Homecoming 2025: Penn Engineering Faculty Lightning Talks
2025-11-07 13:00 - 14:15 (America/New_York) | Amy Gutmann Hall, Auditorium, 3333 Chestnut Street, Philadelphia, PA, 19104, United States
Step back into the classroom with Penn Engineering!
Join us for a series of TED Talk-style Lightning Talks featuring some of our most popular professors as they share their latest groundbreaking research.
“Fragility and Resilience of the Soft Earth”
Doug Jerolmack
Edmund J. and Louise W. Kahn Endowed Term Professor of Earth and Environmental Science
Professor of Mechanical Engineering and Applied Mechanics
“Immune Engineering for Cardiovascular Health”
Noor Momin
Stephenson Foundation Term Assistant Professor of Innovation
Bioengineering
“Advancing Genome Editing for Precision Therapeutics and Molecular Innovation”
Sherry Gao
Associate Professor
Chemical and Biomolecular Engineering
“Building the World’s Smallest Robot”
Marc Miskin
Assistant Professor
Electrical and Systems Engineering
“Uncovering Regulators of Aging with Dynamic Biomaterials”
Chris Madl
Assistant Professor, Chemical and Biomolecular Engineering, Materials Science and Engineering

PICS Colloquium: Influence of particle size distribution on random close packing with Eric Weeks
2025-11-07 14:00 - 15:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
The volume fraction phi for random close packed (RCP) spheres is approximately 0.64. It is well known that higher RCP volume fractions are achieved by using collections of particles with a variety of sizes. The variety of sizes is often quantified by the polydispersity of the particle size distribution: the standard deviation of the radius divided by the mean radius. I’ll show that for 2D and 3D packings, the skewness also plays an important role (related to the third moment of the size distribution). I will also discuss some of our work on random close packing in confined spaces.

Building a Sustainable Future: Empowering the Next Generation.
2025-11-07 15:00 - 16:00 | Amy Gutmann Hall, Auditorium, 3333 Chestnut Street, Philadelphia, PA, 19104, United States

ESE Guest Seminar - "Challenges and Opportunities in Radio Frequency Power Conversion for Semiconductor Plasma Applications"
2025-11-10 10:30 - 11:30 | CTA
Radio frequency (RF) plasma technology is essential in modern semiconductor fabrication, enabling precise processes such as etching and deposition. As the industry advances toward increasingly complex three-dimensional structures and smaller nanoscale features, the demands on plasma-based processing continue to grow. Meeting these demands requires a new generation of RF power conversion and control systems that are robust, accurate, agile, and efficient. Key challenges include delivering precise power across wide dynamic ranges and frequencies, operating under rapidly varying load impedance conditions, maintaining high power efficiency, and incorporating advanced sensing and system analytics. These requirements reveal fundamental limitations in conventional RF power delivery systems.
This presentation offers a brief overview of key plasma processes and typical inductively and capacitively coupled plasma systems. It highlights the core RF power challenges encountered in these platforms and presents recent innovations aimed at addressing them. These include RF inverter designs that maintain high efficiency under varying load conditions, scalable power combining techniques for rapid power control, and phase switched impedance modulation (PSIM) for high-speed impedance matching and transformation. Together, these advances support the development of next generation RF power architectures that enable more capable and efficient semiconductor plasma processing systems.

ESE Ph.D. Seminar: "Temporal Knockoffs: Variable selection for time-varying systems with e-processes"
2025-11-10 13:00 - 14:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
One of the primary goals of ‘explainable AI’ is the identification of a small subset of explanatory variables in an attempt to understand interesting phenomena. The Markov blanket constitutes one such subset, essential for tasks involving causal interpretation, prediction, and robustness. In medical imaging, identifying such variables is particularly important for achieving generalization across sites and mitigating domain shifts induced by scanner or population biases. Existing approaches based on the model-X knockoffs framework (Barber & Candès, 2015) provide finite-sample control of the false discovery rate (FDR) under the IID assumption. However, longitudinal data violate this assumption and exhibit temporal dependencies, non-stationarity, making the standard knockoff constructions invalid. In this work, we explore a principled extension of knockoff-based variable selection to time-varying systems by leveraging ideas from betting games and e-processes in sequential hypothesis testing. We explore its applicability to both synthetic datasets as well as test it on real-world longitudinal neuro-imaging data from ADNI.

FOLDS SEMINAR: The Hidden Width of Deep ResNets
2025-11-10 14:30 - 15:30 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Zoom link: https://upenn.zoom.us/j/6130182858

We present a mathematical framework to analyze the training dynamics of deep ResNets that rigorously captures practical architectures (including Transformers) trained from standard random initializations. Our approach combines stochastic approximation of ODEs with propagation-of-chaos arguments to obtain tight convergence rates to the “infinite size” limit of the dynamics. It yields the following insights:
1/ Depth begets width: infinite-depth ResNets of any hidden width behave throughout training as if they were infinitely wide;
2/ Phase diagram: we derive the phase diagram of the training dynamics, which singles out an “ideal” scaling of hyper-parameters (initialization scale and learning-rates), extending “CompleteP” to more general architectures;
3/ Optimal shape scaling: our analysis suggests how to scale depth, hidden width and embedding dimension of a ResNet when scaling up parameter count. With the optimal shape and a parameter budget P, we argue that the model converges to its limiting dynamics at rate P^{-1/6}.

MEAM Seminar: "Bioelastic State Recovery for Haptic Sensory Substitution"
2025-11-11 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The rich set of mechanoreceptors found in human skin offers a versatile engineering interface for transmitting information and eliciting perceptions, potentially serving a broad range of applications in patient care and other important industries. Targeted multisensory engagement of these afferent units, however, faces persistent challenges, especially for wearable, programmable systems that need to operate adaptively across the body. Here we present a miniaturized electromechanical structure that, when combined with skin as an elastic, energy-storing element, supports bistable, self-sensing modes of deformation. Targeting specific classes of mechanoreceptors as the basis for distinct, programmed sensory responses, this haptic unit can deliver both dynamic and static stimuli, directed as either normal or shear forces. Systematic experimental and theoretical studies establish foundational principles and practical criteria for low-energy operation across natural anatomical variations in the mechanical properties of human skin. A wireless, skin-conformable haptic interface, integrating an array of these bistable transducers, serves as a high-density channel capable of rendering input from smartphone-based 3D scanning and inertial sensors. Demonstrations of this system include sensory substitution designed to improve the quality of life for patients with visual and proprioceptive impairments.

ESE 4070/5070 Guest Speakers: Rouzbeh Yassini-Fard & Paul Nikolich
2025-11-11 17:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States

ESE Fall Seminar - "Engineering with Atomic-Scale Building Blocks: From Complex Properties to Functional Devices"
2025-11-12 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
As the demand for computing power and complexity continues to grow, developing new paradigms of information processing is essential. Unconventional functionalities arising from atomically engineered materials offer pathways to address these challenges. This has motivated the rapid development of atomic-scale materials as building blocks for future nanosystems. Their integration into functional devices, however, is hindered by incompatibility with conventional top-down fabrication processes. We overcome these limitations by leveraging the principles of additive manufacturing to enable atomic-scale control of nanomaterials and their heterogeneous integration into functional structures. Within these building blocks, we embed complex functionalities that can be spatiotemporally controlled, spanning multiple physical domains – including electrical, optical, mechanical, and chemical – to develop platforms for next-generation information processing. In this talk, I will highlight examples based on two-dimensional materials, molecules, and quantum dots. Specifically, I will discuss enhanced two-dimensional devices through van der Waals integration, deterministic on-chip perovskite light sources for photonic quantum technologies, and a nanomechanics-enabled platform for neuromorphic computing.

ASSET Seminar: "Discrete Generative Models for Programmable Molecule Design"
2025-11-12 12:00 - 13:00 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Zoom: https://upenn.zoom.us/j/95189835192
Passcode: [REDACTED]

Fall 2025 GRASP SFI: Martin Nisser, University of Washington, “Computational Fabrication and Assembly for In Situ Manufacturing”
2025-11-12 15:00 - 16:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This is a hybrid event with in-person attendance in Levine 307 and virtual attendance via Zoom.
ABSTRACT
The space environment is remote and unpredictable, and the ability to manufacture in situ offers unique opportunities to address new challenges as they arise. However, the challenges faced in space are often mirrored on Earth. In hospitals, disaster zones, low resource environments and laboratories, the ability to manufacture customized artefacts at points of need can significantly enhance our ability to respond rapidly to unforeseen events. In this talk, I will introduce digital fabrication platforms with codeveloped software and hardware that draw on tools from robotics and human-computer interaction to automate manufacturing of customized artefacts at the point of need. Highlighting three research themes across fabrication machines, programmable materials, and modular assembly, the talk will cover a digital fabrication platform for producing functional robots, a method for programming magnetic material to selectively assemble, and a modular robotic platform for in-space assembly previously deployed in microgravity.

CBE Seminar: “Engineering Soft Matter Systems through the Lens of Plant Physiology” (Jean-François Louf, Auburn University)
2025-11-12 15:30 - 16:30 | Wu & Chen Auditorium
Abstract:
Plants sense local pressure changes in their vasculature and transmit them across tissues via poroelastic coupling, triggering ionic currents in distant mechanosensitive cells to guide growth and biochemical responses. Inspired by this natural mechanotransduction, my lab develops synthetic analogs across soft materials. I will first present a soft robotic skin that mimics plant vasculature, where mechanical strain generates overpressure in embedded channels to enable remote contact detection and stiffness sensing; this frugal science tool, built for under $50, can measure the Young’s modulus of soft objects, serving as a low-cost alternative to commercial mechanical testers. Next, I will introduce a piezoionic hydrogel that transduces pressure into ionic fluxes, acting as a mechano-ionic interface suitable for biointerfacing with tissues like the heart, where mechanical and ionic signaling are intrinsically coupled. Notably, our material generates ionic currents stronger than any reported to date, enabling direct interfacing with neurons without the need for amplification. Lastly, I will discuss our work on hydrogel-coated seeds, which resolves a longstanding agricultural conundrum: while such coatings have been inconsistently beneficial, we show that the key determinant is not hydration rate, as commonly assumed, but gas permeability—since the coating can block oxygen entry through the micropyle and hilum, delaying germination in some species. Together, these projects illustrate how plant-inspired poroelastic mechanisms can inform the design of soft sensors, actuators, and controlled-release systems.

MSE Seminar : "Semiconducting Materials for Opto/Bioelectronic Applications; Chemistry, Processing and Device Engineering" Antonio Facchetti - Georgia Institute of Technology
2025-11-13 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
In this presentation we report the realization of novel semiconductor materials, as well as thin-film processing and morphology engineering, for flexible and stretchable organic electronic devices such as thin film transistors, solar cells, electrolyte gated transistors, sensors and neuromorphic circuits. On material development, we present “soft” small-molecules and polymers by co-polymerizing pi-deconjugated building blocks, properly designed additives and tuned backbone conformation. New transistor architectures using semiconductor film porosity as the key element for enhancing mechanical flexibility and tune charge transport are also demonstrated. These films, combined with pre-stretched elastomeric substrates, enabling unprecedentedly stable current-output characteristic upon mechanical deformation, which are used for sensing analytes, strain, light, temperature and physiological parameters. Furthermore, we report our recent work on the design of fluorinated acceptors for organic solar cells and a new route to achieve ultra-stretchable solar cell devices. Finally, we report our recent work on molecular n-doping of organic semiconductors using a novel strategy involving catalysts.

FOLDS seminar: Learning in Strategic Queuing
2025-11-13 12:00 - 13:00 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Zoom link: https://upenn.zoom.us/j/98220304722

Over the last two decades we have developed good understanding how to quantify the impact of strategic user behavior on outcomes in many games (including traffic routing and online auctions) and showed that the resulting bounds extend to repeated games assuming players use a form of learning (no-regret learning) to adapt to the environment. However, these results assume that there is no carry-over effects between rounds: outcomes in one round have no effect on the game in the future. Many repeated games have an evolving state resulting in direct carry-over effect, such as repeated auctions with budgets, as well as queuing systems. In this talk we will study this phenomenon in the context of a game modeling queuing systems: routers compete for servers, where packets that do not get served need to be resent, resulting in a system where the number of packets at each round depends on the success of the routers in the previous rounds. We study the required excess server capacity needed to guarantee that all packets get served in two different queuing systems (with or without buffers) despite the selfish (myopic) behavior of the participants.

ESE Ph.D. Thesis Defense: "Microfabricated devices for in-vivo sensing for mitochondrial assessment"
2025-11-14 09:00 - 11:00 | Room 221, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Monitoring of oxygen concentration in biological tissues is essential for understanding cellular metabolism, mitochondrial function, and tissue regeneration. Mitochondrial dysfunction is linked to a wide range of metabolic and degenerative diseases, yet current diagnostic approaches lack the capability to continuously measure oxygen and metabolite dynamics in vivo. This work introduces an implantable electrochemical sensor platform developed for real-time monitoring of tissue oxygen tension to support diagnosis and study of mitochondria-related disorders. The research encompasses device design, fabrication, preclinical validation, and clinical testing within a translational framework. A complementary lactate sensor further extends the platform’s diagnostic capabilities, establishing groundwork for future metabolic monitoring technologies.

Fall 2025 GRASP on Robotics: Jan Peters, Technische Universität Darmstadt & German Research Center for Artificial Intelligence, "Inductive Biases for Robot Learning"
2025-11-14 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This event will be in-person ONLY in Wu and Chen Auditorium.
ABSTRACT
The quest for intelligent robots capable of learning complex behaviors from limited data hinges critically on the design and integration of inductive biases—structured assumptions that guide learning and generalization. In this talk, Jan Peters explores the foundational role of inductive biases in robot learning, drawing from insights in control theory, neuroscience, and machine learning. He discusses how exploiting physical principles, modular control structures, symmetry, temporal abstraction, and domain-specific priors can drastically reduce sample complexity and improve robustness in robotic systems.
Through a series of concrete examples—including robot table tennis, tactile manipulation, quadruped locomotion, and dynamic motor skill learning on anthropomorphic arms—Peters illustrates how inductive biases enable efficient policy search, reinforcement learning, and imitation learning. These applications demonstrate how embedding prior knowledge about motor primitives, control hierarchies, or contact dynamics helps robots acquire versatile skills with minimal data. The talk concludes with a vision for future robot learning systems that integrate such structured biases with modern data-driven methods, enabling scalable, adaptive, and generalizable autonomy in real-world environments.

MEAM Seminar: "Multiplying the Coolness of Gels: Messy Networks, Double Networks, and More"
2025-11-18 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Many materials we eat, spread, squeeze, or 3D print are gels, soft amorphous solids whose solid component comprises self-assembled networks of particles, fibers, or agglomerates of proteins, polymers, and colloids. The space between and within human cells is permeated by self-assembled gel networks, the extra-cellular matrix and the cytoskeleton, whose self- organization and heterogeneity is central to biological functions. Self-assembled gels have adaptive, tunable, and nonlinear rheology determined by a complex interplay between the molecular cohesion and surface interactions, the aggregation kinetics that drive formation of various types of structures, and the effect of external forces that can promote breaking or reforming of the load-bearing backbone. Solidification processes are typically sources of frozen-in stresses and help build a memory of the processing history in these amorphous solids. Disorder and self-organization determine stress localization under load and the feedback between stress heterogeneities, structural disorder, and nonequilibrium conditions is therefore key to the mechanical response of these fascinating and ubiquitous materials.

ESE Fall Seminar - "Developing Custom Portable Low-Field MRI for Point-of-Care Imaging"
2025-11-18 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
MRI remains the gold standard in neuroimaging, but its high costs, large footprint, and infrastructure requirements limit deployment in many settings—including intensive care units (ICUs), emergency vehicles, and neonatal intensive care units (NICUs). These challenges demand portable, low-cost MRI systems specifically engineered for operation in space- and power-limited environments. Rather than adapting commercial scanners, our work focuses on the engineering design and prototyping of custom low-field MRI systems, using permanent magnets and compact hardware tailored to specific use cases. We hardware development with model-based reconstruction techniques to reduce system complexity and improve performance. This presentation will highlight several systems developed in our lab at the MGH Martinos Center—including a dedicated neonatal brain scanner—and will emphasize the technical and practical considerations involved in building accessible MRI from the ground up.

Penn AI Presents: “How Brains and Machines Solve the Binding Problem”
2025-11-18 12:00 - 14:00 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Despite decades of research, we still do not know how the brain integrates the many features of an object into a coherent whole, or whether artificial systems perform similar binding. In our first study, we find that large self-supervised vision transformers spontaneously develop a low-dimensional “same-object” representation that predicts whether two image patches belong to the same object with over 90% accuracy. Removing this signal disrupts segmentation, showing that object binding naturally emerges in deep networks trained on natural images. In our second study, we develop a mechanistic model of attention and binding that captures core neurobiological phenomena such as selective focus and inhibition of return. Together, these results suggest a shared computational principle: binding arises from structured interactions between distributed representations, enabling both brains and machines to form coherent percepts from fragmented sensory data.
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CIS Seminar: "Diffusion Generative Models for Non-Euclidean Data"
2025-11-18 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
As a major powerhorse for generative AI, diffusion models have demonstrated great successes in Euclidean spaces, such as for generating images and videos. This talk, on the other hand, will focus on a more nascent aspect, namely non-Euclidean diffusion models. One can for example consider the generative modeling of data that are discrete, living on manifold, constrained, or with multiple such modalities, as they correspond to important applications, some just emerging, including (vision-) language model, robotic motion planning, molecular engineering, and the design of quantum systems. After briefly introducing selected works of ours on these topics, I will expand on one example, where data live on a special type of manifolds known as Lie groups. Such a setting arises in the Gen-AI design of protein, robotic planning, and quantum problems. By leveraging and meshing variational optimization, delicate interplays between continuous- and discrete-time dynamics, and deep connections between optimization, sampling and optimal transport, I will turn our recent accelerated manifold optimization technique, first into a sampler that is fast converging without requiring log-concavity condition or its common relaxations, and then into an efficacious Lie group generative model. If time permits, theoretical understandings of selected diffusion models will also be briefly discussed.

MEAM Ph.D. Thesis Defense: "Mechanical Behavior and Fracture of Fibrous Materials at Large Deformations"
2025-11-19 10:30 - 11:30 | Towne 227 (MEAM Conference Room), 220 S. 33rd Street, Philadelphia, PA, 19104, United States
The mechanical behavior of fiber network materials is characterized by large deformations before failure and a strain-stiffening stress-strain response. In this thesis, this is investigated using discrete simulations at the microstructural level along with theoretical and computational results from continuum models. Cauchy and first Piola-Kirchhoff stress tensors for discrete networks of central-force elements are defined based on the equivalence of virtual power between the discrete system and its continuum representation. The framework is rigorously validated by demonstrating that non-uniform stress fields computed in discrete simulations of networks with defects and inhomogeneities show excellent agreement with continuum predictions for problems involving both infinitesimal and large, non-linear deformations. Network materials are reported to resist fracture in the presence of cracks, and this property is related to their nonlinear behavior. A computational model for the microstructure of network materials is developed to study the effects of a sharp crack under very large deformations. The computed stress field in the discrete network reproduces the asymptotic crack-tip fields based upon a continuum constitutive model derived from representative volume elements of the discrete network. The dominant stress components as a function of the undeformed distance from the crack follow a singular relation that is similar to the classical (small strain) HRR solution. The scaling exponent is determined completely by the constitutive behavior of individual fibers, which directly relates the macroscopic behavior directly to the microstructure. Finally, a continuum model that predicts macroscopic behavior for arbitrary states of deformation, including damage evolution, is constructed from mesoscopic simulations. The continuum model can access length- and time-scales that are inaccessible in a related class of high-fidelity discrete simulations, which allows prediction of fracture toughness, the material property that determines rupture resistance in the presence of defects.

ASSET Seminar: "Testing AI's Implicit World Models"
2025-11-19 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Many of the robustness properties that are required for real-world applications of AI would be realized by a model that has understood the world. But it is unclear how to measure understanding, let alone how to define it. This talk will propose theoretically-grounded definitions and metrics that test for a model’s implicit understanding, or its world model. We will focus on two kinds of settings: one where implicit world models are tested behaviorally, and another that tests a model’s representation. These exercises demonstrate that models can make highly accurate predictions with incoherent world models, revealing their fragility.

Zoom: https://upenn.zoom.us/j/95189835192
Passcode: [REDACTED]

Fall 2025 GRASP SFI: Wei Wang, University of Wisconsin–Madison, “Toward Advanced Autonomy in Complex Aquatic Environments”
2025-11-19 15:00 - 16:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This is a hybrid event with in-person attendance in Levine 307 and virtual attendance via Zoom.
ABSTRACT
Marine robots have undergone significant growth, driven by recent advances in artificial intelligence, sensing technologies, and decision-making systems. As demands for ocean exploration, exploitation, and conservation continue to rise, there is an increasing necessity for advanced autonomy in marine robots at both individual and group levels, as well as for frequent and safe deployment of marine robots at an affordable cost. However, the current marine robots available still fall short in meeting the demands of real-world applications, facing challenges such as robust and safe control in complex environments, self-localization and mapping of their environment, and efficient sensing and coordination within a group. My long-term goal is enabling robots to perform tasks autonomously in challenging marine environments by developing innovative robots and advanced algorithms. In this talk, I will highlight my research in three key areas: 1) learning-based control and navigation of 2D surface vehicles in urban waterways, 2) design and coordination of 2D multi-robot systems on the water surface and 3) developing biologically inspired robots and sensors to address challenges faced by 3D underwater robots and robotic swarms. I will also briefly discuss open research problems on these topics.

MSE PhD Thesis Defense: "Rheology and Clogging Study of Filamentous Suspensions: Bridging Microscopic Dynamics and Macroscopic Behaviors"
2025-11-20 09:00 - 10:00 | Towne 337
Suspensions of filamentous materials, or filamentous suspensions, represent a unique class of complex fluids in which the interplay between particle anisotropy and filament interactions – both intra- and inter-filament associations –gives rise to rich and tunable rheological behaviors. Such suspensions are ubiquitous across natural and engineered systems, with their applications ranging from the locomotion of microorganisms and drag reduction in turbulent flows to fiber processing in papermaking and the development of functional nanomaterials. Their characteristics of a filamentous or fibrillar nature, marked by a high aspect ratio particle structure and finite mechanical flexibility, enable not only the formation of entangled networks in which mechanics and dynamics depend strongly on dimension, concentration, and surface chemistry, but also bring greater complexity to material responses and flow phenomena. Despite their widespread utilization as colloidal materials, understanding how filamentous suspensions are affected by interactions among molecular species and filaments under varying conditions (pH, temperature, ionic strength, etc.), as well as mechanisms governing clogging, remains limited. Such understanding is crucial for tailoring their properties and performance to meet the needs of specific applications, such as rheology modification of complex fluids. To address these knowledge gaps, two complementary studies—rheology and clogging—are performed independently at the microscopic and macroscopic scales, respectively, to elucidate how the filamentous structure and associated physical properties manifest across multiple length scales. First, we systematically investigate the rheology of cellulose nanofibril (CNF) suspensions in the presence of salts and polymers to probe microscopic mechanisms arising from fibril-fibril, fibril-salt, and fibril-polymer interactions. Our studies reveal how ion–colloid and polymer–colloid interactions dictate the formation, structure, and dynamic response of CNF networks, providing a framework for enhanced CNF-containing complex fluid formulations. Secondly, we examine the clogging behavior of suspensions of threadlike colloids to uncover the macroscopic mechanisms underlying collective dynamics in these granular systems. Analysis of clogging probability and flow conditions provides insight into the transition between flow and jammed (clogged) states. Together, these investigations establish a deeper understanding of how anisotropic filamentous geometry and its corresponding interactions govern complex rheological and flow-arrest behaviors, thereby guiding the design of advanced materials and the optimization of processing for filament-based materials.

MSE Seminar: "Building Cyberinfrastructure for Advancing Laboratories of the Future"
2025-11-20 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The development of automated experimental facilities and the growing trend of experimental data digitization brought enormous opportunities for radically advancing laboratories. As many laboratory research tasks involve predicting and understanding previously unknown physical or chemical relationships, the availability of experimental data enables machine learning (ML) approaches to substantially accelerate the conventional design-build-test-learn process. In this talk, we will introduce software packages, including RobustGaSP, RobustCalibration, FastGaSP, DDM-UQ, and AIUQ, for constructing scalable surrogate models to predict complex relationships, enabling efficient inverse estimation and materials characterization. Furthermore, we introduce how artificial intelligence (AI) agents based on large language models can help researchers learn background knowledge in materials science and data science to accelerate discovery processes. We will introduce various case studies enabled by these tools, including universal phase identification of block copolymers by small-angle X-ray scattering, ab initio uncertainty quantification in scattering analysis of microscopy, fast phase prediction of charged polymer blends by white-box machine learning surrogates, and reliable emulation of classical functional theory calculation by active learning with error control.

FOLDS seminar: Function Space Perspectives on Neural Networks
2025-11-20 12:00 - 13:00 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Zoom link: https://upenn.zoom.us/j/98220304722

This talk reviews a theory of the functions learned by neural networks with Rectified Linear Unit (ReLU) activations. At its core is the observation that deep ReLU networks can be characterized as solutions to data-fitting problems in certain infinite dimensional function spaces. The solutions are compositions of functions from Banach spaces of second-order bounded variation, defined in the Radon transform domain. Functions in these spaces exhibit strong smoothness in most directions, making it a natural setting for adapting to intrinsic low-dimensional structure in data. Moreover, the norms in these spaces are closely tied to the size of neural network weights, providing a direct connection between function complexity and network parametrization. In particular, the total variation norm provides an analytic tool for identifying functions that cannot be realized by shallow networks, thereby yielding a precise characterization of depth separation. Representer theorems reveal the solutions are sparse in the number of active neurons per layer. Sparsity provides a principled path to network compression, yet some sparse solutions can suffer from poor generalization. The theory suggests new training strategies to promote solutions that generalize more robustly.

MEAM Ph.D. Thesis Defense: "Exploring Self-Assembly of 2D Materials: Insights from Graphene Auto-Kirigami"
2025-11-20 16:00 - 17:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
In nature, thin sheets bend, fold, and curve to create functional three-dimensional forms—from insect wings to leaves and flower petals. Over the past decades, such behavior has inspired engineered systems ranging from soft robotics to deployable electronics. Extending these ideas to atomically thin two-dimensional (2D) materials such as graphene opens new opportunities: these materials can transform into structures that extend beyond flat geometry through self-driven processes. Graphene, for example, can spontaneously assemble into self-stacked structures through self-folding, self-tearing, and nearly frictionless self-propagation, all driven by interfacial energy. We introduce the term auto-kirigami (AK) to describe these self-folded and self-propagating structures, in analogy to kirigami—the cutting and folding of thin sheets to create complex morphologies, highlighting the spontaneous nature of this process.
AK represents a unique form of self-assembly in 2D materials with implications spanning function, fabrication, and fundamental understanding. Yet, key questions remain regarding how AK initiates and grows, and how it varies across material systems. In particular, the mechanisms that control its onset, directionality, and resulting morphology remain unresolved. In this talk, I will present a comprehensive investigation of AK formation that integrates experiments, continuum modeling, and molecular dynamics simulations. I will show how tip-induced shear in graphene enables reproducible AK formation, how lattice-level anisotropies govern its directional growth and geometry, and how atomistic simulations reveal the initiation pathways inaccessible to experiments. These results provide a unified understanding of AK as a self-driven phenomenon and highlight its potential as a programmable route to reconfigurable nanoscale architecture.

Fall 2025 GRASP on Robotics: Jie Tan, Google DeepMind, “Gemini Robotics: Bringing AI into the Physical World”
2025-11-21 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This event will be in-person ONLY in Wu and Chen Auditorium.
ABSTRACT
Recent advancements in large multimodal models have led to the emergence of remarkable generalist capabilities in digital domains, yet their translation to physical agents such as robots remains a significant challenge. In this talk, I will present Gemini Robotics, an advanced Vision-Language-Action (VLA) generalist model capable of directly controlling robots. Gemini Robotics executes smooth movements to tackle a wide range of complex manipulation tasks while also being robust to variations in object types and positions, handling unseen environments as well as following diverse, open vocabulary instructions. With additional fine-tuning, Gemini Robotics can be specialized to new capabilities including solving long-horizon, highly dexterous tasks, learning new short-horizon tasks from as few as 100 demonstrations and adapting to completely novel robot embodiments. Furthermore, I will discuss the challenges, learnings and future research directions on robot foundation models.

ESE Guest Seminar - "The Nonlinear Small-Gain Theory for Networks and Control"
2025-11-21 12:15 - 13:15 | Greenberg Lounge (Room 114), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
The world is nonlinear and linked. In this talk, I will present the origin of the small-gain theory and show that it serves as an important systematic tool for addressing two fundamental problems for networks: When is a dynamical network robustly stable? When can a dynamical network be made robustly stable by feedback? As an illustration, we show how the small-gain theory can be applied to unify solutions to event-triggered nonlinear control and provide novel solutions to distributed feedback optimization. Finally, if time permits, I will discuss briefly learning-based control, a new direction in control theory, that aims to integrate machine learning and nonlinear control techniques to relax the conservativeness of small-gain designs.

ESE Ph.D. Thesis Defense: "On Riccati Equations in Nonconvex Optimization"
2025-11-21 12:30 - 14:00 | Amy Gutmann Hall, Room 515, 3317 Chestnut Street, Philadelphia, 19104, United States
Riccati equations are ubiquitous in systems/control theory and are frequently solved by the methods of continuous optimization. In some cases, it is known that solutions can be obtained quickly and efficiently by convex-optimization methods, but small modifications to these settings can easily destroy convexity, limiting the applicability of convex-optimization methods. This thesis considers manifold and stochastic optimization techniques to address cases where Riccati-Equations/Riccati Constraints cannot be addressed by convex-optimization methods. Specifically, this thesis studies system-identification, where Riccati constraints are required to ensure synthesizability, and in control synthesis, where Riccati constraints ensure stabilization. The methods developed here yield tractable procedures for identification and synthesis, extending the reach of Riccati-based design beyond what is accommodated by convex-optimization frameworks.
