Penn Engineering Events

Energy Week: "Climate 101: Climate Change and Artificial Intelligence"
2025-02-13 11:00 - 12:00 (America/New_York) | Annenberg School for Communication, Room 300, 3620 Walnut Street, Philadelphia, PA, 19104, United States
The Environmental Innovations Initiative’s Climate 101 series demystifies climate change and its link with a diversity of disciplines. Experts from across Penn share how they think about climate through the lens of their research and teaching, helping to raise our climate literacy and prepare us to tackle one of the planet’s greatest challenges: the climate crisis.
Join us for February’s Climate 101 event with Benjamin Lee, Professor of Electrical and Systems Engineering and Computer and Information Science, where he will talk about Climate Change and Artificial Intelligence. This event takes place during Energy Week at Penn.
All events are Hybrid, and are held in Annenberg School for Communication, Room 300.
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IDEAS/STAT Optimization Seminar: "Foundations of Deep Learning: Optimization and Representation Learning"
2025-02-13 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Deep learning’s success stems from the ability of neural networks to automatically discover meaningful representations from raw data. In this talk, I will describe some recent insights into how optimization enables this learning process. First, I will show how optimization algorithms exhibit surprisingly rich dynamics when training neural networks, and how these complex dynamics are actually crucial to their success – enabling them to find solutions that generalize well, navigate challenging loss landscapes, and efficiently adapt to local curvature. I will then explore how optimization enables neural networks to adapt to low-dimensional structure in the data, how the geometry of the loss landscape shapes the difficulty of feature learning, and how these ideas extend to in-context learning in transformers.

Zoom link: https://upenn.zoom.us/j/93151261686 (Meeting ID: 931 5126 1686)

BE Seminar - "Engineered bacteria for cancer therapy" (Tal Danino, Columbia University)
2025-02-13 15:30 - 16:30 | Berger Auditorium (Room 13), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
Synthetic biology is driving a new era of medicine through the genetic programming of living cells. One particular focus has been the engineering of bacteria as therapeutic delivery systems for cancer, where microbes colonize and release payloads within tumors. Here, genetic circuits are designed to enhance spatial and temporal control of microbial behaviors for improved safety and efficacy. In this talk, I will highlight our recent efforts to develop bacterial biosensors, encapsulation strategies, and effective delivery systems for payloads ranging from cytotoxic to immunomodulatory agents. I will also discuss the progression from bacteria as single agents toward their engineered interactions with other programmable medicines using synthetic biology.

CIS Seminar: "Bridging Informal and Formal AI Reasoning"
2025-02-13 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Neural language models have opened a fascinating, flexible platform for reasoning in mathematics, programming, and beyond. This talk will explore the intersection of these models and the rigor of formal reasoning. First, I discuss my work on building foundation models for mathematics and using language to guide the search for formally verified proofs. Then, I present our research on inference-time reasoning, which uncovers new scaling laws for reasoning based on optimally combining generators and verifiers. Finally, I discuss the challenge of building AI systems that improve their reasoning capabilities over time by learning from both formal and informal feedback. I close by discussing opportunities and future directions in mathematics, programming, agents, and beyond.

PICS Colloquium: Powering decarbonization with modeling and optimization of renewables in the multi-scale atmosphere
2025-02-14 14:00 - 15:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
To meet net-zero carbon emissions targets by mid-century, up to a -fold increase in wind power capacity is required. Acceleration to this rate requires urgent improvements to efficiency and reliability of installed wind farms, as well as cost reductions for future offshore farms. To expand energy production, wind turbines are rapidly increasing in size, wind farms are proliferating to new locations and are increasing in size and siting density, and novel wind farm design and control methods are increasingly deployed. But current engineering models driving wind power design and control rely on idealized theory that neglects key aspects of the rotor aerodynamics and the turbulent atmospheric boundary layer, which are increasingly important for larger turbines and farms. We revisit the first-principles of mass, momentum, and energy conservation to develop a Unified Momentum theory for rotors across operating regimes, accounting for arbitrary misalignments between rotor and inflow and thrust coefficients. The model is validated against large eddy simulations and generalizes and replaces both classical momentum theory and the Betz limit. Going from the scale of a turbine to a farm, wake losses
can reduce farm energy by 30%, a significant loss that negatively impacts economics and is increasing given wind power expansion. Using large eddy simulations of wind turbines operating in a range of atmospheric conditions, we systematically uncover the significant roles of Coriolis effects and stability on wake recovery, trajectory, and morphology. A new fast-running wind farm model that accounts for the coupled rotor operational and atmospheric effects on wakes is developed. The wind farm model is
leveraged for applications including collective control and for control co-design, applied in both simulations and utility-scale field experiments. Collective control can increase the energy generation of wind farms through software modifications, without additional turbines or hardware. Going from the scale of a wind farm to the energy system, we leverage an integrated climate and energy system modeling framework to design minimum-cost decarbonized energy systems. Energy system optimization with high-resolution atmospheric predictions reveals opportunities for complementarity between spatiotemporal variations in wind and solar supply to align with energy demand and to lower the cost of decarbonized energy systems.

MEAM Seminar: "Miniaturized Robots and Probes for Precision Health"
2025-02-18 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Designing miniaturized robots and bioelectronic devices will enable access throughout the entire human body, leading to novel procedures at the cellular level and offering localized diagnosis and treatment with unprecedented precision and efficiency. However, the soft, complex, and multi-dimensional nature of biological systems poses significant challenges for mechanical design, manufacturing, materials engineering, and functional integration in miniaturized devices. My research focuses on developing miniaturized robots and probes that seamlessly interface with and precisely interact with biological systems. By leveraging precision 3D nanomanufacturing, novel biomaterials, and emerging biophysical principles, we aim to engineer functional microrobots and bioelectronic devices to deepen physiological understanding, enable predictive diagnostics, and advance precision therapeutics. In this talk, I will first provide an overview of the challenges and design rationales behind micro/nanorobotics and mechanically compliant bioelectronics, followed by highlights of a few recent advances from my lab, inspired by biological systems or driven by biomedical problems. These include 3D-printed microrobots capable of magnetic actuation, imaging, and magnetothermal hyperthermia; hyperelastic microrobots with extreme deformability for navigating complex physiological constraints; adaptive, skin-conformal soft microelectrode arrays for high-fidelity electromyography recordings and robotic control; and microfabricated neural probe arrays for brain-wide neural circuit interrogation. These miniaturized robots and probes will enable unprecedented interactions across biological, physical, and virtual domains, driving the next generation of precision healthcare.

ESE Spring Seminar - "AI as a Lens: Expanding Vision for Scientific Discovery"
2025-02-18 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Conventional approaches to scientific discovery often prioritize building larger sensors, gathering more data, and scaling up computational power. In this talk, I will present a complementary perspective: extracting insights hidden in the data we already have. The key lies in using AI not as a black-box predictor, but as a tool for interpreting data through its underlying physical process.
I will demonstrate how AI, when integrated with the physics of light propagation, can serve as a computational lens to overcome fundamental limitations in fields ranging from biomedicine to astrophysics. Specifically, I will showcase two compelling applications: non-invasive imaging through scattering biological tissues, and detecting faint exoplanets against the overwhelming brightness of their host stars.
These methods represent a departure from traditional learning-based approaches that rely on fitting models to training labels and hoping for generalization. Instead, with physics-informed strategies that decode how light propagates, we can transform raw measurements into scientifically meaningful insights—without requiring costly hardware upgrades or human-annotated datasets. Finally, I will outline future directions for combining AI with physical principles, enabling us to unlock more phenomena once considered hidden and accelerating discoveries in healthcare, astronomy, and beyond.

CIS Seminar: "Efficient Probabilistically Checkable Proofs from High-Dimensional Expanders"
2025-02-18 15:30 - 16:30 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
The PCP theorem, proved in the 90’s, shows how to encode a proof for any theorem into a format where the theorem’s correctness can be verified by making only a constant number of queries to the proof. This result is a significant milestone in computer science and has important implications for approximation algorithms, cryptography, and cloud computing.
In this talk, I will cover some exciting progress on constructing efficient PCPs. My work gives a systematic way to construct PCPs, improving upon the state-of-the-art PCP constructions from nearly 20 years ago. This implies that many well-known approximation algorithms are nearly-optimal under well-believed complexity-theoretic conjectures. In the process, we also solve various conjectures in property testing and distributed computing. These advances are facilitated by the newly-emerging theory of high-dimensional expansion and draw upon connections with various areas of theoretical computer science and mathematics.

MEAM Seminar: "Real-Time Safe and Energy-Efficient UAV Motion Planning in Windy Urban Environments"
2025-02-19 10:15 - 11:15 | Towne 319, 220 S. 33rd Street, Philadelphia, 19104, United States
Recent advancements in hardware and software are bringing autonomous aerial vehicles closer than ever to finally delivering on futuristic visions of flying cars and package delivery drones. However, the safe deployment of autonomous aircraft at scale in urban environments poses significant challenges, one of which being uncertainties contributed by complex spatial and temporal winds. Clusters of buildings generate dangerous high speed wind flow patterns which increase both the costs and risks for aerial operations. Existing approaches for modeling and planning for urban winds rely on either distributed sensors, extensive exploration, or accurate global representations of the environment and expensive offline calculations.
In this talk, we first introduce a novel approach to predicting wind flow fields in real time, without GPS or prior knowledge of the environment, and only using on-board sensing with minimal compute requirements. We accomplish this by strategically reducing the problem to predicting the wind flow field on a local robot-centered domain, and by leveraging the predictability of the wind flow field induced by conservation of mass and momentum constraints from nearby topography. Using vast amounts of simulated winds through procedurally-generated urban environments, we train deep neural networks to synthesize navigational LiDAR scans with sparse in-situ measurements of the wind, achieving real-time prediction rates and surprising generalizability. In the latter half of the talk, we demonstrate how the local wind prediction can be incorporated into a receding horizon optimal control architecture to improve the accuracy of trajectory forecasting and cost functions associated with energy consumption. The result is a system that is capable of perceiving surrounding winds in real time using readily-available on-board sensors, and then adjusting the planned route accordingly to avoid environmental hazards and maximize energy efficiency along the way.

ASSET Seminar: "Demystifying the Inner Workings of Language Models"
2025-02-19 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract:
Large language models (LLMs) power a rapidly-growing and increasingly impactful suite of AI technologies. However, due to their scale and complexity, we lack a fundamental scientific understanding of much of LLMs’ behavior, even when they are open source. The “black-box” nature of LMs not only complicates model debugging and evaluation, but also limits trust and usability. In this talk, I will describe how my research on interpretability (i.e., understanding models’ inner workings) has answered key scientific questions about how models operate. I will then demonstrate how deeper insights into LLMs’ behavior enable both 1) targeted performance improvements and 2) the production of transparent, trustworthy explanations for human users.
Zoom Link (if unable to attend in-person): https://upenn.zoom.us/j/99113576305

Spring 2025 GRASP SFI: Qinghua Liu, Microsoft Research, “When Is Partially Observable Reinforcement Learning Not Scary?”
2025-02-19 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
Partial observability is ubiquitous in Reinforcement Learning (RL) applications, where agents must make sequential decisions despite lacking complete information about the latent states of the controlled system. Partially observable RL is notoriously challenging in theory—well-known information-theoretic results show that learning partially observable Markov decision processes (POMDPs) requires an exponential number of samples in the worst case. However, this does not rule out the existence of interesting subclasses of POMDPs that encompass a diverse set of practical applications while remaining tractable.
In this talk, we identify a rich family of tractable POMDPs, which we call weakly revealing POMDPs. This family excludes pathological cases where observations provide so little information that learning becomes infeasible. We prove that for weakly revealing POMDPs, a simple algorithm combining optimism and Maximum Likelihood Estimation (MLE) is sufficient to guarantee polynomial sample complexity. Finally, we discuss the practical implications of this theory, including strategies for collecting samples in partially observable tasks and the limitations of purely model-free algorithms.

CBE & BE Seminar: "Targeting the Brain and Behavior to Probe the Dynamics of Aging" (Claire Bedbrook, Stanford University)
2025-02-19 15:30 - 16:30 | Wu & Chen Auditorium
Bio & Abstract:
Claire Bedbrook is an engineer and neuroscientist working to extend lifespan by modulating the brain. Claire was trained in chemical engineering at the University of California, Berkeley. She completed her Ph.D. in Bioengineering at the California Institute of Technology with Professor Frances Arnold and Professor Viviana Gradinaru, where she engineered molecular tools for controlling and recording the brain using machine-learning-based protein engineering. Claire is now a Helen Hay Whitney Fellow and Wu Tsai Neurosciences Institute Interdisciplinary Postdoctoral Scholar in the labs of Professor Karl Deisseroth and Professor Anne Brunet at Stanford University, where she is pioneering approaches to understand and engineer the process of aging. Studying intact systems longitudinally over life in high dimensions is a fundamental unmet need in biology. If achieved, this would uncover key principles of aging, the leading driver of most chronic diseases. However, given the long timescale of vertebrate aging, recording this continuous, lifelong transformation is highly challenging. Claire’s research seeks to overcome this challenge by leveraging the African killifish, a genetically tractable vertebrate model with a naturally compressed lifespan that recapitulates key aging features. Using the short-lived killifish, she engineered a platform to track every moment of a vertebrate animal’s life, capturing multidimensional behavioral dynamics across timescales from milliseconds to entire lifespans. This system offers an unprecedented, unbiased view into the progression of aging.

ESE Spring Seminar - "Quantum Simulation of Electronic Materials with a Superconducting Qubit Array"
2025-02-20 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Arrays of coupled superconducting qubits are a compelling platform for analog quantum simulations of solid-state matter and many-body physics. These devices natively emulate the Bose-Hubbard model while offering a high degree of control, fast operation rates, and site-resolved readout. We discuss recent experiments using a 4-by-4 array of transmon qubits. By adopting a parametric coupling scheme, we emulate a 2D material in an adjustable electromagnetic field and demonstrate the Aharonov-Bohm effect, Faraday’s law of induction, and the Hall effect. We then emulate a material with flat bands, and we study the interplay between band structure and disorder-induced localization and delocalization. We conclude by discussing a near-term path towards utility-scale analog quantum simulators based on superconducting qubit arrays.

IDEAS/STAT Optimization Seminar: "Theoretical foundations for multi-agent learning"
2025-02-20 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
As learning algorithms become increasingly capable of acting autonomously, it is important to better understand the behavior that results from their interactions. For example, a pervasive challenge in multi-agent learning settings, which spans both theory and practice and dates back decades, has been the failure of convergence for iterative algorithms such as gradient descent. Accordingly, a longstanding central question with broad relevance is: how quickly can we compute solution concepts, i.e., equilibria, in multi-agent settings?

I will discuss results which address this question at a variety of levels, starting from foundational settings involving normal-form games and building up to complex problems such as multi-agent reinforcement learning which more aptly model realistic situations. First, I will present a result establishing a near-optimal convergence rate for a simple online learning algorithm in normal-form games, resolving a decade-long line of work which gave suboptimal bounds. I will then discuss a new algorithm for minimizing swap regret exponentially faster than previous approaches. Our algorithm allows us to answer several open questions, such as by establishing the first PTAS for correlated equilibria in extensive-form games.

Beyond contending with agents’ differing incentives, the increasing use of machine learning algorithms presents other challenges, such as the proliferation of AI-generated content. In the latter part of the talk, I will discuss an approach to detect such content via watermarking. Our watermarking scheme is the first to embed a watermark in a language model’s output in a way which only leads to negligible changes in the distribution of the output but which is robust to adversarial edits.

Zoom link: https://upenn.zoom.us/j/98090439937 (Meeting ID: 980 9043 9937)

CIS Seminar: "Learning Theoretic Foundations for Modern (Data) Science"
2025-02-20 15:30 - 16:30 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
In this talk, I will explain how fundamental problems in computational learning theory are at the heart of modern problems in machine learning and scientific applications and how algorithmic insights in mathematically tractable models can inspire new solutions in a wide variety of domains.
I will explore two directions. First, I will explore algorithmic foundations for model stealing of language models. Model stealing, where a learner tries to recover an unknown model through query access, is a critical problem in machine learning. Here, I will aim to build a theoretical foundation for designing model stealing algorithms. Second, I will introduce Hamiltonian learning, a central computational task towards understanding and benchmarking quantum systems. I will highlight how the lens of learning theory plays a key role in identifying and circumventing previous barriers and allows us to give efficient algorithms in settings that were previously conjectured to be intractable.

GRASP Industry Talk: Honda Research Institute, "Working with Imperfect Prediction on Autonomous Vehicles"
2025-02-21 10:45 - 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
State of the art prediction can fail catastrophically when operating outside of the training distribution. We show methods that can be used to reduce these failures. We then outline how delayed decision making can be used to safely plan within the constraints of these block box prediction methods.

PICS Colloquium: Unraveling Internal Friction in a Coarse-Grained Protein Model
2025-02-21 14:00 - 15:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
Understanding the dynamic behavior of complex biomolecules requires simplified models that not only make computations feasible but also reveal fundamental mechanisms. Coarse-graining (CG) achieves this by grouping atoms into beads, whose stochastic dynamics can be derived using the Mori-Zwanzig formalism, capturing both reversible and irreversible interactions. In liquid, the dissipative bead-bead interactions have so far been restricted to hydrodynamic couplings. However, friction does not only arises from the solvent but notably, from the internal degrees of freedom missing in the CG beads. This leads to an additional “internal friction” whose relevance is studied in this contribution. By comparing with all-atom molecular dynamics (MD), we neatly show that in order to accurately reproduce the dynamics of a globular protein in water using a coarse-grained (CG) model, not only a precise determination of elastic couplings and the Stokesian self-friction of each bead is required. Critically, the inclusion of internal friction between beads is also necessary for a faithful representation of protein dynamics. We propose to optimize the parameters of the CG model through a self-averaging method that integrates the CG dynamics with an evolution equation for the CG parameters. This approach ensures that selected quantities, such as the radial distribution function and the time correlation of bead velocities, match the corresponding MD values.

MEAM Ph.D. Thesis Defense: "Leveraging Impedance-Related Properties for Free Self-Sensing in Actuators for Compact Robots"
2025-02-24 13:00 - 14:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Robotic systems, particularly at small scales, require efficient actuation and sensing solutions that maintain compactness. We are interested in systems where sensing and actuation are seamlessly integrated, specifically using impedance-related properties—such as electrical resistance, induced electromotive force (emf), and inductance— for free self-sensing in actuators without additional sensors. We explore three main example applications: (1) Resistance-based sensing in I-cord knitted shape memory alloy (SMA) actuators enables real-time strain estimation, allowing direct feedback for robotic motion, (2) Induced emf sensing in custom linear solenoid actuators provides contact and velocity feedback, demonstrated in applications such as bistable origami grippers and artificial facial muscle devices for facial reanimation surgery, and (3) Inductance-based sensing further enables position and flow monitoring in active valve systems for bidirectional swimming of underwater swimmer robots, offering a self-contained alternative to external instrumentation. This thesis presents the theoretical foundations, experimental validation, and real-world robotic demonstrations of these sensing approaches. By embedding sensing directly into actuators, this work advances the development of compact and robust robotic systems with potential applications in origami-inspired soft robots, bio-inspired robots, and medical implant devices.

Spring 2025 GRASP Seminar: Sarah Keren, Technion - Israel Institute of Technology, “Encouraging Autonomous Agents to Behave Nicely”
2025-02-24 15:00 - 16:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This will be an in-person event only with attendance in Levine 307. This seminar will NOT be recorded.
ABSTRACT
Autonomous AI agents are deployed in increasingly complex and uncertain environments where they must account for the presence of other agents while trying to achieve their own objectives. Moreover, such agents may require assistance from other agents to efficiently accomplish their assigned task or even be able to complete it at all.
This work aims to develop theoretical foundations for agents to learn and adopt cooperative behaviors by introducing Value of Assistance (VOA) – a novel measure of the potential performance improvement achieved by assistive actions. The benefit of using VOA will be demonstrated in multi-robot navigation and collaborative manipulation settings. In addition, adaptations of VOA to other stochastic and partially observable multi-agent settings will be discussed, including our ongoing work on AI-enabled energy systems.

MEAM Seminar: "Neural Operator for Scientific Computing"
2025-02-25 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Accurate simulations of physical phenomena governed by partial differential equations (PDEs) are foundational to scientific computing. While traditional numerical methods have proven effective, they remain computationally intensive, particularly for complex, large-scale systems. This talk introduces the neural operator, a machine learning framework that approximates solution operators in infinite-dimensional spaces, enabling efficient and scalable PDE simulations across varying resolutions and scales. Beginning with the Fourier Neural Operator (FNO) architecture, we explore recent advances in self-supervised learning using scale-consistent learning techniques and modeling complex geometries using adaptive mesh methods. We demonstrate the framework’s practical impact through real-world applications in weather prediction, carbon capture, and plasma dynamics. The talk concludes by examining how foundational tools in computational mathematics can advance efficient architecture design, highlighting the expanding intersection between machine learning, computational science, and engineering.

Spring 2025 GRASP Seminar: Shenlong Wang, University of Illinois Urbana-Champaign, "Interactive Images, Videos, and Worlds"
2025-02-25 10:30 - 11:30 | Levine 512
This will be a hybrid event with in-person attendance in Levine 512 and virtual attendance on Zoom.
ABSTRACT
Our group’s goal is to build a world simulator from visual observations that can answer “what-if” questions. In pursuit of this goal, we develop various methods for modeling the world from images and harnessing physical simulation and generative models to create immersive new experiences. In today’s talk, we will focus on creating realistic and immersive experiences that allow users and agents to interact with images, videos, and 3D worlds captured from the real world. We will cover applications in content creation, autonomy, and agriculture. Finally, I will provide a brief personal outlook on open research topics that make all images and videos interactive.

ESE Spring Seminar - "Towards quantum interconnects: entangling microwave and optical photonic qubits"
2025-02-25 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Modern computing and communication technologies, such as supercomputers and the internet, are based on optically-linked networks of information processors operating at microwave frequencies. An analogous architecture has been proposed for quantum networks using optical photons to distribute entanglement between remote superconducting quantum processors. Here I will discuss our recent demonstration of a chip-scale source of entangled optical and microwave photonic qubits – an essential milestone towards realizing such an architecture. Our device platform integrates a piezo-optomechanical transducer with a superconducting resonator that is robust under optical illumination. We drive a photon-pair generation process and employ a dual-rail encoding to prepare entangled states of microwave and optical photons. This entanglement source can directly interface telecom wavelength time-bin qubits and GHz frequency superconducting qubits; two well-established platforms for quantum communication and computation, respectively.

ESE Guest Seminar - "On Team Decision Problems with Nonclassical Information Structures"
2025-02-25 13:00 - 14:00 | Greenberg Lounge (Room 114), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
Team theory is a mathematical formalism for decentralized stochastic control problems in which a “team,” consisting of a number of members, cooperates to achieve a common objective. It was developed to provide a rigorous mathematical framework of cooperating members in which all members have the same objective yet different information. In static team problems, the information received by the team members is not affected by the decisions of other team members, while in dynamic team problems, the information of at least one team member is affected by the decisions of other team members. If there is a prescribed order in which team members make decisions, then such a problem is called a sequential team problem. The information structures in sequential team decision problems designate who knows what about the status of the team and are classified as classical, partially nested, and non-classical. In classical information structures, all team members receive the same information and have perfect recall. In partially nested information structures, some team members have a nonempty intersection of their information structures while they have perfect recall. Any information structure that is not classical or partially nested is called nonclassical. In this talk, I consider sequential dynamic team decision problems with nonclassical information structures. First, I will address the problem from the point of view of a “manager” who seeks to derive the optimal strategy for the team in a centralized process. I provide structural results that yield an information state for the team, which does not depend on the control strategy, and thus, it can lead to a dynamic programming decomposition where the optimization problem is over the space of the team’s decisions. I will then provide structural results for each team member that yield an information state that does not depend on their control strategy, and thus, it can lead to a dynamic programming decomposition where the optimization problem for each team member is over the space of their decisions. Finally, I will show that the solution of each team member is the same as the one derived by the manager. Therefore, each team member can derive their optimal strategy, which is also optimal for the team, without the manager’s intervention.

Spring 2025 GRASP Seminar: Erdem Bıyık, University of Southern California, “Robot Learning with Minimal Human Feedback”
2025-02-25 14:00 - 15:00 | 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
The lack of large robotics datasets is arguably the most important obstacle in front of robot learning. While large pretrained models and algorithms like reinforcement learning from human feedback led to breakthroughs in other domains like language and vision, robotics has not experienced such a significant influence due to the excessive cost of collecting large datasets. In this talk, I will discuss techniques that enable us to train robots from very little human feedback, as little as one demonstration or one language instruction, or their natural eye gaze. I will dive into reinforcement learning from human feedback, and propose an alternative type of human feedback based on language corrections to improve data-efficiency. I will finalize my talk by presenting how existing large pretrained vision-language models can be used to generate direct supervision for robot learning.

CIS Seminar: " Specializing LLMs for Reliability"
2025-02-25 15:30 - 16:30 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Large language models (LLMs) have advanced the frontiers of AI reasoning: they can synthesize information from multiple sources, derive new conclusions, and explain those conclusions to their users. However, LLMs do not do this reliably. They hallucinate facts, convincingly state incorrect deductions, and exhibit logical fallacies like confirmation bias. In this talk, I will describe my lab’s work on making LLM systems reliable by introspecting their behavior. First, I will demonstrate that better understanding of LLMs helps us train them to be more reliable reasoners. Our work shows that model interpretation techniques can advance training methodology and dataset curation for reasoning models. Second, I will argue that automating fine-grained evaluation of LLM output provides a level of understanding necessary for further progress. I will describe the ingredients of effective automated evaluators and a state-of-the-art factuality evaluation system, MiniCheck, showing that analyzing the nature of hallucinations can help reduce them. Finally, I will describe how deeper understanding of LLMs will let us tackle their most fundamental limitations, such as their inconsistency when given different inputs. I will propose how these pieces might soon be combined to form reliable AI systems.

Spring 2025 GRASP SFI: Harshil Parekh, BotBuilt, “From GRASP to BotBuilt: Using Robotics and AI to Revolutionize Construction”
2025-02-26 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
Construction remains one of the least automated industries, struggling with labor shortages, inefficiencies, and rising costs. At BotBuilt, we are revolutionizing the way homes are built by leveraging robotics and AI to automate framing, making construction faster, safer, and easier.
In this talk, I will discuss the key challenges we are solving at BotBuilt, from automating complex construction tasks to integrating robotics seamlessly into existing workflows. I will then share how my experiences at the GRASP Lab have equipped me to tackle these problems. By bridging research with real-world applications, I’ll highlight how the skills I developed at Penn directly contribute to advancing robotics and AI in construction.

CBE Seminar: "Prioritization of Research, Development, and Deployment Pathways for a Circular Bioeconomy" (Jeremy Guest, UIUC)
2025-02-26 15:30 - 16:30 | Wu & Chen Auditorium
Abstract:

Societies have prospered using a linear “take-make-use-dispose” approach, extracting natural resources to make products, using them, and ultimately discarding them or their residues. This unsustainable approach has exploited natural resources at a rate that has caused excessive pollution and loss of biodiversity, and is leading to a global climate crisis. In response to this challenge, industries are seeking technological solutions that will meet societal needs in a way that is financially viable while supporting the pursuit of broader goals for sustainability (e.g., resource circularity, carbon neutrality, equity). This transition has become a catalyst for research and development, but a critical challenge to achieving rapid and transformative innovations has been the expansive landscape of technology development pathways and the lack of a transparent and consistent framework to target investment.
This presentation will focus on the prioritization of research, development, and deployment (RD&D) pathways for the conversion of renewable resources into biofuels and bio-based products. Using a structured methodology – Quantitative Sustainable Design (QSD) – we integrate process design, simulation, techno-economic analysis (TEA), and life cycle assessment (LCA) under uncertainty to elucidate drivers of system sustainability, identify performance gaps, evaluate tradeoffs and optimize across alternatives, and assess context-specific implications of technology advancement and deployment. Leveraging examples from the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), we will focus on the conversion of perennial grasses (Miscanthus, switchgrass, sugarcane) and agricultural residues to biofuels (sustainable aviation fuel [SAF]) and bioproducts (e.g., 3-hydroxypropionic acid [3-HP], triacetic acid lactone [TAL]), including the exploration of opportunities for the integration of decarbonized processes with existing infrastructure (e.g., co-processing of SAF). In addition to demonstrating specific potential pathways to advance the circular bioeconomy, this presentation will make the process of QSD more accessible to researchers and technology developers across disciplines, including for the broader prioritization of RD&D for novel technologies.

CBE Seminar: "Machine-learning-assisted Atomistic Modeling and Design of Complex Ionic Conductors for Next-Generation Energy Storage" (KyuJung Jun, MIT)
2025-02-27 10:15 - 11:15 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
Fast solid-state Li-ion conductors are a crucial class of materials with the potential to enable all-solid-state batteries, offering enhanced safety and energy density. However, these materials remain rare, and progress in developing novel solid electrolytes has been hindered by a lack of clear descriptors for superionic conductivity and a limited understanding of ion transport mechanisms across diverse conductors, from inorganic crystals to polymers. Building on recent advances in computing power,machine-learning algorithms, material representations, and analysis tools, my research directly addresses these challenges, guiding experimental efforts to discover new superionic conductors. In this talk, I will present three of my representative efforts in this direction. First, I will discuss how identifying structural features of superionic conductors enabled high-throughput screening, leading to the discovery of over 20 novel inorganic superionic conductors. Second, I will share how my research has resolved a long-standing debate on the lithium transport mechanism—known as the ‘paddlewheel effect’ in plastic crystal phases—by providing temporally and spatially resolved correlation insights. Third, I will introduce new algorithms that I have developed to decompose Onsager transport coefficients, allowing us to identify and quantify the contributions of various transport mechanisms in lithium polymer electrolytes, with potential applications to inform mechanistic understanding in any complex ion-conducting medium. Bringing these efforts together, I will discuss how these correlation analysis tools, machine learning interatomic potentials, and generative models represent a breakthrough in achieving both high accuracy and computational efficiency, opening up unprecedented opportunities to model and understand complex dynamic phenomena that were previously inaccessible with traditional ab initio calculations or classical models.

IDEAS/STAT Optimization Seminar: "ML for an Interactive World: From Learning to Unlearning"
2025-02-27 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
The remarkable recent success of Machine Learning (ML) is driven by our ability to develop and deploy interactive models that can solve complicated tasks by understanding and adapting to the ever-changing state of the world. However, the development of such models demands significant data and computing resources. Moreover, as these models increasingly interact with humans, new post-deployment challenges emerge, including privacy concerns, data integrity, and the potential for model misuse. Addressing these issues necessitates innovative algorithmic solutions.
Reinforcement Learning (RL) is the preferred method for training interactive models. In the first part of my talk, I will discuss my work on Hybrid RL, which has led to the development of the first general-purpose, computationally efficient, and theoretically rigorous algorithms for RL. Our method learns effective policies by integrating the trial-and-error processes of RL with pre-collected interaction data logs, demonstrating strong performance in practical applications.
In the second half of my talk, I will discuss my work on the foundations of machine unlearning, a newly emerging field with significant practical applications. Machine unlearning involves updating trained ML models to exclude specific data samples from the trained model upon their deletion request, without retraining from scratch. I will delve into how machine unlearning presents a more viable alternative to traditional methods like differential privacy for data deletion, thus providing a more practical solution for ensuring data privacy post-deployment.

Zoom link: https://upenn.zoom.us/j/94999851890 Meeting ID: 949 9985 1890

BE Seminar - "Scaffold-Modulated Healing in Irradiated Bone" (Katie Hixon, Dartmouth Engineering)
2025-02-27 15:30 - 16:30 | Berger Auditorium (Room 13), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
Bone is the third most common site for cancer metastasis, affecting ~66% of patients with common cancers—breast, lung, prostate, renal, thyroid—incurring skeletal events in up to 400,000 people in the US/year. Metastatic bone disease (MBD) results in weakened bone, leading to refractory pain and pathological fracture that increase disease state morbidity. Despite bone tissue’s dynamic nature and robust capability to remodel and regenerate following injury, MBD-affected bone does not heal according to normal principles, with average fracture healing rates for pathological fractures reported to be 8% at six months following injury. Further, the use of bone grafts for treatment offers additional complications including infection potential and donor site morbidity, where an improved treatment option is necessary. To combat this, cryogel scaffolds have been identified as ideal constructs to support bone formation following traumatic injury/disease. Preliminary work by our group has shown that cryogels do not lose their advantageous physical properties following radiation therapy (RT). Therefore, our overall premise is that MBD patients require alternative treatment options to i) improve bone formation and ii) accelerate healing. The central hypothesis of this study is that chitosan/gelatin cryogels will induce osseointegration and bone formation in MBD patients, while modulating tissue damage caused by RT. This hypothesis is based on current literature reviews, as well as previously published work by our group demonstrating cryogels and animal models for studying and improving bone formation. Our approach for creating a cost-effective, time-sensitive, and biologically improved targeted treatment option consists of optimized cryogel fabrication for MBD patients.
