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 (America/New_York) | 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.

A Fireside Chat with Rajeev Misra: Global Trends in Technology, Business and Human Capital
2025-04-03 16:00 - 17:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Misra will share insights on the intersection of technological innovation, business strategy, and talent in the AI era, drawing on his leadership experience in global finance and tech investment. The program will also include the presentation of the 2024 D. Robert Yarnall Award.
Click here to RSVP to this event.

Penn AI Governance Workshop
2025-04-04 09:00 - 16:00 | Perry World House, 3803 Locust Walk, Philadelphia, PA, 19104, United States
This event is presented by the Wharton Accountable AI Lab and co-sponsored by the Wharton AI & Analytics Initiative; Penn Engineering; the Center for Technology, Innovation & Competition; and the Perry World House.
The Penn AI Governance Workshop will feature panel discussions, lightning talks and networking opportunities, and will conclude with a reception.
The workshop brings together leading Penn researchers to explore key topics, including AI safety and evaluation, AI policy, AI ethics and responsibility, and teaching AI governance at Penn. Throughout the day, panel discussions will be interspersed with lightning talks from researchers.

CIS Seminar: "Pareto-efficient AI systems: Expanding the quality and efficiency frontier of AI"
2025-04-04 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: Reid Simmons, Carnegie Mellon University, “AI-Based Assistants for the Elderly”
2025-04-04 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
As the population ages, the need grows for AI agents to assist people to remain living independently. Older adults are typically set in their ways, so AI agents should adapt to their ways of doing things, rather than the other way around. To that end, we are exploring various approaches to learning to personalize assistive agents, including the use of bandit algorithms, foundational models, neuro-symbolic architectures, and theory of mind. This talk will present our approaches and results in several assistive areas, including meal preparation and exercise coaching, as well as work in learning policies from humans. Much of the research is being supported by AI-CARING, an NSF-sponsored Institute devoted to developing AI technologies to help older adults with cognitive and physical decline remain in their homes.

Women in Data Science @ Penn Conference: From Data to Discovery: Exploring AI with a Patient Case Study, ChatGPT and Generative Models
2025-04-04 13:00 - 15:00 | Jon M. Huntsman Hall, 3730 Walnut Street, Philadelphia, PA, 19104, United States
In today’s data-driven landscape, vast unstructured data sources — like documents and electronic health records (EHRs) — demand advanced AI tools to unlock their full potential. Generative AI, powered by large language models (LLMs), is becoming indispensable for processing and extracting insights from complex language-based data.
Participants will explore:

Identifying key problems that lend themselves to AI-driven solutions.
Collecting and preparing data, building models and running state-of-the-art algorithms.
Validating models and interpreting their results.

CBE Doctoral Dissertation Defense: "Understanding and Modulating Interactions between Polymers and Nanoparticles for Effective Catalyst Design in Polymer Upcycling" (Anirban Majumder)
2025-04-07 14:00 - 15:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Abstract:

Despite recent advances in catalytic conversion of plastic waste into high-value chemicals, the interactions between the polymers and catalysts, which are highly porous nanomaterials, are not well understood. Fundamental understanding of these interactions and the ability to modulate them would allow us to design effective catalysts for polymer upcycling reactions. To study the interactions between polymers and nanomaterials, quantifying them is essential. In this thesis, we employ a quantitative technique to directly measure the contact angle between polymers and nanoparticles, enabling us to investigate various aspects of polymer-catalyst interactions that are crucial for polymer upcycling reactions. We measure the contact angle of polyolefins with silica nanoparticles, a commonly used support in heterogeneous catalysis, and modify the surface chemistry of these nanoparticles by depositing catalytic metals and metal oxides via atomic layer deposition (ALD) and using silane chemistry. Our findings show that the polarizability of polyolefins plays a significant role in their interactions with catalytic support materials, and modifying the polarity of the support material could be an effective way to tune polymer-catalyst interactions. Further, we find that the polymer-catalyst interactions are dominated by interactions of the polymers with the support materials and not with the metal catalytic sites. We also probe the influence of common plastic additives, namely primary antioxidants (PAOs), benzophenones and hindered amine light stabilizers (HALS), on the polymer-catalyst interactions by adding them to purified high-density polyethylene (HDPE) and measuring the contact angle on silica nanoparticles. We observe that while the addition of PAOs and benzophenones to polyolefins does not affect their interactions with silica significantly, HALS strongly alter the polymer-silica interactions. Hence, the presence of different types of additives in plastics might necessitate different strategies to design catalysts for polymer upcycling reactions. Overall, this thesis sheds light on some key unanswered questions on polymer-catalyst interactions and paves the way for future innovations in polymer upcycling and engineering catalysts for other polymeric reactions.

MEAM Seminar: “Learning Memory and Material Dependent Constitutive Laws”
2025-04-08 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The theory of homogenization provides a systematic approach to the derivation of macroscale constitutive laws, obviating the need to repeatedly resolve complex microstructure. However, the unit cell problem which defines the constitutive model is typically not amenable to analytical solution. It is therefore of interest to learn constitutive models from data generated by the unit cell problem. Many viscoelastic and elastoviscoplastic materials are characterized by memory-dependent constitutive laws. Furthermore, in order to amortize the computational investment in finding such memory-dependent constitutive laws, it is desirable to learn their dependence on the material microstructure.
Whilst the learning of memory dependence and material dependence have been considered separately, their joint learning has not been considered. This talk is focused on the joint learning problem and proposes a novel neural operator framework to address it
In order to provide firm foundations, the homogenization problem for linear Kelvin–Voigt viscoelastic materials is studied. The theoretical properties of the cell problem, in this Kelvin–Voigt setting, are used to motivate the proposed general neural operator framework; these theoretical properties are also used to prove a universal approximation theorem for the learned macroscale constitutive model. This formulation of learnable constitutive models is then deployed beyond the Kelvin–Voigt setting. Numerical experiments are presented showing that the resulting data-driven methodology accurately learns history- and microstructure-dependent linear viscoelastic and nonlinear elastoviscoplastic constitutive models; numerical results also demonstrate that the resulting constitutive models can be deployed in macroscale simulation of material deformation.
Joint work with Kaushik Bhattacharya, Lianghao Cao, George Stepaniants and Margaret Trautner (all Caltech).

ASSET Seminar: "Alignment and Control with Representation Engineering"
2025-04-09 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract:
Large Language Models (LLMs) are vulnerable to adversarial attacks, which bypass common safeguards put in place to prevent these models from generating harmful output. Notably, these attacks can be transferrable to other models—even proprietary ones—potentially compromising a wide range of AI systems with a single exploit. This surprising fragility underscores a critical weakness in current AI safeguards.
In this talk, we illustrate how these attacks are discovered, and several recent advances that take advantage of models’ internal representations to thwart them. Unlike much prior work that relies on adversarial training methods, this approach directly controls neural representations responsible for harmful and unwanted behaviors, while remaining agnostic to particular attacks. Notably, in start contrast with prior work we show that these methods can remain effective while preserving the model’s performance on non-adversarial inputs. Our findings suggest that achieving robust safety in generative models may be an attainable goal.
Zoom Link:https://upenn.zoom.us/j/95869536469

Spring 2025 GRASP SFI: Lillian Ratliff, University of Washington, “Fragile Foundations? Building Robustness into Reasoning with Algorithmic Agents”
2025-04-09 13:00 - 14:00 | 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
As AI-enabled systems become integral to critical domains, their robustness is increasingly tested by dynamic environments, continual learning, and inferential uncertainty. Whether an AI proxy informs high-stakes medical decisions or an embodied agent relies on a foundation model to reason across modalities, today’s training and deployment methodologies remain inherently fragile. This fragility often stems from a reliance on stationarity assumptions, overly symmetric training paradigms, and a failure to account for other adapting agents—leading to systems that generalize poorly, misestimate uncertainty, and break down in interactive settings.

This talk presents recent theoretical contributions and algorithmic design principles for robust inference and influence when reasoning with algorithmic agents. In particular, it explores how tools from control and game theory—when integrated into machine learning, and vice versa—enable uncertainty adaptation and the synthesis of decision-making strategies for influencing algorithmic agents. Through motivating examples, the talk will illustrate how bridging these disciplines leads to more robust AI systems that can reason, adapt, and interact effectively in complex, non-stationary environments. The first part will focus on algorithms with non-asymptotic convergence guarantees in time-varying settings with a hierarchical game structure. The second part will address uncertainty quantification and adaptation in safety-critical, multi-agent, embodied AI systems. The talk will conclude with a discussion of open questions and future directions.

CBE Seminar: "Value-added Transformations in Electrocatalysis and Graduate Education" (Maureen Tang, Drexel University)
2025-04-09 15:30 - 16:30 | Wu & Chen Auditorium
Abstract:

Electrifying the chemical industry has been much touted as a path to a low-carbon future, but nearly all pathways of interest are electrochemical reductions. If we want water-to-hydrogen, CO2-to-chemicals, or nitrogen-to-ammonia, from where will we get the electrons? Water-to-oxygen is thermodynamically expensive, kinetically slow, and generates a zero-value product. This talk will discuss two potential avenues of investigation for value-added electron-holes. In the first, we seek to determine the mechanism of six-electron water oxidation to ozone. We discuss our efforts to identify intermediates, understand the role of dopants, and deconvolute catalysis from corrosion in electrochemical ozone production. In the second, we investigate the feasibility of electrocatalytic cyclohexane oxidation. Our results show that molecular oxygen, not water, is the primary oxygen source, with implications for radical intermediates. We furthermore demonstrate the impact of cross-over in these systems and point to the importance of electrochemical reactor design in lab-scale studies. In the final part of this talk, I will discuss non-technical limitations to the aforementioned approach. I will incorporate advances in behavioral economics and organizational behavior into a novel mentoring activity for PhD students while exploring themes of scarcity and abundance in the modern university.

MEAM Ph.D. Thesis Defense: "Macroscopic Ensemble Methods for Multi Robot Task Assignment in Dynamic Environments"
2025-04-10 10:00 - 11:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
With finite resources to complete tasks like monitoring, coverage, and search, the challenge lies in identifying and performing tasks that can change both in frequency and location. One potential solution is teams of robots equipped with the necessary capabilities to complete the desired tasks. However, robot teams require methods that effectively assign robots to tasks, which is also known as the Multi Robot Task Allocation (MRTA) problem. Existing MRTA approaches assign each individual robot to a task. This works well if the team is small (less than 20 robots) and if the individual task specification does not change (monitoring a static environment). Unfortunately, these solutions require solving a combinatorial optimization problem which has poor computational scalability as the team and number of tasks increase, and is further exacerbated by changing task or environment conditions. An alternative perspective is to look at how biologists study animal resource selection. Instead of posing the problem of assigning animals to resources, animals randomly select a resource weighted by the perceived resource value resulting in beneficial population configurations. Taking inspiration from biology, we model team-wide objectives using macroscopic ensemble allocation. These methods allow robots to select stationary task regions, are known to easily control large robot teams (more than 50 robots), and can even describe robot team heterogeneity. Nevertheless, macroscopic ensemble methods require extensions to effectively solve the MRTA problem for tasks where conditions change, e.g., monitoring spatiotemporal processes. The main contributions of this dissertation include online adaptive macroscopic allocation, distributed adaptive macroscopic allocation, and macroscopic allocation via robot-robot collaboration. Our results show robot teams monitoring spatial temporal environments using simulation and robot experiments.

MSE/MEAM Seminar: "Converting Scientific Discovery and Disruptive Ideas Into Impactful Energy Technologies with ARPA-E" Laurent Pilon: ARPA-E
2025-04-10 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This talk presents the Advanced Research Projects Agency-Energy (ARPA-E) and how stakeholders at the University of Pennsylvania can engage in a dialog with the funding agency. ARPA-E advances high-risk high-impact transformational technologies to generate, store, and use energy. As part of the US Department of Energy, ARPA-E’s mission is to enhance the economic and energy security of the United States through the development of energy technologies that (i) reduce energy import, (ii) improve energy efficiency, (iii) reduce greenhouse gas emissions, and (iv) ensure the resilience, reliability, and security of the U.S. energy infrastructure. Another mission of ARPA-E is to ensure that the United States maintains a technological lead in developing and deploying advanced energy technologies. These missions are achieved through rigorous program development, competitive project selection, and active program management to ensure impactful expenditures. We will discuss the different funding mechanisms, the lifecycle of ARPA-E programs and how stakeholders can engage at different stages. This discussion will be illustrated with the speaker’s experience as a faculty member at UCLA and in his roles at ARPA-E where he developed and managed the following programs:

IGNIITE supporting early-career innovators seeking to convert disruptive ideas into impactful new technologies across the full spectrum of energy applications.
CIRCULAR developing foundational technologies to achieve a circular electric vehicle battery supply chain.
Superconducting Tape program aiming to manufacture high performance, low cost, high temperature superconducting tapes to support developments in fusion reactors, electric aviation, and power transmission.

IDEAS/STAT Optimization Seminar: "Gradient Equilibrium in Online Learning"
2025-04-10 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
We present a new perspective on online learning that we refer to as gradient equilibrium: a sequence of iterates achieves gradient equilibrium if the average of gradients of losses along the sequence converges to zero. In general, this condition is not implied by, nor implies, sublinear regret. It turns out that gradient equilibrium is achievable by standard online learning methods such as gradient descent and mirror descent with constant step sizes (rather than decaying step sizes, as is usually required for no regret). Further, as we show through examples, gradient equilibrium translates into an interpretable and meaningful property in online prediction problems spanning regression, classification, quantile estimation, and others. Notably, we show that the gradient equilibrium framework can be used to develop a debiasing scheme for black-box predictions under arbitrary distribution shift, based on simple post hoc online descent updates. We also show that post hoc gradient updates can be used to calibrate predicted quantiles under distribution shift, and that the framework leads to unbiased Elo scores for pairwise preference prediction.

Zoom link: https://upenn.zoom.us/j/98220304722

Spring 2025 GRASP on Robotics: Lingjie Liu, University of Pennsylvania, “Towards Next-Gen 3D Reconstruction and Generation: From Visual Fidelity to Multimodal and Physical Understanding”
2025-04-11 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. This seminar will NOT be recorded.
ABSTRACT
Recent years have witnessed remarkable progress in 3D reconstruction and generation. However, most existing methods primarily focus on modeling geometry and appearance. I believe the next generation of 3D reconstruction and generation should go further in two key directions. First, it should be well-aligned with other modalities—such as language and images—so that 3D representations can play an important role in the multi-modal era. Second, it should incorporate physical understanding to ensure reconstructions and generations are physically plausible, which will ultimately make them more applicable in robotics. In this talk, I will present our recent efforts toward these goals and discuss the challenges that lie ahead.

CBE Doctoral Dissertation Defense: "Context-Dependent Protein Surface Hydrophilicity" (Lilia F. Escobedo)
2025-04-11 11:30 - 13:00 | Vagelos Institute for Energy Science and Technology, Room 121, 231 S 34th Street, Philadelphia, PA, 19104, United States
Abstract:
Proteins perform many important biological functions while also avoiding fouling in an aqueous and crowded cellular environment. Their surfaces have evolved to be both chemically heterogeneous (containing nonpolar, polar, and charged groups) as well as hydrophilic. While nonpolar groups are known to induce hydrophobicity, surface heterogeneity has been found to enhance hydrophilicity and the resistance of non-specific adsorption of biomolecules. Yet, the exact relationship between the nature of heterogeneous surfaces and hydrophilicity is not fully understood. Therefore, protein surfaces offer a promising avenue of study to help elucidate this relationship. While many characterizations of protein surface hydrophilicity sum the individual hydrophilicity of amino acids, surface hydration of heterogeneous surfaces has been found to be highly context-dependent and thus non-additive. In this dissertation, we utilize molecular dynamics simulations to characterize the atomic-level, context-dependent hydrophilicity of protein surfaces to better understand how the chemical composition and surface patterning of protein surfaces enhance hydrophilicity. We demonstrate that charged moieties play a much more significant role in enhancing protein surface hydrophilicity than polar atoms do. In fact, we also demonstrate that chemical composition alone is insufficient to distinguish between hydrophilic and hydrophobic protein surface regions. Furthermore, we use these findings to develop protein-inspired design rules for heterogeneous non-fouling surfaces. The work in this dissertation could be used to inform the design of superhydrophilic materials as well as to elucidate the relationship between surface heterogeneity and hydrophilicity. Additionally, it could be used to not only better understand biomolecular interactions through a context-dependent characterization of protein hydrophilicity, but also to inform protein engineering.
Zoom Information:
Meeting ID: 953 9917 7738
Passcode: [REDACTED]

Penn Engineering Entrepreneurship (EENT): Generative AI Panel
2025-04-11 13:00 - 14:15 | Amy Gutmann Hall, Auditorium, 3333 Chestnut Street, Philadelphia, PA, 19104, United States
A panel hosted by Penn Engineering Entrepreneurship (EENT) highlighting the importance of AI for the next generation of leaders and showcasing how Penn has been, and will continue to be, at the forefront of this evolving field.
Panelists include:
Elizabeth (Liz) Golden
CEO & Co-Founder @ Wavelet Medical
Mel Tang
Founding Operating Partner & CFO of Matter Venture Partners; Formerly CFO for Ring.com
Nat Trask
Associate Professor of Mechanical Engineering and Applied Mechanics; formerly Principal @ Sandia National Laboratories
Mark Weber
Director’s Fellow of MIT Media Lab & Investor; formerly Strategy and Ops Lead @ MIT-IBM Watson AI Lab
Brian Halak
Practice Professor of Engineering Entrepreneurship and Managing Partner @ Medical Excellence Capital

PICS Colloquium: Multiscale simulations of soft matter: from block copolymers to biomolecular condensates
2025-04-11 14:00 - 15:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
Polymers are ubiquitous in both synthetic and biological materials and underlie technologies as diverse as surfactants, adhesives, proteins and DNA. One of the defining features of all polymeric materials is that they are characterized by a wide range of length scales, often involving phenomena that span nanometers to microns. This hierarchy of length scales presents significant challenges for polymeric simulations. Existing models that can resolve phenomena at monomeric length scales (e.g. Atomistic simulations) are far too expensive to access mesoscopic processes, whereas coarse-grained models that can access these large length scales must necessarily omit chemical details, which often enter calculations through phenomenological parameters. As such, there is a significant need for new computational techniques that can accurately and efficiently predict how chemical changes at the smallest length scales will propagate up to the largest length scales in a material.

In this talk, I describe recent efforts by my group to develop multiscale simulations of polymeric materials that can link monomeric to mesoscopic length scales. In the first part of the talk, I describe a new “multi-representation” simulation method where particle-based and field-theoretic simulations are linked together into a unified framework. This approach can accelerate polymer simulations by several orders of magnitude and can rapidly equilibrate mesoscopic length scales while preserving monomer-scale details and dynamics. Notably, this multi-representation approach leverages the formal equivalence between particle and field-based models and involves no approximation. The utility of this approach is illustrated by examining the self-assembly of complex sphere phases in block copolymer melts. In the second part of the talk, I demonstrate how these multi-representation simulations can be extended to explain the phase separation of biomolecular condensates. Our approach can model proteins at amino acid resolution yet is efficient enough to access the long length and time scales that characterize condensate formation. Our approach can recapitulate recent experimental data on prion-like domains and can resolve how subtle modifications to the amino acid sequence can modulate their phase behavior. Finally, we examine the phase separation of chromatin and how post-translational modifications to histone proteins can lead to transitions between liquid-like and solid-like behavior. Taken together, this work demonstrates that multi-representation simulations that combine particle and field-theoretic simulations can unlock new insights into the multiscale physics that characterize synthetic and biological polymers.

AI Infrastructure: Foundations for Energy Efficiency and Scalability
2025-04-14 - 2025-04-15 | Jon M. Huntsman Hall, 3730 Walnut Street, Philadelphia, PA, 19104, United States
Click here for more details.
The workshop will explore the state of the art in sustainable computing and share recent research at the intersection of technology, economics, and policy. Through invited talks, panel discussions, and breakout sessions, participants will help shape a research agenda for the field. The workshop aims to produce a white paper and publish a research perspective in a major journal or media outlet.
Organization. The workshop is organized by Carbon Connect (https://carbonconnect.eco/), a team of researchers who have been awarded an Expedition in Computing by the National Science Foundation. It is co-hosted by Penn Engineering and Wharton School.
The workshop program agenda is available here.

Spring 2025 GRASP Seminar: Sebastian Scherer, Carnegie Mellon University, "Resilient Autonomy for Extreme and Uncertain Environments"
2025-04-14 15:00 - 16:00 | Amy Gutmann Hall, Room 306, 3317 Chestnut Street, Philadelphia, PA, 19104, United States
This will be an IN-PERSON event ONLY with in-person attendance in AGH 306.
ABSTRACT
Robots show great promise if they can get out of the lab into the field and go beyond a single-operator per robot paradigm. However, the unstructured nature of the real-world requires nuanced decision making of the robot. In this talk I will outline some of our approaches, progress, and results on multi-modal sensing, providing nuanced perception inputs, as well as navigation in difficult terrain, and extensions to multi-robot teams, and future directions of our research.

MEAM Seminar: "Microscopic Mayhem: Cancer in Three-Dimensions"
2025-04-15 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Critical to advancing immunotherapy and cell therapy in cancer is developing a deeper understanding pf the dynamics of immune cell-mediated cytotoxicity. The results from the multidisciplinary effort reported here include numerous measurements and movies of immune cell-mediated cytotoxicity with striking examples of serial killing, foraging, path-tracking, cytokine gradients at tumor margins, and killing dynamics, in some cases revealing peak apoptotic signatures just minutes after T Cell engagement.
In vitro studies of immune cell killing are traditionally performed using time-lapse imaging and biochemical assays, but these methods are often limited by spatial and temporal resolution, throughput, and the ability to extract the dynamics of cellular interactions. This study integrates high-resolution and high-speed laser scanning confocal microscopy with artificial intelligence (AI), and machine learning (ML) approaches to provide a high-resolution data-driven analysis of immune cell killing dynamics in vitro.
We have engineered a perfusion-enabled 3D culture system integrated microscopy to assess cellular dynamics for extended periods of time. Perfusion culture maintains the interstitial flow of liquid culture media, clearing the microenvironment of toxic metabolites and reactive oxygen species. This platform uses a Liquid-Like Solids (LLS) to mimic the transport dynamics of a capillary bed. Integrated microscopy allows in situ quantification of spatiotemporal cytokine concentrations, immune cell tracking, immune cell killing dynamics, and invasion dynamics.

What Does AI Tell Us About What It Means to Be Human
2025-04-15 12:00 - 13:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Please RSVP here.
We are living in an age where capabilities previously thought to be hallmarks of human intelligence are increasingly being replicated, or at least mimicked, in artificial systems.
Tasks involving language, reasoning, perception and even interaction with the real world have all been demonstrated in silico. What does this fact tell us about the nature of human intelligence? In what ways is human intelligence similar to or different from machine intelligence? How have philosophers and other thinkers conceptualized issues related to thought and agency over the centuries and which of these ideas are relevant to today’s context?
Join us for a panel discussion where faculty members from Philosophy, Cognitive Science and Computer Science will exchange perspectives on these issues.
Camillo Jose Taylor (Moderator)
Raymond S. Markowitz President’s Distinguished Professor
Computer and Information Science
Chris Callison Burch
Professor of Computer and Information Science
Program Director of Online Master of Science in Engineering in Artificial Intelligence
Charles Yang
Professor of Linguistics and Computer Science
Director, Program in Cognitive Science
Carlos Gray Santana
Associate Professor of Philosophy
