Spring 2024 GRASP on Robotics: Jessy Grizzle, University of Michigan, "Michigan’s Robotics Department and Undergrad Curriculum - 'Non c'è scommessa più persa di quella che non giocherò' (There’s no bet more lost than the one I won’t even play)" -- Ora by Jovanotti
2024-04-05 10:30 - 11:45 (America/New_York) | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom.
ABSTRACT
After 39 years as a faculty member with continuous NSF support, the speaker has graduated his last PhD students, closed his lab, and turned 100% to teaching. From June 2016 through June 2021, he led Michigan’s Robotics Institute (aka, he took that bet) and transformed it into a full-fledged department with UG, MS, and PhD degrees. The talk will focus on the undergraduate program and its highly innovative math sequence: ROB 101 Computational Linear Algebra has already taught over 800 Michigan engineers, 90 HBCU students, and a handful of HS students; and ROB 201 Calculus for the Modern Engineer, which will be piloted in Fall 2024. Both courses are focused on equity because, while Talent is Uniformly Distributed, Opportunity is Not!

Penn Engineering 2023-24 Heilmeier Faculty Award Lecture: Arjun Raj
2024-04-08 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
“Can a cell learn?”
Ever since the genetic code was deciphered, we have increasingly come to view cellular control through the lens of genetic determinism. In this paradigm, a cell’s fate is already written into its DNA, which is in turn shaped by Darwinian evolution over the course of many generations. At the same time, an essential part of our experience as human beings is our ability to learn: our past shapes our present in a multitude of ways, all within a single lifetime. Is it possible that cells can adapt to their environment by learning, thereby overcoming their genetic destiny? We explore this possibility by tracing the life history of individual cells. In the context of drug resistance in cancer, we show that there is a special subset of cells that can store memories of past events. These memories allow cells to rewire themselves at the molecular level to adapt to challenges that evolution may have never encountered. We posit that cellular learning may be occurring across many biological systems, affording new opportunities for the engineering of cellular behavior.

ASSET Seminar: "What Should We “Trust” in Trustworthy Machine Learning?" (Aaron Roth, University of Pennsylvania)
2024-04-10 12:00 - 13:30 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
ABSTRACT:

“Trustworthy Machine Learning” has become a buzz-word in recent years. But what exactly are the semantics of the promise that we are supposed to trust? In this talk we will make a proposal, through the lens of downstream decision makers using machine learning predictions of payoff relevant states: Predictions are “Trustworthy” if it is in the interests of the downstream decision makers to act as if the predictions are correct, as opposed to gaming the system in some way. We will find that this is a fruitful idea. For many kinds of downstream tasks, predictions of the payoff relevant state that are statistically unbiased, subject to a modest number of conditioning events, suffice to give downstream decision makers strong guarantees when acting optimally as if the predictions were correct — and it is possible to efficiently produce predictions (even in adversarial environments!) that satisfy these bias properties. This methodology also gives an algorithm design principle that turns out to give new, efficient algorithms for a variety of adversarial learning problems, including obtaining subsequence regret in online combinatorial optimization problems and extensive form games, and for obtaining sequential prediction sets for multiclass classification problems that have strong, conditional coverage guarantees — directly from a black box prediction technology, avoiding the need to choose a “score function” as in conformal prediction.

This is joint work with Georgy Noarov, Ramya Ramalingam, and Stephan Xie

ZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/96814843409

MEAM Ph.D. Thesis Defense: "Implementation and Performance of Wall Models for Large Eddy Simulation of Non-equilibrium Turbulent Boundary Layers"
2024-04-10 13:00 - 14:00 | Room B13, Chemistry Building, 231 S. 34th Street, Philadelphia, PA, 19104, United States
Accurate prediction of high-Reynolds-number wall-bounded turbulent flows is essential for the understanding and flow control of many engineering applications such as aircraft, turbomachinery, and marine vehicles. Additionally, most practical flows exhibit nonequilibrium effects such as pressure gradient, flow separation, and mean three-dimensionality. However, the direct numerical simulation (DNS) of high-Reynolds-number wall-bounded turbulent flows is not feasible owing to the prohibitive computational cost of resolving small-scale eddies near the wall. Wall-modeled large-eddy simulation (WMLES) presents an affordable predictive alternative to the DNS via the approximate modeling of flow physics near the wall (through a wall model) while resolving the outer (larger) scales directly on the computational grid. In this work, we focus on two aspects of wall models, (i) development and implementation of new/existing wall models, and (ii) application and comparison of different wall models in various nonequilibrium turbulent boundary layers. In the first part, we develop a novel spectral formulation for the ODE equilibrium wall model, showing its superior efficiency to the traditional approach. Furthermore, we extend the integral nonequilibrium wall model to an unstructured-grid LES solver. In the second part, we explore three wall models with varying degrees of computational complexity and physical fidelity, to assess their performance in two controlled but reasonably realistic nonequilibrium flows over a flat plate. The first flow features a turbulent boundary layer undergoing a series of complex pressure gradient effects, while the second exhibits turbulent flow separation induced by suction and blowing. While in the latter case, the more complex model clearly produces a superior prediction of the wall shear stress, the same is not necessarily true in the former case, highlighting that there still exists the need to adapt the existing wall models to different flow physics by modifying their underlying formulation or assumptions. Finally, a physic-based decomposition of skin friction, that shows separable contributions from various physical processes in the flow, is employed to explain the differing mechanisms of success/failure of wall models in different flows.

ESE PhD Thesis Defense: "Scalable and Risk-Aware Verification of Learning Enabled Autonomous Systems"
2024-04-10 13:30 | Moore 317, 200 S 33rd Street, Philadelphia, PA, 19104, United States
As autonomous systems become more prevalent, ensuring their safety will become more and more important. However, deriving guarantees for these systems is becoming increasingly difficult due to the use of black box, learning enabled components and the growing range of operating domains in which they are deployed. The complexity of the learning-enabled components greatly increases the computational complexity of the verification problem. Additionally, the safety predictions from verifying these systems must be conservative. This thesis explores two high-level methods for verifying autonomous systems: probabilistic model checking and statistical model checking. Probabilistic model checking methods exhaustively analyze a model of the system to reason about its properties. These methods generally suffer from scalability issues, but if the abstraction is built correctly then the results will be provably conservative. On the other hand, statistical model checking methods draw traces from the system to reason about its properties. These methods don’t suffer the scalability drawback of probabilistic model checking, but their guarantees are weaker and may not even be conservative. This thesis introduces methods for improving the scalability of verifying autonomous systems with probabilistic model checking methods and incorporating notions of conservatism into statistical model checking.
On the probabilistic model checking side, this thesis first explores using engineering intuitions about systems to reduce probabilistic model checking complexity while preserving conservatism. Next, standard conservative probabilistic model checking techniques are used to synthesize runtime monitors that are conservative and lightweight. Finally, this thesis presents a run-time method for composing monitors of verification assumptions. Verification assumptions are critical for simplifying verification problems so that they become computationally feasible.
For statistical model checking, this thesis first leverages a method called conformal prediction to bound the errors of trajectory predictors, which enables safe (i.e. conservative) planning in dynamic environments. Additionally, a method for producing less conservative conformal prediction regions in time series settings is developed. Then a method called risk verification is developed, which uses statistical methods to bound risk metrics of a system’s performance. Risk metrics, which capture tail events of the system’s performance, offer a statistical equivalent of worst case analysis.

ESE Spring Seminar - "Solving Inverse Problems with Generative Priors: From Low-rank to Diffusion Models"
2024-04-10 13:30 - 14:30 | Towne 337
: Generative priors are effective countermeasures to combat the curse of dimensionality, and enable efficient learning and inversion that otherwise are ill-posed, in data science. This talk begins with the classical low-rank prior, and introduces scaled gradient descent (ScaledGD), a simple iterative approach to directly recover the low-rank factors for a wide range of matrix and tensor estimation tasks. ScaledGD provably converges linearly at a constant rate independent of the condition number at near-optimal sample complexities, while maintaining the low per-iteration cost of vanilla gradient descent, even when the rank is overspecified and the initialization is random. Going beyond low rank, the talk discusses diffusion models as an expressive data prior in inverse problems, and introduces a plug-and-play posterior sampling method (Diffusion PnP) that alternatively calls two samplers, a proximal consistency sampler solely based on the forward model, and a denoising diffusion sampler solely based on the score functions of data prior. Performance guarantees and numerical examples will be demonstrated to illustrate the promise.

Spring 2024 GRASP SFI: Michel Hidalgo, Ekumen, "Doing robotics in digital labs: Or how simulations fuel robotics development"
2024-04-10 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. This week’s speaker will be virtual.
Seminar attendees are also invited to a group discussion with Michel Hidalgo on Thursday, April 11th from 3:00 PM to 4:00 PM on this link.
ABSTRACT
How do you do robotics without robots? Ekumen has been profitably providing software consulting services to robotics companies for over a decade, 10000 miles from relevant technological hubs. While multi-causal, a non-negligible factor in the company’s success were the advancements in multi-body dynamics simulation. From prototyping through validation and testing, the robotics industry relies on these simulations to speed up and scale its development workflows. As a byproduct, this tendency has alleviated the need for locality with hardware assets. I will discuss present-day technology in multi-body dynamics simulation, some of our past experience with it in real-world applications, and what we have learned from practice along the way.

CBE Seminar: "Role of Water in Underwater Adhesion" (Ali Dhinojwala, University of Akron)
2024-04-10 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract
Roughness and wetness can disrupt interfacial bonding and reduce adhesion, and this phenomenon is of relevance for many biological and engineering applications. I will discuss how roughness affects both dry and wet adhesion as well as provide an overview of our current theoretical understanding in this area. My specific interest is in underwater adhesion, focusing on overcoming the challenges for achieving adhesion in confined water, which reduces molecular contact (particularly when in contact with rough surfaces). The trapping of confined water is a function of roughness, surface chemistry, and kinetics, making this a difficult problem to explain using theoretical models. Interestingly, nature has developed a wide range of strategies that enable organisms to stick to rough and wet surfaces. For example, geckos and insects use fibrillar structures to create molecular contact and to improve water drainage, spiders use hygroscopic salts to reduce interfacial water next to hydrophilic surfaces, and mussels use specific chemical groups (catechol) to bind to polar surfaces. I will share new strategies inspired by these natural systems for improving adhesion and discuss how they are applied to biomedical and engineering applications that require adhesion to wet and rough surfaces.

MSE Seminar: "Thermal Architecture"
2024-04-11 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Air conditioning accounts for nearly 20% of the total electricity used in buildings globally and cooling energy demand is predicted to significantly increase over the next decades due to urbanization, population growth, and global warming. Heat stress is a major environmental justice concern, disproportionally impacting disadvantaged communities. What are the paths to reduce the massive energy consumption of the building sector and at the same time still provide people with healthy living environments? With COVID-19 and extreme heat stress events placing a strong focus on the precarious relationship between indoor spaces and human well-being, we need a new paradigm for environmental control in buildings.
The Thermal Architecture Lab seeks to find sustainable and equitable cooling alternatives to replace current building practices. Working at the intersection of heat transfer, architectural design, and material science, we develop novel technologies and design strategies to simultaneously reduce buildings’ energy demand and provide thermal shelter to people in a warming world. In this lecture, a series of projects will be presented, exploring various methods to provide both ventilation and climatic adaptation to interior spaces across different climatic zones.

MEAM Ph.D. Thesis Defense: "Controlling Fracture Behavior Through Architecture"
2024-04-12 09:30 - 10:30 | DRL A8, 209 S. 33rd Street, Philadelphia, PA, 19104, United States
Many natural materials achieve excellent combinations of mechanical properties through their micro- and nano-scale structures, which leverage a level of complexity currently unmatched in engineering design. Recent advances in digital manufacturing have enabled the introduction of these fine-scale architectures to improve the mechanical properties of materials, but their intricacy still lags far behind that of natural materials. In particular, the potential of these structures to create materials with enhanced fracture resistance has remained limited, primarily due to a narrow design focus on simple, repetitive structures optimized for idealized materials. Improving the damage-tolerance of materials is critical to the mechanical performance of structures and interfaces, as cracks and defects often lead to failure at far-field loads that are significantly lower than the theoretical strength of the system. This thesis will demonstrate how leveraging disordered structures and considering material behavior beyond the idealized elastic-brittle regime can significantly enhance the fracture resistance of architected interfaces. Specifically, three key aspects influencing the failure of architected interfaces are examined: the effects of plasticity, the advantages of disordered structures, and the impacts of stochastic material failure. Through a synthesis of mechanics frameworks, computational modeling, and experimental mechanics including full-field analyses using digital image correlation and photoelasticity, it is shown that properly designed architectures lead to tunable and enhanced fracture resistance. These architectures enlarge the region of damage around the crack tip, delocalizing stresses and increasing the resistance to crack propagation, while also revealing novel properties such as the decoupling of toughness and strength.

Spring 2024 GRASP on Robotics: David Fridovich-Keil, University of Texas at Austin, "Information-Aware Algorithms for Smooth Dynamic Games"
2024-04-12 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom.
ABSTRACT
This talk introduces dynamic game theory as a natural modeling tool for multi-agent interactions ranging from large, abstract systems such as ride-hailing networks to more concrete, physically-embodied robotic settings such as collision-avoidance in traffic. We present the key theoretical underpinnings of dynamic game models for these varied situations and draw attention to the subtleties of information structure, i.e., what information is implicitly made available to each agent in a game. Thus equipped, the talk presents a state-of-the-art technique for solving several variants of these games, as well as a set of “dual” techniques for the inverse problem of identifying players’ objectives and other structures based on observations of strategic behavior.

PICS Colloquium: "Modeling Lone Pair Dynamics in Materials"
2024-04-12 14:00 - 15:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
Materials properties are governed by the structure and dynamics of the bonds between their constituent atoms. In addition to covalent, metallic, and ionic interactions that we typically think about, lone pair electrons can result in non-trivial directional interactions in materials. I will discuss molecular interactions involving lone pairs in materials, focusing on results from molecular simulations of the electronic structure and dynamics of model halide perovskites and solid-state ionic conductors of interest for applications in energy science. After a brief discussion of lone pair-driven interactions in materials, I will present recent results predicting the existence of electronic plastic crystals, crystalline solids that exhibit dynamic rotational disorder of lone pairs. We predict that electronic plastic crystals are found in a wide range of molecular and ionic materials, including halide perovskites, where we anticipate that dynamic lone pair disorder plays an important role in photophysical processes. I will then present recent results from our ongoing investigations into electron pair dynamics in solid-state electrolytes for energy storage applications. We predict that rotational motion of electron pairs is coupled to translational dynamics of conducting ions, forming electronic paddle-wheels in solid-state electrolytes. Finally, I will discuss our ongoing efforts to reach the length and time scales necessary to model electrochemical processes in these and related systems by developing machine learning-based models that simultaneously describe electronic effects and coupling to long-range electrostatic fields. By focusing on the correct physics, the resulting models are partially transferable and can describe electronic and nuclear response to external fields. I will then demonstrate the accuracy and transferability of this neural network approach – the self-consistent field neural network (SCFNN) – on model aqueous systems before closing with a discussion of implications of our results for the development of machine learning models.

Career Steps Before Graduation Seminar
2024-04-12 15:00 - 16:30 | Auditorium, LRSM Building, 3231 Walnut Street, Philadelphia, PA, 19104, United States
On Friday, April 12,2024, the Department of Materials Science and Engineering is hosting an information session dedicated to equipping you with insights on the essential steps to secure a career after graduation, with staff from UPenn Career Services and ISSS (International Student and Scholar Services) lending their expertise on prevailing job trends and providing guidance on navigating the intricacies of securing employment.
What sets this presentation apart is the tailored approach. Rather than offering generic advice, our goal is to tailor the presentation to address YOUR questions and concerns. That’s why we’re asking you to register (which is required) and submit any questions that you have in advance. Whether you’re curious about visa regulations, resume tips, using OPT, or hiring trends, we want to provide as much support as possible for your career goals.
This opportunity is extended to all SEAS students who are approaching graduation. Therefore, we ask you to secure your spot at your earliest convenience, ensuring that you don’t miss out on this opportunity.

CBE Doctoral Dissertation Defense: "A Multifaceted Approach to CO2 Emissions Reductions and Removals" (Maxwell Pisciotta)
2024-04-12 15:00 - 17:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
The scientific consensus is that climate change is not only actively occurring, but that it is irrevocably due to human activities associated with greenhouse gas emissions. Greenhouse gas emissions have been accumulating in the atmosphere since the beginning of the industrial revolution. This thesis specifically focuses on one greenhouse gas in particular, CO 2 . The continued CO 2 emissions from human activity can be quantified with the atmospheric concentration, which amounts to upwards of 420 ppm today. To mitigate the harmful impacts of climate change, these CO 2 emissions must be mitigated, through pathways such as reducing their initial generation, capturing them when they are unable to be avoided, and removing them from the atmosphere when they cannot be captured at the source. This thesis investigates different technologies that fit into these broad categories, notably, deploying carbon capture technologies on natural gas combined cycle power plants, decarbonizing industrial sectors, and pairing direct air capture technologies to geothermal energy. To readily address the CO 2 emissions from natural gas combined cycle power plants, a novel approach of using thermal energy storage was developed and evaluated to ensure its technological performance and economic viability. By integrating natural gas combined cycle power plants with carbon capture and storage (CCS) and thermal energy storage opportunities, the economic viability of these plants improve. This was measured using the net present value of each of the configurations assessed over real-world locational marginal pricing (LMP) signals from NYISO and CAISO. Of the thermal energy storage options, eight of the 19 thermal energy storage configurations led to an increased net present value on 11.5% – 98% of the LMP signals. Additionally, a framework was developed and used to identify opportunities to integrate direct air capture (DAC) systems with geothermal energy resources to maximize the CO 2 abatement potential. The Geothermal-Framework can be used with various geothermal resources ranging from 86ºC – 225ºC, using various working fluids, and brine salinity ranging from 0-6%. When the integration of geothermal energy and DAC systems are compared to geothermal energy being used to generate low-carbon electricity, the CO 2 abatement potential is increase by 105% to 452% when geothermal energy is integrated with DAC systems. This illustrates beneficial synergies between the two technologies, namely being able to use geothermal energy as thermal energy rather than solely converting it to electricity. Lastly, the Geothermal-DAC Framework was used to showcase opportunities for integrating DAC with the geothermal resources near Gerlach, NV, in preparation for a community meeting. The community feedback was then incorporated, facilitating updates to the Geothermal-DAC Framework to account for community needs, illustrating that engineering can be community-centered from the start of the project. All the approaches explored in this thesis highlight the need for a diverse portfolio of solutions to address the ongoing CO 2 emissions and abatement required to avoid the most harmful impacts of climate change. Furthermore, the efforts of researchers, scientists, policymakers and frontline communities will be needed in concert to deploy a portfolio that meets the needs to address climate change and protect against further environmental injustices.

BE Doctoral Dissertation Defense: "The Origin and Factors Affecting Differentiation of Progenitor Cells in Tendon-to-Bone Integration" (Tim Kamalitdinov)
2024-04-16 09:00 - 11:00 | Smilow Center for Translational Research in SCTR 11-146AB
The Department of Bioengineering at the University of Pennsylvania and Dr. Nat Dyment are pleased to announce the Doctoral Dissertation Defense of Tim Kamalitdinov.

Title: The Origin and Factors Affecting Differentiation of Progenitor Cells in Tendon-to-Bone Integration
Date: April 16, 2024
Time: 9:00 AM
Location: SCTR (Smilow Center for Translational Research) 12-146AB

The public is welcome to attend.

ESE PhD Thesis Defense: "Multiferroic Micro Electromechanical Systems for Magnetic Sensing and Wireless Power Transfer in Biomedical Applications"
2024-04-16 09:00 - 11:00 | Fisher Bennett Hall, Room 401, 3340 Walnut Street, Philadelphia, PA, 19104, United States
Multiferroic micro-electromechanical systems (MEMS) enable small, room temperature, low power magnetic sensing and wireless power transfer (WPT) in biomedical applications. Current biomagnetic sensing relies on sensitive magnetometers like superconducting quantum interference devices (SQUIDs), but their reliance on cryogenic temperatures is undesirable.

This thesis presents the theory, design, microfabrication, and characterization of multiferroic MEMS magnetic sensors and WPT devices. Iron cobalt/silver (Fe50Co50/Ag) magnetostrictive material is coupled to piezoelectric aluminum nitride (AlN) to form a multiferroic sensor. Low frequency biomagnetic signals are upconverted around the length-extensional beam’s 7-16 MHz mechanical resonance to provide Q enhancement to the sensitivity. The up conversion exploits a nonlinear phenomenon of magnetostrictive materials with applied mechanical strain. For two devices studied, modulated sensitivities of 58.4 mA/T and 37.7 mA/T were observed along with resolutions of 5.03 nT/√Hz and 2.72 nT/√Hz over a bandwidth larger than the biomagnetic frequency spectrum (0.1Hz to 1kHz). The sensors’ sensitivity was limited by Duffing nonlinearity and the relatively low piezoelectric coefficients of AlN.

To improve sensitivity, magnetoelectric sensors were fabricated using (Fe0.5Co0.5)0.92Hf0.08 coupled to 28% aluminum scandium nitride (Al0.72Sc0.28N). Increasing sensitivity improved the resolution from 5.03 nT/√Hz to 2.16 nT/√Hz. To delay the onset of thermal Duffing nonlinearity, various anchoring tether lengths were explored in Fe0.5Co0.5/Ag – AlN magnetoelectric sensors to provide better heat conduction away from the structure. Also, silicon dioxide (SiO2) was added to compensate the temperature coefficient of frequency (TCF). Larger achievable strain was verified before the onset of Duffing nonlinearity, providing increased modulation of the Fe0.5Co0.5/Ag and a resolution of 1.11 nT/√Hz, an 86% improvement when compared to a long tether device with the same layer stack (8.02 nT/√Hz) and a 78% improvement over the initial (Fe50Co50/Ag) – AlN long tether devices with no SiO2 thermal compensation.

WPT measurements were taken using (Fe50Co50/Ag) – AlN magnetoelectric devices. By sending a magnetic field at the device resonance frequency, optimal WPT can be achieved. Devices were packaged with a magnetic bias circuit and the output power was measured. For a device at 7.44MHz, an output power of 126.8 nW and a power density of 1196.2 uW/mm3 is projected when measuring with both electrodes.

MEAM Seminar: "Cardiovascular Engineering – A 'Personal' Journey from Bench to Bedside"
2024-04-16 10:00 - 11:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Over the past few decades, significant contributions have been made by engineers to healthcare. The successful translation of fundamental engineering concepts has helped improve patient care and diagnosis. This impact has been particularly evident in the field of cardiovascular medicine where the roles of fluid and solid mechanics, and imaging are critical. In ~45 years of pioneering research, Professor Ajit Yoganathan’s Cardiovascular Fluid Mechanics Laboratory at the Georgia Institute of Technology & Emory University, has been in the vanguard of this movement: advancing knowledge and technology in native and replacement heart valves, cardiovascular diagnostic techniques, and pediatric surgical/interventional planning. Using state-of-the-art fluid dynamic measurement techniques, Dr. Yoganathan and his group have developed methods to enable the optimization of replacement heart valve designs. Novel techniques in the assessment of native heart valve function have provided clinicians with improved tools to assess disease severity and helped identify effective treatment options.
For the treatment of congenital heart defects, the development of novel computational modeling tools to simulate surgical procedures and their fluid dynamics outcomes have provided clinicians with new ways to plan for treatments for individual patients to increase the probability of success. Combined, these advances have helped bridge the lab bench to the patient’s bedside/bassinet and integrate engineering science with the art of medicine.

ESE Spring Seminar - "Scaling Deep Learning Up and Down"
2024-04-16 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Deep learning with neural networks has emerged as a key approach for discovering patterns and modeling relationships in complex data. AI systems powered by deep learning are used widely in applications across a broad spectrum of scales. There are strong needs for scaling deep learning both upward and downward. Scaling up highlights the pursuit of scalability – the ability to utilize increasingly abundant computing and data resources to achieve superior capabilities, overcoming diminishing returns. Scaling down represents the demand for efficiency – there is limited data for many application domains, and deployment is often in compute-limited settings.
In this talk, we present several studies in both directions. For scaling up, we first explore the design of scalable neural network architectures that are widely adopted in various fields. We then discuss an intriguing observation on modern vision datasets and its implication on scaling training data. For scaling down, we introduce simple, effective, and popularly used approaches for compressing convolutional networks and large language models, alongside interesting empirical findings. Notably, a recurring theme in this talk is the careful examination of implicit assumptions in the literature, which often leads to surprising revelations that reshape community understanding. Finally, we discuss exciting avenues for future deep learning and vision research, such as next-gen architectures and dataset modeling.

Raj and Neera Singh Program in Artificial Intelligence Town Hall
2024-04-16 12:00 - 13:30 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States

Webinar: "Engineering in the Age of AI"
2024-04-17 12:00 - 13:00 | PA
Join Penn Engineering faculty to learn how to harness the power of AI for innovation.
Dean Vijay Kumar will moderate a discussion with Michael Kearns, Professor and National Center Chair in Computer and Information Science (CIS); Surbhi Goel, Magerman Term Assistant Professor in CIS; and René Vidal, Rachleff University Professor, with joint appointments in Electrical and Systems Engineering and Radiology. These experts will guide you through the cutting-edge tools, techniques and methodologies transforming industries and reshaping engineering.
The event is open to everyone.
Register here

ASSET Seminar: "Reasoning Myths about Language Models: What is Next?" (Dan Roth, University of Pennsylvania)
2024-04-17 12:00 - 13:30 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
ABSTRACT:
The rapid progress made over the last few years in generating linguistically coherent natural language has blurred, in the mind of many, the difference between natural language generation, understanding, and the ability to reason with respect to the world. Nevertheless, robust support of high-level decisions that depend on natural language understanding, and one that requires dealing with “truthfulness” are still beyond our capabilities, partly since most of these tasks are very sparse, often require grounding, and may depend on new types of supervision signals.
I will discuss some of the challenges underlying reasoning and argue that we should focus on LLMs as orchestrators – coordinating and managing multiple models, applications, and services, as a way to execute complex tasks and processes. I will discuss some of the challenges and present some of our work in this space, focusing on supporting task decomposition and planning.
ZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/92067502115

BE Doctoral Dissertation Defense: "Deep Learning for Unpaired Domain Adaptive Medical Image Segmentation" (Yuemeng Li)
2024-04-17 13:00 - 15:00 | PA
The Department of Bioengineering at the University of Pennsylvania and Dr. Yong Fan are pleased to announce the Doctoral Dissertation Defense of Yuemeng Li.

Title: Deep Learning for Unpaired Domain Adaptive Medical Image Segmentation
Date: April 17, 2024
Time: 1:00PM-3:00PM
Location: BRB Auditorium
Zoom link

The public is welcome to attend.

ESE PhD Thesis Defense: "Cellular Cosheaves, Graphic Statics, and Mechanics"
2024-04-17 14:00 | Greenberg Lounge (Room 114), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
Methods from algebraic topology enable simplifications and extensions of fundamental concepts in structural and mechanical engineering. Chief among these tools are cellular sheaves and cosheaves – abstract mathematical data structures over polyhedra and discrete spaces. The homology of cellular cosheaves (and cohomology of cellular sheaves) combines and distills distributed data into the most meaningful algebraic-topological features of the underlying system. While sheaves in general have a rich lineage in pure mathematics, only recently has this theory been simplified and streamlined towards practical applications.
The main contribution of this thesis is in describing and enriching graphic statics, a structural design method that emerged in the 19th century. This is a geometric form of Poincaré duality where primal and dual graphs encode the form and forces of a truss structure. We first model planar truss statics using cosheaves, then prove that the long exact sequence of cosheaf homology precisely recovers the graphic statics relationship. This relation further extends to the equivariant setting, where the statics of symmetric structures (under finite group action) splits by irreducible representations (and symmetry types). The cosheaf method proves invaluable in the modern 3D polyhedral setting, where a spectral sequence is used to untangle the linear relations between a range of filtered geometric cosheaf equilibrium spaces. Here many novel results are derived, interlinking the statics of geometrically dual trusses and other systems.
There are several secondary results presented in this thesis. We connect the statics of trusses with the statics of rigid frames, deriving the novel anchored frame system. We prove that mechanical linkage kinematics are in fact encoded by anchored frame self-stresses. The kinematics of rigid origami surfaces is described by cellular cosheaves as well in dual systems. The Jacobian between hinge angular velocities and spatial velocity vectors is shown to be a connecting homomorphism, connecting different origami models and extending widely used linearization techniques in closed-chain kinematics.

Spring 2024 GRASP SFI: Karl Pertsch, University of California, Berkeley and Stanford University, "Building Open-Source Generalist Robot Policies"
2024-04-17 14:00 - 15:00 | Towne 337
This will be a hybrid event with in-person attendance in Towne 337 and virtual attendance on Zoom.
ABSTRACT
Generalist robot policies, trained on large and diverse robot datasets, have the potential to transform how robot learning research is done: in the same way that current models in NLP are almost universally derived from pretrained large language models, future robot policies might be initialized from generalist robot models and finetuned with only modest amounts of target domain data.
In this talk I will discuss our efforts on building such generalist robot policies. I will focus on two key ingredients: data and models. On the data side, I will discuss our recent works on building the largest open-source real robot manipulation datasets to date, the Open X-Embodiment dataset and DROID, with a total of 2M+ robot trajectories. On the model side, I will summarize our learnings from building RT-X and Octo, the first generalist robot policies trained on the Open X-Embodiment dataset. I will discuss their current limitations and outline important steps for future research towards ubiquitous robot foundation models.

CBE Seminar: "Creating Real Steak Without the Cow: Using Insights from Wine and Biopharmaceutical Production to Commercialize Cultivated Meat"
2024-04-17 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract
Between a growing global population and increased consumption of meat from developing countries, it is projected that meat production will have to increase by at least 60% by 2050 to meet demand. It is unlikely that expanded conventional animal agriculture alone will be able to meet this need. Therefore, alternatives to conventional meat will be required at a very large scale. This is likely to include plant- and fungal-based meat alternatives, as well as cultivated meat—the growth of animal stem cells in large-scale fermentors with subsequent differentiation into muscle, fat, and connective tissue cells. While close to 150 companies have formed in the last eight years internationally to commercialize this technology, only three products are currently on the market, a chicken nugget sold at two restaurants in Singapore, and two chicken products recently approved for sale in the US, but not yet widely available. Aside from federal regulatory hurdles, difficult technical hurdles remain. These include development of cell lines well-suited for production, inexpensive growth and differentiation media, creation of structure into whole cuts like marbled steaks, and scale-up to a commercial size potentially 10 times larger than anything previously attempted for cell-culture-based processes. After presenting the field and our consortium-based approach to addressing these hurdles, this talk will focus on media optimization, as over 80% of the cost of these products is projected to be from the nutrients used to grow the cells. For optimization, using both spent media analysis and AI-based efficient experimental design techniques for complex optimization problems will be discussed. In addition, initial efforts to facilitate the scale-up of processing will be discussed. While the field of cultivated meat is extremely new, many of the problems facing large-scale commercialization of this fermentation process are not. Thus, we can look to gain critical insight from decades of research in allied fields from wine to biopharmaceutical production.

MEAM Ph.D. Thesis Defense: "Do the Twist: Toward Agile Control of an Axially Twisting Robotic Quadruped"
2024-04-18 10:00 - 11:00 | David Rittenhouse Laboratory Building, Room 4C4, 209 S. 33rd Street, Philadelphia, PA, 19104, United States
Even as they continue to improve, legged robots pale in comparison to their biological counterparts. This discrepancy is at least partly due to robots possessing an order of magnitude fewer degrees of freedom. In fact, most dynamically capable quadrupedal robots lack any degrees of freedom in the torso, opting instead for a simpler, single, rigid body. This rigidity results in the legs competing for workspace and power optimality during locomotion. Furthermore, although some quadrupeds do feature a flexible torso, most research primarily focuses on bending in either the sagittal or lateral planes. In contrast, this thesis explores the integration of the often-neglected axially twisting spinal degree of freedom into quadrupedal robotic platforms.
First, taking inspiration from biomechanical reorientation data in geckos, the thesis develops an axial twisting strategy that reduces the effort required for a robot to right itself. Following this, a trajectory-optimization-based study compares the energetic and dynamic performance of two quadrupedal models, one with a rigid torso and one with a twisting torso, across various dynamic and aperiodic locomotory tasks. Hoping to realize these results, this work introduces “Twist”, a novel quadrupedal robotic platform with an axially twisting spine, and proceeds to develop controllers for agile, spatial locomotion. Starting in the sagittal plane, an angular-momentum-based coordinate is developed for a three-degree-of-freedom, extensible inverted pendulum model and is shown to be an approximate asymptotic phase variable and to produce an input decoupling. Toward generalizing those results, the underactuation of the spatial floating torso model during two-point contact is thoroughly examined and informs a composition-based controller for the “Twist” platform. Finally, integrating these ideas, this thesis develops a trotting gait, which shows promising results using this composition for spatial quadrupedal locomotion.

A Franklin Medal Laureate Lecture: "Building Therapies Layer-By-Layer"
2024-04-18 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Recipient of the 2024 Benjamin Franklin Medal in Chemistry

Benjamin Franklin Medal in Mechanical Engineering Lecture: "Molecular and Micro-Structural Mechanics and Design of Soft Materials"
2024-04-18 11:00 - 12:00 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Soft synthetic and natural polymeric-based materials offer particular new avenues for the design and fabrication of materials and devices. Engineering the molecular and geometrical structures of the constituent materials, together with utilizing their ability to sustain large deformations enables materials and designs with novel properties and functional behavior. We begin with the development of physically-based models of elastomeric mechanics, from the elegant simplicity of the “8-chain” network model of rubber elasticity to the more complex enhancements that capture the molecular mechanisms of nonlinear time-dependent behavior. We also address the behavior of versatile co-polymers, which can form micro-composites of “hard” and “soft” domains, providing an ability to engineer unique combinations of highly resilient elastic and dissipative systems. The structure and behavior of natural analogs such as mussel byssal threads are included and shown to yield further insights. Finally, we present the ability to tailor constituent geometrical features of soft composites using new fabrication techniques including 3D printing. Exemplars include patterned and layered structures which exhibit deformation and instability-induced pattern transformations. These structural transformations result in concomitant changes in a multitude of behaviors giving super-elastic and multi-linear elastic response, enhanced mechanisms for energy storage, switchable band gaps, soft actuators, and morphable surface topology. Looking to the future, the predictive ability of multi-scale nonlinear mechanics of soft materials, combined with the rapid developments in fabrication techniques provide profound opportunities to truly design functional materials, devices and products.

ESE Spring Seminar - "Miniaturized Biomedical Devices for Navigation, Sensing and Stimulation"
2024-04-18 12:00 - 13:00 | Towne 327
Medical electronic devices are an integral part of the healthcare system today and are used in a variety of applications around us. The design of such devices has several stringent requirements, the key being miniaturization, low-power operation, and wireless functionality. In this talk, I will present CMOS-based miniaturized, low-power and wireless biomedical devices in three broad domains: (a) in-vivo navigation and tracking, (b) in-vivo sensing of biomarkers and physiological signals, and (c) in-vivo stimulation and drug delivery. For the first part, I will talk about ingestible and implantable devices that can be used to achieve sub-mm tracking accuracy in 3D and in real time inside the human body, which is very useful for localizing devices in the GI tract, during precision surgeries and minimally invasive procedures. In the second part, I will present the design of a novel on-chip 3D magnetic sensor that is highly miniaturized and low-power, thus making it suitable for many biomedical applications. In the last part, I will briefly talk about my recent work on a wearable device for multi-modal sensing from sweat, followed by ongoing work on devices for stimulation and drug-delivery in the GI tract. I will end the talk with a glimpse of my future research direction.

BE Doctoral Dissertation Defense: "Proteome-seq: Sequencing-Based Readout of Proteomic Analytical Assay" (Mariia (Masha) Alibekova Long)
2024-04-18 13:00 - 15:00 | Smilow Center for Translational Research in SCTR 11-146AB
The Department of Bioengineering at the University of Pennsylvania and Dr. Alex Hughes are pleased to announce the Doctoral Dissertation Defense of Mariia (Masha) Alibekova Long.

Title: Proteome-seq: Sequencing-Based Readout of Proteomic Analytical Assay
Date: April 18, 2024
Time: 1:00 PM
Location: SCTR (Smilow Center for Translational Research) 11-146AB

Zoom option:

Topic: Mariia Alibekova Long’s PhD Thesis Defense
Time: Apr 18, 2024 01:00 PM Eastern Time (US and Canada)
Join Zoom Meeting
https://upenn.zoom.us/j/98332256725?pwd=[REDACTED]
Meeting ID: 983 3225 6725
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The Public is welcome to attend.
