Andrew W. Lo

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Andrew W. Lo

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Affiliated MIT Sloan Group

MIT Department

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.

Honors

JOIM honors Kritzman and Lo

February 20, 2024

Lo wins 2021 digital teaching award

June 3, 2021

Pinnacle Achievement Award Given to Lo

Markowitz Award given to Lo

GARP honors Lo as Risk Manager of the Year

Lo wins Jamieson Prize

Lo’s book wins PROSE Award

Publications

"Estimating Correlations Between Clinical Trial Outcomes Using Generalised Estimating Equations."

Dai, Yuehao, Andrew W. Lo, Manish Singh, Qingyang Xu, and Ruixun Zhang. Oxford Bulletin of Economics and Statistics. Forthcoming.

"Optimal Financing Design for Drug Development Firms."

Thakor, Richard T. and Andrew W. Lo. Research Policy. Forthcoming. SSRN Preprint.

"Paying off the Competition: Contracting, Market Power, and Innovation Incentives."

Li, Xuelin, Andrew W. Lo, and Richard Thakor. Review of Finance. Forthcoming. SSRN Preprint.

"Use of Bayesian Decision Analysis in the Design of Patient-Centered Clinical Trials for Kidney Failure Devices."

Ben Chaouch, Zied, Qingyang Xu, ... and Andrew W. Lo et al. Computers in Biology and Medicine Vol. 198, No. Part B (2025): 111150.

"Performance Attribution for Portfolio Constraints."

Lo, Andrew W., and Ruixun Zhang. Management Science Vol. 71, No. 9 (2025): 7537-7559. SSRN Preprint.

"The Evolution of Discrimination Under Finite Memory Constraints."

Lo, Andrew W., Ruixun Zhang, and Chaoyi Zhao. Scientific Reports Vol. 15, (2025): 31774.

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Recent Insights

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The AI developments finance pros should be tracking

A new MIT Sloan executive education course led by professor Andrew W. Lo explores machine reasoning, quantamental investing, AI governance, and more.

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Ideas Made to Matter

New MIT Sloan courses focus on deep learning, gen AI, and fintech

Additions to the MIT Sloan 2025 – 2026 course list include Intensive Hands-On Deep Learning, AI and Money, and The Arrhythmia of Finance.

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Media Highlights

Press CNBC

AI may replace your financial advisor, MIT professor says — but there's one big hurdle

"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.

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Press Fortune

The $3.4 billion lesson Big Pharma needs to learn: Its shelved drugs could save millions of patients

Professor Andrew W. Lo and co-author wrote: "Across pharma and academia, an estimated 5,000+ shelved drug candidates were discontinued for reasons unrelated to safety or efficacy. Each represents a potential therapy for conditions that, in many cases, have no approved treatment at all. Industry stakeholders have a unique opportunity to collaborate on identifying these compounds. Aligning these assets with capable and motivated partners will benefit both drug developers and patients."

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Executive Education

Executive Education Course

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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.

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  • Apr 15-Jun 2, 2026
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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.

  • Aug 12-Sep 29, 2026
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