BE Seminar - Peng Yin, "DNA-based molecular measurement tools"
2025-12-11 15:30 (America/New_York) | 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.

PICS Colloquium (Virtual): "Modelling active matter on large length and time scales" with Robert Jack
2026-02-20 14:00 - 15:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
Abstract: Active matter consists of particles that do work on their environment, for example by propelling themselves through a solvent. Fluids made from many interacting self-propelled particles have become popular as models of non-equilibrium matter: they are challenging for theory and computation because their steady states cannot be derived by minimizing a free energy, nor any other thermodynamic potential. I will discuss their behaviour on large length and time scales, including possibilities for dynamical pattern formation, glassy behavior, and surface phase transitions (wetting).

ASSET Seminar: "Title TBD"
2026-02-25 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract TBD

Zoom: https://upenn.zoom.us/j/97254514949

ASSET Seminar: "Title TBD"
2026-03-04 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract TBD

Zoom: https://upenn.zoom.us/j/94502467828

Spring 2026 GRASP on Robotics: Nikolay Atanasov, University of California San Diego, "Elements of Generalizable Robot Autonomy"
2026-03-06 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
Recent years have seen a transformation in artificial intelligence fueled by the convergence of machine learning models, internet-scale data, and large training infrastructures. Vision-Language Models (VLMs) have enabled unprecedented progress in aligned vision-language processing, while Vision-Language-Action (VLA) models and deep reinforcement learning (RL) have dominated the synthesis of intelligent robot behavior. Yet, most VLA and RL methods are model-free, relying on raw image sequences and expert demonstrations to make decisions. This raises concerns regarding scaling to complex tasks, which benefits from extended spatial and temporal context, and generalization to new operational conditions, which benefits from modular understanding of robot, environment, and task properties.

This seminar explores model-based techniques for robot behavior synthesis that integrate robot, environment, and task models, constructed from sensor observations. First, we present a physics-informed approach for learning robot models using neural ordinary differential equations that guarantee energy conservation and kinematic constraints by construction. Next, we focus on learning metric-semantic environment models from RGB and depth observations using implicit neural features. Finally, we discuss learning task models as automata labeled with observation features and trained from demonstrations. We evaluate our techniques in autonomous robot navigation and manipulation examples.

ASSET Seminar: "Title TBD"
2026-03-18 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract TBD

Zoom: https://upenn.zoom.us/j/91712393729

ASSET Seminar: "Title TBD"
2026-03-25 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract TBD

Zoom: https://upenn.zoom.us/j/97160689874

ASSET Seminar: "Title TBD"
2026-04-08 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract TBD

Zoom: https://upenn.zoom.us/j/92802010147

ASSET Seminar: "Title TBD"
2026-04-15 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract TBD

Zoom: https://upenn.zoom.us/j/97940296114

Spring 2026 GRASP on Robotics: Francesco Bullo, University of California, Santa Barbara, "Contraction Theory for Optimization, Control, and Neural Networks"
2026-04-17 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
This talk surveys recent advances on contraction theory for dynamical systems, as a robust, computationally-friendly and modular stability theory. Starting from basic notions, I will present novel theoretical properties and examples of contracting dynamics, including gradient systems, controlled Lure’ systems, constrained optimization solvers, and multiplayer games. As first application I will discuss online feedback optimization, where a dynamic plant is interconnected with a controller based on first-order optimization methods. Second, I will discuss the contractivity properties of recurrent neural networks and briefly review applications to unsupervised representation learning, implicit learning models, and reservoir computing.

ASSET Seminar: "Title TBD"
2026-04-22 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract TBD

Zoom: https://upenn.zoom.us/j/91849643116
