Navigating the Digital Future: AI and Technology Strategy
Book your hotel room with a group rate ($269+tax). A limited block of rooms has been reserved at the Marriott. Please make your reservation using the reservation link by March 13, 2026.
Review our 2025 AI Conference summary and examine the year’s key developments.
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AI is advancing faster than many organizations can adapt, raising urgent questions about how emerging technologies can be deployed reliably and at scale. The 2026 MIT AI Conference looks ahead to the next chapter of AI—examining not only what is possible, but what is practical, responsible, and achievable in the years to come.
The conference highlights MIT’s most influential work from the past year and the promising future it is shaping. The program includes topics such as future AI architectures, AI management and deployment, AI applications, social impact, and policy. Together, these themes offer a forward-looking view of where AI is headed—and how society can prepare.
*Current MIT Community: Livestream registration must be completed with an mit.edu email address.
**STEX Community: please email ocrevents@mit.edu for complimentary access.
Cancellation Policy: You may cancel your registration for a full refund through April 7. Refunds will be issued to the original form of payment. After April 7, partial refunds will be available, minus a service fee ($300 for in-person registrations and $100 for virtual). No refunds will be issued after April 13. To cancel, please email ocrevents@mit.edu.
MIT Alum, Sloan Exec Ed, and Professional Education Member: Please email ocrevents@mit.edu for a 70% discount code.
Visiting MIT: https://www.mit.edu/visitmit/ Where to Stay: https://institute-events.mit.edu/visit/where-to-stay Registration Questions: ocrevents@mit.edu
The agenda will be announced soon.
Dr. Srinivasan is a distinguished scientist who received her PhD in Microbiology from The Ohio State University in 2004, where she contributed to the discovery of the 22nd amino acid, Pyrrolysine (2002). She first came to MIT as an NIH Postdoctoral Fellow in Prof. Tom Rajbhandary’s lab, where her research focused on understanding protein synthesis mechanisms in Archaea.
Dr. Srinivasan subsequently moved into the business development and technology licensing space, serving in MIT’s Technology Licensing Office, where she helped commercialize technologies in medical devices and alternative energies. She then moved to UMass Medical School’s Office of Technology Management in 2009 and to Emory University in Atlanta in 2014 as the Director of Public and Private Partnerships for the Woodruff Health Sciences Center. In 2019, Dr. Srinivasan joined Emory’s Office of Corporate Relations as Executive Director, and in 2021, she led the Office of Corporate and Foundation Relations.
Managing Director, MIT Machine Intelligence for Manufacturing and Operations (MIMO)
Bruce Lawler is a software executive, entrepreneur, venture capitalist, and private equity investor with multiple successful software products and venture exits and deep expertise in artificial intelligence, CAD/CAM, manufacturing, and telecommunications.
As Managing Director of the MIT Machine Intelligence for Manufacturing and Operations (MIT MIMO), Bruce drives research and education in AI and Generative AI for industrial applications. As President, DIGITAL at Re:Build Manufacturing, a private equity firm, he led digital transformation and AI innovation, focusing on revitalizing American industry through strategic acquisitions in CAD/CAM and the development of next-generation CAD, CAM, and IIoT solutions.
Bruce is a founder of inOvate Communication Group, a venture capital firm that played a pivotal role in the digitization of mobile communications, investing alongside Cisco and top-tier VC firms such as Accel, CRV, KPCB, Redpoint, Sequoia, and SoftBank. He was a founder of iBeam Broadcasting (a pioneering cloud-based internet video service), which had a multi-billion-dollar IPO, and Kodiak Networks (a cloud-based wireless SaaS platform), which was successfully acquired by Motorola Solutions.
An early innovator in AI, Bruce wrote one of the first AI-driven tool path generators for CNC machines using LISP, contributing to the U.S. Navy’s Rapid Acquisition of Manufactured Parts (RAMP) project. He was recognized with the MIT Internet Entrepreneur Award by Tim Berners-Lee for his transformational work in cloud-based internet video.
Bruce holds dual Master’s degrees in Engineering and Business Administration from MIT and the MIT Sloan School of Management, as well as a Bachelor’s degree in Engineering from Purdue University. He is an inventor with five patents, a lecturer in AI at MIT and UCSD, and a published author in IEEE Transactions, Harvard Business Review, and other leading academic and business journals.
A sought-after speaker, Bruce has presented at MIT, Harvard Law School, McKinsey & Company, the Brookings Institution, and major industry events. He is also an active startup advisor and angel investor, with portfolio companies successfully acquired by firms such as Google.
Generative AI is delivering real, measurable value in industry—but only for organizations that focus on the right use cases and execution patterns. This talk draws on a large-scale study of AI adoption across manufacturing and operations to highlight where Generative AI is creating the greatest impact today, from maintenance and quality to knowledge capture and decision support.
Beyond showcasing proven use cases, the presentation introduces a practical framework for identifying and prioritizing high-impact GenAI opportunities based on business value, data readiness, and time-to-value. Attendees will gain insight into how leading organizations move from experimentation to production, and how others can apply these lessons to accelerate their own GenAI initiatives.
Gordon Kaufman Professor of Management, MIT Sloan School of Management Professor of Work and Organization Studies, MIT Sloan School of Management Faculty Director, MIT Behavioral Research Lab
Jared Curhan is the Gordon Kaufman Professor and a Professor of Work and Organization Studies at the MIT Sloan School of Management, as well as Faculty Director of MIT’s Behavioral Research Lab.
Curhan specializes in the psychology of negotiation and conflict resolution. A recipient of support from the National Science Foundation, he has pioneered a social psychological approach to the study of “subjective value” in negotiation—that is, the feelings and judgments concerning the instrumental outcome, the process, the self, and the relationship. His research uses the Subjective Value Inventory (SVI; Curhan et al., 2006) to examine the precursors, processes, and long-term consequences of subjective value in negotiation. He also studies the dynamics of negotiation and brainstorming.
Curhan is Vice Chair for Research and a member of the Executive Committee of the Program on Negotiation (PON) at Harvard Law School, a world-renowned inter-university consortium dedicated to developing the theory and practice of negotiation and dispute resolution. He is also Director of the PON Research Lab and Director of MIT's Negotiation for Executives Program.
Curhan founded the Program for Young Negotiators, Inc., an organization dedicated to the promotion of negotiation training in primary and secondary schools. His book, Young Negotiators (Houghton Mifflin, 1998) is acclaimed in the fields of negotiation and education, and has been translated into Spanish, Hebrew, and Arabic. It has been used to train more than 35,000 children across the United States and abroad to achieve their goals without the use of violence.
Deeply committed to education at all levels, Curhan has received the Stanford University Lieberman Fellowship for excellence in teaching and university service, as well as MIT's Institute-wide teaching award, MIT Teaching with Technology Award, and MIT Sloan's Jamieson Prize for excellence in teaching. His three-day crash course, Negotiation Analysis, is open to all MIT students via lottery. He also offers a 10-week, open-enrollment online course with live negotiations, Mastering Negotiation and Influence.
Curhan holds an AB in psychology from Harvard University and an MS and a PhD in psychology from Stanford University.
Negotiation is a daily practice in business—with clients and partners, vendors and suppliers, supervisors and colleagues, employees and recruits. In today's complex and interconnected world, the art of negotiation has never been more crucial. Successful negotiation requires self-awareness, preparation, and practice. However, it's challenging to find partners with whom you can do the latter.
What if AI could help?
Join MIT Sloan Professor Jared Curhan as he delves into best practices and groundbreaking research surrounding AI as a tool to hone your negotiation skills in a variety of situations.
Professor, MIT Department of Brain and Cognitive Sciences
Josh Tenenbaum is a Professor of Computational Cognitive Science in the Department of Brain and Cognitive Sciences at MIT, a principal investigator at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), and a thrust leader in the Center for Brains, Minds, and Machines (CBMM). His research centers on perception, learning, and common-sense reasoning in humans and machines, with the twin goals of better understanding human intelligence in computational terms and building more human-like intelligence in machines. The machine learning and artificial intelligence algorithms developed by his group are currently used by hundreds of other science and engineering groups around the world.
Tenenbaum received his PhD from MIT in 1999 and was an Assistant Professor at Stanford University from 1999 to 2002 before returning to MIT. His papers have received awards at the Cognitive Science (CogSci), Computer Vision and Pattern Recognition (CVPR), Neural Information Processing Systems (NIPS), and Uncertainty in Artificial Intelligence (UAI) conferences, the International Conference on Learning and Development (ICDL) and the International Joint Conference on Artificial Intelligence (IJCAI). He has given invited keynote talks at all of the major machine learning and artificial conferences, as well as the main meetings of the Cognitive Science Society, the Cognitive Development Society, and the Society for Mathematical Psychology, and held distinguished lectureships at Stanford University, the University of Amsterdam, McGill University, the University of Pennsylvania, the University of California, San Diego, and the University of Arizona. He is the recipient of the Early Investigator Award from the Society of Experimental Psychologists, the Distinguished Scientific Award for Early Career Contribution to Psychology from the American Psychological Association, and the Troland Research Award from the National Academy of Sciences, and is a fellow of the Society of Experimental Psychologists and the Cognitive Science Society.
Lister Brothers Career Development Assistant Professor, MIT Assistant Professor, MIT Department of Electrical Engineering and Computer Science Faculty/PI, MIT Lab for Information and Decision Systems Assistant Professor, MIT Statistics and Data Science Center
Ashia Wilson is a Lister Brothers Career Development Assistant Professor at MIT whose research builds the theory and practice of reliable AI. Her group studies four core themes: privacy and unlearning mechanisms for modern models, optimization and sampling methods for large-scale training, the dynamics of homogenization and algorithmic influence, and evaluation frameworks that enable rigorous measurement of model behavior. She draws on statistics, optimization, and dynamical systems to analyze and design AI systems that are both scientifically grounded and socially responsible. Ashia earned her Ph.D. in statistics from UC Berkeley and previously held a postdoctoral position at Microsoft Research. Her work has been recognized with best paper and spotlight awards at FAccT, NeurIPS, and OptML.
The rapid diffusion of open-weight generative models has transformed creative practice but has also introduced new security risks, including large-scale misuse and the proliferation of illegal content such as non-consensual intimate imagery (NCII) and child sexual abuse material (CSAM). As generative systems become increasingly modular and decentralized, harmful capabilities often arise not from base models themselves but from lightweight fine-tuning and recombination strategies that are easy to distribute, difficult to trace, and hard to audit. This creates a fundamental challenge for trustworthy AI: platforms and regulators are expected to detect and mitigate high-risk models, yet legal, ethical, and adversarial constraints make direct content generation or inspection infeasible.
In this talk, I argue that securing open-weight generative ecosystems requires a shift from downstream content moderation to upstream, generation-free risk assessment at the level of model parameters. I highlight recent work showing that malicious or abusive fine-tuning objectives leave detectable signatures in weight space, enabling scalable screening and monitoring without prompting models, generating outputs, or accessing training data. More broadly, I outline a research agenda for weight-space accountability as a security primitive for open generative AI, with implications for platform governance, regulatory compliance, and the design of preventive safeguards as AI development continues to decentralize.
Professor of Philosophy, MIT Department of Linguistics and Philosophy
Caspar Hare is a professor of philosophy in the Department of Linguistics and Philosophy. From 2022 to 2025 he served as associate dean for Social and Ethical Responsibilities of Computing (SERC) in the MIT Schwarzman College of Computing. He has been working to create multidisciplinary connections on campus and to weave social, ethical, and policy considerations into the teaching, research, and implementation of computing.
A member of the MIT faculty since 2003, Hare’s main interests are in ethics, metaphysics, and epistemology. The general theme of his work has been to bring ideas about practical rationality and metaphysics to bear on issues in normative ethics and epistemology. He is the author of three books: “On Myself, and Other, Less Important Subjects” (Princeton University Press 2009), about the metaphysics of perspective, “The Limits of Kindness” (Oxford University Press 2013), about normative ethics, and “Living in a Strange World” (Oxford University Press, forthcoming in March 2026), about the practical implications of cosmology.
We want agential AI to reflect our values, pursue our goals, and act in our interests, but what exactly are these values, goals, and interests? In this talk, Caspar will explore this foundational question, offering a framework for thinking about human objectives in the context of AI alignment.
Dibner Professor of the History of Engineering and Manufacturing, MIT Professor, MIT AeroAstro
David Mindell is Dibner Professor of the History of Engineering and Manufacturing and Professor of Aerospace Engineering at MIT. He is a leading authority on generations of inventors, engineers, and entrepreneurs within the great arcs of technological change that make them successful. He has led or participated in more than 25 oceanographic expeditions, written seven books, and holds thirty-four patents in RF navigation, autonomous systems, and AI-assisted piloting. David co-Chaired MIT’s Task Force on the Work of the Future. He is also the Founder and Executive Chair of Humatics Corporation, which develops technologies to transform how robots and autonomous systems work in human environments, and co-Founder of Unless, an investment firm that is catalyzing the future of industry. He is the author of seven books, including his latest, The New Lunar Society: An Enlightenment Guide to the Next Industrial Revolution.
Based on the ideas in his recent book The New Lunar Society: An Enlightenment Guide to the Next Industrial Revolution, in this talk, Mindell lays out some principles for envisioning the future of industry within a world of Artificial Intelligence. Marry product innovation to process innovation. Think in systems. Emphasize adoption. Design for resilience and flexibility. Get excited about maintenance and repair. Value knowledge at every level of work. See the human intelligence embodied in every product or system. Each is supported by examples from the original Industrial Revolution with relevance for the next one.
Samuel A. Goldblith Career Development Professor, MIT Assistant Professor, MIT Department of Electrical Engineering and Computer Science Assistant Professor, MIT Schwarzman College of Computing
Yunha Hwang is an Assistant Professor at MIT with a shared appointment between Biology, EECS, and the Schwarzman College of Computing. She is also a Co-founder and Chief Scientist at Tatta Bio, a scientific nonprofit dedicated to advancing genomic AI for biological discovery. She completed her Ph.D. in Biology from Harvard University and B.S. in Computer Science from Stanford University. Her research interests span machine learning for sustainable biomanufacturing, microbial evolution, and open science.
Microbial genomes encode the largest molecular, biochemical and functional diversity on Earth, however, much of this diversity remains uncharacterized. Dr. Hwang's talk will focus on machine learning models and experimental approaches to discover and design novel microbial functions.These approaches enable more systematic identification of microbial capabilities and have applications in biomanufacturing, natural product discovery, and microbial technologies.
Assistant Professor, MIT EECS' Faculty of AI and Decision Making Assistant Professor, MIT Computer Science & Artificial Intelligence Laboratory
Sara Beery is an Assistant Professor in MIT EECS’ Faculty of AI and Decision Making and at CSAIL. She was previously a visiting researcher at Google, where she worked on the Auto Arborist project. Motivated by a lifelong appreciation for the natural world and the growing need for technology-driven solutions to conservation and sustainability challenges, her research focuses on developing computer vision methods for global-scale environmental and biodiversity monitoring across data modalities. Her work addresses real-world challenges such as strong spatiotemporal correlations that lead to domain shift, imperfect data quality, fine-grained categories, and long-tailed distributions.
Dr. Beery received her PhD in Computing and Mathematical Sciences from Caltech, where she was advised by Pietro Perona and awarded the Amori Doctoral Prize for her dissertation. She has been recognized with an AI2050 Early Career Fellowship, an NSF CAREER Grant, a PIMCO Data Science Fellowship, an Amazon AI4Science Fellowship, and the NSF Graduate Research Fellowship. Her research has been supported by the NSF, NASA, Google, Microsoft, IBM, the U.S. Air Force, MIT J-WAFS, and the Caltech Resnick Sustainability Institute.
Committed to breaking down knowledge barriers across disciplines, Dr. Beery founded the AI for Conservation Slack community, which now includes more than 2,500 members. She is a Co-PI on the NSF/NSERC Global Center on AI and Biodiversity Change, serves as the Biodiversity Community Lead for Climate Change AI, and is the founding director of the Caltech Summer School on Computer Vision Methods for Ecology. She works closely with industry partners—including the MIT-IBM Watson AI Lab, Microsoft AI for Good, Google Research, Wildlife Insights, and Wild Me—to translate research into deployed, real-world tools.
Drawing on her experiences as a professional ballerina and a nontraditional student, Dr. Beery values diverse perspectives both within and beyond the research community. She is deeply committed to expanding access to and capacity in STEM through mentorship, teaching, and outreach, and her efforts have been recognized with the inaugural Caltech Computing and Mathematical Sciences Department Gradient for Change Award and the inaugural Caltech Engineering and Applied Science Division New Horizons Award.
Multimodal biodiversity monitoring integrates data from diverse sources, such as remote sensing, bioacoustic recordings, camera traps, citizen science, and scientific literature, to provide a more comprehensive and timely understanding of ecosystems. These modalities are complementary but heterogeneous, and traditional multimodal AI assumptions about co-registration, prevalence, and shared information content across modalities don't always hold. This talk will explore the principles, opportunities, and challenges of multimodal biodiversity monitoring, highlighting real-world applications and future directions for research.