Penn Engineering Events

Sustainable Catering - Earth Week 2024
2024-04-23 11:00 - 14:00 (America/New_York) | 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.

MEAM Seminar: "The Mechanics of Animal Collective Behavior: From Insect Swarms to Fish Schools"
2024-05-14 10:00 - 11:30 | David Rittenhouse Laboratory Building, Room A5, 209 S. 33rd Street, Philadelphia, PA, 19104, United States
An incredible diversity of organisms, from bacteria and social insects to bird flocks and fish schools, self-organize to achieve collective tasks. They have inspired the development of decentralized algorithms and robotic swarms. While numerous models have been proposed to understand the self-organization of animal groups, the role of mechanics was rarely considered. In this talk, I will demonstrate experiments and models of the collective behaviors of two insect species, red imported fire ants (Solenopsis invicta) and black soldier fly larvae (Hermetia illucens), in both static and moving fluids. Particular attention will be paid to how their collective behaviors are affected by fluid forces. My results suggest a timescale associated with the limit of animal perception and locomotion. Collective phenomena shorter than such a timescale are driven by mechanical forces and random movements of the individuals. To conclude, I will extend the framework to consider the hydrodynamics of fish schools and discuss how I use computer vision and biomimetic robots to seek insights into this complex system.

MEAM Seminar: "Computational FSI Modeling of the Cardiovascular System"
2024-05-15 14:00 - 15:00 | Moore 212
Computational modeling and simulation has become a routine part of cardiovascular clinical research. These techniques leverage medical imaging to construct patient-specific models that can be used to study disease processes, design and evaluate medical devices, perform predictive surgery, and aid in clinical decision-making. Modern cardiovascular simulations often require millions of elements and tens of thousands of time steps. Thus, there is a need for accurate, robust, and efficient computational techniques to simulate complex pulsatile hemodynamics potentially coupled with deformable vessel walls or heart valves. In this talk, I will discuss computational methods for simulating blood flow and for modeling fluid-structure interaction problems in the cardiovascular system. I will present improvements to existing finite element solver technologies, including development of a block preconditioning technique for fully implicit time integration schemes coupled to reduced dimension models of the cardiovascular system (e.g. Windkessel model). Mass conservation properties of various techniques will be investigated in a patient-specific aorta model. Next, I will show how these improved techniques can be leveraged to simulate fluid-structure interaction problems using the arbitrary Lagrangian-Eulerian method combined with a quasi-Newton solution procedure. Lastly, I will present an immersed approach to computational modeling of fluid-structure interaction problems and demonstrate the potential of the method to simulate heart valves dynamics over the cardiac cycle using an idealized problem.

Penn Engineering Commencement 2024: Doctoral Ceremony
2024-05-16 16:00 - 17:15 | Irvine Auditorium, 3401 Spruce Street, Philadelphia, PA, 19104, United States
Celebrate Penn Engineering’s 2024 Doctoral Graduates. Additional information is available on the Penn Engineering Commencement website.

Penn Engineering Commencement 2024: Master's Ceremony
2024-05-17 14:00 - 16:30 | Palestra, 223 South 33rd Street, Philadelphia, PA, 19104, United States
Celebrate the Penn Engineering 2024 Master’s Graduates. Additional information is available on the Penn Engineering Commencement website.

Celebrating Penn Engineers: Past, Present and Future
2024-05-18 09:00 - 10:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Penn Engineering invites all alumni, family and friends to join us for the dedication of two new hallway installations that honor the past while inspiring the future. Join Dean Kumar to reconnect with classmates and hear remarks from notable alumni. Breakfast will be provided at 9:00am, program begins at 9:45 a.m. Click to see the full schedule of events for Penn Alumni Weekend.

Penn Engineering Commencement 2024: Undergraduate Ceremony
2024-05-18 14:00 - 16:00 | Palestra, 223 South 33rd Street, Philadelphia, PA, 19104, United States
Celebrate the Penn Engineering Undergraduate Class of 2024. Additional information is available on the Penn Engineering Commencement website.

Penn Engineering 2024 Commencement Open House
2024-05-20 12:15 - 14:15
On Monday, May 20, 2024, from 12:15-2:15 p.m., Penn Engineering will host an Open House for the School’s returning graduates and their families throughout the first floors of the main Engineering complex, immediately following the University Ceremony.
Included are photo booth opportunities, a t-shirt giveaway (including a live t-shirt press!), and light refreshments. Please join us!

GRASP Seminar: Yinghao Xu, Stanford University, "Large Reconstruction Model for Efficient 3D Reconstruction and Generation"
2024-05-24 13:00 - 14:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
*This seminar will be held in-person in Wu and Chen as well as virtually via Zoom.
ABSTRACT
Over the past year, the large language model has achieved significant milestones, approaching human-like intelligence across various domains. However, there has been limited investigation into large-scale 3D reconstruction in the literature. In this talk, I will primarily focus on our recent advancements in large-scale 3D reconstruction.
I will start with an introduction to the basics of the Large-scale Reconstruction Model (LRM), aiming to develop a robust and highly generalizable 3D reconstruction system utilizing high-quality 3D data. I will also explain how LRM can be used to efficiently perform high-quality text-to-3D and image-to-3D generation tasks, such as Instant3D and DMV3D.Finally, I will highlight our recent work, specifically our progress in large-scale 3D reconstruction using Gaussian Splatting (GRM). As a large-scale reconstructor, GRM can reconstruct a 3D asset from sparse-view images in about 0.1 seconds. Moreover, GRM shows promising potential in generative tasks, such as text-to-3D and image-to-3D, through its integration with existing multi-view diffusion models.
