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In this talk, I will describe a nacre-inspired hard-soft architecture and an uncertainty-aware tension-shear-chain model showing how interfacial load transfer can suppress the propagation of constituent-level variability into stiffness and failure. I will also introduce a high-throughput photoelastic testing platform that resolves stress localization and fracture in recycled thermoplastics under coupled chemical and mechanical loading, capturing ductile-to-brittle transitions and degradation mechanisms under realistic environments while enabling data-efficient service-life prediction. Finally, I will show how these studies motivate fundamental questions in nonlinear fracture mechanics, including whether stable crack growth in thermoplastic sheets is governed by a material-specific, constant crack-tip opening angle.

Together, these advances provide new tools for understanding, predicting, and improving the mechanical performance of recycled polymers, supporting their broader adoption in engineering applications.

Bio:

Prof. Christos Athanasiou is a mechanician and physicist focusing on materials whose growing importance to a circular and resource-efficient economy has outpaced our understanding of how they deform and fail. His group develops advanced experiments and physically interpretable models to reveal the fundamental mechanics of these materials and translate this new knowledge into design strategies for reliable, resource-efficient, and sustainable systems. He is an Assistant Professor in the Daniel Guggenheim School of Aerospace Engineering at the Georgia Institute of Technology. Before launching his independent career, he conducted postdoctoral research at Brown University and the MIT Media Lab and received his Ph.D. in Photonics from EPFL in Switzerland. Prof. Athanasiou has been recognized with the NSF CAREER Award, the ASME Orr Early Career Award, the ASME Eshelby Mechanics Award for Young Faculty, and a Climate Tech Fellowship from the The New York Climate Exchange supporting the translation and commercialization of his research.

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Speaker: Abe Davis, Assistant Professor of Computer Science, Cornell University

Abstract: AI is rapidly changing the roles that humans play as developers and users of new technology. In this talk, I will discuss three of those shifting roles and how my group addresses them through technical innovation and interdisciplinary design. Across these projects, understanding human roles is central to determining what systems should optimize, what data they need, and how they can be trusted.

Humans as objective functions: Many real-world applications involve optimizing user-specific objectives, but users’ descriptions can be vague, inconsistent, or self-contradictory. I will discuss Interactive Explainable Ranking (Best Paper, CHI 2026), which combines optimization, AI, and interaction design to help users identify and resolve contradictions in how they evaluate items—from open-ended course projects to purchasing options or contestants in a competition to find the best cat.

Humans as a source of data: AI is only effective if we have the right data, and many important applications depend on specific or individualized data captured in uncontrolled settings. Projects in my group design systems that help people capture such data with mobile devices. This work connects capture guidance, sampling theory, computational imaging, and interaction design to support downstream learning, with applications spanning remote healthcare, infrastructure, and agriculture.

Humans as adversaries: Some advances in AI carry significant potential for harm, calling for new approaches to protect against adversarial use. Video forensics is particularly challenging. I will discuss coded illumination systems that add near-imperceptible watermarks to lighting in high-stakes environments, making manipulation in recorded videos easier to detect without access to or control over recording cameras.

Speaker Bio: Abe Davis is an assistant professor in the Computer Science Department at Cornell University, where his group works at the intersection of human-AI systems and visual computing. He earned his Ph.D. in EECS at MIT. His dissertation received the Sprowls Award for Best PhD Thesis in Computer Science at MIT and an ACM SIGGRAPH Outstanding Doctoral Dissertation Honorable Mention. He has received the NSF CAREER Award and a Sloan Research Fellowship, published award-winning papers across visual computing and HCI, and been recognized in Forbes’ 30 Under 30 and Business Insider’s 50 Scientists Who Are Changing the World.

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Orit Shaer is a professor of computer science at Wellesley College and a leading scholar in human-computer interaction whose work advances human-centered approaches to artificial intelligence for creativity, learning, and collaboration. At Radcliffe, she will write “Weaving Wonder: Human Flourishing in the Age of Artificial Intelligence,” a book that guides readers in shaping AI technologies that expand rather than diminish human potential.

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Abstract: Hyperscalers are racing to train ever larger models, and the electricity needed to power them is rising just as fast. The critical issue is timing: generation and transmission upgrades often take 5 to 10 years, while data centers can be built in a fraction of that time, making the grid an immediate bottleneck for scaling AI infrastructure. How can we unlock gigawatts of grid capacity on a much faster timeline while keeping electricity affordable for existing customers and long-term planning on track?
The first part of this talk focuses on accelerating large load interconnection via flexible connection programs that explicitly use load flexibility to advance energization. Focusing on partially measured distribution grids, where only some loads are metered and communicated in real time, I will discuss why such programs can unlock substantial grid hosting capacity while requiring only infrequent load interventions (e.g., ~1% annual curtailment). I will also show how a flexibility-aware notion of hosting capacity allows utilities to trade off load flexibility, sensing and communication, and asset upgrades. The second part surveys two related lines of work from my group: quantifying and limiting the impact of flexible connection on the wholesale prices paid by existing customers, and modeling the couplings between the grid and flexible load systems such as data centers and electric vehicles. Together, these tools and insights point to practical pathways for mobilizing near-term grid capacity while informing longer-horizon planning, market design, and policy.

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The Su Family Gallery is located by the Northeast entrance of the SEC (Western Ave/Stadium Road entrance) on the main level.

Advance registration not required – register for email reminder and event updates.

Formlabs Website

Capital One Website

Care Hero Website

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Title: Quality Control Algorithms for Pattern Counting

Speaker: Cassandra Marcussen, graduate student at Harvard SEAS

Abstract: When can we trust an algorithm that works well on random inputs on real-world data? I will introduce quality control algorithms, which accept random inputs while rejecting inputs for which a prediction is substantially wrong, with high probability. I will focus on permutation patterns (the relative order of entries in a sequence) and predictions from an i.i.d. model. For patterns of length k, estimating their frequency on arbitrary inputs requires exponentially many queries. We show that quality control requires only polynomially many queries. Based on joint work with Ronitt Rubinfeld and Madhu Sudan.

Speaker Bio: Cassandra Marcussen is a fifth-year Ph.D. student in computer science at Harvard University, advised by Madhu Sudan. Her research focuses on sublinear and local algorithms, average-case complexity, and quasirandomness. She is particularly interested in how computation can remain reliable when only limited information about the input is available. Her research is supported by an NDSEG Fellowship.

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Title: The Power of Adaptivity in Quantum Learning

Speaker: Weiyuan Gong, graduate student at Harvard SEAS

Abstract: Adaptivity is one of the most powerful ideas in machine learning and AI: what a learner has learned so far should decide what data it collects next. In active learning, binary search finds a threshold with exponentially fewer labels than random sampling. Yet in quantum learning, where every measurement disturbs the state it probes, adaptivity has been proven to offer little or no advantage for many basic tasks. In this talk, I will show that adaptivity can nonetheless bring dramatic improvements to learning from quantum data. For stabilizer states, an essential subclass of quantum states, adaptive single-copy measurements need quadratically fewer copies than non-adaptive ones. For predicting many properties of a multi-qubit system, first learning where to look saves exponentially many copies over comparable non-adaptive strategies. For Gaussian states of light, adaptivity cuts the number of copies needed from linear to doubly logarithmic in the energy, which we prove is optimal for any strategy using Gaussian measurements. In short, what you learn should decide what you measure in the quantum world, with savings ranging from quadratic to doubly exponential.

Speaker Bio: Weiyuan Gong is a PhD student at Harvard SEAS, advised by Sitan Chen. He works on quantum learning theory, quantum algorithms, and quantum complexity theory.

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Thursday, October 22
3 - 4pm
Maxwell Dworkin 119

Neural Network Scaling Limits: From Theory to Practice
Boris Hanin
Associate Professor, Operations Research & Financial Engineering (ORFE)
Princeton University

Abstract: A fundamental question in deep learning is to describe the scaling limits of neural networks by answering how and what neural networks learn in regimes where both model and dataset size diverge. From the perspective of theory the goal is to identify what infinite size limits of neural network training dynamics exist and to analyze the mechanisms by which architecture and optimizer combine to learn data-dependent features. The aspiration is to develop an analog of the ideal gas law, where out of the collective motion of many weakly interacting molecules (in this analogy network parameters) emerge a small number of thermodynamic variables like pressure, temperature, and volume that give simple and predictive insights into the properties of realistic gasses. From the practical perspective the objective is to design a wind tunnel for neural networks. The goal is to predict reliably from experiments on small models and datasets the most performant ways to align architecture, data, and optimizer at large scale. In this talk I will give a survey of some of my work on neural network scaling limits emphasizing the interplay between theory and practice.

Bio: Boris Hanin is an Associate Professor at Princeton ORFE. His research uses tools from probability and mathematical physics to study deep learning theory. He is currently supported by a 2024 Sloan Fellowship in Mathematics.

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If you would like to attend the dinner, please RSVP here. This event is open to all current Harvard students from all religious, spiritual, and ethical traditions.

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Bacteria and the viruses that infect them, known as phages, are locked in a continual evolutionary competition. In this talk, I will present three stories illustrating the surprisingly sophisticated strategies used by both sides. First, we will see how bacteria can learn about danger from their fallen comrades: when cells are killed and burst open, they release a small molecule that serves as a “death signal” to warn their neighbors of impending attack. Second, dead bacteria can continue to influence the battle. The debris left behind by lysed cells can strongly absorb phages, effectively removing them from circulation. We have found that mechanisms known as superinfection exclusion, which protect infected bacteria from additional infections by the same phage, can importantly also prevent cell debris from absorbing those phages. Finally, I’ll turn to the information available to phages themselves. Temperate phages can either kill their host or remain quietly inside it, and this decision can depend on how many phages are infecting a cell and on chemical “quorum-sensing” signals reporting the abundance of nearby bacteria. Using neural networks and automatic differentiation to discover optimized phage strategies, we uncover some surprising answers about when information helps—and when it does not.

Bio:

Ned Wingreen received his Ph. D. in theoretical condensed matter physics from Cornell University in 1989. He did his postdoc in mesoscopic physics at MIT before moving, in 1991, to the newly founded NEC Research Institute in Princeton. At NEC, he continued to work in mesoscopic physics, but also started research on the statistical mechanics of protein folding. Thinking about proteins led him inexorably down the path into biology. During a sabbatical at UC Berkeley in 1999, his primary focus shifted to systems biology of bacteria. Wingreen joined Princeton University as a Professor of Molecular Biology in 2004, with a joint appointment in the Lewis-Sigler Institute as of 2008. Wingreen's current research focuses on modeling the biophysics of bacteria and their viruses (phage), intracellular phase separation, and, most recently, immunology.

For more information on Alma and the Alma Dal Co Foundation ETS, visit https://almadalcofondazione.org/

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How we design and deploy autonomous robots such as self-driving cars is evolving rapidly. Many technical challenges arise as the size of the fleet scales up, and especially ensuring robustness and safety. I will describe some of the design decisions in developing a self-driving robotaxi system at scale, some of the candidate solutions, and open questions for the future.

Nicholas Roy is the Autonomy Architecture lead at Zoox. He and his team address technical challenges that cut across the autonomy verticals, leading the design and deployment of cross-functional capabilities in the Zoox autonomy system. He is also the Hunsaker Professor of Aeronautics & Astronautics and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology. His research focuses on decision-making under uncertainty, mobile robot autonomy and human-robot interaction.

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SEAS faculty, staff, students, & families are welcome to attend at a discounted rate. Email events@seas.harvard.edu to request the link to purchase tickets.

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The Computer Science Colloquium Series will be held weekly on Thursdays at 2:30pm in SEC LL2.224. There will be refreshments before the talk at 2:00pm.

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Robots operating alongside people need to do more than reproduce behaviours learned from fixed datasets: they need to understand, learn from, and adapt to the humans with whom they interact. In this talk, I will explore how humans can be placed within the robot learning loop, from sensing and modelling human movement and behaviour to learning controllers that adapt to individual users, changing capabilities, and physical interactions. I will discuss approaches for learning personalised assistance, extracting low-dimensional structure from human motion, and enabling robots to adapt their actions during shared physical tasks. Through examples, I will consider how sensing, learning, control, and robot design can be jointly developed around the human. Ultimately, this work asks how we can move from robots that simply learn from humans towards robots that continuously learn and adapt with them.

Dr. Letizia Gionfrida is a Senior Lecturer in Computer and Robot Vision in the Department of Informatics at King’s College London, where she leads the Vision in Human Robotics Lab and is the Head of the Robotics, Autonomous Systems and Planning (RASP) Group. Her research sits at the intersection of computer and robot vision, human movement understanding, biomechanics, robot learning and human–robot interaction. Her work investigates how information about human movement and behaviour can be sensed and modelled, and how these representations can be translated into adaptive robotic systems. Before joining King’s, she was a Postdoctoral Research Fellow at Harvard John A. Paulson School Of Engineering And Applied Sciences working on computer vision for wearable robotics, following a PhD in Bioengineering from Imperial College London. She holds a Royal Academy of Engineering Research Fellowship and is the recipient of a Philip Leverhulme Prize.

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No translation or venturing experience or knowledge required. Just bring your curiosity and questions for a no-risk discussion with the Grid's Executive Director Paul Hayre, plus resident i-lab experts Julien Pham (Healthcare), and Phil Green (B2B) . Feel free to join in for any duration. Pizza lunch and good conversation provided. Open to the Harvard community.

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The Computer Science Colloquium Series will be held weekly on Thursdays at 2:30pm in SEC LL2.224. There will be refreshments before the talk at 2:00pm.

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Determining stratospheric transport from trace gas measurements has a long history, dating back to Brewer over 75 years ago. The broad features of the stratospheric transport have been known since then and have been refined over the years in numerous ways. But we have been limited in making a detailed quantitative description of the Brewer-Dobson circulation climatology and trends due to a lack of sufficiently precise and consistently measured trace gases in the stratosphere. In this talk I’ll give an overview of some of my research journey using primarily in situ trace gas measurements to quantify various aspects of stratospheric transport, in particular the mean age of air. I’ll briefly describe my recent work on stratospheric mean age trends, the value of simultaneously measuring multiple trace gases and conclude with some thoughts on what realistic measurements would be most useful for this topic moving forward.

Eric Ray is a senior research scientist at the Cooperative Institute for Research in Environmental Sciences at the University of Colorado and works at the NOAA Chemical Sciences Laboratory. Dr. Ray has spent his entire career at CIRES and NOAA since obtaining his PhD in 1997 from the University of Washington.

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Bio:

Pilar Cossio joined the Center for Computational Mathematics in April 2021 as the Research Scientist and Project Leader for Biomolecular and Biophysical Inference (BBI). Previously, Cossio was Max Planck Tandem Group Leader associated with the University of Antioquia (Colombia) and the Max Planck Institute of Biophysics (Germany). She also has held postdoctoral positions at the National Institute of Health (NIH, USA) and the Max Planck Institute of Biophysics. Cossio focuses on the development of mathematical and computational methods to characterize proteins’ structures and dynamics from cryo-electron microscopy, single-molecule spectroscopy and biomolecular simulations. Cossio holds a Ph.D. in Physics and Chemistry of Biological Systems from the International School for Advanced Studies (SISSA) in Italy and a B.S. in Physics from the University of Antioquia.

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Advances in robotic technology, wearable sensors, and signal processing have enabled a new generation of human-machine interfaces for studying and assisting human movement. Research in the UF Human Neuromechanics Laboratory focuses on the neural control and biomechanics of human locomotion and other whole body movements. Through the use of robotic exoskeletons, bionic prostheses, immersive virtual reality, and high-density electroencephalography, we identify general principles governing human movement in health and disability, and strive to develop new technologies that can enhance motor training, rehabilitation, and overall health.

Bio:

Daniel Ferris is the Robert W. Adenbaum Professor of Engineering Innovation at the University of Florida. His research focuses on human-machine interactions, including both mechanical and electrical dynamics. He completed his Ph.D. at UC Berkeley, M.S. at the University of Miami, and B.S. at the University of Central Florida. After completing postdoctoral fellowships at the UCLA Department of Neurology and the University of Washington Department of Electrical Engineering, he joined the faculty at the University of Michigan for 16 years. In 2017, Dr. Ferris moved to the J. Crayton Pruitt Family Department of Biomedical Engineering at the University of Florida. He is a Fellow of the American Association for the Advancement of Science (AAAS), American Society of Biomechanics (ASB), American Institute for Medical and Biological Engineering (AIMBE), and National Academy of Kinesiology (NAK).

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Lucía F. Franchini is a molecular evolutionary biologist based in Buenos Aires, Argentina, whose aim is to unravel the genetic bases underlying the evolution of the human brain. At Radcliffe, she will collaborate with Harvard neurobiologists to gain insight into functional consequences of human-specific changes to neuron development and function using state-of-the-art genetic engineering techniques and transcriptomics tools.

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Speaker: Michael Hicks, the Cecilia Fitler Moore Professor and Associate Chair in the Department of Computer and Information Science at the University of Pennsylvania and Director of the Schlein Center for Cybersecurity

Abstract: Cedar is an authorization policy language: Rather than embed authorization logic in an application’s code, developers can write that logic as Cedar policies and delegate access decisions to Cedar’s evaluation engine. Cedar is designed to be ergonomic, fast, safe, and analyzable. Cedar’s simple and intuitive syntax supports common authorization use-cases with readable policies, naturally leveraging concepts from role-based, attribute-based, and relation-based access control models. Cedar’s policy structure enables access requests to be decided quickly. Cedar’s design has been finely balanced to allow for a sound and complete logical encoding, which enables precise policy analysis, e.g., to ensure that when refactoring a set of policies, the authorized permissions do not change. Cedar is open-source, implemented in both Rust and Go for fast execution, and faithfully modeled in the Lean verification-oriented programming language, to support proof of important security and correctness properties. These implementations are kept in sync using differential random testing. Cedar is now used in a variety of real-world settings, including as a means to authorize AI agent actions, and these deployments have led to further expansion and extension of its capabilities. This talk will present a full overview of Cedar, how it is used today, and research underway to expand and improve its capabilities.

Speaker Bio: Michael Hicks is the Cecilia Fitler Moore Professor in Computer and Information Science at the University of Pennsylvania, and Director of the Schlein Center for Cybersecurity. He was previously a Senior Principal Applied Scientist at Amazon Web Services (2022-2025) and a Professor at the University of Maryland (2002-2021). His research explores how to apply novel programming languages, program analysis, and automated testing techniques to solve problems in security, software engineering, systems, databases, networks, and quantum computing.

The Computer Science Colloquium Series will be held weekly on Thursdays at 2:30pm in SEC LL2.224. There will be refreshments before the talk at 2:00pm.

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Current and future challenges, as well as opportunities, for materials solutions will be highlighted for both Logic and Memory applications.

Bio:

Glen Wilk is a Vice President of Technology at ASM, where he has specialized in ALD materials for over 20 years, including high-k gate dielectrics. In addition, while at ASM he has also focused Si and Ge Epitaxy technology for advanced devices.

He previously worked at Texas Instruments and Bell Laboratories in Murray Hill, NJ, where he developed novel materials for Logic and Memory applications.

Glen received a B.S in Materials Science from Cornell University, and a PhD in Applied Physics from Harvard University.

He won the 2026 Distinguished Career Achievement Award in Materials Science from Cornell University.

His publications include an invited review paper in the Journal of Applied Physics on high-k gate dielectrics with approximately 6,000 citations.

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Sulfur is an element critical to growth of all living organisms, as well as a useful ‘tool’ employed throughout human history, as a pesticide, preservative, and component of fossil fuel sources and fertilizer production. In this talk, I will present the results of several new efforts by my research group that provide new knowledge about the changing global sulfur (S) cycle. At local-to-regional scales, my group has used the stable isotopes of S and mercury (Hg) to investigate the connection between runoff from agricultural S applications to croplands and stimulation of methylmercury production—a neurotoxin—in downstream wetlands. At national-to-global scales, we are evaluating both trends in atmospheric S deposition and farmers’ use of fertilizer S applications to sustain plant nutrition. In addition, we probe novel data sources—such as shipping traffic in the Strait of Hormuz—to understand changes in the S industry and its large implications for fertilizer availability, food supply chains, and economic welfare. Altogether, the results of these studies provide evidence of our deep reliance on S, a shift in how we are interacting with and using this critical element, and the consequences of excess S in the environment for wildlife and human health.

Eve-Lyn Hinckley is an Associate Professor of Ecology and Evolutionary Biology and Fellow of the Cooperative Institute for Research in Environmental Sciences at the University of Colorado, Boulder. Her research focuses on studying the elements that underlie all life on Earth, with an emphasis on how they are changed by human activities and how those changes feed back to affect human welfare. Dr. Hinckley earned her Ph.D. in Geological and Environmental Sciences from Stanford University, and B.A. in Environmental Studies from Middlebury College. Her research on modern changes to the global sulfur cycle is supported by multiple funding agencies, including the prestigious CAREER program through The U.S. National Science Foundation, a grant from U.S. Department of Agriculture, the National Geographic Society, the International Fertilizer Association, and The Sulphur Institute. Dr. Hinckley has been recognized as a University of Colorado Research & Innovation Office Faculty Fellow and Arts & Sciences Support of Education Through Technology Teaching Fellow.

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This event is open to all Harvard University, Boston University, Olin College, Tufts University, MIT, and Yale University engineering students and postdocs.

Students: Register Here

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The Computer Science Colloquium Series will be held weekly on Thursdays at 2:30pm in SEC LL2.224. There will be refreshments before the talk at 2:00pm.

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Dr. Jessica Stark is an Assistant Professor in the departments of Biological and Chemical Engineering and an intramural member of the Koch Institute for Integrative Cancer Research at MIT. The Stark Lab is developing biological technologies to realize the largely untapped potential of glycans for immunological discovery and immunotherapy. The lab’s work has been recognized with the Burroughs Wellcome Fund Career Award at the Scientific Interface, the Society for Immunotherapy of Cancer Rosenberg Scholar Award, a V Scholar Award from The V Foundation, and a Pershing Square Sohn Cancer Prize.

Jessica’s postdoctoral work with Prof. Carolyn Bertozzi at Stanford University established a new class of protein therapeutics that makes it possible to target glycans for cancer immunotherapy. This technology is in translation to the clinic via her co-founded company, Valora Therapeutics. During her PhD with Prof. Michael Jewett at Northwestern University, Jessica developed cell-free technologies for protein therapeutic and vaccine production that promise to enable portable and personalized medicine. Previously, she received her B.S. in Chemical and Biomolecular Engineering from Cornell University and worked at Genentech, Inc. in process development and research and development roles.

The Stark Lab is working to train the next generation of scientists and engineers through mentoring, outreach, and service activities. To support this work, Jessica co-developed and commercialized BioBits® educational kits that promise to increase access to high-quality biology education by facilitating hands-on learning.

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This event is open to all Harvard University and MIT students and alumni.

Students: Register Here

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Further information and registration details are available on the event page:

https://www.eventbrite.com/e/entrepreneurship-conference-2026-at-harvard-business-school-tickets-1994640530521

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The Computer Science Colloquium Series will be held weekly on Thursdays at 2:30pm in SEC LL2.224. There will be refreshments before the talk at 2:00pm.

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No translation or venturing experience or knowledge required. Just bring your curiosity and questions for a no-risk discussion with the Grid's Executive Director Paul Hayre. Feel free to join in for any duration. Pizza lunch and good conversation provided. Open to the Harvard community.

FIRST COME, FIRST SERVE

Seating at all events is on a first come, first serve basis. RSVPs are encouraged so we have an accurate headcount for classroom size and catering purposes.

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Bio: Sujit Datta is Professor of Chemical Engineering, Bioengineering, and Biophysics at Caltech, where his group integrates experiment, theory, and computation to study transport processes of complex fluids, gels, and microbes in complex environments. He is also Editor-in-Chief of Reviews of Modern Physics. His scholarship has been recognized by awards from a range of different communities, including the Allan P. Colburn and 35 Under 35 Awards of the AIChE, three awards from the APS (Early Career Award in Biological Physics, Andreas Acrivos Award in Fluid Dynamics, and Apker Award), Pew Biomedical Scholar Award, Arthur Metzner Award of the Society of Rheology, Unilever Award of the ACS, Camille Dreyfus Teacher-Scholar Award, NSF CAREER Award, and Soft Matter Lectureship of the Royal Society of Chemistry. He received his undergraduate degrees in Physics and Mathematics at the University of Pennsylvania, his PhD in Physics from Harvard, and postdoctoral training at Caltech. Prior to moving to Caltech, Sujit was Associate Professor and Director of Graduate Studies of Chemical & Biological Engineering at Princeton University.

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Assistive technology offers promise to improve personal mobility for hundreds of millions of people. However, determining how to assist people to accommodate their unique mobility needs during everyday activities is an open challenge. Methods for personalizing assistance to each user have helped systematically improve mobility outcomes in laboratory settings but are difficult to extend beyond the laboratory. This talk outlines a vision to advance assistance personalization methods and robotic technology for enhancing mobility in everyday settings, with specific applications for patient populations. This work merges advances across fundamental biomechanical science to understand people’s movement goals and needs, engineering development to create real-world assistance optimization methods, and translational studies to address specific patient population challenges.

Patrick Slade is an Assistant Professor of Bioengineering at Harvard University. He received his bachelor’s at University of Illinois at Urbana-Champaign and completed his M.Sc., Ph.D., and postdoctoral studies at Stanford University. He leads Harvard Slade Lab that combines robotics, biomechanics, and human-centered AI to build intelligent health models and assistive devices. His work explores how to understand the biomechanics of mobility-related challenges and personalize technology to improve people’s independence. Dr. Slade's work has been recognized for both mentorship (Everett I. Mendelsohn Excellence in Mentoring) and innovation (Forbes, MIT Technology Review, Amazon Robotics).

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