German Ramirez holds a B.Sc. in Industrial Engineering and a B.Sc. in Chemical Engineering from La Universidad de los Andes. Upon graduation he joined Lumni, a company that manages investment funds to finance low-income students through Income-Share Agreements (ISAs). At Lumni, he redesigned an algorithm to price ISAs, using Gaussian Quadrature rather than Monte Carlo Simulations. This change allowed them to price ISAs more efficiently. Moreover, German led a team of engineers to integrate the pricing algorithm with the students' application platform, making the pricing process fully automated and scalable. In order to broaden his knowledge in finance and macroeconomics, German joined the Central Bank of Colombia. Here he was responsible for overseeing the credit risk of the Foreign Reserves and Sovereign Wealth Funds, both worth over USD 47 billion. To do so, he developed tools to measure credit risk and to identify issuers with deficient credit capacity. In addition, he managed the relationship with external managers from the most competitive firms in the industry. German is a CFA charter holder since 2015. In his spare time, German enjoys playing tennis and running.
Achuthan Sekar graduated with a Bachelor of Technology in Electrical Engineering from IIT Madras where he got solid training in probability, statistics and time series analysis. His internship in summer of 2013 with the rates quantitative strategy team at Deutsche Bank in Mumbai gave him a foray into the exciting world of finance and investments. During the internship, Achuthan worked on implementing and back testing a trading strategy based on digital signal processing applications to USD rates futures. The project aimed to identify business cycles using Fourier and Wavelet transforms. After his internship, Achuthan decided to pursue his interest in finance and accepted the pre placement offer he got from Deutsche bank. He joined as the only analyst at the Emerging Market Credit Derivative Trading team. As a part of his job, Achuthan priced credit default swaps, credit linked swaps, enhanced repos, researched and identified potential relative value trades in the region, optimally hedged various risks in the portfolio, performed scenario analysis on exposures to distressed credit names and participated in balance sheet compression activities. Achuthan cleared the CFA Level 1 exam in December 2016 and plans to get the full charter. During his time at IIT, he taught students preparing for JEE as a teacher at Avanti Fellows. In his spare time, Achuthan likes to play and watch tennis, sing and listen to music, read and travel.
Manas Shah graduated from IIT Bombay with a Bachelors of Technology degree in Aerospace Engineering. During his undergraduate studies, he undertook projects in subfields of stochastic processes and optimization techniques and subsequently authored two publications in peer-reviewed journals. Manas previously worked as a quantitative researcher at iRageCapital (Mumbai) with the key responsibility of developing sub-millisecond market making strategies for options. He researched and implemented signals based on the concepts of Hayashi Yoshida nonsynchronous estimator (lead-lag) and Hawkes process (order-book). He also worked as a remote global alpha researcher at Trexquant Investment (NY) where he developed medium frequency statistical arbitrage alphas for trading global equities. He studied research papers and analyzed wide range of datasets to devise systematic signals based on concepts of post-coincidence comovements, continuous-discontinuous beta, up-down volatility. Additionally, he has worked with Deutsche Bank (Mumbai) where he supported the FX Trading team in pricing G10 Forwards and NDFs. Manas looks forward to opportunities in the data science and fin-tech industry.
Jie Sheng received her bachelor degree in quantitative finance from the National University of Singapore. During her undergraduate studies, she developed further knowledge in mathematics, programming and finance. Her final year project was focused on game theory specialized in optimal salary bargaining problem. Combining Monte Carlo simulation and mathematical deduction, her project provided assessment on various bargaining strategies. Prior to attending the Berkeley MFE program, Jie worked at Development Bank of Singapore (DBS) as a rates controller where she developed market sensitivity and accumulated product knowledge. By providing daily pricing support, her experience also supported her study of the impact of market news on various asset classes including fixed income, forex and structured products. Jie Sheng led several process automation projects which improved working flow efficiency and enhanced data quality control. She was also an active member in DBS's finance social committee and promoted fun spirit at work. She is a certified FRM and CFA level III candidate. In her spare time, she enjoys working out, photography, travel and reading.
Amneet Singh received an MBA from Indian Institute of Management Bangalore and a Bachelors in Engineering from Punjab Engineering College. At IIM Bangalore, Amneet authored research papers involving, among others, pricing of exotic derivative structures and financial time series analysis, that were selected for presentation at academic conferences in the US and Greece. Amneet worked for more than 5 years' across diverse Sales and Trading roles. He was working as a FX and Derivative Sales Management Associate in Citi's Corporate Sales and Structuring team; handling a portfolio of more than 100 corporate clients and later as a discretionary Interest Rates trader at a Fortune 150 corporate where he contributed in the reduction of the borrowing costs on a USD 8 billion LIBOR indexed loan book. Prior to gaining experience in financial markets, he worked as a software programmer with Accenture on data warehousing projects. While working as an interest rate trader, Amneet extensively leveraged his programming experience and developed systemic multi- factor trading models that integrated sentiment, market positioning and technical analysis to generate economically significant trading signals. He further authored research papers outlining systemic trading strategies leveraging on machine learning techniques such as artificial neural networks and principal component analysis.
Mathieu Sjoholm 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. Previously, he worked on an 8-month project with ENGIE (French multinational electric utility company) where he focused on a forecast model of the energy consumption in the US using Time-Series and Econometrics techniques. Implemented in R; these estimations were then used for the climate change correction. In addition, Mathieu worked for ten months at BNP Paribas Corporate and Institutional Banking in London as a Structurer in Equity Derivatives. His team was in charge of the development of systematic investment strategies for any type of packaging. Mathieu worked on a multitude of projects using Python as implementation of strategies, replication of indexes, statistical analysis, model calibration and development of optimization tools. He also developed a portfolio optimization model using Equally Risk Contributions methods on Smart Beta factors. He strongly developed his programming skills as he was constantly using programming tools like Python, JAVA and VBA. With five of his peers, he also founded an Investment Club in which they managed a portfolio following a strategic asset allocation. Mathieu is presently working on an academic project within the MFE in collaboration with Orbis Asset Management on strategy analysis and optimization of contemplated strategies. In his spare time, he enjoys all kind of sports, especially soccer and track and field. He also likes the acting, and has had acting classes for four years at the French Conservatoire.
Qiyi Song graduated with a B.S. in Mathematics/Economics and minor in Accounting from the University of California, Los Angeles, in 2016. During her undergraduate studies, she developed sound analytical and quantitative skills through rigorous coursework and research experience. Prior to joining the Berkeley MFE Program, Qiyi worked as an analyst intern at Greysteel, a commercial real estate firm, where she conducted transactional analysis on Los Angeles's real estate industry and participated in property valuation. She also interned in the Investment Banking Department of China International Capital Corporation Limited (CICC), where she estimated the internal rate of return for a new fund of funds, analyzed real estate bonds and assisted with IPO valuation. In addition, Qiyi gained experience in investment management through her internship at China Galaxy Securities, where she assessed fixed income products' credit risk and interest risk. In her spare time, she enjoys traveling, snorkeling and hiking.
Qingyao (Patrick) Sun received his undergraduate and master's degree in Mathematics from the University of Oxford. He is well versed in areas of applied mathematics such as partial differential equations, numerical methods, derivatives pricing, probability and statistics as well as the pure aspect of the subject, including linear algebra and measure theory. Through years of rigorous training, he has acquired a logical mindset and the ability to systematically tackle convoluted problems by breaking them into smaller and more tractable ones. He has also developed strong programming skills using Matlab, C++ and Python. Prior to joining the Berkeley MFE program, Patrick applied his mathematical toolbox to the credit risk modeling sector through an internship at The Bank of East Asia's risk strategy and governance department in Hong Kong. During his internship Patrick studied in detail the updated bank credit rating models of rating agencies such as Moody's and S&P in order to reevaluate the BEA's current probability of default model. Subsequently he conducted research and analysis on the significance of the new rating methodologies, by collecting the relevant rating changes and programming various statistical tests for the data. Out of the classroom Patrick loves playing golf, swimming, weightlifting and badminton.
Shravan Sunkada graduated in 2008 from the Indian Institute of Technology, Bombay with a Master's degree in Metallurgical Engineering and Materials Science. He has over eight years of experience delivering advanced analytical solutions for the Consumer Banking business of Citigroup with the responsibility of formulating, executing, implementing and tracking high priority analytical initiatives and leveraging advanced Machine Learning techniques. Shravan has passed all three levels of the CFA exams. Upon graduation, Shravan plans to utilize his strong quantitative and problem-solving skills in quantitative finance or the Fintech industry. In his spare time he enjoys listening to music, playing chess and table tennis.
Yuzhe (Michael) Tang graduated with a Bachelor of Economics in Finance and Banking from Peking University in 2016. During his undergraduate studies, he developed solid quantitative and statistical analysis skills, strong programming skills and deep market insights. His research focused jumps in volatility in option pricing models and circuit breakers mechanism in the equity market. He also founded a club and ran his startup business. After graduation, Michael took his internship at Wealth Evolution Wealth Management as a quantitative researcher, where he developed strategies related to Funds of Funds (FoF). He implemented machine learning (such as LASSO, SVM and random forest algorithms) and data analytics techniques (web crawler, PCA and etc.) to develop new strategies and launch new quantitative products. He also has a ten-year personal trading and portfolio management experience and passed the CFA level I in 2015 and FRM in 2016. In his spare time, Michael enjoys tennis, basketball, and photography. He is an active member in volunteer associations.
Christopher Teixeira graduated with a Master's degree in Finance from Neoma Business School and a Bachelor's degree in Technology from Telecom SudParis & Management in France. Following graduate school, Christopher joined the Capital Markets Company as a consultant in risk management. He worked on the implementation of the new Standardized Approach for measuring Counterparty Credit Risk Exposures (SA-CCR) following Basel requirements at BNP Paribas. He designed and built the prototype in both C++ and VBA that is now under construction in the bank systems. Prior to this, Christopher worked two years for Lyxor Asset Management in New York, a subsidiary of Société Générale CIB, as junior portfolio manager in the fund of hedge funds business ($4bn of AuM). He conducted quantitative studies on hedge funds and fund of hedge funds (i.e. portfolio construction, optimization, correlation and scenario analysis, as well as risk and liquidity reviews) for existing and prospective portfolios. He was also actively involved in advisory mandates for major US pension funds on the Alternative Risk Premia topic. During this time, he created new and improved tools in VBA and used his quantitative skills in his work with the Lyxor portfolio managers. He worked on portfolio construction using a variety of frameworks and scenario analysis while also incorporating mandate constraints. Prior to joining Lyxor, Christopher interned as a market risk analyst. He also passed the CFA level I Exam. In his spare time, Christopher enjoys playing basketball and billiards, and traveling.
David Thai 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. Previously, he worked on an 8-month project with INSEE (French National Institute of Statistics and Economic Studies) on Signal and Information Theory where he designed error correcting codes with Gallager's methods using Python and reconstructed degraded images and videos using LPDC codes and Belief Propagation Algorithm. Prior to joining the Berkeley MFE, David worked for BNP Paribas in London for nine months within the Credit Repo Trading desk. Beyond the daily tasks which involved credit derivatives pricing, risk management, conception of new trading strategies as well as creation of analysis tools to understand and optimize the cash position of the Repo book, David also worked on the development of an in-house trading platform for Repo Desk which facilitated more automated, faster trade execution. David is currently working in collaboration with Orbis Asset Management on Strategy Analysis and Optimization. During his spare time, he loves travelling. He is also fond of sports and regularly enjoys playing soccer and swimming.
Tao (Tony) Tong graduated from the University of California at Berkeley with a PhD in microelectromechanical (MEMS) systems and nano-materials engineering in 2007. Since graduation, he's been in the solid-state lighting industry participating in the LED lighting revolution. He has a proven track record of delivering technical innovations as individual contributor as well as driving a team of scientists and engineers in delivering greater impacts (30 issued USPTO patents so far and 13 peer reviewed journal publications). Tony has been practicing stocks investment for about 9 years with various fundamental and technical analyses. Recently, he has developed a strong interest in deep neural network based machine learning techniques and applying them into understanding financial markets. Tony is proficient with Python in data analysis and rapid programming development (TensorFlow for deep learning), and deploying into AWS cloud for scalable high performance computing. Aside from work, he enjoys spending time with his wife and two wonderful young kids.
Cyrus Vesvikar received his Bachelor's and Master's dual degree in Computer Integrated Manufacturing from the Indian Institute of Technology, Bombay. In his thesis, he worked on modelling thermal and vibration dynamics in machining using partial differential equations and published three research papers through the effort. Upon graduation, he worked on developing low latency algorithmic trading strategies for inter-exchange arbitrage and FX flow execution through liquidity aggregation. This equipped him with a thorough understanding of order book mechanics and trading system architecture. Later on, at Citigroup Global Markets, as a part of the Market Quantitative Analysis team, he implemented and calibrated the market impact cost model for single stock, ETF, index and custom portfolio trading. Additionally, he developed investing style factor flow indices, helped develop tools for index tracking and inventory optimization and conducted market micro-structure research. In his spare time Cyrus loves playing table-tennis and multi-player on line games.
Brian Vo received his bachelors degree from UC Berkeley with a major in computer science, where he also cultivated a strong foundation in mathematics, probability and statistics, and finance. During his undergraduate studies, Brian won the first place grand prize in the Big Ideas at Berkeley project competition for his application of artificial intelligence and machine learning techniques in prototyping a campus safety mobile application. After graduation, he joined Goldman Sachs Asset Management in New York where he designed and implemented software solutions for the Fundamental Equities group. Specifically, he led development for a cutting edge proprietary trading and order management system used to manage over 200 billion USD from both institutional and ultra high net worth clients. He also contributed to a software platform that reconciles data from in-house and vendor sources in order to generate risk, performance, and flows reports delivered to portfolio managers, strategists, and clients. In his spare time, Brian enjoys powerlifting, dragon-boat, travelling, and reading about macroeconomic trends.