Miquel Noguer Alonso:
Co – Founder and Chief Science Officer, Artificial Intelligence Finance Institute – AIFI
Miquel Noguer Alonso: Co – Founder and Chief Science Officer, Artificial Intelligence Finance Institute – AIFI
Miquel Noguer is a financial markets practitioner with more than 20 years of experience in asset management, he is currently Head of Development at Global AI ( Big Data Artificial Intelligence in Finance company ) and Head on Innovation and Technology at IEF.
He worked for UBS AG (Switzerland) as Executive Director.for the last 10 years. He worked as a Chief Investment Office and CIO for Andbank from 2000 to 2006.
He is professor of Big Data in Finace at ESADE and Adjunct Professor at Columbia University teaching Asset Allocation, Big Data in Finance and Fintech. He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain).
Michael Oliver Weinberg:
Co – Founder and Chief Executive Officer
Michael has 25 years of experience investing directly at the security level and indirectly as an asset allocator in traditional and alternative assets. He is the Chief Investment Officer, and a Senior Managing Director of MOV37 and Protégé Partners. His portfolio management experience includes Soros Fund Management LLC, Credit Suisse First Boston, and Financial Risk Management (FRM).
Michael is a published author and keynote speaker at conferences and universities. He received an M.B.A. from Columbia Business School, where he is now also an Adjunct Professor of Finance and Economics, and a B.S. in Economics from New York University.
Senior Portfolio Manager, GSA Capital
Gordon Ritter: Senior Portfolio Manager, GSA Capital
Gordon Ritter completed his PhD in mathematical physics at Harvard University in 2007, where his published work ranged across the fields of quantum computation, quantum field theory, differential geometry and abstract algebra.
Prior to Harvard he earned his Bachelor’s degree with honours in Mathematics from the University of Chicago. Gordon is currently a senior portfolio manager at GSA Capital, and leader of a team trading a range of high-Sharpe absolute return strategies across geographies and asset classes. GSA Capital has won the Equity Market Neutral & Quantitative Strategies category at the Eurohedge awards four times, with numerous other awards including in the long-term performance category.
Prior to joining GSA, Gordon was a Vice President of Highbridge Capital and a core member of the firm’s statistical arbitrage group, which although less than 20 people, was one of the most successful quantitative trading groups in history, responsible for billions in pro_t and trillions of dollars of trades across equities, futures and options.
Concurrently with his positions in industry, Gordon teaches courses ranging from portfolio management to econometrics, continuous-time finance, and market microstructure in the Department of Statistics at Rutgers University, and also in the MFE programs at Baruch College (CUNY) and New York University (both ranked in the top 5 MFE programs).
He has published several articles on modern portfolio theory in top practitioner journals including Risk, and academic journals including European Journal of Operational Research.
Assistant Professor in AI Research, Illinois Institute of Technology, Chicago
Matthew Dixon: Assistant Professor in AI Research, Illinois Institute of Technology, Chicago
Matthew Dixon is a co-founder of the Thalesians and an Assistant Professor of Finance at the Illinois Institute of Technology, Chicago. He began his career as a quant at Lehman Brothers before moving to Silicon Valley where he worked in data science. His research, funded by Intel, focuses on the application of high performance computing and machine learning to trading and risk modeling. Matthew holds a PhD in Applied Math from Imperial College and has held visiting appointments at Stanford and UC Davis. He has published over 20 peer-reviewed papers including a recent article on deep learning in Algorithmic Finance.
Professor, Courant Institute of Mathematical Sciences, NYU
Petter Kolm: Director of the Mathematics in Finance Master’s Program and Clinical Professor, Courant Institute of Mathematical Sciences, New York University
Petter Kolm is a Clinical Professor and the Director of the Mathematics in Finance Master’s Program at Courant Institute of Mathematical Sciences, NYU. Previously, Petter worked in the Quantitative Strategies Group at Goldman Sachs Asset Management where his responsibilities included researching and developing new quantitative investment strategies for the group’s hedge fund. Petter has coauthored numerous academic articles and four books: Financial Modeling of the Equity Market: From CAPM to Cointegration (Wiley, 2006), Trends in Quantitative Finance (CFA Research Institute, 2006), Robust Portfolio Management and Optimization (Wiley, 2007), and Quantitative Equity Investing: Techniques and Strategies (Wiley, 2010). He holds a Ph.D. in Mathematics from Yale, an M.Phil. in Applied Mathematics from the Royal Institute of Technology, and an M.S. in Mathematics from ETH Zurich.
Petter is a member of the editorial boards of the International Journal of Portfolio Analysis and Management (IJPAM), Journal of Investment Strategies (JoIS), Journal of Portfolio Management (JPM) and Journal of Financial Data Science (JFDS). He is an Advisory Board Member of Betterment (one of the largest robo-advisors) and the Alternative Data Group (ADG). Petter is also on the Board of Directors of the International Association for Quantitative Finance (IAQF) and the Yale Graduate School Alumni Association (GSAA).
Petter’s teaching, work and research interests include alternative data, data science, econometrics, forecasting models, high frequency trading, machine learning, portfolio optimization w/ transaction costs and taxes, quantitative and systematic trading, risk management, robo-advisory and investing, smart beta strategies, transaction costs, and tax-aware investing.
Chief Intelligence Architect, MIT Quest for Intelligence
Josh Joseph: Chief Intelligence Architect, MIT Quest for Intelligence
Josh Joseph is the Chief Intelligence Architect of the Bridge, the application arm of MIT’s Quest for Intelligence Initiative. Previously, Josh was the Chief Science Officer of Alpha Features, an alternative data distribution platform, and co-founded a proprietary trading company based on machine learning driven strategy discovery and fully autonomous trading. Additionally, he has done a variety of consulting work across finance, life sciences, and robotics. He has a Ph.D. in Aeronautics and Astronautics from MIT where his research focused on methods for learning models of complex systems for decision making.
Chief Investment Officer, Springfield Capital | Adjunct Professor, Columbia & NYU
George is a statistics expert with a decade of experience in applying quantitative models in the real world. He has worked in various capacities at leading financial institutions, such as Morgan Stanley, BNP Paribas, Citigroup, and Hutchin Hill Capital. He has also held faculty positions at Columbia University and NYU, where he has taught courses in machine learning and applied statistics and econometrics. His professional expertise includes the application of statistics, machine learning, and AI to finance and economics. He is currently the chief data scientist and manager of Springfield Capital Management. He holds a PhD, MPhil, and BA from Oxford University and an MPhil from Cambridge University.
Associate Professor, Cold Spring Harbor Laboratory
Mickey Atwal: Associate Professor, Cold Spring Harbor Laboratory
Mickey Atwal is an associate professor at Cold Spring Harbor Laboratory where he
undertakes machine learning research and builds tools to analyze vast datasets in
cancer genomics and immunology. He was awarded the Winship Herr Award for
Excellence in Teaching a record three times, developing courses at the interface of
machine learning, molecular biology, and neuroscience. He has trained in theoretical
physics from the University of Cambridge, Cornell University, and Princeton University
Professor, MIT Computer Science and Artificial Intelligence Laboratory
Larry Rudolph: Professor, MIT Computer Science and Artificial Intelligence Laboratory
Larry Rudolph is a researcher at the MIT Computer Science and Artificial Intelligence
Laboratory. Larry received his PhD also in Computer Science in 1981 from the Courant
Institute at NYU. He was on the faculty at University of Toronto, Carnegie-Mellon
University, and The Hebrew University, before joining MIT as a principal research
scientist, in 1995.
Way back in 1978, he helped start the Ultracomputer, a high performance parallel
computer architecture, many ideas of which can be found in current multi-core
computer chips. VP (Member of Labs) at Two Sigma Investments