Xiaowen Yu graduated with a Bachelor's degree in Business Administration from Shandong University in 2010 and with a Ph.D. in Management Science and Engineering from Peking University in 2015. During her graduate studies, she developed solid quantitative and analytical skills. Her doctoral thesis focused on measuring the economic development performance of a carbon-emission conscious society. Xiaowen visited the University of Texas at Austin as a Ph.D. student and worked with research fellows of IC2 Institute on efficiency of university technology transfer offices. Xiaowen previously interned at China Securities Company as a bond underwriting analyst and Yingda Asset Management Company as an equity research analyst. During her internship at Yingda Asset Management Company, she mined stock data and calculated through Wind and Matlab to assist investment manager in selecting stocks, and also assisted in building up an Access-based support decision system for stock selection, and in improving the background VBA program. After her graduation from Peking University, she worked at CITIC Trust Company in Beijing as a full-time researcher. Besides generating research reports on macroeconomics, capital market and influences of new governmental policies, her job also included participating in finance projects on real estate trust and Asset-back Securitization. Xiaowen is a CFA level III candidate. Xiaowen is passionate about voluntary activities; she joined the stray dogs and cats protection association and volunteer teaching teams.
Hua Zhang graduated from Zhejiang University in China in 2012 with an Honors Bachelor degree in Finance where she won the Outstanding Student Leader and Academic Scholarship. Upon graduation, she joined Deloitte Consulting as a full-time quantitative risk management consultant where she was awarded the highest yearly assessment score due to her excellent work performance. Before joining the Berkeley MFE program, she worked at PricewaterhouseCoopers Management Consulting. There, she focused on financial instrument pricing, market risk internal model validation, risk data warehouse design. portfolio allocation and performance analysis in her four-year practical experience. She is good at R, MATLAB and VBA and she is using Python developing portfolio allocation strategy by herself. She is an FRM charter holder and CFA Level III candidate. In her spare time, she enjoys photographing and traveling.
Yi Zhang attended ENSAE ParisTech (France) 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. He also completed a BSc degree in Mathematics at Pierre and Marie Curie University (France). Previously, he worked as a real estate analyst at an insurance company Generali, where he calibrated the property risk of Asian countries for Solvency II regulatory purposes with Matlab. In addition, he carried out a one-year-long project for CNED where he and his teammates successfully described the efficient frontier of an equity portfolio, by modeling the returns and covariance using parametric models (GARCH, ARMA). Yi further developed his skills in quantitative finance by carrying out numerous financial projects, such as stock prices prediction by artificial neural network. Prior to joining the Berkeley MFE program, he worked for six months as a quantitative analyst at Natixis Asset Management, focusing on developing an R program for forecasting the credit spreads of different fixed-income European and American indexes, by applying the multivariate time series analysis (VARIMAX, VECM and BVAR models). During his spare time, Yi enjoys hiking, swimming and playing badminton. He speaks English, French, and Chinese fluently.
Zibo Zhao received his Bachelor degree in Economics and Finance from Tsinghua University, to which he was guaranteed admission by winning the First Prize in Chinese Mathematical Olympiad in High School. He interned at WorldQuant since the end of junior year and was later promoted to be a quantitative researcher. During that period, Zibo developed alphas in various categories including fundamental, news, analyst, etc. for the US equity market and ranked first among all the new recruits in the alpha simulated weight ranking. He then joined Shanghai Enigma Investment Corporate as a quantitative investment associate. His strategies managed 75 million USD (35% AUM of the firm) and generated 40% annual return (transaction fee and market impact considered) in China A-share market with a Sharpe ratio of 5.24 and a max drawdown of 5% in 2014-2016. He passed the CFA level 1 exam after graduation, and developed his own alpha simulation platform in Python & Cython. In his spare time, Zibo enjoys trading, playing poker, ballroom dancing (US national qualified dancer) and traveling.
Jia Shuo (John) Zhou attended the University of British Columbia, where he completed his bachelor's degree with a double major in honors Economics and Mathematics. During the last undergraduate year, John completed his honor's thesis by utilizing a statistical inference technique known as stepwise hypothesis testing to compare the returns of momentum strategies with various benchmarks in the Fama and French's factor models and institutional returns using financial equity data from 1926-2014. John's research paper would then go on to be selected as a global winner by the Undergraduate Award (2016) as the best paper in the business category. During his undergraduate period, John also worked as a research assistant doing computational work in matlab to an econometrics professor in a research project that focused on non- parametric statistical modelling using networks and graphs. The project primarily consisted of performing inference to quantify the graph concordance—a measure used to determine the empirical relevance of a network in explaining the cross-sectional dependence of the outcomes—using monte-carlo simulations. John also worked as an intern at China Merchant Securities as a summer analyst in the Capital Markets department. John came to the MFE program to learn practical applications of theoretical concepts in finance. In his spare time, John enjoys going to the parks at snow resorts with his snowboard, watching the NBA, travelling, hiking and golfing.
Lingxin Zhou graduated with a Bachelor of Arts in Mathematics and Statistics and minor in Computer Science from Rutgers University – New Brunswick in 2016. During her undergraduate studies, Lingxin developed solid analytical and programming skills through academic research and internships in numerical analysis and machine learning. In the Rutgers Aresty Research Assistant program, she used a sequential importance-sampling algorithm to optimize the computation of Alpha-Permanent. Lingxin interned at UCLA and Polaris, where she became interested in machine learning. Prior to joining the Berkeley MFE program, Lingxin interned in the Investment Banking Division at Central China Securities, where she worked with clients on IPOs on Chinese National Equities Exchange and Quotations (NEEQ) for small-to-medium enterprises. To get more exposure in investment and financial markets, Lingxin worked for five months at CASH Capital Investment Management under the M&A group, in which she engaged in investments in information technology industry and conducted market research with a concentration on TMT. In her spare time, Lingxin enjoys fishing and learning to play the violin.
Prior to joining the Berkeley MFE program, Pedro Zonari received his Bachelor and Master's degree in Mathematics from Universite Paris 7 Diderot. Upon graduation, he joined Societe Generale (Paris) as an intern in the Electronic Market Making division where he implemented different optimization algorithms to make tighter markets. He later joined the Equity Derivatives division at BNP Paribas (New York) as a Delta One trader assistant. During this time, he worked on the ETF creation-to-lend business and the equity financing one, optimizing firm inventory through swaps on GC and Naturals baskets, ETFs and through options. In his spare time, Pedro enjoys drawing and reading.
Huang Zou earned his Bachelor's degree and Master's degree in Software Engineering from Shanghai Jiao Tong University. Throughout his study at STJU, he focused on fields combining mathematics and computer science, such as Machine Learning and Natural Language Processing. He applied several machine learning and deep learning methods for textual sentiment analysis and personalized recommend, including GBDT, CRF, SVM, and RNN. He gained a good understanding of data analyzing with his models trained by millions of data items. With an interest to apply mathematics and programming knowledge to the finance industry, Huang interned as a quantitative researcher at a local hedge fund at Shanghai, where he analyzed the textual information for stock reports and financial news for Chinese stock markets, and proposed several alpha strategies. He also developed a back-testing system with python for his strategies. In his spare time, he enjoys reading history and listening to classic music.