Penn Engineering Events

The Harold Berger Distinguished Award Lecture 2025
2025-03-19 15:30 - 16:30 (America/New_York) | Wu & Chen Auditorium
Quantum Dots: From Curiosity to Technological Impact
The combination of quantum effects, nanometer dimensions and a chemical synthesis make quantum dots a platform for exploring new size-dependent fundamental properties and a sandbox for developing new applications. This talk will cover the origin story of chemically synthesized quantum dots, their basic physics, the synthesis that unlocked their widespread study and applications, an overview of their properties and a few of their applications, including in bio-imaging and as emissive materials in displays.
Register Now

MSE Seminar: "Soft Microparticle Assemblies to MAP Scaffolds" Tatiana Segura - Duke University
2025-03-20 10:30 - 12:00 | Wu & Chen Auditorium
Particle assemblies form interconnected pockets of empty space that are hot spots for activity in many applications and natural phenomenon that deal with particulate matter. In my lab we explore the use of interlinked soft microparticle assemblies as scaffolds for cell culture in vitro and to promote regenerative wound healing in vivo. The open space that is created by these soft microparticle assemblies can be engineered to guide cellular behavior through restricting the space occupied by cells by modulating particle size, changing the chemical composition of the open space, and activating the cells with soluble factors. Using these approaches, we have studied the role of open space on macrophage polarization, neuroprogenitor cell growth, and vascular morphogenesis. Injection of these materials into wounds result in the local modulation of immune cells that turn the environment towards a reparative environment and leads to scarless healing skin. In this talk, I will discuss our latest results in our creation of soft particle assemblies for biomedical applications.

ESE Spring Seminar - "Can Robots Learn from Machine Dreams? - Robot Learning via GenAI-powered World Models"
2025-03-20 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Over the past decade, large-scale pre-training followed by alignment has revolutionized natural language processing and computer vision. Yet, robotics remains constrained by the scarcity of real-world data. In this talk, I will present our systematic approach to overcoming this bottleneck by building increasingly rich world models from data. I will first introduce our distilled feature field work that represents a robot’s action in relation to multi-modal descriptors in a static environment. Building upon this foundation, I will demonstrate LucidSim, a GenAI-powered simulator that significantly narrows the sim-to-real gap in the visual domain. I will show the initial steps we have taken toward a robot foundation model by extending this framework from legged locomotion to manipulation tasks. These results suggest that synthetic data sourced from world models can accelerate robot learning beyond what is possible with real-world data alone. I will conclude by outlining a roadmap that begins with enabling machines to dream a million worlds that will ultimately allow AI to enter the physical world.

IDEAS/STAT Optimization Seminar: "Statistics-Powered ML: Building Trust and Robustness in Black-Box Predictions"
2025-03-20 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Zoom link:
https://upenn.zoom.us/j/98220304722
Abstract:
Modern ML models produce valuable predictions across various applications, influencing people’s lives, opportunities, and scientific advancements. However, these systems can fail in unexpected ways, generating unreliable inferences and perpetuating biases present in the data. These issues are particularly troubling in high-stakes applications, where models are trained on increasingly diverse, incomplete, and noisy data and then deployed in dynamic environments—conditions that often exacerbate test-time failures.
In response to these challenges, this talk explores a key question: How can fundamental statistical principles be harnessed to produce trustworthy predictive inference?
In the first part, I will present a new advancement in conformal prediction—a statistical wrapper for any black-box model that provides precise error bounds on ML predictions. I will focus on scenarios where training data is corrupted or biased, such as through missing features and labels, and introduce a framework for constructing predictive uncertainty estimates that remain valid despite distribution shifts between the available corrupted data and unknown clean data.
In the second part, I will show how sequential statistical testing can enable a novel test-time training scheme, allowing a pre-trained model to adapt online to unfamiliar environments. For instance, consider an image classification task where test images are captured under varying illumination conditions that differ from the training setup. Building on conformal betting martingales, I will first introduce a monitoring tool to detect data drifts. Using this tool, I will derive a rigorous ‘anti-drift correction’ mechanism grounded in (online) optimal transport principles. This mechanism forms the foundation of a self-training scheme that produces robust predictions invariant to dynamically changing environments.

CIS Seminar: "AI for Materials Discovery: Graphs, Language Models, and Agents"
2025-03-20 15:30 - 16:30 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Artificial intelligence (AI) is transforming scientific discovery, particularly in materials science, by accelerating the prediction and design of materials with desired properties. Traditional physics-based modeling of atomic systems is computationally prohibitive for large-scale problems, and AI addresses this challenge by learning the underlying physics from data, thereby accelerating discoveries. In this talk I will present advances in AI-driven materials discovery, focusing on integrating physical principles, such as symmetry and equivariance, into AI models for accurate prediction and generation. My key approaches include geometric deep learning, language models, and AI agents, which collectively enhance the efficiency of materials discovery and strengthen the connection between AI and physical sciences. I will discuss my ongoing and future work, aiming at developing foundation models for materials and generic atomic systems, along with automated AI-driven discovery pipelines. I will conclude by presenting my future plans in tackling more complex and multiscale systems, spanning disordered materials and high-entropy alloys to general AI for science problems. Altogether, my current and future research demonstrates the close synergy among AI, physics, chemistry, and materials science.

MEAM Seminar: "Multiscale and Multi-physics Mechanics Involving Highly Flexible Nano and Continuum Rods"
2025-03-25 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Flexible rod-like structures such as nanotubes and nanowires have found significant interest in nanoelectronic applications. Likewise, with the recent advancement in soft robotics and additive manufacturing, an important goal is to optimally design architected slender metamaterials and further derive their effective mechanical properties through homogenization techniques. However, modeling such structures as a flexible continuum body poses multiscale challenges. Lately, there is also a surge in interest for understanding the motion and deformation of such slender structures in the presence of surrounding fluid or external electric/magnetic field. In this talk, I will touch upon the mechanics and numerical challenges faced while addressing the above mentioned problems.

ESE Spring Seminar - "Engineering and utilizing interactions between spins and light with molecular qubits"
2025-03-25 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Coupling of spins and light can enable photon-mediated scaling and control in quantum technologies, as demonstrated in trapped atom, ion, and solid-state spin qubits. Molecular analogs of such systems hold promise as a nascent qubit platform that can leverage the tools of synthetic chemistry to tailor quantum properties and integrate in nanoscale devices but have thus far been limited by broad optical transitions. In this talk I describe the development of optically addressable molecular qubits utilizing a central spin-bearing rare-earth ion. We demonstrate coherent microwave control of the spin ground-state and high-resolution spin-selective optical transitions. The resulting spin-optical interface enables high-contrast detection and optical generation of non-equilibrium spin polarization. This approach opens new doors for engineering and utilizing spin-photon coupling in heterogeneous molecular devices with capacity for chemical design.

CIS Seminar presents: " Cyber-Physical Security Through the Lens of AI-Enabled Systems
2025-03-25 15:30 - 16:30 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Cyber-physical systems (CPS), powered by emerging artificial intelligence (AI) technologies, have become integral to various critical domains such as the Internet of Things (IoTs), medical devices, and autonomous vehicles. A unique aspect of these systems lies in their interactions with the physical world, by perceiving environments through heterogeneous modalities (perception), processing digital data with intelligence algorithms (computing), and autonomously actuating controls that affect physical processes (actuation). While this intricate fusion of cyber and physical components has unlocked unprecedented capabilities, it has also introduced new security challenges. However, traditional security measures often fall short in addressing these multifaceted threats. Under this paradigm shift, I systematically explore and mitigate the threats inherent in AI-enabled cyber-physical systems. The research objectives are threefold: (1) investigating how the interplay of cyber and physical components opens up novel attack and defense vectors, (2) developing robust defense strategies grounded by physical laws and reasoning, and (3) benchmarking and theoretically analyzing security trade-offs from algorithmic, system-level, and human-centric perspectives. By bridging the gap between cyber and physical domains, my research enhances the resilience and trustworthiness of modern CPS while retaining system efficiency and usability.

ASSET Seminar: "Controlling Language Models"
2025-03-26 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract:
Controlling language models is key to unlocking their full potential and making them useful for downstream tasks. Successfully deploying these models often requires both task-specific customization and rigorous auditing of their behavior. In this talk, I will begin by introducing a customization method called Prefix-Tuning, which adapts language models by updating only 0.1% of their parameters. Next, I will address the need for robust auditing by presenting a Frank-Wolfe-inspired algorithm for red-teaming language models, which provides a principled framework for discovering diverse failure modes. Finally, I will rethink the root cause of these control challenges, and propose a new generative model for text, called Diffusion-LM, which is controllable by design.
Zoom Link (if unable to attend in-person): https://upenn.zoom.us/j/93867005722

Spring 2025 GRASP SFI: Danna Ma, Cornell University, “Harnessing Physical Intelligence for Collective Motion in Robotic Matter”
2025-03-26 15:00 - 16:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom.
ABSTRACT
In recent years, the field of swarm robotics has seen rapid advancements, from research to industry. While most work focuses on programmed intelligence, swarms in nature demonstrate that physical intelligence—where agents perform tasks based on their morphology and physical interactions with the environment—also plays a critical role. In my work, I have developed a robotic swarm of smart particles, or ‘smarticles,’ which can serve both as a taskable robotic platform and as a smart material. In this talk, I will discuss the electro-mechanical design, sensory feedback mechanisms, and adaptive control strategies that enable smarticles to exhibit emergent collective motion, demonstrating how they can operate autonomously without the need for a central controller, high-bandwidth communication, or extensive pre-planning.

ESE Spring Seminar - "Wavelength-Encoded Nanolaser Particles for Highly Multiplexed Single-Cell Analysis"
2025-03-27 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Understanding single-cell heterogeneity in biological systems is considered the holy grail of biomedicine. However, conventional single-cell analysis methods are constrained by the destructive readout process of DNA barcodes and the broad emission linewidths of fluorescence barcodes, limiting their ability to capture dynamic information and achieve high multiplexing capabilities.
This seminar explores the transformative potential of “nanolaser particles.” By leveraging the broad gain bandwidths of III-V semiconductor materials, these nanolaser particles generate ultra-narrowband stimulated emissions (<1 nm), enabling high-throughput combinatorial living cell barcoding on a scale of millions.
Key advancements include: (i) large-scale fabrication of injectable, biocompatible nanolaser particles; (ii) the invention of Laser Particle Stimulated Emission Microscopy (LASE) for super-resolution, real-time in vitro and in vivo cell tracking, as well as spatial transcriptomics of migrating cells; and (iii) the development of the world’s smallest laser (170 nm), achieving the fundamental half-wave size limit and paving the way for molecular-sized laser probes.

IDEAS/STAT Optimization Seminar: "The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws"
2025-03-27 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Zoom link: https://upenn.zoom.us/j/98220304722
Abstract:
We study the generalization properties of neural networks through the lens of data complexity. Recent work by Buzaglo et al. (2024) shows that random (nearly) interpolating networks generalize, provided there is a small “teacher” network that achieves small excess risk. We give a short single-sample PAC-Bayes proof of this result and an analogous “fast-rate” result for random samples from Gibbs posteriors. The resulting oracle inequality motivates a new notion of data complexity, based on the minimal size of a teacher network required to achieve any given level of excess risk. We show that polynomial data complexity gives rise to power laws connecting risk to the number of training samples, like in empirical neural scaling laws. By comparing the “scaling laws” resulting from our bounds to those observed in empirical studies, we provide evidence for lower bounds on the data complexity of standard benchmarks.

Joint work with G. K. Dziugaite.

CIS Seminar: "Unlocking Scalable Robot Learning in the Real World"
2025-03-27 15:30 - 16:30 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Many domains of machine learning, from language modeling to computer vision, have recently undergone a shift towards generalist models, whose broad generalization abilities are fueled by large and diverse real-world training datasets and high-capacity model architectures. In robotics, however, it has been challenging to apply the same recipe: after all, we cannot easily scrape millions of hours of robot data from the internet and existing model architectures for scalable learning in vision or language modeling are not designed for the continuous control tasks we need to solve in robotics. In this talk, I will describe my work on unlocking scalable robot learning in the real world. I will discuss important differences between robotics and other machine learning domains, and describe how we can adapt scalable learning approaches for robotics to yield robot policies that generalize out-of-the-box to unseen environments, and can be quickly adapted to new tasks or robot embodiments. Through a combination of community-wide data sharing efforts, improved model design and training objectives, my work has enabled the construction of the largest robot learning datasets to date, and the training of generalist robot policies that can perform a range of complex, long-horizon manipulation tasks simply by prompting them in natural language. I will close with a description of current limitations and open challenges towards building truly general robot control policies.

CIS Seminar: "Learning to Perceive the 4D World"
2025-03-28 09:30 - 10:30 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Perceiving the 4D world (i.e., 3D space over time) from visual input is essential for human interaction with the physical environment. While computer vision has made remarkable progress in 3D scene understanding, much of it remains piecemeal—for example, focusing solely on static scenes or specific categories of dynamic objects. How can we model diverse dynamic scenes in the wild? How can we achieve online perception with human-like capabilities? In this talk, I will first discuss holistic scene representations that enable long-range motion estimation and 4D reconstruction. I will then introduce a unified learning-based framework for online dense 3D perception, which continuously refines scene understanding with new observations. I will conclude by discussing future directions and challenges in advancing spatial intelligence.

CIS Seminar: "Pareto-efficient AI systems: Expanding the quality and efficiency frontier of AI"
2025-03-28 09:30 - 10:30 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
We have made exciting progress in AI by massive models on massive amounts of data center compute. However, the demands for AI are rapidly expanding. I identify how to maximize performance under any compute constraint, expanding the Pareto frontier of AI capabilities.

This talk builds up to an efficient language model architecture that expands the Pareto-frontier between quality and throughput efficiency. In motivation, the Transformer, AI’s current workhorse architecture, is memory hungry, severely limiting its throughput, or amount of text it can process per second. This has led to a Cambrian explosion of alternate efficient architecture candidates proposed across prior work. Prior work has painted an exciting picture: there exists architectures that are asymptotically faster than Transformers, while also matching quality. However, I ask, if we’re using asymptotically faster building blocks, are we giving something up in quality?

In part one, we build understanding. Indeed, there’s no free lunch! I present my work to identify and explain the fundamental quality and efficiency tradeoffs between different classes of architectures. Methods I developed for this analysis are now ubiquitous in the development of language models.

In part two, we measure how AI architecture candidates fare on the tradeoff space. A major hurdle, however, is that we lack implementations of the architectures that that run at peak-efficiency on modern hardware. Further, many proposed architectures are asymptotically fast, but not wall-clock fast. I present ThunderKittens, a new programming library I built to help AI researchers write simple, hardware-efficient algorithms across hardware platforms.

In part three, we expand the Pareto-frontier of the tradeoff space. I present the BASED architecture, which is built from simple, hardware-efficient components. I released the state-of-the-art 8B-405B Transformer-free language models, per standard evaluations, all on an academic budget.

Given the massive investment into language models, this work has had significant impact and adoption in research, open-source, and industry.

Spring 2025 GRASP on Robotics: Mac Schwager, Stanford University, “Perception-Rich Robot Autonomy with Neural Environment Models”
2025-03-28 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This will be a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom.
ABSTRACT
Recent advances in computer vision have led to the rise of highly expressive 3D scene models such as NeRFs and GSplats. More than just rendering lifelike images, these models allow robots to ground visual, semantic, physical, and affordance properties in a common 3D model, to rearrange objects in the scene and even simulate physical interactions. In this talk I will describe our efforts to build new robot autonomy features around these models, while preserving safety, modularity, and interpretability. I will present navigation algorithms for robots to safely maneuver through their environment using NeRFs and GSplats, even while training the model online in a SLAM-like fashion. I will describe methods to embed semantic and affordance information into radiance fields, giving robots a 3D grounding for understanding and executing tasks from natural language commands. Finally I will describe using these neural models as high-fidelity training environments for learning end-to-end visuo-motor policies. I will demonstrate such a policy for navigating a drone through an obstacle-rich environment while being robust to significant visual distractors. I will conclude with future opportunities and challenges in neural environment models for robotics.

CBE Doctoral Dissertation Defense: "Computational Materials Design for Exerting Control over Ice Formation" (Zachariah Vicars)
2025-03-28 13:30 - 16:00 | Room 313, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:

Ice formation plays an important role in a wide range of contexts, from serving as nuclei for clouds in the upper atmosphere to forming on the surfaces of aircraft and ships, reducing their performance. Controlling the formation of ice using additives or novel coatings has, consequently, been a long-standing pursuit in materials science. Here, we employ molecular simulations to explore the nano-scale detail of ice formation on a variety of surfaces and determine what molecular features are necessary to create surfaces that display extreme ice-phobicity and ice-philicity by modulating lattice match, surface polarity, and surface attractions. We then outline some recent advances in quantifying ice-philicity on chemically and topologically complex materials, like textured surfaces and anti-freeze proteins. These results serve as a foundation for future molecular studies of ice formation in all sorts of environments, with the goal of designing materials that can reliably control the formation of ice.

Zoom Information:
Link: https://us05web.zoom.us/j/84021428899?pwd=vafTqLwDqy7UxUJYhGnbX3kXswpKVp.1
Passcode: 2kdaS5

Celebration of Community
2025-03-28 15:00 - 18:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The Cora Ingrum Center for Community and Outreach is planning its annual Celebration of Community gala to showcase Penn Engineering students, staff, and faculty in their multi-talented richness. The event will consist of guest speakers, performances, presentations from student groups, and a variety of cuisines. Do not hesitate to contact Dr. Ocek Eke (ocek@seas.upenn.edu) and André Marston (amarston@seas.upenn.edu) with any questions.

If you are interested in performing, fill out this form and look out for updates!

2024-2025 Heilmeier Award and Lecture
2025-03-31 15:30 - 16:30 | Wu & Chen Auditorium
“Colloidal Nanocrystal Materials and Devices with Extraordinary Structures and Functions”
Colloidal nanocrystals (NCs) can serve as “artificial atoms” to construct materials and devices with extraordinary structures and functions.
This talk will discuss the process of designing materials and devices from single- and multiple-types of NCs. These designs can leverage the unique characteristics of NCs, in particular their size- and composition-dependent physical properties.
This approach has already led to several advancements, including flexible NC electronics, the first NC integrated circuits and magnetically and acoustically driven active matter.
Register Now

Frontiers in Science: Engineering RNA and AI
2025-04-01 09:00 - 13:00 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
This free, half-day event for undergraduates is hosted by the National Science Foundation Artificial Intelligence-driven RNA BioFoundry (AIRFoundry).
Led by experts from the University of Pennsylvania and University of Puerto Rico, Mayagüez, the event will highlight how AI can enhance RNA, with applications in medicine, agriculture and more.
Students will also be able to tour cutting-edge labs and engage with graduate researchers at a poster session.
Breakfast and lunch will be provided.
Speakers include: Professors Yoseph Barash, Lijun Zhou, Camilo Mora.
Tours include: Singh Center for Nanotechnology, Penn Institute for RNA Innovation and SMART Lab

MEAM Seminar: "Sticky Situations: Cohesion in Granular Flows"
2025-04-01 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
From silo discharge and granular avalanches to the erosion of granular beds, granular materials play a central role in many industrial and environmental processes. While significant advances have been made in understanding the statics and dynamics of cohesionless grains, the role of interparticle adhesion, which fundamentally alters bulk rheology and transport mechanisms, remains elusive. In this talk, we will discuss recent experimental efforts to systematically control and characterize model cohesive granular materials, allowing us to quantify the impact of cohesion in various configurations. By using model adhesive grains, we probe the role of interparticle adhesion in several canonical flow scenarios: granular discharge from a silo, where cohesion governs clogging onset and flow rate reduction; erosion of a granular bed by an impinging turbulent jet, where cohesive forces delay particle entrainment; and the collapse of a cohesive granular column, where cohesion enhances stability. These experiments provide a framework to rationalize the dynamics of cohesive granular materials, bridging the gap between idealized numerical models and real-world applications. The study of cohesive granular materials offers predictive frameworks for controlling granular materials in engineering applications, with implications ranging from powder handling in industrial processes to geophysical flows.

CBE Doctoral Dissertation Defense: "Tailoring MXene Chemistry through Composition and Undercoordination for Applications in Hydrogen Chemistries" (Yamilée Morency)
2025-04-01 11:00 - 13:00 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract:
Global transition to clean energy relies on the development of efficient hydrogen storage materials and hydrogen evolution reaction (HER) catalysts—two primary chemistries investigated in this thesis. MXenes, a family of two-dimensional transition metal carbides and nitrides, have emerged as promising candidates for hydrogen technologies due to their high electrical conductivity, chemical tunability, and structural versatility. Yet, the mechanisms by which their chemical composition and morphology—particularly atomic coordination environments—influence performance in hydrogen-related chemistries remain insufficiently understood.
This work employs atomic-scale high-throughput modeling, primarily based on density functional theory (DFT), to systematically investigate how compositional tuning and under-coordination affect hydrogen adsorption and catalytic activity across a wide range of MXene structures. The role of under-coordination is probed through the modeling of MXene nanoribbons and nanoparticles, revealing that edge environments significantly impact hydrogen adsorption, with certain configurations exhibiting enhanced catalytic activity relative to basal planes.
The study further explores the chemistry and HER performance of representative MXenes, including Mo₂TiC₂O₂ and other double-transition-metal systems, and identifies key structural and electronic features governing their behavior. The results demonstrate that both composition and under-coordination are powerful levers for optimizing MXenes in hydrogen storage and HER applications. By establishing structure–property relationships and uncovering relevant electronic descriptors, this thesis provides guidance for the design of MXene-based hydrogen technologies. These findings support the development of low-cost, earth-abundant alternatives to platinum catalysts and contribute to the broader effort of enabling sustainable hydrogen solutions through computational materials discovery.

CIS Seminar: "Generative Computer Vision for the Physical World"
2025-04-01 15:30 - 16:30 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Generative models are revolutionizing our world, with the ability to generate photorealistic visual content that are indistinguishable from reality. Despite their overwhelming presence in the cyber world, they haven’t been very useful in the physical world that we live in. In this talk, I will present how the rich priors learned by large-scale generative models—ranging from shape and geometry to motion and dynamics—can be harnessed for real-world perception and interaction tasks. I will showcase how these models can facilitate tasks like 3D reconstruction and robotic manipulation by incorporating the structure of the physical world. Moreover, I will discuss methods to further refine and adapt these systems through self-learning, enabling machines to continually improve as they explore new scenarios and environments. Together, these breakthroughs build the foundation for my vision of creating self-supervised machines that can perceive and interact with the physical world.

AI Across Disciplines: A Penn Initiative
2025-04-01 16:30 - 18:30 | Amy Gutmann Hall, Auditorium, 3333 Chestnut Street, Philadelphia, PA, 19104, United States
Join President J. Larry Jameson and Provost John L. Jackson, Jr. along with the Penn AI Council for the launch of Penn AI.
Senior Vice Provost for Research Dawn Bonnell will lead a discussion with members of the Penn AI Council to explore AI’s societal implications and Penn’s potential to influence a more sustainable future.
Panelists
Marylyn Ritchie, the Edward Rose, M.D. and Elizabeth Kirk Rose, M.D. Professor and Vice Dean of Artificial Intelligence and Computing at the Perelman School of Medicine (PSOM)
René Vidal, Rachleff University Professor and Penn Integrates Knowledge University Professor, with joint appointments in PSOM and the School of Engineering and Applied Science (Penn Engineering), and Director of the Center for Innovation in Data Engineering and Science (IDEAS)
Lynn Wu, Associate Professor of Operations, Information and Decisions at the Wharton School
Bhuvnesh Jain, Walter H. and Leonore C. Annenberg Professor in the Natural Sciences and Co-Director of the Penn Data Driven Discovery Initiative in the School of Arts & Sciences
Duncan Watts, Stevens University Professor and Penn Integrates Knowledge University Professor in Penn Engineering, the Annenberg School for Communication and Wharton
Reception to follow

ASSET Seminar: "Getting Lost in ML Safety Vibes"
2025-04-02 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract:
Machine learning applications are increasingly reliant on black-box pretrained models. To ensure safe use of these models, techniques such as unlearning, guardrails, and watermarking have been proposed to curb model behavior and audit usage. Unfortunately, while these post-hoc approaches give positive safety ‘vibes’ when evaluated in isolation, our work shows that existing techniques are quite brittle when deployed as part of larger systems. In a series of recent works, we show that: (a) small amounts of auxiliary data can be used to ‘jog’ the memory of unlearned models; (b) current unlearning benchmarks obscure deficiencies in both finetuning and guardrail-based approaches; and (c) simple, scalable attacks erode existing LLM watermarking systems and reveal fundamental trade-offs in watermark design. Taken together, these results highlight major deficiencies in the practical use of post-hoc ML safety methods. We end by discussing promising alternatives to ML safety, which instead aim to ensure safety by design during the development of ML systems.
Zoom Link (if unable to attend in-person): https://upenn.zoom.us/j/91619533220

Spring 2025 GRASP SFI: Ke Li, Simon Fraser University, “The Devil Is in the Gaps: How (Not) To Interpolate Between Discrete Points”
2025-04-02 15:00 - 16:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom.
ABSTRACT
What do diffusion models/flow matching, Gaussian splatting and efficient transformer architectures have in common? Under the hood, they all turn a discrete set of points into a function defined everywhere. In the case of diffusion models/flow matching, the points are training data points, and the function is the probability density. In the case of Gaussian splatting, the points are splat centres and the function is the volume density. In the case of efficient transformers, the points are keys used by attention and the function is the mapping from query to attention weights.

It turns out that *how* gaps between points are filled in is critical — in this talk, I will show how seemingly innocent choices made in popular techniques give rise to profound consequences. Such choices make diffusion models/flow matching data-hungry and slow to sample from, Gaussian splats hard to move and edit, and hashing-based efficient transformers error-prone. To address these issues, I will give an overview of three methods my lab developed, Implicit Maximum Likelihood Estimation (IMLE), Proximity Attention Point Rendering (PAPR) and IceFormer, and show applications in few-shot image synthesis, trajectory prediction, visuomotor policy learning, novel view synthesis, 3D shape and albedo editing, scene interpolation and language modelling.

CBE Seminar: "Metabolic Engineering and Synthetic Biology for a Sustainable Future" (Hal Alper, UT Austin)
2025-04-02 15:30 - 16:30 | Wu & Chen Auditorium
Abstract:
Recent technical advances are leading to a rapid transformation of the chemical palette available in cells, thus making it conceivable to produce nearly any organic molecule of interest—from biofuels to biopolymers to pharmaceuticals. However, these feats require the ability to “hijack” native cellular machinery and metabolism and navigate the complexity inherent in cellular regulation. This talk will cover examples of how microbial systems and enzyme can achieve the goal of sustainable chemical production and waste valorization. Examples include rewired cells for polymer precursors, unconventional carbon utilization including plastic waste, and platform strain engineering. Throughout the talk, there will be a focus on the biological developments necessary to achieve a sustainable, circular economy.

MSE Seminar: "Multifunctional Nanoparticles for Targeted Cancer Imaging and Treatment," Miqin Zhang - Kyocera Chair Professor of Materials Science and Engineering University of Washington
2025-04-03 10:30 - 12:00 | Wu & Chen Auditorium
One of our key research areas focuses on developing innovative nanoplatforms for cancer diagnosis, treatment, and therapy-response monitoring. We aim to understand material-biosystem interactions and design nanoparticles capable of delivering effective therapeutic payloads. This presentation will discuss strategies for synthesizing and engineering nanoparticles to ensure stability, extended blood circulation, and controlled drug release while overcoming biological barriers. I will also highlight the applications of our nanoparticle formulations in targeted delivery of DNAs, siRNAs, and mRNAs, integrated with chemotherapy, radiation, and immunotherapy. Our scalable and safe nanoparticles demonstrate effectiveness in overcoming therapy resistance, metastasis, and tumor recurrence in various murine models.

IDEAS/STAT Optimization Seminar: "Stochastic-Gradient-based Algorithms for Solving Nonconvex Constrained Optimization Problems"
2025-04-03 12:00 - 13:00 | Amy Gutmann Hall, Room 615
Zoom link: https://upenn.zoom.us/j/98220304722

Abstract
I will present recent work by my research group on the design and analysis of stochastic-gradient-based algorithms for solving nonconvex constrained optimization problems, which may arise, for example, in informed supervised learning. I will focus in particular on algorithmic strategies that have consistently been shown to exhibit the best practical performance, rather than focus on regularization-based methods that are popular for theoretical analyses, but often fail to yield satisfactory results. Our algorithms possess solid theoretical convergence guarantees and preliminary experiments motivate continued study.

CIS Seminar: "Learning, Reasoning, and Planning with Neuro-Symbolic Concepts"
2025-04-03 15:30 - 16:30 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
I aim to build complete intelligent agents that can continually learn, reason, and plan: answer queries, infer human intentions, and make long-horizon plans spanning hours to days. In this talk, I will describe a general learning and reasoning framework based on neuro-symbolic concepts. Drawing inspiration from theories and studies in cognitive science, neuro-symbolic concepts serve as compositional abstractions of the physical world, representing object properties, relations, and actions. These concepts can be combinatorially reused in flexible and novel ways. Technically, each neuro-symbolic concept is represented as a combination of symbolic programs, which define how concepts can be structurally combined (similar to the ways that words form sentences in human language), and modular neural networks, which ground concept names in sensory inputs and agent actions. I show that systems that leverage neuro-symbolic concepts demonstrate superior data efficiency, enable agents to reason and plan more quickly, and achieve strong generalization in novel situations and for novel goals. This is illustrated in visual reasoning in 2D, 3D, motion, and video data, as well as in diverse decision-making tasks spanning virtual agents and real-world robotic manipulation.
