ESE Ph.D. Thesis Defense: "Training Adaptive and Sample-Efficient Autonomous Agents"
2025-04-16 14:30 (America/New_York) | Room 512, Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
AI agents, both in the physical and digital worlds, should generalize from their training data to three increasingly difficult levels of deployment: training tasks and environments, training tasks and environments with variations, and completely new tasks and environments. Moreover, like humans, they are expected to learn from as little training data as possible, especially in the physical world, and adapt with as little adaptation data as possible. This thesis is founded around and describes work that tackles these levels of generalization with an additional emphasis on sample-efficiency.
We start with a focus on training data efficiency and the simplest level of generalization from training data to training tasks and environments (a.k.a., level 1). AI agents, especially in the physical world, are usually trained via one of two paradigms: imitation learning or reinforcement learning. First, we propose a plug-in model class to improve behavior cloning with any deep neural network (DNN) backbone that is particularly effective in the low-data regime. Second, we leverage our proposed model class to guarantee the conformance of any DNN world model to physics and medical constraints, in a highly data-efficient manner. Third, we improve the sample-efficiency of reinforcement learning agents, by an order of magnitude, by leveraging expert interventions.
Next, we tackle the challenge of generalization to training tasks and environments with variations as well as completely new tasks and environments (a.k.a., levels 2 and 3), keeping both training and adaptation sample-efficiency in mind. Here, we pre-train REGENT, a retrieval-augmented generalist agent that can adapt to unseen robotics and game-playing environments via in-context learning, without any finetuning. REGENT outperforms state-of-the-art generalist agents after pre-training on an order-of-magnitude fewer datapoints and with up to 3x fewer parameters. We also propose a strategy, inspired by adaptive control, to improve the robustness of the image encoder of REGENT, an essential component for handling environment variations.
Finally, we bring REGENT to the real world by converting a Vision Language Action model (VLA) to a REGENTic VLA capable of generalizing to unseen objects and tasks through retrieval-augmentation and in-context learning. Further task-specific REGENTic-tuning substantially improves reliability, surpassing a VLA directly fine-tuned on the same data.
We conclude by outlining future directions to expand the envelope of tasks and environments to which a general AI agent can adapt.

Spring 2025 GRASP SFI: Anastasia Bizyaeva, Cornell University, “Nonlinear dynamics of social decision-making and belief formation”
2025-04-16 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
Motivated by the study of complex social behavior and by the bottom-up design of collaborative autonomy, we present and analyze a nonlinear dynamic model of social belief formation. In our framework, belief updates of individuals are informed by the interplay of external factors, i.e. social network effects, and internal factors, i.e. individual biases and interdependencies between different belief dimensions. The model accounts for networked relationships between an individual’s internal belief representations and nonlinear processing of social information. Our analysis sheds light on mechanistic principles that enable groups to make fast and flexible collective decisions, overcoming deadlocks to form strong beliefs when it is urgent to do so. We show how the structure of a social network and of the underlying belief system graph shapes emergent social decisions in the group, and how effects of local biases and inputs can be amplified through feedback to enable tunably sensitive informed collective decisions. This work provides novel insights into the dynamics of complex social systems and motivates a new approach for the design of distributed decision-making strategies in engineered networks of social agents.

CBE Seminar: "Computational Design and Simulations of Soft Matter: From Molecular Insights to Functional Materials" (Antonia Statt, UIUC)
2025-04-16 15:30 - 16:30 | Wu & Chen Auditorium
Abstract:
I will present the phase separation behavior of different sequences of a coarse-grained model for sequence defined macromolecules. They exhibit a surprisingly rich phase behavior, and not only conventional liquid-liquid phase separation is observed, but also reentrant phase behavior. Most sequences form open phases consisting of large, interconnected aggregates (e.g. string-like or membrane-like clusters), rather than a conventional dense liquids. Minor alterations in the sequence may lead to large changes in the overall phase behavior, a fact of significant potential relevance for biology and for designing self-assembled structures using block copolymers. I will also discuss recent results from unsupervised manifold learning (UMAP) to classify the different aggregate types and what we can learn from machine learning. Using a bidirectional Recurrent Neural Network (RNN), we can now predict which sequence will self-assemble into what aggregate structure. With this framework, we can investigate the effects of dispersity and sequence errors, which is of immediate importance for experimental investigations.
Additionally, I will briefly discuss how block copolymers interact with biological lipid membranes to form hybrid membranes. Hybrid phospholipid block copolymer bilayers display many properties, seen in biomembranes such as selective transport phenomena, synergistic elastic properties, and structural phase transformations. Just like in biomembranes, these fundamental properties of hybrid bilayers are often regulated by lateral phase separation. Understanding the molecular and physical cues that determine the formation of rafts or domains in hybrid membranes, their size, and morphology is paramount to elucidating and programming their function. We find that at low polymer content, a new structure develops in which the bilayer leaflets unzip (but remain continuous) to incorporate nanodomains of hydrophobic butadiene globules. Our findings offer new insights into the morphology of biomembranes upon the insertion of transmembrane proteins with bulky hydrophobic residues.

MSE Seminar: "Foundry Enabled Chip-Scale Photonics Technology and Applications" Shaya Fainman - University of California, San Diego (UCSD)
2025-04-17 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Dense photonic integration requires miniaturization of materials, devices, circuits and systems, including passive components (e.g., engineered composite metamaterials, filters, etc.), active components (e.g., modulators and nonlinear wave mixers) and integrated circuits (Fourier transform spectrometer, programmable phase modulator of free space modes, linear algebra processors, etc.). In this talk we will discuss recent progress in developing CMOS compatible nonlinear optical materials as well as examples of foundry enabled silicon photonic circuits and systems. Specifically, we will review silicon photonics-based Fourier transform spectrometer (Si-FTS) that can bring broadband operation and fine resolution to the chip scale. Here we will present the modeling and experimental demonstration of a thermally tuned Si-FTS accounting for dispersion, thermo-optic non-linearity, and thermal expansion. We show how these effects modify the relation between the spectrum and interferogram of a light source and we develop a quantitative correction procedure through calibration with a tunable laser. Providing design flexibility and robustness, the Si-FTS is poised to become a fundamental building block for on-chip spectroscopy. Moreover, taking advantage of nanofabrication we will discuss on-chip spectrometers using stratified waveguide filters and machine learning. Moving forward, we will discuss chip-scale integrated circuit/system that will allow to realize linear algebra accelerators with superior performance in speed, energy consumption and size compared to its electronic counterpart. Such a system can be manufactured using monolithic CMOS process and impact such applications as 5G/6G and beyond wireless MIMO systems as well as deep learning and artificial intelligence.

IDEAS/STAT Optimization Seminar: Resilient Distributed Optimization for Cyberphysical Systems
2025-04-17 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:
This talk considers the problem of resilient distributed multi-agent optimization for cyberphysical systems in the presence of malicious or non-cooperative agents. It is assumed that stochastic values of trust between agents are available which allows agents to learn their trustworthy neighbors simultaneously with performing updates to minimize their own local objective functions. The development of this trustworthy computational model combines the tools from statistical learning and distributed consensus-based optimization. Specifically, we derive a unified mathematical framework to characterize convergence, deviation of the consensus from the true consensus value, and expected convergence rate, when there exists additional information of trust between agents. Under certain conditions, we show that the consensus protocol has almost sure convergence to a common limit value is possible even when malicious agents constitute more than half of the network; the deviation of the converged limit, from the nominal no attack case can be bounded with probability that approaches 1 exponentially, and that correct classification of malicious and legitimate agents can be attained in (random) finite time almost surely. Further, the expected convergence rate decays exponentially with the quality of the trust observations between agents. We then combine the trust-learning model within a distributed gradient-based method for solving a multi-agent optimization problem and characterize its performance.

CIS Seminar: "Correctness Matters: Automatic Software Engineering in the age of Generative AI"
2025-04-17 15:30 - 16:30 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Software engineers never start from a blank page, but rather from an extant and usually long-running project in need of modification (for repair, extension, update, etc.). One way to view modern programming is thus as a continual process of iteratively transforming existing programs into something new, and hopefully better.
In this talk, I will discuss my work on techniques to automate a broad range of software engineering and programming tasks. I position program transformation as a search problem over a space of potential program edits; automating transformation entails careful design choices to manage and successfully traverse this trivially infinite space. I will focus especially on the fundamental challenge of ensuring that automatically transformed code is of acceptable quality, and ways to tackle that challenge, especially in light of recent advances in generative AI. Throughout, I will highlight my vision of how to develop future-generation tools to help engineers make better software, and make existing software better, by carefully integrating domain knowledge and semantics-based reasoning with powerful heuristic search.

The Future of AI: A Fireside Chat with Yann LeCun, Chief AI Scientist at Meta
2025-04-17 17:30 - 19:00 | Amy Gutmann Hall, Auditorium, 3333 Chestnut Street, Philadelphia, PA, 19104, United States

ESE Ph.D. Thesis Defense: "Neural Compression: Estimating and Achieving the Fundamental Limits"
2025-04-18 12:00 - 14:00 | Amy Gutmann Hall, Room 515, 3317 Chestnut Street, Philadelphia, 19104, United States
Neural compression, which pertains to compression schemes that are learned from data using neural networks, has emerged as a powerful approach for compressing real-world data. Neural compressors often outperform classical schemes, especially in settings where reconstructions that are perceptually similar to the source are desired. Despite their empirical success, the fundamental principles governing how neural compressors operate, perform, and trade off performance with complexity are not well-understood compared to classical schemes.
We aim to develop some of the fundamental principles of neural compression. We first introduce neural estimation methods that can estimate the theoretical rate-distortion limits of lossy compression for high dimensional sources using techniques from generative models. These methods illustrate that recent neural compressors are sub-optimal. Next, we build on these insights to discuss neural compressors that approach optimality yet remain low-complexity through the use of lattice coding techniques. These are shown to approach the rate-distortion limits on high-dimensional sources without incurring a significant increase in complexity. Finally, we develop low-complexity compressors for the rate-distortion-perception setting, where an additional perception constraint ensures the source and reconstruction distributions are close in terms of a statistical divergence. These compressors combine lattice coding with the use of shared randomness via dithering over the lattice cells, and provably achieve the fundamental rate-distortion-perception limits on the Gaussian source.

MSE PhD Defense: “Chromatin as an Active and Adaptive Material”
2025-04-18 14:30 - 17:00 | CEMB Conference room, LRSM, 3231 Walnut Street, Room 112-C, Philadelphia, PA, 19104, United States
The three-dimensional organization of chromatin within the cell nucleus plays a critical role in regulating gene expression, maintaining genome stability, and guiding cellular responses to environmental cues. Despite advances in imaging and sequencing technologies, the fundamental principles governing chromatin architecture and dynamics, particularly the role of associated proteins like HP1α in driving these processes, remain poorly understood. Additionally, although multiple studies have demonstrated the sensitivity of chromatin organization to microenvironmental cues, the underlying physical mechanisms driving this sensitivity and its impact on transcription, especially in response to mechanical cues, remain largely unexplored. Therefore, elucidating the mechanisms that regulate chromatin organization, particularly those shaped by the interplay of molecular interactions and external mechanical forces, is crucial for understanding how genome structure and function are interrelated and how they might be controlled for therapeutic purposes.
To bridge this gap, this thesis integrates theoretical modeling, computational simulations, and experimental approaches to investigate the principles that regulate genome organization. To explore the molecular drivers of chromatin structure, we developed a novel polymer-based model using kinetic data of the chromatin architectural protein HP1α, extracted from FRAP experiments in vivo. This model was designed to predict both the structural organization and dynamic behavior of constitutive heterochromatin, which comprises gene-poor, transcriptionally silent regions essential for genome stability. By incorporating HP1α binding kinetics and its affinity for methylated chromatin, the model accurately predicts heterochromatin domain sizes and sub-diffusive motion. These predictions were validated using Hi-C and high-resolution imaging data, revealing how transient HP1α interactions contribute to heterochromatin structure and mobility. Moreover, the model provides a mechanistic explanation for the maintenance of epigenetic memory within these regions.
Next, to investigate how the mechanical properties of the cellular microenvironment influence genome architecture, we designed experiments that replicate cellular responses to changes in tissue stiffness, guided by predictions from our stiffness-dependent polymer model. Focusing on lung fibrosis, we used IMR90 cells cultured on synthetically engineered hydrogels with defined stiffnesses representing healthy and fibrotic (disease-like) pulmonary environments. As substrate stiffness increased from soft, healthy-like conditions to stiff, fibrotic-like conditions, cells exhibited marked changes in chromatin accessibility, nuclear epigenetic landscape, and gene expression. These effects were characterized using RNA-seq, ATAC-seq, and super-resolution OligoSTORM imaging. Our findings uncovered a mechanosensitive mechanism by which chromatin reorganization mediates both transcriptional responses and epigenetic regulation, offering insights into the gene-level consequences of microenvironmental changes during disease progression.
Together, this thesis presents a multiscale integrative framework that combines computational modeling with biological experimentation to advance our understanding of genome organization. By bridging the gap between molecular-scale interactions and tissue-level mechanical cues, it provides new insights into how chromatin architecture encodes both dynamic responsiveness and long-term cellular identity. Ultimately, this work contributes to a broader understanding of how biophysical principles shape gene regulation, with implications for development, disease progression, and the design of novel therapeutic strategies.

ESE Ph.D. Thesis Defense: "Machine Learning for Large-Scale Cyber-Physical Systems"
2025-04-21 09:00 - 11:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Directly training deep learning models for applications in large-scale cyber-physical systems can be intractable due to the large number of components and decision variables. Instead, we focus on exploiting spatial symmetries in systems by designing size-generalizable architectures. Once trained on small-scale examples, such architectures exhibit equivalent or comparable performance on large-scale systems. The first example we consider is a fully convolutional neural network, for which we prove a bound that guarantees generalization performance. We demonstrate generalizability empirically with applications to multi-target tracking and mobile infrastructure on demand. Next, we introduce a novel spatial transformer architecture design with two key properties in mind: locality and shift-equivariance. The proposed architecture uses shift-equivariant positional encodings and spatially windowed attention. Our experiments in two distributed collaborative multi-robot tasks show that these design features are necessary for size generalizability. Moreover, we demonstrate that the spatial transformer architecture is capable of decentralized execution, robust to communication delays, can generalize to unseen tasks, and performs state-of-the-art graph neural networks. Finally, we refocus on a particularly challenging optimization problem in power systems: optimal power flow (OPF). By appropriately formulating the Lagrangian dual problem, we train graph attention networks with improved optimality and feasibility. The training performance can also be reproduced on new power systems without further hyperparameter tuning.

Confirmation Bias, the Original Error. A master class with Prof. Konrad Kording
2025-04-21 12:00 - 13:00
RSVP at https://bit.ly/3RzmdVH
Learn what confirmation bias is, how to identify it in your own research, and acquire the skills to mitigate it. Yes, it turns out, we’re all biased and this can negatively impact your research. Join Professor Konrad Kording in this live training session based on the Community for Rigor’s new educational unit.
Community for Rigor is a new Center at Penn, an NIH/NINDS-funded initiative to create a free, online curriculum to learn, practice, and promote scientifi rigor.

ESE 5160 Special Lecture: "Taking RoboRacer Off-Road: Learning Extreme Off-Road Mobility"
2025-04-21 12:00 - 13:30 | Towne 327
In this guest lecture, we will cover two recent research thrusts from the RobotiXX lab in taking RoboRacer off-road: high-speed off-road navigation and wheeled mobility on vertically challenging terrain. For high-speed off-road navigation, we will introduce a sequential line of work with every work inspired by and built upon its prior work, ranging from inverse kinodynamics learning based on inertia and vision to forward kinodynamics to enable competence awareness. For wheeled mobility on vertically challenging terrain, we will introduce this new problem formulation and a recently developed ecosystem around this topic, from research infrastructure, kinodynamics modeling and planning, to off-road mobility learning. We will also share the latest research on combining these two thrusts, i.e., making high-speed vehicles safely fly over uneven terrain through in-air maneuvers.

ESE Ph.D. Thesis Defense: "Inverse design for engineering complex light-matter interaction"
2025-04-21 12:00 - 14:00 | Moore 317, 200 S 33rd Street, Philadelphia, PA, 19104, United States
The inverse design paradigm has emerged as a transformative approach for the synthesis of nanophotonic structures, offering a powerful alternative to conventional intuition-driven design. By approaching photonic device design as a computational optimization problem, inverse design enables the systematic exploration of high-dimensional parameter spaces to uncover non- intuitive structures that meet complex performance targets. This dissertation advances the application of inverse design methodologies across multiple photonic platforms, spanning from silicon photonics to lithium niobate, and targets critical functionalities, including analog optical computation, nonlinear power limiting, compact second-harmonic generation, efficient electro- optic modulation, and flat antennas with desired radiation properties.
The first part of the thesis focuses on the design of compact, planar silicon photonic structures for wave-based analog computation. Using topology optimization, I designed and demonstrated a spatially patterned monolithic silicon slab structure that performs optical vector–matrix multiplication by manipulating in-plane modes. Through a collaborative effort with the Aflatouni group, this device, fabricated using a CMOS-compatible process, validates the ability of inverse design to encode mathematical operators on light propagation through patterned, compact, and mass-producible photonic architectures.
The second part of the thesis introduces an inverse-designed three-port Kerr nonlinear cavity that realizes optical intensity limiting. By tailoring the nonlinear response of a high-Q cavity under increasing optical input power, the design achieves dynamic power redistribution between ports, suppressing excess transmission in a self-regulating manner. This structure demonstrates how inverse design can be employed to precisely engineer a cavity-modal structure and nonlinear interactions to achieve dynamic functional behavior such as optical limiting.
Building upon these insights, the third part addresses the challenge of designing efficient and compact electro-optic (EO) modulator devices based on lithium niobate on insulator (LNOI). Traditional modulators face trade-offs among voltage-length product, footprint, and bandwidth. Here, I present an inverse-designed EO phase modulator cavity that substantially reduces a phase modulator footprint from traditional cm-long waveguides into a micron-scale cavity featuring similar phase modulation performance. This significant reduction in size highlights the potential of inverse design to enable scalable, high-density modulator architectures for applications in optical computing and high-speed data transmission.
Throughout the thesis, I develop and employ a general framework for photonic inverse design based on the time-harmonic Maxwell equations, adjoint sensitivity analysis, and density-based topology optimization. This framework is adapted to accommodate platform-specific constraints, including fabrication limitations and incorporating material nonlinearities for novel functionalities, while enabling the design of freeform photonic structures with thousands of degrees of freedom. The designs presented in this dissertation outperform conventional counterparts and highlight inverse design’s power to fundamentally reshape how we conceive, engineer, and deploy photonic systems.

CBE Doctoral Dissertation Defense: "Structure and transport properties of nanoporous polymers derived from lyotropic mesophases" (Christopher Johnson)
2025-04-21 13:45 - 15:30 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Abstract:

Burgeoning energy and water scarcity challenges motivate the development of new membrane materials for charge transport as well as chemical and water separations. This in turn requires an improved understanding of the physics that govern charged and uncharged solute transport in membranes, and particularly the motion of such species in nm-scale confinement in polymeric materials. This dissertation addresses transport in porous polymers with highly ordered nm-scale constrictions made of lyotropically assembled surfactant mesophases. The primary concerns of the thesis include the extent to which one can control the bulk material properties of a polymerized lyotropic mesophase material and what affects the bulk and dynamic pictures of transport in the resulting nanoconfined spaces. The explored polymers have controlled dimensions, curvature, and solid volumes, allowing for in-depth discussion and alteration of the membrane’s internal environment. Fine control over the initial lyotropic mesophase is shown through a study incorporating a spiropyran dopant into a bicontinuous gyroid, where pore size is actively reduced by 5 % in response to stimuli, lowering acid vapor flux by 30 %. Membrane tensile properties are improved by careful adjustment of crosslinking groups in a similar bicontinuous gyroid mesophase. By optimizing the number of -diene crosslinking groups, improvements in tensile strength are found in comparison to the unaltered polymer blend. Anion conductivities of two differing morphologies – the Ia3d gyroid and direct hexagonal cylinders – are investigated, with a focus on determining differences in bulk properties on the basis of morphology, anion identity, and external conditions such as temperature and relative humidity. Potassium ion transport through another lyotropically self-assembled mesophase is performed and deemed a suitable candidate for further study. The self-assembled materials presented in this dissertation are found to be resilient, with higher conductivity than previously reported ordered polymeric materials. Solvent composition is found to be a first order effect on conductivity, and anion identity shows that nanoconfinement enhances differences in diffusivity due to solvation shell depletion and condensed charge pair formation. Additional work varying relative humidity and pore size unites bulk activation energies and conductivities with short-time dynamic phenomena. These findings motivate future work in understanding the dynamics of these systems and putting these porous polymers into useful scenarios.

MEAM Master's Thesis Defense: "Investigating Jet Interactions in the Multi-Jet SALP Robot"
2025-04-22 09:00 - 10:00 | David Rittenhouse Laboratory Building, Room 4E19, 209 S. 33rd Street, Philadelphia, PA, 19104, United States
Jet propulsion is a common locomotion strategy in nature. We developed an underwater particle image velocimetry (PIV) system to investigate the hydrodynamic effects of the SALP (Salp-inspired Approach to Low-energy Propulsion) robot, a soft underwater robot that swims using jet propulsion. Multiple SALP units can be physically connected to form a multi-SALP system, coordinating their jets to achieve different motions. This thesis explores jet interactions in the multi-SALP robot under various physical arrangements and propulsion modes. PIV analysis identified three types of interactions: interaction, weak interaction, and no interaction. Results show that, compared to non-interacting jets, interacting jets can exhibit up to a 25% reduction in average velocity. This research enables further exploration of flow-field-enhanced performance in multi-SALP systems.

MEAM Master's Thesis Defense: "In Situ Additive Manufacturing of Metal-Graphene Composites by Upcycling Polymers"
2025-04-22 10:00 - 11:00 | 4E9, DRLB, 209 S. 33rd Street, Philadelphia, PA, 19104, United States
Laser powder bed fusion (LPBF) is a bourgeoning additive manufacturing technique for rapid prototyping and creating unconventional designs using metal alloys. In parallel, graphene has garnered significant research interest since its discovery, owing to its remarkable mechanical and transport properties. Driven by the potential advances in additive manufacturing, this project aims to harness the intrinsically high energy densities characteristic of LPBF for in situ formation of graphene using polymers as a direct carbon source during the metal 3D printing process. This innovative approach provides a comprehensive understanding of the underlying graphene formation mechanisms and to thoroughly characterize the resulting metal-graphene systems produced via LPBF. This thesis project investigates the processing parameters and properties of these 3D metal-graphene composites in comparison to conventional metal alloys.

Spring 2025 GRASP Seminar: Robin Walters, Northeastern University, "Pushing the Limits of Equivariant Neural Networks"
2025-04-22 10:00 - 11:00 | Amy Gutmann Hall, Room 306, 3317 Chestnut Street, Philadelphia, PA, 19104, United States
This will be a hybrid event with in-person attendance in AGH 306 and virtual attendance on Zoom.
ABSTRACT
Despite the success of deep learning, there remain challenges to progress. Deep models require vast datasets to train, can fail to generalize under surprisingly small changes in domain, and lack guarantees on performance. Incorporating symmetry constraints into neural networks has resulted in models called equivariant neural networks (ENN) which have helped address these challenges. I will discuss several successful applications, such as trajectory prediction, ocean currents forecasting, and robotic control. However, there are also limits to the effectiveness of current ENNs. In many applications where symmetry is only approximate or does apply across the entire input distribution, equivariance may not be the correct inductive bias to aid learning and may even hurt model performance. I will discuss recent work theoretically characterizing errors that can result from mismatched symmetry biases which can be used for model selection. I will also suggest different methods for relaxing symmetry constraints so that approximately equivariant models can still be used in these situations.

MEAM Seminar: "Biomedical Innovations for Global Health Research and Technology (BIGHEART): NOAS, EXODUS, iTEARS, and BOAS"
2025-04-22 10:15 - 11:15 | Wu & Chen Auditorium
This presentation will discuss the concept of BIGHEART and present various illustrative examples of this approach. The initial focus will be on NOAS (Nanoscale Optical Antennas) within the context of quantitative life sciences and transformative medicine. NOAS facilitates the visualization of quantum biological electron transfer processes occurring in mitochondria within living cells, supports the precise release of siRNAs with accurate spatiotemporal control, enables the detection of oscillatory communication among living bacteria via extracellular vesicles, and innovates ultrafast photonic PCR technology for enhanced precision in preventive medicine. EXODUS (Exosome Detection via the Ultrafast-Purification System) is meticulously designed to facilitate accurate diagnostics and therapeutic applications through the utilization of exosomes. The effective purification of exosomes from patients’ liquid biopsies enables comprehensive analyses and the advancement of translational medical treatments. iTEARS (Integrated Tear Exosome Analysis via Rapid-Isolation System) allows the detection of protein and miRNA biomarkers, thereby enhancing the diagnostic capability of various diseases through the analysis of tear samples. BOAS (Brain Organoid Analysis System), which integrates biosensors and EEG for real-time, non-invasive monitoring of brainwaves and extracellular vesicles (EVs), was developed to explore the connections between molecular signals and neurophysiological brainwaves. This study critically analyzes and offers insights into the interrelationships among secretomes, electrophysiological brainwaves, and the networks generated from human brain organoids. The in vitro models of BOAS serve as valuable tools for researchers investigating neuropathogenesis, developing treatments for neurodegenerative diseases, and exploring preventive medical therapies by studying the interactions between EVs and brainwaves. Furthermore, the BIGHEART initiative endeavors to create solutions for preventive and personalized medicine that contribute to affordable global healthcare.

CBE Doctoral Dissertation Defense: "Designing Solvent-Based Order Parameters for Characterizing Binding of Surfaces with Different Hydrophobicity Using Molecular Dynamics Simulations" (Jun Lu)
2025-04-22 10:15 - 11:45 | Greenberg Lounge (Room 114), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
Abstract:

Life is dependent on water: most self-assembly and binding processes of biomolecules take place in water. Water-mediated interactions are an essential driving force behind these processes, which is largely affected by the hydrophobicity of the binding surfaces. As many biomolecular binding interfaces are amphiphilic, the hydrophobic interactions are largely affected by polar and charged groups near hydrophobic binding domains. Therefore, the study of interactions between hydrophobic surfaces and hydrophilic surfaces are of great importance. We find that on the two extremes: purely hydrophobic and hydrophilic systems, traditional sampling approaches in molecular dynamics (MD) simulations become ineffective for different reasons. Here, we study the binding of surfaces with different hydrophobicity using the MD approach with specifically designed solvent-based order parameters to control. By sensibly choosing order parameter sets from 1. Separation distance, 2. Solvent coordinates, and 3. Electrostatic energy to control, sampling pathologies can be mostly solved and the thermodynamics of binding of surfaces with different hydrophobicity can be studied.

ESE Ph.D. Thesis Defense: "Graph Neural Networks for Communication in Multi-Agent Systems"
2025-04-22 12:15 - 13:15 | Room 313, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Communication networks support a wide range of applications in multi-agent systems by solving core problems such as routing, scheduling, and resource allocation. In this thesis, we focus on data-driven routing and scheduling strategies using local information subject to constraints using Graph Neural Networks (GNNs). First, we study information routing in communication networks with constant channel conditions and formulate it as a constrained learning problem. We propose a novel State Augmentation strategy to achieve faster convergence and achieve decentralized implementation using GNNs. The state augmentation based optimization framework leverages graph convolutions to generate optimal routing decisions using only local information from the nearby neighbors and achieves competitive performance without the need for supervision or global knowledge. Second, we extend the framework to opportunistic routing in wireless networks, where we leverage the broadcast nature of wireless channels for dynamic relay node selection. We integrate state augmentation with GNN-based distributed optimization to learn efficient routing policies that maximize end-to-end throughput. The learned models can be generalized across varying network sizes and multiple flows which are very robust to network variations. Third, we design a real-time wireless ad-hoc network testbed to validate the proposed routing strategies under realistic channel conditions. Our evaluation demonstrates that the state augmentation combined GNN framework validates the simulational algorithms in terms of queue length stability while retaining stability and transferability properties without the requirement for retraining. Overall, this thesis presents a scalable and decentralized approach to intelligent routing and scheduling in multi-agent systems by bridging graph-based learning with network optimization, offering practical solutions for large-scale and dynamic communication systems.

ESE Guest Seminar - "Efficient Computing for AI and Robotics: From Hardware Accelerators to Algorithm Design"
2025-04-23 10:45 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
The compute demands of AI and robotics continue to rise due to the rapidly growing volume of data to be processed; the increasingly complex algorithms for higher quality of results; and the demands for energy efficiency and real-time performance. In this talk, we will discuss the design of efficient tailored hardware accelerators and the co-design of algorithms and hardware that reduce the energy consumption while delivering swift real-time and robust performance for applications including deep neural networks, data analytics with sparse tensor algebra, and autonomous navigation. Throughout the talk, we will highlight important design principles, methodologies, and tools that can facilitate an effective design process and various forms of co-design that can broaden the design space.
*Distinguished lecture supported by the IEEE Solid-State Circuits Society

ASSET Seminar: "Fake News, Echo Chambers, and Algorithms: A Data Science Perspective"
2025-04-23 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract:
American democracy has been undermined by an “infodemic” of fake news, coupled with the widespread segregation of consumers into ideologically homogenous echo chambers by inscrutable algorithms deployed by rapacious social media platforms—or so we are told. In this talk, I will critically examine claims of this sort—made frequently by politicians, journalists, and public intellectuals—summarizing several recent papers that leverage large-scale representative panel data for US media consumption. Contrary to conventional wisdom, I argue that fake news is relatively rare, echo chambers on television are much larger and stickier than their online equivalents, and individual preferences dominate algorithmic filtering in determining consumption patterns. I further argue that it is trivially easy to mislead people without resorting to outright falsehoods and that researchers should accordingly pay more attention to biased information, even when it is factually accurate. I conclude by introducing the media bias detector, a recently launched project of Penn’s Computational Social Science Lab, that seeks to characterize and expose bias in mainstream media.
Zoom Link: https://upenn.zoom.us/j/94075987313

Spring 2025 GRASP SFI: Haimin Hu, Princeton University, “From Gambits to Assurances: Game-Theoretic Integration of Safety and Learning for Human-Centered Robotics”
2025-04-23 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
From autonomous vehicles navigating busy intersections to quadrupeds deployed in household environments, robots must operate safely and efficiently around people in uncertain and unstructured situations. However, today’s robots still struggle to robustly handle low- probability events without becoming overly conservative. In this talk, I will discuss how planning in the joint space of physical and information states (e.g., beliefs) allows robots to make safe, adaptive decisions in human-centered scenarios. I will begin by introducing a unified safety filter framework that combines robust safety analysis with probabilistic reasoning to enable trustworthy human–robot interaction. I will discuss how robots can reduce conservativeness without compromising safety by closing their interaction–learning loop. Next, I will show how game-theoretic reinforcement learning tractably synthesizes a safety filter for high-dimensional systems, guarantees training convergence, and reduces the policy’s exploitability. Finally, I will present a scalable game-theoretic algorithm for optimizing social welfare in multi-agent coordination scenarios. I will conclude with a vision for next-generation human-centered robotic systems that actively align with their human peers and enjoy verifiable safety assurances.

CBE Seminar: "From Molecules to Supply Chains: Transforming Data to Decisions using Geometry, Optimization, and Machine Learning" (Victor Zavala, University of Wisconsin-Madison)
2025-04-23 15:30 - 16:30 | Wu & Chen Auditorium
Abstract:

We discuss how geometry, optimization, and machine learning are key technologies that are revolutionizing the way we think about data and the way we transform data into actionable models and decisions. Specifically, we explain how complex data (e.g., text, molecules, time series, images/video, supply chain flows) can be represented as geometrical objects and how this facilitates the interpretation and extraction of useful information from data. We also discuss how extracted information can be mapped into decisions using optimization and machine learning models. We illustrate how to use these powerful math tools in innovative ways for analyzing complex datasets arising in molecular dynamics simulation, microscopy, chemical processes, and supply chains. Specifically, we show that these tools can help link the microstructure of soft gels to their rheological properties, can help analyze complex responses of liquid crystals from video data, and can help detect faults and optimize large-scale systems.

MSE Seminar: "Engineered Biomaterials for Regeneration, Therapy, and Beyond" Akhilesh K Gaharwar, Texas A&M University
2025-04-24 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Engineered biomaterials have emerged as powerful tools for a range of biomedical applications, including regenerative medicine, drug delivery, and additive manufacturing. These engineered biomaterials possess tunable biophysical properties, specific biochemical cues, and complex architecture, enabling precise control over cellular behavior. In this talk, I will outline three biomaterials-based approaches developed in our lab for biomedical applications. Firstly, I will highlight how engineered biomaterials can be used to control and direct cellular functions. Our work has resulted in a new class of biomaterials for bone regeneration, and mitochondrial biogenesis. The second approach emphasizes the design of biomaterials tailored for the sustained and controlled release of therapeutics, targeting osteoarthritis treatment, angiogenesis promotion, and wound healing. We have pioneered a suite of nano-toolkits adept at delivering both small molecular drugs and sizeable proteins, characterized by efficient loading and adaptable release dynamics. Lastly, I will demonstrate the design of 3D printing bioelectronics and anatomical-size tissue constructs. These advanced tissue structures enable the creation of physiologically accurate tissue models, replicating complex disease conditions like vascular pathophysiology and intricate vascularized tumor representations.

IDEAS/STAT Optimization Seminar: "Negative Stepsizes Make Gradient-Descent-Ascent Converge"
2025-04-24 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: Solving min-max problems is a central question in optimization, games, learning, and controls. Arguably the most natural algorithm is Gradient-Descent-Ascent (GDA), however since the 1970s, conventional wisdom has argued that it fails to converge even on simple problems. This failure spurred the extensive literature on modifying GDA with extragradients, optimism, momentum, anchoring, etc. In contrast, we show that GDA converges in its original form by simply using a judicious choice of stepsizes.
The key innovation is the proposal of unconventional stepsize schedules that are time-varying, asymmetric, and (most surprisingly) periodically negative. We show that all three properties are necessary for convergence, and that altogether this enables GDA to converge on the classical counterexamples (e.g., unconstrained convex-concave problems). The core intuition is that although negative stepsizes make backward progress, they de-synchronize the min/max variables (overcoming the cycling issue of GDA) and lead to a slingshot phenomenon in which the forward progress in the other iterations is overwhelmingly larger. This results in fast overall convergence. Geometrically, the slingshot dynamics leverage the non-reversibility of gradient flow: positive/negative steps cancel to first order, yielding a second-order net movement in a new direction that leads to convergence and is otherwise impossible for GDA to move in. Joint work with Henry Shugart.

MEAM Master's Thesis Defense: "Learning a Vision-Based Footstep Planner for Hierarchical Walking Control on Unstructured Terrain"
2025-04-24 13:00 - 14:00 | David Rittenhouse Laboratory Building, Room 4C4, 209 S. 33rd Street, Philadelphia, PA, 19104, United States
Bipedal robots demonstrate high potential in navigating challenging terrains through dynamic ground contact. However, current frameworks often depend solely on proprioception or use manually designed visual processing pipelines, which are fragile in real-world settings and complicate real-time footstep planning in unstructured environments. To overcome this problem, this work proposes a vision-based hierarchical control framework that integrates a reinforcement learning-based footstep planner, which generates footstep commands based on a local elevation map, with a low-level model-based controller that tracks the generated trajectories. The proposed framework is evaluated using the underactuated bipedal robot Cassie in both simulation and hardware. A detailed analysis identifies key challenges in sim-to-real transfer and potential strategies to improve the robustness and real-world applicability of hierarchical control frameworks.

BE-Grace Hopper Distinguished Lecture: Melody Swartz, PhD "Immunoregulatory roles of lymphatic vessels in cancer and opportunities for immunoengineering"
2025-04-24 15:30 - 16:30 | Berger Auditorium (Room 13), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
Tumor lymphangiogenesis, which involves both the activation and growth induction of surrounding lymphatic vessels, is well-known to correlate with tumor progression and metastasis in many solid tumors. While it is typically assumed that lymphangiogenesis supports an ‘escape route’ for cells to leave the primary tumor, the tumor-draining lymph node serves as the key site of immune surveillance. Our lab has been exploring how lymphatic involvement affects the tumor immune microenvironment and anti-tumor immunity while promoting metastasis at the same time. In doing so, we have discovered new fundamental roles for lymphatic endothelial cells (LECs) as direct modulators of immunity. This is important because LECs are constantly bathed with peripheral antigens, cytokines, danger signals and immune cells travelling from peripheral tissues to lymph nodes. In terms of promoting metastasis, we have learned that tumor-activated lymphatics alter the tumor microenvironment in multiple ways, including (i) increasing immune suppressive cell types and factors in the tumor microenvironment both directly and indirectly, (ii) inhibiting maturation of antigen-presenting cells and T cell activation, and (iii) driving changes in the stromal microenvironment that promote both cancer invasion and immune suppression. However, lymphatic activation also enhances communication with cells in the draining lymph node by antigen and cell transport, and leads to increased immune cell infiltration within the tumor. As a consequence, lymphangiogenic tumors can be exceptionally responsive to immunotherapy, paradoxically. This ‘lymphangiogenic potentiation’ of immunotherapy depends on tumor cell infiltration of both cross-presenting dendritic cells and naïve T cells, driving local T cell education post-immunotherapy and antigen spreading. On the translational side, we are engineering novel strategies to exploit lymphangiogenesis for cancer immunotherapy. Beyond cancer, our findings suggest that LECs may be potential targets for immunomodulation in vaccination, autoimmunity, and allergy.

Spring 2025 GRASP on Robotics: Phillip Isola, Massachusetts Institute of Technology, “Robots and Artificial Life from Visual Foundation Models”
2025-04-25 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
I will talk about two ways we can design agents with the help of powerful vision/graphics models. In the first project, LucidSim, we augment a traditional robotics simulation engine (MuJoCo) with visual detail from an image generative model. The generator adds diversity and realism to the barebones MuJoCo content, and results in a RGB-only policy trained entirely in sim that generalizes zero-shot to the real world. In the second project, ASAL, we use a visual recognition model to search for artificial lifeforms that display distinct and interesting behaviors. This process can discover cellular automata that are open-ended like Conway’s Game of Life, particle swarms that flock like Boids, and more.

MEAM Seminar: "Atmospheric Boundary Layer Simulations for Wind Energy"
2025-04-29 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The growth in wind energy is driving a need to understand how wind turbines perform under a range of atmospheric conditions. Under stably stratified conditions, for example, wind turbine wakes can persist for long distances and impact the performance of downwind turbines. Additionally, variations in surface topography, from shallow depressions to steep mountains, can deflect wind turbine wakes upward or downward. These and other factors must be captured accurately by atmospheric models that provide predictions for wind turbine micro-siting and operational wind power forecasting over complex terrain. Here we present examples of mesoscale to microscale simulations at real wind farms with complex terrain, including implementation of detailed models for turbine wake effects. The focus is on understanding how simulation of wind turbine wakes is influenced not only by topography and thermal stratification, but by numerical techniques such as turbulence closure models and grid nesting strategies. The Weather Research and Forecasting (WRF) model is used here in grid nested configurations starting from the mesoscale (~10 km resolution) and ending with fine scale resolutions (~10 m) suitable for large-eddy simulation (LES). Wind turbine wake behavior is examined under stably stratified and convective conditions over complex terrain with comparison to field campaign data.
