Penn Engineering Events

BE Doctoral Dissertation Defense: "Proteome-seq: Sequencing-Based Readout of Proteomic Analytical Assay" (Mariia (Masha) Alibekova Long)
2024-04-18 13:00 - 15:00 (America/New_York) | 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=UjU2MXllaHlqMFdHemZaL2VHeTQ5UT09
Meeting ID: 983 3225 6725
Passcode: 183014

The Public is welcome to attend.

BE Seminar: "Using Computers to Derive Protein Structure from Sparse Data – A Case Study for Mass Spectrometry" (Steffen Lindert, Ohio State)
2024-04-18 15:30 - 16:30 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Mass spectrometry-based methods such as covalent labeling, surface induced dissociation (SID) or ion mobility (IM) are increasingly used to obtain information about protein structure. However, in contrast to other high-resolution structure determination methods, this information is not sufficient to deduce all atom coordinates and can only inform on certain elements of structure, such as solvent exposure of individual residues, properties of protein-protein interfaces or protein shape. Computational methods are needed to predict high-resolution protein structures from the mass spectrometry (MS) data. Our group develops algorithms within the Rosetta software package that use mass spectrometry data to guide protein structure prediction. These algorithms can incorporate several different types of mass spectrometry data, such as covalent labeling, surface induced dissociation, and ion mobility. We developed scoring functions that assess the agreement of residue exposure with covalent labeling data, the agreement of protein-protein interface energies with SID data and the agreement of protein model shapes with collision cross section (CCS) IM measurements. We subsequently rescored Rosetta models generated with de novo protein folding and protein-protein docking and we were able to accurately predict protein structure from MS labeling, SID and IM data. Future work is focusing on developing custom machine learning models to predict protein structure from MS data.

Spring 2024 GRASP on Robotics: Kristi Morgansen, University of Washington, "Integrated Sensing and Actuation for Robust Flight Systems"
2024-04-19 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
A fundamental element of effective operation of autonomous systems is the need for appropriate sensing and processing of measurements to enable desired system actions. Model-based methods provide a clear framework for careful proof of system capabilities but suffer from mathematical complexity and lack of scaling as probabilistic structure is incorporated. Conversely, learning methods provide viable results in probabilistic and stochastic structures, but they are not generally amenable to rigorous proof of performance. A key point about learning systems is that the results are based on use of a set of training data, and those results effectively lie in the convex hull of the training data. This presentation will focus on use of model-based nonlinear empirical observability criteria to assess and improving and bounding performance of learning pose (position and orientation) of rigid bodies from computer vision. A particular question to be addressed is what sensing data should be captured to best improve the existing training data. The particular tools to be leveraged here focus on the use of empirical observability gramian techniques being developed for nonlinear systems where sensing and actuation are coupled in such a way that the separation principle of linear methods does not hold. These ideas will be discussed relative to both engineering applications in the form of motion planning for range and bearing only navigation in autonomous vehicles, vortex position and strength estimation from pressure measurements on airfoils, and effective strain sensor placement on insect wings for inertial measurements.

2024 Bioengineering Graduate Research Symposium
2024-04-19 12:30 - 18:00 | Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Join the Graduate Association of Bioengineers (GABE) for the 2024 Graduate Research Symposium!

When: April 19, 2024 from 12:30-6:00 PM
Where: The Singh Center for Nanotechnology
What: Keynote by Dr. David Kaplan; BE graduate student posters and presentations; food buffet and reception; BE swag and awards.

Registration is free and is open to anyone affiliated with the Department of Bioengineering. Register here.

Should you have any questions, contact the symposium co-chairs: Dimitris Boufidis boufidis@seas.upenn.edu & Miles Arnett mjarnett@seas.upenn.edu

PICS Colloquium: "Physics-compatible kinetic-energy and entropy preserving (KEEP) scheme for high-fidelity simulation of compressible turbulence"
2024-04-19 14:00 - 15:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
Low (or ideally zero) numerical dissipation is always critical for high-fidelity scale-resolving flow simulations, as numerical dissipation prevents the physics of inviscid kinetic energy and entropy conservation, which is an essential attribute of compressible turbulence. However, contrary to the requirement, numerical schemes in compressible flow heavily rely on numerical dissipation for stable computation, preventing high-fidelity simulations, especially for flows around complex geometries. We address this challenge by devising a physics-compatible numerical scheme that satisfies the kinetic energy and entropy preservation (KEEP) properties by discretely satisfying the analytical relations of the governing equations. The KEEP scheme is highly stable without introducing numerical dissipation, something that existing numerical schemes fail to do. The stability stems from the significant improvement of entropy preservation in the KEEP scheme. The KEEP scheme allows robust and high-fidelity simulation not only for academic purposes but also for engineering applications with complex geometries. We also discuss an illustrative application to near-stall flows around complex full aircraft configurations with high-lift devices to show the capability of our numerical framework.

CBE Doctoral Dissertation Defense: "Identifying Material Fingerprints of Relevance to Understand Adsorbate-Surface Interactions Using First Principles Modeling and Machine Learning" (Genesis Quiles-Galarza)
2024-04-19 15:30 - 17:30 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Abstract:

Adsorption of chemical species on surfaces of materials is one of the critical phenomenon governing the reactivity and activity of the material for surface and interface driven chemical reactions. At the core of the analytical $d$-band adsorption model is the correlation between the adsorption energy of a chemical species (molecule or reaction intermediate) on the metal surface and an electronic material property, namely the d-band center from the density of states (DOS). Although very successful, the d-band model has its limitations and cannot be applied to all materials. Therefore, efforts have been devoted to discover material fingerprints that can be used to describe adsorption of chemical species on more complex surfaces and materials. Herein, we use first principles methods (density functional theory, DFT) and machine learning (ML) to elucidate what kind of material fingerprints or features are needed to describe the interaction between an adsorbate and the surface of a metal material, a two-dimensional (2D) transition metal carbide and nitride compound materials known as MXenes, and a hybrid (molecular catalyst supported on a heterogeneous surface) catalyst material. The ML models used in this study belong either to the “black-box” or “glass-box” category, enabling not only prediction of the adsorption energy with small errors, but also allow insight into the material physics governing the adsorbate-surface interaction. These ML studies indisputably demonstrate that the electronic fingerprints of the material are the most critical features in reliably determining the adsorbate-surface interactions.For metals, we confirm the findings of the analytical d-band model by achieving adsorption formulas with contributions from both the sp and d-DOS bands, as well as multiple higher order contributions. For MXenes, we find that the adsorbate-surface interaction is complex with significant contributions from the terminating functional group atom, specifically their sp-DOS band features. Generally, our studies shows that the nature of the adsorbate-surface interactions cannot be fully captured by single or simple linear correlations between the adsorbate energy and a materials feature, but instead require higher order, multi-dimensional feature combinations. These findings imply that further investigations are needed to develop physically-sound, multi-dimensional features which could be used as descriptors to predict adsorbate-surface interactions with an accuracy comparable to that of DFT.

MEAM Master's Thesis Defense: "Optical Analysis of Buckling-Induced Micro-Robotic Membranes"
2024-04-22 12:00 - 13:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
In recent years, micro-robotic membranes have attracted increasing interest due to their unique properties and potential applications in various fields. The optical properties of these membranes have been playing a crucial role in the design and development of optical devices such as reflective displays with customizable colors. The primary challenge to understanding the mechanical-spectral interaction is the limitation of conventional microscopic techniques. The AFM cannot be employed when voltage is applied. Conversely, hyperspectral imaging offers insights into the spectral response but lacks the capacity to infer topological characteristics directly. In this research, I build an optical model that stands on the theoretical foundation laid by Maxwell’s equations, Fresnel equations, and the Transfer Matrix Method (TMM). By feeding the hyperspectral imaging, the model can reconstruct the 3D topologies of buckling membranes. This is achieved through least-square regressions to accurately predict height data across various points. Through this methodology, this research offers a novel framework for understanding the complex interplay between mechanical deformation and optical phenomena.

MEAM Seminar: "Flows About Superhydrophobic Surfaces"
2024-04-23 10:00 - 11:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Superhydrophobic surfaces, formed by air entrapment within the cavities of hydrophobic solid substrates, offer a promising potential for hydrodynamic drag reduction. In several of the prototypical surface geometries the flows are two-dimensional, governed by Laplace’s equation in the longitudinal problem and the biharmonic equation in the transverse problem. Moreover, low-drag configurations are typically associated with singular limits. Accordingly, the analysis of liquid slippage past superhydrophobic surfaces naturally invites the use of both singular-perturbation methods and conformal-mapping techniques. I will discuss the combined application of these methodologies to several emerging problems in the field.

Sustainable Catering - Earth Week 2024
2024-04-23 11:00 - 14:00 | Lobby and Mezzanine, Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Learn about sustainable products and practices your caterer can implement to reduce waste, minimize plastic and lower carbon footprint. Planet-friendly menu Plastic-alternative packaging and utensils Nutrition label for customized eating preferences Vendor engagement beyond delivery.

MEAM Master's Thesis Defense: "Gaussian Process-Based Active Exploration Strategies in Vision and Touch"
2024-04-23 12:00 - 13:00 | Meyerson Hall, Room B2, 210 S. 34th Street, Philadelphia, PA, 19104, United States
Robots struggle to understand object properties like shape, material, and semantics due to limited prior knowledge, hindering manipulation in unstructured environments. In contrast, humans learn these properties through interactive multi-sensor exploration. This work proposes fusing visual and tactile observations into a unified Gaussian Process Distance Field (GPDF) representation for active perception of object properties. While primarily focusing on geometry, this approach also demonstrates potential for modeling surface properties beyond geometry.
The GPDF encodes signed distance, gradients, and uncertainty estimates. Starting with an initial visual shape estimate, the framework iteratively refines the geometry by integrating dense vision measurements using differentiable rendering and tactile measurements at uncertain regions. By quantifying multi-sensor uncertainties, it plans exploratory motions to maximize information gain for recovering precise 3D structures. To improve scalability, it investigates approximation methods like inducing point parameterization for Gaussian Processes. This probabilistic multi-modal fusion enables active exploration and mapping of complex object geometries.

PICS Colloquium: "Exploiting time-domain parallelism to accelerate neural network training and PDE constrained optimization"
2024-04-24 10:00 - 11:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
This talk will explore methods for accelerating numerical optimization constrained by transient problems using parallelism. Two types of transient problems will be considered. In the first case training algorithms for Neural ODEs will be discussed. Neural ODEs are a class of neural network architecture where the depth of the neural network (the layers) is modeled as a continuous time domain. For the second case, transient PDE-constrained optimization problems will be described. In either case, simulation-based optimization requires repeated executions of the simulator’s forward and backward (adjoint) time integration schemes. Consequently, the arrow of time creates a major sequential bottleneck in the optimization process. Second, for performance these methods rely strongly on the available parallelization for the forward and adjoint solves. Thus, when forward and adjoint solvers are already operating at the limit of strong scaling and hardware utilization, the arrow-of-time bottleneck cannot be overcome by additional parallelization across the spatial grid or network layers.
Deep neural networks are a powerful machine learning tool with the capacity to‚ learn complex nonlinear relationships described by large data sets. Despite their success training these models remains a challenging and computationally intensive undertaking. We will present a layer-parallel training algorithm that exploits a multigrid scheme to accelerate both forward and backward propagation. Introducing a parallel decomposition between layers requires inexact propagation of the neural network. The multigrid method used in this approach stitches these subdomains together with sufficient accuracy to ensure rapid convergence. We demonstrate an order of magnitude wall-clock time speedup over the serial approach, opening a new avenue for parallelism that is complementary to existing approaches. We also discuss applying the layer-parallel methodology to recurrent neural networks and transformer architectures.
The second half of this talk focuses on PDE-constrained optimization formulations. Solving optimization problems with transient PDE-constraints is computationally costly due to the number of nonlinear iterations and the cost of solving large-scale KKT matrices. These matrices scale with the size of the spatial discretization times the number of time steps. We propose a new 2-level domain decomposition preconditioner to solve these linear systems when constrained by the heat equation. Our approach leverages the observation that the Schur-complement is elliptic in time, and thus amenable to classical domain decomposition methods. Further, the application of the preconditioner uses existing time integration routines to facilitate implementation and maximize software reuse. The performance of the preconditioner is examined in an empirical study demonstrating the approach is scalable with respect to the number of time steps and subdomains.

ASSET Seminar: "Statistical Methods for Trustworthy Language Modeling" (Tatsu Hashimoto, Stanford University)
2024-04-24 12:00 - 13:15 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
ABSTRACT:
Language models work well, but they are far from trustworthy. Major open questions remain on high-stakes issues such as detecting benchmark contamination, identifying LM-generated text, and reliably generating factually correct outputs. Addressing these challenges will require us to build more precise, reliable algorithms and evaluations that provide guarantees that we can trust.
Despite the complexity of these problems and the black-box nature of modern LLMs, I will discuss how in all three problems — benchmark contamination, watermarking, and factual correctness — there are surprising connections between classic statistical techniques and language modeling problems that lead to precise guarantees for identifying contamination, watermarking LM-generated text, and ensuring the correctness of LM outputs.

ZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/94597712175

Spring 2024 GRASP SFI: Harish Ravichandar, Georgia Institute of Technology, "New Wine in an Old Bottle: A Structured Approach to Democratize Robot Learning"
2024-04-24 15:00 - 16:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom.
ABSTRACT
Decades of rigorous research in dynamical systems and control helped us integrate robots into a wide variety of domains, ranging from factory floors to the moon. Today, it would appear that deep learning has taken over the torch and will bring robots to our homes, freeing us all from banal chores. In this utopian vision, learning-based approaches tend to replace analytical methods. Moving away from handcrafted bespoke solutions to generalist robots that can operate in unstructured environments. But one can instead view learning-based and analytical approaches as two ends of a broad spectrum, with one end optimizing for reliability (at the cost of human effort) and the other for emergent intelligence (at the cost of data and computation). In this talk, I will argue why it is better for robots to be in the middle of this broad spectrum. Using manipulation as a case study, I will discuss how our lab combines ideas from dynamical systems and machine learning to overcome three often-overlooked issues with contemporary methods: i) high barrier to entry due to demands for expensive computational resources and annotated data, ii) inability to handle new tasks without relying on significant user expertise (e.g., for reward or controller design, hyperparameter tuning, data collection and curation), and iii) unreliable behaviors due to inscrutable and unpredictable learned policies. Addressing these issues will enable robot learning to escape the confines of well-resourced research labs and positively impact the larger society.

John A. Quinn Distinguished Lecture in Chemical Engineering: "Exploring the Physics, Materials Science, and Biological Implications of Polyelectrolyte Complexation" (Matthew Tirrell, University of Chicago)
2024-04-24 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract
The richness of liquid-liquid phase separation behavior in mixtures of oppositely-charged polyelectrolyte has been greatly illuminated recently in the polymer physics literature. Precise determinations of phase diagrams, measurements of interfacial tension, scattering measurements of chain configurations, and increasingly insightful theory are all producing a clearer understanding of these phenomena. In parallel, physics is also being brought to bear on manifestations of these behaviors in biology. Diverse biological examples related to liquid0liquid phase separation of polyelectrolyte complexes include membraneless organelles, biological condensates that enhance transcription or protect from stress shock, and other biological functions. This talk will spell out current understanding of the various contributions to the phase behavior, including the role of various entropic contributions, as well as the effects of charge density of the macromolecules. New
results on asymmetric mixtures will be presented, which are more the norm in nature than the perfectly symmetrical mixtures in polymer physics studies.

Tech Talks: Bridging Academia and Innovation in Philadelphia's Tech Hub
2024-04-24 18:00 - 20:00 | Jon M. Huntsman Hall, 3730 Walnut Street, Philadelphia, PA, 19104, United States
Join us at the University of Pennsylvania for an event co-organized by the Mack Institute for Innovation Management and the City of Philadelphia Department of Commerce. This event is set to explore and expand collaboration opportunities that are remaking Philadelphia’s tech industry into a diverse and dynamic hub of innovation. Hear from city and commonwealth officials about their take on Philadelphia’s tech ecosystem and how the University of Pennsylvania can help solidify the city’s future as a leader in innovation. We’ll also hear from Penn entrepreneurs about their contribution to the local ecosystem and the benefits of starting their venture in Philadelphia.
Confirmed Panelists:

Tempest Carter, Director of Strategic Tech Initiatives at the City of Philadelphia
John Swartley, Chief Innovation Officer, University of Pennsylvania
Jen Gilburg, Deputy Secretary for Technology and Entrepreneurship for the Commonwealth of PA, Department of
Community and Economic Development, Commonwealth of PA
Steven Nichtberger, MD; CEO, Cabaletta Bio; Adjunct Professor, Healthcare Management; Senior Fellow, Vagelos LSM Program, University of Pennsylvania

Moderator:

Dr. Valery Yakubovich, Executive Director, Mack Institute of Innovation Management

About the Organizers:
Department of Commerce for the City of Philadelphia champions the innovation economy, with the Philadelphia Most Diverse Tech Hub (MDTH) initiative leading the charge in making the city a top, inclusive tech destination. Launched by the Department in August 2023, Tech Talks offer a platform to share resources, discuss innovative ideas, and foster community connections.
Mack Institute for Innovation Management at the Wharton School is a premier hub for innovation research and practice, driving forward the integration of academic insights with real-world application.
REGISTER HERE

MSE Seminar: "Probabilistic Digital Twins for Structure Preserving Simulation and Scientific Discovery"
2024-04-25 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Despite the recent flurry of work employing machine learning to develop surrogate models to accelerate scientific computation, the “black-box” underpinnings of current techniques fail to provide the verification and validation guarantees provided by modern finite element methods. In this talk we present a data-driven finite element exterior calculus for building accelerated reduced-order models of multiphysics systems when the governing equations are either unknown or require closure. Key to the framework is a fully differentiable partition of unity which provides a machine learnable alternative to a traditional computational mesh, upon which we simultaneously learn physical relevant control volumes alongside corresponding integral balance laws. We demonstrate that resulting models may realize speedup of over 1000x over traditional finite element simulations, while guaranteeing the exact treatment of physical constraints and numerical stability. We then briefly summarize recent work developing Bayesian underpinnings for these models, providing characterization of epistemic uncertainty which may be used to drive active learning tasks.
With tools for building probabilistic digital twins in hand, we then turn to our work integrating physical models into high-throughput material discovery experiments to characterize process-structure-property relationships. In material science, datasets are comparatively small relative to the combinatorially massive space of potential designs. We combat this by fusing information spanning multimodal characterization (e.g. XRD,TEM,SEM,EBSD) of differing fidelity and throughput and incorporating data-driven models. We end by summarizing some campaigns conducted at Sandia National Laboratories applying these tools to physical vapor deposition, metal additive manufacturing, and electrodeposition.

PRECISE Seminar: Optical Coherence Tomography - From Conception to Current Frontiers
2024-04-25 14:00 - 15:00 | Room 307, Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Optical coherence tomography (OCT) is a technology invented in 1991 to image small critical tissue structures throughout the body with micrometer resolution. It is widely used in the management of eye and coronary heart diseases. In 2023, OCT received broad attention when its inventors received the prestigious Lasker-DeBakey Clinic Medical Research Award and the National Medal of Technology and Innovation from President Biden. For me, it was the culmination of 3 decades of work as an engineer, clinician, and translational researcher, as well as an even longer journey as an immigrant who tapped into the potential of America’s great research universities.
I will present OCT from an inventor’s perspective. The physical principles will be explained with illustrations on measuring the time-of-flight of light with interferometry. I will tell the story of the aha moment when the idea of OCT came to my mind, as well as the rapid pace of development that made OCT a clinical reality. The biggest applications of OCT in the management of eye diseases will be shown. Recent advances made at OHSU that enable OCT to advance beyond the imaging of tissue structure to the detection of blood flow and photoreceptor function will be described.
OCT is still a rapidly developing technology. The technical capabilities have improved in many aspects, but the most astounding has been the continual improvement in imaging speed, which has doubled approximately every 2.5 years over the past 3 decades. The technological advances have made more and more clinical applications feasible. I will present a vision for the broader applications of OCT, which includes imaging the eye to assess brain and cardiovascular diseases, as well as direct OCT imaging of other target organs such as the skin, digestive tract, brain, inner/middle ear, and teeth.

Spring 2024 GRASP on Robotics: Marco Pavone, Stanford University & NVIDIA, "Rethinking AV Development with AV Foundation Models"
2024-04-26 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
Foundation models, trained on vast and diverse data encompassing the human experience, are at the heart of the ongoing AI revolution influencing the way we create, problem solve, and work. These models, and the lessons learned from their construction, can also be applied to the way we develop a similarly transformative technology, autonomous vehicles. In this talk, I will highlight recent research efforts towards rethinking elements of an AV program both in the vehicle and in the data center, with an emphasis on (1) composing ingredients for universal and controllable end-to-end simulation, (2) architecting autonomy stacks that leverage foundation models to generalize to long-tail events, and (3) ensuring safety with foundation models in the loop.

MEAM Master's Thesis Defense: "The Rheology and Microphysics of Monodisperse Synthetic Mucin"
2024-04-26 12:00 - 13:00 | Room 2C8, David Rittenhouse Laboratory Building, 209 South 33rd Street, Philadelphia, PA, 19104, United States
Mucus, a complex fluid produced by every living organism, has multiple essential functions including acting as an effective barrier layer in various bodily processes, many of which involve important rheological (flow) and tribological (adhesive, lubricative) functions. The primary component of mucuses are mucins – highly glycosylated, linear polypeptides. Understanding how the structure and properties of mucins control the overall behavior of mucus is thus of substantial interest. However, natural mucus is often contaminated with non-mucin constituents that affect the rheological response, and purifying mucus without damaging the mucins themselves is difficult. Here, fully synthetic, monodisperse mucins have been prepared by a collaborator. Aqueous solutions of these mucins have been studied for comparison to the rheological response shown by natural mucin solutions. The purity and structural control of these synthetic mucins provide a model system where the mechanistic sources of the mucus’ rheological response can be isolated and identified. Experimental bulk rheometry demonstrates a shear-thinning behavior with a yield-stress fluid response. This behavior is attributed to associations between mucin molecules localized to the liquid-air interface, which contradicts previous literature on natural mucins. This conclusion is supported by interfacial rheology measurements and by a mathematical model encapsulating the dynamics of a thin mucin layer under shear. This work furthers the understanding of the dynamics of mucin solutions and the qualitative microphysics surrounding their dynamics.

PICS Colloquium: "Representations Learnt from Synthetic Volumes Enable Training-free Medical Image Analysis"
2024-04-26 14:00 - 15:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
Current medical image analysis projects involve months to years of data annotation and custom technical development. This talk introduces methods to train networks that generalize out-of-the-box to new modalities, anatomies, and datasets all without retraining for the specific use case. Our key contributions include (A) generative models driven by biomedical shape priors that synthesize wildly variable training data, and (B) a multi-scale dense representation learning algorithm that leverages the synthetic data to learn contrast-invariant representations. We will show that a single U-Net pretrained in this manner can then extract features that enable state-of-the-art 3D multimodality image registration and can also serve as a general-purpose foundation model for few-shot segmentation across arbitrary biomedical datasets. We will also briefly demonstrate translational applications of the proposed methods to ongoing studies of disordered pregnancies in fetal and maternal MRI.

Entrepreneurship Seminar Series: Pathways to Impact
2024-04-26 14:00 - 16:00 | Towne 327
Entrepreneurship Seminar Series: Pathways to Impact
This session brings together a panel of current and former faculty and PhDs that have brought their technology to market and have worked in both academia and commerce. Panelists will discuss the opportunities and approaches they took to create companies, leverage experience in academia, and drive research into commercial success.
Refreshments will be provided.
Who: Faculty, Post-Docs, & PhD students
Where: Towne 327
When: 2-4:00 pm, Friday April 26, 2024
Add to calendar: Apple Google Office 365 Outlook Outlook.com Yahoo
RSVP Here (Not required)

MEAM Master's Thesis Defense: "Modeling Off-Grid Photovoltaics Integrated with Micro-Cogeneration and Electrical Energy Storage"
2024-04-29 14:00 - 15:00 | Room 2C6, David Rittenhouse Laboratory Building, 209 S. 33rd Street, Philadelphia, PA, 19104, United States
Due to the elevated expenditure of fossil fuels and their adverse impacts on climate change resulting from greenhouse gas emissions, it is imperative to integrate clean energy sources alongside fossil fuels. This study presents the design, simulation, and optimization of an integrated system comprising solar photovoltaics, micro-cogeneration, and electrical energy storage to achieve energy self-sufficiency independently from external energy sources. Moreover, the study determines the maximum cost of the converter that can be economically added to the heating system, enabling it to generate both heat and electricity simultaneously. It also calculates the unit electricity cost for each combination. The modeled system is capable of meeting all required electricity and heat demands. Yet, the model presents a range of alternatives for configuring the capacity required for PV panels, batteries, and converter, each resulting in varied overall costs. It has been determined that when conversion efficiency increases, the costs associated with PV panels and batteries decrease, as do the overall cost. However, the maximum cost of the converter necessary for achieving cost-effectiveness is not constant, but rather changes according to its efficiency. Ultimately, the unit electricity cost varies, decreasing with increasing converter rated power until a certain value, then increasing.

MEAM Seminar: "Collective Transitions in Beating Cilia and Swimming Fish"
2024-04-30 10:00 - 11:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
I will discuss the collective modes that spontaneously emerge in ciliary carpets and fish schools. In both systems, the fluid medium couples the motion of individuals in the group. Flow coupling is dominated by viscous forces in cilia and by inertial interactions in fish. I will show, numerically and analytically in the continuum limit, that the waves that emerge in ciliary carpets are robust to morphological and kinematic perturbations of the ciliated tissues. In fish schools, I will show intermittent collective behavior with back-and-forth switching between schooling and milling, and I will analyze the bifurcations underlying phase transitions by lifting the stochastic dynamics of individual fish onto a Fokker-Planck equation that governs the group dynamics. These problems highlight how structure, activity, and environment interconnect to produce emergent biological function; our work provides a framework for establishing structure-to-function maps across multiple length scales to help understand, probe, and engineer emergent phenomena in biological and biomedical systems.

AI Month: Closing Keynote Address
2024-04-30 13:00 - 14:00 | Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
AI Month at Penn concludes with closing remarks by Vijay Kumar, Nemirovsky Family Dean, Penn Engineering. This will be followed by a panel discussion moderated by Susan Davidson, Weiss Professor, Computer and Information Science (CIS). Panel guests include: Zachary Ives, Adani President’s Distinguished Professor, CIS;
George Pappas, UPS Foundation Professor of Transportation, Electrical and Systems Engineering; and Chris Callison-Burch, Associate Professor, CIS.
Panel: 1:00-2:00 p.m.
Glandt Forum, Singh Center
A reception will follow.

ASSET Seminar: "Lifelong Learning for Autonomous Systems: Progress and Challenges" (Eric Eaton, University of Pennsylvania)
2024-05-01 12:00 - 13:30 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
ABSTRACT:
Research in lifelong or continual machine learning has advanced rapidly over the past few years, primarily focusing on enabling learned models to acquire new tasks over time while avoiding catastrophic forgetting of previous tasks. However, autonomous systems still lack the ability to rapidly learn new generalizable skills by building upon and continually refining their learned knowledge—a hallmark of lifelong learning in humans and animals. In this talk, I will examine our progress toward lifelong machine learning and its application to autonomous systems, exploring recent work in compositional representations and self-supervision. We’ll then discuss how far we have come and the open problems that remain toward learning generalizable skills in lifelong settings.

ZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/94522983788

Penn NRT Soft AE Annual Symposium: Exploring research & educational intersections of soft materials, autonomous experimentation & science policy
2024-05-02 10:00 - 15:30 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Please register at the event webpage, where you will also find abstracts and bios for the speakers.
Invited talks include:
Exploring Polymer Blend Directed Self-Assembly Using Autonomous X-Ray Scattering
Greg Doerk, PhD, Brookhaven National Lab
Machine-Learning-Guided Discovery of New Electrochemical Reactions
Andrew Zahrt, PhD, University of Pennsylvania
Ask an Expert about ChatGPT
Chris Callison-Burch, PhD, University of Pennsylvania
Engineering’s Hardest Problem
Gwen Ottinger, PhD, Drexel University

Spring 2024 Robotics MSE Thesis Presentations
2024-05-07 09:30 - 14:00 | GRASP Treehouse – Pennovation Complex; LAB Building (Bldg 250 – Suite 250), 3401 Grays Ferry, Philadelphia, 19146, United States
This is a hybrid event with in-person attendance in the GRASP Treehouse and virtual attendance via Zoom.
RSVPs required for in-person attendees. Please RSVP here.

9:30am – Welcome Remarks
Dr. Ani Hsieh – ROBO Program Chair
9:35am – Beiming Li
Advised by: Dr. Vijay Kumar
“Depth Completion of Glass Surfaces from a Single-View Polarization Image“

10:05am – Derek Cheng
Advised by: Dr. Vijay Kumar
“Multimodal Neural Rendering using Agricultural Robotics Field Data”

10:35am – Coffee Break
10:50am – Daryl Hurwitz
Advised by: Dr. Flavia Vitale
“Rapid Fabrication of Patient Specific High Density Microelectrode Arrays”

11:20am – Sharanya Venkatesh
Advised by: Dr. Michael Posa
“Approximating global MPC for Contact Rich Manipulation Using Local Feedback”

11:50am – Jiaqi Lian
Advised by: Dr. Linh Thi Xuan Phan
“Online Rotor Fault Detection and Isolation for Vertical Takeoff and Landing Vehicles”

12:20pm – Lunch
12:50pm – Yifan Wang
Advised by: Dr. Linh Thi Xuan Phan
“FDIR Verification for Faults in eVTOL Vehicles: Dynamic Analysis and Control”

1:20pm – James Springer
Advised by: Dr. Dinesh Jayaraman
“Leveraging Privileged Information for Sample-Efficient Reinforcement Learning”

1:50pm – End of Presentations

IEEE Grace Hopper A-0 Milestone Celebration
2024-05-07 10:00 - 12:00 | Greenberg Lounge (Room 114), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States

CBE Seminar: "Chemical Synthesis Enabled by Information" (Tim Cernak, University of Michigan)
2024-05-08 15:30 - 16:30 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Abstract
Advancing the synthesis of small molecules is critical to the advent of new medicines, materials, and agrochemicals. Our lab has been exploring strategies in chemical synthesis — both in reaction method development and total synthesis — that leverage modern data science techniques and robotics. This presentation will share some recent results using informatics to target novel amine-acid coupling reactions, and algorithms to streamline multistep synthesis. Chemical synthesis enabled by data science techniques and automation will be a consistent theme of the research, aiming towards a future state where medicines are invented at a rapid pace.

ESE PhD Thesis Defense: "Low Noise and Low Power Front-end Circuit Design for Bio-Signal Recordings"
2024-05-13 15:00 - 17:00 | Room 221, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Bio-signals in living objects are the signals thatcarry physiological information from one part of the body to another. Studying bio-signals can extract data that maps health status or body activities for medical purposes. Implantable and wearable devices of small volume for measuring different bio-signals are desired for medical applications where the devices measure the signals with further processing to provide feedback for diagnosis and treatment. Bio-signals produced by the body usually have a small amplitude requiring low noise and low power analog front-end amplifiers and signal processing circuitry.
This talk will focus on low noise and low power readout circuits for magnetic sensing systems to acquire bio-magnetic fields. Compared to bio-electrical recordings, bio-magnetic sensing is non-invasive and non-contact. PCB-based readout electronics for strain modulated multiferroic sensors with a bandwidth of 3.4 kHz and a magnetic noise floor at 1 kHz of 98.5 pT/√Hz is implemented for understanding the circuit and the sensor noise models for modulated multiferroic sensor-readout systems. To reduce the power consumption, an ASIC readout circuit in 180 nm CMOS for the sensor is designed and implemented. By utilizing a demodulator first architecture, measurements for the sensor-readout system demonstrate a 127 pT/√Hz magnetic noise floor at 1 kHz and a low power consumption of 5.9 mW. To further improve the noise performance at low frequencies, readout circuit in BiCMOS and a differential structure are implemented to achieve a magnetic noise floor of 85 pT/√Hz at 1 kHz, and 300 pT/√Hz at 10 Hz with a power consumption of 5.6 mW. In addition, a low intermediate frequency (low-IF) demodulation readout circuit is implemented and measured to eliminate the 1/f flicker noise and realizes a noise floor of 722 pT√Hz at 1 Hz with the power consumption of 2.9 mW. The noise and power consumption that the magnetic sensing systems have achieved are significantly lower than alternative magnetic sensor systems of similar volume, which outlines an excellent solution for low-power, low-noise, wearable, on-body sensing in the future.
