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).

Gordon Ritter: Founder, CIO, Ritter Alpha, LP

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.

Prof. Marcos López de Prado: Global Head – Quantitative Research & Development, ABU DHABI INVESTMENT AUTHORITY (ADIA)

Prof. Marcos López de Prado is the Global Head – Quantitative Research & Development, ABU DHABI INVESTMENT AUTHORITY (ADIA), and professor of practice at Cornell University’s School of Engineering. He has over 20 years of experience developing investment strategies with the help of machine learning algorithms and supercomputers. He launched TPT after he sold some of his patents to AQR Capital Management, where he was a principal and AQR’s first head of machine learning. Marcos also founded and led Guggenheim Partners’ Quantitative Investment Strategies business, where he managed up to $13 billion in assets, and delivered an audited risk-adjusted return (information ratio) of 2.3.

Nicole Königstein: Chief Data Scientist, Head of AI & Quant Research, Wyden Capital AG

Nicole Königstein is a distinguished Data Scientist and Quantitative Researcher, currently working as Data Science and Technology Lead at impactvise, an ESG analytics company, and as Head of AI and Quantitative Research at Quantmate, an innovative FinTech startup focused on alternative data in predictive modeling. Alongside her roles in these organizations, she serves as an AI consultant across diverse industries, leading workshops and guiding companies from the conceptual stages of AI implementation through to final deployment.

As a guest lecturer, Nicole shares her expertise in Python, machine learning, and deep learning at various universities. She is a regular speaker at renowned AI and Data Science conferences, where she conducts workshops and educational sessions. In addition, she is an influential voice in the data science community, regularly reviewing books in her field and offering her insights and critiques. Nicole is also the author of the well-received online course, “Math for Machine Learning.

Igor Halperin: AI Research Associate, Fidelity Investments

Igor Halperin is an AI Research Associate at Fidelity Investments. His research focuses on using methods of reinforcement learning, information theory, neuroscience and physics for financial problems such as portfolio optimization, dynamic risk management, and inference of sequential decision-making processes of financial agents. Igor has an extensive industrial and academic experience in statistical and financial modeling, in particular in the areas of option pricing, credit portfolio risk modeling, portfolio optimization, and operational risk modeling. Prior to joining Fidelity, Igor worked as a Research Professor of Financial Machine Learning at NYU Tandon School of Engineering.  Before that, Igor was an Executive Director of Quantitative Research at JPMorgan, and a quantitative researcher at Bloomberg LP. Igor has published numerous articles in finance and physics journals, and is a frequent speaker at financial conferences. He has co-authored the books “Machine Learning in Finance: From Theory to Practice” (Springer 2020) and “Credit Risk Frontiers” (Bloomberg LP, 2012). Igor has a Ph.D. in theoretical high energy physics from Tel Aviv University, and a M.Sc. in nuclear physics from St. Petersburg State Technical University.

Matthew Dixon: Stuart School of Business, Illinois Institute of Technology

Matthew Dixon, Ph.D, FRM, began his career as a quant in structured credit trading at Lehman Brothers. He has consulted for numerous investment management, trading and financial technology firms in machine learning and risk analytics. He is the author of the 2020 textbook “Machine Learning in Finance: From Theory to Practice” and has written over 20 peer reviewed papers on machine learning and computational finance, including SIAM J. Financial Mathematics and the Journal of Computational Finance. He is the recipient of an Illinois Tech innovation award, and his research has been funded by Intel and the NSF.  Matthew has recently contributed to the CFA syllabus on machine learning and he currently serves on the CFA advisory committee for quantitative trading. He has been invited internationally to give talks at prestigious seminars organized by investment banks and universities in addition to being quoted in the Financial Times and Bloomberg Markets.  He holds a Ph.D. in Applied Math from Imperial College, has held visiting academic appointments at Stanford and UC Davis, and is a tenure-track Assistant Professor at Illinois Tech.

Petter Kolm: Clinical Full Professor and Director of the M.S. in Mathematics in Finance Program, Courant Institute of Mathematical Sciences, New York University & Partner, CorePoint-Partners.com

Petter Kolm is Clinical Full Professor and Director of the M.S. in Mathematics in Finance Program at the Courant Institute of Mathematical Sciences, New York University, since 2007. He is also Partner at CorePoint-Partners.com. Previously, Petter worked in the Quantitative Strategies group at Goldman Sachs Asset Management, developing proprietary investment strategies, portfolio and risk analytics in equities, fixed income and commodities.

Petter is the co-author of numerous academic journal articles and several well-known finance books including, ​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).

Petter is a frequent speaker, panelist and moderator at academic and industry conferences and events. He is a member of the editorial boards of the International Journal of Portfolio Analysis and Management (IJPAM), Journal of Financial Data Science (JFDS), Journal of Investment Strategies (JoIS), and Journal of Portfolio Management (JPM). Petter is an Advisory Board Member of Alternative Data Group (ADG), AISignals and Operations in Trading (Aisot), Betterment (one of the largest robo-advisors) and Volatility and Risk Institute at NYU Stern. He is also on the Board of Directors of the International Association for Quantitative Finance (IAQF) and Scientific Advisory Board Member of the Artificial Intelligence Finance Institute (AIFI).

As an advisory board member, consultant, and expert witness, Petter has provided services in areas including alternative data, data science, econometrics, forecasting models, high frequency trading, machine learning, portfolio optimization with transaction costs, quantitative and systematic trading, risk management, robo-advisory, smart beta strategies, trading strategies, transaction costs, and tax-aware investing.

He holds a Ph.D. in Mathematics from Yale University; an M.Phil. in Applied Mathematics from the Royal Institute of Technology, Stockholm, Sweden; and an M.S. in Mathematics from ETH Zurich, Switzerland

Stefan Jansen: Professor, Founder & Lead Data Scientist, Applied AI

Stefan is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms and startups across industries on data & AI strategy, building data science teams, and developing machine learning solutions. Before his current venture, he was a partner and managing director at an international investment firm where he built the predictive analytics and investment research practice. He also was a senior executive at a global fintech company with operations in 15 markets. Earlier, he advised Central Banks in emerging markets, consulted for the World Bank, and has worked in six languages across Asia, Africa, and Latin America. Stefan holds Master degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin and is a CFA Charterholder. He has also been teaching data science at Datacamp and General Assembly.

Tim Leung: Boeing Professor of Applied Math; Director of Computational Finance & Risk Management, University of Washington

Tim Leung is the Boeing Full Professor in the Department of Applied Mathematics and the Director of the Computational Finance & Risk Management (CFRM) program at University of Washington in Seattle. Previously, he was a tenure-track Assistant Professor in the Department of Applied Mathematics & Statistics at Johns Hopkins University and in the Department of Industrial Engineering & Operations Research at Columbia University in New York City. He obtained his BS from Cornell University and PhD from Princeton University. His research in Quantitative Finance has been funded by the National Science Foundation (NSF). He has published over 60 peer-reviewed articles and two books respectively, on the topics of Mean Reversion Trading, and ETFs. Professor Leung served as the Chair for the INFORMS Finance Section and Vice Chair for the SIAM Activity Group on Financial Mathematics & Engineering. He is the founding editor of the book series, Modern Trends in Financial Engineering, and is also on the editorial board of multiple journals, including Applied Mathematical Finance, SIAM Journal on Financial Math, IEEE Intelligent Systems, and Stochastic Models.

Sonam Srivastava: Founder/Portfolio Manager, Wright Research

Sonam is the founder of Wright Research, an investment advisory firm that uses advanced statistics and quantitative techniques to democratise access to research backed products. She is a leading researcher in the field of machine learning in finance and quantitative methods. She has more than nine years experience in this field. She has led teams at Central Risk Book and Equity Structuring desks at HSBC; at Edelweiss for the Algorithmic Trading desk and Qplum as a quantitative portfolio manager for US and Indian equities.

She is a IIT Kanpur graduate and a Masters in Financial Engineering from Worldquant University.

William J. Kelly: CEO, CAIA Association

William (Bill) J. Kelly is the CEO of the CAIA Association. Bill has been a frequent industry speaker, writer, and commentator on alternative investment topics around the world since taking the leadership role at the CAIA Association in January, 2014. Previously, Bill was the CEO of Boston Partners and one of seven founding partners of the predecessor firm, Boston Partners Asset Management which, prior to a majority interest being sold to Robeco Group in Rotterdam in 2002, was an employee-owned