Macroeconomic Announcements: Evidence from Overnight Currency Options - Grad
The dynamic relation between CDS markets and the VIX index - Figuerola-Ferretti, Paraskevopoulos
A Different Way of Exploring Value versus Growth - Branch, Qiu
Value and Momentum in Frontier Emerging Markets - Swinkels, Pang, Groot
Feasible momentum strategies in the US stock market - Ammann, Moellenbeck, Schmid
Gradual Diffusion of Upstream and Downstream Earnings News - Implications for Stock Prices - Chen
Creative Destruction and Asset Prices - Jank, Gramming
Is Contrarian Investment Performance Conditional Upon Relative Price Levels? - Wu, Li, Hamill
If it's good for the firm, it's good for me: Insider trading and repurchases motivated by undervaluation - Jalegaonkar
Does Investor Relations Add Value - Agarwal, Bellotti, Taffler
Spot and forward volatility in foreign exchange - Della Corte, Sarno, Tsiakas
An investigation of customer order flow in the foreign exchange market - Cerrato, Sarantis, Saunders
Active Currency Investing and Performance Benchmarks - Melvin, Shand
Volatility Term Structure and Option Returns - Vasquez
Persistence of derivative returns through the financial crisis - Onn and Sinnakkannu
Black Swans, Beta, Risk, and Return - Estrada and Vargaas
Can exchange traded funds be used to exploit country and industry momentum - Andreu, Swinkels,
Crash worries and stock returns - Baltussen
Another Look at Trading Costs And Short-Term Reversal Profits - De, Huij, Zhou
Does the market know better? The case of strategic vs. non-strategic bankruptcies - Coelho, John, and Taffler
Explaining Stock Returns with Intraday Jumps - Amaya and Vasquez
Geographic Dispersion and Stock Returns - Garcia and Norli
Prior Earnings, Dividend-Reducing Announcement Returns and Future Earnings Performance - Asern
The Relative Leverage Premium - Ippolito, Steri, and Tebaldi
A New Anomaly: The Cross-Sectional Profitability of Technical Anlaysis - Han, Yang, Zhou
As Told by The Supplier: Trade Credit and The Cross Section of Stock Returns
The effect of the US holidays on the European markets, When the cat's away - Muga, Casado, Santamaria
Search Frictions and the Liquidity of Large Blocks of Shares - Schroth and Albuquerque
Economic Risk Premia in the Fixed Income Markets - Balduzzi and Moneta
Why Does Treasury Issue TIPS? The TIPS - Treasury Bond Puzzle - Lustig, Longstaff, Fleckenstein
Know When to Hold 'Em, and Know When to Fold 'Em: The Success of Frequent Hedge Fund Activists - Boyson and Mooradian
Volatility Term Structure and the Cross-Section of Option Returns
Do Firms Buy Their Stock at Bargain Prices? Evidence from Actual Stock Repurchase Disclosures
Do Mutual Fund Managers Trade on Stock Intrinsic Values?
As Told by The Supplier: Trade Credit and The Cross Section of Stock Returns
How does Portfolio Disclosure affect Institutional Trading? Evidence from their Daily Trades -Wang
Buy High and Sell Low - Wang
Capital Utilization and Stock Returns - Balvers, Gu, and Huang
Investor Sentiment, Risk Factor and Asset Pricing Anomalies - Ho and Hung
IQCAPM: Asset Pricing with Information Quality Risk - Jacoby, Lee, Paseka & Wang
Post Earnings Announcement Drift and Value-Glamour Anomaly - Yan and Zhao
Profitable Mean Reversion after Large Price Drops - Dunis, Laws, and Rudy
New Evidence on the Relation between the Enterprise Multiple and Average Stock Returns - Loughran and Wellman
Variance Risk Premium and Cross-Section of Stock Returns: Han and Zhou
Contrarian and Momentum Strategies: The Impact of the Business Cycle - Filbeck, Li, and Zhao
Crash Worries and Stock Returns - Baltussen
Acquisitions of Foreign Divested Assets - Ngo and Jory
Streaks in Earnings Surprises and the Cross-Section of Stock Returns - Loh and Warachka
Bond Variance Risk Premia - Mueller, Vedolin, and Yen
Short and Long Slopes of Yield Curves Have Different Economic and Asset Pricing Implications - Lee
Cross-Section of Option Returns and Idiosyncratic Stock Volatility – Cao and Han
On the Timing and Pricing of Dividends - Van Binsbergen, Brandt and Koijen
Global Tactical Sector Allocation: A Quantitative Approach: Doeswijk, Van Vliet
Is Momentum Really Momentum?: Novy-Marx
How does Portfolio Disclosure affect Institutional Trading? Evidence from their Daily Trades -Wang
Information Content When Mutual Funds Deviate from Benchmarks: Jiang, Verbeek, and Wang
The Baltic Dry Index as a Predictor of Global Stock Returns, Commodity Returns, and Global Economic Activity: Bakshi, Panayotov, and Skoulakis
The Share of Systematic Variation in Bilateral Exchange Rates: Verdelhan
Can Oil Prices Forecast Exchange Rates?: Ferraro, Domenico, Rossi, Barbara and Rogoff
Carry Strategies in Global Asset Classes: Koijen, Tobias Moskowitz, Lasse H. Pedersen, Evert
International Diversification: An Extreme Value Approach: Cholette et al.
A Simple Way to Estimate Bid-Ask Spreads from Daily High and Low Prices: Corwin and Schultz
Are Mutual Funds Sitting Ducks?: Shive, Sophie and Yun, Hayong
The Road Less Traveled: Strategy Distinctiveness and Hedge Fund Performance: Sun et al.
Uncovering Hedge Fund Skill from the Portfolio Holdings They Hide, Agarwal et al.Goldman Sachs Alumnus' $400 Billion Asset Manager Hands-On Team Leadership Project
• Quantitative Finance & Risk Modeling, Econometric Modeling, & Numerical Programming Projects, 2009-Current
120+ SSRN Top-10 Research Rankings:
Computational Quant Analytics, Machine Learning, AI & Modeling, Financial Econometrics, & Risk Analytics, 2015-2018
Computational Mathematical Models & Quantitative Methods for Uncertainty & Risk Management,
Quantitative Finance, Asset Valuation, Risk Arbitrage, Trading & Hedging Strategies.
Hands-On Technologies: MATLAB, SAS, C++, MS-Excel, VBA, Bloomberg, NYSE-TAQ, Time Series Analysis, Neural Networks, Web Analytics
- Princeton Quant Trading Conference 2015 (April 04, 2015), Princeton University: Knight Reconsidered:
'Future of Finance Beyond Flash Boys': Risk Modeling for Managing Uncertainty in an Increasingly Non-Deterministic Cyber World
(Invited Research Presentation on Post-HFT Finance and Risk Models & Methodologies)
- Risk, Uncertainty, and Profit for the Cyber Era: Model Risk Management of Cyber Insurance Models Using Quantitative Finance and Advanced Analytics
- Beyond ‘Bayesian vs. VaR’ Dilemma to Empirical Model Risk Management: How to Manage Risk (After Risk Management Has Failed).
- Markov Chain Monte Carlo Models, Gibbs Sampling & Metropolis Algorithm for High-Dimensionality Complex Stochastic Problems.
- Quantitative Modeling of Trust and Trust Management Protocols in Next Generation Social Networks Based Wireless Mobile Ad Hoc Networks.
- A Risk Management Framework for Penetration Testing of Global Banking & Finance Networks Voice over Internet Protocols.
- Cryptology beyond Shannon’s Information Theory: Technical Focuson Number Field Sieve Cryptanalysis Algorithms for Prime Factorization.
- A Framework for Penetration Testing & Security of Network Protocols for Global Banking & Finance Call Centers.
- Future of Bitcoin & Statistical Probabilistic Quantitative Methods: Global Financial Regulation (Interview: Hong Kong Institute of CPAs).
- Bitcoin Protocol: Model of 'Cryptographic Proof' Based Global Crypto-Currency & Electronic Payments System.
- Quantum Computing, Quantum Cryptography, Shannon's Entropy and Next Generation Encryption & Decryption.
- Cryptology Beyond Shannon's Information Theory: Preparing for When the 'Enemy Knows the System'.
- C++11 Concurrency and Multithreading Programming Logic for High Frequency Trading and Hedge Funds.
- Measuring & Managing Financial Risks with Improved Alternatives beyond Value-At-Risk (VaR).
- VLANs Implementation, inter-VLAN Routing & VLAN Trunking Protocol using Cisco Network Security Best Practices (Cisco VLANs).
- Network Intrusion Detection and Prevention & Active Response: Frameworks, Systems, Methods, Tools & Policies (Cisco IDS/IPS).
- Analysis of Attack Trees for Mitigating Cybersecurity Attacks on Global Banking & Finance and SCADA Systems.
- Analysis of FIX and FAST as Financial Securities Trading and Transactions Messaging Network Protocols.
- Threats and Vulnerabilities: A First 'Appetizer' to Cybersecurity: 15 Minutes to Minimizing 95% Threats.
- C++ Options & Financial Derivatives Pricing Algorithms and Quantitative Finance Design Patterns.
- Adaptive Neuro-Fuzzy Inference System Models for Forecasting Nonlinear Chaotic Time Series Signals.
- Machine Learning & Java Neural Networks Algorithms for Non-Linear & Non-Normal Signal Processing of Financial Time Series.
- A Probabilistic Mathematical Analysis Model of the Financial Market as a Bayesian Learner.
- Algorithm Models of Social Networks, Graph Theory, Game Theory & Nash Equilibrium.
- Vector Autoregressive Models of Market Microstructure for Analyzing High Frequency Econometric Time Series.
- Empirical Replication of Yield Curve Decomposition Models (Based on Cochrane and Piazzesi study, 2008).
- Empirical Replication of Ho Lee Merton Short Rate and Term Structure Models for Bond Options Pricing.
- Empirical Calibrations of Hull White Model and Merton Tree Model for Modeling Interest Rates and Bond Prices.
- Empirical Models of Monetary Neutrality, Real Income Growth, Nominal Income Growth, and Inflation.
- Empirical Models of Purchasing Power Parity and Fisher Equation for Prices, Interest Rates, and Exchange Rates.
- Empirical Replication of JP Morgan Credit Default Swaps (CDS) Models for CDS Mark to Market Valuations.
- Empirical Replication of Merton's Model of Default Probabilities with Debt as an Option on Firm Assets.
- Empirical Replication of the Gaussian Copula Model for Time to Default for Four Different Firms.
- Empirical Replication of the Nth to Default Swap Pricing Model for Risk Pooling Strategy for Risky Bonds.
- Empirical Replication of Merrill Lynch Gaussian Copula Model for Nth to Default Swap Pricing over Multiple Periods.
- Empirical and Simulation Models of Large Portfolio Approximation (LPA) of Credit Default Probabilities.
- Worst Case Default Rates (WCDR) and VaR Models for Bank Loans Based Upon Gaussian Copula Correlations.
- WCDR and Risk Weighted Assets (RWA) Models for Bank Loans Given Probability of Default (PD) and Loss Given Default (LGD).
- CreditMetrics Methodology Models and Simulation for Assessing Credit VaR & Economic Capital for a Bond Portfolio.
- Moody's KMV Model for Distance-to-Default, Expected Default Frequency (EDF) and CDS Fair Value Spreads Estimation.
- Counterparty Default Risk Models for Semi-Annual and Annual Forward Rate Contracts for Currency Swaps.
- Bank's Credit Derivative Valuation Models of Ratings Transition Matrices, Real and Risk Neutral Default Probabilities.
- Monte Carlo Simulation and Option Pricing in C++: A Monte Carlo Pricer for Path Dependent Financial Options.
- Maximum Likelihood Estimation of GARCH Models for Empirical Analysis of Asset Prices and Returns Time Series.
- Maximum Likelihood Estimation of Cox-Ingersoll-Ross Model for Empirical Analysis of Federal Interest Rates.
- Maximum Likelihood Estimation of 2-regime Markov Regime Switching Model for Empirical Analysis of Federal Interest Rates.
- Econometric Analysis and Volatility Modeling Using GARCH and VaR for Stock, Index, and Commodity Time Series.
- VaR Modeling with Monte Carlo and Historical Simulation (HS), Weighted HS, and Filtered HS for Multiple Time Series.
- Back Testing Model Comparisons of Unconditional and Conditional VaR Models for Multiple Financial Time Series.
- Black-Scholes Model Based Monte Carlo Engine for Derivatives Pricing of Exotic Options in C++.
- Jump Diffusion Analysis of Option Price Sensitivity to Simulations in Comparison of Black‐Scholes and Monte Carlo Models.
- A Comparison of CAPM, Constrained Portfolio Optimization, MACD, and Black Litterman Model Portfolio Optimization Strategies.
- Empirical Models of ARCH/GARCH Volatility and Black-Scholes Simulations for Pricing Vanilla and Barrier Options.
- Transient Directional Volatility Arbitrage & Volatility Neutralizing Hedging Strategies for Portfolio Management.
- Volatility Trading and Volatility Markets Using VIX, VIX Futures, VIX Options, and VIX Term Structure Models.
- Financial Statements Analysis Models of Constant Growth HPR, Steady State Dividend Growth, FCF and Abnormal Earnings.
- Investment Strategy Portfolio Simulations and Comparative Volatility of Investment Portfolio and Market Porfolio Models.
- Financial Asset Valuation Models in Corporate IPOs, Bankruptcies, Liquidations, Restructurings, Mergers and Acquisitions.
- Fundamental Shifts in Financial and Accounting Risk Management Pertaining to Global Finance and Capital Markets.
- Fundamental Shifts in Efficient Markets Hypothesis, 'New Normal' Outside +/- 3-Sigma, and Market Microstructure.
- Accounting Measurement and Reporting for Fair Value Accounting and 'Mark-to-Market' Transactions and Events.
- Advanced Financial Auditing of Simulated Corporation Financial Statements Using ACL for Auditing and Compliance.
- Financial Auditing & Assurance Simulation Using ACL for Risk Assessment of Firm's Enterprise Business Processes.
- Assessing Financial Audit Risk of Big-4 Firm's Proposed Audit Client Acquisition of Oracle Corporation on Sun Acquisition.
- FASB-IASB Convergence of US GAAP and IFRS Asset Fair Value Measurement Standards Based Upon SFAS 157.
- Forensic Accounting & Analysis of Financial Statements of Goldman Sachs, Morgan Stanley, and Berkshire Hathaway.
- Financial Accounting Analysis of Statutory Merger of Burlington Northern Santa Fe Corporation in Berkshire Hathaway Inc.
• Discrete, Continuous & Stochastic Time Series Signal Processing Finance & Risk Models
Economic Capital, Capital Adequacy, Basel/US Federal Reserve/OCC Frameworks & Regulations, Portfolio Risk, Liquidity Risk, Credit Risk, Market Risk, Econometric Analysis, Market Microstructure, Interest Rate Derivatives, Stochastic Volatility, Fixed Income, Equity, Derivatives (Options, Futures, Forwards, Swaps, Swaptions)
Credit Risk Models
Credit Default Swaps, Default Probabilities, Gaussian Copula, Nth to Default Swaps, Simulations, Large Portfolio Approximation, CreditMetrics, KMV, VaR, Expected Default Frequency (EDF), Counterparty Risk, Credit Valuation Adjustment (CVA), Stress Testing, Basel II/III, Worst Case Default Rate (WCDR), Exposure at Default (EAD), Loss Given Default (LGD), Probability of Default (PD), Risk Weighted Assets (RWA)Market Risk Models
Volatility Modeling, GARCH/Extensions, MLE, Variance/Correlation Models, Portfolio VaR, QMLE, Non-Normality, Cornish-Fisher, Extreme Value Theory (EVT), Expected Shortfall (ES), Coherent/Spectral Risk Measures, Weighted/Filtered/Historical Simulation, Monte Carlo, Backtesting VaRs/ES, Stress Testing, Basel II/III
Interest Rate Derivatives Models
Simulations, Analytic Expectation, Tree Models, Calibrations; Continuous Time, CIR,Vasicek, Merton, Hull-White, BDT, & HJM Models; Bond Options, Treasuries, Coupon Bonds, Caplets, Floorlets, Swap Contracts, Bond Risk Premia, Yield Curve, Markov Regime Switching ModelsEquity Portfolio Models
Derivatives, Mean-Variance Portfolios, CAPM, Passive/Active Portfolio Performance, Multi-Factor Models, Cross-Sectional Returns, Asset Allocation, Risky/Risk-Free Portfolios, Diversification, Risk Pooling, CAPM, Anomalies, Dividend Discount/Growth Models
Fixed Income Portfolio Models
Bond Valuations, Derivatives, Yields, Term Structure, Credit Spread, Credit Risky Bonds, Interest Rate Risk, Portfolio Performance, Passive/Active/Liability Funding, Hedging, Swaps, Forwards, Futures, ABS, MBS.
• Technologies of Computational Quantitative Modeling, Quantitative Finance & Risk Management
Algorithms: Graph Theory, Dynamic & Linear Programming, Computational Complexity
Algorithms: Social Networks Analysis, Game Theory, Nash Equilibrium, Financial Markets
Algorithms: Mathematical Models of Automata, Computability & Formal Languages
Algorithms: Computational Mathematical Models of Cryptography & Encryption Protocols
Advanced Statistical Models & Machine Learning Numerical Methods for Large Data Frameworks
Bayesian Inference & Markov Chain Monte Carlo Models for High-Dimensional Stochastics
C++11 Concurrency & Multi-threading, Machine Learning, & Java Neural Network Models
C++ Mathematical Finance Derivatives Pricing & Software Engineering Algorithms
C++ Design Patterns Financial Programming for Derivatives & Options Pricing
C++ Financial Programming for Quantitative Finance Models & Applications
C++ Programming for Financial Engineers Course, University of California Berkeley
Cybersecurity-Signal Processing: Cryptography, Finance Protocols, Information Assurance
Network Penetration Testing & Protocols Analyses: Metasploit Pro, Nmap, Wireshark, etc.
Network Security: CCNA Security, ICND1, ICND2; Network Intrusion Detection & Prevention
Statistics for Financial Engineers Course, University of California Berkeley
Math Foundations for Financial Engineers Course, University of California Berkeley
MATLAB Advanced Financial Econometrics Markov Chain & Monte Carlo Models
MATLAB Market Risk, Credit Risk, Volatility, VaR, ARCH, GARCH, EVT, ES Models
MATLAB/MS-Excel/C++ Credit Risk Management & Credit Risk Derivatives Models
MATLAB Stocks and Equity Portfolio Management & Equity Derivatives Models
MATLAB Continuous Time Interest Rates, Yield Curve, Fixed Income Derivatives Models
MATLAB Stochastic Numerical Methods & Mathematics for Quantitative Finance
MATLAB Artificial Intelligence-Machine Learning-Fuzzy Logic-Chaotic Time Series Models
MATLAB Advanced Statistical, Financial Econometrics & Optimization Models
MATLAB Advanced Finance Portfolio Theory, CAPM & APT Matrix Algebra Models
MS-Excel Market Risk, Credit Risk, Volatility, VaR, ARCH, GARCH, EVT, ES Models
MS-Excel/VBA Hedge Fund Statistical Risk/Returns, Asset Pricing, Market Risk Models
MS-Excel/VBA Fixed Income Portfolio Management & Fixed Income Derivatives Models
MS-Excel/VBA Advanced Quantitative Models of Utility Theory & Portfolio Management
MS-Excel/VBA Advanced Statistical, Financial Econometrics & Optimization Models
MS-Excel/VBA/ACL Advanced Financial Accounting & Financial Auditing Models
MS-Excel/VBA/Solver/Macros for Operations Research & Network Programming Models
MS-Excel/VBA/Solver/Macros for Finance, Investments, Accounting Decision Models
SAS Advanced Programming, SAS SQL Processing & SAS Macro Programming Courses
SAS Large Scale Data Models of High-Frequency Econometrics & Market Microstructure
SAS Advanced Quantitative Models of Macroeconomics & Microeconomics Analysis
SAS/SPSS Statistical Analysis of Variance (ANOVA) & Co-Variance (ANCOVA) Models
SAS/SPSS Applied Multivariate Analysis & Applied Regression Analysis Models
SAS/SPSS Correlation, Multivariate Regression & Inferential Statistics Models
SAS/SPSS Quantitative Statistical Structural Equation Models in Behavioral Science
SAS/SPSS Quantitative Statistical Methods in IT, Organizations & Social Sciences
Quantitative Structural Equation Models of Risk Management, Controls & Compliance
Statistical Multivariate Regression Models of Risk Management, Controls & Compliance
Qualitative Survey Research Methods in Organizational Controls & Compliance Analysis
• Algorithms & Computational Finance: SAS, MATLAB, C++, C++11, Machine Learning, Signal Processing
C++ Design Patterns, Monte Carlo Models, Black-Scholes Model, C++11 Multithreading and Concurrency, SAS Applied Data Science, SAS Advanced Data Mining Models, Uncertainty Modeling, Machine Learning, Computer Algorithms, Mathematical Computation, Computational Cryptography, Artificial Intelligence & Modeling, Machine Learning, Soft Computing, Multivalent Logic, Fuzzy Systems, Computational Complexity, Computational Economics, Graph Theory, Social Networks Analysis, Game Theory, Bayesian Models, Automata, Computability, Formal Languages
Algorithms & Mathematical Models of Computing Machines
Complexity theory, Computability theory, Automata theory, Regular Languages, Finite Automata, Nondeterminism, Regular Expressions, Nonregular Languages, Pumping Lemma, Context-Free Languages, Context-Free Grammars, Pushdown Automata, Non-Context-Free Languages, Church-Turing Thesis, Turing Machines, Variants of Turing Machines, Hilbert’s Problems, Decidable Languages, Undecidability, Undecidable Problems from Language Theory, Computation Histories, Mapping Reducibility, Time Complexity, Measuring Complexity, Class P, Class NP, P versus NP, Cook-Levin Theorem, NP-complete Problems.
Algorithms & Computational Complexity
Big-O and Small-O, Primality Testing, Euclid's Algorithm, Fermat's Little Theorem, Recurrence Relations, Divide-and-Conquer Algorithms, Fast Fourier Transform, Undirected Graphs, Depth-First Search, Directed Graphs, Directed Acyclic Graphs (DAGs), Breadth-First Search, Dijkstra's Algorithm, Shortest Path Algorithms, Bellman-Ford Algorithm, Greedy Algorithms, Minimum Spanning Trees, Kruskal's Algorithm, Prim's Algorithm, Huffman Encoding, Horn Formulas, Dynamic Programming, Topological Ordering, Knapsack Problem, Floyd-Warshall Algorithm, Traveling Salesman Problem, Linear Programming, Duality, Complexity Reductions, Network Flows, Max-Flow Minimum Cut Algorithm, Bipartite Matching, Simplex Algorithm, NP-Completeness, Satisfiability (SAT), Integer Linear Programming, Vertex Cover, Clique, NP-Complete Reductions.
Algorithms, Cyber Networks & Computational Economics
Graph Theory, Social Networks Analysis, Network Strength, Network Structure, Graph Partitioning, Homophily, Structural Balance, Game Theory, Dominant Strategies, Nash Equilibria, Mixed Strategies, Evolutionarily Stable Strategies, Braess's Paradox, Auctions and Pricing, Auction Formats, Bidding Strategies, Matching Markets, Bipartite Graphs, Market-Clearing Prices, Equilibria in Trading Networks, Power in Social Networks, Nash Bargaining Solution, Modeling Network Exchange, Information Networks, WWW Link Analysis, PageRank, Spectral Analysis, VCG Principle, VCG Prices, Bayes' Rule, Information Cascades, Network Effects, Negative Externalities, Power Laws, Rich-Get-Richer Models, Long Tail, Information Cascades, Decentralized Search, Epidemic Models, Wisdom of Crowds Models, Asymmetric Information, Reputation Systems, Voting Systems.Algorithms, Cryptography, Cryptology & Cyber Security
Shannon's Information Theory, Modular Arithmetic, Number Theory, Symmetric Cryptography, Data Security, Stream Ciphers, Linear Feedback Shift Registers (LFSR), Data Encryption Standard (DES), Triple DES (3 DES), Galois Fields, Advanced Encryption Standard (AES), Block Ciphers (ECB, CBC, OFB, CFB, CTR, GCM), Public-Key Cryptography, RSA Cryptosystem, Public-Key Cryptosystems, Discrete Logarithm Problem, Diffie-Hellman Key Exchange, Elgamal Encryption Scheme, Elliptic Curve Cryptosystems, Digital Signatures, RSA Signature Scheme, Elgamal Signature Scheme, Digital Signature Algorithm, Elliptic Curve Digital Signature Algorithm, Hash Functions, Hash Algorithms, Message Authentication Codes (MACs, HMAC, CBC-MAC, GMAC), Key Establishment (Symmetric and Asymmetric), Key Derivation.
C++ Mathematical Finance, Risk, Design Patterns & Derivatives Pricing Models
C++ Software Engineering Design Patterns: C++ Algorithms, Creational patterns, Virtual Copy Constructor, Factory Pattern, Singleton Pattern, Structural patterns, Adapter Pattern, Bridge Pattern, Decorator Pattern, Behavioral patterns, Strategy Pattern, Template Pattern, Iterator; C++ Computational Finance Options and Derivatives Pricing Applications: Monte Carlo Model, Black Scholes Model, Monte Carlo Call Option Pricer, Encapsulation, Open Closed Principle, Inheritance, Virtual Functions, Virtual Constructor, Bridge Pattern, Statistics Gatherer, Wrappers, Convergence Table, Decorator Pattern, Random Number Generators, Linear Congruential Generator, Anti-Thetic Sampling, Exotics Engine, Template Pattern, Black Scholes Path Generation Engine, Asian Option, Tree Class, Pricing On Trees, Solvers, Templates, Implied Volatilities, Function Objects, Bisections, Newton Raphson Method, Smart Pointers, Exceptions.
C++11 Multithreading & Concurrency Standard Extensions and Operating Systems
Threads, Lambda Expressions, Thread Execution Modes, Thread Termination Modes, References in Multi-threading Mode, Exception Management for Threads, Resource Acquisition is Initialization (RAII), Thread Execution and Document Management, Parameter Passing in Threads, Object References in Threads, std::thread Standard Thread Library, C++ smart pointers, Inter-Thread Execution Transfer, Hardware Concurrency for Multi-Threading, Thread IDs, Preventing Broken Invariants, Mutexes and Race Conditions, Runtime Functions and Arguments Passing, Stack-Related Interface Issues and Race Conditions, std::lock Standard Thread Library, Preventing Deadlocks in Multi-threading, std::lock_guard Standard Thread Library, std::unique Standard Thread Library, std::defer Standard Thread Library, Mutex Ownership Transfers, Efficient Locking of Mutexes, compare vs. swap, Data Initialization and Race Conditions, Initialization of Static Variables, Single Writer & Multiple Readers.
Machine Learning, Signal Processing, Uncertainty & Risk Modeling, Econometric Modeling
Multivalent Logic, Uncertainty Modeling, Interval Arithmetic, Multi-Level Interval Numbers, Fuzzy Numbers, Fuzzy Arithmetic, Fuzzy Sets, Fuzzy Operations, Fuzzy Relations, Many-Valued Logic, ANFIS (Adaptive Neuro-Fuzzy Inference System) Models, MATLAB, Java Neural Network Models, C, Approximate Reasoning, Algorithms, Data Mining, Machine Learning, Supervised Learning, Unsupervised Learning, Semi-supervised Learning, Dimensionality Reduction, Pattern Recognition, Classification, Clustering, Overfitting, Underfitting, K-Means Clustering Algorithms, K-Nearest-Neighbor Algorithms, Feature Selection, Nearest Neighbor Classifiers, Naive Bayes Classifier, Bayesian Classifiers, Differential Misclassification, Bootstrap Aggregating (Bagging), Boosting, Single Link Clustering, Complete Link Clustering, Novelty Detection, Receiver Operating Characteristic (ROC), Decision Trees, Genetic Algorithms, Neural Networks, Wrappers vs. Filters, ID3 Algorithms, C4.5 Algorithms, C5.0 Algorithms, Entropy Estimation.
SAS Applied Data Science & Advanced Data Mining Models
SAS Programming Advanced Techniques and Efficiencies: User-Defined Functions, Controlling I/O Processing and Memory, Accessing Observations, Using DATA Step Arrays, Using DATA Step Hash and Hiter Objects, Combining Data Horizontally; SAS SQL: SQL Queries, Displaying Query Results, SQL Joins, Subqueries, Set Operators, Creating Tables and Views, Advanced PROC SQL Features; SAS Macros: Macro Variables, Macro Definitions, DATA Step and SQL Interfaces, Macro Programs; SAS Data Manipulation Techniques: Controlling Input and Output, Summarizing Data, Reading Raw Data Files, Data Transformations, Debugging Techniques, Processing Data Iteratively, Restructuring a Data Set, Combining SAS Data Sets, Creating and Maintaining Permanent Formats; SAS Programming: SAS Programs, Accessing Data, Producing Detail Reports, Formatting Data Values, Reading SAS Data Sets, Reading Spreadsheet and Database Data, Reading Raw Data Files, Manipulating Data, Combining SAS Data Sets, Creating Summary Reports.
Leading the Silicon Valley Again!: Post-COVID SocioTechnical Digital Transformation Networks:
Digital Transformation Networks Leading Silicon Valley-Wall Street-Pentagon-Global CEOs-CXOs:
Financial & Crypto Network Protocols Analyses Indicate Critical Risks, Threats & Vulnerabilities
Bayesian Models beyond VaR Model Risks Exposed by the Global Financial Crisis of 2008-2009
Griffiss Cyberspace Cybersecurity Venture Aims to Span Wall Street and Hi-Tech Research• Cybersecurity, Ethical Hacking, Intrusion Detection & Prevention, Networks Protocols Analysis
(Applied R&D in Authorized Closed Private Networks isolated from other Private and Public Networks)Network & Computer Security, Ethical Hacking, Intrusion Detection & Prevention, Financial & Networks Protocols Analysis
Access Control Lists, Anomaly Based Intrusion Detection, Application Layer Attacks, Application Layer Protocols, ARP Cache Poisoning, ARP Protocol, ARP Spoofing, Attack Trees for Mitigating Attacks on Banking & Finance Systems, Attack Trees for Mitigating Attacks on SCADA Systems, Backdoors, Behavior Based Intrusion Detection, Bitcoin Protocol, Buffer Overflow Attack, Cisco ASA Firewalls, Cisco Routers, Cisco Switches, Cisco VLANs, Common Vulnerabilities & Exposures, Compromised Key Attack, Crypto-Currency, 'Cryptographic Proof' Based Systems, Denial of Service Attacks, DNS Cache Poisoning , Eavesdropping Attack, FAST Financial Securities Trading Network Protocol, Firewall Architectures, Firewall Configuration, FIX Financial Transactions Messaging Network Protocol, Format String Overflow Attack, FTP Protocol, Heap Overflow Attack, High Interaction Honeypots, Honeynets, Honeypots, Honeypots Legal Issues, Host Based Intrusion Detection Systems, HTTP Protocol, ICMP Attacks, ICMP Protocol, Incident Analysis, Incident Containment Strategy, Incident Documentation, Incident Evidence Gathering & Handling, Incident Handling & Incident Response, Incident Handling of Denial of Service Attacks, Incident Handling of Inappropriate Usage , Incident Handling of Malicious Code Attacks, Incident Handling of Multiple Component Incidents, Incident Handling of Unauthorized Access, Incident Prioritization, Intrusion Detection & Prevention Forensics, Intrusion Detection Systems, Intrusion Prevention Systems, IP Address Spoofing, IP Attacks, IP Fragmentation Attacks, IP Fragmentation Flooding, IP Packet Fragmentation, IP Protocol, IPSec, Keyloggers, Knowledge Based Intrusion Detection, LAN Design, LAN Switching, Layer-2 Connection Hijacking, Low Interaction Honeypots, MAC Spoofing , Malware, Man in the Middle Attacks, Medium Interaction Honeypots, Misuse Based Intrusion Detection, Network Address Translation, Network Based Intrusion Detection Systems, NMap Network Analyzer, OSI Model, OSSEC, Packet Filtering, Padded Cells, Password Attacks, Phishing Attacks, Ping Flooding Attack, Ping of Death Attack, Port Address Translation, Port Forwarding Attack, Port Redirection Attack, Proxy Services, Reconnaissance Attacks, Router Configuration, Router Operation, Router Security, Routing Attacks, Routing Protocols, Rule Based Intrusion Detection, Signature Based Intrusion Detection, SMTP Protocol, Smurf Attacks, Sniffing Attack, Snort, Social Engineering Attacks, Spear Phishing Attacks, SSL/TLS Protocols Security and Vulnerabilities, Stack overflow Attack, Stateful Firewalls, Statistical Based Intrusion Detection, Subnetting, Suricata, Switch Configuration, Switch Security, SYN Flood Attack, TCP Attacks, TCP Dump Network Analyzer, TCP Layer Attacks, TCP Port Scanning Attacks, TCP Protocol, TCP Session Hijack, TCP Session Poisoning, TCP SYN Flooding, TCP/IP, TCP/IP Connection Hijacking, TCP/IP Model, TCP/IP Security Flaws, Teardrop Attack, Tracking Cookies, Traffic Amplification, Trojan Horses, Trust Exploitation Attacks, UDP Flood Attacks, Virtual Private Networks, Viruses, VoIP Phishing Attacks, VPN, VPN Security, Wireless Intrusion Detection Systems, Wireless Intrusion Prevention Systems, Wireless LAN Attacks, Wireless LAN Threats, Wireless LAN Security, Wireshark Network Analyzer, Worms.
Financial & Networks Protocols Analysis; Networks Penetration Testing & Ethical Hacking with Metasploit, Nmap, Wireshark, etc.
Access Control Misconfiguration Vulnerabilities, Active Dictionary Attack, Active Footprinting, Active Information Gathering, Apache Vulnerability Analysis, Asterisk Exchange Server Configuration, Asterisk Virtual Machine Configuration, Audacity Audio Editor & Recorder, Banner Grabbing, Brute Force Password Attacks, Brute Forcing with Dictionary Attacks and NCrack, Cain & Abel ARP Poison Routing, Cain & Abel IAX2 Packet Flooding Attack, Cain & Abel Man in the Middle Attack, Cain & Abel Network Sniffing Attack, Cain & Abel Passive Eavesdropping Attack, Cain & Abel Password Cracking Attack, Cain & Abel VoIP Traffic Hijacking Attack, Covert Penetration Testing, Dictionary Password Attacks, Digiphone Hard Phones Configuration, E.164 Alias Enumeration, Ekiga Softphone Configuration, Enumerated Open and Closed Ports and Services, enumIAX for exploiting IAX Vulnerabilities, Ethical Hacking Challenges, Exploitation, Fingerprinting Remote Host Services, Flag Capture Challenge Competitions, Getif for SNMP Exploitation, H.323 Debugging, H.323 Device Enumeration, H.323 Username Enumeration, Hashcat Password Cracking of Hashed Salted 64-bit SHA256 Passwords , Hydra Password Attack with Wordlists, IAX Username Enumeration, IAXComm Softphone, Intelligence Gathering Using CLI, Intelligence Gathering Using WWW, John the Ripper Password Cracking Attacks, Kali Virtual Machines Configuration, Key Generation Vulnerabilities, Linux Misconfiguration Vulnerabilities, Linux Virtual Machines Configuration, Man-in-the-Middle Attack on IAX MD5 Authentication, md5 Hash Generator Password Cracker, Metasploit Pro Framework and Associated Tools & Scripts, Metasploit Pro Advanced Nmap Scanning, Metasploit Pro Armitage, Metasploit Pro Basic Exploitation, Metasploit Pro Brute Forcing Ports, Metasploit Pro Brute Forcing SSH Login Using SNMP, Metasploit Pro Creating & Executing Single Encapsulation Payload, Metasploit Pro Creating & Executing Multiple Encapsulation Payload, Metasploit Pro Delivering Payload through xp_cmdshell, Metasploit Pro Executing Exploit as a Background Job, Metasploit Pro Exploitation of Linux Machine, Metasploit Pro Exploitation of Windows Machine, Metasploit Pro Exploits, Metasploit Pro Framework, Metasploit Pro FTP Scanning, Metasploit Pro Meterpreter Compromising Windows Machine, Metasploit Pro Meterpreter Dumping the Password Hashes, Metasploit Pro Meterpreter Extracting the Password Hashes, Metasploit Pro Meterpreter Killing Antivirus Software, Metasploit Pro Meterpreter Leveraging Post Exploitation Modules, Metasploit Pro Meterpreter Migrating a Process, Metasploit Pro Meterpreter Obtaining System Password Hashes, Metasploit Pro Meterpreter Passing the Hash, Metasploit Pro Meterpreter Pivoting onto Other Systems, Metasploit Pro Meterpreter Privilege Escalation, Metasploit Pro Meterpreter Scraping a System, Metasploit Pro Meterpreter Upgrading Command Shell to Meterpreter, Metasploit Pro Meterpreter Using Persistence, Metasploit Pro Meterpreter Using Scripts, Metasploit Pro Meterpreter Viewing Traffic on Target Machine, Metasploit Pro MS SQL Attacks , Metasploit Pro MS SQL Server Brute Forcing, Metasploit Pro MSF Exploit Execution, Metasploit Pro Msfcli, Metasploit Pro Msfconsole, Metasploit Pro MSFencode, Metasploit Pro Payloads, Metasploit Pro Port Scanning, Metasploit Pro Post Exploitation , Metasploit Pro Reverse TCP Payload Using Meterpreter, Metasploit Pro Server Message Block Scanning, Metasploit Pro SNMP Sweeping, Metasploit Pro SSH Server Scanning, Metasploit Proable Virtual Linux Machine Attacks, MySQL Vulnerability Analysis, Nessus for Discovering Vulnerable Services, Netcat to Create Backdoor Tunnel into Target Host, Netcraft Passive Information Gathering, Netstat to Display Kernel IP Interface table, Netstat to Display Kernel IP Routing table, Network Vulnerabilities Scanning, Nikto for Scanning Web Management Interfaces, Nmap Aggressive Network Scanning, Nmap Brute Forcing HTTP Authentication, Nmap Brute Forcing Password Auditing Joomla! Sites, Nmap Brute Forcing Password Auditing WordPress Sites, Nmap Brute Forcing SMTP Passwords, Nmap Detecting Backdoor SMTP Servers, Nmap Discovering UDP Services, Nmap Enumerating Host IP Protocols, Nmap Enumerating Users in an SMTP Server, Nmap Finding SQL Injection Vulnerabilities in Web Applications, Nmap Fingerprinting Host Operating System, Nmap Host Discovery, Nmap ICMP Ping Scans, Nmap Interactive Execution on Remote Host, Nmap List Scan, Nmap Matching Services with Security Vulnerabilities, Nmap Passive Network Scanning, Nmap Scanning for Open Ports and Services, Nmap Script Scanning, Nmap TCP ACK Ping Scans, Nmap TCP Idle Scan, Nmap TCP SYN Ping Scans, Nmap Testing Default Credentials in Web Applications, Nmap UDP Ping Scans, Nmap Vulnerability Script Scanning, NSLookup Passive Information Gathering for DNS, Offline Dictionary Attack, OpenSSH Vulnerability Analysis, OSINT, Overt Penetration Testing, Passive Footprinting, Passive Information Gathering, Password Retrieval, Penetration Testing Execution Standard (PTES), PTES From Start to End, Penetration Testing From Start to End for a Client, Port Scanning with Metasploit Pro, Post Exploitation, Post-Engagement Reports, Pre-Engagement Activities Report, Pre-engagement Interactions, Protocol Authentication/Registration Sniffing, Protocol Device Enumeration, Remote Code Execution Vulnerability Exploitation, Remote Host User Password Cracking, Remote Host User Privilege Escalation, Reporting, RPC Vulnerability Analysis, rtpbreak for RTP Stream Analysis, rtpflood for Denial of Service Attack, Session Initiation Protocol, Session Initiation Sniffing, Setting Up IVR with Asterisk, sflphone Softphone Configuration, SIP Device Enumeration, SIP Encryption Vulnerabilities, SIP Protocol, SIP Username Enumeration, SIPVicious Security Tools, SIPVicious svcrack, SIPVicious svcrash, SIPVicious svmap, SIPVicious svreport, SIPVicious svwar, SSH for Proxying Remote Host Services Using SOCKS, SSH Tunneling Between Multiple Hosts, SSH Tunneling Using Local Forwarding, SSH Tunneling Using Remote Forwarding, SSH Tunneling Using Port Forwarding, SSH Vulnerability Analysis, Stealth Scanning by Spoofing IP Address, Targeted Host Reconnaissance, The Phases of the PTES, Threat Modeling, vnak Multiple Protocol Attacks, VoIP Caller ID Spoofing , VoIP Phishing Attacks, Vulnerability Analysis, Vulnerability Scanners, Whois Lookups (Diverse) Passive Information Gathering, Windows XP Virtual Machines Attacks, Wireshark Network Protocol Analysis, Wireshark Data Stream Capturing, Wireshark Data Stream Playback, Wireshark Packet Capture Analysis, Wireshark RTP Stream Analysis, Wireshark Sniffing, Wireshark Traffic Graph Analysis, Wireshark UDP Stream Analysis, Xlite Softphone Configuration, ZenMap Network Scans using Diverse Modes.
Ethical Hacking and Countermeasures for Penetration Testing Official Curriculum, v8, EC-Council
Sample of Global Management Consulting Clients
Global and National Honors for Global CxO Practices Leadership -
CxO Think Tank - CxO Practices - CxO Guidance - CxO Keynotes
Includes strategic partnerships based upon invitation by the other party.
(USA, North America, Europe, Asia)Arthur Andersen Consulting (Accenture) (Managing Partners & Founders)
Bank of America
Banque Indo-Suez (Hong Kong)
British Telecom (UK)
Conference Board
Emerald Group Publishing (UK)
European Bank Merger (European Union)
Government of Mexico (Mexico: National Cabinet)
Government of Netherlands (Netherlands: National Cabinet)
Harvard Business School
Hewlett-Packard
IBM
Institute for Supply Management
Intel Corporation
JP Morgan
Knowledge Management Consortium International (Board of Directors)
Maeil Business TV Network & Newspaper (S. Korea)
Microsoft
MIT
National Science Foundation
Northrop Grumman Corporation
Ogilvy & Mather
Royal Philips Electronics N.V. (Netherlands)
Siemens AG
Silicon Valley Venture Capitalists and Tech CEOs
Tata Group (India)
Turkish Steel Conglomerate
U.S. Federal Government
Unisys Corporation
United Nations (World Headquarters)
UPMC
Vision Korea Campaign (S. Korea)
Wall Street Investment Bank(s)
Xerox
Ziff DavisPioneering Computational Quant Cyber Finance-IT-Risk Management ICT Digital Transformation
Princeton University Invited FinTech Research Presentations on Model Risk Management
Pioneering 'Open Systems Finance', 'Model Risk Arbitrage', and, 'Cyber Finance'
2015-2018: 58 SSRN Top-10 Research Rankings: Top-3% SSRN Authors:
Computational Quant Analytics; AI & Decision Modeling; Algorithms & Machine Learning
SSRN Top-10 Research Ranking Categories:
• Accounting Technology & Information Systems,
• Accounting, Corporate Governance, Law & Institutions,
• Artificial Intelligence,
• Banking & Insurance,
• Capital Markets,
• Cognition in Mathematics Science & Technology,
• Community College Education,
• Computational Biology,
• Computational Techniques,
• Computer Science,
• Computing Technologies,
• Conflict Studies,
• Corporate Governance Practice Series,
• Corporate Governance: Disclosure Internal Control & Risk-Management,
• Cultural Anthropology,
• Cyber-Conflict (Inter-State),
• Cyberlaw,
• Decision-Making under Risk & Uncertainty,
• Econometric & Statistical Methods,
• Econometric Modeling,
• Econometric Modeling: Capital Markets - Risk,
• Econometrics,
• Econometrics: Econometric & Statistical Methods,
• Econometrics: Mathematical Methods & Programming,
• Economics of Networks,
• Forensic Accounting,
• Government Expenditures & Education,
• Hedging & Derivatives,
• Information Systems & Economics,
• Information Systems: Behavioral & Social Methods,
• Information Technology & Systems,
• Innovation & Management Science,
• Innovation Finance & Accounting,
• Innovation Law & Policy,
• Inter-State Conflict,
• Interorganizational Networks & Organizational Behavior,
• IO: Productivity, Innovation & Technology,
• IO: Regulation, Antitrust & Privatization,
• Labor: Human Capital,
• Legal Perspectives in Information Systems,
• Machine Learning,
• Mathematical Methods & Programming,
• Microeconomics,
• Microeconomics: Decision-Making under Risk & Uncertainty,
• Military & Homeland Security,
• Mutual Funds, Hedge Funds, & Investment Industry,
• Operations Research,
• Pedagogy,
• Political Economy - Development: Public Service Delivery,
• Postsecondary Education,
• Risk Management,
• Risk Management & Analysis in Financial Institutions,
• Risk Management Controls,
• Risk Modeling,
• Risk, Regulation, & Policy,
• PSN: Security & Safety,
• Social Network Analysis,
• Sociology of Innovation,
• Stochastic Models,
• Sustainable Technology,
• Systemic Risk,
• Telecommunications & Network Models,
• Uncertainty & Risk Modeling,
• VaR Value-at-Risk.
Top Wall Street Banks' Model Risk Management Beyond VaR for Extreme Risks
FinTech Technical Expert to Top MDs Team for World's Largest Investment Bank
Beyond 'Bayesian vs. VaR' Dilemma to Empirical Model Risk Management: How to Manage Risk (After Risk Management Has Failed).
(Global Risk Management Network, LLC, 2012, 2014)
Measuring & Managing Financial Risks with Improved Alternatives Beyond Value-At-Risk (VaR)
(Global Risk Management Network, LLC, 2012).
FinTech Markov Chain Monte Carlo Models and Bitcoin Block Chain Encryption Protocols
Markov Chain Monte Carlo Models,
Gibbs Sampling, and, Metropolis-Hastings Algorithms
(Global Risk Management Network, LLC, 2013).
Complex Stochastics Hi-Dimensional Statistical Analysis
Bitcoin Protocol: Model of 'Cryptographic Proof' Based Global Crypto-Currency & Electronic Payments System
(Global Risk Management Network, LLC, 2013).
First Report on the Bitcoin Cryptographic-Proof-of-Work
Bayesian vs. VaR for Hedge Funds
Model Risk Management
Beyond "Bayesian vs. VaR" Dilemma to
Empirical Model Risk Management: How to Manage Risk
(After Risk Management Has Failed) for Hedge Funds'"It is this "true" uncertainty, and not risk, as has been argued, which forms the basis of a valid theory of profit and accounts for the divergence between actual and theoretical competition... It is a world of change in which we live, and a world of uncertainty...If we are to understand the workings of the economic system we must examine the meaning and significance of uncertainty; and to this end some inquiry into the nature and function of knowledge itself is necessary."
-- Frank H. Knight in Risk, Uncertainty, and Profit
(Boston, MA: Hart, Schaffner & Marx; Houghton Mifflin Co), 1921.
(Boston, MA: Hart, Schaffner & Marx; Houghton Mifflin Co), 1921.
'Knight Reconsidered':
Risk, Uncertainty, and, Profit for the Cyber Era: Model Risk Management of Cyber Insurance Models using Quantitative Finance and Advanced Analytics
(Global Risk Management Network, LLC, 2015).
Toward Integrated Enterprise Risk Management, Model Risk Management, & Cyber-Finance Risk Management:
Bridging Networks, Systems, and, Controls
(Global Risk Management Network, LLC, 2015).
Advancing Beyond 'Predictive' to 'Anticipatory' Risk Analytics:
CyberFinance: Why Cybersecurity Risk Analytics must Evolve to Survive 90% of Emerging Cyber Financial Threats, and, What You Can Do About It?
(Global Risk Management Network, LLC, 2016).
FinTech Enterprise Risk-Model Risk Management meet Penetration Testing-Ethical Hacking
Cybersecurity & Cyber-Finance Risk Management: Strategies, Tactics, Operations, &, Intelligence: Enterprise Risk Management to Model Risk Management: Understanding Vulnerabilities, Threats, & Risk Mitigation
CSO-CxO Plenary Keynote, National Cybersecurity Summit, Altria Group Inc. Headquarters, VA, 2015
A Framework for Pen Testing Network Protocols for Global Banking & Finance Call Centers: Bridging Networks, Systems, and, Controls Frameworks for Cybersecurity Curricula & Standards Development
(Innovative Design and Development Practices)
New York Cyber Security and Engineering Technology Association (NYSETA) Conference, 2015.
Griffiss CyberspaceTM Cybersecurity Venture Spans Wall Street & Cyber Research
Quantitative Modeling of Trust and Trust Management Protocols in Next Generation Social Networks Based Wireless Mobile Ad Hoc Networks
(Global Risk Management Network, LLC,
December 18, 2014.)
Griffiss Cyberspace Cybersecurity Venture Aims to Span Wall Street and Hi-Tech Research,
Cybersecurity, Financial Protocols & Networks Protocols Analysis, and, Penetration Testing
(Global Risk Management Network, LLC,
Summer 2013.)
FinTech Cognitive Analytics and Cryptanalytic Algorithms for Quantum Computing & Quantum Biology
Future of Bitcoin & Statistical Probabilistic Quantitative Methods: Interview by Hong Kong Institute of Certified Public Accountants
(Global Risk Management Network, LLC, January 20, 2014.)
Cryptology beyond Shannon's Information Theory: Preparing for when the 'Enemy Knows the System': Beyond NSF Cryptanalytic Algorithms
(Global Risk Management Network, LLC, 2013.)
AACSB Recognizes Real Impact among Nobel Laureates such as Black-Scholes
Pioneered Anticipatory Risk Analytics Frameworks Applied by Top Investment Banks
There are many examples illustrating that advances in basic research have had a substantial impact on practice. Exemplars of this phenomenon can be seen in finance through academic publications on the theories of portfolio selection (Markowitz, 1952), irrelevance of capital structure (Modigliani and Miller, 1958), capital asset pricing (Sharpe, 1964), efficient markets (Fama, 1965 and 1970), option pricing (Black and Scholes, 1973), and agency theory (Jensen and Meckling, 1976). All are well-known for their substantial impact on both theory and practice. In information systems, the research of Malhotra (Malhotra, 2004) has helped companies to understand why knowledge management systems fail...
The new business model of the Information Age, however, is marked by fundamental, not incremental, change. Businesses can't plan long-term; instead, they must shift to a more flexible "anticipation-of-surprise" model.
-- Yogesh Malhotra in CIO Magazine interview, Sep. 15, 1999.
Leading Global Enterprise Risk Management and Model Risk Management Practices
"The future is moving so quickly that you can't anticipate it& We have put a tremendous emphasis on quick response instead of planning. We will continue to be surprised, but we won't be surprised that we are surprised. We will anticipate the surprise."
The new business model of the Information Age, however, is marked by fundamental, not incremental, change. Businesses can't plan long-term; instead, they must shift to a more flexible "anticipation-of-surprise" model.
-- Yogesh Malhotra in CIO Magazine interview, Sep. 15, 1999.
[A Decade Later... Wall Street CEO, CFOs, & CROs know so... ]Digital Transformation Research & Practices Leading Global Firms & Governments
Pioneering e-Services & Knowledge Management Digital Transformation Practices
Global & National Thought Leader for UN, NSF, US & World Governments & Parliaments
Digital Transformation Venture Clients, Patrons, & SubscribersA sample of our corporate and organizational clients, patrons, and users is listed below:
FinTech Firms: Goldman Sachs, Google, HP, IBM, Intel, Microsoft, Ogilvy, Wells Fargo
Consulting Firms: Accenture, Ernst & Young, McKinsey, PricewaterhouseCoopers
World Governments: Australia, Canada, European Union, United Kingdom, United States
U.S. Defense: AFRL, Air Force, Army, CCRP, Comptroller, DISA, DoD, NASA, Navy, RAND
World Defense: Australia (Air Force), Canada (Defence R&D), UK (Ministry of Defence)
Business Schools: Harvard, MIT, Princeton, Stanford, UC Berkeley, Wharton
Associations: AACSB, ABA, ACM, AICPA, AOM, APICS, ASTD, ISACA, IEEE, INFORMS
"Founder Yogesh Malhotra says his vision is to fill the gaps between business and technology, data and knowledge, and, theory and practice..."
- Dr. Yogesh Malhotra in Fortune Interview
Digital Transformation Ventures in Global Business & Technology Press
Digital Transformation Research Interviews in Global Business & Technology Press
Cyber Transformation Practices Guiding US DoD Commanders & CxOs
MORE...
("Obsolete what you know before others obsolete it..."
- Dr. Yogesh Malhotra in Inc. Interview)
"If you spend some time at [the digital research lab] founded by Dr. Malhotra youwill be blessed by some of the world's most astute thinking on the nature of knowledge and its value."
- U.S. Army Knowledge Symposium, Theme: "Knowledge Dominance: Transforming the Army...from Tooth to Tail", US Department of Defense, United States Army.
"There are many definitions of knowledge management. It has been described as "a systematic process for capturing and communicating knowledge people can use." Others have said it is "understanding what your knowledge assets are and how to profit from them." Or the flip side of that: "to obsolete what you know before others obsolete it." (Malhotra) "
- U.S. Department of Defense, Office of the Under Secretary of Defense (Comptroller)
"KM is obsoleting what you know before others obsolete it and profit by creating the challenges and opportunities others haven't even thought about -- Dr. Yogesh Malhotra, in Inc. Technology Interview"
- U.S. Defense Information Systems Agency Interoperability Directorate
in Knowledge Management
(Ranked in Drexel University Global Survey of IS Practice)
Dr. Yogesh Malhotra among other 'Vision Korea' National Campaign Keynote Speakers in Vision Korea National Campaign (2000): Dr.Charles Lucier of Booz Allen Hamilton, Dr.David Snowden of IBM, Dr.Robert H. Buckman of Buckman Labs, Dr.Hubert Saint-Onge of Canadian Imperial Bank of Commerce, Professor Dr.Ikujiro Nonaka of Hitotsubashi UniversityPioneering AI & Decision Modeling; Algorithms & Machine Learning; CyberSecurity Risk Engineering
2015 & 2016: Princeton Quant Trading Conference, Invited Research Presentations
Sponsors: Princeton University, Goldman Sachs, Citadel, SIG, KCG.
AACSB & Scientific Studies Research Impact among Finance & IT Nobel Laureates,
Reviewed & Referenced in Top Business-IT-Finance Institutions & Publications.2015-2018: 58 SSRN Top-10 Research Rankings: Top-3% SSRN Authors:
Computational Quant Analytics; AI & Decision Modeling; Algorithms & Machine Learning.
RESEARCH & PUBLICATIONS - Download Full-Text Articles
AI & Decision Modeling; Algorithms & Machine Learning;
CyberSecurity Risk Engineering; Quantum Computing:
Frameworks, Models, Methods & Metrics in Applied Research
Digital Transformation BPR, e-Services, &, Knowledge Management Practices Pioneer
Led Global Virtual Team of 200-PhD Experts & CxOs to Publish Pioneering Research Leading Global Practices
"In his latest book, Knowledge Management and Virtual Organisations, KM luminary, Dr. Yogesh Malhotra, offers some cautionary advice. He exposes three myths often associated with KM solutions."
- Microsoft
Published: 2001"Knowledge Management and Business Model Innovation is an important addition to the IS researcher's bookshelf. It brings together the latest thinking on issues at the forefront of teaching innovation and professional imagination."
- M. Lynne Markus, Professor and Department Chair of Electronic Business, City University of Hong Kong
Developed Computerworld's Top Digital Research Site, Top-3 Search Engine, & Top-10 Social Network
Top-Ranked Digital Research Site: Computerworld Best Web Site Award
Top-3 Search Engine: Carnegie Mellon University Industry.Net National AwardsSample of Worldwide Editorial Reviews
"A Pretty Powerful Portal. Smart Stop on the Web."
- American Institute of Certified Public Accountants"A Virtual Library of the Best Sources for Knowledge Management and Intellectual Capital."
- Ellen M. Knapp, Vice Chairman & CKO, Coopers & Lybrand (later PwC) in Fast Company
"Will keep enthusiasts of Knowledge Management entertained for hours."
- Harvard Business Publishing"Tool for raising your company's IQ..." - Forbes
"What every CEO should know..." - Business Week
"Best business information source..." - Business Week
"A practical guidepost..." - Chief Executive
"Thumbs up for this serious surfer's tool useful for managers..." - Fortune
"Contemporary business management and technology issues..." - Wall Street Journal
"Pool of largest collection of knowledge management literature..." - Wall Street Journal
"One of the best HR sites on the Internet..." - Wall Street Journal: Career Journal
"Complexity theory made easy..." - Wall Street Journal
"Invaluable for applying complexity theory to business management..." - New York Times
"First on the list for in-depth sites for company and industry research..." - San Jose Mercury News
"If @Brint doesn't have it, then you probably don't need it." - Fast Company
"Best source for knowledge management and intellectual capital..." - Fast Company
"Best site for information technology and business information..." - Computerworld
"Wealth of incredibly rich, useful and interesting information..." - CIO Magazine
"Unparalleled in depth and relevance for business research..." - Information Week
"Best web site for keeping up with hi-tech industry developments..." - InfoWorld
"Best web site on the topic of knowledge management..." - InfoWorld Top-10 Social Network: Popular Rankings among others such as LinkedIn
Led Global Virtual Community of Practice of 130,000+ to Pioneer Digital Transformation Practices
Millions of worldwide users included Global-2000 Corporations and G-20 World Governments.Top-3 Search Engines Ranked in the Carnegie Mellon University: National Industry.Net Awards
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