Artificial Intelligence Finance Institute

Matthew Dixon - Technical Co-Founder & Chief Risk OfficerTechnical Co-Founder & Chief Risk Officer CFX Labs

FinTech entrepreneur and innovator in the area of statistical and mathematical algorithms with a software engineering background. Author of a Machine Learning in Finance textbook and several Journal papers on algorithms and models for machine learning, blockchain based technologies. RISK Magazine’s Buy-side Quant of the Year (2022). College of Computing Dean’s Excellence in Research Award (Junior Professor level), 2021. Tenured Professor (currently on leave) and PI/Co-PI of research funding from Intel, Dell, NASA JPL, and NSF. Research featured in the Financial Times, Bloomberg Markets, Barron’s Advisor.

Member of the CFA NY Quant Investing Committee. Chartered Financial Risk Manager (FRM). Editorial Associate for the World Scientific Annual Review of FinTech and the AIMS Journal of Dynamics & Games. Google Summer of Code Mentor for the R Statistical Computing Project (2017). Chair of the IEEE/ACM Workshop on High Performance Computational Finance (2010-2015).

Marcos Lopez de Prado - Professor

Prof. Marcos López de Prado is the CIO of True Positive Technologies (TPT), 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

Sonam Srivastava - Quantitative researcher

Sonam Srivastava is a quantitative researcher with 10+ years of experience in quantitative trading & research. She is the founder and CEO at Mumbai based Wright Research, which is a data driven investment management & advisory firm.

She has worked at HSBC as a quant building factor driven portfolio solutions enabling large scale trading at the central risk book & structuring desks. She has worked as an algorithm designer at Edelweiss institutional equity execution & arbitrage desks and at Qplum, in a portfolio management role for artificial intelligence driven robo-advisor in the US and India.

She graduated studying computational chemical engineering from IIT Kanpur and has a master’s in financial engineering from Worldquant University.

Dr. rer. nat. Daniel T. Schmitt - Theoretical physicist

Dr. rer. nat. Daniel T. Schmitt is theoretical physicist by training with a passion of learning from data. He has more than 12 years of experience building and running quantitative investment strategies at Credit Suisse and SIMAG (ETH-spinoff). Currently, Head of Systematic Strategies at SG Value Partners AG where he and his team develop and run investment strategies using various machine learning techniques.

Stefan Jansen - Founder and Lead Data Scientist

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.

Miquel Noguer Alonso - Co-Founder & Chief Science Officer, Artificial Intelligence Finance Institute – AIFI

Miquel Noguer i Alonso is a financial markets practitioner with more than 20 years of experience in asset management, he is the Founder of Artificial Intelligence Finance Institute. Head of Development at Global AI and co-Editor of the Journal of Machine Learning in Finance. He serves in the advisory board of Financial Data Professional Institute FDPI and the CFA New York Quant Investing Group.

He worked for UBS AG (Switzerland) as Executive Director. He is member of European Investment Committee for the last 10 years. He worked as a Chief Investment Office and CIO for Andbank from 2000 to 2006. He started his career at KPMG.

He is Visiting Professor at NYU Courant Institute of Mathematical Sciences and the CQF institute and Tandon. He has been Adjunct Professor at Columbia University teaching Asset Allocation, Big Data in Finance and Fintech. He is also Professor at ESADE teaching Hedge Fund, Big Data in Finance and Fintech. He taught the first Fintech and Big Data course at the London Business School in 2017.

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). He completed a Postdoc in Columbia Business School in 2012. He collaborated with the Mathematics department of Fribourg during his PhD. He also holds the Certified European Financial Analyst (CEFA) 2000. He also holds the ARPM certificate.

William J. Kelly - Keynote Speaker

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 firm. Bill’s career in the institutional asset management space spans over 30 years where he gained extensive managerial experience through successive CFO, COO and CEO roles.

In addition to his current role, Bill is a tireless advocate for shareholder protection and investor education and is currently the Chairman and lead independent director for the Boston Partners Trust Company. He has previously served as an independent director and audit committee chair for ’40 Act Mutual Funds and other financial services firms. He is also currently an Advisory Board Member of the Certified Investment Fund Director Institute which strives to bring the highest levels of professionalism and governance to independent fund directors around the world. A member of the board of the CAIA Association, Bill also represents CAIA in similar capacities via their global partnerships with other associations and global regulators.

Bill began his career as an accountant with PwC and is a designated Audit Committee Financial Expert in accordance with SEC rules.

Nicole Königstein - Data Scientist

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.

Faculty

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