Stevens Institute of Technology

-- Developing new financial technology courses focusing on practical skill development 
-- Coordinating industry/academic partnerships with a research focus for the NSF sponsored CRAFT Fintech Center

Associate Tch. Professor/Research Director of CRAFT Fintech Center

Geodesic Solutions

• Managing consulting relationships from initial design to final delivery and continued maintenance for a variety of quantitative finance, fintech, and technology oriented clients.  website: geodesic.pro 
• Example projects include, model development, review, and trading strategy optimization for proprietary trading and family office clients, runtime and memory profiling and associated refactoring for a large sell-side client, developing time series forecasting methods and related infrastructure for tech startup clients, and developing asset allocation algorithms for a wealth management robo-advisor startup.
• We have also worked over ten firms mostly in the finance area to design and deliver customized Python development training courses as well

President

New York University

Working with graduate students in the Courant Institute on a research project related to the design and implementation of a stable-coin cryptocurrency.

Visiting Professor

New Jersey Institute of Technology

• Design curricular for and instruct data science and financial technology courses for doctoral and undergraduate students focused on first learning theory and then constructing an implementation, from first principles, in the Python programming language
• Manage approximately a dozen research projects for students ranging from ambitious high school students, undergraduate capstone projects, and master and doctoral student research
• Teach students practical technology skills in the area of quantitative finance with a main focus on assisting them to gain entry level employment in the finance, insurance, and technology industries

Chair of Fintech Cluster and Assistant Professor of Finance

TUDOR INVESTMENT CORPORATION

-- Creating and searching a library of financial timeseries using nearest neighbor search to identify which subseries of all library timeseries most closely match a candidate series.
-- Exploring a variety of closeness definitions between two timeseries using Euclidean distance, correlation, as well as Fourier and wavelet decompositions. Plan on developing predictive analytics in order to identify potential patterns between features of the candidate series and its k-nearest neighbors with a focus on linear, support vector, and random forest regression methods. 
-- Examining how altering portfolio manager risk constraints such as volatility, VaR, and drawdown limits can improve individual and firmwide performance; proposing new risk limits based on results 
-- Developing and implementing new PCA based risk metrics for fixed income securities that generalize the idea of DV01.
-- Fitting option chain prices using nonlinear parametric regression; designing loss functions and regularization terms tailored to each option type and asset class (focused on commodities, equities, FX, and rates) and developing an optimization method appropriate for the objective function and constraints
-- Using option fits to estimate the future price distribution of the underlying asset at expiration.  Using the Boost.Python C++ library, CFFI, numpy vectorization, and multiprocessing/joblib to speed up Python computations, writing batch processes, logging functionality, unit tests, and monitoring nightly fit jobs
-- Recruiting, interviewing, and managing the projects of two summer interns.  Organizing a series of five quantitative finance lectures for the intern program.  Designing and conducting a quantitative analytics competition as part of the intern program
 -- Giving a technical talk at PyData NYC on correlation filtering and portfolio optimization: https://www.youtube.com/watch?v=kbK53gkPaRQ

Quantitative Analyst

Hutchin Hill Capital

My duties include:
--  Developing interactive Python/Bokeh web based dashboards as a front end for internal SQL databases that provide graphical time series and histogram displays of trading strategy P​&L, risk metrics, and accounting data
-- Creating, maintaining, refactoring, and at times reimplementing Python scripts that produce HTML based daily graphical and tabular risk reports that were distributed to senior risk staff and principals. The content of these reports involved monitoring individual strategy P&L, volatility, drawdowns, VaR, scenario analysis results, internal risk measures related to portfolio composition, portfolio betas to market indices, gross market values of security classes, etc.
-- Actively monitoring quantitative long short equity, global macro, credit, and associated hedging portfolios with an approximate aggregate market value of $10bn (total AUM is $4.2bn). Reported irregularities to senior management.  Specific securities monitored included equities, ETFs, futures, options (single name, index, and various FX option types), FX forwards, single name and index CDS, interest rate swaps, and corporate, sovereign, and convertible bonds 
-- Maintaining code that used Amazon Web Services to run distributed Monte Carlo simulations and aggregated summary statistics.  The results were used to assess how the fund's risk profile changed as capital allocations between different trading strategies were altered 
-- Speaking at Fordham, St. John's, and CUNY on the 2008 financial crisis as well as different industry quant roles.  Gave a technical talk on filtering noise from correlation matrices at the PyDataNYC August 2015 meetup
-- Programming in Python (NumPy, SciPy, pandas, matplotlib, scikit-learn, etc.), C/C++, q/Kdb+, SQL, MATLAB, Mathematica, Linux/bash/ksh, and occasionally VBA and R as well as using HTML/CSS, Javascript, and Excel for deployment

Quantitative Researcher

Morgan Stanley

My group interacts with traders, IT staff, and business managers to oversee the construction of  municipal bond trade databases, development of hedging and trading strategies for our desk, and design, development, and deployment of supporting analytics related to both the institutional and retail municipal bond market.  

Specific responsibilities include: 

-- Cleaning, wrangling, and analyzing a MSRB based municipal bond trade data feed
-- Using Bloomberg's MATLAB, Python, and Excel API to extract data needed for desk operations and to patch holes in house data stores
-- Using kernel regression to identify outlying bonds within indices of comparable bonds on an OAS basis and returning ranked results to traders that are used for subsequent fundamental analysis
-- Developing new hedging strategies and portfolios using futures, options, and liquid rates and credit structured products' price series to formulate and solve associated optimization problems
-- Interacting with traders to specify, implement, and optimize trading strategies while accounting for appropriate desk and firm risk limits   
-- Creating risk metrics for firm and regulatory compliance purposes and aiding in the preparation of associated risk reports
-- Mentoring NYU and Columbia financial mathematics students by leading an industry practicum project related to developing and backtesting corporate bond based strategies that utilizes statistics, stochastic processes, signal processing, and optimization methods

My main programming tools are Mathematica, MATLAB, Python (NumPy, SciPy, matplotlib, scikit-learn, etc.), C/C++, q/Kdb+, SQL, Linux/bash/ksh, and occasionally VBA. We deploy analytics using HTML/CSS, Javascript, and Excel to traders, risk analysts, and business managers.

Exams: Passed Series 7 and Series 63

Municipal Bond Strat Associate

Worked with a group of four NYU quantitative finance masters students on constructing portfolio level corporate bond valuation and risk measures as an industry mentor for their capstone project.

Industry Mentor

University of Chicago

Worked with a group of six U of C quantitative finance masters students on the development and implementation of Bayesian based value at risk metrics as an industry mentor.  Whitepaper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2490240

MIT Lincoln Laboratory

I use mathematics, statistics, and numerical analysis to address decision support problems.

Selected Projects: 

-- Designing a wavelet based synthetic aperture radar data compression algorithm
-- Developing a synthetic aperture radar change detection algorithm
-- Constructing a performance model for infrared object detection algorithms
-- Creating a phased array receiver area coverage model

Technical Staff

Interested in the application of mathematics, statistics, and optimization methods to practical data analysis problems with the aim of producing high quality objective data driven solutions;  Am dedicated to continually learning new technical topics, developing my skills, and making a meaningful contribution.

Please contact me if you are interested in discussing consulting opportunities.  

Consulting Site: https://www.geodesic.pro/

Github: https://github.com/steve98654


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