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

CIS Seminar: "Realizing the Promise of Language-level Security in Real Systems"
2025-02-27 15:30 - 16:30 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Promises are cheap. Software vendors routinely describe their offerings as “secure”, but few are based on designs that can guarantee even the most basic security properties. To address this problem, services like Cloudflare, Android, and Firefox are increasingly relying on languages like Rust and WebAssembly to provide safety by design. But these promises too can fall short: any vulnerability in the execution stack—compiler, runtime, OS, or hardware—can undermine language-level security.

In this talk, I present my research towards delivering on the promise of language-level security by building an execution stack that guarantees that these security properties are preserved. In particular, I will describe how three of my systems, VeriWasm, WaVe, and VTock, guarantee safety of the compiler, language runtime, and OS respectively. I will focus on how the designs of these systems provide formal guarantees while still adhering to production constraints that allow them to be deployed in real systems used by millions of users. I will conclude by discussing some future directions for this work, such as retrofitting formal safety guarantees to safety-critical embedded systems and clean-slate OS design using language-level isolation.

CIS Seminar: "Privacy, Copyright, and Data Integrity: The Cascading Implications of Generative AI"
2025-02-28 10:00 - 11:00 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
The rapid adoption of generative AI has created a cycle where personal information cascades perpetually: from people to models to applications and online platforms, then back through scrapers into the system. Simple blanket rules such as “don’t train on this data” or “don’t share sensitive information” are inadequate, as we face training data scarcity while these models are already deeply integrated into people’s daily lives. In this talk, rather than examining data, people, and models in isolation and setting rigid rules, we will reason about their interplay by discussing three research directions: (1) measuring the imprint of data on models through novel membership inference attacks and uncovering memorization patterns, (2) developing algorithmic approaches to help people control the exposure of their data while preserving utility, and (3) grounding model evaluations in legal and social frameworks, particularly the theory of contextual integrity. Looking ahead, we discuss emerging directions in building on-device privacy controls and nudging mechanisms, formalizing semantic memorization, and developing model capabilities such as abstraction, composition, and inhibition to enable controllable generation of outputs.

Spring 2025 GRASP on Robotics: Bruno Olshausen, University of California, Berkeley & Redwood Center for Theoretical Neuroscience, “Invariance and equivariance in brains and machine”
2025-02-28 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
The goal of building machines that can perceive and act in the world as humans and other animals do has been a focus of AI research efforts for over half a century. Over this same period, neuroscience has sought to achieve a mechanistic understanding of the brain processes underlying perception and action. It stands to reason that these parallel efforts could inform one another. Here I propose an approach to the long-standing problem invariant and equivariant representation in vision – that is, how do we recognize objects independent of pose, lighting and other variations, and how do we perceive such variations independent of object shape? The approach is rooted in observations of animal behavior and informed by both neurobiological mechanisms (recurrence, dendritic nonlinearities, phase coding) and mathematical principles (group theory, residue numbers). What emerges from this approach is a neural circuit for factorization that can learn about shapes and their transformations from image data, and a model of the grid-cell system based on high-dimensional encodings of residue numbers. These models provide efficient solutions to long-studied problems that are well-suited for implementation in neuromorphic hardware or as a basis for forming hypotheses about visual cortex and entorhinal cortex.

CIS Seminar: "Intelligence Augmentation for Scientific Researchers"
2025-02-28 11:00 - 12:00 | PA
Special location for this talk: 105 Amy Gutmann Hall
Recent advances in Artificial Intelligence are powering revolutionary interactive tools that will transform the very nature of the scientific enterprise. We describe several large-scale projects at the Allen Institute for AI aimed at developing open models, agentic platforms, and novel interaction paradigms in order to amplify the productivity of scientists and engineers.

MSE Thesis Defense: "Chain and Pendant Architecture Effects in Associating Polyolefins" Eli Jared Fastow
2025-02-28 13:00 - 15:00 | Wu & Chen Auditorium
The United States recycles less than 9% of plastic waste, representing a tremendous environmental catastrophe and a loss of embodied value. This dissertation presents the structure-property relationships of functional polymers made from an upcycling approach targeting polyolefins for polymer-to-polymer conversions. The overall strategy proceeds by first dehydrogenating polyolefins, then functionalizing the resulting C=C to generate functional polymers. This upcycling route retains the backbone architecture and enables control of the pendant, accessing architectures otherwise unattainable by the free radical polymerization (FRP) that is typically used to produce polyolefin copolymers. Within this larger strategy, this dissertation explores the chain and pendant effects on the structure-property relationships of the resulting associating polymers. Starting with polycyclcooctene (PCOE), a model for partially dehydrogenated polyethylene, the polymer is functionalized with alcohol, carboxylic acid, and acrylate groups. These polymers have a linear backbone, in contrast to commercial functional polymers with branched backbones.
X-ray scattering reveals that long pendants terminating in OH (-S(CH2)2OH, or SC2OH) do not incorporate into polymer crystals when added to PCOE. By contrast, when added directly to the backbone, OH incorporates into crystals. Linear OH-PCOE crystallizes faster and to a greater extent than commercial branched EVOH, as demonstrated by differential scanning calorimetry and in situ X-ray scattering. This difference in structure as a consequence of pendant architecture tunes the surface and adhesive properties. The more crystalline OH-PCOE exhibits a higher contact angle (lower surface polarity) than commercial EVOH or SC2OH-PCOE; this difference in contact angle is explained by the distribution of OH groups between the amorphous and crystalline regimes. The difference in surface polarity leads SC2OH-PCOE to exhibit greater adhesive strength than OH-PCOE at functionalization <15 % of ethylene units. At higher functionalization, the loss of crystallinity in SC2OH-PCOE decreases adhesive strength below that of OH-PCOE due to a loss of bulk strength.
Functionalizing PCOE with COOH-terminated pendants with a variety of methylene spacers (-S(CH2)nCOOH, where n = 1, 2, 7) enables further exploration of pendant effects on mechanical properties. Dynamic mechanical analysis identifies that the storage modulus in the rubbery plateau (E’ rubbery) depends on both the extent of functionalization and pendant length. Similarly, functionalizing PCOE with ethyl acrylate (EA) decreases E’ rubbery and tunes tensile mechanical properties. From 0 to 18 mol% EA functionalization, extensibility increases to over 4000% strain at break. An increase in molar mass between entanglements facilitates the high elongation of EA-PCOE. Ultimately, the polymer-to-polymer upcycling route discussed here enables a greater degree of control over the chain and pendant architecture than is conventionally achievable by FRP. This dissertation elucidates the structure-property relationships tuned by the chain and pendant architecture of associating polyolefins, contributing to a broader strategy for polymer-to-polymer upcycling.

Spring 2025 GRASP Seminar: James Tompkin, Brown University, “Joint Depth and 3D Motion Estimation Two Ways”
2025-02-28 14:00 - 15:00 | Levine 512
This will be a hybrid event with in-person attendance in Levine 512 and virtual attendance on Zoom. This seminar will NOT be recorded.
ABSTRACT
Dynamic scene reconstruction from monocular cameras often requires us to simultaneously estimate depth and 3D motion, where knowledge of either one would help to constrain the other. I will review two different approaches to resolving this problem, describe their relative merits, and discuss what they tell us (if anything!) about this fundamental problem. The first approach uses active illumination to augment the scene via continuous-wave time of flight measurements. I will explain how this additional depth input only superficially helps—itself introducing new problems—and how we can resolve them using self-supervision and physically-based rendering. This lets us reconstruct objects under fast motion like swinging baseball bats (ECCV 2024, ongoing work, and arXiv). The second uses supervised learning to directly predict depth and 3D scene flow from two RGB images only. Here, providing generalization is the key challenge, where factors like motion parameterization and data scaling are critical. Careful empirical work lets us use a single feed-forward neural network to predict depth and motion for casual videos, robotic manipulation videos, and autonomous driving videos (arXiv). Finally, after all that hard work, if there’s time then I’ll show some pretty pictures of shiny objects, because who doesn’t like those (SIGGRAPH Asia 2024)?!

ESE Spring Seminar - "Versatile RF Interconnects and Electronics for Extreme Environment Sensing and Communications"
2025-03-03 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Future emerging applications like extreme environment electronics, high-performance computing, space sensing, and brain-machine interfaces share a critical goal: massive bandwidth and deployment scalability. To this end, my research group is investigating energy-efficient and scalable sensing and communication techniques, emphasizing circuits, advanced packaging, and signal processing innovations for the above applications.
To begin with, I will briefly discuss technology gaps in information-carrying wiring harnesses and illustrate the new use cases of millimeter-wave dielectric fibers and short-distance wireless interconnects as alternative mediums, mainly as thermal isolating channels. Then, I will describe the challenges of hypersonic radio interference and harsh environment antenna remoting systems for the DoD and aerospace industries. I will introduce the high-temperature electronics framework as my future work to address the above challenges, focusing on high-temperature metrology and scalable signal-harnessing techniques at millimeter-wave frequencies. To show the feasibility of the new methods, I will illustrate the ceramic fiber’s capability of electromagnetic wave propagation, extreme heat tolerance, and extreme heat isolation, assisted by automatic gain-controlled millimeter-wave CMOS transceivers.
In the second part, I will discuss the need for large-scale cryogenic interconnects for future high-performance computing and demonstrate the potential adoption of wireless interconnects between cryogenic devices and room-temperature electronics. As an initiative, our lab demonstrated the feasibility of removing thermally loaded wires/connectors between 7K and 290K thermal break and efficiently packing more data in a millimeter-wave carrier enabled by multi-level signaling and digital pre-distortion in CMOS technologies. As a part of the scalable interconnect theme within high-performance computing, I will also describe my collaborative efforts with academic and industry partners on massively scalable RF connectors for future artificial intelligence and data centers.
Lastly, I will elaborate further on my research activities in space sensing and brain-machine interface, where massive scalability matters most. I will discuss their challenges, opportunities, and future directions.

MEAM Seminar: "Engineering Soft Medical Robots and Devices to Solve Hard Health Problems in Extreme Body Environments"
2025-03-04 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Advances in engineering have enabled a new generation of soft medical robots and devices with unique theranostic capabilities for interfacing with delicate organs. However, challenges remain in achieving spatial and temporal precision in extreme body environments, particularly within the digestive system. This talk will highlight three recent preclinical innovations addressing these challenges: (i) BIOSENTER: a bioinspired soft enteroscopic robot for locomotion, steering, and intervention in the deep small intestine. (ii) IngRI: an ingestible, battery-free, tissue-adhering robotic interface for prolonged gut electrostimulation. (iii) e-GLUE: an electroadhesive hydrogel interface for enhanced mucosal retention of ingestible devices. These platforms demonstrate significant potential for managing chronic digestive conditions and beyond.

ASSET Seminar: "From Data to Insights: Trustworthy Solutions for Imaging Problems"
2025-03-05 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract:
Extracting insights from imaging data used to be straightforward: every component of imaging systems was engineered by humans, the analysis and interpretation of the collected data was driven by human understanding and experience, and only humans were responsible for the impact of the decisions stemming from such insights. Today, however, machine learning permeates every stage of image acquisition and analysis, challenging our understanding of robustness, interpretability, and fairness in automated decision making. This talk will present three approaches to leverage the power of data-driven models for imaging applications while increasing their trustworthiness, focusing on biomedical imaging. These approaches enable precise mathematical claims about what modern networks compute in the context of inverse problems (via learned proximal networks), facilitate efficient and rigorous testing for interpretable concepts for classification problems (via testing by betting), and ensure compliance with fairness guarantees even in incomplete-data regimes (via proxy attributes).
Zoom Link (if unable to attend in-person): https://upenn.zoom.us/j/93115449335

Spring 2025 GRASP SFI: John Suehle, Image Engineering, “The Magic of Lasers in Entertainment – Behind the Technology”
2025-03-05 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
It was not long after Theodore Maiman published the first demonstration of the ruby laser in the August 6, 1960 issue of Nature1 that it was realized that such a unique light source could be used in many applications, including entertainment. In fact, four years later a laser was initially used and then simulated in a scene from the James Bond movie Goldfinger. Later in the 1970s high-speed galvanometers were developed to quickly scan laser beams to produce 2-dimensional images. The laser light show was born in 1973 when filmmaker Ivan Dryer formed Laser Images, Inc. and performed “Laserium” at Griffith Observatory in Los Angeles. The show was expanded to 46 cities.
Today, advanced material science allows the fabrication of laser diodes emitting in the red, blue and green wavelengths with optical powers exceeding 4 watts from a single emitter. Lasers are now exhibiting 40% efficiency up from .05% compared to their ion laser ancestors. Such an increase in efficiency and decrease in size have made the laser ubiquitous in entertainment. Hundreds of watts of optical power are routinely used by my company Image Engineering, Inc, in live tours such as Paul McCartney and the Trans-Siberian Orchestra. It is now possible to use a large number of laser sources to produce compelling volumetric kinetic displays of laser light.
My presentation will cover the many applications we used this unique light source in visually captivating displays in concert touring, professional sports, and architectural lighting. I will also discuss projects of using lasers in new lighting technologies such as laser stimulated phosphor light engines.

CBE Seminar: "Multifunctionality of MXenes for Sensor Technologies" (Lia Stanciu, Purdue University)
2025-03-05 15:30 - 16:30 | Wu & Chen Auditorium
Abstract:

MXenes, a versatile family of 2D materials, have garnered interest for applications in sensing technologies due to their unique combination of conductivity, hydrophilicity, and surface tunability. This talk explores the integration of MXenes into sensor platforms, and discusses their potential in electrochemical, optical, and environmental sensing. Their surface functional groups can be leveraged for biomolecular interactions and their ability to increase signal transduction can be exploited for low-concentration analyte detection.
The presentation will discuss a few case studies: hybrid MXene-MoS₂ composites for the detection of volatile organic compounds (VOCs), which uses their high surface area and conductivity; nucleic acid biosensors employing MXenes for the detection of viral RNA, which takes advantage their excellent biocompatibility and signal transduction capabilities; and surface-enhanced Raman scattering (SERS) sensors incorporating MXenes for the highly sensitive detection of Listeria monocytogenes in food safety applications. The presentation will also address current challenges for in the deployment of these materials at scale and discuss their potential in real-world applications that require high sensitivity, selectivity, and scalability.

MEAM Seminar: "Exploring Structure, Mechanics, and Function in Soft Biological Systems Across Scales"
2025-03-06 10:15 - 11:15 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Soft biological systems exhibit intricate hierarchical organization, with structures spanning multiple length scales, from tissues to cells and molecules. This structural complexity gives rise to rich mechanical behaviors that profoundly influence biological function. My research integrates soft matter physics and biology to establish a unified framework that connects structure, mechanics, and function in soft biological systems.
In this talk, I will demonstrate how this framework applies to diverse biological components, including biomaterials, cells, and molecular assemblies. At the bulk material level, I reveal how the hierarchical microstructure of biomaterials, such as fibrin, underpins their extraordinary resilience and complex mechanical behavior. At the cellular level, I uncover the distinct roles of cytoskeletal components in shaping the cell’s internal mechanical environment. At the molecular level, I explore biomolecular condensates, which are dynamic, membrane-less compartments that are essential for cellular processes, highlighting how chromatin organization and mechanics influence their formation and behavior. Building on these insights, I will demonstrate how stem cells sense and respond to structural cues in their microenvironment, influencing key processes such as spreading, migration, and differentiation.
By integrating insights across these scales, my work advances our understanding of the physical principles that govern soft biological systems, and informs strategies for designing cell-instructive biomaterials and guiding cellular behavior for tissue engineering applications.

IDEAS/STAT Optimization Seminar: "Data-Driven Algorithm Design and Verification for Parametric Convex Optimization"
2025-03-06 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
We present computational tools for analyzing and designing first-order methods in parametric convex optimization. These methods are popular for their low per-iteration cost and warm-starting capabilities. However, precisely quantifying the number of iterations required to compute high-quality solutions remains a key challenge, especially in real-time applications. First, we introduce a numerical framework for verifying the worst-case performance of first-order methods in parametric quadratic optimization. We formulate this as a mixed-integer linear program that maximizes the infinity norm of the fixed-point residual after a given number of iterations. Our approach captures a broad class of gradient, projection, and proximal iterations through affine or piecewise-affine constraints, with strong polyhedral formulations. To improve scalability, we incorporate bound-tightening techniques that exploit operator-theoretic bounds. Numerical results show that our method closely matches true worst-case performance, achieving significant reductions in worst-case fixed-point residuals compared to standard convergence analyses. Second, we present a data-driven approach for analyzing the performance of first-order methods using statistical learning theory. We establish generalization guarantees for classical optimizers using sample convergence bounds and for learned optimizers using the Probably Approximately Correct (PAC)-Bayes framework. We then apply this framework to learn accelerated first-order methods by directly minimizing the PAC-Bayes bound over key algorithmic parameters (e.g., gradient steps and warm-starts). Numerical experiments demonstrate that our approach provides strong generalization guarantees for both classical and learned optimizers, with statistical bounds that closely match true out-of-sample performance.

CIS Seminar: "Pareto-efficient AI systems: Expanding the quality and efficiency frontier of AI"
2025-03-06 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
We have made exciting progress in AI by massive models on massive amounts of data center compute. However, the demands for AI are rapidly expanding. I identify how to maximize performance under any compute constraint, expanding the Pareto frontier of AI capabilities.

This talk builds up to an efficient language model architecture that expands the Pareto-frontier between quality and throughput efficiency. In motivation, the Transformer, AI’s current workhorse architecture, is memory hungry, severely limiting its throughput, or amount of text it can process per second. This has led to a Cambrian explosion of alternate efficient architecture candidates proposed across prior work. Prior work has painted an exciting picture: there exists architectures that are asymptotically faster than Transformers, while also matching quality. However, I ask, if we’re using asymptotically faster building blocks, are we giving something up in quality?

In part one, we build understanding. Indeed, there’s no free lunch! I present my work to identify and explain the fundamental quality and efficiency tradeoffs between different classes of architectures. Methods I developed for this analysis are now ubiquitous in the development of language models.

In part two, we measure how AI architecture candidates fare on the tradeoff space. A major hurdle, however, is that we lack implementations of the architectures that that run at peak-efficiency on modern hardware. Further, many proposed architectures are asymptotically fast, but not wall-clock fast. I present ThunderKittens, a new programming library I built to help AI researchers write simple, hardware-efficient algorithms across hardware platforms.

In part three, we expand the Pareto-frontier of the tradeoff space. I present the BASED architecture, which is built from simple, hardware-efficient components. I released the state-of-the-art 8B-405B Transformer-free language models, per standard evaluations, all on an academic budget.

Given the massive investment into language models, this work has had significant impact and adoption in research, open-source, and industry.

Grace Hopper Distinguished Lecture: Melody Swartz
2025-03-06 15:30 - 16:30 | Berger Auditorium (Room 13), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
“Immunoregulatory Roles of Lymphatic Vessels in Cancer and Opportunities for Immunoengineering”
Melody Swartz will discuss her pioneering research on the lymphatic system. This talk will highlight her translational work in immunotherapy, including lymph node-targeting vaccines and engineering tumor microenvironment models for therapeutic discovery.

CIS Seminar: "Next Generation Operating Systems for the Cloud"
2025-03-07 10:00 - 11:00 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Modern datacenters must handle an ever-growing array of real-time and data-intensive workloads, such as interactive web services and AI models, that demand both low latency and high throughput. However, traditional operating systems introduce significant I/O overhead, degrading performance and reducing efficiency. A common solution is to let applications directly communicate with hardware, bypassing the operating system altogether. While this greatly improves performance, it sacrifices compatibility with existing software and requires operators to dedicate hardware to each application, ultimately reducing overall resource utilization.
In this talk, I will present a new datacenter operating system design that achieves high I/O performance without making these tradeoffs. First, I will introduce Shenango, which allows applications that directly access hardware to efficiently share CPU cores with other tasks, maintaining high performance without requiring dedicated resources. Next, I will discuss Caladan, a system that prevents performance degradation by managing interference among co-located applications. Finally, I will describe Junction, a library operating system that extends these benefits to unmodified applications, unlocking higher performance and efficiency across diverse workloads.

Spring 2025 GRASP on Robotics: Terry Fong, NASA Ames Research Center, “The NASA Volatiles Inspecting Polar Exploration Rover (VIPER) Mission”
2025-03-07 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
The Volatiles Investigating Polar Exploration Rover (VIPER) is a NASA mission designed to explore the extreme environment of the Moon in search of water ice. VIPER is intended to land at the South Pole of the Moon and spend approximately 100-days mapping and surveying four different “ice stability regions”. Determining the distribution, physical state and composition of water ice deposits will help increase understanding the sources of lunar polar water, as well as providing insight into the distribution and origin of volatiles across the solar system.
In this talk, I will present an overview of the VIPER mission, the rover’s design, lunar surface simulation, and mission operations. During VIPER’s exploration of the Moon, the rover will endure extreme temperature conditions, dynamic lighting and complex terrain, while near-real-time rover driving will present new planetary mission operational challenges.

MEAM Seminar: "Three-Dimensional Biointerfaces: Soft Bioelectronics for Complex Biological Geometries"
2025-03-11 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Cutting-edge biological and medical research demands innovative methods for sensing and modulating complex tissues, organs, and organ systems. Recent progress in bioelectronics enables multimodal interfacing for broad fundamental and therapeutic applications. However, key challenges persist in interfacing with complex biological geometries, particularly for applications requiring conformal contact in electrical, chemical, or mechanical biointerfaces. In this talk, I will introduce our work on three-dimensional biointerfaces to address this challenge. These 3D biointerfaces feature precisely programmable geometries and/or ultrasoft nature that enable conformal contacting with multiscale tissues and organs. The first part of the talk focuses on our work in full-surface 3D interface for 3D-shaped neural organoids. These 3D programmable frameworks enable nearly complete surface accessibility of neuronal populations for the first time. They support high-resolution recording and stimulation via hundreds of individual channels and allow for 3D reconstruction for spatial electrophysiology. These capabilities enable monitoring 3D network-level neural activities, supporting broad biological and medical research as demonstrated with neural activity manipulation, pharmacological studies, and modeling neural disease phenotypes. Next, I will discuss our work on organ interface. We develop hydrogel-based ultrasoft thin platforms that enable real-time pH sensing. These platforms integrate ultrasound imaging for deep tissue monitoring, and address the long-standing clinical challenge of early detection of gastrointestinal (GI) system leakage after surgery. In the last part of the talk, I will present our work on novel nanofabrication techniques for large scale and aligned 3D nanostructured surfaces which match the scale of cellular and subcellular sensing. Together, these works open new possibilities for precision engineering of advanced bioelectronics with 3D features that allow sufficient interfacing with complex geometries, enabling transformative applications in organoid research and neural engineering, precision medicine, and spatial characterization platforms for complex in vitro and in vivo biological systems for advanced medical and therapeutic applications.

Seminar: "Merging Bio-integrated Materials and Devices with Ultrasound: Opportunities in Sensing, Modulation and Actuation" - Dr. Jiaqi Liu, Ph.D.
2025-03-12 10:30 - 12:00 | LRSM Reading Room, 3231 Walnut St., Philadelphia, PA, 19104, United States
Soft, bio-integrated materials and devices exhibit the ability to collect physiological signals and offer therapeutic functions, posing a substantial impact on our understanding of biology and paving the way for precision medicine. With the assistance of advanced ultrasound technology, the innovative integration holds immense promise for sensing, modulation and actuation in deep tissues and complex anatomical structures that are otherwise difficult to approach. In this talk, I will highlight our recent advances in bioresorbable, shape-adaptive materials structures that enable real-time monitoring of deep-tissue homeostasis using ultrasound instruments. I will showcase another example of a bioresorbable implant based on radio-frequency coupling for chemical sensing. I will then summarize our previous efforts in materials design that lead to programmable dual responsive soft actuators. Finally, I will discuss opportunities in leveraging functional soft materials, ultrasound imaging and its various forms of energy for measuring biosignals, provide targeted modulation and programmable actuation to decipher biological systems and improve healthcare.

Spring 2025 GRASP Seminar: Shubham Tulsiani, Carnegie Mellon University, "Reconstructing and Generating 3D"
2025-03-13 14:30 - 15:30 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
This will be a hybrid event with in-person attendance in Raisler Lounge (Towne 225) and virtual attendance on Zoom.
ABSTRACT
Modern reconstruction systems, leveraging advances in pose estimation and generative modeling, can reconstruct everyday objects and scenes from casually captured images. In this talk, I will highlight recent work from our group that pushes these frontiers. I will first present a learning-based approach to the structure-from-motion task, highlighting how we can rethink camera parametrization for neural prediction methods. Building on this, I will describe a framework for combining generative priors and pose estimation for high-fidelity 3D reconstruction ‘in-the-wild’. Finally, I will outline an approach for ‘ultra-fast’ 3D generation that allows users to synthesize high-fidelity 3D assets in under a second.

BE Seminar - “Probing nuclear structure and function with novel genomic tools” (Sofia Quinodoz, Princeton University)
2025-03-13 15:30 - 16:30 | Berger Auditorium (Room 13), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States

MEAM Seminar: "Bioinspired Robotic Sensorimotor Systems via 3D Printed Soft and Architected Materials"
2025-03-18 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Animals can easily adapt their bodies and movements to new, unstructured environments and situations. Robots cannot. While engineers construct robots from rigid, motorized mechanisms to precisely control their movements, vertebrates leverage compliant, deformable musculoskeletal systems to adaptively navigate complex environments and produce dynamically stable gaits and motions. Providing robots with an equivalent musculoskeletal system will open new opportunities for achieving bioinspired motility, adaptability, robustness, and performance. However, this vision is stymied not only by limitations in current materials and manufacturing methods, but also in how to strategically integrate soft and rigid materials in robot bodies. With these challenges in mind, I will present approaches for engineering artificial musculoskeletal systems via 3D printed soft and architected materials. First, I will introduce a flexible, architected soft actuator unit for motorized extensional motion. I will introduce techniques for sensorizing these architected actuators, assembling them for locomoting soft robots, and interfacing them with skeletal elements for force transmission. Finally, I will discuss new strategies for architecting soft ionic conductors for distributed sensing. These efforts aim to embody both physical and computational intelligence into real-world-deployable soft robots with practical task capabilities.

ESE Spring Seminar - "Generalization, Memorization, and Privacy in Trustworthy Machine Learning"
2025-03-18 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Machine learning is transforming numerous aspects of modern society, and its expanding use in high-stakes applications calls for responsible development. In this talk, I will present my research on the foundations and methodologies for building trustworthy ML, centered on three interconnected challenges: generalization, memorization, and privacy. First, I will show how information-theoretic tools can be used to analyze generalization across different learning setups. Next, I will describe my work on the fundamental limits of memorization in certain high-dimensional convex settings, showing a precise trade-off between memorization and accuracy. Finally, I will propose adaptive and efficient optimization algorithms under differential privacy—a well-established framework designed to protect sensitive data and limit memorization risk—that adapt to the properties of the dataset, resulting in smaller error. My results highlight how these three pillars interact, and I will conclude by outlining my plans for future research.

CIS Seminar: "Probabilistic Experimental Design for Petascale DNA Synthesis"
2025-03-18 15:30 - 16:30 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Generative modeling offers a powerful paradigm for designing novel functional DNA, RNA and protein sequences. In this talk, I introduce probabilistic experimental design methods to efficiently manufacture samples from generative models of biomolecules in the real world. These algorithms merge computational techniques for approximate sampling with physical randomness. I also develop tools to rigorously evaluate the quality of manufactured samples, including nonparametric
two-sample tests with consistency guarantees and scalable algorithms. I demonstrate synthesizing ~10^16 samples from a generative model of human antibodies, at a sample quality comparable to state-of-the-art protein language models, and a cost of ~$10^3. The library yields candidate therapeutics for “undruggable” cancer targets. Using previous methods, manufacturing a DNA library of the same size and quality would cost roughly ~$10^15.

ASSET Seminar: "Algorithmic Stability for Trustworthy Machine Learning and Statistics"
2025-03-19 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract:
Data-driven systems hold immense potential to positively impact society, but their reliability remains a challenge. Their outputs are often too brittle to changes in their training data, leaving them vulnerable to data poisoning attacks, prone to leaking sensitive information, or susceptible to overfitting. Establishing fundamental principles for designing algorithms that are both stable—to mitigate these risks—and efficient in their use of resources is essential for enabling trustworthy data-driven systems.
In this talk, I will focus on statistical estimation under differential privacy—a rigorous framework that ensures data-driven system outputs do not reveal sensitive information about individuals in their input. I will present algorithmic techniques that take advantage of beneficial structure in the data to achieve optimal error for several multivariate tasks without requiring any prior information about the data, by building on robustness against data poisoning attacks. Lastly, I will highlight the deeper connection between differential privacy and robustness that underpins these results.
Zoom Link (if unable to attend in-person): https://upenn.zoom.us/j/94645455801

Spring 2025 GRASP SFI: Student Lightning Talks, Session 2
2025-03-19 15:00 - 16:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom.
ABSTRACT
The Spring 2025 GRASP SFI Student Lightning Talks will highlight the research of three GRASP Lab Master’s or early PhD students whose presentation topics have been nominated by their faculty advisors and voted on by their GRASP peers.
Ahmad Amine (PhD, ESE)
SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks
Iterative tasks are common in robotics applications ranging from pick-and-place tasks to autonomous racing. Finding optimal trajectories for these tasks has been thoroughly explored in literature through techniques like trajectory planning, optimal control, and reinforcement learning. Learning Model Predictive Control (LMPC) solves for the optimal trajectory that maximizes performance for a given task by learning the value function from trajectories executed in previous iterations. Unlike reinforcement learning, LMPC provides desirable theoretical properties for safety and convergence, which is crucial for safety-critical applications. We present an approach for extending the LMPC problem to stochastic nonlinear systems using Model Predictive Path Integral (MPPI). Previous methods utilizing optimization-based techniques or Cross-Entropy-Method sampling to solve the LMPC problem fall short when dealing with high-dimensional, nonlinear dynamics. Our MPPI-based framework optimizes the control policy from an information-theoretic perspective and overcomes these limitations by providing a systematic way of handling constraints without sacrificing sample spread. This is achieved by incorporating penalty functions and optimizing over multipliers to balance out constraint satisfaction with controller performance. The proposed algorithm can be parallelized allowing for real-time control on limited compute hardware. We validate our approach through simulations and real-world experiments, demonstrating significant improvements in constraint satisfaction and final trajectory performance.
Erica Santos (MSE, ROBO)
Temporarily, Robots Unite to Surmount Sandy Entrapments, then Separate (TRUSSES)
The TRUSSES project is developing methods for teams of robots to jointly overcome environmental hazards on the Moon by attaching to each other to form larger and more stable, maneuverable structures. The robots will use their interactions with the ground to form a map of safe and risky terrain, attach to each other as support when the ground traversal risk is high, move in a coordinated fashion once joined, and, once the maneuver has been successfully completed, separate to continue their original individual missions.
Darshan Thaker (PhD, CIS)
Frequency Guided Posterior Sampling for Diffusion-based Image Restoration
Image restoration aims to recover high-quality images from degraded observations. When the degradation process is known, the recovery problem can be formulated as an inverse problem, and in a Bayesian context, the goal is to sample a clean reconstruction given the degraded observation. Recently, modern pretrained diffusion models have been used for image restoration by modifying their sampling procedure to account for the degradation process. However, these methods often rely on certain approximations that can lead to significant errors and compromised sample quality. We present a simple modification to existing diffusion-based restoration methods that helps mitigate these errors based on a frequency analysis of the degraded observations. This results in significant improvement on challenging image restoration tasks such as motion deblurring and image dehazing.

The Harold Berger Distinguished Award Lecture 2025
2025-03-19 15:30 - 16:30 | Wu & Chen Auditorium
Quantum Dots: From Curiosity to Technological Impact
The combination of quantum effects, nanometer dimensions and a chemical synthesis make quantum dots a platform for exploring new size-dependent fundamental properties and a sandbox for developing new applications. This talk will cover the origin story of chemically synthesized quantum dots, their basic physics, the synthesis that unlocked their widespread study and applications, an overview of their properties and a few of their applications, including in bio-imaging and as emissive materials in displays.
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MSE Seminar: "Soft Microparticle Assemblies to MAP Scaffolds" Tatiana Segura - Duke University
2025-03-20 10:30 - 12:00 | Wu & Chen Auditorium
Particle assemblies form interconnected pockets of empty space that are hot spots for activity in many applications and natural phenomenon that deal with particulate matter. In my lab we explore the use of interlinked soft microparticle assemblies as scaffolds for cell culture in vitro and to promote regenerative wound healing in vivo. The open space that is created by these soft microparticle assemblies can be engineered to guide cellular behavior through restricting the space occupied by cells by modulating particle size, changing the chemical composition of the open space, and activating the cells with soluble factors. Using these approaches, we have studied the role of open space on macrophage polarization, neuroprogenitor cell growth, and vascular morphogenesis. Injection of these materials into wounds result in the local modulation of immune cells that turn the environment towards a reparative environment and leads to scarless healing skin. In this talk, I will discuss our latest results in our creation of soft particle assemblies for biomedical applications.

ESE Spring Seminar - "Can Robots Learn from Machine Dreams? - Robot Learning via GenAI-powered World Models"
2025-03-20 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Over the past decade, large-scale pre-training followed by alignment has revolutionized natural language processing and computer vision. Yet, robotics remains constrained by the scarcity of real-world data. In this talk, I will present our systematic approach to overcoming this bottleneck by building increasingly rich world models from data. I will first introduce our distilled feature field work that represents a robot’s action in relation to multi-modal descriptors in a static environment. Building upon this foundation, I will demonstrate LucidSim, a GenAI-powered simulator that significantly narrows the sim-to-real gap in the visual domain. I will show the initial steps we have taken toward a robot foundation model by extending this framework from legged locomotion to manipulation tasks. These results suggest that synthetic data sourced from world models can accelerate robot learning beyond what is possible with real-world data alone. I will conclude by outlining a roadmap that begins with enabling machines to dream a million worlds that will ultimately allow AI to enter the physical world.
