Top Questions: Cornell Financial Engineering Manhattan 2025 Future of Finance & AI Conference
My questions for the speakers inside to help you prepare for the Rebellion Research hosted conference on Sep 19th in NYC.
On Friday, September 19, 2025, Cornell Financial Engineering Manhattan (CFEM) hosts its annual Future of Finance & AI Conference in New York City.
To help the data community prepare, I've created targeted questions for each speaker and panel. There are 3 key themes to my questions:
AI’s role in investing
Data economics
Infrastructure and governance
Link to agenda: https://www.rebellionresearch.com/cornell-financial-engineering-manhattan-2025-future-of-finance-ai-conference
Welcome to the Data Score newsletter, composed by DataChorus LLC. This newsletter is your source for insights into data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. I remain active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser and creator of data, I have a unique perspective, which I am sharing through the newsletter.
Note: Agenda as of September 16th
08:50 – Opening Address
Prof. Victoria Averbukh, Director, CFEM
Alexander Fleiss, CEO, Rebellion Research
Question: Which AI and data engineering skills are most overlooked in today’s financial engineering curricula, and how will CFEM address them?
08:55 – Panel & Keynote Debate
Chat-GPT & The Future of AI in Finance!
Debate: What is the Future of Alternative Data for Markets?
Tony Berkman (ex-Two Sigma, on garden leave) vs Carson Boneck, Chief Data Officer, Balyasny Asset Mgmt
Moderator: Dr. Amal Moussa, MD, Head of US Single-Stocks Exotic Derivatives Trading, Goldman Sachs; Adjunct Prof., Columbia
Question: I suspect that both Tony and Carson will take the view that generative AI is a tool for humans to leverage to go deeper into analytics and not a human replacement (my view too). But, as a thought experiment, how could an independent autonomous AI analyst be integrated into the investment decision process with the human-on-the-loop instead of in-the-loop?
09:30 – Panel: Changing Landscape of Data & Investing
Moderator: Rafa López Espinosa, COO of Equities, Citadel
Panelists:
Natalya Dmitriyeva (Schonfeld),
Nancy Davis (Quadratic Capital),
Wenqi Zhou (Engineers Gate),
Mark Fleming-Williams (CFM),
Mike Soss (Millburn)
Question: Looking 10 years ahead, will advances in technology and AI eliminate alpha opportunities in systematic investing? If not, which market, data, or technology dynamics would sustain alpha generation?
10:05 – Conversation: Data Disrupting
Paul Humphrey, CEO, BMLL
Christina Qi, CEO, Databento
Moderator: Tim Baker, CEO viaNexus
Question: Will consumption-based pricing disrupt traditional enterprise models? If so, how might data vendors adapt their product roadmaps and go-to-market strategies?
10:55 – Panel: Where Do Equities & AI Go From Here?
Moderator: Naim El-Far, Head of Equities Investment Engineering, Bridgewater
Panelists:
Boyu (Daniel) Wu (Vanguard),
Samson Qian (Citadel),
Renee Yao (Neo Ivy),
Mike Purewal (Millennium),
Kevin Mahn (Hennion & Walsh)
Question: Which generative AI tools are now used daily in equity signal research, and what measurable uplift have they delivered?
11:35 – Panel: Generative AI for Investment Research
Moderator: Andrew Chin, Chief AI Officer, AllianceBernstein
Panelists:
Peter Cotton (author Microprediction),
Kathryn Zhao (Cantor Fitzgerald),
Sean Slotterback (Quant PM),
William Wu (Menos AI),
Charlie Marin (Quanted)
Question: Acknowledging that today’s AI will be the worst AI we work with going forward, yet large language models have clear limitations in quantitative analytics. Which real applications are already adding production value, and where are researchers still testing boundaries?
1:00 – Panel: Future of AI & Data for Modeling?
Moderator: Matthew Lyberg, Head of Asset Mgmt AI, Manulife IM
Panelists:
Daniel DeWoskin (Graham Capital),
Atlas Wang (XTX Markets),
Petros Zerfos (IBM Research)
Question: As firms adopt more advanced AI, what is today’s biggest constraint on modeling: algorithms, data quality and governance, or institutional willingness to productionize under uncertainty? How should these bottlenecks be prioritized over the next few years?
1:40 – Talk: The Current Research Focus of CFEM
Sasha Stoikov, Head of Research, CFEM
Question: If you could launch one open-source benchmark for generative AI in 2026, what would it target?
1:55 – Panel: Building Models From Real-World Data Sets
Moderator: Gordon Ritter, CEO Ritter Alpha
Panelists:
Ioana Boier (NVIDIA),
Evan Reich (Verition),
Paul Krueger (Millennium),
Alex Unterrainer (DefconQ),
Morgan Slade (Exponential Technology)
Question: How do you measure the “new information” in a dataset relative to existing ensembles to avoid overpaying for correlated alpha?
2:35 – Panel: Allocating to Quant Funds
Moderator: Jonathan Larkin, Columbia Investment Mgmt Co.
Panelists:
Patrick Hop (Draco Ova),
Abhi Kane (400 Capital),
Yury Rojek (MSIM),
Josh Shapiro (UNC Mgmt Co.)
Question: What verifiable artifacts do you require before allocating to a quant manager claiming an ML/AI edge?
3:35 – Panel: Understanding the Future of AI in the 21st Century
Moderator: Prof. Jim Liew, Johns Hopkins
Panelists:
Zach Golkhou (JPMorgan),
Arkin Gupta (Citadel),
Keywan Rasekhschaffe (Code Willing),
Ramit Sawhney (Tower Research),
Igor Halperin (Fidelity)
Question: To unlock AI’s long-term potential, which frontier is most critical in the next two years: (a) building trustworthy enterprise platforms, (b) advancing model architectures for alpha discovery, or (c) creating infrastructure and governance for reliable scaling?
4:15 – Panel: Future of Interpreting Data
Moderator: Christina Qi, CEO Databento
Panelists:
Nan Xiao (Greenland Capital),
Ethan Geismar (Jefferies),
Rahul Gupta (DRW),
Judith Gu (Scotiabank),
Winston Ma (Professor),
Andreea Minca (Cornell)
Question: As financial data becomes more complex and central to decision making, what should be the top priority for the industry to build interpretation frameworks that deliver actionable insights while also meeting the rising expectations of regulators and clients for transparency and trust?
What questions do you have for the panelists? Leave a comment below
Know someone attending or presenting at the conference? Feel free to forward this to them.
- Jason DeRise, CFA
