Global Risk Management Network, LLC, 757 Warren Rd, Cornell Business & Technology Park, Ithaca, NY 14852-4892

World-Leading Hi-Tech Research Pioneering World-Leading Hi-Tech Digital Transformation Practices^{TM}:

AI, Algorithms & Machine Learning; Computational Quant Analytics; Cyber Security Risk Engineering; Quantum Computing^{}.

Who's Who in America^{®}, Who's Who in the World^{®}, Who's Who in Finance & Industry^{®}, Who's Who in Science & Engineering^{®}.

Post-Doc Princeton Quant Trading Presentations: AI & Decision Modeling, Algorithms, Machine Learning; Computational Quant Analytics; Cybersecurity Risk Engineering.

Top-10 PhD IT-Statistics Double Doctorate, MSQF, MSCS, MSNCS, MSAcc, MBAEco, BE, CEng, CISSP, CISA, CEH, CCP-CDP, CPA Education.

INTERVIEWS IN WORLDWIDE BUSINESS & TECHNOLOGY PRESS: INSIGHTS & FORESIGHTS LEADING GLOBAL PRACTICES

EXECUTIVE SUMMARY

Model Risk Management Impact among Finance Nobel Laureates such as Black-Scholes, AACSB.

Post-Doc Princeton Quant Trading Presentations: AI & Modeling, Algorithms, Machine Learning.

Wall Street Hedge Funds Quant: Global Financial Systems Leader, Big-3 Finance-IT firms.

Hands-on Strategic, Operational, Tactical Leader:

Wall Street Investment Banks-Hedge Funds (Midtown Manhattan): Senior Quants Leader: MDs & PMs: $1 Trillion AUM: JP Morgan. Founder, CxO Computational Quant Risk Analytics Digital Ventures: Clients: Goldman Sachs, Google, Harvard, IBM, Intel, Microsoft: Recommended by Microsoft founder Bill Gates, Big-4 CxOs, US DoD CIOs. Big-3 Finance-IT (US, Hong Kong, India): Bank of America. Advisor: $100 Billion Hi-Tech Firms: Intel, BT (UK), Silicon Valley VCs-CEOs, US & World Governments, UN, NSF.

Post-Doc Computational Quant Research:

Pioneering AI & Modeling, Algorithms, Machine Learning: Princeton Quant Trading Conference: Sponsors: Goldman Sachs, Citadel: 41 Top-10 SSRN Rankings: Decision Modeling, Uncertainty & Risk Modeling, Mathematical Methods & Programming, Stochastic Models, Econometrics, Operations Research.

Computational Quant Finance-Risk Analytics Projects Leader:

Wall Street investment banks with $1 Trillion AUM such as JP Morgan: Quant Finance, Liquidity Risk, Market Risk, Credit Risk, Financial Econometrics & Programming, Large-Scale Data Modeling, Interest Rate Derivatives, Fixed Income & Equity Portfolio Modeling: SAS, MATLAB, C++, MS-Excel, VBA, Bloomberg.

Post-Doc Princeton Quant Trading Presentations: AI & Modeling, Algorithms, Machine Learning.

Carnegie Mellon, Kellogg: Executive Education Faculty.

Professor: Computer Scientist, Management Scientist, Information Scientist. Chartered Engineer.

Top-10 PhD IT-Statistics Double Doctorate: UPMC-Pitt.

5 Computational Quant Analytics Masters: 2 Computer Science, 3 Quant Finance.

Global CEO, CIO, CFO, CRO Benchmarks: Worldwide Press:

CIO, Wall Street Journal, New York Times, Fortune, Inc...Post-Doctoral Research: Computational Quantitative Analytics: AI & Decision Modeling, Algorithms, Machine Learning, Quantum Computing

40 Top-10 SSRN Research Rankings: Top 10% SSRN Authors; Princeton Quant Trading Conference Presentations;

Professor-Faculty: Computer Scientist, Management Scientist, Information Scientist; Chartered Engineer (C.Eng.)

Top-10 MIS PhD Double Doctorate: IT and Statistics: '45-Cr PhD' Credits inbothIT-Quantitative Methods & Statistics-Quantitative Methods: 91 Cr PhD.

- PhD Thesis Field Study: University of Pittsburgh Medical Center (UPMC): Digital Transformation of the UPMC: Quantitative Models.

PRMIA Executive Education: Quantitative Risk Management & Qualitative Risk Management, Kellogg School of Management.

MFE Executive Education: Computational Quant Analytics Financial Engineering C++, Statistics, Maths, University of California Berkeley.

MS* Cybersecurity: Cyber Risk Insurance, Algorithms, Machine Learning, AI & Modeling, Pen Testing Champion.

MS Computer Science: Computational Finance, Algorithms, Machine Learning, AI & Modeling, Cryptography.

MS Quantitative Finance: Derivatives, Stochastics, Securities Pricing, Risk Modeling, Risk Management.

MS* Accountancy: Finance, Auditing, Derivatives, Valuations, Global Financial Crisis: Risk Management.

MBA* Hypermedia Computing, Quant Economics, Advanced Statistics, Econometrics, Quant Modeling Champion.

BE Mechanical Engineering with Distinction: Dynamics of Machines, Electrical Engineering, Electronics,

Fluid Dynamics, Fluid Mechanics, Heat Transfer, Kinematics of Machines, Mechanical Engineering,

Engineering Design, Mechanics, Physics, Statistical Quality Control, Thermal Engineering.

Certifications: C.Eng., CISSP, CISA, CEH, CCP/CDP, CPA-Education,

SAS, MATLAB, SAP-ERP, SAP-CRM, AIB/ABA, Kauffman Foundation.

*Top Rank in Program, Outstanding Student Award, Perfect GPA Award, etc.

[41 SSRN Top-10 Research Rankings: Computational Quant Analytics: AI & Decision Modeling, Algorithms, & Machine Learning].

[Digital Transformation Pioneer] [AI, Algorithms & Machine Learning] [Computational Quant Finance] [FinTech: 'Rethinking Finance'] [CyberSecurity Risk Engineering]

Dr. Yogesh Malhotra: RESEARCH: Wall Street Quant: Big-3 Finance-IT Leader: Research Impact among Nobel Laureates: Princeton Quant Trading Presentations: Ventures:

[Digital Transformation Pioneer] [AI, Algorithms & Machine Learning] [Computational Quant Finance] [FinTech: 'Rethinking Finance'] [CyberSecurity Risk Engineering]

2015 & 2016 Princeton Quant Trading Conference: Sponsors: Goldman Sachs, Citadel, SIG, KCG Holdings.,

2008: AACSB: Model Risk Management Research Impact among Nobel Laureates such as Black-Scholes.

Research Impact Beyond 'Prediction' Future of Finance Beyond VaR Model Risk Management Future of Risk Cyber Risk SSRN Google Scholar Publications

Projects Goldman Sachs JP Morgan Wall Street Hedge Funds Princeton Presentations Model Risk Arbitrage Cyber Finance Cyber Risk Insurance Quantum Crypto

Bayesian vs. VaR Markov Chain Monte Carlo Wireless Mobile Trust Models VoIP Pen Testing Frameworks Bitcoin Cryptanalytics NFS Cryptanalytics Algorithms

Advancing on leadership of top Wall Street investment banks and hedge funds and having examined next-generation computational probabilistic modeling of financial econometric signals processing for global financial markets using computer science algorithms and machine learning, Dr. Yogesh Malhotra's computational quantitative risk management focus advanced to include global electromagnetic spectrum networks and global networking and encryption protocols. Spanning

deterministic,stochastic, andnon-deterministiccomputational statistical modeling methodologies, his current Computer Science, Computational Quantitative Analytics, &, Cybersecurity-Finance applied research focus is on high-dimensionality complex stochastic problems involving computational time complexity and computational space complexity relevant to emergingModel Risk ArbitrageandModel Risk Managementconcerns of Wall Street Chief Risk Officers such as the following."Recently, such probabilistic, statistical, and numerical methods related concerns are in globally popular press related to cybersecurity controls and compliance. Earlier, similar probabilistic, statistical, and numerical methods related concerns were in the global popular press in the context of the Global Financial Crisis. Future questions focused on the underlying assumptions and logic may focus on related implications for compliance, controls, valuation, risk management, etc. Likewise, recent developments about mathematical entropy measures shedding new light on apparently greater vulnerability of prior encryption mechanisms may offer additional insights for compliance and control experts. For instance, given related mathematical, statistical and numerical frameworks, analysis may also focus on potential implications for pricing, valuation and risk models. The important point is that many suchfundamental assumptions and logicunderlying widely used probabilistic, statistical, and numerical methodsmay not as readily meet the eye."

Source:Interview: Bitcoin BlockChain Cryptographic Protocols, Hong Kong Institute of CPAs, January 20, 2014.

Reference: First Research Report on Bitcoin 'Cryptographic Proof' preceding Goldman Sachs, December 04, 2013.How applied research shapes worldwide practices - an exemplary illustration in context (Retained Executive Search):

"[T]he approaches to mitigate operating risk associated with the use of models need to evolve to reflect recent trends in the Finance Industry. In particular there are a number of new areaswhere it is not possible for the "human eye" to necessarily detect material flaws: in the case of models operating over very small time scales in high frequency algorithmic trading, or for portfolio risk measurement models where outputs lack interpretability due to high-dimensionality and complex interactions in inputs, the periodic inspection of predicted versus realized outcomes is unlikely to be an effective risk mitigate. These situations require a holistic validation framework of the system focused on identifying and mitigating potential failures, taking into account the models’ objectives, their implementation including the joint interaction of software and hardware, their response to potential input shocks in real time and the fail-safe mechanisms."

Model Risk Management Managing Director/Executive Director, NYC, Top Wall Street Investment Bank; Interviewed: March 21, 2014.

Advancing on Top Wall Street Investment Banks’ Computational Quant Risk Management & Risk Analytics practices leaderships & Research Impact recognized among Finance-Economics and IT Nobel Laureates by AACSB & scientific impact studies continuing global leadership of Computational Quant Risk Management & Risk Analytics practices.

## Risk Management Analytics beyond 'Prediction' to 'Anticipation of Risk'™

• Risk Management Analytics research program that anticipated needs of Wall Street CEOs, CFOs, CROs to “anticipate risk” over a decade before they all said "we must anticipate risk" with invited machine learning research and media interviews in premiere press since 1990s.

• Advancing Fed/OCC SR11-7 and OCC 2011-12 Model Risk Management (MRM) Execution.

• Named among others such as Black-Scholes, Markowitz, Sharpe as "exemplar" of "considerable impact on actual practice"

- AACSB International Impact of Research Report, 2008.

• CNET Networks Corporate Computing Award for Most Influential Research, 2002.

• Interviews and Reviews of Related Research in: Wall Street Journal, Fortune, Inc., CIO, etc.

2015-2017: 41 SSRN Top-10 Research Rankings: Top-10% SSRN Authors:

Computational Quant Analytics; AI & Decision Modeling; Algorithms & Machine Learning:

SSRN Top-10 Research Ranking Categories:

• Capital Markets,

• Cognition in Mathematics, Science, & Technology,

• Computational Biology,

• Computational Techniques,

• Computing Technologies,

• Corporate Governance: Disclosure, Internal Control, & Risk-Management,

• Cyberlaw,

• Decision-Making under Risk & Uncertainty,

• Econometric & Statistical Methods,

• Econometric Modeling,

• Econometrics,

• Hedging & Derivatives,

• Information Systems & Economics,

• Interorganizational Networks & Organizational Behavior,

• Mathematical Methods & Programming,

• Microeconomics,

• Operations Research,

• Risk Management,

• Risk Management Controls,

• Risk Modeling,

• Social Network Analysis,

• Stochastic Models,

• Systemic Risk,

• Telecommunications & Network Models,

• Uncertainty & Risk Modeling,

• VaR Value-at-Risk.

Other Categories:

• Banking & Insurance

• Cultural Anthropology,

• Economics of Networks,

• Innovation Law & Policy,

• Mutual Funds, Hedge Funds, & Investment Industry,

• Sociology of Innovation

2016 Princeton Quant Trading Conference, Princeton University

Sponsors: Princeton University, Goldman Sachs, Citadel

Model Risk Arbitrage and Open Systems Finance, 'The Biggest Short'

Beyond Model Risk Management to Model Risk Arbitrage for FinTech Era

How to Navigate ‘Uncertainty’... When ‘Models’ Are ‘Wrong’... and ‘Knowledge’... ‘Imperfect’!

Knight ReconsideredAgain: Risk, Uncertainty, & Profit beyond ZIRP & NIRP

• 2016 Princeton Quant Trading Conference invited Post-Doc Presentation.

- Beyond Stochastics to Non-Deterministic Finance: Model Risk Arbitrage-Open Systems Finance:

Focus: Cybersecurity, Quant FinTech, Bayesian Networks, Non-Deterministic Probability-Statistics.

• Pioneered Computational Quantitative Analytics industrial research in Non-Deterministic Finance, Model Risk Arbitrage, Open Systems Finance, and, Black Hat Finance invited for presentation at the 2016 Princeton Quant Trading Conference.

2015 Princeton Quant Trading Conference, Princeton University

Sponsors: Princeton University, Princeton Bendheim Center & ORFE, Citadel, KCG Holdings

Future of Finance beyond 'Flash Boys'

Risk Modeling for Managing Uncertainty in Increasingly Non-Deterministic Cyber World• 2015 Princeton Quant Trading Conference invited Post-Doc Presentation.

Knight Reconsidered: Risk, Uncertainty, and Profit for the Cyber Era:

Future of Finance: Cyber-Finance?: Uncertainty Modeling & Model Risk Management

- Quantitative Finance Models for Extreme Risks: Cyber Risk Insurance Modeling & Finance.

Focus: Cyber Finance: Quantitative Finance, Computer Science, Cybersecurity, Machine Learning.

• Pioneered Computational Quantitative Analytics industrial research in Cyber Finance & Cyber Risk Insurance Modeling in collaboration with the committee of distinguished research scientists from AFRL/NYS-CRI invited for presentation at the 2015 Princeton Quant Trading Conference.

• Pioneering Quantitative Finance Analytics: Statistical Computing Methods & Algorithms

Cyber Finance: Future of Finance Beyond 'Flash Boys': Tail Risks & Systemic Risks:

Risk Modeling for Managing Uncertainty in an Increasingly Non-Deterministic Cyber World.

Advanced Risk Modeling Statistical Techniques: Econometrics & Time Series Models, Bayesian Inference, Markov Chain Monte Carlo, Capital Markets, Derivatives, Portfolio Construction & Optimization Models, Volatility Models, VaR, ARCH, GARCH, Multifactor Pricing Models, Market Risk, Credit Risk, Liquidity Risk, Cyber Risk Insurance Models.

• Invited ERM/MRM Keynotes to Top Investment Bank HQ and National CROs/CSOs.

• Invited Cyber-RM Expert Advice & Invited Interviews by MDs-Teams of Top Wall Street Firms.•

Math-FinTech-Algorithms: Transformation of Finance for Switzerland

Sponsor: Switzerland Federal Department of Economic Affairs.

Mathematics-FinTech-Algorithms: Digital Transformation of Global Banking & Finance, 2016.

Switzerland Federal Department of Economic Affairs, Education & Research EAER.

Enterprise Cyber Risk Management for CROs-CSOs National Plenary KeynoteInvited Plenary Keynote to National Fortune 100 Chief Risk Officers & Chief Security Officers:

National Chief Security Officers Summit, Philip Morris/Altria World HQ, Richmond, VA, Sep 2015.## State Street Bank World HQ Keynote on Modeling Extreme Cyber Finance Risks

Extreme Cyber Risk Computational Quant Finance Models, Aug. 2015:

State Street Bank World HQ, Boston, MA.Post-Doctoral Research: Computational Quant Analytics: AI, Algorithms & Machine Learning Research.

Princeton University Presentations on the Future of Finance: 'Rethinking Finance' Global Networked Digital Finance.

2016 Princeton Quant Trading Conference Presentation: Beyond Stochastic Models to Non-Deterministic Methods.

2015 Princeton Quant Trading Conference Presentation: Beyond Risk Modeling to Knightian Uncertainty Management.

Beyond 'Bayesian vs. VaR' to Model Risk Management: How to Manage Risk After Risk Management Has Failed.

Markov Chain Monte Carlo, Gibbs Sampling, Metropolis Algorithm for High-Dimensional Complex Stochastic Problems.

Risk, Uncertainty & Profit for Cyber Era: 'Knight Reconsidered': Model Risk Management: Cyber Risk Insurance Models.

Cybersecurity & Cyber-Finance Risk Management: Strategies, Tactics, Operations, &, Intelligence: ERM to MRM.

Number Field Sieve Cryptanalysis Algorithms for Most Efficient Prime Factorization on Composites: Beyond Shannon.

Bitcoin Protocol & Bitcoin Block Chain: Model of 'Cryptographic Proof' Global Crypto-Currency & Electronic Payments.

2015-2016 39 SSRN Top-10 Rankings: Computational Quant Analytics AI, Algorithms, Machine Learning Research.

2008 AACSB International Impact of Research Report: Among Black-Scholes, Markowitz, Sharpe, Modigliani & Miller.## Pre-empting the forthcoming Global Cyber Insurance Crisis by Pioneering Cyber Risk Insurance Models beyond Value-At-Risk (VaR) Analytics

• To avert the impending Global Cyber Insurance Crisis resulting from large-scale commercial reliance upon quantitative models with inherent model risks, tail risks, and systemic risks in current form, this dissertation makes the following key contributions.

• First, we develop the first known Cyber-Finance-Trust™ framework for Cyber insurance modeling to analyze how finance risk entangled with Cyber risk further exacerbates the systemic, interdependent, and correlated character of Cyber risks.

• Second, we develop the first known model risk management framework for Cyber insurance modeling as model risk management has received sparse attention in Cyber risk assessment and Cyber insurance modeling.

• Third, our review of quantitative models in Cyber risk and Cyber insurance modeling develops the first known analysis establishing significant and extreme model risks, tail risks, and, systemic risks related to predominant models in use.

• Fourth, we develop an empirical study of VaR and Bayesian statistical inference methodologies with specific guidance for containing model risks by applying multiple simple and advanced models for cross-checking the reliability of VaR.

• Fifth, we develop an analysis of the Markov Chain Monte Carlo Models, Gibbs Sampling and Metropolis-Hastings statistical computing algorithms for enabling Bayesian statistical inference methodologies to minimize model risk in Cyber risk and Cyber insurance risk modeling for the specific context of cybersecurity.

• Sixth, we develop the first known portfolio theory based framework for Cyber insurance modeling with guidance to minimize model risks, tail risks, and systemic risks inherent in models in commercial Cyber insurance modeling.

• Finally, given increasing role of uncertainty in cyber (and financial) risk modeling and management, we develop a framework for enabling Knightian uncertainty management relating it to model risk management.Wall Street Investment Banks Project Leaderships of Risk Modeling & Analysis for Banks with $1 Trillion AUM:

- JP Morgan Private Bank Multi-Asset Portfolio Fund of Funds with $500-600 Billion AUM, Midtown Manhattan.

- Mentor: JP Morgan Global Head of Quantitative Research & Analytics and US Head of Portfolio Construction

- Goldman Sachs Alumnus' Asset Management Firm with $400-500 Billion AUM, Midtown Manhattan.

Quantitative Finance, Risk Modeling, Computational Finance, AI-Modeling, Algorithms, Machine Learning, Computer Science, Network Science.•Project Leader: JP Morgan, Wall Street Hedge Funds, & Venture Capital Finance Projects.

Technologies: SAS, MATLAB, C++, MS-Excel, VBA, Bloomberg, NYSE-TAQ, CRSP.

Models: Derivatives, Credit Risk, Market Risk, Interest Rates, Equity & Fixed Income Portfolios.Credit Risk Models

Probability of Default (PD), Loss Given Default (LGD), Expected Default Frequency (EDF), Basel II/III, Exposure at Default (EAD), Worst Case Default Rate (WCDR), Risk Weighted Assets (RWA), Counterparty Risk, CreditMetrics, KMV, VaR, Credit Valuation Adjustment (CVA), Credit Default Swaps, Default Probabilities, Gaussian Copula, Simulations, Large Portfolio Approximation, Stress Testing

Market Risk Models

Volatility Models, ARCH/GARCH, MLE, 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, 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

Equity 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.

Project Leader, JP Morgan Private Bank Portfolio Liquidity Risk Modeling Framework, reporting to and guiding

Global Head of Quantitative Research & Analytics, US Head of Portfolio Construction and team. [Midtown Manhattan, New York City]

JP Morgan (JPM) Hands-On Team Leadership Projects Leader•

JP Morgan Fund of Funds Liquidity Assessment Framework Development LeaderMentor: JPM Global Head of Quantitative Research & Analytics, JPM US Head of Portfolio Construction:

JPM Top-4 Leadership ED in Global Financial Crisis Management, Harvard Case Study.

Advised: Team of Senior EDs/MDs, Portfolio Managers, Quants.JP Morgan Portfolio Construction & Optimization Liquidity Assessment Framework

Asset Pricing, Risk Management, Liquidity Risk, Market Risk, Credit Risk, ALM Risk, Portfolio Risk, Investment Risk, Non-Normality, Non-Linearity. MATLAB, SAS, C++, MS-Excel, VBA, Bloomberg.Developed Large Equities17-Asset Class Portfolio Liquidity Assessment & Stress Testing Research & Analysis

Developed Small Equities

Emerging Equity

Unlisted Equity

Various Commodities

Government Bonds

Investment Grade Bonds

Inflation-Linked Bonds

High Yield Corporate Bonds

Emerging Market Hard Currency Bonds

Emerging Market Local Currency Bonds

Major Currencies

Statistical Arbitrage Hedge Funds

Equity Hedge Hedge Funds

Merger Arbitrage Hedge Funds

Macro Hedge Funds

Relative Value Hedge Funds.

Technical Framework & Project Management Foundation:

Exhaustive Review of Recent 25-Years of Liquidity Measurement Research

Academic, Policy, and Practice Literatures:

Technical Liquidity Risk Models, Methods, & Measures Research:

~5,000 documents ~ 60,000 pages.Project Leader, JP Morgan Private Bank Portfolio Optimization & VaR Stress Testing, reporting to and guiding

Global Head of Quantitative Research & Analytics, US Head of Portfolio Construction and team. [Midtown Manhattan, New York City]

JP Morgan (JPM) Hands-On Team Leadership Projects Leader•

JP Morgan Portfolio Construction, Optimization & VaR Stress Testing LeaderMentor: JPM Global Head of Quantitative Research & Analytics, US Head of Portfolio Construction.

Technologies: MATLAB, SAS, C++, MS-Excel, VBA, Bloomberg

Alternative Investments, Hedge Funds, Equities, Commodities, Fixed Income, Bonds, Currencies

Asset Pricing, Risk Management, Liquidity Risk, Market Risk, Credit Risk, ALM Risk, Portfolio Risk, Investment Risk, Non-Normality, Non-Linearity.

Led quantitative portfolio liquidity modeling for $500B fund-of-funds & hedge funds (HF).

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 and presentations to Senior Quants, 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.

Led evaluation of third-party models, data, software for diverse asset classes.

Led inventorying of model assumptions and assessment of model risks for all assets.

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.Project Leader, Wall Street Hedge Funds Systematic Trading: State Street Trading Strategies Analysis

and High Frequency Econometric Liquidity Microstructure Modeling

reporting to and guiding Sr. VP/Portfolio Manager. [Midtown Manhattan, New York City]

Project: Hedge Fund Quantitative Finance & Quantitative Risk Modeling•

Goldman Sachs Alumnus' $400 Billion+ Asset Management Firm.Mentor: Wall Street SVP Hedge Fund Manager with Top Wall Street Investment Banks:

Harvard Computer Scientist & Mathematician Alumnus Wall Street Hedge Fund SVP/PM.

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.SAS High Frequency Econometric Modeling of Market Microstructure

400 State Street Advisors Trading Strategies Analysis for Alpha and Risk

Hedge Fund Performance Analysis

Quantitative Finance, Quantitative Risk ModelingAnalyzed 400 State Street Associates Trading, Hedging, and Risk Management Strategies.

Replicated /Analyzed Large Scale Data HF Econometrics Models of Market Microstructure.

Critical Review of State Street Associates Trading, Hedging, and Risk Management Strategies.

High Frequency Econometrics Models of Trade Price Impact & Market Microstructure.

Researched Co-Integrated Time Series for Ultra-High Frequency Tick-and-Quote (TAQ) Data.

Presented and Taught VARMAX Models of Co-Integrated Time Series for HF Econometrics.

Analysis: Why Existing `Alpha´ Research Is Insufficient for Profitable Hedge Fund Asset Management.Equity Portfolio & Risk Management Strategist, developed Financial Risk Analytics with 200 market data sources, did 3,500 equity trades in double-digit million US$ of 250 companies using technical, fundamental, structural analysis.

• Risk Analytics & Risk Management Specialist-Portfolio Strategist• Prior

* Execution & Risk Management of Long/Short Equity Trades

- 3,500 Buy/Sell Transactions in Double-Digit Million US$

- Equity Trades of 250 Companies across Diverse Sectors

- Technical/Fundamental/Structural Analysis

- Using Aggregated Data from 200 Market Data Sources.

* Development of Financial Risk Analytics Technologies

- Development Technologies

Unix, CGI, Perl, MySQL, PHP, C++, etc.

* Financial Modeling, Time Series Modeling, Structural Equation Modeling

SAS, SPSS, MATLAB, MS-Excel, VBA, AMOS, PLS, Compustat, WRDS, CRSP.Banking & Finance Analytical & Modeling Project Leadershipsfor Bank of America, Las Vegas; Crédit Agricole CIB, Hong Kong Govt. Treasury Management; Wells Fargo, and, Big-3 IT for Global Financial Systems of worldwide banks.

• Global Banking Financial Systems Projects Leader for Global Banks, USA & Hong Kong.• Founder,

Global Financial Systems Modeling, Models Quality Assurance, Development & Implementation.• Bank of America merger (Las Vegas), Bank of America Nevada.

Site Leader, Models Quality Assurance & Models/Systems Integration.

Senior Analysts/Analysts Team Leader, Systems Implementation.• Crédit Agricole Corporate and Investment Bank (Hong Kong)

Formerly, Banque Indo-Suez, Hong Kong

Algorithms Strategist & Technical Lead

Hong Kong Treasury Management, Multi-Currency/Forex Arbitrage.• Wells Fargo Bank (Davenport)

Formerly Davenport Bank & Trust Company• Big-3 IT, Unisys Global Financial Services (Atlanta, Norcross)

- CxO Management Consultant: TATA-Unisys Facilitation with CIO Mr. James A. Unruh.

- Senior Analysts/Analysts Team Leader.

Led Modeling & Development of Global Financial Systems Used by Worldwide Banks.• Tata Group Financial Services Division (Bombay, Delhi): Banking Projects, USA & Hong Kong.

Corporate Strategy Keynote to Strategic & Senior Leadership at the Global Big-3 IT Firm:

- Advancing Beyond Mainframes to Unix & C Software Services for Global IT Market Dominance.

Modeling & Development of Global Financial Systems Used by Worldwide Banks.

- Promoted to Systems Analyst, Global Financial Services, SWOT Mentor: Corporate SVP.

Programmed Algorithmic Language (ALGOL), 3GL & 4GL Systems, Hierarchical DBMS.Risk Analytics Ventures with clients & patrons such as Goldman Sachs, Google, IBM, Intel, Microsoft, Harvard.

Global Thought Leader & Advisor for Big-4, Fortune 100 CxOs, Silicon Valley VCs & CEOs

• Global CxO Risk Management Advisory & Consulting Practice.

- Invited Thought Leader, Accenture Consulting, Senior MDs & Practice Founders/Owners.

- $100 Billion Firms such as Intel Corporation, British Telecom (UK), Philips (Netherlands),

- Silicon Valley VCs & CEOs: 300 Venture Capitalists, Tech-CEOs, and Angel Investors.Founding Chairman & Chief Knowledge Architect, CEO/CIO/CTO, Risk Analytics Ventures.

• Venture Clients/Patrons: Goldman Sachs, Google, Harvard, IBM, Intel, Microsoft, NASA, etc.

• Founder & Executive Director, Global CxO ventures on Risk Management & Risk Analytics.

- Recommended by Top Tech Visionaries such as:Microsoft founder Bill Gates, Big-4 (PwC, E&Y) CxOs,

Harvard Business School Professors, US & World Governments.

- Recommended by U.S. AFRL/Army/Navy/Air Force/NASA CxOs.

Algorithms & Computational Finance: SAS, MATLAB, C++, C++11, Machine Learning, Signal ProcessingC++ 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 MachinesComplexity 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 SecurityShannon'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 SystemsThreads, 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 ModelingMultivalent 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.

Technologies of Computational Quantitative Modeling, Quantitative Finance & Risk ManagementAlgorithms: 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.

2015-2017: 41 SSRN Top-10 Research Rankings: Top-10% SSRN Authors:

AI & Decision Modeling; Algorithms & Machine Learning:

SSRN Top-10 Research Ranking Categories:

• Capital Markets,

• Cognition in Mathematics, Science, & Technology,

• Computational Biology,

• Computational Techniques,

• Computing Technologies,

• Corporate Governance: Disclosure, Internal Control, & Risk-Management,

• Cyberlaw,

• Decision-Making under Risk & Uncertainty,

• Econometric & Statistical Methods,

• Econometric Modeling,

• Econometrics,

• Hedging & Derivatives,

• Information Systems & Economics,

• Interorganizational Networks & Organizational Behavior,

• Mathematical Methods & Programming,

• Microeconomics,

• Operations Research,

• Risk Management,

• Risk Management Controls,

• Risk Modeling,

• Social Network Analysis,

• Stochastic Models,

• Systemic Risk,

• Telecommunications & Network Models,

• Uncertainty & Risk Modeling,

• VaR Value-at-Risk.

Other Categories:

• Banking & Insurance

• Cultural Anthropology,

• Economics of Networks,

• Innovation Law & Policy,

• Mutual Funds, Hedge Funds, & Investment Industry,

• Sociology of Innovation

Recent Research Presentations and Research Reports

Princeton University Presentations on the Future of Finance: 'Rethinking Finance' for Era of Global Networked Digital Finance.

2016 Princeton Quant Trading Conference Invited Research Presentation: Beyond Stochastic Models to Non-Deterministic Methods.

2015 Princeton Quant Trading Conference Invited Research Presentation: Beyond Risk Modeling to Knightian Uncertainty Management.

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.

Risk, Uncertainty, and Profit for the Cyber Era: 'Knight Reconsidered': Model Risk Management of Cyber Risk Insurance Models.

Cybersecurity & Cyber-Finance Risk Management: Strategies, Tactics, Operations, &, Intelligence: ERM to Model Risk Management.

Number Field Sieve Cryptanalysis Algorithms for Most Efficient Prime Factorization on Composites: Beyond Shannon's Maxim.

Bitcoin Protocol & Bitcoin Block Chain: Model of 'Cryptographic Proof' Based Global Crypto-Currency & Electronic Payments System.

2015-2016 40 SSRN Top-10 Research Rankings for Computational Quantitative & Risk Analytics Algorithms Machine Learning Research.

2008 AACSB International Impact of Research Report: Named among Black-Scholes, Markowitz, Sharpe, Modigliani & Miller

Top Wall Street Investment Banks Quantitative Finance Projects & FinTech Ventures

• Princeton: Future of Finance: 'Rethinking Finance' for Era of Global Networked Digital Finance

• 2016 Princeton Quant Trading Conference: Invited Research Presentation: Model Risk Arbitrage

• 2015 Princeton Quant Trading Conference: Invited Research Presentations: Future of Finance

• Quantitative Finance Risk Analytics Modeling Wall Street Investment Banks & VC Projects

• Model Risk Management: Risk Management Analytics from 'Prediction' to 'Anticipation of Risk'

• Quantitative Finance Risk Analytics, Econometric Analytics, Numerical Programming Models

• Quantitative Finance Model Risk Management for Systemic-Tail Risks in Cyber Risk Insurance

• JP Morgan Portfolio Optimization, VaR & Stress Testing: 17-Asset Class Portfolio

• JP Morgan Portfolio Liquidity Risk Modeling Framework for $500-600Bn Portfolio

• Bayesian VaR Beyond Value-At-Risk (VaR) Model Risks Exposed by Global Financial Crisis

• Goldman Sachs Alumnus Asset Manager Large-Scale Data High Freq Econometric Models

• Quantitative Finance, Risk Modeling, Econometric Modeling, Numerical Programming

• Technologies of Computational Quantitative Finance & Risk Analytics and Risk Management

• Algorithms & Computational Finance: C++, SAS, Java, Machine Learning, Signal Processing

• Cybersecurity, Financial Protocols & Networks Protocols Analysis, and, Penetration Testing

• Quantitative Finance, Quantitative Risk Analytics & Risk Management Projects Impact

• Digital Social Enterprise Ventures Creating Trillion $ Practices for Hundreds of Millions

Named among FinTech Finance & IT Nobel laureates for Real World Impact of Research

• FinTech Innovations: Model Risk Arbitrage, Open Systems Finance, Cyber Finance, Cyber Insurance

• AACSB International Reports Impact of Research among Black-Scholes, Markowitz, Sharpe

• Research Impact Recognized among Finance & Information Technology Nobel laureates

• 40 SSRN Top-10 Rankings: Computational Quant Analytics: Algorithms, Methods & Models

• FinTech Innovations: Model Risk Arbitrage, Cyber Finance, Cyber Risk Insurance Modeling

• Computational Quantitative Finance Modeling & Risk Management Research Publications

• Model Risk Management of Cyber Risk Insurance Models & Quantitative Finance Analytics

• Thesis on Ongoing Convergence of Financial Risk Management & Cyber Risk Management

• U.S. Federal Reserve & Office of the Comptroller of the Currency Model Risk Guidance

• Bayesian VaR Beyond Value-At-Risk (VaR) Model Risks Exposed by Global Financial Crisis

• Markov Chain Monte Carlo Models & Algorithms to Enable Bayesian Inference Modeling

• OCC Notes Cybersecurity Risk & Cyber Attacks as Key Contributor to Banks' Financial Risk

• Future of Bitcoin & Statistical Probabilistic Quantitative Methods: Global Financial Regulation

• Models Validation Expert Panels: IT, Operations Research, Economics, Computer Science

Global, National, & Enterprise CxO Level FinTech-Cyber-Risk Analytics Ventures

• CxO Think Tank that pioneered 'Digital' Management of Risk, Uncertainty, & Complexity

• CxO Consulting: Global, National & Corporate Risk Management Practices Leadership

• CxO Guidance: Cyber Defense & Finance-IT-Risk Management: Uncertainty & Risk

• CxO Keynotes: Conference Board, Silicon Valley, UN, World Economy: Uncertainty & Risk

• The Future of Finance Project Leading Quantitative Finance Practices at Elite Conferences

• The Griffiss Cyberspace Cybersecurity Venture Spans Wall Street and Hi-Tech Research

• UN Quantitative Economics Expert Paper & Keynote on Global Economists Expert Panel

• National Science Foundation Cybersecurity & Cybercomputing National Expert Panels

• Digital Social Enterprise Innovation Ventures Pioneering the Future of Risk and Quant

• Global Footprint of Worldwide World-Leading CxO Risk Management Ventures & Practices

FinTech: Wall Street & IT: Goldman Sachs, Google, HP, IBM, Intel, Microsoft, Ogilvy, Wells Fargo: Accenture, Ernst & Young, McKinsey, PricewaterhouseCoopers

Consulting Firms

Business Schools: Harvard, MIT, Princeton, Stanford, UC Berkeley, Wharton: AACSB, ABA, ACM, AICPA, AOM, APICS, ASTD, ISACA, IEEE, INFORMS

Associations

World Governments: Australia, Canada, European Union, United Kingdom, United States: AFRL, Air Force, Army, CCRP, Comptroller, DISA, DoD, Marines, NASA, Navy

U.S. Defense

World Defense: Australia (Air Force), Canada (Defence R&D), UK (Ministry of Defence)

: World Health Organization (WHO), U.S. Department of Health & Human Services,WorldHealth

European Health Management Association, U.K. Department of Health, UNESCO, UNDP

Larger Sample