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Abitboul, Yohan Yohan Abitboul attended ENSAE Paris where he studied mathematics, statistics, and economics; he will officially receive his master’s degree from ENSAE upon completion of the Berkeley MFE program. While at ENSAE, Yohan completed several statistical and programming projects including a visual association exercise using NLP. Before joining the Berkeley MFE program, he worked as a researcher in quantitative strategies at BNP Paribas and contributed to a research paper on reversal strategy. He also worked at Datascientest, a start-up that teaches online data science courses. During this internship, Yohan helped design curricula in various fields such as computer vision and reinforcement learning and held meetings for client companies' programmers. In his spare time, Yohan enjoys playing soccer and chess.
Ahmad, Aman Danish Aman Danish graduated from the Indian Institute of Technology Kanpur with a bachelor’s degree in materials science and engineering. Before joining the Berkeley MFE program, Aman worked as a quantitative analyst at Credit Suisse in the Credit Analytics department. He was responsible for conducting quantitative impact studies to assess default risk of the bank’s trading books, where he recommended risk mitigation strategies that were further utilized by trading desks for effective hedging. Aman also assisted the strategy team in development and implementation of core default risk charge methodologies using Monte Carlo simulation in R. Using the Merton model, he extensively employed multifactor models to simulate issuer asset returns, which were used to determine credit risk. Before Credit Suisse, Aman worked as an Associate at Axtria, a data science firm where he employed test -control and regression techniques to deliver insights to Fortune 500 companies. Besides quantitative modelling, Aman is also enthusiastic about learning Machine Learning applications in finance. Recently, while working on his Nanodegree AI for Trading hosted by Udacity and WorldQuant, he generated trading signals on Python and learnt how alternative datasets can capture alpha. Aman has passed the CFA level
- In his free time, Aman likes to practice Latin dance forms such as Salsa, Bachata, Jive and enjoys participating in various dance festivals.
Ali, Hamza Hamza holds an undergraduate degree in computer science from the University of Punjab with a parallel undergraduate degree in Accounting and Finance from the University of Derby. During his undergraduate studies, Hamza built a strong foundation in economics, finance theory, statistics, and machine learning. While at it, Hamza also cleared CFA Level I and it was this that sparked his interest in risk modeling. After moving to the US in 2019, Hamza worked at USAA with the CCAR Team. It was here that he developed a keen interest in securitized assets and derivatives trading especially with interest rates and credit derivatives. At USAA, Hamza used real-time market data to build interest rate models such as HJM and Cox-Ingersoll-Ross models. These models were then utilized to simulate valuations for the Bank’s Bond Portfolio as well as valuation of various interest rate derivatives to be used for ALM and hedging purposes as a part of the CCAR exercise. After USAA, hamza interned at IvyLine Capital Group to gain exposure into the trading area. At IvyLine, Hamza implemented lattice and tree-based simulation strategies to build quantitative simulations on equity derivatives. He used Time Series models such as Garch and EWMA to model volatility and mean-reversion. He also implemented regression-based machine learning models to build factor models for equity assets and implemented PCA to identify key factors and clean the model.
Benard, Sheldon Sheldon Benard graduated with a bachelor's degree from McGill University on the Dean’s Honour List. Majoring in mathematics and computer science and minoring in statistics, Sheldon received both the J. W. McConnell Scholarship and Faculty of Science Scholarship for his academic performance. While at McGill, Sheldon focused on Machine Learning, participating in the ICLR Reproducibility Challenge where his team successfully replicated the results of a Discriminator Actor-Critic adversarial reinforcement learning algorithm. His studies equipped him with a solid foundation in stochastic processes, time-series analysis, probability theory, and statistical machine learning. Sheldon was introduced to financial markets while interning at BMO Capital Markets. During the internship, Sheldon and the Fixed Income Analytics team leveraged the Quorum blockchain platform to deliver the first-ever Canadian dollar fixed-income issuance on the blockchain. Sheldon subsequently joined the BMO Capital Markets Rotational Program as a Quantitative Analyst. Over the past 2 years, he has been applying technology to solve problems in the ETF, Fixed Income, and Structured Notes space. During his time on the ETF desk, Sheldon had the opportunity to explore state-of-the-art financial reinforcement learning academic papers, specifically with regard to portfolio optimization. On the Fixed Income Analytics desk, he developed a Fixed Income optimizer based on principal component analysis and formulated an attribution model for the optimizer. Further, he leveraged the Financial Information eXchange (FIX) protocol to implement a Fixed Income trading platform. With the Equity-Linked Structured Notes team, Sheldon built processes to generate bid/ask prices for BMO’s structured notes and implemented a pricer for the new Fund Basket Option notes.In his leisure time, Sheldon enjoys watching, betting on, and following the statistics of hockey. Sheldon also plays electric guitar and is part of an intramural soccer team.
Bhatia, Chandi Chandni Bhatia graduated with a bachelor’s degree from the University of Mumbai and post graduate Diploma from the National Institute of Securities Markets. Before joining the Berkeley MFE program, Chandni worked as a Market Risk Quant in the model risk management department at Morgan Stanley, where she evaluated a broad spectrum of risk and capital models in use within the bank. Her role involved questioning risk modeling methodologies and assessing model performance against actual market data. Chandni was involved in the development of various machine learning and deep learning challenger models in Python for existing market risk and fraud models for the bank. These challenger models consisted of techniques including Random forest, KNN, Convolution Neural Network etc. Leveraging her strong skills in Python, Chandni built a class based architecture to automate data parsing steps for the Credit VaR model. Under the new Basel FRTB regulations, she capitalized Morgan Stanley’s linear and nonlinear market risks. At Credit Suisse, Chandni worked in two different departments: she worked as a Market risk analyst where she was primarily responsible for FDSF submission. In her second role, Chandni worked as Quantitative Credit Risk modeler where she was involved in backtesting of Credit Suisse exposure models. Her work involved running Monte Carlo simulations in R to evaluate the risk models that simulate risk factors and the consequent exposure numbers for credit products. During her tenure at Morgan Stanley and Credit Suisse, Chandni has cataloged substantial experience working with models in areas of credit risk, market risk, and PPNR. She joined the Reserve Bank of India in the Advanced Financial Research department after a postgraduate course where she worked on various projects in trade finance, banking, and Prime Minister schemes. This experience helped her develop expertise in programming languages. Chandni has cleared FRM and CQF certifications. In her spare time, she likes to play badminton, travel and listen to music.
Chen, Zefu Zefu Chen received his bachelor’s degree in agricultural engineering from Zhejiang University. During his undergraduate studies, Zefu pursued a comprehensive curriculum including statistics, applied mathematics, programming and finance, gaining analytical and technical skills which included machine learning and data mining. Before joining the Berkeley MFE program, Zefu interned at Egret Asset in China, where he designed and implemented a custom genetic programming framework to generate features of commodity futures market data for return prediction and performed statistical analysis on these. He also investigated the intra-week and intra-day pattern of stock index futures and converted these into timing strategies. Prior to his role at Egret, Zefu worked in the research department of Hwabao Securities as a quantitative research intern. He conducted Maximum Entropy Spectral Analysis (MESA) to measure the cycle of index price and generated adaptive cycle indicators and developed timing strategies using the cycle and momentum effect of index data. Zefu also interned as a strategy analyst at Industrial Securities in Shanghai. There he conducted exploratory data analysis on foreign fund holdings in listed BRICS companies and generated statistical features of the data for stock market trend prediction. Apart from his internships in the financial sector, Zefu worked as a data mining research assistant for a project on Brexit public opinion, jointly run by Zhejiang University and China Central Television. Zefu collected and processed large amounts of text data from media with web-crawler and NLP methods such as word embedding. In his spare time, Zefu loves playing guitar and travelling.
Chen, Ziao Chen Ziao obtained his double bachelor’s degree with honours in computer science and business from Singapore Nanyang Technological University. Before joining the Berkeley MFE program, Ziao worked as a data scientist at GIC, the Singapore sovereign wealth fund. There he was responsible for building statistical and machine learning models for the external funds investment group. He worked closely with the portfolio managers in fields such as portfolio construction, portfolio monitoring and portfolio optimisation. One of the projects he worked on was to apply Natural Language Processing to extract text signals from fund reports and meeting notes, which demonstrated high predictive correlation to fund performance. Previously, Ziao also worked as a quantitative researcher in the systematic investment group where he improved an equity stock selection model to improve the sharpe ratio through residual study and factors selection. In addition, he also constructed machine learning portfolios in US equities using neural networks and gradient boost trees, which achieved >1.5 sharpe ratio. Ziao has passed CFA level III and FRM level I exams. In his spare time he enjoys badminton, basketball and dragon boating.
Cohen, Emile Emile attended Ecole des Ponts ParisTech where he studied applied mathematics, machine learning, economics and corporate finance. He also attended Ecole Normale Supérieure (ENS) Paris-Saclay for the MVA master program focusing on time-series, computer vision and deep learning. Upon completion of the Berkeley MFE Program, he will hold two master’s degrees from Ponts ParisTech and Ecole Normale Supérieure. During his studies at ENS, Emile worked as software/data engineer with Nalia, an early stage startup in customer success, and built a fully automated pipeline predicting the churn probability of customers using tools such as Python, JavaScript and various AWS services. He led a deep learning research project reproducing SOTA results on a video question answering task using ResNet, BERT and a new Hierarchical Conditional Relational Network (HCRN), coded in PyTorch. He also developed two financial algorithms: a portfolio optimization algorithm for decentralized finance (ethereum blockchain) based on sharpe ratio, and a debt pricing model relying on financial and macroeconomic parameters for AgDevCo, a specialist investor in African agribusinesses. As an undergraduate, Emile worked as a NLP Data Scientist with Dathena in Singapore and created a fine-tuned Named Entity Recognition (NER) tool using BERT. Emile has an entrepreneurial spirit and enjoys discussing new ideas in new markets. He is a basketball player and a fan of sliding sports (surf and snowboard).
Dang, Surbhi Surbhi graduated from Birla Institute of Technology and Science Pilani with a bachelor’s degree in computer science engineering and a master’s degree in economics. During her undergraduate studies, she developed a solid theoretical and practical background in finance, statistics, mathematics, as well as a penchant for competitive programming. Her academic projects helped sharpen her technical acumen in machine learning, econometric modelling, and macroeconomic scenario analysis. She interned at IBM India Software Labs, where she used Natural Language Generation to automate comment generation for IBM’s proprietary language. In her final year, as part of her Masters’ thesis, she used multiple measures of liquidity, profitability, and leverage, as well as stock prices to compare and evaluate strategies in Mergers and Acquisitions. Before joining the MFE program, Surbhi spent over two years with Tesco as a Software Developer.As part of the forecasting team, she developed a highly versatile platform analysing Tesco’s daily predicted sales. Surbhi also worked on a streaming application using Kafka Streams to process invoices generated every hour across all Tesco stores. As a passion project, Surbhi developed a trading bot to implement custom trend, momentum, and swing strategies to execute favourable risk/reward trades. In her spare time, Surbhi enjoys practicing badminton, boxing, and table tennis. She also likes playing poker, painting, and singing.
Das, Pradeepta Pradeepta received his bachelor’s degree in electrical engineering from the Indian Institute of Technology in Kharagpur. During his pre-final year, Pradeepta interned at Goldman Sachs in the Equity One-Delta team where he undertook various latency reduction projects for the algorithmic trading platform. Alongside his coursework he researched various applications of Deep Learning-based super-resolution algorithms in bio-medical imaging which led to the publishing of two research abstracts and a bachelor’s thesis. After graduation, he joined JPMorgan as a Quantitative Researcher where he supported volatility desks in the equity derivatives group. Working with the sales & trading team, he conceptualized and developed Factor Certificate - a structured product offering constant leverage, which is now traded on the Stuttgart Exchange. Pradeepta has also worked on a deep hedging research project where he explored the hedging behavior of barrier and cliquet products under market frictions with various Neural Network architectures such as FNN, RNN and LSTM. During the Covid sell-off period, he implemented spot correlation marking support for Monte Carlo local volatility-based derivative pricing engines as they failed to deliver the marked terminal correlations for the basket payoffs. He is passionate about contributing to the future where machines become more intelligent agents and work together with humans. In his spare time, Pradeepta enjoys following Formula 1, cricket, tennis and playing PC games.
Desai, Karan Karan Desai graduated from BITS Pilani with a bachelor’s in computer science and a minor in finance. As a part of his undergraduate studies, he carried out research in denoising stock market returns using autoencoders and PCA. As an undergraduate student, he interned at the Custody and Fund Services quant department at JP Morgan, after which he was offered a return offer in the Treasury Credit Risk division. During his time there, he was tasked with building a model for current expected credit losses for US municipal bonds which was subsequently used in CCAR as a part of Basel III regulations. This experience helped him understand econometric techniques used to model call risk and default risk in bonds. He was also responsible for developing a framework using Python and VBA to assess the impact of Covid-19 on CIO’s portfolio as well as the broader credit market. Through these responsibilities, he became proficient in Python and was also able to apply his theoretical knowledge to real life problems in finance and gain market intuition. Karan has passed the CFA Level 1 exam and also has a Bloomberg Market Concepts certification. In his leisure time, he likes to read, play soccer, and travel.
Garg, Srajan Srajan graduated with a Bachelor of Technology (B. Tech) in Computer Science with honors from the Indian Institute of Technology (IIT), Bombay in 2018. Apart from gaining an excellent command of core computer science concepts, he also built a strong foundation in mathematics, statistics and applied machine learning. An internship at Jane Street Capital as a software developer served as a solid introduction to the world of finance and he subsequently joined Tower Research Capital as a High Frequency Strategist. There, he traded several commodity derivatives across financial exchanges, which recorded a profit of over $40 million in
- He was primarily responsible for managing trading activities in asset classes including precious metals and petrochemicals in the Multi Commodity Exchange (MCX) in Mumbai, Shanghai Futures Exchange (SHFE), and Shanghai Gold Exchange (SGE) in China. He was responsible for building new technical indicators to model market movements, improving the existing signal building and strategy optimization pipeline, developing and maintaining a highly optimized C++ framework used to write strategies and analyzing market opportunities and behavior to determine the risk and exposure to be taken. Finally, he was also responsible for deploying, executing and monitoring the final strategy. He now looks forward to gaining a new perspective on the overall market dynamic through a more rigorous and theoretical understanding of financial markets. His short-term career goals aim at utilizing state of the art machine learning to quantitatively trade portfolios in a reputed hedge-fund or high-frequency trading firm. He’s excited about grasping the workings of hedge funds and would like to expand his horizon on mid-frequency trading, while leveraging the insights and foundation he has gained from his high-frequency experience. He pursues swimming and acrylic painting in his spare time and holds a penchant for maintaining an eclectic and fresh music taste.
Gautier, Marin Marin Gautier has managed to combine two passions at a high level: math and sports. Upon completion of the Berkeley MFE Program, he will also officially graduate with dual master’s degrees in mathematics and finance at ENSTA and ENSAE Paris. In parallel, after reaching national level in climbing and biathlon, Marin is now competing in the French professional triathlon league. While pursuing his master’s at ENSAE, Marin completed various projects on pricing and hedging a wide range of exotic financial derivatives through different methodologies including Monte-Carlo methods and partial differential equations. He also developed specific knowledge in machine learning, neural networks and linear regression with numerous programming languages: Python, Matlab, RStudio and C++. Mathematics is a real passion for Marin. He has a strong background in probability, statistics, stochastic calculus, and time-series analysis. He has also been able to share his knowledge as an instructor at engineering schools. Marin has enjoyed the discipline of high performance and achievement that both sports and mathematics demand.
Goel, Krishna Krishna graduated from Rice University with a bachelor’s in computer science and extensive coursework in statistics. While in college, he interned as a software engineer at a loan aggregator his sophomore year and was part of a founding team for an e-commerce platform his junior year. During his studies Krishna developed a keen interest in artificial intelligence, which he pursued through his academics while simultaneously undertaking research projects with professors. These included using various data science packages in Python such as Keras and Tensorflow to create CNNs and GANs. In his final year Krishna interned at AQR Capital as a software engineer where his performance led to a return offer. That same summer, Krishna started trading in equities and equity-options, thereby discovering his passion for investing. After graduating in May 2020, Krishna started to work at AQR full time as part of their systematic trading engineering team. He quickly learned and utilized languages he did not have prior exposure to such as Go and C# and consistently received excellent feedback from his managers regarding his performance. Krishna pursued his interests in investing and quantitative research on financial markets by partaking in seminars at AQR which addressed portfolio management and research. In his spare time Krishna enjoys playing card games, chess, jogging, and practicing the guitar.
Gong, Chu Chu Gong graduated from Beihang University in Beijing with a bachelor’s degree in financial engineering and double major in applied mathematics. Before joining the Berkeley MFE program, Chu worked at Harvest Fund Management DataLab as a data modeling intern. During this experience, she was responsible for modeling implied market valuation for corporate bonds via probability models and developing liquidity and trade cost measures for fixed-income traders. Chu also built a complete Python package for valuation and liquidity modules to achieve an automatic pipeline system for ETL, model training and results presentation. During her undergraduate studies, Chu interned at PBC School of Finance, Tsinghua University, and conducted quantitative research on factor models, which greatly improved her understanding about factor investing and how these effects vary in Chinese market. Chu has passed the CFA Level I exam. Besides professional certificates, Chu pursued online Coursera courses to satisfy her curiosity and passion about new trends and technology. In her spare time, Chu enjoys listening to music, playing piano and photography.
Gulati, Neil (Part-Time)
Neil graduated from University of California, Irvine in 2013 with a bachelor’s degree in mathematics. For much of his life, Neil was an international trampoline gymnast and competed in various events including: as a member of the Senior National Team, 5 World Championships, 3 Olympic Trials, 4 World Cup Circuits, and the World Games. While a student, Neil worked as an acrobatic performer to help fund his education, and performed half-time shows for NFL, NBA, FIFA, and NHL games. He has performed for crowds of over 100,000 people and has competed on NBC’s ‘Ninja Warrior’ and ‘America’s Got Talent’. In order to help fund his training expenses, Neil worked as a rank 1 coach and rank 1 judge working with athletes competing at the national level and served as the chair judge for several National Championships. After graduating from university, Neil successfully passed the probability and financial mathematics actuary exams, the latter sparking his interest in quantitative finance. Prior to joining the Berkeley MFE, he worked as a supply chain analyst in the medical device industry which is where he developed a passion for data analysis and cultivated his leadership skills. Neil is pursuing a career in financial engineering as it encompasses his passion for mathematics and finance and requires the dedication and drive of an athlete. In his spare time, Neil enjoys surfing, freediving and playing tennis.
Gupta, Ayush Ayush Gupta graduated with bachelor’s and master’s degrees in computer science from the Indian Institute of Technology, Delhi. During his studies, Ayush undertook a project in computer vision for tracking socio-economic conditions using satellite imagery which led to publication. For his masters thesis, he worked on building a pipeline for constructing Open Knowledge Graphs from large unstructured texts using NLP and Information Retrieval algorithms. He also served as a teaching assistant for post-graduate advanced data structures class and freshman CS class. After graduation, Ayush joined Goldman Sachs as a Strat in the Global Markets Division. He worked on modelling discount curves required for derivative pricing. In addition, he was responsible for building Infrastructure critical for Libor Transition of firm-wide trades. Ayush volunteered for mentoring new analysts in a Covid Visualisation Datathon and designing quant questions for university recruitment. Ayush also interned at Works Applications where he built a recommendation engine for their enterprise software using Data Mining techniques. Ayush also worked as a Summer Analyst in the Risk Division at Goldman Sachs where he built a data-driven framework for finding errors in regulatory stress reports. Ayush is a bronze medallist in the Asian Physics Olympiad. He enjoys listening to EDM, lo-fi Music and loves reading Philosophy, Tech and Productivity Blogs.
Hong, Taige Taige graduated from Northwestern University with a bachelor’s degree in industrial engineering and a combined master’s degree in computer science. During his studies, he interned at a Beijing-based securities company where he designed and prototyped an automatic factor mining program using Python to support the multifactor investment decision model. Taige also regularly immersed himself in the field of data science: he enrolled in several courses in machine learning and developed a deep understanding of the models’ uses and limitations, along with their application to the finance market. Before joining the Berkeley MFE program, Taige worked in a medical research lab to further strengthen his data analysis skills. He optimized the processing time of a series of RNA-sequencing experiments on large raw data sets and greatly accelerated the lab research. During his spare time, Taige enjoys playing card games and cooking.
Huang, Edward Eddie Huang attended UC Berkeley where he majored in Economics. During his undergraduate studies, Eddie developed a foundation in mathematics, statistics, and physics, but specialized in financial markets and macroeconomic policy. Eddie started his career as a Sales and Trading Analyst at Citigroup where he developed regression models to identify price to fair value dislocations and used quantitative skills to analyze data and propose trading ideas. While working at GitHub, Eddie leveraged his programming skills to lead data driven business decisions in launching and pricing new products as well as engineer new database infrastructure. Eddie has combined the programming and finance skills as a Quantitative Researcher at GMO to research and create alpha signals. Personal projects Eddie has taken on include building machine learning models aimed at predicting user churn and spam detection. In his free time, Eddie enjoys surfing, skiing, cycling, and playing soccer.
Jain, Apeksha Apeksha Jain graduated with honours from BITS Pilani in India with a bachelor's degree in computer science and a master’s degree in mathematics. Her summer internship in the quantitative research team at JP Morgan introduced her to the field of quantitative finance. There she worked on the development and the implementation of a closed form solution for T/T-1 Corridor Variance Swap Instruments. During her final year of graduation, she served as an off-cycle intern at Moody’s Analytics Knowledge Services in their Quantitative Services team where she primarily worked on providing FX and interest rates hedging solutions to large enterprises in collaboration with a European bank. Post graduation, she joined JP Morgan full-time as part of the Equity Derivatives Group Modelling Team where she worked on various projects which helped strengthen her programming skills in Python and C++ and further sharpened her acumen in mathematics and finance. She was tasked with performing reviews of the firm’s mathematical models for Barrier and Delta 1 products in the equity domain for the Market Risk Team and was also instrumental in automating the Model Usage Restrictions framework and implementing a framework for the Fundamental Review of Trading Book. As a part of her thesis at the Hanlon Financial Systems Lab at Stevens Institute of Technology, under the supervision of Dr. Ionut Florescu and Dr. Mayank Goel, Apeksha developed a feature selection algorithm for accurately predicting the credit ratings of any company from its balance sheet financials and financial ratios. This helped her hone her data science skills and expand her statistical knowledge. Apeksha has passed the CFA Level 1 exam. She is a trained Indian Classical singer and a dance enthusiast. In her leisure time, she also enjoys reading, graphic designing and playing basketball.
Jantanasaro, Taweepol Taweepol holds a master’s degree in finance and a bachelor’s degree in economics from Thammasat University in Thailand. He has three years of experience working as an internal auditor at the Bank of Thailand with a focus on auditing the reserve management process. This work has allowed him to understand the end-to-end investment process ranging from portfolio construction and trading through to risk management.He later moved internally to the financial risk management department where he worked for another three years and was assigned many important projects including revising the risk management framework of reserve management and developing tools to monitor risk using advanced financial knowledge. This involved incorporating vine copulas into risk analysis and machine learning to assist in developing early warning indicators. He was also involved in setting up different emergency policies to alleviate the financial and economic shock from the Coronavirus pandemic. Outside of his formal responsibilities, he was chosen to be a member of the Risk Management Subcommittee for Bank of Thailand Employees’ Thrift and Credit Cooperatives which oversees the investment risk of the employees’ savings. He has passed the CFA Level 3 exam. In his spare time, Taweepol enjoys playing soccer, badminton, and table tennis as well as reading economics and sports news.
Kazmane, Ali Ali will hold a Master of Financial Engineering from UC Berkeley and a Master in Financial Economics - Applied Mathematics (Double Major) from EDHEC upon completion of his coursework at Berkeley. Using different programming languages (Python, R and C++), Ali completed several research projects including his master’s thesis on factor selection in finance. He was able to review asset pricing models and risk premia using machine learning and dimension reduction techniques (Python implementation). He has a solid understanding of complex financial products and has participated in many automatization projects and coded a (faster) PNL reconciliation program in Python. During an asset management internship he conducted research on the application of Arima/Garch in portfolio optimization problems (R implementation). As a high school student in Morocco, Ali played in an U17 soccer team which helped him develop a winning mindset and discover the importance of a team. He is also interested in geopolitics and Formula 1.
Kumar, Pankaj Pankaj Kumar holds a bachelor’s degree from the Indian Institute of Technology (IIT) Bombay, where he developed a strong foundation in mathematics, statistics, and programming through his involvement in various research projects and internships. Overall, he has more than four years of professional experience in the investment management and financial services domain. After graduating, he joined MSCI’s quantitative risk team where he was responsible for creating and maintaining models for the calculation of Credit and Market exposures. He worked with risk team members in Boston and Japan for reporting existing and incremental risk metrics in swaps and other exotic instruments across major asset classes. He also built analytical tools and computations using Excel and Python. Pankaj then worked in the Global Equity Beta Solutions team at StateStreet Global Advisors as a quantitative ESG researcher. He was responsible for creating equity portfolios for institutional clients and conducting performance attribution analysis using factset and Bloomberg. He was actively involved in research on new ESG factors including GHG emissions and climate metrics and carried out factors integration (reduced carbon, mitigation - adaptation) into client’s systematic investment strategies. Pre-MFE, Pankaj interned at a medium frequency hedge fund where he worked on generating systematic trading signals (alphas) for Indian equity markets. His research ideas leveraged news-based sentiments, technicals, group momentum, ex-ante & ex-post returns. He has also completed independent coursework in Machine Learning Engineer hosted by Udacity. He has cleared CFA level 3. Pankaj is an avid cricket enthusiast and likes to listen to music in his spare time.
Lei, Ranran Ranran Lei received her bachelor’s degree in finance from Fudan University. During her undergraduate studies, Ranran enlisted in a comprehensive curriculum including statistics, programming and finance and her interest in data science encouraged her to take some basic courses in machine learning. Before joining the Berkeley MFE program, Ranran interned as a quantitative researcher at Luoshu Investments and Brightridge Investments. At Luoshu, Ranran investigated the momentum effect and CTA strategies of commodity futures and created effective factors, reaching a Sharpe ratio of 2.5. On a further project, she utilized the BAW model to calculate the implied volatility through optimization methods, such as Newton-Raphson and Dichotomy. Ranran continued to apply her interest in quantitative finance with a project in high- frequency trading research. Based on the microstructure of Chinese financial markets (including stocks, futures, convertible bonds) and the bitcoin market, Ranran utilized C++ programming to develop and implement several price forecast signals from the orderbook and transaction data, such as the imbalance of ask and bid size. Not only did she guarantee the stability of signals, but ensured prompt convergence and norm- like distribution of all the signals without weakening the performance. She also applied some machine learning models such as ridge regression in her predictions. In her spare time, Ranran enjoys Chinese traditional painting and climbing.
Lertpienthum, Kanchanit Kanchanit graduated with a bachelor’s degree in economics from Thammasat University in Thailand. While an undergraduate, she participated in many competitions including National Economics Contests, CFA Research Challenge and various Business Case competitions. Her first internship was with the Bank of Thailand where she performed research on the labor market, identified key gaps between labor demand and supply and then tried to solve the existing gap by utilizing a scholarship scheme Later, she interned as an investment banking analyst,where her responsibilities included conducting a valuation analysis for IPO stocks and real estate investment trusts, collecting and analyzing the related market data, running sensitivity analysis on the valuations , as well as writing and compiling data for the filing documents. Her most recent internship was as a project analyst for a real estate development company where she analyzed the project feasibility, financing options and projected cash flow for a residential project with a market value of $8,000,000. Kanchanit is currently working as a risk management officer at the Bank of Thailand with responsibility for overseeing portfolio performance, assessing the validity of current portfolio mandates, investment criteria and risk measures, quantifying risks of new mandates and investment schemes, as well as keeping track of potential risks arising from the global markets.In her spare time, she enjoys reading, and coaching for the business case competition club that she co-founded at Thammasat university.
Li, Xinyu Xinyu Li graduated from Tsinghua University with a bachelor’s degree in automation and a double major degree in economics. He will also receive a master’s degree in finance from Tsinghua University upon completion of the Berkeley MFE program. During his studies, Xinyu gained a solid background in deep learning, statistics, econometrics, and financial derivatives. In addition, he gained proficiency in C++, Python, Matlab, and C#. During a research internship at Tsinghua University, Xinyu theoretically modeled the turbine engine degradation and used the LSTM model for dynamic maintenance planning, the results of which have been applied in multiple practical engineering projects. Xinyu also completed four internships in quantitative finance covering Alpha research, CTA strategy, FOF investment strategy, and derivatives trading. As a quantitative intern at Lingjun Investment, the top quantitative hedge fund in China, Xinyu conducted research in developing daily high-frequency alphas using Level-2 price and volume data. He identified over 50 qualified alphas with a Sharpe ratio over 5 and correlation under 0.7 based on Linux system backtesting. He also interned as a financial engineer with Huatai Securities, where he built a fund-style classification algorithm and initiated the FOF strategy based on a Fourier transform and multifactor model. While working as a futures quantitative research intern at Shenwan Hongyuan Group, he independently proposed the main position analysis model based on resistance support relative strength and developed the trading strategy. In his spare time, Xinyu enjoys socializing with friends, playing badminton, and cycling.
Li, Yijie Yijie received her bachelor’s degree in economics from Nanjing University. She is passionate about financial engineering and interested in applying quantitative methods to solve financial problems. As a quantitative strategy assistant, she built econometric models to reveal the relationships among the prices of different futures to develop trading strategies, and optimized trading signals in the backtesting. During her internship with Guotai Junan Securities, Yijie developed insights into commodities by optimizing a statistical arbitrage strategy on the silver price difference between two markets. More recently, Yijie worked at a fintech where she used Python and SQL to design and backtest factors for industry research and model development. Yijie is also interested in machine learning and data analysis and has earned the machine learning certificate from Coursera. On one project, Yijie adopted the Gini Index and Information Gain method for data mining, and employed LSTM, GRNN, and PNN to predict the stock market. Her main hobbies include piano, Chinese Calligraphy, hiking, and watching tennis.
Liang, Rongbing Rongbing received his bachelor’s degree in finance from Zhejiang University and holds a master’s degree in statistics from Columbia University. He most recently worked full-time as a CTA strategy developer in a small Beijing-based hedge fund where he devised a short-term trend-following system with EDR position sizing to trade on selected futures. He also examined and identified momentum patterns and tested multiple settings of trend-following trading rule optimizations. In addition, Rongbing interned with several large securities companies where one of his assignments was to develop a risk monitoring system algorithm which tracked listed companies via NLP techniques and ensemble modeling based on SVM and random forest. Apart from finance and statistics, he is proficient in accounting and is a member of CICPA (Chinese Institute of Certified Public Accountants). Rongbing has enhanced his machine learning skills by taking electives and participating in Kaggle competitions and recently gained certification as a junior psychological counselor.
Liu, Yupeng Yupeng earned his bachelor's degrees summa cum laude in financial mathematics from CUNY Baruch College and Southwestern University of Finance and Economics in a double-degree program. Before joining the MFE program, he interned at Guotai Jun’an Securities, one of the largest securities companies in China, as a quantitative researcher focusing on Fund-of-Funds (FOF) strategies and asset allocation models. He carried out factor-mining for funds, constructed a FOF strategy which tried to exploit market signals and select high-alpha funds, and built a model completing the holdings of funds with limited public information, which was published in a widely-read research report. He also researched multiple asset allocation models including Hidden Markov Chain. Yupeng also gained experience at Suntime Corporation, a leading Chinese fintech, where he successfully constructed a factor model for stock based on a factor database, and built an entire top-down quantitative strategy including asset allocation, sector rotation and fund evaluation. Yupeng also worked as a research assistant in Support Vector Machine which served as a solid foundation in machine learning. In his spare time, Yupeng enjoys traveling, playing soccer and skiing.
Madayan, Alec Alec holds a bachelor’s degree in econometrics and a master’s degree in economics from University of Paris, Panthéon-Assas. He also graduated from ESSEC Business School with a master’s degree in financial techniques. He enrolled in a master of research in artificial intelligence and data science from PSL University (joint degree between Paris-Dauphine, ENS Paris & Mines ParisTech) before attending the Berkeley MFE. Deeply interested in investing, has considerable experience in quantitative investment strategies and economic modelling. First, at Exane in fixed income research, where he developed a tool for pricing and trading CoCo bonds using R, involving exotic options, stochastic volatility models and Monte-Carlo simulations. He also worked on ARDL-ECM modeling for government debt pricing. This led to the redaction of a master’s thesis at ESSEC, where he was the only A+ of the cohort. He then worked in quantitative research at BNP Paribas Asset Management on both volatility and systematic option overlay strategies. The strategies were developed in Python mainly using equity options but involved some aspects on the credit side as well. Alec is also interested in artificial intelligence and machine learning. He got entrepreneurial experience at Dental Clinic Awada where he developed deep learning algorithms to detect dental anomalies on dental panoramic X-Rays. Also, in the context of his master in artificial intelligence, he worked on several long-term projects covering, among others, linear bandits and reinforcement learning, NLP for investing, prototypical network and latent space, optimization and mathematical properties. In his spare time, Alec has been the co-manager and a player of Mythix Esport, one of the best professional teams at the time in video game tournaments. He is also a long-term subscriber to the Financial Times and has a strong interest in soccer, Middle East politics, macroeconomics, and Lebanese cuisine.
Marshall, John John graduated from the University of Chicago with a bachelor’s in economics and statistics. After graduating, he joined a proprietary market making firm, Cardinal Capital Management, as an assistant trader. He spent a year in this role to learn the industry by clerking on both the Chicago Mercantile Exchange and the Chicago Board of Options Exchange before becoming a floor trader on the CBOE’s S&P 500 trading pit and joins the Berkeley MFE program with more than five years’ experience as a floor trader John and all the floor traders participated in daily morning meetings breaking down part of the term structure to communicate the most pressing positional concerns to the risk management team to properly hedge their aggregate portfolio. In addition, the floor traders would communicate recent order flow trends for the respective brokerage group they covered to be prepared for market moving orders. In the evenings, he would collaborate with his upstairs team on data driven projects to help improve his firm’s algorithmic strategies and look for market pricing inefficiencies. He also worked with CCM’s team of developers to help them design new functionalities necessary to thrive in the modern-day floor market making world. John has taken various MOOC courses including the machine learning certificate from Coursera, Baruch University’s C++, and several others to better position himself for the future of the industry. John is also passionate about education and has spent the past four years volunteering for Minds Matter Chicago, first as a test prep instructor and then as a co-director of the test prep program. In his leisure time, John enjoys playing chess, reading philosophy and baseball analytics.
Peng, Yuting Yuting received his bachelor’s degree from Southwestern University of Finance and Economics with a major in finance. He will also receive a master’s degree in finance from Tsinghua University upon completion of the Berkeley MFE program. During his graduate studies, Yuting completed coursework in programming, mathematics, statistics and finance. While interning at China International Capital Corporation as an equity derivatives trading intern, he designed and priced 8 new exotic options and swaps as well as developing an algorithm to plot volatility surfaces. At Huatai Securities, he gained exposure to asset allocation strategies and obtained periodic signals from security indexes and commodity prices and transformed periodic signals to market timing parameters. Yuting is also passionate about machine learning and has completed various projects through Coursera. In his spare time, Yuting enjoys soccer, going to the gym, tennis and travel.
Prasad, Shiwangi Shiwangi holds a bachelor's degree in electronics and communication from BIT Mesra and an MBA from Indian Institute of Management, Indore with a specialization in finance. She is a certified FRM and has successfully passed the CFA Level 2 exam. She joins Berkeley with more than 5 years of work experience in financial risk management. Shiwangi started her career with Royal Bank of Scotland as a Risk Analyst where she managed the risk of a ~£40bn RBS pension portfolio. She conducted regulatory stress testing (e.g. EBA and BoE) to assess the impacts of extreme stresses on the capital requirement of the bank. Shiwangi successfully migrated the stress testing process from London to India, and worked on streamlining the process which resulted in considerable cost saving for the bank. Subsequently, Shiwangi worked with Deutsche Bank as an Associate in their treasury risk team where she was responsible for managing risk projections. She built a Python-based analytical tool which supported the assessment of the impact of new trades on Value- at-Risk (VaR). In preparation for the MFE program, she also completed independent coursework in machine learning, deep learning, Python and R programming. In her free time, Shiwangi likes playing board games and reading. She also enjoys travelling and has explored 17 countries to date.
Ren, Zhihao (Chris) Zhihao (Chris) Ren obtained his bachelor’s degree at The Hong Kong Polytechnic University, majoring in applied mathematics - investment science, where he gained essential statistics, finance, and computer science knowledge. He also obtained the Entry Full Scholarship and the Outstanding Student Award at the School of Applied Science. His final year thesis --‘Optimal Stopping under Model Ambiguity’-- centered on a time equilibrium approach for optimal stopping which was applied to options exercise strategies. Zhihao is enthusiastic about quantitative research and quantitative risk management, areas in which he has corresponding professional experience. During his 4-month contract at Rivermap Quantitative Research in Hong Kong, an internship at Ageon-Industrial Fund and a project at GuoFu Futures, he had hands-on experience in constructing factor strategies and smart beta indices, applying techniques of signal processing and machine learning on portfolio construction, and building the database for production. Working as a summer intern in the Financial Risk Management team at KPMG Hong Kong, he helped build models to forecast important ratios for banks and gained an understanding of how risk affects the finance industry and how it is regulated. Zhihao has passed CFA Level I and has earned a machine learning engineer nanodegree on Udacity. He is a team player and loves to tackle challenges. In his spare time, Zhihao enjoys detective novels, anime, badminton, and swimming.
Sadler, Alex Alex Sadler graduated from Northeastern University with a bachelor’s degree in mathematics and business and a minor in data analytics. While at Northeastern, Alex completed three extended internships in the financial sector at Wellington Management, Morgan Stanley, and most recently at Grantham, Mayo, Van Otterloo & Co. (GMO). During his 10 month internship at GMO in their quantitative asset allocation division, he led the creation of a new framework for systematically forecasting REITS in MATLAB using statistical and machine learning techniques based on fundamental and economic data. He also worked on other projects such as improving the efficacy of real carry forecasts, creating index proxies for internal use, and migrating manual, Excel-based models into production code. At Morgan Stanley, Alex worked within the prime brokerage division where he helped produce research reports for clients based on data analytics of the hedge fund industry. Leading up to the MFE, Alex worked on strengthening his quantitative background through online classes and textbooks in machine learning, deep learning, derivatives, linear algebra, and analysis. In his spare time, Alex enjoys traveling/backpacking, golfing, and snowboarding.
Shah, Jill Jill Shah received his dual master’s and bachelor’s degrees in mathematics and electrical-electronics engineering from BITS Pilani. During his time at BITS, Jill interned with Credit Suisse in their Market Risk Management division. At the end of his internship, Credit Suisse offered him a full-time offer where he worked for another 2 years. In 2017, Jill joined a high-frequency trading firm where he researched and developed ultra-low latency market-making algorithms for the Indian equity market using C++ and Python. The algorithm traded more than 500 stocks simultaneously each with a holding period less than a few milliseconds and ended up being one of the most profitable equities strategies for the firm. Before joining the Berkeley MFE program, Jill worked as a quantitative researcher with IDFC Asset Management. Here he was part of the fund management team which managed various funds under the alternative investment division. He was predominantly involved in the management of Neo Equity PMS where he worked on building a multi-factor model using Machine Learning techniques in Python. Neo Equity--the only AI-driven PMS on the mainstream Asset Management Platform in India--outperformed its benchmark index by 9% in the 12 months after Jill joined the team. His firm awarded him the ‘Star of the Quarter’ award for his contributions to the fund. Jill passed the CFA Level II exam in
- In his spare time, he likes to trek and play cricket. He is also an avid gamer and likes to play strategy and first-person shooter games.
Shetty, Prarthana Prarthana Shetty obtained her bachelor’s degree in computer science and engineering with a specialisation in data science and a minor in management studies with distinction from PES University. She was also the recipient of the CNR Rao Scholarship, awarded to the top performers in the Computer Science Department. In addition, she attended the Financial Risk Management summer school program at the London School of Economics, has completed Level I of the FRM and is a CFA Level III Candidate. Prarthana has a wide range of experience ranging from advanced quantitative analytics to devising investment strategy solutions for institutional clients. She worked as a Quantitative Analyst in the Treasury Department of State Street Corporation for 2 years where she collaborated on a broad range of projects including “Dynamic Deposit Forecasting based on Macroeconomic factors”, “Optimal Interest- Rate Beta Estimation”, “Term Structure Modelling” and “Client Attrition Modelling”. Prarthana developed deep domain expertise in Statistical and Machine Learning models to generate insights from large financial data sets using Python and R. She is also skilled in breaking down complex, technical topics and communicating them effectively to clients. With this unique skill set, she moved to a front-office Investment Strategy & Research Analyst role at State Street Global Advisors where she worked on devising Multi-Asset Class Investment Portfolio solutions for Institutional Clients. Her research projects during this role included: Measuring Climate Risk Resilience of Portfolios, A Case for Multi-Factor Investment Strategies and Long Term Allocation to Emerging Markets (Debt & Equity). In order to bridge the knowledge gap that exists in the domain of finance, she launched an education platform called “Coffee Time Finance”, with a mission of demystifying financial concepts to readers from across all educational backgrounds. The platform currently has a readership base of 3000 subscribers. Prarthana is also an artist and hopes to showcase her artwork in galleries across the globe. She enjoys reading books on Psychology and Philosophy, has been professionally trained in 5 dance forms, has a flair for public speaking and was the President of the State Street Toastmasters club.
Shi, Kefan Kefan Shi graduated from Nanjing University with a bachelor’s degree in financial engineering where she developed a solid foundation in finance and math. Kefan also gained skills in programming and has expertise in Python and C++. Prior to joining the Berkeley MFE program, she completed several projects in CNN, LSTM and GAN. During her most recent internship with a hedge fund, she adopted technical indicators, buy/sell imbalance and pattern recognition to produce mid-frequency factors. Using the XGBoost model, she adopted CNN with customized channels to mine stock factors. She also interned at a mutual fund, where she ascribed the return over fundamental factors based on the Brinson Model and constructed a fund manager performance evaluation system. In addition, she designed a GAN algorithm to generate more robust trading signals from top fund managers' actions and macroeconomic data. Her prior internship at GF Securities involved developing a strategy based on various performance data. She searched optimal hyper parameters and industry rotation by taking into consideration correlation coefficients between net money flow and returns of individual industry indexes. In her spare time, she enjoys solving sudoku and watching movies.
Silantyev, Dmitry Dmitry holds a master’s degree in quantitative finance from Bocconi University and a bachelor’s degree in economics from MGIMO in Moscow. He also spent a summer term at Harvard University, studying mathematics. Before deciding to pursue graduate studies, Dmitry managed a securitized derivatives book at Renaissance Capital, an EM-dedicated investment bank. In that role, he also developed a target-volatility systematic investment strategy and introduced an FVA management routine that helped optimize the firm’s collateral profile. Dmitry started his career in cross-asset derivatives sales at Goldman Sachs, where he launched two new interest rate products aimed at the local market and designed to minimize CVA charges. He also worked on several thematic indices, which included both factor- and sector-based rebalancing strategies. Most recently, Dmitry interned in Quantitative Research at Rationis in partial fulfillment of the requirements for his Master’s degree at Bocconi. There, he implemented and deployed a proprietary fundamental factor model for long- term bond returns in R. Dmitry began his journey into the data science world by passing the Applied Data Science Module at WorldQuant University with honors. In October 2020, he won the Citadel Europe Regional Datathon, a week-long competition with 500+ applicants from Europe’s top universities, by applying clustering methods to identify New York neighborhoods undergoing gentrification. Dmitry is a Chartered Financial Analyst (CFA) and holds a Certificate in Quantitative Finance (CQF) with distinction.
Singh, Jorawar (Part-Time)
Jorawar Singh is currently working in the Multi-Asset (MA) Investment risk team at T. Rowe Price. Previously he worked at MSCI for 8 years, most recently as an Analytics Consultant. At MSCI, Jorawar was in a role where he helped clients with custom analysis, questions, and implementations using various MSCI risk models. He has extensive knowledge about risk models and in framing portfolio and risk problems in a way conducive to solving them with risk & quantitative models. His 11 years of industry experience also includes positions with Avendus Capital Private Limited and Credit Suisse before joining MSCI. He earned a Bachelor and Master of Technology in Civil Engineering from the Indian Institute of Technology and is a CFA charterholder. Jorawar has extensive experience in delivering risk and multi-factor performance attribution solutions to institutional clients.
