Spring 2023 GRASP SFI: Ram Vasudevan, University of Michigan, "Can't Touch This: Real-Time, Provably Safe Motion Planning and Control for High Dimensional Autonomous Systems"
2023-03-29 15:00 - 16:00 (America/New_York) | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This is a hybrid event with in-person attendance in Levine 307 and virtual attendance via Zoom. This week’s presenter will be in-person as well.
ABSTRACT
A key challenge to the widespread deployment of robotic manipulators is the need to ensure safety in arbitrary environments while generating new motion plans in real-time. This talk describes a technique that constructs a parameterized representation of the forward reachable set that it then uses in concert with predictions to enable certified, collision checking. To improve computational speed, this talk describes how to represent this parameterized reachable set using a neural implicit representation without sacrificing any safety guarantees. This approach, which is guaranteed to generate safe behavior, is demonstrated across a variety of different real-world platforms including ground vehicles, manipulators, and walking robots.

MSE Seminar: "Natural Structural Materials: Lessons on Toughening Mechanisms, Weight Reduction, and Multifunctionality" (Virginia Tech University)
2023-03-30 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Structural materials that are damage tolerant, lightweight, multifunctional, and sustainable are highly desirable for many engineering applications. Such combinations of properties are often found in the biological world. Organisms from nature construct a variety of different biological structural materials for protection, predation, body support, camouflage, etc. Despite the fact that these materials are made from limited constituent materials with usually poor intrinsic mechanical properties, such as brittle minerals and soft biopolymers, biological materials are often able to achieve remarkable mechanical properties while offering additional functionalities simultaneously, such as low density, coloration, transparency, flexibility, visual sensitivity, etc. In this talk, I will present our recent work in elucidating the fundamental structure-property relationships in some natural structural materials by focusing on their strategies in achieving damage tolerance, weight reduction, and multifunctionality. In particular, I will present a unique damage-tolerant, dual-scale, single-crystalline, low-density microlattice that we recently discovered in an echinoderm skeletal system. Our research combines quantitative multiscale 3D structural analysis, in-situ mechanical analysis, theoretical and computational modeling, and design and manufacturing of bio-inspired materials. I hope this talk can stimulate more discussions among research areas such as material synthesis, biomimetics, mechanics, biology, and manufacturing.

ESE & MEAM Seminar - "Enabling Self-sufficient Robot Learning"
2023-03-30 12:30 - 13:30 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Autonomous exploration and data-efficient learning are important ingredients for helping machine learning handle the complexity and variety of real-world interactions. In this talk, I will describe methods that provide these ingredients and serve as building blocks for enabling self-sufficient robot learning.
First, I will outline a family of methods that facilitate active global exploration. Specifically, they enable ultra data-efficient Bayesian optimization in reality by leveraging experience from simulation to shape the space of decisions. In robotics, these methods enable success with a budget of only 10-20 real robot trials for a range of tasks: bipedal and hexapod walking, task-oriented grasping, and nonprehensile manipulation.
Next, I will describe how to bring simulations closer to reality. This is especially important for scenarios with highly deformable objects, where simulation parameters influence the dynamics in unintuitive ways. The success here hinges on finding a good representation for the state of deformables. I will describe adaptive distribution embeddings that provide an effective way to incorporate noisy state observations into modern Bayesian tools for simulation parameter inference. This novel representation ensures success in estimating posterior distributions over simulation parameters, such as elasticity, friction, and scale, even for scenarios with highly deformable objects and using only a small set of real-world trajectories.
Lastly, I will share a vision of using distribution embeddings to make the space of stochastic policies in reinforcement learning suitable for global optimization. This research direction involves formalizing and learning novel distance metrics on this space and will support principled ways of seeking diverse behaviors. This can unlock truly autonomous learning, where learning agents have incentives to explore, build useful internal representations and discover a variety of effective ways of interacting with the world.

BE Seminar: "Engineering therapeutic immunity using (nano)biomaterials" (Natalie Artzi, Harvard Medical School)
2023-03-30 15:30 - 16:30 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
This seminar will be held in person only; snacks will be provided.
Immunomodulatory therapies have advanced to clinical trials over the past decade for the treatment of a range of diseases and disorders, from cancer to diabetes to transplant rejection. However, the efficacy of these therapies remains limited, as challenges associated with off-target drug toxicity, poorly controlled drug pharmacokinetics, and an incomplete understanding of real-time therapy responses prevent effective therapeutic windows from being realized. Here, I will highlight some of our work on the design, fabrication, and characterization of biomaterial-based delivery technologies for the controlled delivery of combination immunotherapies and for the non-invasive monitoring of their associated immune responses for cancer therapy. We show that the design of materials and their delivery context can influence therapeutic outcomes and alter the spatiotemporal characteristics of the incited immunomodulatory responses. By adroitly designing and utilizing our material delivery platforms, we can deliver immunotherapies with tailorable pharmacokinetics and enhanced efficiency to improve long-term therapeutic outcomes and tolerability and enable studying basic questions in immunobiology as we seek to generate a ‘living’ therapeutics.

Spring 2023 GRASP on Robotics: Pulkit Agrawal, MIT, "Fun with Robots and Machine Learning"
2023-03-31 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 via Zoom. This week’s presenter will be in-person as well.

ABSTRACT
Robots are getting smarter at converting complex natural language commands describing household tasks into step-wise instructions. Yet, they fail to actually perform such tasks! A prominent explanation for these failures is the fragility and inability of the low-level skills (e.g., locomotion, grasping, pushing, object re-orientation, etc.) to generalize to unseen scenarios. In this talk, I will discuss a framework for learning low-level skills that surpasses limitations of current systems at tackling contact-rich tasks and is real-world-ready: generalizes, runs in real-time with onboard computing, and uses commodity sensors. I will describe the framework using the following case studies:
(i) a dexterous manipulation system capable of re-orienting novel objects.
(ii) a quadruped robot capable of fast locomotion and manipulation on diverse natural terrains.
(iii) learning from a few task demonstrations of an object manipulation task to generalize to new object instances in out-of-distribution configurations.

Spring 2023 GRASP Seminar: Larry Matthies, Jet Propulsion Laboratory, California Institute of Technology, "Autonomous mobility in Mars exploration: recent achievements and future prospects"
2023-03-31 13:30 - 14:30 | Berger Auditorium (Room 13), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
This is a hybrid event with in-person attendance in Berger Auditorium (Skirkanich Floor B) and virtual attendance via Zoom. This week’s presenter will be in-person as well.

ABSTRACT
This talk will summarize key recent advances in autonomous surface and aerial mobility for Mars exploration, then discuss potential future missions and technology needs for Mars and other planetary bodies. Among recent advances, the Perseverance rover that is now operating on Mars includes new autonomous navigation capability that dramatically increases its traverse speed over previous rovers. Perseverance also carried the Ingenuity helicopter to Mars, which is a technology demonstration of the first heavier-than-air aircraft ever to operate on another planet. The current mission objective for Perseverance involves driving a total distance of about 60 kilometers in about 10 Earth years. Rover mission concepts recently suggested for the Moon would drive about 1500 to 2000 km in under 4 years, which requires significant advances in autonomy. Successors to the Ingenuity helicopter are now under development for use in a mission planned for later this decade to return Mars samples to Earth that Perseverance is collecting. Much larger helicopter concepts are being studied to enable carrying larger science instrument payloads for potential future Mars missions. Robotic surface and aerial vehicles, as well as drilling systems for subsurface access, potentially could play a role in NASA’s goals for a human mission to Mars roughly two decades from now.

MEAM Seminar: "Wall-modeled Large-eddy Simulation of the Turbulent Boundary Layer with Mean-flow Three-dimensionality"
2023-04-03 14:00 - 15:00 | Towne 309, 220 S. 33rd Street, Philadelphia, PA, 19104, United States
The capability to predict high-Reynolds-number turbulent flows is essential for many natural and engineering flows such as external aerodynamics of wind turbines and aircraft wings, flow over the hull of marine vehicles, atmospheric boundary-layer flow over complex landscapes and cityscapes. However, due to extreme disparity of scales present in high-Reynolds-number wall-bounded turbulent flows, any attempt to simulate these flows directly on a computational grid without resorting to modeling of some sort results in prohibitively large computational cost. Wall-modeled large-eddy simulation (WMLES) show perhaps the most promise in being able to capture more of the relevant flow physics while keeping computational cost tractable in simulating these flows. There have been many novel wall models being developed during the last decades. However, the applications of most of the models are limited to canonical two-dimensional turbulent flows such as the turbulent channel flow where non-equilibrium effects including pressure gradient and mean-flow three-dimensionality are missing.
In this talk, I will present a comparative study of WMLES of a turbulent boundary layer with mean-flow three-dimensionality developing on the floor of a bent square duct which mimics the flow over the swept wing of the aircraft. The predictive capabilities of three widely used wall models, namely, a simple equilibrium stress model, an integral nonequilibrium model, and a PDE nonequilibrium model, have been investigated. These models potentially span the complete spectrum of wall models with varying physical details and complexity. While the wall-stress magnitudes predicted by the three wall models are comparable, the PDE nonequilibrium wall model produces a substantially more accurate prediction of the wall-stress direction, followed by the integral nonequilibrium wall model. The wall-stress direction from the wall models is shown to have separable contributions from the equilibrium stress part and the integrated nonequilibrium effects, where how the latter is modeled differs among the wall models. Budget analyses have been conducted to elucidate precise mechanisms by which the three wall models produce different predictions of the wall shear stress directions given almost identical inputs. The physical characteristics of the three-dimensional turbulent boundary layer including the generation mechanism of mean-flow three-dimensionality and the anisotropy of turbulence will also be discussed in the talk.

5th Annual Mechanobiology Symposium
2023-04-04 - 2023-04-05 | Smilow Center Auditorium, 3400 Civic Center Blvd, Philadelphia, PA, 19104, United States
This two-day symposium is organized by the Center for Engineering MechanoBiology (CEMB) with support from the National Science Foundation. The theme will be MechanoImmunology and Epigenetics.
The CEMB is a Science and Technology Center funded by the National Science Foundation; our research focus is to advance the study of mechanical forces in molecules, cells, and tissues in plants and animals. The theme of this 5th symposium will be the role of forces on immunology and epigenetics. We are inviting leaders in these fields to join a relatively small group environment where new state-of-the-art research can be presented and discussed.
This meeting will be in-person at University of Pennsylvania in Philadelphia, PA. While we encourage in-person attendance, this meeting will also be streamed for registrants that cannot attend in person.

Please register by March 1, 2023 to gain access to the meeting.
We are currently accepting poster abstracts for our poster session. If you are interested in presenting a poster, please submit a title and abstract (1500 character limit) HERE by March 1, 2023.
Invited speakers:

Adelin Barbacci, French National Centre for Scientific Research, National Institute for Agriculture, Food, and Environment
Marco Fritzsche, Rosalind Franklin Institute, Kennedy Institute for Rheumatology, University of Oxford
Claire Hivrov, Institut Curie
Lance Kam, Columbia University
Matthew Lang, Vanderbilt University
Song Li, University of California, Los Angeles
Yekaterina Miorshnikova, National Institutes of Health
Maeli Melotto, University of California, Davis
Guilherme Nader, Children’s Hospital of Philadelphia
Verena Ruprecht, Centre for Genomic Regulation

For inquiries, please contact:
Symposium logistics: Annie Jeong, CEMB Managing Director (annjeong@seas.upenn.edu)
Scientific content: Vivek Shenoy, CEMB Director (vshenoy@seas.upenn.edu)
CEMB event website: https://cemb.upenn.edu/research/mechanobiology-annual-symposium/

MEAM Seminar: "Material-Enabled Technologies for Soft and Fluidic Robots"
2023-04-04 10:00 - 11:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The emerging field of soft robotics, which incorporates unconventional or compliant materials in autonomous systems, has simultaneously reshaped traditional robotics applications and introduced new use cases for robots. However, many useful classes of materials remain relatively unexplored, and furthermore, the vast majority of soft robotics research has targeted actuation and sensing, with power and control schemes still relying on bulky, rigid electronic components. My research program addresses open questions in these domains by applying our expertise in energy, fluids, and materials. For instance, biotic materials—non-living materials derived from living organisms—have remained underutilized in robotics, despite having played a role in human development since the times our early ancestors wore animal hides as clothing and used bones for tools. In the first part of my talk, I describe how we repurposed an inanimate spider as a ready-to-use actuator requiring only a single fabrication step, initiating the area of “necrobotics” in which biotic materials are used as robotic components. The second part of my talk focuses on assistive wearable robots, which currently rely on bulky and hard control systems and power supplies, or alternatively require cumbersome tethers to external infrastructure. To address this limitation, my group has developed completely soft fluidic digital logic components fabricated entirely from textiles. Our fluidic logic platform enables integrated memory, decision making, and the ability to interact with and adapt to stimuli and the environment, all without the use of rigid valves or electronics. Meanwhile, we address limitations in power delivery by developing “self-powered” textile-based wearable robots that harvest energy from the motion of the human body. The integration of fluidic logic and energy harvesting in textile architectures represents an important step toward fully soft, self-sufficient wearable robots that are as comfortable, resilient, and practical as everyday clothing.

(Re)Connecting in the Classroom: Evan C Thompson Lecture on Excellence in Teaching
2023-04-04 16:00 - 17:00 | Van Pelt Library, Kislak Center, 6th Floor, 3420 Walnut Street, Philadelphia, 19104, United States
Lately there has been much talk in universities and the public about “the great disengagement.” As we emerge from the pandemic era, many students and instructors alike are experiencing a lack of energy and motivation, and we are seeking encounters in the classroom that are transformative, rather than simply transactional.
In this talk, Daeyeon Lee will reflect on his own evolution as a teacher, from his early focus on content delivery to his current practice of infusing classes with intrigue, storytelling and humor. Speaking from his own experiences, Lee will argue that creative exchanges with our students are just as important as content coverage in today’s classrooms. He will share some of his strategies for engaging students, practices that have enabled him to establish meaningful relationships with his students while also maintaining high academic standards. He shows us that taking time to connect with each other socially and emotionally is an essential part of revitalizing our intellectual culture and community.

MEAM Ph.D. Thesis Defense: "Towards Digital Twins for Cardiovascular Flows: A Hybrid Machine Learning and Computational Fluid Dynamics Approach"
2023-04-05 09:00 - 10:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
To design personalized treatment strategies, a number of measurable quantities (biomarkers) that relate a patient’s clinical representation to the existence, progress, and outcome of a disease need to be identified and measured. In cases where a biomarker is strongly correlated with the disease outcome, e.g. vascular pressure for hypertension, changes to the biomarker will perfectly describe changes of the disease outcome. However these ofter require invasive procedures to be measured. When the desired biomarkers correspond to physical properties, computational mechanics can be leveraged to obtain predictions in-silico. Unfortunately, computational models require a list of patient specific parameters, such as precise boundary conditions, which also cannot be easily measured in-vivo. Inaccurate calibration of these parameters is often the cause of poor predictions, therefore hindering the translational impact of computational methods. These challenges motivate the need flexible and computationally efficient frameworks that can operate under uncertain model assumptions and partial measurements.
The goal of this thesis is to introduce a novel approach to precision medicine by synthesizing artificial intelligence (AI) and computational modeling. We start by exploring how one can use available patient data to estimate parameters in computational fluid dynamics models of arterial blood flow, and show that this is prohibitively expensive. Then we accelerate the prediction of biomarkers by training surrogates to reconstruct available measurements by building physics-informed machine learning models to infer correlations between measurable (e.g., blood velocity) and unmeasurable quantities (e.g., vascular pressure) through underlying laws of fluid mechanics. We show that even though this is a successful approach it also faces challenges in generalizing to new clinical scenarios. Finally we propose a purely data-driven approach for making online biomarker predictions. In many biological scenarios the data acquisition process can be expensive and time consuming, limiting the amount of available training data. For this purpose, we propose creating a virtual patient database via computational fluid dynamics to train a neural operator model which we then use to make online predictions for new patients and clinical conditions. This computational efficiency that this brings has the potential to bridge the gap between modeling and clinical decision making.

ASSET Seminar: Lockout: Sparse Regularization of Neural Networks, Gilmer Valdes (UCSF)
2023-04-05 12:00 - 13:30 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
ABSTRACT:
Many regression and classification procedures fit a function f(x;w) of predictor variables x to data 〖{x_i,y_i}〗_1^N based on some loss criterion L(y,f(x;w)). Often, regularization is applied to improve accuracy by placing a constraint P(w)≤t on the values of the parameters w, where P is a monotonic increasing function of the absolute values of the parameters (e.g. Lasso, Ridge). Although efficient methods exist for finding solutions to these constrained optimization problems for all values of t≥0 in the special case when f is a linear function, none are available when f is non-linear (e.g. Neural Networks). Here we present a fast algorithm that provides all such solutions (path) for any differentiable function f and loss L, and any differentiable constraint P that is an increasing monotone function of the absolute value of each parameter. Applications involving sparsity inducing regularization of arbitrary Neural Networks are discussed. Empirical results indicate that these sparse solutions are usually superior to their dense counterparts in both accuracy and interpretability (sometimes strikingly better). This improvement in accuracy can often make Neural Networks competitive with, and sometimes superior to, state-of-the-art methods in the analysis of tabular data. Specific applications to Medicine, in particular, Microarray data are discussed.

Spring 2023 GRASP SFI: Matt Zucker, Swarthmore College, "Robotics education and research at a liberal arts college"
2023-04-05 15:00 - 16:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This is a hybrid event with in-person attendance in Levine 307 and virtual attendance via Zoom. This week’s presenter will be in-person as well.
ABSTRACT
In this talk, I will describe the transition from a research-intensive PhD to a position at a teaching-focused undergraduate institution in the context of my own career trajectory. Key topics include connections between robotics and the liberal arts, and guidelines for graduate students who are curious about pursuing teaching-focused faculty jobs.

MSE Seminar: "Designing Quantum Properties and Functionalities in Heterostructures" (MIT)
2023-04-06 10:30 - 11:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Interfaces in heterostructures often exhibit emergent physical properties that are absent in the constituent layers. These heterostructures, together with their compatibility with nanofabrication, are promising for constructing functional quantum devices. I will illustrate the capability of such heterostructure engineering by taking two examples. I will first explain our discovery of a versatile method for synthesizing ferroelectric materials from non-ferroelectric two-dimensional (2D) materials: by physically stacking two monolayer boron nitrides at controlled angles, novel types of ferroelectricity emerge at the interface. I will demonstrate its functionality as one of the world’s thinnest ferroelectric non-volatile memories at room temperature. As another example, I will demonstrate the versatile spintronic functionalities of topological insulator heterostructures synthesized by a thin film growth method. I will conclude by discussing how we can overcome the limits of existing heterostructure engineering for the future design of quantum materials and functionalities.

BE Doctoral Dissertation Defense: "Investigating the Role of Lipid Droplets as Intracellular Mechanical Stressors in Non-alcoholic Fatty Liver Disease" (Abigail Lonker)
2023-04-06 13:00 - 15:00 | Smilow Center for Translational Research in SCTR 11-146AB
The Department of Bioengineering at the University of Pennsylvania and Dr. Rebecca Wells are pleased to announce the Doctoral Dissertation Defense of Abigail Lonker.
Title: Investigating the Role of Lipid Droplets as Intracellular Mechanical Stressors in Non-alcoholic Fatty Liver Disease
Date: April 6, 2023
TIme: 1:00 PM
Location: Smilow Center for Translational Research in SCTR 11-146AB
The public is welcome to attend.

IoT4Ag Seminar: Sensing and Robotics for Specialty Crop Production
2023-04-06 14:30 - 15:30 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Register to attend via Zoom

PICS Colloquium: "Deep Anomaly Detection using Coincident Learning"
2023-04-07 14:00 - 15:00 | https://upenn.zoom.us/j/96715197752
Anomaly detection is a crucial task in the operation of complex systems such as industrial facilities, manufacturing plants, and large-scale science experiments. Failures in a sub-system can result in low yield, faulty products, or damage to components, making it essential to detect anomalies as quickly as possible. Despite the abundance of data available for complex systems, labeled anomalies are rare and expensive to obtain. To address this issue, we present a novel approach called CoAD that trains anomaly detection models on unlabeled data by leveraging the correlation between sub-systems and products. CoAD works by analyzing two data streams, s and q, which represent subsystem diagnostics and final product quality, respectively. We define an unsupervised metric, akin to the supervised classification F_beta statistic, to assess the performance of independent anomaly detection algorithms on s and q based on their coincidence rate. Our method is demonstrated in four cases, including a synthetic outlier data set, a synthetic imaging data set generated from MNIST, a metal milling data set, and a data set obtained from a particle accelerator. By using CoAD, we can detect anomalies in complex systems more effectively, even when labeled anomalies are scarce.
Email jnespos@seas.upenn.edu for the Zoom link.

ESE Spring Seminar - "Towards Scalable Multi-User Wireless Networking in mmWave and Terahertz Spectrum"
2023-04-10 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Millimeter-wave and terahertz bands are emerging as the most promising spectrum to meet the data-rate and latency demands of future wireless applications, including virtual reality and autonomous cars. Moreover, the large spectral availability together with the mm-scale wavelength, opens the opportunity of scaling the capacity of future wireless networks by supporting highly directional, high data rate multi-user transmission and reception. My research builds a foundation for scalable multi-user wireless systems in such high-frequency regimes yielding a paradigm shift in the design and development of future wireless systems. In this talk, I will begin by presenting emerging transceiver architecture that can enable directional sub-THz steering without traditional multi-antenna arrays. I will discuss how to exploit the key characteristics of sub-THz signals and the proposed architecture to enable the first scalable single-shot single-antenna multi-user system in THz bands with angularly dispersive links that are robust to client and environmental mobility. By exploiting electro-magnetics of antenna to protocol design, signal processing, and end-to-end system design with analytical model-driven evaluations and over-the-air experiments, I will show how the multi-user performance of an angularly dispersive THz link fundamentally depends on frequency, angle, and bandwidth utilized by users, through non-linear mechanisms and achieving close to Tb/s aggregate data rates using just a single-element antenna link. I will then discuss the opportunities offered by this platform to enhance next-generation communication and sensing capabilities in unprecedented ways. In particular, we tackle the mobility, blockage, and scalability challenges of highly directional wireless networks by efficiently adapting steering direction for mobile users. Finally, I will share several research directions in wireless networking, sensing, and security in mmWave and THz networks that I would like to pursue in the future.

BE Doctoral Dissertation Defense: "Fibrillar Collagen Modulation of Extracellular Matrix Structure and Organization Following Tendon Injury" (Jaclyn Carlson)
2023-04-10 14:00 - 16:00 | CRB Auditorium, 415 Curie Boulevard, Philadelphia, PA, 19104, United States
The Department of Bioengineering at the University of Pennsylvania and Dr. Louis Soslowsky are pleased to announce the Doctoral Dissertation Defense of Jaclyn Carlson.
Title: Fibrillar Collagen Modulation of Extracellular Matrix Structure and Organization Following Tendon Injury
Date: April 10, 2023
Time: 2pm
Location: CRB, Austrian Auditorium
Zoom Link: https://upenn.zoom.us/j/97931643342
The public is welcome to attend.

MEAM Seminar: "Mechanically Guided Assembly of Complex 3D Mesostructures and Shape-Programmable Surfaces"
2023-04-11 10:00 - 11:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The development of approaches to design and manufacture complex 3D functional mesoscopic structures in advanced materials is a topic of increasing research interest. Previous options in forming 3D mesostructures are, however, constrained by a narrow accessible range of materials or 3D geometries. In this talk, I will first introduce a versatile, mechanical approach to deterministically assemble sophisticated 3D mesoscale structures, guided by mechanics analysis, from planar 2D structures through controlled compressive buckling. To enhance the geometric diversity and functionality of 3D mesostructures, various mechanics-guided design strategies, for both the 2D precursor structures and the supporting substrates, will be demonstrated. Based on this mechanical assembly approach, many unique opportunities for 3D bio-integrated functional systems exist, for example, 3D multifunctional neural interfaces for cortical spheroids and 3D artificial microvascular networks. Precisely defined 3D geometries and deterministically distributed functional components through well-defined volumetric spaces, for unconventional approaches to neuromodulation, sensing, and regulation, highlight the design versatility driven by mechanics analysis. I will also briefly present a soft, shape-programmable system that exploits liquid metal microfluidic networks embedded in an elastomer matrix, with electromagnetic forms of actuation, to achieve a unique set of properties. Key features include fast and continuous surface shape morphing and reprogramming with access to a diverse set of 3D shapes originating from a single 2D planar configuration and well-controlled 4D (spatiotemporal) electronic programmability. Mechanics methods capable of precisely predicting complex 3D surface shape transformations in non-uniform magnetic fields serve as the design tool for various 3D target shapes.

ASSET Seminar: Machine Learning: A Data-Centric Perspective, Aleksander Madry (Massachusetts Institute of Technology)
2023-04-12 12:00 - 13:30 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
ABSTRACT:
The training data that modern machine learning models ingest has a major impact on these models’ performance (as well as failures). Yet, this impact tends to be neither fully appreciated nor understood at a fine-grained enough level.
In this talk, we will discuss some of the key ways in which training data influences not only what but also how models “learn” as well as tools to dissect this influence. In particular, we will present a new framework—called datamodeling—for directly casting predictions as functions of training data and the corresponding model class. This framework enables us to perform a range of model class-driven data analysis, including discovery of subpopulations, quantifying brittleness of model predictions, and diagnosing other shortcomings of the training set.

Spring 2023 GRASP SFI: Wenzhong Yan, UCLA, "Mechanical Intelligence for Compliant Robots"
2023-04-12 15:00 - 16:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This is a hybrid event with in-person attendance in Levine 307 and virtual attendance via Zoom. This week’s presenter will be in-person as well.
ABSTRACT
Biological systems with deformable bodies exhibit remarkable abilities. However, compliant/soft robots still can not match the capabilities of their biological analogs in terms of adaptability, physical robustness, and autonomy partially due to the reliance on rigid, bulky silicon-based electronics. My research aims to address this challenge by creating self-sustained, autonomous soft machines through Mechanical Intelligence (MI) — embedding necessary functionalities into compliant materials and structures — with minimal or even no silicon-based electronics. The resulting material-like soft robots can behave like biological organisms, obtaining energy from the ambiance, moving on various terrains, and surviving extreme conditions, which may find applications in environment monitoring, rescue, exploration, human-machine interaction, education, etc.
In this talk, I will demonstrate the power of MI by achieving locomotion and autonomous interaction with the environment of soft robots in compliant origami materials and structures with high robustness to adversarial events, e.g., radiation and physical deformation. Firstly, I will show how to realize self-sustained oscillation by incorporating bistable mechanisms and conductive artificial muscles without discrete components or electronic control hardware. This oscillation can be used to generate locomotion for robots with only constant electrical power. Then, I will present an efficient method to rapidly design such oscillators from desired behavioral specifications, i.e. frequency. This method provides a powerful tool for facilitating the modeling, designing, and prototyping of such complicated dynamic compound systems. I will also introduce a method to achieve complete sense-decide-act loops in compliant materials for autonomous interaction with environments, demonstrated with several robots. I will close my talk with a preview of how to use MI to improve adaptability and convert/harvest environmental energy to power robots, toward achieving my ultimate goal of creating self-sustained soft robots to allow their widespread deployment into complex, extreme environments to perform challenging tasks.

Seminar: Accelerating Clean Energy Technologies: Pathways to Commercial Liftoff
2023-04-12 17:00 - 19:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Wednesday, April 12, 2023
5:00 PM
Wu and Chen Auditorium

Anna Siefken
Department of Energy
Office of Technology Transitions

President Biden’s Bipartisan Infrastructure Law and Inflation Reduction Act provided DOE billions of dollars to invest in and support large-scale demonstration and deployment of clean energy technologies over the next decade. These historic investments are intended to drive commercialization and unlock trillions in private investment to set the nation on a course to hit critical long-term decarbonization objectives while creating high quality American jobs, strengthening domestic supply chains and global competitiveness, and facilitating an equitable energy transition. By 2030, DOE reports that cumulative investments must increase from approximately $40 billion to $300 billion across the hydrogen, nuclear, long duration energy storage, and carbon management sectors. DOE’s Office of Technology Transitions (OTT) is responsible for technology commercialization and engagement with the business and industrial
sectors across the United States.

On March 8, 2023, the DOE announced a Partnership with the Edison Electric Institute, the Electric Power Research Institute (EPRI) and the Long Duration Energy Storage Council to support the development and domestic manufacture of long-duration energy storage (LDES) technologies that can meet all U.S. market demands by 2030 with the goal of reducing the cost of grid-scale energy storage by 90% for systems that deliver 10+ hours of duration. With input from industry, investors, academics, and other stakeholders, the OTT prepared “Liftoff Reports” for emerging clean energy technologies which provide constant updates on market conditions, technology advances, governance issues, and community concerns. DOE also encourages direct public input, which can be submitted via email to liftoff@hq.doe.gov.
Reception to follow sponsored by the Environmental Innovations Initiative.

Seminar Flyer

MSE Seminar: "'Low Dimensional' Rare-Earth-Free Permanent Magnetic Materials" (University of California, Riverside)
2023-04-13 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Critical elements such as rare-earth (RE) metals that are subject to supply risks and are incorporated in critical materials, play a central role in the function of these materials. They dictate the properties that control the function of critical materials, including both molecules and materials, used in a broad range of technologically important and energy relevant properties such as superconductivity, magnetism, quantum phenomena, light generation, magnetocaloric behavior, and catalytic activity. It is of high importance to the Department of Energy (DOE) to enable the discovery and design of alternatives to critical materials that reduce or eliminate the need for critical elements. In this seminar, I will present our recent research focused on “designing” new quasi low-dimensional rare-earth-free magnetic materials, and new van-der-Waals (vdW) crystals showing both high Curie temperatures and large magnetic anisotropy as well as efficient spin-orbit torque. Furthermore, I will present a new reaction developed in our group that enables the synthesis of some of these materials at the nanoscale, a major step toward fulfilling their huge potential.

BE Doctoral Dissertation Defense: “Generation of Chimeric Antigen Receptor Macrophages to Target Pathogenic Protein Aggregates in Alzheimer’s Disease” (Matias Porras Paniagua)
2023-04-13 15:15 - 17:00 | Smilow Center for Translational Research in SCTR 11-146AB
The Department of Bioengineering at the University of Pennsylvania and Drs. Saar Gill & Frederick Bennett are pleased to announce the Doctoral Dissertation Defense of Matias Porras Paniagua.

Title: Generation of Chimeric Antigen Receptor Macrophages to Target Pathogenic Protein Aggregates in Alzheimer’s Disease
Date: April 13, 2023
Time: 3:15pm
Location: Smillow Center for Translational Medicine: SCTR 11-146AB
Optional Zoom Link: https://upenn.zoom.us/j/96832299770

The public is welcome to attend.

BE Seminar: "MAPing Principles and Applications to Endogenous Repair" (Tatiana Segura, Duke University)
2023-04-13 15:30 - 16:30 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
This seminar will be held in person in Glandt Forum; snacks will be served.
“MAPing Principles and Applications to Endogenous Repair”
Microporous annealed particle (MAP) scaffolds are materials composed of hydrogel microparticle (HMP) building blocks. Thus, rather than use polymers as the building block that form the hydrogel, we use particles. This makes MAP scaffolds granular materials, which open unique properties such as inner porosity, exterior porosity, injectability, and heterogeneity. We have found that these properties make MAP uniquely suited for applications in tissue regeneration applications. We have found that simple changes in the MAP composition can have dramatic changes in the immune response to the material and subsequent regenerative healing response. This talk will cover the concept of MAP, software that we have developed to understand MAP microstructure, and some of our findings that relate the immune response and regenerative healing.

Spring 2023 GRASP on Robotics: Luca Carlone, MIT, “Next-Generation Robot Perception: Hierarchical Representations, Certifiable Algorithms, and Self-Supervised Learning”
2023-04-13 15:30 - 17:00 | 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 via Zoom. This week’s presenter will be in-person as well.

ABSTRACT
Spatial perception —the robot’s ability to sense and understand the surrounding environment— is a key enabler for robot navigation, manipulation, and human-robot interaction. Recent advances in perception algorithms and systems have enabled robots to create large-scale geometric maps of unknown environments and detect objects of interest. Despite these advances, a large gap still separates robot and human perception: Humans are able to quickly form a holistic representation of the scene that encompasses both geometric and semantic aspects, are robust to a broad range of perceptual conditions, and are able to learn without low-level supervision. This talk discusses recent efforts to bridge these gaps. First, we show that scalable metric-semantic scene understanding requires hierarchical representations; these hierarchical representations, or 3D scene graphs, are key to efficient storage and inference, and enable real-time perception algorithms. Second, we discuss progress in the design of certifiable algorithms for robust estimation; in particular we discuss the notion of “estimation contracts”, which provide first-of-a-kind performance guarantees for estimation problems arising in robot perception. Finally, we observe that certification and self-supervision are twin challenges, and the design of certifiable perception algorithms enables a natural self-supervised learning scheme; we apply this insight to 3D object pose estimation and present self-supervised algorithms that perform on par with state-of-the-art, fully supervised methods, while not requiring manual 3D annotations.

PICS Colloquium: "Bridging scales in aerosol modeling with particle-resolved simulations"
2023-04-14 14:00 - 15:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
The aerosol lifecycle consists of processes that act on the micro-scale, yet the aerosols’ climate impacts are perceived on regional or global scales. Capturing this multiscale nature of the atmospheric aerosol poses considerable challenges for aerosol models since computational constraints limit the detail of aerosol representation, yet these details matter in determining large-scale aerosol impacts. This presentation will illustrate how high-detail, particle-resolved simulations can serve as a bridge between scales in aerosol modeling. The particle-resolved approach represents the aerosol using individual computational particles that evolve in size and composition as they undergo aging processes in the atmosphere. While computationally expensive, this approach is therefore not limited by assumptions about particle composition within a given size range and can represent the full aerosol mixing state. My presentation will show how this modeling approach, leveraged by machine learning, can efficiently bridge from the process scale to the global scale. I will present a summary of our knowledge to what extent simplifying the diversity of aerosol composition introduces errors in our estimates of cloud condensation nuclei concentration and aerosol optical properties. I’ll conclude by highlighting the unique measurement challenges that we face in constraining these models but that provide an unprecedented opportunity in “getting the right answer for the right reasons.”

ESE PhD Thesis Defense: "Leveraging Models to Improve Data Efficiency: Navigation, Reinforcement Learning, and Lie Group Convolutions"
2023-04-17 14:00 - 15:00 | Greenberg Lounge (Room 114), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
Consider a system which takes data as an input, processes the data with a model, and outputs a decision for a particular objective. We call the measure of the amount of data used to complete the objective with some performance metric as data efficiency. Across many domains, it is advantageous to reduce the amount of data to achieve the same or better level of performance. In this thesis, we exploit the model of the system in order to improve the data efficiency across three distinct domains of interest: robot navigation in ellipsoidal worlds, reinforcement learning, and Lie group convolutions.

MEAM Seminar: "Nano and Polymers and Mechanics and Data"
2023-04-18 10:00 - 11:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
For polymer composites, nanocomposites and polymer thin film systems, the local properties of polymers can be altered by the chemical and physical interactions with substrates and embedded particles over a length scales exceeding 100nm. The mechanisms and impact of confined polymers remains still an active area of research and debate. Here we will review methods to explore nanoscale polymer properties near surfaces, with a focus on scanning probe methods to quantitatively measure mechanical response and the interesting mechanics problems that arise. In multiphase soft materials, local changes in the sample modulus, tip-sample interactions and stress field interaction effects impact the acquired force curves. Coupling experimental data with simulations of indentations enable the structural effects of the particle-polymer-tip system to be accurately estimated and removed, revealing the effects of confinement on property gradients. Capturing and archiving this data allows case studies which connect the property-structure-property domains through a combination of machine learning and physics-based modeling. We demonstrate the ability to identify the most critical features influence properties and the ability to acquire new insights from ensembles of unrelated data. The importance of data, data resources and leverage of this knowledge in new physics based and interpretable machine learning methods is discussed. Overall this work illustrates new approaches combining physics and data based models and experiments to tackle materials design principles for the complex, high dimensional problems inherent in the multi-phase polymer space.
