BE Doctoral Dissertation Defense: "High-throughput and high-dimensional single-cell analysis of antigen-specific CD4+ T cells" (Yuwan Guo)
2024-12-05 11:00 - 13:00 (America/New_York) | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
The Department of Bioengineering at the University of Pennsylvania and Dr. Jenny Jiang are pleased to announce the Doctoral Dissertation Defense of Yuwan Guo.

Title: High-throughput and high-dimensional single-cell analysis of antigen-specific CD4+ T cells
Advisor: Dr. Jenny Jiang

Date :Thursday, Dec 5, 2024
Time: 11am – 1pm
Location: Glandt Forum, 3rd floor Singh Center

The public is welcome to attend.

ESE PhD Seminar: "Multiferroic MEMS Magnetic Field Sensors for Biomedical Applications"
2024-12-05 12:00 - 13:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
The human body produces magnetic fields wherever ion exchange occurs. Detecting these pico-Tesla level magnetic fields enables non-invasive monitoring of brain and heart health, but medical-grade sensing methods require large equipment with high power consumption. This talk will detail a solution using microelectromechanical systems (MEMS) composed of a magnetostrictive and piezoelectric material. After an introduction to the sensor design and operation, we will discuss the design process, electrical and magnetic characterization, and results.

BE Doctoral Dissertation Defense: "Understanding the genome's structure-function relationship in early neural lineage commitment" (Katelyn Titus)
2024-12-05 15:30 - 16:30 | CRB Auditorium, 415 Curie Boulevard, Philadelphia, PA, 19104, United States
The Department of Bioengineering at the University of Pennsylvania and Dr. Jennifer Cremins are pleased to announce the Doctoral Dissertation Defense of Katelyn Titus.

Title: Understanding the genome’s structure-function relationship in early neural lineage commitment
Date: December 5, 2024
Time: 3:30 PM
Location: CRB Austrian Auditorium

The public is welcome to attend.

Fall 2024 GRASP on Robotics: Bill Smart, Oregon State University, "Privacy-Sensitive Robotics"
2024-12-06 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This will be a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom.
ABSTRACT
As robots become more and more prevalent, both in the workplace and outside it, they will have greater access to the details of our lives. Sensors used by these robots to make intelligent decisions about what to do can also be used to record the people and things around them. These sensors are fundamentally different from existing fixed infrastructure, such as surveillance cameras, because they are attached to a mobile system, capable to operating autonomously. How should we think about the new privacy risks that such systems bring with them? How can we mitigate these risks, while still reaping the benefits offered by the widespread use of robots? What do we even mean when we say the word “privacy”? In this talk, we’ll give a framework for how to think about privacy in the context of mobile robot systems, outline some of the potential risks introduced by this new technology, and show some examples of technical mitigations that can preserve privacy, while still allowing the robot to do its job.

ESE Fall Seminar - "Quantum information processing stack: from bottom to top and back"
2024-12-06 11:00 - 12:00 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Quantum processors have become quite large and sophisticated machines over the last several years, with many tech companies racing to develop the first quantum computer of practical utility. While the progress has been impressive, quantum processors still face significant hurdles such as short coherence times and high error rates. They are not yet able to compete with classical information processing technologies in solving problems of practical interest. I will review some of the recent advances in the field and discuss my group’s contributions across the quantum information processing stack, from the control of quantum hardware to quantum algorithm development and back.

BE Doctoral Dissertation Defense: "Clonal differences underlie differential responses to initial, sequential, and prolonged drug treatment" (Dylan Schaff)
2024-12-06 13:00 - 15:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The Department of Bioengineering at the University of Pennsylvania and Dr. Sydney Shaffer are pleased to announce the Doctoral Dissertation Defense of Dylan Schaff.

Title:”Clonal differences underlie differential responses to initial, sequential, and prolonged drug treatment”
Date:December 6, 2024
Time: 1pm-3pm
Location: Wu and Chen auditorium, Levine Hall

The public is welcome to attend.

PICS Colloquium: Lipid Membrane Remodeling by Proteins and Peptides: Mechanistic insights from multi-scale analysis
2024-12-06 14:00 - 15:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
We will discuss the analysis of membrane remodeling by proteins and peptides using multi-scale computational methods; these include mainly molecular dynamics simulations at atomistic and coarse-grained levels, although we will also touch upon analyses using lattice models and a mean- field theory. The discussions will cover several systems that we have analyzed in recent studies, which include the SAR1 protein from the COPII machinery and the ESCRTIII complex; we will also briefly discuss how protein condensates interact with lipid membranes, especially in terms of their mutual influence on morphology and phase behaviors. These examples illustrate different molecular properties and mechanisms that are potentially relevant to membrane remodeling, as well as the values and limitations of various computational methodologies in such context.

MEAM Seminar: "Leveraging Impedance Properties for Free Self-Sensing in Actuators for Compact Robots"
2024-12-12 12:00 - 13:00 | Towne 319, 220 S. 33rd Street, Philadelphia, 19104, United States
Self-sensing actuators provide a compelling approach to designing compact robotic systems by integrating sensing capabilities directly into the actuator, eliminating the need for external sensors. This presentation, titled “Leveraging Impedance Properties for Free Self-Sensing in Actuators for Compact Robots,” highlights how the intrinsic impedance properties of actuators—resistance, inductance, and induced EMF—can be harnessed to achieve low-volume solutions of self-sensing actuators. Evidence for this approach is demonstrated through three projects: a self-sensing I-cord knitted SMA actuator that uses resistance measurements to monitor its state, applied in an inchworm robot and a gripper; a custom PET solenoid actuator that utilizes induced EMF to sense movement and velocity, enabling applications such as an origami-inspired bistable gripper and a facial reanimation device acting as an artificial muscle; and a custom acrylic linear solenoid actuator that leverages inductance to self-sense plunger position, demonstrated as an active valve in underwater swimming robots. These examples illustrate the potential of impedance-based self-sensing to streamline actuator design, enhance functionality, and enable new possibilities in robotics across domains.

CBE Doctoral Dissertation Defense: "Engineering Biomolecular Condensates: Insights Into Catalytic Activities and Structural Design" (Muyang Guan)
2024-12-13 14:30 - 16:00 | https://upenn.zoom.us/j/96301746733
Abstract:
Eukaryotic cells orchestrate cellular processes through spatiotemporal organization achieved by compartmentalization. Biomolecular condensates, formed via liquid-liquid phase separation (LLPS), serve as vital hubs for enzymatic reactions and mediate cellular functions.
To investigate catalytic activities within biomolecular condensates, a light-emitting enzyme (NanoLuc) was incorporated into LAF-1 RGG model condensates. Incorporation into condensates led to a significantly enhanced enzymatic reaction rate for the two-phase system compared to the single-phase reaction. The reaction exhibited diffusion-limited behavior in the condensed phase, with light output affected by condensate viscosity. Given the low viscosity of our model condensates, this diffusion-limited behavior is anticipated in most condensates, suggesting the potential use of a condensate-NanoLuc system for identifying drug molecules that modulate condensate viscosity. In addition, complementation of enzyme fragments inside condensates and recruitment of cargo proteins at condensate surface was demonstrated.
To study the miscibility between different condensed phases, a second model condensate from the low complexity (LC) domain of human protein FUS was constructed. At physiological salt concentration, FUS LC and RGG condensates formed discrete condensed phases that are immiscible. The two condensates partially wet each other, indicating similar surface tensions. At high salt concentrations, the two phases formed a core-shell structure, suggesting the effect of charge interactions on interfacial tension. Additionally, viscoelasticity affected miscibility between two condensed phases. Further, dynamically driven substructures were observed as the system underwent out-of-equilibrium transition. This aspect can be harnessed to produce multiphasic condensates by kinetically trapping FUS LC inside RGG condensates. To systematically coordinate the interaction between the two phases, a designer “surfactant” protein was developed, reducing the FUS LC – RGG interfacial tension and enabling the integration of complex assemblies of two phases. These strategies demonstrated the ability to generate novel structures by bridging two phases that do not normally integrate, showcasing the potential for controlling condensate architecture.
In summary, this work provides valuable insights into the principles and strategies for modulating biomolecular condensate functions and structures, laying a foundation for further exploration and potential therapeutic applications.

ESE Ph.D. Thesis Defense: "Wave Interaction with Nonreciprocal Swift-Electron Platforms and Reconfigurable Metasurfaces"
2025-01-13 10:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
The study of electromagnetic wave interactions with various media is of fundamental significance in both theoretical and applied sciences. Understanding how electromagnetic waves propagate, reflect, refract, and scatter when encountering different materials is essential for the design of advanced technologies in telecommunications, imaging, sensing, and computation. Furthermore, the ability to manipulate wave behavior within diverse media presents new possibilities for developing high-performance devices, such as antennas, sensors, and optical systems, with applications ranging from wireless communications to medical diagnostics.
Nonreciprocal devices, which enable unidirectional signal propagation, are critical for protecting systems from interference and enhancing the performance of telecommunications and photonic technologies. Achieving a robust nonreciprocal response is vital for ensuring signal integrity and efficiency in these systems. In this study, I present theoretical models that explore the strong and tunable nonreciprocal response of swift electrons interacting with various structures, including those in vacuum and graphene-based media. My analytical and numerical analysis demonstrates that guided modes in metallic and dielectric waveguides can be effectively manipulated by fast-moving electrons, resulting in unidirectional propagation regimes and significant nonreciprocity in light-matter interactions. Additionally, I introduce a beam-steering structure based on the interaction between antenna radiation and an electron sheet comprising swift moving electrons at constant velocity. The nonreciprocal strength of these interactions is further modulated through space-time variation of electron velocity and density, demonstrating extra degree of freedom in controlling wave propagation. The strong nonreciprocal response achieved using this method is highlighted through comparative analysis with existing approaches. I also investigate nonreciprocity in graphene-coated optical fibers, where electrically biased electrons moving along the fiber axis interact with the guided modes of the dielectric fiber. Moreover, I explore the impact of electrical bias on the absorption rate and resonance frequency of nano-patterned graphene based metasurface. This study opens new avenues for controlling and manipulating electromagnetic wave propagation in THz systems, where the nonreciprocity strength is not inherently limited by material properties.
Further, I explore the integration of memristors into conventional electromagnetic devices, which represents advancement in information storage and processing. The convergence of memristive properties with electromagnetic wave propagation offers promising opportunities for new computing paradigms, including neuromorphic computing, data storage, and communication systems. I demonstrate theoretically the design and functionality of a metastructure composed of an array of memristors (mem-cells), illustrating how this configuration can effectively capture both the amplitude and phase characteristics of incident electromagnetic waves.

MSE Ph.D. Thesis Defense: "Controlling Assembly of Polymer-grafted Nanoparticles to Enhance Mechanical Properties in Polymer Nanocomposite Films"
2025-01-13 14:00 - 16:00 | LRSM Reading Room, 3231 Walnut St., Philadelphia, PA, 19104, United States
Polymer nanocomposite (PNC) films are of interest for many applications including electronics, energy storage, and advanced coatings. In phase-separating PNCs, the interplay between thermodynamic and kinetic factors governs the assembly of polymer-grafted nanoparticles (NPs), which directly influences material properties. Understanding how processing parameters affect the structure-property relationship of PNCs is important for designing advanced materials. This thesis provides new insight by investigating a model PNC system of poly(methyl methacrylate)-grafted nanoparticles (PMMA-NPs) embedded in a poly(styrene-ran-acrylonitrile) (SAN) matrix.
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) was developed to quantify the distribution of NPs within PMMA-NP/SAN films, enabling precise 3D reconstruction of PNC structures. Experimental parameters such as primary ion beam angle and charge compensation were optimized to enhance secondary ion signals and depth resolution. Upon annealing in the two-phase region, PMMA-NP/SAN films exhibited phase separation and surface segregation, leading to morphological evolutions characterized by atomic force microscopy (AFM), ToF-SIMS, water contact angle measurements, and transmission electron microscopy. By systematically exploring the effects of film thickness on PNC structures, we found that film thickness-induced confinement reduces lateral phase separation and enhances NP dispersion at the surface. A dimensional crossover from three to two dimensions was observed around 240 nm, below which surface-directed spinodal decomposition is suppressed. As a result of phase separation and surface segregation, six distinct bulk morphologies were identified, allowing for the construction of a morphology map correlating film thickness and annealing time. Among these morphologies, percolated structures were found to improve mechanical properties such as hardness and modulus, while interconnected networks show the highest hardness and modulus at both low and high force loadings, as measured using AFM nanoindentation. Additionally, Marangoni-induced hexagonal honeycomb patterns were observed in spin-coated as-cast PMMA-NP/SAN films. By changing to a less volatile solvent, these defects were eliminated, demonstrating the importance of solvent selection in achieving uniform and high-quality thin films.

These findings demonstrate the potential for precise control of surface-enriched and phase-separated microstructures in PNC films through tailoring processing conditions. This thesis advances the understanding of PNC processing-structure-property relationships, providing a foundation for designing highly functional materials with broad industrial applications.

ASSET Seminar: "Poison and Cure: Non-Convex Optimization Techniques for Private Synthetic Data and Reconstruction Attacks"
2025-01-15 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract:
I will survey recent results describing the application of modern non-convex optimization methods to the problems of reconstruction attacks on private datasets (the “poison”), and the algorithmic generation of synthetic versions of private datasets that provably provide strong privacy guarantees (the “cure”).
Zoom Link (if unable to attend in-person): https://upenn.zoom.us/j/97716959173

MSE Grace Hopper Lecture: "Bioelectronics to Probe Brain-Body Physiology" (Polina Anikeeva - Massachusetts Institute of Technology)
2025-01-16 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Biological signaling in the mammalian nervous system spans a dizzying range of spatial and temporal scales. To understand how cellular and molecular signals contribute to physiology and behavior and to treat the neurological and psychiatric conditions our group designs tools that mimic biological complexity yet match the materials properties of tissues. By combining polymer engineering, fiber drawing, and solid-state microelectronics we create scalable fiber-based tools that record and modulate cell signaling in the central and the autonomic nervous systems in behaving rodents. Using these fiber-based tools we reveal the contributions of gut-brain circuits not only to ingestive and metabolic functions but also to high-level behaviors previously attributed exclusively to brain signaling. To probe receptor contributions to neural circuit dynamics, we synthesize magnetic nanotransducers that convert externally applied magnetic fields into thermal, chemical, mechanical, and electrical signals. Since biological tissues exhibit negligible magnetic permeability and low conductivity, magnetic fields can penetrate deep into the body with no attenuation allowing us to apply the nanomagnetic transducers to remotely modulate ion channel function in arbitrarily deep tissues. We employ magnetic neuromodulation to control reward and motivation circuits and extend their applications to relieve motor dysfunctions in mouse models of Parkinson’s disease.

MEAM Seminar: "Modeling the Unique Behaviors of Liquid Crystal Elastomers"
2025-01-21 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Liquid crystal elastomers (LCEs) exhibit complex thermomechanical behaviors that can be harnessed for a wide variety of applications in soft robotics, biomedical devices, and impact protection. The material comprises stiff mesogens bound in an elastomeric network of flexible polymer chains. The mesogens can order and disorder in response to temperature and mechanical deformation. This allows LCEs to undergo reversible phase transitions between the disordered isotropic, ordered monodomain, and polydomain states. The motion of the mesogens relative to the polymer network also leads to unique behaviors, including large reversible actuation response to temperature and soft elasticity. LCEs also display enhanced dissipation over conventional elastomers from the viscous rotation and ordering of the mesogens and relaxation of the network chains. The viscoelastic dissipation mechanisms can be exploited to design LCE materials and structures with extraordinary toughness, impact energy absorption, and mechanical damping. Yet, these same properties may impede the actuation and morphing capabilities of the material. Predictive modeling is needed to efficiently design and optimize LCE structures to achieve the desired performance. This presentation will describe our efforts to develop generalized continuum theories for the thermoviscoelastic behavior of monodomain nematic elastomers that describe the viscous director rotation, viscous mesogen ordering, and viscoelastic network deformation mechanisms. We specified constitutive functions for the nematic and mechanical components of the free energy density and viscosities based on experimental observations, and applied the resulting models to design energy-absorbing architected materials, evaluate the effectiveness of actuators, and optimize the director pattern of monodomain nematic elastomeric sheets to maximize viscoelastic dissipation.

ASSET Seminar: "Towards Improving the Reliability of AI: Perspectives from Uncertainty Quantification and Fairness"
2025-01-22 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract:
Artificial intelligence holds enormous promise to automate our life. At the same time, AI systems can be incredibly brittle, unreliable, and biased. In this talk, I will present a few approaches towards making AI more reliable, trustworthy, and fair, drawing on recent work on uncertainty quantification and algorithmic fairness. Specifically, I will discuss how to make large language models more calibrated, how to construct prediction sets for data with multiple missing outcomes, and how to obtain optimally accurate fair classifiers under constraints on linear disparity measures.
Zoom Link (if unable to attend in-person): https://upenn.zoom.us/j/97670810812

CBE Seminar: "Unlocking New Therapeutic Strategies by Reconstructing Cell Signaling Networks from the Ground Up" (Mohamad Abedi, University of Washington, Seattle)
2025-01-22 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
Immunotherapies have achieved remarkable success in treating hematological cancers, yet the solid tumor microenvironment remains a significant barrier to therapeutic breakthroughs. Machine learning (ML)-driven computational protein design offers a powerful approach to creating novel protein components tailored for specific functions. By combining ML-driven design with synthetic biology and immuno-engineering, we have developed innovative tools to overcome therapeutic barriers within the tumor microenvironment while deepening our understanding of the biological systems involved. In the first half of this seminar, I will introduce three new technologies developed using these approaches: (1) a receptor degradation platform to remodel the tumor microenvironment and enhance therapy, (2) a novel class of receptors that sense soluble tumor markers and localize therapeutic activity, and (3) de novo-designed many-to-many protein networks resembling mammalian signaling complexity. In the second half, I will present a computational protein design pipeline that reconstructs cytokines from the ground up. This approach allows us to design cytokines with customizable receptor clustering geometries, enabling precise exploration of their cell-specific effects and the ability to fine-tune their naturally pleiotropic activities. Furthermore, this approach enabled us to transcend nature’s designs by creating entirely novel cytokines that cells have never encountered before, thus unlocking new therapeutic opportunities to manipulate the tumor microenvironment and expand our understanding of the underlying signaling mechanisms.

ESE Seminar - "A changing grid powered by the new generations of power conversion, control, and energy management"
2025-01-23 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
The electric grid is undergoing a transformative paradigm shift, driven by sweeping changes in generation, demand, and energy storage. By 2035, solar PV alone is expected to supply 40% of U.S. electricity, with substantial additional contributions from wind, geothermal, and hydroelectric sources—creating a renewable-dominant energy landscape. Meanwhile, electricity demand is accelerating due to rapid growth in electric vehicle (EV) charging, data centers, industrial manufacturing, and universal electrification across sectors. By 2050, EV charging is anticipated to represent 23% of the nation’s electricity demand. Energy storage system installations are also expanding to manage the variability of renewable sources, support dynamic loads, and ensure grid stability.
This seminar will discuss the pivotal role of high power, high voltage, and high frequency power converters in creating a flexible, efficient, and resilient grid. We will examine integration challenges for 15kV-class converters within the AC grid, as well as the opportunities these converters present for real-time voltage stabilization, frequency regulation, and dynamic grid management. High-bandwidth converters are key enablers of a smart, adaptable grid, facilitating higher renewable penetration, dynamic load management, and enhanced compatibility with energy storage. A few projects on grid-connected power electronics will highlight how these advancements can drive the evolution of a robust and versatile grid architecture to meet the demands of a power electronics-driven energy future.

BE Seminar - "The Neurobiology of Mistakes" (Becket Ebitz, Université de Montréal)
2025-01-23 15:30 - 16:30 | Berger Auditorium (Room 13), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
Humans and other animals do not always choose the most rewarding course of action, even when we have ample time and computational resources.Why do we make mistakes? The noiseLab uses a combination of theoretical, behavioral, and neurobiological techniques to address this question. In this talk, Dr. Becket Ebitz will discuss converging evidence that mistakes are the product of representational and temporal nonlinearities in neural activity that constrain our ability to make good decisions. The talk will argue that some of these nonlinearities have long-term adaptive benefits, even when they fail to produce the best decision in the moment.

2025-01-23 15:30 - 16:30 | Berger Auditorium (Room 13), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
Humans and other animals do not always choose the most rewarding course of action, even when we have ample time and computational resources.Why do we make mistakes? The noiseLab uses a combination of theoretical, behavioral, and neurobiological techniques to address this question. In this talk, Dr. Becket Ebitz will discuss converging evidence that mistakes are the product of representational and temporal nonlinearities in neural activity that constrain our ability to make good decisions. The talk will argue that some of these nonlinearities have long-term adaptive benefits, even when they fail to produce the best decision in the moment.

CIS Seminar: "Generative Language Models for Biotherapeutics Design"
2025-01-24 10:30 - 11:30 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States

LRSM Seminar: "Expanding Our Vision of Glasses: Physical Vapor Deposition Prepares Ultrastable and Anisotropic Materials"
2025-01-27 10:30 - 12:00 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Glasses are generally regarded as disordered and the idea of “controlling” molecular packing in glasses is reasonably met with skepticism. However, as glasses are non-equilibrium materials, a vast array of amorphous structures are possible in principle. Physical vapor deposition (PVD) produces glasses with properties that cannot be achieved by other preparation routes, including high stability and controlled anisotropy. The exotic properties of PVD glasses can be explained by a surface equilibration mechanism: mobility near the free surface allows substantial equilibration during deposition, even well below the conventional Tg. Initial work with organic glasses has now been extended to show aspects of ultrastability in metallic and chalcogenide glasses.
The active layers in commercial OLEDs are PVD glasses of organic semiconductors. Ultrastable glasses of organic semiconductors make longer-lasting OLEDs and in-plane orientation of emitter molecules produces more efficient OLEDs. Co-deposition of two organic semiconductors often produces a homogeneous ultrastable glass but, in other systems, component separation occurs in the plane of the sample, on a controllable length scale.

MEAM Seminar: "Robots that Evolve on Demand"
2025-01-28 10:15 - 11:15 | Wu & Chen Auditorium
Soft robots have the potential to adapt their morphologies and behavioral control policies to changing tasks and environments. Inspired by the dynamic plasticity of living organisms and the general adaptability of animals, this talk will discuss several shape-shifting soft robot platforms for multi-task performance and multi-environment locomotion—for example, robotic skins, robotic fabrics, and robots with morphing limbs. The talk will also explore the active material components, such as stretchable electronics and computation, soft actuation, and variable stiffness materials, that enable predictable robot morphology changes. By harnessing these engineered materials and mechanisms, we aim to unlock a wide range of capabilities for increasingly adaptive, evolving robots.

CIS Seminar: "Decentralized Mechanism Design: Cryptography Meets Game Theory"
2025-01-28 15:30 - 16:30 | Wu & Chen Auditorium
In classical auction design, we take it for granted that the auctioneer is trusted and always implements the auction’s rules honestly. This assumption, however, no longer holds in modern auctions based on blockchains, or those mediated by third-party platforms such as Google. For example, in blockchain-based auctions, the consensus nodes that partly act as the auctioneer are incentivized to deviate from honest behavior if profitable. Third-party auction platforms such as Google have also been involved in high-profile anti-trust lawsuits for manipulating their auctions.

In this talk, I will describe our recent work on decentralized mechanism design, where we aim to build a new scientific foundation for emerging auctions that are not backed by a trusted auctioneer. I will characterize the mathematical landscape of decentralized mechanism design, by showing several infeasibility and feasibility results. I will also highlight how cryptography can play an essential role for bypassing impossibility results in decentralized mechanism design, leading to a new class of auctions that not only incentivize bidders to act honestly, but also incentivize the auctioneer to play by the book.

ASSET Seminar: "Efficient Sharing of AI Infrastructures with Specialized Serverless Computing"
2025-01-29 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract:
The efficient sharing of AI infrastructures is becoming increasingly important in both public and private data centers. This demand is driven by two key factors: the proliferation of specialized AI models tailored for different users and applications, and the highly dynamic nature of requests, which are often on-demand. Dedicated GPU allocation in such scenarios results in prohibitively high costs and inefficient resource utilization.
In this talk, I will introduce serverless computing as a promising paradigm for addressing these challenges by enabling efficient, on-demand sharing of AI infrastructures. I will highlight its use cases and discuss key barriers to broader adoption. Following this, I will present ServerlessLLM, a state-of-the-art system designed to tackle key challenges in serverless large language model (LLM) inference, particularly cold-start latency. Specifically, I will cover ServerlessLLM’s novel contributions, including its checkpoint format design, locality-aware scheduling, and inference request live migration. Finally, I will outline open challenges beyond efficiency, such as fairness, privacy, and sustainability, which are critical for the future of serverless AI systems.
Zoom Link (if unable to attend in-person): https://upenn.zoom.us/j/95090162762

Spring 2025 GRASP SFI: Guandao Yang, Stanford University, “Toward Spatial Intelligence with Limited Data”
2025-01-29 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
Modern artificial intelligences (AIs) rely heavily on internet-scale data with unified representations. However, such large-scale homogeneous data isn’t readily available for spatial computing applications involving 3D geometry, hindering the development of spatial intelligence— AIs that can generate and understand 3D spatial data. In this talk, I will present ideas toward building spatial intelligence systems with limited 3D data. I will discuss my work combining existing mathematical models in graphics with foundation models in machine learning to generate and analyze 3D shapes. Finally, I will conclude with a discussion about the future opportunities and challenges in developing data-efficient AIs for spatial computing and beyond.

CBE Seminar: "Engineering Technologies for Enhanced Modeling, Detection, and Treatment of Neurological Disease" (Alice Stanton, MIT)
2025-01-29 15:30 - 16:30 | Wu & Chen Auditorium
Abstract:
Neurological conditions are the leading cause of illness worldwide, though over 92% of clinically tested CNS drug candidates fail to become treatments. Contributing to this high failure rate is a lack of understanding of human disease mechanisms, technologies to address them, and the restrictive blood-brain barrier (BBB), which most compounds fail to cross. New models are critically needed that more faithfully recapitulate human neurological disease, providing a tool for enhanced discovery of biomarkers and targets, effective therapeutic development, and personalized drug screening. In this seminar, I will present my work in (1) developing biomaterials-based platforms to mimic physiological conditions, probe fundamental questions, and utilize as cell-carrying scaffolds, (2) establishing a multicellular human cell-based model of the brain, miBrain, that incorporates neuronal, glial, vascular, and immune components into 3D brain tissue with structural organization, combining the power of induced pluripotent stem cell (iPSC) technology and tissue engineering, and (3) utilizing the multicellular brain model to interrogate disease pathogenesis and delivery across the BBB, harnessing a microfluidic platform I developed to enable 3D vascular perfusion within the miBrain. In my doctoral work, I developed biomaterials-based platforms to probe fundamental questions of stem cell mechanotransduction and to utilize as cell-carrying scaffolds in tissue engineering approaches. In my postdoctoral work, I applied these engineering tools to develop an advanced preclinical brain model, extensively characterized and validated the platform, and leveraged this system to model disease and BBB transport. I differentiate iPSCs into each of the six major brain cell types and assemble them in the Neuromatrix Hydrogel I developed to incorporate brain-niche cues and support cell network co-formation, and culture them in high throughput well format or perfusable chip format. miBrains form integrated 3D immune-glial-neurovascular units with enhanced cell- and tissue-scale phenotypes inclusive of myelinated neuronal networks, microglial immune cells, and BBB. To enable perfusable vasculature within the miBrain, I developed a novel microfluidic platform, the GelChip, via a 3D printing fabrication strategy, that supports 3D network formation within complex co-cultures and engineered hydrogels to form the miBrain-on-Chip. Harnessing iPSCs from patient lines and genome editing to isolate the functional consequences of specific mutations, I can form multicellular brain models across patient cohorts, assess disease susceptibility, probe mechanisms, and screen putative interventions. I have harnessed the miBrain to model APOE4 risk for Alzheimer’s Disease, recapitulating canonical disease hallmarks of increased reactive astrocytes, amyloid aggregates, and neuronal tau phosphorylation. Further, I found that APOE4 astrocytes are sufficient to increase neuronal tau phosphorylation via crosstalk with microglia. I have thus established a novel preclinical brain model with broad utility for dissecting disease mechanisms, assessing delivery to the brain, and probing barrier function and other hallmarks in contexts of disease. In the conclusion of this seminar, I will explore how multicellular human cell-based brain models and their underlying technologies and approaches can be leveraged to accelerate mechanistic understanding, therapeutic target identification, and drug candidate optimization for treatment delivery and efficacy.

MSE Seminar: "Tuning Nanostructured Materials for Combustion Applications" (Kerri-lee A. Chintersingh, Ph.D., New Jersey Institute of Technology)
2025-01-30 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Metals powders like aluminum and boron are attractive potential fuel additives for pyrotechnics, propellants and explosives due to their high energy release upon oxidation. However, they tend to agglomerate, have lengthy ignition delays, and low combustion rates/efficiencies. This work aims to design metal powders with tuned surface, micro-structure, morphology, or chemistries to mitigate these challenges and favor the formation of desired products and high energy release; without jeopardizing thermochemical performance, safety, and stability. One approach used is to incorporate elements (like Fe) that can act as oxygen shuttle catalysts for surface reactions or to introduce elements to form exothermic intermetallic products (like Zr). Ball-milling allows a simple one pot technique to incorporate these elements to form metastable nanocomposite powders with lower ignition thresholds and improved combustion efficiencies. Novel experimental and diagnostic tools like x-ray phase contrast imaging (XPCI) and snapshot hyper-spectral imager for emissions and reactions (SHEAR) have been coupled to capture condensed phase/internal particle features and external optical emissions, temperatures, and gas phase species from combustion reactions, respectively. Machine learning is also used to obtain quantitative data: identify trends, detect anomalies, and classify particle events from the videos produced from combustion scenes. Results show that surface modification to boron reduces ignition delays and doping boron with as low as 1wt% Fe improves surface reaction rates in air. Other additives like Bi and Co also help to change boron’s oxidation mechanism. For aluminum, powders can be modified by emulsion assisted milling to produce spheres of various sizes by changing the milling parameter space with process control agents like acetonitrile and hexane, and the addition of Zr can accelerate combustion in thermite formulations and allow Al powders to burn in multiple phases with improved combustion efficiencies.

IDEAS/STAT Optimization Seminar
2025-01-30 12:00 - 13:00 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States

Herman P. Schwan Distinguished Lecture: "Engineering Proteins, Genomes, Viruses & Organs" (George Church, Harvard & MIT)
2025-01-30 15:30 - 16:45 | Zoom
Our exponential technologies for reading, writing genomes and epigenomes combined with AI-ML has enabled large libraries and selections for radical new functions — e.g. resistance to all viruses, novel delivery vectors for gene therapies, xeno-transplantation, de-extinction, and de-aging.
This lecture will be held remotely.
Zoom Link: https://upenn.zoom.us/j/94497096918?pwd=[REDACTED]

Spring 2025 GRASP Seminar: Nat Trask, University of Pennsylvania, "Geometric structure preservation in probabilistic digital twins"
2025-01-31 10:30 - 11:45 | Wu & Chen Auditorium
This will be a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom.
ABSTRACT
Motivated by the ever-increasing success of machine learning in language and vision models, many aim to build AI-driven tools for scientific simulation and discovery. Contemporary techniques drastically lag behind their comparatively mature counterparts in modeling and simulation however, lacking rigorous notions of convergence, physical realizability, uncertainty quantification, and verification+validation that underpin prediction in high-consequence engineering settings. One reason for this is the use of “off-the-shelf” ML architectures designed for language/vision without specialization to scientific computing tasks. In this work, we establish connections between graph neural networks and the finite element exterior calculus (FEEC). FEEC forms the backbone of modern mixed finite element methods, tying the discrete topology of geometric descriptions of space (cells, faces, edges, nodes and their connectivity) to the algebraic structure of conservations laws (the div/grad/curl theorems of vector calculus). By building a differentiable learning architecture mirroring the construction of Whitney forms, we obtain a de Rham complex supporting FEEC, allowing us to learn models combining the robustness of traditional FEM with the drastic speedups and data assimilation capabilities of ML. We then introduce a novel UQ framework based on optimal recovery in reproducing Hilbert spaces, allowing the model to quantify epistemic uncertainty, providing practical notions of trust where the model may be reliably employed.
