Gabelli School QuantVision Conference Draws Thought-Leaders Across the Quant Finance Spectrum Who Focused on AI’s Growing Role in the Future of Investing
Featured Events | Jun 09, 2025 | Gabelli School of Business
Top Industry Experts and Researchers Examined How AI is Shaping Finance Today and Highlighted New Trends on the Horizon
On May 22, 2025, the leaders and changemakers at the forefront of quantitative finance gathered at Fordham University’s Gabelli School of Business for QuantVision, its annual quantitative conference and data summit. Recognized as one of the leading quant finance conferences in the country, the event is hosted by the Gabelli School’s top-ranked M.S. in Quantitative Finance (MSQF) program in collaboration with Rebellion Research, a global machine learning think tank, artificial intelligence financial advisor, and hedge fund. The symposium featured presentations and engaging panel discussions that explored the many ways AI is having an impact on the quantitative finance world, prompting lively debates and in-depth analysis from influential thought-leaders in the industry. In addition to the program’s thought-provoking content, the audience of over 250 researchers, students, entrepreneurs, and quantitative analysts networked, exchanged ideas, and forged connections that serve to influence the growing community of financial engineers who are shaping the future of the finance and investing industries.
Qing Sheng, Ph.D., associate clinical professor in finance and director of the MSQF program, explained how QuantVision provides students invaluable access to knowledge and expertise in this rapidly evolving industry. “This event gives our students insight into how AI is radically reshaping the quantitative finance field and complements the skills they are developing in the classroom. QuantVision brings to life what they are learning through our innovative curriculum, helping them to prepare them for success in their careers,” she commented.
Faruque Khan (GABELLI’24), a recent MSQF graduate and past co-president of the Fordham Quant Finance Society, along with MSQF current students and co-presidents Sonal Agarwal and Mackenzie Qu, collaborated with Alexander Fleiss, CEO of Rebellion Research and a member of the MSQF advisory board, to convene experts from across the fields of quantitative finance and AI. Their vision focused specifically on exploring the technological advancements taking shape now and how they will serve as catalysts for future developments. Khan commented, “We aimed to bring together the brightest minds to talk about how AI is having a radical impact on careers within quantitative finance. We want to inspire students about how to utilize AI in their careers and to imagine what is possible in the future.”
The program’s seven panels featured the leading minds and decision-makers in quantitative finance and AI from JPMorgan, Two Sigma, Tradeweb, Deutsche Bank, Bloomberg, Jefferies, Airbnb, IBM Research, Morgan Stanley, and Blackrock, among many other top firms. Renowned researchers from Columbia University, Yale University, New York University, UCLA, and Johns Hopkins University also participated.
Over the last two years, QuantVision has provided MSQF students with exposure to the financial engineering world as it responds to the impact of AI. The event also affords them the opportunity to establish invaluable relationships as they begin or continue their careers after graduation. Many of the companies that participated— LSEG (London Stock Exchange Group), DeepMM, Similarweb, Lime Trading, Shakudo, BMLL, Databento, Quantconnect, Flyjets, OpenBB and Zonova—connected students to internships and initiated discussions about future career prospects. The CQF Institute, the fastest-growing network of quant finance professionals, was a conference academic collaborator, as was the CFA Institute and the International Association for Quantitative Finance.
A wide range of subjects were covered during keynote presentations and panel discussions. Industry veterans and data pioneers shed light on how alternative data has transformed—and continues to reshape—how investors make decisions. A panel about the investment process brought together managers in hedge fund and quant investing for a conversation centered on systematic strategy evaluation, regime shifts, the use of AI, and advice for the next generation of investors. Another presentation, by Albert An, CEO of Tower Research Capital, summarized how the worlds of AI and blockchain are merging, creating new forms of digital assets and economic models. He advised student to, “keep learning, embrace innovation, and get involved—the edge of technology is where AI meets crypto.”
In a compelling debate moderated by Francesco Fabozzi, Ph.D., research director, Yale School of Management International Center for Finance, Igor Halperin, Ph.D., AI asset manager at Fidelity Investments, and Peter Cotton, Ph.D., co-founder and chief scientific officer at Crunch Labs, explored opposing visions for financial modeling. They discussed whether should be thought of as structured physical systems, or as chaotic, evolving ecosystems best navigated by crowds of competing algorithms? The two clashed on the value of “black box” models, with Halperin warning of their risks in non-stationary systems and arguing that without understanding, trust is misplaced. Cotton countered that performance, not transparency, is often the more practical benchmark—especially in fast-moving, data-rich environments. However, they agreed machine learning is central to finance but noted key bottlenecks. As markets evolve, the future of finance may lie in blending both approaches—physics meets evolution, structure meets scale.
The afternoon sessions focused on theorizing what potential innovations are in play and what to expect from AI in quantitative finance over the coming years. One panel concluded that AI tools will augment but not replace human judgment. The discussion also accentuated the key point that domain knowledge, scientific rigor, and trustworthiness of models are paramount, stressing that education and evolving the interface between humans and AI are critical next steps. Another panel discussion deliberated the ethics of AI and acknowledged that replacing human labor with AI—such as CEOs using AI to replace contractors—raises ethical questions. However, panel members highlighted that AI also offers a chance to reduce grueling workloads, automate repetitive tasks, and allow humans to focus on higher-value activities. It also was suggested that while the finance industry will increasingly rely on AI for efficiency gains, thoughtful integration is essential. In addition, conversations highlighted a fundamental truth—while data remains the cornerstone of effective decision-making, the assurance of rigorous onboarding, quality control, and thoughtful strategy around data use is essential.
Although the excitement about the potential of AI as it relates to financial engineering was palpable in the room, many presenters warned against buying into the hype. They emphasized that the most effective use of AI is to redefine problems and/or uncover insights others can’t see. In this new era, the message was that it’s not just about having access to data. The skill is in knowing how to ask the right questions, and increasingly, teaching machines to do the same. Those just beginning to build their careers in quantitative finance were advised to gain access to proprietary data, sharpen their technical skills, and understand how to use AI tools. As AI reshapes industries and market dynamics continue to shift, the future of quant finance will belong to those who can combine these assets with their own inquisitiveness and creative, original thinking.
Written by: Michelle Livingston, associate director of communications, Fordham University Gabelli School of Business
