Fall 2025 GRASP Seminar: Aljoša Ošep, NVIDIA, "Segmenting More Than Meets the Eye: Towards Amodal 4D Segmentation"
2025-11-03 13:00 - 14:00 (America/New_York) | Amy Gutmann Hall, Room 306, 3317 Chestnut Street, Philadelphia, PA, 19104, United States
This is an in-person event ONLY in AGH 306.
ABSTRACT
The future of AI is embodied — imagine intelligent agents that can navigate and manipulate the world, from robot assistants helping around the home to autonomous vehicles taking you anywhere safely. To act in the physical world, these agents must do more than process raw sensory inputs: they must reason about the underlying, dynamic world that gives rise to their observations. This requires a 4D scene model — 3D geometry evolving over time — that is object-centric, predictive, and grounded in language. Such representations enable agents to answer questions like “Where am I?”, “What is around me?”, and “How can I interact with this object?”
In this talk, I will advocate for an explicit, amodal representation of world geometry and objects learned from unlabeled sequences. Such a model supports robust perception in dynamic environments and enables language-driven interaction with the world. I will outline a blueprint for building such systems, centered around two complementary components: a slow video object mining method that discovers and tracks arbitrary objects in unlabeled videos, and a fast feed-forward network that learns from these tracks to detect, segment, complete, and forecast object trajectories.
I will trace the progression from early methods for self-supervised object discovery and detection, to recent models capable of promptable, text-conditioned 4D segmentation and amodal scene completion. Taken together, these components form a scalable recipe for learning object-centric 4D representations directly from raw video — a step toward grounded, general-purpose world understanding.

Tedori-Callinan Distinguished Lecture: "Robotic Predictions are Hard, Especially About the Future"
2025-11-04 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Many autonomous systems (e.g, driverless cars and drones) must make decisions based on predictions of the future actions of other nearby agents, whose dynamics and intentions are unknown. E.g., autonomous cars must predict the motions of surrounding vehicles, pedestrians and bicycles. Autonomous racing drones must avoid crashing into other drones on the race course. Unfortunately, only partial and noisy data on the motions of these potential hazards are available. This talk will introduce a novel method to approximate, in real-time, a predictive Koopman operator for each potential hazard from noisy data, quantify the uncertainty of the future predictions, and use the quantified predictions to provide probabilistic collision avoidance guarantees within a real-time model predictive control framework. Experiments with ground robots, a drone, and a semi-autonomous crane on an ocean going vessel will illustrate the ideas.

ESE Fall Seminar - "Diamond and GaN: Wide-Bandgap Allies for Thermal and Power Management from Devices to 3D-Stacked Chips"
2025-11-04 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Once considered exotic, diamond and gallium nitride (GaN) have become practical enablers for next-generation electronic systems. Their convergence—diamond providing exceptional thermal conductivity and GaN delivering high-efficiency power conversion—lays the groundwork for integrated thermal–power co-design. As computing, RF, and high-performance systems push toward higher power densities, conventional packaging and cooling approaches struggle to manage buried hotspots and multilayer bottlenecks. In this talk, I’ll share our journey that began in 2015 with an unconventional idea—integrating thin-film polycrystalline diamond directly onto GaN high-electron-mobility transistors (HEMTs) while preserving their functionality. This effort led to some of our most significant findings, including the development of a low-temperature (400–500 °C), back-end-of-line (BEOL)–compatible diamond growth platform, now extended to silicon, oxides, and nitrides. Our “all-around” diamond-integrated GaN HEMTs achieved an average channel-temperature reduction of ~70 °C at 25 W/mm (DC) (IEDM ’22, ’23), while workload-representative, heater-based experiments demonstrated nearly a tenfold reduction in temperature rise within 3D architectures (IEDM ’24). In collaboration with Prof. Mitra’s team, we are advancing the thermal scaffolding paradigm for 3D chips—a concept that merges materials innovation with architectural design. It is exciting to build upon nearly two decades of GaN and diamond research—dating back to my Ph.D. work on vertical GaN transistors—and to see it evolving toward compact, energy-efficient, and thermally optimized electronics for the AI datacenter era. Much of our research has been carried out in close collaboration with industry partners, and some of our GaN efforts have already transitioned into industrial applications. I will also share some of the key lessons learned along the way, as well as the challenges that continue to shape this evolving field.

Penn AI Seminar Featuring Li Shen: Harnessing Trustworthy AI and Informatics for Dementia and Aging Research
2025-11-04 12:00 - 14:00 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Alzheimer’s disease and related dementias (ADRD) remains a major health crisis with profound social and economic burdens. Innovative strategies are needed to identify genetic risk and protective factors, model disease mechanisms, and accelerate therapeutic discovery. Advances in trustworthy AI and informatics now enable the integration of multimodal genetics, omics, imaging, and outcome data from large biobanks, creating powerful opportunities for biomarker and gene discovery beyond categorical diagnoses. At the same time, generative AI and large language models (LLMs) extend these capabilities to text-rich sources such as the scientific literature, clinical notes, and caregiver narratives. When integrated with knowledge graphs, LLMs can dynamically retrieve and synthesize domain-specific knowledge.

CIS Seminar: "Good Old Fashioned Engineering Can Close the 100,000 Year “Data Gap” in Robotics"
2025-11-04 15:30 - 16:30 | Wu & Chen Auditorium
AI is rapidly advancing the way we think, but we live in a material world. We still need to move things, make things, and maintain things. We need AI-driven robots to support an aging human population that doesn’t have enough workers. Large vision-language models based on internet-scale data can now pass the Turing Test for intelligence. In this sense, data has “solved” language and many claim that data has solved speech recognition and computer vision.
Will data also solve robotics? Rich Sutton points out in the “Bitter Lesson” that data and black-box “end-to-end” models have surpassed all the best-laid analytic work in AI. I accept that this trend will eventually produce general-purpose robots.
But the question is: when?
Using commonly-accepted metrics for converting word and image tokens into time, the amount of internet-scale data (texts and images) used to train contemporary large vision language models (VLMs) is on the order of 100,000 years – it would take a human that long to read or view it [2]. However, the data needed to train robots must combine video with robot motion commands: that data does not yet exist.
One way to collect robot data is teleoperation – where human “trainers” use remote control devices to painstakingly choose every motion of a robot as it performs a task – like folding a towel – over and over again. This is a variant of puppeteering, an ancient artform, that requires extensive human skill and patience. Unlike puppets however, robot joint angles can be precisely recorded, so the exact position history of each motor can be combined with videos from cameras that record the scene from different angles. The data for each “trial” or “trajectory” from start to finish includes a few minutes of video and the position history of all robot motors. Many companies are gearing up with fleets of robots and humans to collect data this way.
However, the largest such dataset reported so far is on the order of 1 year of data (it was collected in under a year by many human-robot systems). This data has been used to train large models and initial results are intriguiging. But this suggests that at current data-collection rates, a general-purpose robot, based on a ChatGPT-sized set of robot data, will be available in…100,000 years.
So how can we close this 100,000-year “Data Gap”?
Researchers are actively pursuing 2 additional methods for generating robot data: simulation and 3D analysis of internet videos.
Digital simulation today looks incredibly life-like – consider the special effects in action movies and the deepfakes generated by AI. It’s relatively easy to create life-like simulations of robot drones flying or robot dogs walking down stairs and doing backflips. Simulations can also provide videos and motor data to train large robot models. Simulation data works well for robots that fly or walk, or even for doing backflips. But it turns out that simulation is notoriously unreliable for robot manipulation.
This Sim2Real “gap” arises because physical manipulation involves precise and changing contacts between the edges and surfaces of objects and grippers, very small but important material deformations, and very nuanced and changing frictional forces due to microscopic surface variations. These factors are extremely difficult to measure and to accurately model. But these very small errors result in simulation data that looks realistic but is physically inaccurate. A submillimeter inaccuracy can make the difference between carrying a glass of water and dropping it. Robots trained on simulation data can work well in simulation but they often fail when manipulating physical objects. Researchers agree that physically-accurate simulation of manipulation is a Grand Challenge.
The third potential source of robot data is videos on the Internet. YouTube includes about 35,000 years of videos. Many of these videos show people manipulating objects, cooking, stacking cups, folding laundry. However, it is extremely difficult to extract precise 3D motion from 2D videos. Computer vision researchers can approximately track the motion of human hands and objects in a video, but the same issues of noise and precision make data from videos unreliable for robot learning. Accurately “lifting” a video image back into 3D to recover precise finger and object motions is a Grand Challenge for computer vision that is not expected to be solved in the forseeable future.
There is a 4th option.
Robot data can be collected from real robots working with real objects in real environments. Industry has thousands of robots doing useful work around the clock. Today, little of this real robot production data is saved. This is partly because most industrial robots perform extremely repetitive tasks like automotive welding and spot-painting that do not vary much. Data to train large models is often diverse – think of the massive range of texts and images on the internet. General-purpose robots need a broad range of data with variations in tasks, objects, and environments.
But real general-purpose robots don’t exist yet, so we can’t collect real robot data from them.
One option is to bootstrap, starting with specific tasks like driving or e-commerce package sorting, where the objects vary but the task and environment don’t vary much, and gradually expanding as specific skills are mastered into adjacent skills. Some companies are developing such robots and putting them to work.
One example is Waymo, which has robot taxis operating in several US cities. These robots have “level 4” autonomy – they rely on human operators who log in remotely to guide robot taxis when unfamiliar circumstances arise.
Another example is Ambi Robotics, which has package sorting and stacking machines operating in postal and warehouse facilities. These robots are fully autonomous – but a few times an hour they drop a package. As with Waymo, human operators help out in such cases.
Both Waymo and Ambi have created a “data flywheel”, where working robots constantly collect data that is used to improve robot performance and to enable adjacent robot skills, like highway merging for Waymo and package stacking (very different from sorting) for Ambi.
One thing that Waymo and Ambi also have in common is that they don’t rely only on “end-to-end” AI models. These companies combine advances in AI and learning from data with rigorous engineering methods like inverse kinematics, 6d motion planning, and digital signal processing.
I call this GOFE (Good Old-Fashioned Engineering). GOFE was developed long before modern AI. GOFE is based on modularity, metrics, and step-by-step algorithms based on geometry and physics that can be fully understood and often guaranteed to perform reliably. GOFE includes Kalman Filters, RANSAC outlier rejection, PID and MPC controllers, etc [3].
Whereas “end-to-end” AI methods are “model-free”, GOFE is model-based. GOFE segments problems into modules, so that each module can be tested, fixed, or fine-tuned independently, and replaced when a better module becomes available. Model-free methods can be combined with model-based methods to “kickstart” robots to achieve the levels of reliability required for adoption in real commercial environments, where they can then begin generating real robot data. I’ve been told such a combination is what’s behind the current success of Waymo, and I know that a combination of model-free and GOFE is behind the success of Ambi. Waymo’s robot taxis are collecting vast amounts of real data, and over the past 4 years, Ambi has collected 22 years of real robot data as they have sorted over 100 million real packages [4].
As noted at the beginning, I don’t disagree with Rich Sutton – I believe that model-free AI will eventually surpass GOFE and that general-purpose robots will be common at some point in our future. I look forward to that future and hope I get to see it.
But when will the general-purpose robots arrive? I’m not sure that the public (or investors) are willing to wait very long. For the next few years, the safest bet for closing the 100,000 year data gap is to get real robots into production by combining GOFE with model-free methods. These real robots can collect data as they perform useful work such as taxi driving and sorting packages. That high-quality data will improve their performance and enable robots to perform adjacent skills, spinning up the data flywheel until it collects enough data to enable general-purpose robots.

ASSET Seminar: "The coverage principle in language models: From pre-training to test-time scaling"
2025-11-05 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Test-time compute has emerged as a new axis for scaling language model capabilities, yet we lack a principled understanding of this paradigm. What are the right algorithms and trade-offs for test-time scaling? What properties of the pre-trained model enable it? And can we better align pre-training recipes for test-time success? This talk addresses these questions through a unified lens of coverage. We first show that test-time scaling strategies like best-of-N sampling succeed if and only if the pre-trained model has coverage over high-quality responses. We then demonstrate that coverage, and hence best-of-N performance, can be improved through deliberate exploration, either purely at test time or via RL-style post-training. Finally, we ask why pre-training via next-token prediction yields models with good coverage in the first place. We uncover a rich theoretical landscape driven by an implicit bias of the next-token prediction objective, while also identifying a fundamental misalignment between next-token prediction and coverage, raising the possibility of future algorithmic innovations.

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

Fall 2025 GRASP SFI: Huy Ha, Columbia University & Stanford University, “UMI on Legs: Making Manipulation Policies Mobile with Manipulation-Centric Whole-body Controllers”
2025-11-05 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
We introduce UMI-on-Legs, a new framework that combines real-world and simulation data for quadruped manipulation systems. We scale task-centric data collection in the real world using a hand-held gripper (UMI), providing a cheap way to demonstrate task-relevant manipulation skills without a robot. Simultaneously, we scale robot-centric data in simulation by training whole-body controller for task-tracking without task simulation setups. The interface between these two policies is end-effector trajectories in the task frame, inferred by the manipulation policy and passed to the whole body controller for tracking. We evaluate UMI-on-Legs on prehensile, non-prehensile, and dynamic manipulation tasks, and report over 70% success rate on all tasks. Lastly, we demonstrate the zero-shot cross-embodiment deployment of a pre-trained manipulation policy checkpoint from prior work, originally intended for a fixed-base robot arm, on our quadruped system. We believe this framework provides a scalable path towards learning expressive manipulation skills on dynamic robot embodiments.

MSE David P. Pope Distinguished Lecture: Ion Migration and Its Impact on the Stability of Halide Perovskite Solar Cells - Prashant Kamat - University of Notre Dame
2025-11-06 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The ability to tune the bandgap of metal halide perovskites by alloying different halide ions is key to advancing tandem solar cells and light-emitting displays. However, this compositional flexibility also introduces challenges, most notably, the photoinduced migration of halide ions, which can degrade device performance. A prominent manifestation is photoinduced phase segregation in mixed-halide perovskites (MHPs), leading to the formation of iodide-rich and bromide-rich domains. These inhomogeneous regions act as charge carrier traps, reducing device efficiency. The thermodynamic and redox characteristics of halide perovskites create a strong driving force for hole trapping and the oxidation of iodide species. As a result, the mobility of halide ions and their vulnerability to hole-induced oxidation are major factors governing the long-term stability of perovskite solar cells.

Surface passivation of 3D perovskites using 2D perovskites, carbazole derivatives has been reported widely. However, interfacial chemistry can pose significant challenges during long-term solar cell operation. Under light and heat, cation migration can substantially alter the 2D/3D interface, leading to performance degradation. Therefore, suppressing both halide and cation migration is essential for enhancing the long-term stability and efficiency of perovskite solar cells and light-emitting devices.
Suggested Readings:
DuBose, J. T.; Kamat, P. V. Hole Trapping in Halide Perovskites Induces Phase Segregation, Accounts of Materials Research 2022, 3, 761-771.
DuBose, J. T.; Kamat, P. V. Energy Versus Electron Transfer: Managing Excited-State Interactions in Perovskite Nanocrystal–Molecular Hybrids, Chemical Reviews 2022, 122, 12475–12494.
Chakkamalayath, J.; Hiott, N.; Kamat, P. V. How Stable Is the 2D/3D Interface of Metal Halide Perovskite under Light and Heat?, ACS Energy Letters 2023, 8, 169-171.
Szabó, G.; Kamat, P. V., How Cation Migration across a 2D/3D Interface Dictates Perovskite Solar Cell Efficiency ACS Energy Letters 2024 9 (1), 193-200
Chakkamalayath, J. et al., Photon Management Through Energy Transfer in Halide Perovskite Nanocrystal–Dye Hybrids: Singlet vs Triplet Tuning. Accounts of Chemical Research 2025, 58, 1461–1472.

FOLDS Seminar: ACS: An interactive framework for machine-assisted selection with model-free guarantees
2025-11-06 12:00 - 13:00 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Zoom link: https://upenn.zoom.us/j/98220304722

In this talk, I will introduce adaptive conformal selection (ACS), an interactive framework for model-free selection with guaranteed error control. Building on conformal selection (Jin and Candès, 2023b), ACS generalizes the approach to support human-in-the-loop adaptive data analysis. Under the ACS framework, we can partially reuse the data to boost the selection power, make decisions on the fly while exploring the data, and incorporate new information or preferences as they arise. The key to ACS is a carefully designed principle that controls the information available for decision making, allowing the data analyst to explore the data adaptively while maintaining rigorous control of the false discovery rate (FDR). Based on the ACS framework, we provide concrete selection algorithms for various goals, including model update/selection, diversified selection, and incorporating newly available labeled data. The effectiveness of ACS is demonstrated through extensive numerical simulations and real-data applications in large language model (LLM) deployment and drug discovery.
The talk is based on https://arxiv.org/pdf/2507.15825.

BE Seminar - Rohit Bhargava, "Chemical imaging: engineering a bridge between morphology and molecular composition in biomedical sciences"
2025-11-06 15:30 - 16:30 | 216 Moore Building

CIS Seminar: "Exams with More Learning and Less Stress with a Computer-Based Testing Facility"
2025-11-06 15:30 - 16:30 | Berger Auditorium (Room 13), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
In this talk, I’ll share (1) research on the benefits of frequent testing and “second-chance testing” (optional exam re-takes) on increased student learning and decreased test anxiety, (2) research on patterns of cheating on unproctored online assessments, and (3) how we’ve reduced the instructor workload at Illinois to implement frequent testing through our Computer-Based Testing Facility (CBTF). The CBTF is a collection of proctored computer labs that, in conjunction with the PrairieLearn open-source question-asking platform, enable our faculty to run sophisticated exams with almost no recurring effort even in the largest classrooms. For example, our CS 1 course for majors (run by a single faculty member) ran weekly exams for 1,150 students. Key enabling ideas for the CBTF include: (1) sophisticated auto-grading questions, (2) question generators, (3) asynchronous exams, and (4) dedicated testing space and proctors. The CBTF has been running for over 10 years and proctored over 100,000 exams last semester.

CIS Seminar: "Exams with More Learning and Less Stress with a Computer-Based Testing Facility"
2025-11-06 15:30 - 16:30 | Berger Auditorium (Room 13), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
Exams are an important tool for summative assessment, whose utility has only grown with the advent of large language models (LLMs) like ChatGPT, because they can be implemented in a trustworthy manner. But exams are generally not well liked by either students or faculty. Students find them stressful. For faculty (and their course staff), they represent a large adminstrative burden to write, proctor, and grade. This large burden means they are done infrequently in many classes, but this infrequent testing encourages cramming and leads to high test anxiety.
In this talk, I’ll share (1) research on the benefits of frequent testing and “second-chance testing” (optional exam re-takes) on increased student learning and decreased test anxiety, (2) research on patterns of cheating on unproctored online assessments, and (3) how we’ve reduced the instructor workload at Illinois to implement frequent testing through our Computer-Based Testing Facility (CBTF). The CBTF is a collection of proctored computer labs that, in conjunction with the PrairieLearn open-source question-asking platform, enable our faculty to run sophisticated exams with almost no recurring effort even in the largest classrooms. For example, our CS 1 course for majors (run by a single faculty member) ran weekly exams for 1,150 students. Key enabling ideas for the CBTF include: (1) sophisticated auto-grading questions, (2) question generators, (3) asynchronous exams, and (4) dedicated testing space and proctors. The CBTF has been running for over 10 years and proctored over 100,000 exams last semester.

Fall 2025 GRASP on Robotics: Neville Hogan, Massachusetts Institute of Technology, “Sensory-motor control in humans and robots”
2025-11-07 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
Despite recent advances, humans are still more agile and dexterous than robots; yet human communication (nerves) and actuation (muscles) are slower and our musculo-skeletal system is more complex. This presentation will consider features of neuro-mechanics that may confer advantage. However, they also impose limitations.
Muscle is highly ‘back-drivable’, enabling our ease with (even preference for) ‘contact rich’ tasks. However, muscle is not just a force-generator. Our endo-skeleton requires muscle stiffness for stability; moreover, stiffness must increase at least in proportion to tension.
Consequently, human strength is not limited by force production but by stiffness production. Recent experiments confirm this.
Measuring stiffness (or its dynamic generalization, mechanical impedance) requires access to three variables, but only two are directly measurable: force and position. ‘Subtracting’ a model of limb mechanical impedance enabled estimating the neurally-defined reference trajectory (the third variable) underlying a simple ‘contact-rich’ task: turning a circular crank. It displayed a coincidence of curvature maxima and speed minima, despite the strictly-constant curvature of the constrained hand path. This feature, as well as an observed dependence on turning direction, was reproduced by a model composing the neurally-defined reference trajectory from superimposed oscillations.
Composing cyclic movements from ‘primitive’ oscillations simplifies control but implies a speed-curvature constraint that is widely reported; it significantly limits human performance. It also accounts for our remarkable inability to control force exerted on a moving robot.
The composability of motion is complemented by the composability of mechanical impedance. That enables a truly modular approach to robot programming. It simplifies transitions between free and constrained motion; manages redundancy without inverse kinematic computations; and enables operation into, at, and out of singular configurations—all features of human sensory-motor control that may benefit robots.

Homecoming 2025: Penn Engineering Faculty Lightning Talks
2025-11-07 13:00 - 14:15 | 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.
