Juntao Fang graduated from Boston University with a Master’s degree in Mathematical Finance and earned his B.A. in Finance at Shanghai University of Finance and Economics. During his graduate studies, he developed a solid foundation in mathematics, stochastic calculus, derivatives pricing and numerical methods. Prior to joining the Berkeley MFE Program, Juntao interned at various financial services companies. He mainly focused on developing factor-based models, using both fundamental and technical factors to develop alpha strategies and back-tested them using historical data to achieve excess returns. At Boston University, he worked on several projects, including utilizing optimal control and DPE to simulate a pairs-trading strategy, applying machine learning techniques like boosted tree based model (GBM, Adaboost, xgboost) to predict stock returns. He has also passed the CFA Level I exam. In his spare time, Juntao enjoys travelling, working out and, playing basketball
Pierre Foret attended ENSAE ParisTech where he studied Applied Mathematics, Statistics, and Economics. He will officially receive his Master's degree from ENSAE upon completion of the Berkeley MFE Program. During his studies, and in association with the French Antitrust Agency, he constructed a game theory model to investigate the effects of leniency programs. With the support of a Microsoft Venture incubator, he developed a face-clustering algorithm used to compute the screen time of various politicians over Youtube videos. Pierre earned a Machine Learning Engineer Nanodegree from Udacity and applied deep learning techniques to the recognition of Devanagari (Nepali) handwritten characters, reducing the state of the art misclassification rate by 74%. As a Fixed Income Strategist at Natixis, he used stochastic calculus and Monte-Carlo methods to create and implement a model for contingent convertible debts, providing clients an innovative tool to price these complex instruments. In his spare time, Pierre enjoys reading French literature, drawing, and playing bass guitar.
Richard Fu graduated from Imperial College London with First Class Honours in Mathematics in 2013. Previously, he interned at PNC financial services, where he used time-series analysis to forecast the probability of default rates of small businesses for the CCAR. He also interned at the Policy and Economic Research Council, where he processed geospatial data on ArcGIS to map alternative data in the San Francisco region. After graduation, Richard worked at China Bohai Bank, where he conducted tests on banking systems for simulated clients for various financial scenarios. Most recently he interned at Haitong International, where he assisted the trade desk by creating a verification algorithm to compare orders submitted by clients through a DMA and the actual orders executed by the traders. In addition, he researched and presented his analysis on characteristics of the Chinese market microstructure, such as factors that could affect the bid-ask spread. Richard also passed the CFA level I exam. In his free time, Richard enjoys playing chess, table tennis, and traveling.
Julien Gille obtained his Master in Economic & Financial Engineering from Université Paris Dauphine with honors. He is also currently completing a Master in Applied Mathematics at Ecole Centrale Paris and will receive his degree upon graduating from the Berkeley MFE program. While at Université Paris Dauphine for his Master’s degree, Julien worked as an Equity Derivatives Trader apprentice in Natixis Paris. As a corporate actions specialist, Julien implemented arbitrage strategies, was involved in risks hedging, tax management, and developed several pricing and reporting tools thanks to his VBA proficiency. He previously worked as a fixed income product specialist in Natixis Asset Management where he performed competitive intelligence analysis. Julien is deeply interested in quantitative finance. He is currently working together with Electricité De France research teams on the Python implementation of quantitative trading strategies using deep learning methods. In his master’s brief, Julien studied the predictability of oil prices using time-series analysis and linear regression methods. During his free time Julien enjoys playing soccer, tennis, and running.
Saurabh Gokhale received a Bachelor's degree in Computer Engineering from the University of Mumbai and a Postgraduate degree in Machine Learning from Rutgers University. During his undergraduate studies, he developed a solid theoretical and practical background in algorithms, while his graduate studies trained him in statistics, econometrics, time series analysis, and machine learning. Prior to joining the Berkeley MFE Program, he worked for three years as a big data developer in healthcare and analytics startups. During this time, he developed large-scale document processing workflows using open source technologies such as Apache Hadoop, Apache Solr and Apache Zookeeper. Saurabh has passed the CFA Level I exam. In his spare time, he enjoys playing cricket and soccer.
Raghav Gumber graduated from the University of Waterloo with a Bachelor in Mathematics concentrating in Statistics and Finance. While in University, Raghav had the opportunity to complete a one-year internship working for ING Investment Management as a Quantitative Analyst, where he was instrumental in developing and validating a new Economic Scenario Generator for their growing European Book of Variable Annuities issued by Insurers. Fascinated with the product and the insurance industry, Raghav went on to work for a preeminent Actuarial Consulting firm, Milliman, upon graduation. At Milliman, Raghav was tasked with running and improving the Hedging Program of various Insurers. Raghav has also been keen on continuing to develop his skillset by always challenging himself with interesting online courses such as Udacity Machine Learning Certificate, C++ Certificate from Baruch University, and additional certificates to further refine his programming knowledge. Apart from work, Raghav is an avid trekker and rower. Raghav has also recently completed three major treks in the Himalayas along with a few in South Indian Rainforests.
Tony He received his Bachelor of Science degree from the University of Toronto, where he earned the Dean's Honors Distinction with the specialization of Actuarial Science and major of Statistics. Tony is a Fellow of the Society of Actuaries and a Fellow of the Canadian Institute of Actuaries. Before joining the Berkeley MFE Program, Tony worked as a manager at Manulife Financial where he prepared product interest risk analysis by divisions and provided risk mitigation recommendations and EaR analysis. Tony also worked as an Assistant Actuary at Sun Life Financial, where he calculated present value for different group benefit products and updated valuation model assumptions. Prior to that, he performed pension liability valuation for US companies using actuarial principles and a variety of valuation and funding methods with Mercer’s pension valuation team. In addition, Tony ran a trading firm through March 2018 with two partners that was incorporated in Toronto. He conducted research on the volatility behaviors of options in the US market to find trading opportunities and trained regression models to improve trading performance. In his spare time, Tony enjoys cycling and tennis
John Hurford received his Bachelor’s degree in Business Administration from the University of San Diego. He worked at Bank of America for 7 years, beginning as an associate and after a number of promotions finished his time in a dual role as Portfolio Manager and Research Analyst. During his time at Bank of America, he implemented derivative strategies encompassing implied volatility, skew, and earnings data for client portfolios using equity options. He also co-authored white papers and research reports on fixed income & FX markets and portfolio construction techniques for dissemination to national portfolio management teams and clients. He researched and developed an FX model based on carry, momentum, and macroeconomic fundamentals to identify undervalued currencies. Additionally, he integrated a quantitative multi-factor model based on valuation, momentum and quality metrics to select equities. John completed the CFA program and is now a charterholder. In his spare time, he enjoys surfing, grilling and smoking various meats and seafood, and Olympic lifting
Victor (Yuhao) Jiang graduated from the University of California, Berkeley with triple majors in Applied Math, Statistics, and Economics (High Distinction) in 2015. He has 3 years of experience in financial risk management. Prior to joining the Berkeley MFE Program, Victor worked as a senior consultant with EY’s quantitative advisory service team, where he helped clients with model development as well as model validation. He has extensive experience in U.S CCAR (Comprehensive Capital Analysis and Review), Basel, and stress testing. Victor assisted 7+ banks with their CCAR projects, including stress testing framework design, credit risk model validation, model document, and model risk management. Victor also has experience with ALLL (Allowance for Loan and Lease Losses) methodology review and CECL/IFRS9 model development. Before he joined EY, Victor also interned with Standard Chartered Advisory, Esurance Insurance Company and Apple Inc. He has completed multiple online machine learning courses in his spare time, and participated in several data competitions. Aside from work and study, Victor enjoys cooking, playing basketball, and making animated videos
Nathan Johnson received his Bachelor of Science in Applied Mathematics from the University of California, Los Angeles. He then passed the first three Society of Actuaries examinations and worked as an actuarial analyst. While completing his Master of Applied Mathematics degree from California State University, Fullerton, Nathan worked with the RAND Corporation to develop a model that can describe and anticipate political conflict within various geospatial regions based on a self-exciting point process. His research resulted in two conference presentations and a first-author journal publication. Prior to joining the Berkeley MFE Program, Nathan worked as a Systems Performance & Algorithms Engineer at The Aerospace Corporation. There, he designed a convolutional neural network, in tandem with a statistical parametric model, to detect and extract features of ground-based targets using only low-resolution satellite imagery; the algorithm’s superior performance replaced prior design architectures. He also produced technical analysis that uncovered operational flaws in radar experimentation conducted by Jet Propulsion Laboratories; the analysis helped inform military acquisition of a new radar matrix. Nathan was also selected to be the TA for Dr. Johan Walden’s course, “Math Foundations for Financial Engineers” at Berkeley in Spring 2018.
Saurabh Kelkar graduated from IIT Bombay with a Bachelors of Technology degree in Chemical Engineering and a Minor in Electrical Engineering. Saurabh has interned with Quant AI Capital, where he has worked on developing medium frequency alphas for the Indian markets and performed research on index arbitrage using PCA for Indian bank index and its components. Prior to that, Saurabh has worked with Credit Suisse as a Senior Quantitative Analyst. He has worked on backtesting stochastic Credit Risk Models for counterparty credit risk and Risk Factor Evolution. His work in the Market Risk Team involved consolidating Value at Risk (VaR) changes due to market movements or model changes. Prior to Credit Suisse, Saurabh worked on several projects at Fractal Analytics where he implemented papers from journals for solving business problems such as expectile-regression for estimating impacts of discounts in CPG industry, synthetic controls modeling of time series for estimating impacts of experiments such as changing the name of the outlets, and a monte-carlo simulation for predicting cricket premier league’s outcomes using an extension of the Elo-Ratings model from chess. Saurabh looks forward to opportunities in data science in quantitative trading and research for strategy.
Abhinay Korukonda completed his Bachelor’s in Chemical Engineering from the Indian Institute of Technology, Bombay. Upon graduation, he worked at Gravitas Technology in the Portfolio Analytics & Risk team where he performed post-trade analysis of an $11 billion trade book. Ensuring that all market risks are analyzed, quantified, and reported in a timely manner to clients. He also executed POCs and pitched relevant risk reports to prospective clients. Additionally, he developed historical stress scenarios for a global macro portfolio using R. Later Abhinay moved to investment services, where he researched mortgage markets and developed a weekly presentation and spreadsheets for the traders’ team and CIO of a major US insurance group. With a strong interest in data science, he completed various projects and coursework online. In his free time, Abhinay likes to play board games and listen to music.
ing Lau graduated from Imperial College London with a Bachelor’s degree in Physics. He then received his Ph.D. in Physics from the Chinese University of Hong Kong, where he focused on finding analytic approximate solutions for free boundary and multi-dimensional PDE problems. His research work was closely related to applications in finance, and the collaboration between his research group and the Hong Kong Monetary Authority led to his first internship in finance, during which he implemented derivatives pricing models to extract market implied correlations from the iTraxx option data and further utilized those data to quantify the lead-lag information flow between the implied and realized correlations. Prior to joining the Berkeley MFE, Sing worked as a quantitative analyst at the market risk department of Goldman Sachs for over three years, where he was responsible for designing, implementing and optimizing the proprietary models for market risk calculations across the firm. He took the initiatives on leading multiple projects, which involved machine learning, algorithms, synchronizing risk models with the latest front-office pricing models, designing a new object-oriented framework to do machine monitoring on model performance. While preparing for the MFE, he discovered his new interests on artificial intelligence, and its applications to finance, and continues to broaden his knowledge in this area through online courses. In his spare time, Sing enjoys cycling and watching movies.
Kentin Le Faou completed a double masters’ degree in Banking and Finance at the Magistère Banque Finance from Panthéon-Assas University in 2017, and will graduate from the Master in Applied Mathematics of Centrale Paris upon graduation from the Berkeley MFE Program in 2018. He previously interned twice at HSBC Paris - first as a fixed income analyst in charge of the offshoring of the STP activity in Manila and Kuala Lumpur and the implementation of a new STP process, then as a trader assistant in charge of the pricing of exotic rates and derivatives products with stochastic volatility models. He recently worked as a quantitative analyst for BNP in Paris conducting research on stress test scenario definition and likelihood using Bayesian Nets with applications and limitations on synthetic and real portfolios. During his time at Panthéon-Assas he was at the managed the trading of the Assas Finance Society and organized the conference “The Influence of Low Rates in the Financial Industry.” He was also the delegate of the 2018 Class in Centrale Paris. In his spare time, he enjoys playing golf, English boxing, and learning new computer programming languages.
Teddy Legros attended ENSAE ParisTech where he studied Applied Mathematics, Statistics, and Economics. He will officially receive his Master's degree from ENSAE upon completion of the Berkeley MFE Program. During his studies, he completed several research projects in Statistics and Machine Learning. He worked on stochastic volatility models using MCMC methods and implemented a neural network in C++ for option pricing. He also conducted research on econometric models to reduce sparsity in OTC derivatives data for Hellebore Capital. Prior to joining the Berkeley MFE Program, he completed courses focusing on Deep Learning and Big Data, and passed the CFA Level I. During his internship with Societe Generale, he worked on statistical arbitrage and machine learning models for the Credit and Equity Derivatives trading desk. He implemented a machine learning pipeline in Python with advanced techniques for features selection, hyperparameters optimization, and model validation. At CrossQuantum, he developed a robot-advisor using risk-factor models, genetic programming, and dynamic portfolio analysis, providing clients with asset allocation strategies. In his spare time, Teddy works with ETFwave, implementing tools for data analytics and backtesting. He also enjoys reading French literature and traveling - he recently participated in a humanitarian race in the Sahara.