Jérôme Dont received his undergraduate degrees in Applied Mathematics and Applied Economics from Université Paris-Dauphine and will graduate from an extensive master's program in Banking, Finance and Insurance in March 2017. Jérôme has previously interned at Société Générale in London, where he worked on delta-one and turbo products structuration. He then interned within the pricing team where he handled the pricing of complex equity and hybrid derivatives products (using Monte-Carlo and Least-Squares Monte-Carlo), generated trade ideas with the salesforce and computed regulatory simulations and back-tests. Jérôme acquired experience in corporate finance when he interned at the Bank of France where he performed financial analysis and evaluated SMEs' capacity to meet their financial obligations over a 3-year horizon. Alongside his Master's, Jérôme was the Treasurer of the Master in Banking, Finance and Insurance students' association where he managed a €70k budget and organized several events with financial practitioners such as seminars, thematic conferences, meetings with alumni or integration events. He volunteered at the French Red Cross and worked at a food distribution center. In his free time, Jérôme enjoys reading French literature, travelling and cooking. He is also fond of tennis, ski and golf.
Shiyu Du graduated from Fudan University in 2015 with a bachelor's degree of Nuclear Physics, which gave her a rigorous training in mathematics, statistics and other quantitative fields. She earned a master's degree of Engineering Management from Duke University in 2016, where she gained a further understanding in econometrics and mathematical finance. Shiyu represented Duke to compete in the Rotman International Trading competition, where she used market making and ETF arbitrage strategies to write algorithmic trading codes. As part of her master's degree, she completed an internship at Lingwang Fund as a quantitative trader, working on the trend following trading strategies for treasury futures in China's market. Shiyu also horned her leadership skills through her presidency of the Duke MEMP finance club, where she organized finance workshops and industry networking events. In her spare time, Shiyu enjoys hiking, reading and swimming.
Xinhui Gu graduated from the National University of Singapore with Bachelor of Science degree in 2014. He developed his data analysis skills, programming skills and markets acumen by taking double majors in Quantitative Finance and Statistics. In his undergraduate thesis supervised by Prof Steven Kou, he implemented the trinomial tree pricing function of Hull White model for interest rate options by adding the analytical estimate to the node displacement and improved the efficiency comparing to Monte Carlo Simulation methods. Prior to joining the Berkeley MFE program, Xinhui worked as a risk and treasury system consultant for ANZ Banking Group on various front office and middle office projects. He integrated the quant library into ANZ's trading platform using Java and C++, as well as resolved day to day issues raised by Sales and Trading teams. He also led development of the trades straight through processing framework for ANZ China rates, FX and precious metal desks to multiple exchanges including CFETS, SGE, SHFE and NIFC in Java. Xinhui is a certified FRM and CFA level II candidate. To prepare for the Berkeley MFE program, Xinhui completed coursework in Python, C++ and Machine Learning. In his spare time, he enjoys jogging, history and calligraphy.
Peng Hao graduated from SUNY at Buffalo in 2015 with a PhD in theoretical and mathematical physics, where he received solid training in advanced math through studying general relativity and quantum field theory. His research focused on modified gravity and black hole formation. Inspired by superstring theory, Peng approached the cosmological constant problem (dark energy) through extra-dimensional multi-brane theory. Peng extended the cascading gravity model from the lower dimensional side. He solved the inconsistency problem and constructed the first robust model based on the idea of vanishing dimensions. Peng has long been curious about economic development and finance. At school, he developed strong interest in machine learning and applying his quantitative skills into practical work. After graduation, he worked at Apple Inc., where he gained experience in maintaining and training fraud detection model with artificial neural network and support vector machine using Gaussian kernel. While working, he completed courses in corporate finance, international finance, security analysis and risk management. He passed the CFA level 1 exam. Aside from work and study, Peng enjoys hiking, running and reading about historical events.
Florian Hayek received his undergraduate degree in Mathematics, Computer Science and Economics from Université Paris-Dauphine and will graduate from the extensive Master in Banking, Finance and Insurance in 2017 (Magistère Banque, Finance et Assurance). He previously interned at Crédit Agricole CIB where he worked to improve partnerships with Regional Banks within the Group. Florian actively participated in mitigating counterparty credit risk and created a dashboard to monitor the activities of sales in the partnership, developing his VBA skills. He then worked for six months at BNP Paribas on equity derivatives structuring where he deepened his knowledge of financial derivatives. He used Monte-Carlo and American Monte-Carlo simulations to simulate prices, suggested trade ideas to sales and back-tested strategies. Before that, Florian interned at Chevron-Oronite as a credit analyst where he evaluated the credit score of customers. During his Master degree, he was the vice-president of the students' association and organized a conference about audit with the Big 4 accounting firms. Passionate about sports and nutrition, Florian enjoys weightlifting and running in his spare time, as well as climbing and board sports.
Zhengyang (Alan) He graduated from Drexel University with a Bachelor's degree in Mathematics in 2013. While in college, Alan got exposure to various research projects in business and behavioral studies, including one where he built a Monte Carlo simulation application for a research team in University of Goettingen. Prior to joining the Berkeley MFE program, Alan worked as pricing actuarial analyst at Esurance, a San Francisco based insurance firm, where he accumulated extensive experience in modeling and analyzing actuarial risks. As part of his job, Alan conducted innovative research projects to implement machine learning techniques (generalized linear regression, decision tree, and clustering) to strengthen the loss estimation and operating expense allocation procedures. To prepare himself for a quantitative finance career, Alan took several graduate level computer science classes and passed the CFA level I exam. Alan is looking forward to enhancing his knowledge and skill set through the program. In his spare time, he follows technological and political news, and enjoys outdoor activities such as biking, hiking, and archery. He is also a fan of Formula One games.
Hang Sun Kim graduated from Sogang University in 2006 with a Bachelor of Economics. Upon graduation, he worked at the Korea Development Bank in the Global Banking Department, where he was responsible for long-term foreign currency funding of approximately USD 10 billion annually through the issuance of debt such as Global Bond and Euro Medium Term Note(EMTN). Hang Sun established two new Emerging Currency (MYR, Thai Baht) funding platforms that served as key alternative for the main G3 currency funding channels during the credit crunch. In recognition of his expertise in international banking, Hang Sun was offered a secondment in Korea's Center for International Finance (KCIF) for one year. He monitored and analyzed the foreign capital flow movement on real-time basis by handling a large database through SQL. Later he worked as F/X & derivatives sales in the Trading Department and as Senior Manager led five team members to cover more than thirty Korean corporate clients. He consulted clients on structuring market risk hedging strategies and provided effective hedging solutions through products such as forwards, interest rate & currency swaps and options. He also successfully initiated and conducted seminars on F/X risk management strategies for over fifty small & medium enterprises(SMEs) in Korea. In his spare time he loves spending time with his family, watching baseball, reading and travelling.
Raghuram Kowdeed graduated with Bachelor's and Master's Degrees in Electrical Engineering from the Indian Institute of Technology, Kanpur in 2013. Prior to Joining the Berkeley MFE program, he worked as a Trader and Quant. At Two Roads Trading, he traded VIX and FVS futures. He augmented HFT strategies performance using KALMAN Filter and machine learning techniques. He constructed vol surface based pure gamma, vega strategies to trade options. Prior to Two Roads , he worked as FX Quant at Goldman Sachs, Interest Rate Quant at Morgan Stanley. As FX Quant, Raghuram built a model to price single barrier basket product which consistently price other barrier products. He developed arbitrage free volatility surface using Regime switching Markov Chain model. At Morgan Stanley, he worked on extending PCA technique to handle missing bonds data. He also generalized pricing engines to handle in- arrear libor payments. During his spare time, Raghuram likes to play badminton, cricket and listen to music.
Jules Landry-Simard graduated in Actuarial Science from Laval University in Canada. During his undergraduate studies, he completed a Master semester at the University in Oslo in Mathematics. He also completed two research projects, the first being an interdisciplinary project which aim was to forecast supply-chain bottlenecks using time series analysis in R, and the second on asset liability management. Jules completed an internship at a major Canadian insurer, working in Risk Management and Capital, where he developed VBA and SQL tools for risk studies. After graduation, he enrolled in many CS courses in data structure and algorithms, machine learning, C++ and database management while working at Fiera Capital. There, he spearheaded the development of a new Monte Carlo stochastic economic scenario generator in MATLAB. He also used his Python and data structure skills to optimize many trading tools as well as developing backtesting tools in Python and VBA. He also passed the first 4 actuarial exams of the Society of Actuaries, as well as the Level 1 CFA exam. In his spare time, he enjoys reading about technology, clean energy, and rocket science as well as rock climbing.
Xiaoyi Li graduated with her Bachelor of Science degree in Statistics and Applied Probability from the National University of Singapore. She developed an early interest in quantitative risk modelling during school. Besides proposing her own one-year honor project thesis which compared two Bayesian methods – INLA and MCMC in portfolio credit risk modelling, she also participated in the Risk Management Institute's Credit Research Initiative project by daily testing its credit rating models and supported the Shenzhen Stock Exchange's text mining tool development which aimed to detect speculative transaction in trading. Upon graduation, Xiaoyi worked at Standard Chartered Bank, where she developed quantitative analysis models to predict customers' default rate probability, the exposure at default and the loss given default. The Internal Rating Models for Thailand Mortgage portfolio she developed had achieved a 20% decrease in risk-weighted assets and was endorsed by Bank of Thailand. She also worked on the IFRS9 Models for Malaysia Mortgage portfolio to estimate the expected credit losses for business clients. In her leisure time, Xiaoyi enjoys spending her time traveling, watching movies, volunteering and doing sports.
Yujing (Eugenie) Li graduated from the University of California, Berkeley with triple majors in Computer Science, Applied Math and Statistics (Highest Honors) in 2016. Prior to joining the Berkeley MFE program, Eugenie worked in the quantitative risk team at Citigroup, where she was responsible for the analysis of risk across the whole firm. She developed machine learning algorithms such as clustering to group thousands of loan tranches to smaller number of groups in order to reduce computational costs. She also worked on statistical tests for PPNR models. Before this internship, Eugenie also acquired experience working with capital stress testing models. In college, Eugenie did multiple research projects in machine learning and text mining. She implemented algorithms to analyze the text sentiment from the press release on crowdfunding and bitcoins. In her spare time, Eugenie enjoys music, traveling, snowboarding and exploring restaurants.
Pierre Monroy attended ENSAE ParisTech as a double diploma with ENS Cachan, where he studied Applied Mathematics, Statistics and Economics. He will officially receive his Master's degree upon completion of the Berkeley MFE program. Between October 2014 and May 2015, he took part in a group project for a company where he focused on predicting startups likelihood of success. These predictions were then used to provide investors an accurate estimation of the risk associated with the funding of specific startups. This project allowed him to improve his MATLAB programming skills and to implement statistics models such as logistic regression model and classification trees on a concrete issue. Pierre then spent six months as a Junior Quantitative Auditor at the french public institution, Caisse des Dépôts. This internship was an opportunity to apply his knowledge in statistics and R programming. He worked on a wide range of financial issues including the improvement of a credit scoring model using machine learning techniques and interest rates prediction applying time series analysis. He joined in January 2016 the investment bank Natixis for a six months internship in the Equity Derivatives Trading team. While working on trading issues, Pierre also implemented a forward valuation model that includes dividends dynamics in C#. He proceeded to backtest his model and managed its integration in the IT structure. During his spare time, Pierre enjoys training for half marathon, playing bass guitar and helping to manage the ENS Cachan Ski Club.
Yasmine Moulehiawy is currently completing her Master's in Applied Mathematics at Ecole Centrale Paris and will receive her degree upon graduating from the Berkeley MFE program. Prior to joining the MFE, she was part of the BNP Paribas business valuation team as an M&A intern for 6 months, before joining Credit Suisse for 10 weeks in the Global Markets team, where she rotated in Sales and Trading desks. Yasmine is deeply interested in quantitative finance and especially in the application of Data Science to finance. Yasmine is currently working on a research project where she is building a strategy trading implied volatility factors using machine learning methods. Yasmine enjoys travelling and discovering new cultures. She recently spent 3 months working at a start-up in Brazil where she discovered some of its most outstanding landscapes. In her spare time, she likes reading and learning new languages. Yasmine speaks English, Arabic, and French fluently and conversational Spanish and Portuguese.
Ivan Nurminsky started his career as a structurer in fixed income, designing custom made hedging and investing solutions in London. He then moved to equity derivatives trading where he ran a number of proprietary investment strategies while also helping build the emerging market platform at BNP Paribas. Prior to joining the MFE, Ivan's role was to lead a fixed income hybrid trading book in Singapore where he managed and further developed the business he initially worked on as a structurer. Ivan's eight years on the trading floor, primarily in exotic derivatives, have provided a detailed understanding of investing across a wide range of financial instruments and markets, alongside experience successfully developing and managing a business platform. Having received his BS in computer science and mathematics from Tufts University, Ivan has always had a passion for applied maths and computing technologies. He joined the MFE to further his knowledge of the latest methods of statistical modelling and machine learning methods and to explore new developments in data analysis. In his free time, Ivan is an avid traveller and swimmer.
Tianyi (Tony) Peng graduated from the University of California, Los Angeles with a B.S. in Applied Mathematics and a B.S. in Statistics in 2016. During his undergraduate studies, he developed a solid foundation in mathematics, statistics and computer programming. Prior to joining the Berkeley MFE program, Tony interned at various financial services companies. He completed an internship at Huatai Securities, where he researched Interbank lending market and conducted data analysis on pledge-style bond repo rates. He also interned as a portfolio manager assistant at Ping An Annuity in the asset allocation management team, where he was responsible for calculating portfolio analytics and providing daily update. After graduation, Tony further developed his quantitative programming skills and knowledge in stock and bond markets at Totum Wealth, a Fintech startup in Los Angeles. He prototyped and built portfolio optimization and Monte Carlo Simulation models, which are implemented for live application. He has passed two actuarial exams (P, FM) and completed the CFA level I exam. In his spare time, Tony enjoys travelling, working out and playing basketball.