Oleksiy Kondratyev is ADIA Lab Research Fellow and Visiting Professor at the Department of Mathematics, Imperial College London. Prior to joining ADIA in 2021 as Quantitative Research & Development Lead, Oleksiy was Managing Director and Head of Data Science & Innovation at Standard Chartered Bank in London.

Oleksiy has over 25 years of quantitative finance experience in both risk management and front office roles and has been recognized as Quant of the Year (2019) by Risk magazine for his research on the application of machine learning techniques to risk factor analysis and portfolio optimisation.

Oleksiy holds MSc in Theoretical Physics from Taras Shevchenko National University of Kyiv and PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine. His research interests are in machine learning and quantum computing.

Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL, a trading and risk technology company. He is also a co-founder of Numerix, where he served as CTO from 1996 to 2003.

Alexander won the 2018 Quant of the Year Award together with Leif Andersen and Michael Pykhtin for their joint work revealing the true scale of the settlement gap risk that remains in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin), joint measure models and the local price of risk (with John Hull and Alan White), the use of autoencoder manifolds for interest rate modelling (with Andrei Lyashenko and Fabio Mercurio), and the mean reversion skew.

Alexander graduated from high school at the age of 14 and earned a PhD from the L.D. Landau Institute for Theoretical Physics at the age of 22. He was the winner of the USSR Academy of Sciences Medal for Best Student Research of the Year in 1988.

Nicole is the Co-Founder, CEO, and Co-Chief AI Officer at Quantmate, a deep-tech fintech company developing AI agents for portfolio management and strategy development via natural language. She is a globally recognized thought leader in large language models (LLMs) and agentic architectures, with a particular focus on their transformative applications in quantitative finance.

As a guest lecturer, Nicole shares her expertise in Python, machine learning, and deep learning at universities. She is also a frequent speaker at AI and quantitative finance events. Nicole has authored Math for Machine Learning and Transformers in Action with Manning Publications. Her forthcoming book, Transformers: The Definitive Guide – Applications Beyond NLP, will be published by O’Reilly Media.

Jesper Andreasen is head of quantitative analytics at Verition Fund Management. Jesper’s career spans 28 years in the industry and he has previously headed quant teams at Saxo Bank, Danske Bank, Bank of America, Nordea and General Re Financial Products. Jesper co-received Risk Magazine’s quant of the year awards in 2001 and 2012 and their in-house risk system of the year in 2015.

He is an honorary professor of mathematical finance at the University of Copenhagen and he holds a PhD in the same subject from Aarhus University.

Gary is the Head of Quant Technology Strategy in the Office of the CTO at Bloomberg. Prior to taking on this role, he created and headed the company’s Machine Learning Engineering group, leading projects at the intersection of computational linguistics, machine learning and finance, such as sentiment analysis of financial news, market impact indicators, statistical text classification, social media analytics, question answering, and predictive modeling of financial markets.

Prior to joining Bloomberg in 2007, Gary had earned degrees with distinction in physics, mathematics, and computer science from Boston University.

He is engaged in advisory roles with FinTech and Machine Learning startups and has worked at a variety of technology and academic organizations over the last 20 years. In addition to speaking regularly at industry and academic events around the globe, he is a member of the KDD Data Science + Journalism workshop program committee and the advisory board for the AI & Data Science in Trading conference series. He is also an adjunct professor at Columbia University, and a co-organizer of the annual Machine Learning in Finance conference at Columbia University.

Petter Kolm is a Clinical Professor at NYU’s Courant Institute, where he directs the Mathematics in Finance Master’s program. In 2021, he was honored as “Quant of the Year” by Portfolio Management Research (PMR) and the Journal of Portfolio Management (JPM) for his significant contributions to quantitative portfolio theory. He has co-authored numerous influential articles and books on quantitative finance and financial data science. Petter serves on several company advisory boards (including Aisot, Axyon AI, and GoQuant), editorial boards for leading academic journals, and boards of directors for professional associations. Previously, he worked in Quantitative Strategies at Goldman Sachs Asset Management. As a consultant and expert witness, Petter provides expertise in machine learning, portfolio management, risk management, and systematic trading. He earned his Ph.D. in Mathematics from Yale University, an M.Phil. in Applied Mathematics from the Royal Institute of Technology (KTH), and an M.S. in Mathematics from ETH Zurich.

Tony Guida is a Quantitative Portfolio Manager, researcher, and author with expertise in systematic investing, machine learning, and financial data science. He currently works on the integration of large language models (LLMs) and knowledge graphs into investment processes for a Swiss asset manager specializing in growth-oriented thematic equity portfolios.

Tony began his career at Unigestion in 2006, joining the quantitative equity team as a research analyst. He later became a member of the Research and Investment Committee for Minimum Variance Strategies, where he led the factor investing research group for institutional clients. In 2015, he joined EDHEC-Risk Scientific Beta as a Senior Consultant, focusing on risk allocation and multi-factor strategies. In 2016, Tony moved to a major UK pension fund, where he built an in-house systematic equity strategy and co-managed a £8 billion portfolio as Senior Quantitative Portfolio Manager.

In 2019, he joined RAM Active Investments as a Senior Quantitative Researcher in equities, later co-heading the systematic macro hedge fund offering. In 2023, Tony co-founded a hedge fund, where he served as Co-Head of Research and Portfolio Manager, leading the development of cross-asset, machine learning-driven investment strategies.

Tony holds Bachelor’s and Master’s degrees in Econometrics, and a Master’s in Economics and Finance. He has authored and edited several industry-leading books, including:

• Big Data and Machine Learning in Quantitative Investment (Wiley, 2018)
• Machine Learning for Factor Investing: R Version (CRC, 2020)
• Machine Learning for Factor Investing: Python Version (CRC, 2023)

He is also an Advisory Board Member for the Financial Data Professional Institute and serves as a peer reviewer for academic journals in machine learning and finance.

Dr. Randeep Gug is the Managing Director of the CQF Institute and the Certificate in Quantitative Finance (CQF). Prior to joining Fitch Learning, Randeep worked in a variety of roles. He spent five years working in the Equities division at Salomon Smith Barney and later traded futures and options on the Indian National Stock Exchange (NSE). More recently he has spent time teaching mathematics at all levels. He is a qualified teacher, holds a 1st class honours degree and a PhD for research in semiconductor physics. He is a CQF Alumnus, achieving a distinction on the programme and his current interests are based around improving and promoting the teaching and learning of Quant Finance.