Joanna Wang obtained her B.A. in Economics and B.A. in Applied Mathematics with a Finance Sequence concentration from Claremont McKenna College. During her undergraduate studies, Joanna acquired in-depth training in finance, mathematics, statistics, and computer science, and explored many facets of finance through multiple internships. During the summer of 2016, Joanna completed an internship at Fortress Investment Group, where she developed automated cash flow models and supported management with portfolio and market analysis. In addition to her buy-side experience, she also explored sell-side finance through her internship as an M&A analyst at PwC Shanghai, where she supported the team with several major acquisitions. Joanna also participated in multiple research projects as a part-time research analyst, and her research focused on macroeconomics forecasting and monetary policy. In her spare time, Joanna enjoys traveling, painting, and working out
Timothy Wong graduated from the University of New South Wales with a Bachelor of Electrical Engineering (Hons 1) and a Bachelor of Commerce majoring in Economics and Finance. During his studies he also performed research in hardware design and image compression under a summer research scholarship and became a 2nd year teaching assistant in digital embedded design. Upon graduation he joined Deutsche bank’s graduate technology program where he received product training in London and worked in Sydney and Singapore. At the end of the program he joined fixed income IT where he provided support and technical expertise for the pricing and risk systems of the electronic trading desk. In 2014 he moved to JP Morgan to provide support and development for the rates and credit OTC derivates desk and later changed roles to be a project lead in the treasury solutions business where he lead technical and functional design. Timothy likes to run, rock climb and develop android programs in his spare time.
Zhaochen (Colin) Xie graduated from the University of California, Los Angeles with a B.S. in Electrical Engineering in 2015. After graduation, he found himself more interested in quantitative finance after building engineering models to processing electrical signals. After graduation, he joined Encore Capital Group as an Operations Analyst. During the year-and-a-half time in Encore’s headquarter in San Diego, he developed mathematical models to predict revenue and cost using SAS and Excel. He also built business cases regarding the future of FinTech industry and ways of increasing revenues. Colin joined the Berkeley MFE program to better understand the mechanisms behind investment products and to further develop his quantitative skillset. Since being admitted, he has taken online courses in Python, R, Partial Differential Equation, and Numerical Analysis. He also passed the CFA level 2 exam and received a Machine Learning Nano-Degree from Udacity.com. He took a gap year in 2017 to appreciate cultural diversity across different countries. During his spare time, he enjoys hiking, skiing, running, swimming, and playing ukulele.
Huiyan Xu graduated from the University of Toronto with a Bachelor’s degree in Statistics, where she received training in mathematics, statistics, and computer programming in R and Python. Huiyan interned at Scotiabank in Toronto where she designed and implemented automation process of industry stress testing on corporate rating migration and expected loss calculation in VBA and SQL. She also completed several model reviews including 1-day VaR and 10-day VaR scaling study, distribution update for recovery rate in Incremental Risk Charge model and impact study on CVA proxies. Upon graduation, Huiyan rejoined Scotiabank and gained experience in both credit and market risk management. She analyzed the impact of the IFRS9 Expected Credit Loss calculation requirement on industry stress testing stress expected loss results. She was highly involved in Scotiabank USA’s CFTC requirement project by generating stress testing results on futures positions. Huiyan has passed CFA Level I and FRM Level I exams. She speaks English, Chinese, Japanese, and is interested in reading about Japanese culture during her spare time.
Yizhou Yan received a Master’s degree in Finance from the Chinese University of Hong Kong (CUHK), and a Bachelor’s in International Economics from Hunan University. She completed several internships in the equity research departments of GF and Sinolink Securities that improved her markets acumen and financial analysis skills. After graduating, Yizhou was also a finance tutor at CUHK where she tutored Stochastic Models, Derivative Markets, Mining Massive Datasets, and Quantitative Risk Management courses. She interned as a quantitative research analyst at Fortune Securities to develop CTA and Alpha trading strategies. She was also a quantitative finance tutor and has passed FRM part 1 & 2 and CFA Level 1 exams. In her spare time, Yizhou enjoys travelling, swimming, and photography.
Ying Yang graduated from Tsinghua University with a Bachelor of Science in Mathematics and Physics and a minor in Economics in 2017. After graduation, Ying joined Wells Fargo as a quantitative analyst in the Capital Market Risk and Model Validation division, where she validated derivative pricing models for CCAR stress testing and developed machine learning algorithms with statistical methods to detect and predict risks in banking. During her junior year, Ying worked with Prof. Jamol Pender at Cornell University to research extending the limitations of the Black-Scholes Formula with an analytical approximation solution to the variable parameter PDE which is published on IJFE in Dec. 2017. Ying has worked as an option market maker at Grand Resources Group and employed wavelet transform for high frequency market data analysis at Shanghai Advanced Institute of Finance. In her spare time, Ying enjoys playing the piano, rock climbing, and traveling with friends.
osang Yoon completed his B.S. in Electrical and Computer Engineering from Seoul National University with valedictorian honors, and his Ph.D. in Applied Physics from Harvard University, after which he worked as a postdoctoral researcher at SNU for 3 years. His research deals with high-frequency measurements of low-dimensional electronic systems, with first-author publications in acclaimed journals such as Nature and Nature Nanotechnology. In addition to experiments, his work involved substantial use of C++ for data acquisition programming and MATLAB for experimental data processing and multidimensional regression in the frequency domain. Hosang developed a strong interest in deep learning and reinforcement learning. As a side project, he built a real-time automated trading system using LSTMs on level 2 market data. The project, implemented in C++ and Python, involved collecting data, developing trading and allocation strategies, training and evaluating various neural network architectures, and building a backtesting simulator and a live-trading system. This system operated successfully in the Korean stock market for 9 months. Hosang has passed the CFA level 1 exam.
Yiming Yu was a fund manager prior to joining the Berkeley MFE Program. He obtained his bachelor’s degree in statistics from Zhejiang University, China in 2015. After graduation, he joined the equity derivatives trading group of J.P. Morgan, Hong Kong, where he was on the derivative warrants and CBBC (a structured product similar to barrier option) desk, responsible for P&L breakdown and reporting, product issuing, trading assistance, as well as risk monitoring. Afterwards, he co-founded a trading firm in China, starting with developing futures and options trading strategies. With intensive research in algorithm optimization and clear financial logic, his quantitative strategy managed more than 100 million CNY with top performance in the market. He also applied state-of-the-art machine learning models in trading, and utilized GPU computing to accelerate model training and parameter calibration. In his free time, Yiming is fond of composing music and enjoys song writing. He also loves travelling and playing sports.
Ruochen Zeng earned his Ph.D. in Statistics from the University of Hong Kong, and obtained his B.Sc. in Risk Management with First Class Honors in 2013. His research area was Financial Time Series, and his thesis topics ranged from quantile spectral analysis to robust nonlinear time series modeling. He applied his expertise in the time series theory to develop a mean-reversion strategy and internship experience at Guotai Junan Securities. He also gained a background in machine learning theory with proficiency in R and Python. Prior to joining the MFE Program, he worked as a quantitative research analyst at Bach Option, founded by a former volatility trader at SAC Capital. He developed a predictive model to forecast the momentum of the market index with outstanding out-of-sample accuracy. Ruochen interned at SAFE Investment Company, applying the functional PCA technique to study the structure of the US equity market. He also interned as a data scientist at Ping An Insurance Group of China, working on credit scoring using logistic regression, support vector machine, and random forest. In his spare time, Ruochen enjoys photography, hiking and playing Age of Empires.
ong Zhang holds a BA with Honors in Mathematics from the University of Cambridge. Upon his graduation in 2013, he worked as a quantitative intern for four months with Manifold Partners LLC, a San Francisco-based company, before joining China International Capital Corp. Ltd (CICC) as an Equity & Derivatives Trader. At CICC, he was fully engaged in the construction of CICC’s total return swap management system, and designed the risk management module, including functions such as portfolio risk analysis, stress test, interest and commission calculation, and real-time risk mark to market. Long later joined Lighthorse Asset Management, a Hong Kong-based multi- strategy hedge fund and was promoted to Senior Trader. He led a team focusing on the methodology development of systematic trading strategies. In 2016, he designed and constructed a robotic trading system for the fund, aimed to trade on market signals automatically, perform real-time volatility-return analysis, and automate order and position management. He helped the company manage up to 20% of the fund portfolio under automated trading. In his spare time, Long enjoys yacht sailing and snowboarding.
Pengfei (Fenix) Zhang graduated from the University of Toronto in 2014 with a Master’s degree in Statistical Science. In 2014 Pengfei started his career as an analyst in the Model Validation and Management team at TD Bank, and he also worked as a research analyst intern on the MBS rating team in DBRS. Before joining the Berkeley MFE Program, Pengfei worked as a quantitative researcher at a Toronto-based hedge fund employing quantitative multi-strategy like volatility arbitrage CTA, spread trading, and global macro. He implemented statistics and machine learning algorithms (LASSO, SVM, neural networks, and random forest, etc.) and data analytics techniques (web crawler, PCA, etc.) to generate investment signals, to perform sentiment analysis on the market, and to develop new strategies. Prior to that, he was a senior quant at S&P Global New York, leading a consulting team to provide quantitative advisory, pricing, portfolio and risk analytics services to several asset management and investment bank clients in the U.S. Pengfei priced options, swaps, and volatility derivatives, tested new factors and portfolio optimization techniques, developed risk management frameworks, and performed scenario analysis and stress testing on multi-asset portfolios. Pengfei also has five years of personal trading and portfolio management experience, and is currently preparing for the CFA level 2 exam. In his spare time, Pengfei enjoys skiing, hiking, and yoga. He is a certified Level 3 National ski instructor in Canada.
Prior to joining the Berkeley MFE Program, Yiwei Zhang worked as a Quantitative Analyst at Goldenwise Capital Management where he conducted research on trading strategies of VIX and equity index futures based on volatility term structure, vol-of-vol term structure and market sentiment indices. He also built a quantitative trading platform in Python, which efficiently captured a trading signal, executed the trade and monitored different risk exposures of the portfolio. Prior to Goldenwise Capital Management, Yiwei interned at Canada Pension Plan Investment Board (CPPIB) in Toronto, where he worked on multi-factor portfolio construction and asset allocations based on given preferences for performance and risk. He successfully constructed hedging strategies against Brexit and the 2016 US election for the managed multi-asset portfolio. Yiwei joined the Global Banking and Markets in Bank of Nova Scotia in 2015 for an internship in the Financial Engineering team in Toronto. As a front desk quant, he conducted research on FX and commodity derivative products and developed their pricing models in C++. He also worked closely with business and technology teams to build solutions for trading desks. Yiwei passed the CFA Level II exam in June 2017. In his spare time, Yiwei enjoys snowboarding, working out, and playing tennis.
Yuandi Zhang obtained a Bachelor’s degree in Mathematical Finance at the University of Waterloo, where he explored the principles and applications of quantitative finance. During his undergraduate study, Yuandi focused on applying theoretical knowledge to the industry through internships. In his second year, Yuandi worked as a summer analyst with the Institutional Client Group at Citigroup where he provided prompt technology support to the equity researchers. In his third year, he completed an internship at the Canada Pension Plan Investment Board (CPPIB) where he performed due diligence and prepared analyses to assist portfolio managers in the hedge fund selection process. Upon graduation, Yuandi joined Connor Clark & Lunn Financial Group as a quantitative analyst on the Portfolio Management desk. As a junior analyst, Yuandi supported the portfolio managers on daily workflow and also took initiative on projects to expand revenue and save costs. One of the major projects that he finished to optimize prime broker operation is estimated to save the firm $80m on the prime broker side.
Yifan Zhao received her Honours Bachelor degree in Economics and Business from Durham University in the UK. Her undergraduate dissertation was about multifactor asset pricing models, where she developed specific asset pricing models for nine different industries. Yifan received her Master of Sciences degree in Applied Analytics from Columbia University in 2018. She studied relational database architecture, data analytics, and machine learning, and she implemented Excel add-ins for derivative pricing models using C++. During her internship at Castle Placement, a New York- based investment brokerage firm, she developed a machine learning model in Python to suggest potential investors for investment opportunities using gradient-boosted tree techniques. While interning at Greensea Capital in London as a financial analyst, she performed industry research and financial modeling for mergers and acquisitions in the energy industry. Yifan also interned at Mirae Asset in Shanghai as a research analyst, where she worked on No. 9 Milestone Scenery Quantitative Hedge Asset Management Project, performing pricing valuation for different financial products such as stock, bond, and stock index futures and tracked the dynamic information of listed companies to look for undervalued stocks. As a competitive dancer since the age of six, Yifan was the captain of Durham University’s cheerleading team that competed in the UK and Europe, and she later joined the Columbia University cheerleading team. She is also trained in gymnastics and enjoys playing the piano.
Yue (Irene) Zhao graduated from the University of Melbourne with a master’s degree in Business Analytics after completing triple majors in Economics, Finance, and Business Management（First Class Honours）from the Royal Melbourne Institute of Technology and Wuhan University of Science and Technology. Before joining the MFE Program, she interned as a data analyst in the Anti-Crime team at National Australian Bank, where she was responsible for enhancing credit card fraud detection. She used machine learning algorithms, such as neural network and random forest with the organization’s existing rule-based system, which reduced the operational cost significantly. She also completed multiple projects related to predictive analysis, such as employing the ARIMAX model to forecast the short-term electricity demand in Victoria, Australia. When she was in China, she established the first Youth League in her school to provide equal education opportunities to mentally-disabled children. Irene is interested in leveraging her knowledge to facilitate the responsible (smart) investing, which aims to focus on the sustainable environment, technological change, and human well-being. In her spare time, Irene loves traveling, reading, yoga, swimming,