Valentin Lepinay will graduate from Paris-Dauphine University’s extensive Master’s program in Banking, Finance and Insurance (MASEF) in March 2019 upon completion of the Berkeley MFE. During his Master’s degree, Valentin worked on math and programming projects including “Game Theory: 3 sided matching”, “Managing the Issues of a Dynamic Hedging Strategy of Equity Vanilla Options,” and “Valuing American Options by Simulation: Simple Least-Squares vs. Monte-Carlo Malliavin Approach.” Valentin has previously interned at the largest secondhand luxury e- commerce company, TheRealReal where he employed his extensive background in machine learning and deep learning to work on churn model, conversion model, and NLP features extraction for pricing model. He also interned at the VC firm Bleu Capital as an Analyst, where he managed over 30 inbound deals every week and conducted market analysis in retail, enterprise solution, and FinTech and took part in all investment decisions. In the same year, Valentin taught as a Teacher’s Assistant for a Data Science class at UC Berkeley, managing projects based on NLP. Outside of his professional interests, Valentin has his Light Aircraft Pilot License and was a sponsored mountain bike athlete for four years.
Jincheng Li completed his Bachelor degree in Computer Science from Xi’an Jiaotong University, and obtained his Master of Engineering Management from the Thayer School of Engineering at Dartmouth College. Upon graduation, he became an early employee and product manager at AgilOne, a startup backed by Sequoia Capital that pioneered the cloud-based marketing software industry. At AgilOne, he conducted extensive industry research and delivered the prototype of a new product. He then became a Technical Product Manager at Expedia and led a team of software engineers developing critical web service products. In 2014, he joined Credit Karma, a fast- growing company in the fin-tech sector, as a product manager and led a team of six software development engineers delivering the pre-qualification platform for credit cards and personal loans. He then co-founded CottonBrew, a startup that leverages data and algorithm to improve efficiency in the traditional tailored-clothing industry; the company was admitted into the 2016 Spring batch of StartX, a community of Stanford’s top entrepreneurs. He is passionate about data, technology, and investing.
Linfeng Li graduated from the University of Waterloo in 2015 with double majors in Actuarial Science and Financial Analysis & Risk Management with a minor in Statistics. During his undergraduate study, Linfeng participated in a co-operative program where he completed several internships in the finance sector. During his internship at the Dominion Bond Rating Services (DBRS), Linfeng researched the correlation between the loss severity rate and the credit rating for European commercial mortgage-backed securities. He also studied how demographics related to the market liquidity for US real estate using regressions. Prior to joining the MFE Program, Linfeng worked at Canadian Imperial Bank of Commerce (CIBC) as a risk data analyst under the Capital Market Trading Credit Risk Group, where he analyzed day-over-day credit exposure changes for fixed income, repo, security lending lines of business and communicated with front office for limit breach. He also streamlined the data reconciliation process for capital reporting and enhanced the process of monitoring the bank’s overall pledging activities. Linfeng has completed Level II of the CFA Program. In his spare time, Linfeng enjoys traveling, basketball, and board games.
Yiheng Li graduated from the University of Toronto in 2016 with an Honours Bachelor of Science degree in Statistics with high distinction. Yiheng has been acknowledged for his innovative skills in translating data into visual insights, and was awarded the top prize at The American Statistical Association DataFest Competition, for his segmentation analyses on consumer use behavior. Upon graduation, Yiheng joined advertience Inc., a consulting startup in the digital advertising industry, where he conducted research on clients' advertising performance and audience segmentations using R and Python. Yiheng led other three team members in developing the company's data-driven business intelligence platform to help marketers to track advertising information and provide analysts with the ability to easily consume and use the information to build required advertising models. In his spare time, Yiheng enjoys exploring new technologies.
Darren Lieu graduated from the University of California, Irvine, in 2016, where he triple- majored in Mathematics for Finance, Quantitative Economics, and Information and Computer Science. During his undergraduate studies, Darren entered and placed in AppJam coding competitions, was the webmaster of Anteaters Math Club, and was the technical director of the Super Smash Bros. club. He also created a technical analysis stock market simulation with his simulation professor, and used techniques such as moving average, head-and-shoulders, and moving average convergence/divergence to simulate trends in the S&P 500. During and after his undergraduate studies, Darren worked at Netaphor as a Data Science intern, and created scripts in Python, C#, and SQL to analyze anomalies in their printer audit software. In his spare time, Darren enjoys MMORPGs with friends and participating in competitive Super Smash Bros. Melee tournaments
Weiqi Liu graduated from New York University with double Bachelor’s degrees in Mathematics and Computer Science. During his undergraduate studies, he took graduate level courses in Artificial Intelligence and Financial Engineering and established solid statistical analysis and programming skills in Python. After graduation, Weiqi worked for a proprietary trading firm as an equity trader in New York. He closely worked with the quantitative team and developed an automated trading strategy that utilized certain stock chart patterns combined with level two volumes. He further improved the strategy by introducing an indicator that quantified momentarily overall market influence upon individual stocks and classified different types of events that might affect the strategy. He also did some arbitrages on Chinese ADR privatizations. In his spare time, Weiqi is a professional level poker player in high stake cash games and has participated in WSOP tournaments.
Xinyuan Liu graduated with a Bachelor of Science in Computer Science with distinction from the University of Minnesota as well as a Bachelor of Economics in Finance from Shandong University of Finance and Economics. He participated actively in academic research and completed an independent research project with Prof. Dan Knights on machine learning repository. In his research, Xinyuan preprocessed microbiome data and applied machine learning algorithms such as random forest with cross validation. Xinyuan has interned at a number of firms in the financial industry with diverse experience at hedge funds, mutual funds, and venture capital environments. This most recent work experience was in a hedge fund in China where he analyzed similarities in time series data that could be used in trading strategies in futures contracts. Furthermore, he implemented a double-layer factor model to select stocks and rebalance positions. Xinyuan has recently completed a research paper on quantamental philosophy in equity management and has passed the CFA Level I exam. In his spare time, Xinyuan volunteers with Chinese Children Care and enjoys playing Texas hold’em.
Zhening (Nini) Liu graduated with a Bachelor of Science degree in Operations Research and Information Engineering from Cornell University in 2015. Upon graduation, she joined the Corporate Technology Services group at Nomura in New York. During her two years with the firm, she worked on multiple automation projects as well as real-time data processing through RDBMS. Later, she transferred to the Quantitative Risk team where she had her first exposure to quantitative pricing and big data technology. Aside from her full-time job, Nini also completed a machine learning project at Columbia University, in which she collaborated with two teammates to fit various algorithms to predict Freddie Mac's single family loan performance. She also passed her CFA Level 2 exam in June 2017. Based on all her professional experience, she realized she had a strong interest in the application of data science / artificial intelligence to finance and is eager to pursue a career in FinTech after graduation. In her spare time, Nini enjoys participating in many sports and is also an amateur singer/songwriter who used to perform in a rock band.
Zhuorui (Jerry) Liu graduated from Tsinghua University with a B.S. in Applied Mathematics. Jerry developed an early interest in trading from his experience in the Chinese stock and future markets. Jerry interned in the Derivatives department of China Grand Enterprises and participated in the Summer Intern Program of China International Capital Corporation (CICC) in 2016. At CICC, he researched practical hedging methods concerning transaction costs and volatility surface generation of Chinese OTC equity derivatives market, and wrote a thesis focused on the volatility premium phenomenon. Prior to joining the Berkeley MFE Program, Jerry worked at Keywise Capital as a quantitative researcher. There, he established a quantitative back-testing framework in Python for multifactor strategies and built two trading strategies utilizing machine learning models and boosting methods. In his spare time, Jerry enjoys playing the piano, reading, travelling, and playing chess.
Matias Lopez obtained a Master of Science and Bachelor of Science degree in Engineering from Pontificia Universidad Catolica de Chile, both with highest distinctions. For his Master’s thesis, he studied affine-diffusion for commodity prices, developing and implementing a multifactor stochastic volatility model for oil prices. The model resulted in a practical tool to price both futures and options with an arbitrary number of factors dedicated to each contract type. During his Master’s, Matias worked at RiskAmerica implementing several multifactor models to price fixed income instruments and foreign currency futures using MATLAB and SQL. Later, he joined the quantitative team at AGF Security, where he designed asset allocation models and other tools aimed at improving portfolio performance and developing new investment strategies for funds. His most remarkable contribution was a model for multi-asset portfolios based on factor investing and volatility management positioning these funds among the top places in Chilean balanced funds for three consecutive years. Matias passed the CFA Level 1 in December 2015. He is fluent in English and Spanish, and his main hobbies include working out, travelling, watching soccer and American football.
Wen Luo obtained a Bachelor’s in Applied Mathematics and minor in Economics at Peking University in China. During his undergraduate studies, he acquired solid skills in mathematics, statistics, computer science and other quantitative fields. He interned at Shenyin & Wanguo Securities in Shanghai, where he conducted research on SME Board and GEM and composed analysis reports. Upon graduation, he worked as a quantitative researcher for a hedge fund in China. There, he developed trend following and cross-product arbitrage strategies on commodity futures. He tested several machine-learning techniques on commodity futures to gain abnormal returns, including SVM, neural networks, decision trees, Hidden Markov Model. He was also responsible for database maintenance, market data cleaning, and the calculation of the positions to ensure the accuracy of program trading. He has passed the FRM Parts 1&2 and CFA Level 2 exams. In his spare time, Wen is passionate about soccer and hiking.
Pingchuan Ma received his Master’s and Bachelor’s degree in Electrical Engineering from Tsinghua University. For his master’s dissertation, he designed a special mechanism to coordinate the separated local calculations to achieve distributed optimization. Relevant analytical and numerical methods were employed, including PCPDIPM and Newton-GMRES methods. Through various project developments, Pingchuan mastered different programming languages, including C++, Java, Python, MATLAB, R and SQL. After graduation, he worked at the headquarters of State Grid Corporation of China (SGCC, ranked 2nd in the Fortune 2017 Global 500) through recruitment examination in which he ranked 2nd among 5 headquarter offers out of more than 900 applicants. But Pingchuan expected his work to be more technique-involved. During his internship at DFC technology, Pingchuan made a summary and comparison of the Moving Average and Kernel Smoothing Method, conducted CTA market research and assisted in formulating new CTA trading strategy using Kernel Smoothing. Pingchuan is also a passionate runner and swimmer who enjoys cooking Chinese cuisine.
Prateek Maheshwari received his Bachelor’s and Master’s dual degree in Chemical Engineering from the Indian Institute of Technology, Bombay. During his studies, he undertook multiple projects in process control, optimization and multivariate statistics, culminating in his thesis, where he designed economically efficient supply chains, robust to supply-side tail risks, for second-generation biofuels. His work was presented at a conference and published in a leading peer-reviewed journal. After graduation Prateek worked with the Edelweiss group, in physical commodity risk management, followed by two years of low frequency systematic trading in G10 and EM FX. After, he joined a Long/Short Equity hedge-fund as an investment research intern, where he covered growth sectors like China fintech and biologics outsourcing. In his spare time, Prateek follows his passion for music, his love for learning string instruments, and he also enjoys globetrotting.
Jaime Maihure graduated from Universidad Nacional de Ingeniería in Lima, Peru with a B.Sc. in Economic Engineering and a focus on Mathematical Economics; he also earned an M.Sc. in Economics and Finance with a focus on Financial Econometrics and Asset Pricing at the Barcelona Graduate School of Economics. Jaime worked at the Central Bank of Peru, after training through both economics and Advanced Finance summer school programs. While in the Economic Studies division, Jaime conducted Economic analysis using DSGE models and econometrics tools for forecasting macroeconomic variables for monetary policy. After 3 years, Jaime joined the Central Bank’s International Reserves Management division, where he rebuilt the Active Asset Allocation model using a Bayesian approach using Matlab. In addition, Jaime coded software for valuation of fixed Income instruments, scenario analysis and stress testing the fixed income portfolio. In particular, he implemented an LMM for pricing Bermuda callable bonds using the Longstaff-Schwartz algorithm. Jaime applied different non- linear optimization models in R using Differential Evolution algorithms for optimizing the components of a basket of FI and FX index trackers, the value of which is linked to a structural bond with nominal of USD$ 1 billion. He also prepared reports on financial and macroeconomics variables for seeking tactical investment opportunities in the US, UK and European Markets. In his spare time, Jaime enjoys hiking, reading about war history, and studying machine learning tools.
Vladimir Morozov graduated with honors from Lomonosov Moscow State University majoring in Applied Mathematics and Computer Science. He worked at the Moscow State University doing research in artificial intelligence and teaching programming classes including C++ and Object-Oriented Analysis and Development. He went on to earn a Master of Science in Finance degree from the New Economic School in Moscow, finishing at the top of his class. After graduation, Vladimir joined Barclays Investment Bank in London for a six-month internship on the Quantitative Analytics Market Risk team, where he created a framework for generating scenarios commodities volatility surfaces and used it to compare and backtest different approaches to scenario generation. He received a full time conversion offer from Barclays and continued to implement a component of a new market risk framework using highly vectorized Python code. He passed the CFA Level I exam in June 2017. In his spare time, Vladimir enjoys reading and going to the theatre.