Faculty
Andrew W. Lo is the Charles E. and Susan T. Harris Professor at the MIT Sloan School of Management and the director of MIT's Laboratory for Financial Engineering. He is also a Principal Investigator at the Computer Science and Artificial Intelligence Laboratory (CSAIL), an affiliated faculty of the Department of Electrical Engineering and Computer Science, a member of the Operations Research Center (ORC) and the Institute for Data, Systems, and Society (IDSS), all at MIT. He is also an external faculty at the Santa Fe Institute, Santa FE, NM. He received his AM and PhD in economics from Harvard University, his BA in economics from Yale University, and graduated from the Bronx High School of Science. He began his academic career at the University of Pennsylvania's Wharton School, where he was an Asistant and Associate Professor.
His current research spans several areas: evolutionary models of investor behavior and adaptive markets; systemic risk and financial regulation; quantitative models of financial markets; financial applications of machine-learning techniques and secure multi-party computation; healthcare finance; and deep-tech investing, including fusion energy and advanced manufacturing. Recent projects include:
An evolutionary model of asset prices based on the Adaptive Markets Hypothesis
New financing methods/ business models for accelerating biomedical innovation
Quantitative approaches to deep-tech investing
Applications of AI, especially machine learning and LLMs, to financial advice, “quantamental investing,” and healthcare finance
Lo has published extensively in academic journals (see http://alo.mit.edu) and his most recent book is The Adaptive Markets Hypothesis: An Evolutionary Approach to Understanding Financial System Dynamics. His awards include Sloan and Guggenheim Fellowships, the Paul A. Samuelson Award, the Harry M. Markowitz Award, the CFA Institute’s James R. Vertin Award, as well as election to Academia Sinica, the American Academy of Arts and Sciences, the American Finance Association, the Econometric Society, and TIME’s 2012 list of the “100 most influential people in the world.” His trade book Adaptive Markets: Financial Evolution at the Speed of Thought published in 2017 has also received a number of awards, listed here, and he has received multiple teaching awards from the University of Pennsylvania and MIT.
Lo is also a research associate of the National Bureau of Economic Research; a cofounder and board member of BridgeBio Pharma and Uncommon Cures; a cofounder of AlphaSimplex Group, QLS Advisors, QLS Technologies, Quantile Health, and Rutherford Energy Ventures; a board member of GCAR, n-Lorem, and Vesalius; and an investor in and advisor to a number of biotech companies and non-profit organizations. For a complete list of Lo’s affiliations and conflicts of interest disclosure, please click here.
Dai, Yuehao, Andrew W. Lo, Manish Singh, Qingyang Xu, and Ruixun Zhang. Oxford Bulletin of Economics and Statistics. Forthcoming.
Thakor, Richard T. and Andrew W. Lo. Research Policy. Forthcoming. SSRN Preprint.
Li, Xuelin, Andrew W. Lo, and Richard Thakor. Review of Finance. Forthcoming. SSRN Preprint.
Shukla, Chinmay, Irwin Tendler, Neil Kumar, and Andrew W. Lo. Drug Discovery Today Vol. 31, No. 1 (2026): 104583. Download PDF.
Fengze Liu, Haoyu Wang, Joonhyuk Cho, Dan Roth, and Andrew W. Lo. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Suzhou, China: November 2025. Download PDF.
Ben Chaouch, Zied, Qingyang Xu, ... and Andrew W. Lo et al. Computers in Biology and Medicine Vol. 198, No. Part B (2025): 111150.
MIT Sloan professor Andrew Lo says Al is good at explaining trade-offs and exploring scenarios but weak at precise tax optimization, math, and regulatory compliance.
A new MIT Sloan executive education course led by professor Andrew W. Lo explores machine reasoning, quantamental investing, AI governance, and more.
Professor Andrew W. Lo said that AI struggles with tax optimization, doesn't understand regulatory nuance and — unlike a human financial adviser — isn't subject to legal requirements, such as acting in a client's best interest. He stressed that it's important to ask critical questions when using AI for retirement advice, such as prompting an AI to say where it might be wrong and to list its assumptions and uncertainties.
Professor Andrew W. Lo expects AI models to quickly advance and be able to serve as fiduciaries in coming years. As of now, he cautioned, they aren't ready for prime time."We believe that it is possible to train an LLM, just like we train humans, to provide fiduciary duty," Lo said. "But they don't have it right now, and the guardrails aren't there to protect individuals."
"I think that there's a real art and science to prompt engineering," said professor Andrew W. Lo. "When it comes to specific calculations of your own personal situation, that's where you have to be very, very careful," he said. AI can also sometimes provide wrong answers due to so-called "hallucination" of the algorithm. "One of the things about large language models that I find particularly concerning is that no matter what you ask it, it'll always come back with an answer that sounds authoritative, even if it's not," Lo said.
"The problem that we have to solve is not whether AI has enough expertise," said professor Andrew W. Lo. "The answer right now is, clearly, AI has the financial expertise. What they don't have is that fiduciary duty. They don't have the ability to suffer consequences if they make a mistake to the same degree that a human advisor does." The notion of putting a client's interest ahead of yours "has no teeth" without responsibility or legal liability, he said.
Machine learning, a branch of artificial intelligence, is the science of programming computers to improve their performance by learning from data. Dramatic progress has been made in the last decade, driving machine learning into the spotlight of conversations surrounding disruptive technology. This six-week online program from the MIT Sloan School of Management and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) aims to demystify machine learning for the business professional – offering you a firm, foundational understanding of the advantages, limitations, and scope of machine learning from a management perspective.
This online program from the MIT Sloan School of Management and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) challenges common misconceptions surrounding AI and will equip and encourage you to embrace AI as part of a transformative toolkit. With a focus on the organizational and managerial implications of these technologies, rather than on their technical aspects, you’ll leave this course armed with the knowledge and confidence you need to pioneer its successful integration in business.
