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

™

World Leading Hi-Tech Research Defining World Leading Digital, Computational, Quant & Cyber Risk Analytics Practices

Dr. Yogesh Malhotra: LinkedIn: Beyond 'Prediction' to 'Anticipation of Risk': Research Impact among Nobel Laureates: Princeton University Presentations: Digital Ventures:

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

Post-Doc Quant Presentations at Princeton,Quant Top-10 PhD Double Doctorate,MSQF,MSCS,MSNCS,MSAcc,MBAEco,BE,CEng,CISSP,CISA,CEH,CCP/CDP,CPA Education.

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

Post-Doc Computational Analytics-AI & Decision Modeling-Algorithms-Machine Learning: Princeton Quant Presentations,

PhD,MSQF,MSCS,MSNCS,MSAcc,MBAEco,

BE,CEng,CISSP,CISA,CEH,CCP/CDP

Who's Who in America^{®},

Who's Who in the World^{®},

Who's Who in Finance & Industry^{®},

Who's Who in Science & Engineering^{®}.

RESEARCH

SSRNGoogleScholar

Princeton

Syracuse

Research Impact among Nobel Laureates: Computer Scientist, Management Scientist, Information Scientist.

Wall Street Investment Banks-Hedge Funds Quant: Invited Princeton University Quant Trading Presentations.

Global Computational Quant Leaderships: Digital, Computational, Quant, Cyber Risk Management-Analytics.

World's Largest Banking & Finance and IT & Telecom Firms; Silicon Valley VCs & CxOs, Wall Street CxOs,

National Science Foundation; United Nations; US-World Governments, Economies & Defense Agencies.

E-mail: Dr.Yogesh.Malhotra[at]gmail.com LinkedIn: linkedin.com/in/yogeshmalhotra

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

2016 Princeton Quant Trading Conference Invited Research Presentation: Sponsor: Princeton University.

2015 Princeton Quant Trading Conference Invited Research Presentation: Sponsor: Princeton University.

2015-2016: 39 Top-10 SSRN Research Rankings: Digital, Computational, Quantitative & Cyber Risk Analytics.

2016 New York State Cyber Security Conference Research Presentation: Sponsor: New York State Governor.

2008: AACSB: Real Impact of Research among Nobel Laureates such as Black-Scholes & William Sharpe.

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

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

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

Dr. Yogesh Malhotra: LinkedIn: Risk Analytics Beyond 'Prediction' to 'Anticipation of Risk': Princeton University Presentations on FinTech CyberFinance

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

2015 & 2016 Princeton Quant Trading Conference Presentations: Computational Quant & Crypto Machine Learning Algorithms,

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

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

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

Research Impact Future of Finance Beyond VaR Model Risk Management SR11-7 OCC2011-12 Future of Risk Cyber Risk SSRN Google Scholar Publications

Top-10 IT & Statistics Double Doctorate PhD Computer Scientist, Management Scientist, Information Scientist, Chartered Engineer: Research Impact: Ranked among Finance-IT Nobel Laureates by AACSB & scientific impact studies. Senior Quant leaderships for MDs & PMs, Wall Street Banks & Hedge Funds with $1 Trillion AUM such as JP Morgan. Founder: Digital, Computational, Quant & Cyber Risk Analytics Ventures with CxO clients-patrons: e.g. Goldman Sachs, Google, Harvard, IBM, Intel, Microsoft: Recommended by IT visionaries such as Microsoft founder Bill Gates; Big-4 CxOs; US Army, Navy & Air Force CIOs. Invited Advisor: $100 Billion hi-tech firms such as Intel, Silicon Valley VCs-CEOs, US & World Governments, UN, NSF. Global Financial Systems Leader: Big-3 Finance-IT Firms such as Bank of America.

Princeton Invited Quant Presentations: Post-Doctoral Quant Analytics Research:

Global FinTech-TechFin Expert Leading Industry Practices: National Association of Insurance Commissioners, Cyber Risk Insurance Underwriting; Government of Switzerland, FinTech Algorithms; Invited Princeton Quant Trading Presentations, Sponsors: Princeton University, Goldman Sachs; 39 Top-10 SSRN Rankings: Decision Modeling, Stochastic Models, Econometrics, Mathematical Methods & Programming, Uncertainty & Risk Modeling. Computational Quant Finance-Risk Analytics Leader: MDs & PMs with Wall Street Banks & Hedge Funds 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 with SAS, MATLAB, C++, MS-Excel, VBA, Bloomberg.

Carnegie Mellon, Kellogg: Invited Executive Education Faculty.

Professor: Computer Scientist, Management Scientist, Information Scientist.

Invited Interviews-Editorial Reviews: Industry Benchmark: CEOs, CFOs, CIOs, CROs:

in global press including CIO, Wall Street Journal, New York Times, Fortune, Inc., etc.Post-Doctoral research in Computational Quantitative Analytics: AI & Modeling, Algorithms, Machine Learning

- Having received admission offers from Top-10 PhD Programs in both Accountancy and Economics

- Adding ~ 2x Credits of the 91 Cr 'Double Doctorate' PhD including four new Computational Quant Analytics graduate degrees.

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

PRMIA Executive Education in Quantitative Risk Management, Kellogg School of Management.

MFE Executive Education in Computational Quant Analytics, 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, Optimization Champion.

Certifications: C.Eng., CISSP, CISA, CEH, CCP/CDP, CPA-Education, SAS, MATLAB, SAP-ERP, SAP-CRM, AIB/ABA.

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 emerging Model Risk Arbitrage and Model Risk Management concerns 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 such fundamental assumptions and logic underlying widely used probabilistic, statistical, and numerical methods may not as readily meet the eye."

Source:Invited Interview on Bitcoin BlockChain Cryptographic Protocols, Hong Kong Institute of CPAs.

Reference: First Research Report on Bitcoin 'Cryptographic Proof' preceding Goldman Sachs & other reports.How applied research shapes worldwide practices - an exemplary illustration in context:

"[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."

Retained Exec Interview Spec: Managing Director/Executive Director, NYC, Top Wall Street Investment Bank; Interviewed: March 21, 2014.

2015-2017: 39 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,

• Computational Techniques,

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

• Cyberlaw,

• Decision-Making under Risk & Uncertainty,

• Econometric & Statistical Methods,

• Econometric Modeling,

• Econometrics,

• Hedging & Derivatives,

• Information Systems & Economics,

• Mathematical Methods & Programming,

• Microeconomics,

• Operations Research,

• Risk Management,

• Risk Management Controls,

• Risk Modeling,

• Stochastic Models,

• Systemic Risk,

• Uncertainty & Risk Modeling,

• VaR Value-at-Risk.

Other Categories:

• Banking & Insurance

• Cognition in Mathematics, Science, & Technology,

• Computational Biology,

• Cultural Anthropology,

• Economics of Networks,

• Innovation Law & Policy,

• Mutual Funds, Hedge Funds, & Investment Industry,

• Sociology of Innovation

## Math-FinTech-Algorithms: Transformation of Finance for Switzerland

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.## Princeton University: Offensive Cybersecurity & Model Risk Arbitrage

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

- Sponsors: Princeton University, Goldman Sachs, Citadel, SIG, Apr 16, 2016.

- 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.## Princeton University: Defensive Cybersecurity & Model Risk Management

• 2015 Princeton Quant Trading Conference invited Post-Doc Presentation.

- Sponsors: Princeton University, Citadel, KCG Holdings, Apr 4, 2015.

- 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.## Journal of Defense Modeling and Simulation (JDMS): STIX, TAXII, MCMC, Bayesian Networks

40+ Scientific Research Journals & Proceedings Editorial & Review Boards:

ACM, IEEE, IBM, MISQ, ISR, JMIS, Decision Sciences, JDMS:

Latest Expert Panel: Journal of Defense Modeling and Simulation (JDMS), 2016

(Cybersecurity, Bayesian Networks, Markov Chain Monte Carlo Models, STIX, TAXII, etc.)

- Society for Modeling and Simulation International.

Tier-1 Global Refereed Research Journals, Conference Proceedings, and Research Monographs:

100+ Computational Quant Analytics Modeling Reviews for Tier-1 Research Publications

Academy of Management Best Reviewer Award: Review of Structural Quantitative Models Best Paper.

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

Research Committee: Distinguished Scientists, Air Force Research Lab, Cyber Research Institute, SUNY.• 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

post-doctoral dissertationmakes 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.

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.

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 39 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

• 39 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

2015-2017: 39 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,

• Computational Techniques,

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

• Cyberlaw,

• Decision-Making under Risk & Uncertainty,

• Econometric & Statistical Methods,

• Econometric Modeling,

• Econometrics,

• Hedging & Derivatives,

• Information Systems & Economics,

• Mathematical Methods & Programming,

• Microeconomics,

• Operations Research,

• Risk Management,

• Risk Management Controls,

• Risk Modeling,

• Stochastic Models,

• Systemic Risk,

• Uncertainty & Risk Modeling,

• VaR Value-at-Risk.

Other Categories:

• Banking & Insurance

• Cognition in Mathematics, Science, & Technology,

• Computational Biology,

• Cultural Anthropology,

• Economics of Networks,

• Innovation Law & Policy,

• Mutual Funds, Hedge Funds, & Investment Industry,

• Sociology of Innovation