Penn Engineering Events

Rigorous and Glamorous in 100 Words or Less 2.0 - An Abstract-Writing Workshop
2025-12-02 12:00 - 13:30 (America/New_York) | PA
It’s hard to write an abstract. It’s harder to write a glamorous one that tells your story without overselling your results. Last year, we hosted a virtual abstract-writing workshop where our P.I., Konrad Kording, reviewed and refined students’ abstracts live. Back by popular demand, the workshop returns — this time with a bonus! Participants can use the scientific project planner app, PlanYourScience.com, to kickstart a new study or fine-tune an existing one.

RSVP here and submit your abstract for a chance to have it workshopped with Konrad Kording, live.
We will notify you if your abstract was selected for review.

You can also join the workshop without submitting an abstract.

*Community for Rigor will not use submitted work for any purposes outside of this event
*If you have any questions, email us at carogar@seas.upenn.edu

Academic Innovation Speaker Series: “A’s for All” (as time and interest allow)
2025-12-02 16:00 - 17:00 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Traditional grading paradigms often fail to cater to diverse learning paces and styles, potentially sidelining those needing more time to learn the material. “A’s for All” (as time and interest allow) is an innovative pedagogical philosophy that builds on grading for equity, mastery learning, growth mindset, and competency-based approaches. It gives every student the opportunity and time to learn the material, including working beyond the term. It removes barriers and late penalties, and sends a strong signal to students that they can achieve any level of mastery they want, if they are willing to put in the time and effort. We will share the principles of “A’s for All” and actionable insights to refine and enhance teaching methodologies. We hope this talk will sow the seeds for a more inclusive, flexible, and student-centric future in STEM education.

Fall 2025 GRASP SFI: Minghan Zhu, University of Pennsylvania, "3D Robot Vision for Structured World Understanding"
2025-12-03 15:00 - 16:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This presenter is one of the winners of the 2025 GRASP vote for internal PhD or postdoc SFI Speakers!
This is a hybrid event with in-person attendance in Levine 307 and virtual attendance via Zoom.
ABSTRACT
Deploying robots in diverse real-world environments is a fundamental challenge. While recent AI advances are impressive, robots still struggle to generalize. I argue that a key missing piece in embodied intelligence is the structured understanding of the world: how geometries compose, how physics governs interactions, and how dynamics unfold. My research in 3D vision develops this understanding with two complementary principles: physics-based reasoning and symmetry-aware learning. First, I present Vysics, fusing vision and contact-rich physics to overcome heavy occlusions in object reconstruction, and my recent follow-up work that further incorporates 3D generative priors for reconstructions with both high visual fidelity and physical compliance. Then, I demonstrate my work on leveraging symmetry for efficient modeling of 3D geometry and dynamics. I introduce my algorithmic contributions in equivariant learning, including E2PN, which improves the efficiency of point cloud learning by 7x compared with prior work, and Lie Neurons and Reductive Lie Neurons, which expand the scope of symmetry preserved by equivariant networks from rotations to general linear transformations. These advances enable significant progress in various robotic tasks by incorporating symmetry, from segmentation and place recognition to odometry and dynamics learning. I will close with my vision of building structured world representations that are simultaneously grounded in physics, informed by data, and structured by symmetry, toward robots that truly understand their physical world.

CBE Seminar: "New Technologies for Delivery of Nucleic Acids and Proteins to Plants” (Markita Landry, UC Berkeley)
2025-12-03 15:30 - 16:30 | Wu & Chen Auditorium
Abstract:
Genetic engineering of plants is at the core of sustainability efforts, natural product synthesis, and agricultural crop engineering. Plant bioengineering requires delivery of biomolecules for genetic or post-transcriptional manipulation of plants, however, current delivery methods either suffer from host range limitations, low transformation efficiencies, tissue regenerability issues, tissue damage, or unavoidable DNA integration into the host genome. We briefly discuss prior efforts in delivering DNA plasmids and small interfering RNAs to mature plant cells [1-7], and challenges in implementing these approaches for the delivery of proteins. While there exist numerous methods for nucleic acid delivery in plants, few options exist for protein delivery: biolistics and proptoplast-based PEG transformation, each with considerable drawbacks. Here, we report the discovery that plant homeoproteins [8], specifically the 3rd alpha helix of the homeodomain, are generally cell penetrating to walled plant cells with implications for both plant biotechnology and fundamental plant biology. We catalog plant homeoproteins and explore natural variation in the 3rd alpha helix of homeodomains derived from plant homeoproteins. We select a subset of identified 3rd alpha helix peptides for their ability to penetrate mature plant cells and demonstrate these plant derived peptides internalize to plant cells in an amino acid sequence and concentration dependent manner. Finally, we demonstrate delivery of recombinant protein cargo Cre recombinase and transcription factors utilizing these plant-derived peptides, into a broad range of plant tissues ranging from single-cell zygotes through mature plant tissues. Our work provides the foundation for tagging proteins with CPPs for their seamless and biolistic-independent delivery to walled plant cells, enabling a broad range of transgene-free plant biotechnology applications.
1. Demirer, G.S., Zhang, H., Goh, N.S., Grandio, E.G., Landry, M.P.‡, Carbon nanotube-mediated DNA delivery without transgene integration in intact plants. Nature Protocols (2019)
2. Demirer, G.S., Zhang, H., Matos, J., Goh, N., Cunningham, F.J., Sung, Y., Chang, R., Aditham, A.J., , Chio, L., Cho, M.J., Staskawicz, B., Landry, M.P.‡, High Aspect Ratio Nanomaterials Enable Delivery of Functional Genetic Material Without DNA Integration in Mature Plants. Nature Nanotechnology (2019)
3. Landry, M.P.‡, Mitter, N.‡ How nanocarriers delivering cargoes in plants can change the GMO landscape. Nature Nanotechnology (2019)
4. Demirer, G.S. ‡, Silva, T.N., Jackson, C.T., Thomas, J.B., Ehrhardt, D., Rhee, S.Y. ‡, Mortimer, J.C. ‡, Landry, M.P. ‡ Nanotechnology to advance CRISPR/Cas genetic engineering of plants. Nature Nanotechnology (2021)
5. Zhang, H., Zhang, H., Demirer, G.S., Gonzales-Grandio, E., Fan, C., Landry, M.P.‡ Engineering DNA nanostructures for siRNA delivery in plants. Nature Protocols (2020)
6. Zhang, H.*, Demirer, G.S.*, Zhang, H., Ye, T., Goh, N.S., Aditham, A.J., Cunningham, F.J., Fan, C., Landry, M.P. Low-dimensional DNA Nanostructures Coordinate Gene Silencing in Mature Plants. PNAS (2019)
7. Zhang, H.*, Goh, N.S.*, Wang, J., Demirer, G.S., Butrus, S., Park, S-J, Landry, M.P.‡ Nanoparticle Cellular Internalization is Not Required for RNA Delivery to Mature Plant Leaves. Nature Nanotechnology (2022)
8. Wang, J.W., Goh, N., Lien, E., Gonzalez Grandio, E., Landry, M.P.‡ Quantification of cell penetrating peptide mediated delivery of proteins in plant leaves. Nature Communications Biology (2023)

MSE Seminar: "Ion Transport in Hydrated Anion Conducting Polyelectrolytes" - Paul F. Nealey - Pritzker School of Molecular Engineering of the University of Chicago, Senior Scientist at Argonne National laboratory
2025-12-04 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The realization of next-generation electrochemical technologies demands anion-conducting polymers that break the fundamental trade-offs between conductivity, stability, and swelling. Ion transport in these materials is a complex, emergent property that arises from the coupling of water dynamics, nanoscale morphology, and polymer chemistry across multiple length and time scales.
In this talk, I will present a unified mechanistic picture of ion motion in a versatile family of polynorbornene-based polyelectrolytes. We have developed a multiscale framework that integrates simultaneous measurements of ion transport and water uptake in thin films with advanced spectroscopy, scattering, and molecular dynamics simulations. This approach enables us to directly connect local solvation dynamics on sub-picosecond time scales to the formation of a percolated, water-mediated network that governs macroscopic conductivity.
The first part of the talk I focus on how water facilitates ion transport across different conductivity regimes, revisiting long-standing concepts such as vehicular diffusion. I then show how the interplay between water-enabled transport and the thermodynamics of water uptake produces an unexpected convergence in conductivity across polymers with different ion-exchange capacities. Building on this insight, in the second part I demonstrate a strategy to overcome the usual relationship between transport and swelling. By engineering polymers with spatial charge density fluctuations, it is possible to decuple percolation from bulk water content and achieve high conductivity even at substantially reduced water and charge concentrations. These findings establish a mechanistically grounded design principle for anion-conducting polymers that achieve high performance while maintaining mechanical and chemical resistance.

BE Seminar - Xiaojing Gao, "Synthetic Biology for Human Health: RNA Sensors, Protein Circuits, and Machine-Guided Humanization"
2025-12-04 15:30 - 16:30 | 216 Moore Building

Responsive Materials Meet Intelligence
2025-12-05 08:45 - 18:30 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Fold It. Stretch It. Build it. Shape it.
Responsive Materials Meet Intelligence.
Keynote Speakers:
H. Jerry Qi
Woodruff Endowed Professor
George W. Woodruff School of Mechanical Engineering
Georgia Institute of Technology
Talk title: “Photocuring: grayscale digital light processing printing for pixel-level property manipulations”
Jenny Sabin
Arthur L. and Isabel B. Wiesenberger Professor in Architecture
Chair, Department of Design Tech
Cornell University
Principal, Jenny Sabin Studio
Talk title: “Biosynthetic Design: Towards Adaptive Architecture”
Wei Chen
Wilson-Cook Professor in Engineering Design
Professor and Chair, Department of Mechanical Engineering
Northwestern University
Talk title: “Autonomous co-design and fabrication of multi-stimuli responsive material systems”
Glaucio Paulino
Margareta Engman Augustine Professor of Engineering. Professor of Civil and Environmental Engineering and the Princeton Materials Institute
Princeton University
Talk title: “Super modular chiral origami metamaterials”
Register by November 14 to secure your spot. Registration is required.

LAMP Seminar: "Mechanics of Contact, Friction, and Fracture (by design) - Towards the Interaction between the Natural and Built Environment"
2025-12-05 12:00 - 13:00 | LRSM Reading Room, 3231 Walnut St., Philadelphia, PA, 19104, United States
Contact, friction, and fracture are fundamental processes governing the emergent (mechanical) behaviors of various complex (material) systems, from as large as tectonic slip to as small as third-body wear. In this talk, we briefly discuss (1) challenges in understanding the (nonlinear) mechanics of these processes, specifically focusing on those designed into metamaterials, and (2) novel engineering applications they may lead to, specifically in the context of enabling sustainable interaction between the natural and built environment.

MEAM Ph.D. Thesis Defense: "Real Time Local Wind Inference for Robust Autonomous Navigation"
2025-12-10 11:00 - 12:00 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Urban air mobility and autonomous package delivery represent promising avenues for integrating aerial robots into everyday life. However, operating these systems safely and efficiently in windy urban environments remains a major challenge due to the complexity of urban wind flow fields. Existing methods for predicting and navigating wind fields rely on precise environmental knowledge, distributed sensor networks, or extensive exploration. On the contrary, this thesis presents a solution that enables aerial robots to reason about surrounding wind flow fields in real time using on board sensors and embedded flight hardware.
The core novelty of this research is the fusion of range measurements with sparse in situ wind measurements to predict surrounding flow fields. We aim to address two fundamental questions: first, the sufficiency of topographical data for accurate wind prediction in dense urban environments; and second, the utility of learned wind models for motion planning with an emphasis on energy efficiency and obstacle avoidance. Drawing on tools from deep learning, fluid mechanics, and optimal control, we establish a framework for local wind prediction using navigational LiDAR, and then incorporate local wind model priors into a receding-horizon optimal controller to study how local wind knowledge affects energy use and safety during autonomous navigation.
Through simulated demonstrations in diverse urban wind scenarios we evaluate the predictive capabilities of the wind predictor, and quantify improvements to autonomous urban navigation in terms of crash rates and energy consumption when local wind information is integrated into the motion planning. Sub-scale free flight experiments in an open-air wind tunnel demonstrate that these algorithms can run in real time on an embedded flight computer with sufficient bandwidth for stable control of a small aerial robot.
Philosophically, this thesis contributes a new paradigm for localized wind inference and motion planning in unknown windy environments.
By enabling robots to rapidly assess local wind conditions without prior environmental knowledge, this research furthers the safe introduction of aerial robots into increasingly challenging environments.

ASSET Seminar: "Reality Checks"
2025-12-10 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Despite its success, leaderboard chasing has become something researchers dread and mock. When implemented properly and executed faithfully, leaderboard chasing can lead to both faster and easily reproducible progress in science, as evident from the amazing progress we have seen with machine learning, or more broadly artificial intelligence, in recent decades. It does not however mean that it is easy to implement and execute leaderboard chasing properly. In this talk, I will go over four case studies demonstrating the issues that ultimately prevent leaderboard chasing from a valid scientific approach. The first case study is on the lack of proper hyperparameter tuning in continual learning, the second on the lack of consensus on evaluation metrics in machine unlearning, the third on the challenges of properly evaluating the evaluation metrics in free-form text generation, and the final one on wishful thinking. By going over these cases, I hope we can collectively acknowledge some of our own fallacies, think of underlying causes behind these fallacies and come up with better ways to approach artificial intelligence research.

Zoom: https://upenn.zoom.us/j/96405514259

BE Seminar - Peng Yin, "DNA-based molecular measurement tools"
2025-12-11 15:30 | 216 Moore Building

Fall 2025 Robotics MSE Thesis Lightning Talks and Poster Session
2025-12-12 10:00 - 11:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This is an in-person event with in-person attendance in Levine 307.
10:00am – Lightning Talks and Poster Session
Welcome Remarks
Dr. Ani Hsieh – ROBO Program Chair
Kashish Garg
Advised By: Dr. Vijay Kumar
Aero-Simian: A Bio-Inspired, Brachiating-Flying Robot

JiYoon Kang
Advised By: Dr. Mark Yim
Birddy: A Multimodal, Offline Health Check-In System for Older Adults

Jessica Liang
Advised By: Dr. Jianbo Shi
Interpretable and Compressed Vision-Language Models

Dixuan Lin
Advised By: Dr. Kostas Daniilidis
Zero-shot Reconstruction of In-Scene Object Manipulation from Video

Doctoral Dissertation Defense - Spencer Averbeck "Advancing neural interface technologies for high-fidelity cortical recording and stimulation"
2026-01-12 11:00 - 12:00 | Class of 62 Auditorium, John Morgan Building, 3620 Hamilton Walk, Philadelphia, PA, 19104

ASSET Seminar: "Formal Methods for Language Model Systems"
2026-01-14 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Formal methods are often dismissed as too rigid, complex, or unscalable for frontier language model systems (e.g., LLMs, VLMs, agentic systems). In this talk, I will challenge this assumption with both theoretical insights and empirical evidence across various domains, including chatbots, autonomous driving, mathematical reasoning, code generation, and agentic AI.

I will present a new set of efficient formal frameworks for LLMs that:

Specify and verify safety properties (e.g., secure code generation, catastrophic risk), yielding stronger guarantees than standard evaluation methods such as benchmarks or red teaming.
Guide generation with semantic guardrails, ensuring outputs respect formal constraints, substantially improving both reasoning performance and safety.
Train models that are more performant and safer, and synthesize agents that provably adhere to formally specified constraints (e.g., privacy, resource consumption).

Together, these advances demonstrate that formal methods provide a principled foundation for improving the utility, safety, and efficiency of frontier language model systems.

Zoom: https://upenn.zoom.us/j/98203000874

MSE Seminar: "Polymer Chain-End Chemistry: Unlocking Next-Generation Functional Materials"
2026-01-15 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
End-group functionalization has become a powerful and versatile strategy in polymer science, enabling precise control over physical properties, nanoscale self-assembly, and interfacial functionality without modifying the polymer backbone. In our group’s research, we have investigated (1) how tailored end groups affect intrinsic polymer characteristics such as thermal transitions, solubility, and crystallization behavior, and (2) how end-group interactions direct polymer self-assembly, particularly by modulating chain packing,interfacial curvature, and phase behavior in block copolymer systems, including the formation of complex network morphologies. Our goal is to develop synthetic methodologies for polymers with rationally designed end-functional groups, allowing systematic study of their thermodynamic phase-transition behavior through precise control of molecular interactions. By identifying and tuning key variables, our work aims to deepen fundamental understanding of polymer science. These insights highlight the transformative potential of end-group chemistry for next-generation polymer materials and provide a foundation for designing functional nanomaterials for emerging applications, including solid-state battery electrolytes, mechanical metamaterials, and optical metamaterials.
References
1) Hojun Lee, Sangwoo Kwon, Jaemin Min, Seon-Mi Jin, Jun Ho Hwang, Eunji Lee, Won Bo Lee, and Moon Jeong Park*, “Thermodynamically Stable Plumber’s Nightmare Structures in Block Copolymers”, Science 2024, 383, 70.
2) Hojun Lee, Jihoon Kim, and Moon Jeong Park*, “Block Copolymer Electrolytes with Double Primitive Cubic Structures: Enhancing Solid-State Lithium Conduction via Lithium Salt Localization”, ACS Nano 2025, 19, 1251.
3) Gyuha Jo, Hyungmin Ahn, Moon Jeong Park “Simple Route for Tuning the Morphology and Conductivity of Polymer Electrolytes: One End Functional Group is Enough”, ACS Macro Lett. 2013, 2(11), 990-995.

Doctoral Dissertation Defense - Paul Jacobs, "Novel Metasurfaces for Mitigation of B1 Inhomogeneities in NOE and CEST MRI at 7T: Clinical Applications in White Matter Demyelinating Pathology"
2026-01-15 14:00 - 15:00 | BRB Auditorium

BE Doctoral Dissertation Defense - David Gonzalez-Martinez, "Dissecting mechanisms and consequences of oncogenic RTK fusion signaling"
2026-01-16 10:00 - 11:00 | Class of 62 Auditorium, John Morgan Building, 3620 Hamilton Walk, Philadelphia, PA, 19104

MEAM Seminar: "Nonlinear Modeling, Synthesis, and Design-for-Additive Manufacturing of Smart Compliant Mechanisms"
2026-01-20 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Compliant mechanisms have been the subject of intense research in recent decades. Making compliant mechanisms “smart” to form flexible, adaptive structures is the focus of my research group, with applications ranging from medical devices to aerospace structures. This seminar will describe recent work on modeling and synthesis approaches for compliant mechanisms with both superelastic material behavior and large deformations, which have not been considered previously in the literature. This approach allows for design of compliant mechanism-based metamaterials with highly nonlinear tailorable stiffness and enhanced energy absorption. Additionally, the presentation will cover our method for systematic design of active compliant mechanisms that change shape on demand due to application of external stimulus such as magnetic field. Methods to model and design soft magneto active elastomer devices produced via additive manufacturing will be described. These devices are designed to be robust to geometry variation inherent in additively manufacturing processes.

ESE 2026 Jack Keil Wolf Lecture - "Convex Optimization"
2026-01-26 15:00 - 16:15 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Convex optimization has emerged as useful tool for applications that include data analysis and model fitting, machine learning and statistics, resource allocation, engineering design, network design and optimization, finance, and control and signal processing. We give an overview of the basic mathematics, algorithms, and software frameworks for convex optimization, and give a few examples. We describe real-time embedded convex optimization, in which small convex optimization problems are solved repeatedly in time frames measured in milliseconds.

MEAM Seminar: "Constitutive Modeling of Rubbery Networks: From Microscale Physics to Macroscopic Behavior"
2026-01-27 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Rubbery polymer networks, including elastomers and hydrogels, are increasingly employed in advanced applications such as biomedical implants, drug delivery systems, and smart sensors and actuators. Their macroscopic mechanical properties, such as stiffness, strength, and stretchability, are largely governed by network-level features, including polymer chain length distribution, crosslink density, and network heterogeneities and topological defects. Consequently, recent material design strategies have focused on tailoring network architecture to achieve enhanced mechanical performance. Despite these efforts, predictive tools that directly connect molecular- and network-scale parameters to macroscopic mechanical response remain scarce. Atomistic simulations are computationally prohibitive at relevant length and time scales, while continuum models often rely on phenomenological fitting parameters. In this talk, I will present our recent progress toward a predictive multiscale modeling framework for soft rubbery networks that incorporates microscale physics at the single-chain and network levels.

Spring 2026 GRASP on Robotics: Ming C. Lin, University of Maryland, College Park, "Learning the Dynamic World "
2026-01-30 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This event will be in-person ONLY in Wu and Chen Auditorium.
ABSTRACT
With increasing availability of data in various forms from images, audio, video, 3D models, motion capture, simulation results, to satellite imagery, representative samples of the various phenomena constituting the world around us bring new opportunities and research challenges. Such availability of data has led to recent advances in data-driven modeling. However, most of the existing example-based synthesis methods offer empirical models and data reconstruction that may not provide an insightful understanding of the underlying process in a dynamic world or may be limited to a subset of observations.
In this talk, I present recent advances that integrate classical model-based methods and statistical learning techniques to tackle challenging problems that have not been previously addressed. These include flow reconstruction for urban traffic, learning heterogeneous crowd behaviors from video, simultaneous estimation of deformation and elasticity parameters from images and video, and example-based multimodal display for VR systems. These approaches offer new insights for learning and understanding complex collective behaviors, developing better models for complex dynamical systems from captured data, delivering more effective medical diagnosis and treatment, as well as design and prototyping of personalized apparel. I conclude by discussing some possible future directions and challenges.

Joseph Bordogna Forum: 2026 Lecture
2026-02-02 13:00 - 14:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Engineering Leadership in a World of Accelerating Change
Engineering bridges science and technology, driving innovation for the benefit of people and society. Today we live in an increasingly technology-dependent world shaped by engineers — and the pace of change is increasing exponentially. This calls for a reexamination of how we educate and empower engineers to advance technology for the long-term welfare of humanity and the planet. In this talk, I will share ideas for transforming engineering education, taken from relevant publications of the National Academies, and discuss ways the National Academy of Engineering can promote coordination and foster collaboration to build an engineering workforce that is fit to shape a brighter future for all.

Spring 2026 GRASP on Robotics: Roni Sengupta, University of North Carolina at Chapel Hill, "From Pixels to Physics: Understanding and Manipulating Physics from Images"
2026-02-06 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This event will be in-person ONLY in Wu and Chen Auditorium.
ABSTRACT
A hallmark of human vision is the ability to reason about the physics of the world: we can infer the shape of the object, how light reflects off the object, and how the object deforms under force. Yet today’s AI systems still lack this kind of physical intuition. Enabling machines to perceive and manipulate physics would mark a major step toward grounding AI in the real world.
In this talk, I will present my lab’s research at the intersection of computer vision, graphics, and machine learning that takes an inverse graphics perspective: rather than forward-simulating the physics of light, materials, and deformation as in decades of computer graphics, we aim to invert this process to infer and control these properties directly from images. The first part of the talk will focus on explicitly estimating physics, with examples in recovering lighting, reflectance, and object deformation. The second part will show how controlled generative models allow us to manipulate physics, with applications in relighting and simulating facial aging. Together, these efforts highlight a path toward AI systems with a deeper and more actionable understanding of the physical world.

PICS Colloquium: "Stochastic Reaction-Diffusion-Dynamics Modeling of whole systems: application to fibrin clot contraction and fibrin clot rupture" with Valeri Barsegov
2026-02-06 14:00 - 15:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
Abstract: Stochastic Reaction-Diffusion-Dynamics Model (SRDDM) for particle-based simulations of mechanochemical processes for thermodynamically large systems with high spatial and temporal resolution will be presented. The SRDDM couples the spatially inhomogeneous reaction-diffusion master equation to account for chemical reactions and molecular transport within the Langevin Dynamics (LD) framework to describe force-dependent dynamic processes at the whole system level (e.g. fibrin gel, eukaryotic cell). This computational infrastructure developed allows for the simulation of hours of mechanical processes in reasonable wall-clock time. The SRDDM will be applied to explore: 1) the kinetics, thermodynamics, and mechanisms of blood clot contraction – platelet-driven fibrin network remodeling; and 2) the strength, deformability, damage and fracture toughness of fibrin clot – a prime example of fibrous material networks.

PICS Colloquium: "Learning parsimonious models by covariance balancing" with Clarence Rowley
2026-02-06 14:00 - 15:00 | PA
Abstract: Data-driven reduced-order models often struggle with high-dimensional nonlinear systems sensitive to low-variance coordinates, which are typically truncated. To address this, we use ideas from balanced truncation and active subspaces to identify low-dimensional coordinate systems that balance adjoint-based sensitivity information with state variance along trajectories. Our method, analogous to balanced truncation, replaces system Gramians with state and adjoint-based gradient covariance matrices, maintaining key transformation laws. We also present a further refinement whereby the resulting oblique projection is iteratively optimized to minimize forecasting error. We demonstrate and compare these techniques with other methods on a challenging toy problem and a nonlinear axisymmetric jet flow simulation with 100,000 state variables.

Eli Burstein Lecture in Materials Science: "Toward Intelligent Metamaterial Machines," Katia Bertoldi - Harvard University
2026-02-11 10:30 - 12:00 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Flexible mechanical metamaterials are engineered structures whose unique geometries allow them to display remarkable behaviors, especially in the nonlinear regime. These systems hold promise for enabling the next generation of smart materials and devices, offering capabilities such as shape morphing, programmable nonlinear responses, and energy manipulation. By embedding programmable mechanics, shape-shifting functions, and computational abilities directly into a single monolithic structure, we open the door to a new class of machines that require minimal electronic input and derive their advanced functionality from their intrinsic architecture.
In this talk, I will highlight our recent progress toward integrating shape morphing, sensing, and intelligence within a single synthetic material system to realize such machines.

ASSET Seminar: "Title TBD"
2026-02-11 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract TBD

Zoom: https://upenn.zoom.us/j/93936800903

Spring 2026 GRASP on Robotics: George Konidaris, Brown University, "Unifying the Stack: A Principled Structuralist Approach to Intelligent Robot Control"
2026-02-13 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This event will be in-person ONLY in Wu and Chen Auditorium.
ABSTRACT
There are two dominant approaches to designing intelligent robots. One, typified by language behavior models, leverages unstructured deep neural networks and learning from demonstration to generate behavior. These approaches have had several impressive successes but face scaling, trust, and explainability challenges. The second approach seeks to integrate, rather than discard, technologies from existing subfields (like motion planning and SLAM) into a coherent control architecture that retains their favorable properties while accessing the strengths of deep network. The primary challenge here is that there is no unifying theoretical framework for all of robotics: each subfield was designed and studied largely in isolation.
I will propose a unifying framework that models the control stack as layers of increasingly abstract decision processes. Each layer combines perceptual and action abstractions, to generate a more tractable decision process by exploiting structure in the world or the robot. Existing technologies fit naturally into this stack as observation or action abstractions. The result is a natural hierarchy with a few missing technologies. I will discuss my group’s recent results on both filling in these missing technologies, and more generally in learning decision process abstractions from pixel-level data.

ASSET Seminar: "Title TBD"
2026-02-18 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract TBD

Zoom: https://upenn.zoom.us/j/95008740148

Spring 2026 GRASP on Robotics: David Held, Carnegie Mellon University, "Precise and Generalizable Robot Manipulation"
2026-02-20 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This event will be in-person ONLY in Wu and Chen Auditorium.
ABSTRACT
Robots in factories are still largely limited to structured environments with known object models. How can we bring robots into the more diverse, unstructured settings of our daily lives, where objects may be deformable or articulated and vary widely in shape and appearance, while maintaining reliable performance? A popular direction is to train generalist robot policies on large-scale internet data and broad robot datasets. However, today’s generalist policies still lack the precision needed for robust real-world operation. In this talk, I argue that closing this gap requires learning a hierarchy over robot motion: learning both what subgoals to achieve as well as how to move the robot end-effector to achieve them. I will present hierarchical motion policies that combine high-level subgoal prediction with a learned low-level policy, enabling tasks such as articulated object manipulation and precise insertion, while generalizing to unseen objects and novel configurations.
