Penn Engineering Events

MEAM Seminar: "Harnessing Physical Intelligence for High-Performance Soft Robots"
2022-09-29 10:30 - 11:30 (America/New_York) | Towne 227 (MEAM Conference Room), 220 S. 33rd Street, Philadelphia, PA, 19104, United States
Different from neuron-based computational intelligence through the brain, physical intelligence leverages structural designs and smart materials to physically encode sensing, actuation, control, adaption, and decision-making into the body of an agent. The stimuli-responsive body materials can enable autonomous sensory, actuation, powering, and other physical intelligence functions. The structural designs of soft body can simplify the required actuation for deformation and motion, as well as enable real-time feedback control-free locomotion and self-adaption.
In this talk, I will discuss our recent work in embodying mechanical intelligence of structural designs and/or materials intelligence of soft active materials in soft robotics, for achieving delicacy in manipulation, high speed and high efficiency in locomotion, and autonomy and intelligence. First, I will talk about utilizing the ancient paper cutting art of kirigami for programming 3D curved shape shifting via geometric mechanics guided design, as well as its application in nondestructive and delicate grasping. Then, I will discuss how to leverage bistability and multistability for achieving high-speed and high-efficient terrestrial and aqueous soft robots. Finally, I will discuss examples of integrating structural designs with soft active materials for achieving autonomy and intelligence in soft robots.

MEAM Ph.D. Thesis Defense: "Room-temperature Electrochemical Healing of Structural Metals"
2022-09-29 10:00 - 11:00 | Greenberg Lounge (Room 114), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
For over 6,000 years, repairing high-strength metallic materials has required high temperatures and large energy inputs. Likewise, recent innovations in self-healing and repairable metals have remained limited by the need for heating, the small size of repairable cracks, and the low strength and constrained chemical composition of healed metals. While welding remains the most widely used approach to repair metals, the increasing ubiquity of digital manufacturing and “unweldable” alloys call for radically different approaches. This thesis pioneers a new approach for repairing structural metals at room-temperature, termed “electrochemical healing”. First, by mimicking the transport-mediated healing of bone, selective nickel electrodeposition enables rapid, effective, low-energy, and room-temperature healing of a cellular metal. A polymer coating restricts electrodeposition only at fracture or high stress sites, and a statistical method quantifies and predicts the probability of a target recovery of tensile strength based on energy input. This thesis extends room-temperature healing to low-carbon steel, a widely used structural metal, by elucidating how ion transport and electrolyte chemistry influence growth morphology and strength in fractured steel wires repaired with nickel electrodeposition. Pulsed electroplating and electrolyte chemistry selection improve nickel adhesion and enable fully fractured steel wires to recover up to 69% of their pristine strength. Finally, this thesis presents a framework for effective room temperature electrochemical healing based on a quantitative model that links geometric, mechanical, and electrochemical parameters to the recovery of tensile strength in repaired metals. This framework enables full recovery of tensile strength in a variety of structural metals, including “unweldable” alloys and a 3D-printed difficult-to-weld funicular shellular structure, as well as over 100% recovery of toughness in an aluminum alloy. The model reveals scaling relationships for the energetic, financial, and time costs of repairing metals that facilitate the practical adoption of electrochemical healing. Room-temperature electrochemical healing could open exciting possibilities for the scalable, autonomous, repeatable, and prophylactic repair of metals in structures and robots, enable cellular materials that respond to environmental stimulus with growth and morphogenesis, and advance the life cycle sustainability of structural metals.

Fall 2022 GRASP Seminar: Guillermo Gallego, Technical University Berlin, "Stereo depth and optical flow estimation via contrast maximization of event camera data”
2022-09-29 10:00 - 11:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
*This is a HYBRID Event with in-person attendance in Levine 307 and virtual attendance via Zoom.
ABSTRACT
Event cameras are novel vision sensors that mimic functions from the human retina and offer potential advantages over traditional cameras (low latency, high speed, high dynamic range, etc.). They acquire visual information in the form of pixel-wise brightness changes, called events. This talk presents event processing approaches for motion estimation in computer vision and robotics applications. In particular, we will discuss recent advances by the Robotic Interactive Perception Lab at TU Berlin in extending the contrast maximization framework to stereo depth and optical flow estimation while avoiding its Achilles’ heel: event collapse.

Fall 2022 GRASP SFI: Millind Tambe, Harvard University, "Results from deployments for public health and conservation"
2022-09-28 15:30 - 16:30 | 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 via Zoom here…

ABSTRACT
With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. I will focus on domains of public health and conservation, and address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. I will present results from work around the globe in using AI for challenges in public health such as Maternal and Child care interventions, HIV prevention, and in conservation such as endangered wildlife protection. Achieving social impact in these domains often requires methodological advances. To that end, I will highlight key research advances in multiagent reasoning and learning, in particular in, restless multiarmed bandits, influence maximization in social networks, computational game theory and decision-focused learning. In pushing this research agenda, our ultimate goal is to facilitate local communities and non-profits to directly benefit from advances in AI tools and techniques.

ASSET Seminar: Equivariance in Deep Learning, Kostas Daniilidis (University of Pennsylvania)
2022-09-28 12:00 - 13:30 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
ABSTRACT
Traditional convolutional networks exhibit unprecedented robustness to intraclass nuisances when trained on big data. Generalization with respect to geometric transformations has been achieved via expensive data augmentation. It has been shown recently that data augmentation can be avoided if networks are structured such that feature representations are transformed the same way as the input, a desirable property called equivariance. In this talk, we show how equivariance can be realized via group convolutions, how to deal with vector and tensor fields, and how we achieve equivariance in transformers. We present results on 3D shape classification and scene reconstruction based on learning only objects but not scenes.
BIO
Kostas Daniilidis has been faculty at the University of Pennsylvania since 1998. He is an IEEE Fellow. He was the director of the GRASP laboratory from 2008 to 2013. He obtained his Ph.D. in Computer Science from the University of Karlsruhe (now KIT) in 1992. He is a co-recipient of the Best Conference Paper Award at ICRA 2017. Kostas’ main interest today is in geometric deep learning, event-based neuromorphic vision, and their applications in vision-based manipulation and navigation.

MEAM Ph.D. Thesis Defense: "Surface and Interface Engineering in Manipulation and Fabrication of Colloid-Based Sub-Microporous Hierarchical Materials and Their Applications"
2022-09-28 12:00 - 13:00 | Moore 212
Nanolattices exhibit attractive mechanical, energy conversion, and optical properties, but it is challenging to fabricate nanolattices in large scale while maintaining the dense hierarchical nanometer features that enable their properties. Current advanced fabrication methods, like 3D printing or self-assembly, are significantly limited by their scalability or the cracking problem in the assembled templates. This work focuses on self-assembly of metallic inverse opals, a particular type of nanolattices, to overcome these limitations via developing a theoretical model for understanding the cracking problem and a crack-free self-assembly method to scale-up the fabrication and to characterize and explore applications of metallic inverse opals.
The developed model incorporates film yielding, particle order, and interfacial friction to explain several experimental observations and helps solving the cracking problem. It is found that the key to solving the cracking problem is to manipulate the surface and interface properties of particles and substrates. The developed crack-free self-assembly approach results in centimeter-scale nickel inverse opals with much larger crack-free area than prior self-assembled and much more unit cells than 3D-printed nanolattices, demonstrating a tensile strength of 260 MPa. It is also found that drop-casting can achieve fast, high-quality, and large-scale self-assembly via pre-assembly in highly concentrated micro/nanoparticle suspension.
Based on these development and findings, two applications of metallic inverse opals have been demonstrated, including a mechanochromic bending sensor and a magnetic sorting chip for capturing disease-related extracellular vesicles. The developed sensor is wireless and power-free, can utilize full visible spectrum, and has a 10X higher strain sensitivity than other mechanochromic sensors. The fabricated sorting chip achieves >109 nanoscale magnetophoretic sorting devices in a postage-stamp-sized lattice with >70x magnetic traps and >20x improved enrichment for magnetic nanoparticles versus previous studies.
The understanding of cracking in particle templates, the developed self-assembly methods, and the application demonstrations reported in this work may advance the fabrication and applications of high-strength multifunctional porous materials, providing fundamental insights into the design, synthesis, and control of complex hierarchical materials that employ colloid self-assembly.

MEAM Seminar: "Development of Astronomical Instrumentation to Study the Birth and Evolution of the Universe"
2022-09-27 10:00 - 11:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The study of the early universe requires deep high-resolution maps of the sky at millimeter and submillimeter. This requires the development of state-of-the-art cryogenic receivers and custom built telescopes. These instruments operate in extreme locations including from NASA launched high-altitude balloons over Antarctica and high (5,200m/17,000ft) mountain tops in Northern Chile adding a level of planning and complexity beyond what is normally required for astronomical observations. I will discuss the science goals of these instruments and how we develop instruments at Penn to meet these goals.

ESE Ph.D. Thesis Defense: "Lattice Theory in Multi-Agent Systems"
2022-09-26 14:00 - 15:30 | Moore 317, 200 S 33rd Street, Philadelphia, PA, 19104, United States
Ordered sets model signals such as binary relations, concepts, partitions, rankings, matchings, events, as well as other taxa of information, temporal, hierarchical, relational, or, in general, logical in nature. We argue that (order-) lattice-based (networked) multi-agent systems constitute a broad class of systems in which data fusion, consensus, synchronization, and other collaborative tasks are described with lattices and Galois connections (maps between lattices that preserve structure). Mathematically speaking, these systems are network sheaves. Motivated by analogous vector diffusion and consensus algorithms, we initiate a discrete Hodge theory with the Tarski Laplacian, a diffusion operator—analogous to the graph Laplacian and the graph connection Laplacian—acting on assignments of lattice-valued data to the nodes of a network. The Hodge-Tarski theorem (Main Theorem) relates the fixed point theory of the Tarski Laplacian to the global sections (consistent signals, equilibria, if you will) of the sheaf. We present novel applications in signal processing and multi-modal semantics where we design a consensus algorithm on statements such as “I know that she knows that he doesn’t know that I’m defending my thesis.”

PSOC Seminar: "Compromised nuclear envelope integrity leads to tumor cell invasion" (Guilherme Nader, CHOP)
2022-09-26 13:00 - 14:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Fall 2022 Hybrid-Seminar Series
Mondays 1.00-2.00 pm (EST)
Towne 225 / Raisler Lounge
For Zoom link, please contact <manu@seas.upenn.edu

MEAM Seminar: "Manually-Operated, Slider Cassette for Multiplexed Molecular Detection at the Point of Care"
2022-09-23 13:30 - 14:30 | Room 2C8, David Rittenhouse Laboratory Building, 209 South 33rd Street, Philadelphia, PA, 19104, United States
Effective control of epidemics, individualized medicine, and new drugs with virologic response-dependent dose and timing require, among other things, simple, inexpensive, multiplexed molecular detection platforms suitable for point of care and for home use. Conventional molecular detection methods such as PCR tests, require bulky and expensive equipment, trained personnel, and specialized laboratories, limiting their use to centralized facilities.
In this talk, I will describe our 3D-printed slider cassette for the co-detection of the human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV) – three blood-borne pathogens that co-infect numerous people worldwide with severe personal and public health consequences. Novel features of our cassette include the integration of sample processing; nucleic acid isolation and concentration, isothermal amplification, minimally instrumented and instrument free detection; refrigeration-free storage of reagents; the ability to co-detect multiple pathogens, and minimal requirements from the user. Furthermore, the various processes in our devices can be incubated electricity-free with heat provided by an exothermic reaction and temperature control with phase change materials.

Theory Seminar- Recent Developments in Combinatorial Auctions, Matt Weinberg (Princeton University)
2022-09-23 12:00 - 13:00 | Room 401B, 3401 Walnut, 3401 Walnut Street, Philadelphia, PA, 19104, United States
Abstract: In a combinatorial auction there are m items, and each of n players has a valuation function v_i which maps sets of items to non-negative reals. A designer wishes to partition the items into S_1,…,S_n to maximize the welfare (sum_i v_i(S_i) ), perhaps assuming that all v_i lie in some class V (such as submodular, subadditive, etc.).
Within Algorithmic Game Theory, this problem serves as a lens through which to examine the interplay between computation and incentives. For example: is it the case that whenever a poly-time/poly-communication algorithm for honest players can achieve an approximation guarantee of c when all valuations lie in V, a poly-time/poly-communication truthful mechanism for strategic players can achieve an approximation guarantee of c when all valuations lie in V as well?
In this talk, I’ll give a brief history, then survey three recent results on this topic which:
– provide the first separation between achievable guarantees of poly-communication algorithms and poly-communication truthful mechanisms for any V (joint works with Mark Braverman and Jieming Mao, and with Sepehr Assadi, Hrishikesh Khandeparkar, and Raghuvansh Saxena).
– revisit existing separations between poly-time algorithms and poly-time truthful mechanisms via a new solution concept “Implementation in Advised Strategies” (joint work with Linda Cai and Clayton Thomas).
– resolve the communication complexity of combinatorial auctions for two subadditive players (joint work with Tomer Ezra, Michal Feldman, Eric Neyman, and Inbal Talgam-Cohen, time-permitting).

Fall 2022 GRASP Seminar: GRASP Affiliated Faculty Research Overview
2022-09-23 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.

Dr. James Pikul
Simon Kim, AIA (via Zoom)
Dr. Rahul Mangharam
Dr. Robert Stuart Smith (via Zoom)

For more details, please check out the full speaker line-up here.

MSE Seminar: "Materials Growth and Discovery for Magnetic and Quantum Applications"
2022-09-22 10:30 - 11:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
For functional materials that are in a nascent stage, such as the antiferromagnetic spintronics, quantum information storage, and new semiconducting compounds, it is not clear what will be the high-performance materials of tomorrow. There is a pressing need to examine the complex properties of these emerging materials, and growing single crystals is a crucial step toward investigating their properties in detail. I will explain why measuring transport, optical, and magnetic properties are important in these systems, and how to determine their anisotropy. Due to the required millimeter dimensions, they must be grown from solutions, fluxes, or vapors. This process is often hard to observe, and highly kinetically dependent, so in situ techniques can be especially valuable to understand how to grow larger or better crystals, how to choose phases more precisely, and how to discover entirely new materials. With a clearer view of how materials form, we can critically evaluate computational predictions (ab initio or machine-learned methods) and explore novel reactions to target new phases.

CBE Seminar Series: "Quantitative Insights for Rapid Improvement of Sustainable Energy and Chemical Technologies" (Micah Ziegler, Massachusetts Institute of Technology)
2022-09-21 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States

Fall 2022 GRASP SFI: Zhongyu Li, University of California Berkeley, “Can We Bridge Model-based Control and Model-free RL on Legged Robots?”
2022-09-21 15:00 - 16:00 | Room 307, Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
*This will be a HYBRID Event with in-person attendance in Levine 307 and Virtual attendance via Zoom.
In this talk, I will provide a brief introduction about our recent progress in applying optimal control and deep reinforcement learning (RL) on legged robots in the real world. I will then dive into our recent work to bridge model-based safety-critical control and model-free RL on a highly nonlinear and complex system, such as a bipedal robot Cassie. Bridging model-based safety and model-free RL for dynamic robots is appealing since model-based methods are able to provide formal safety guarantees, while RL-based methods are able to exploit the robot agility by learning from the full-order system dynamics. I will discuss a new method to combine them by explicitly finding a low-dimensional model of the system controlled by a RL policy and applying stability and safety guarantees on that simple model.

ASSET Seminar: Explainable AI via Semantic Information Pursuit (René Vidal, Johns Hopkins University)
2022-09-21 12:00 - 13:30 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Presentation Abstract:
There is a significant interest in developing ML algorithms whose final predictions can be explained in domain-specific terms that are understandable to a human. Providing such an “explanation” can be crucial for the adoption of ML algorithms in risk-sensitive domains such as healthcare. This has motivated a number of approaches that seek to provide explanations for existing ML algorithms in a post-hoc manner. However, many of these approaches have been widely criticized for a variety of reasons and no clear methodology exists for developing ML algorithms whose predictions are readily understandable by humans. To address this challenge, we develop a method for constructing high performance ML algorithms that are “explainable by design”. Namely, our method makes its prediction by asking a sequence of domain- and task-specific yes/no queries about the data (akin to the game “20 questions”), each having a clear interpretation to the end-user. We then minimize the expected number of queries needed for accurate prediction on any given input. This allows for human interpretable understanding of the prediction process by construction, as the questions which form the basis for the prediction are specified by the user as interpretable concepts about the data. Experiments on vision and NLP tasks demonstrate the efficacy of our approach and its superiority over post-hoc explanations. Joint work with Aditya Chattopadhyay, Stewart Slocum, Benjamin Haeffele and Donald Geman.
Speaker Bio:
Dr. René Vidal is the Herschel Seder Professor of Biomedical Engineering, and the Director of the Mathematical Institute for Data Science (MINDS), the NSF-Simons Collaboration on the Mathematical Foundations of Deep Learning and the NSF TRIPODS Institute on the Foundations of Graph and Deep Learning at Johns Hopkins University. He is also an Amazon Scholar, Chief Scientist at NORCE, and Associate Editor in Chief of TPAMI. His current research focuses on the foundations of deep learning and its applications in computer vision and biomedical data science. He is an AIMBE Fellow, IEEE Fellow, IAPR Fellow and Sloan Fellow, and has received numerous awards for his work, including the IEEE Edward J. McCluskey Technical Achievement Award, D’Alembert Faculty Award, J.K. Aggarwal Prize, ONR Young Investigator Award, NSF CAREER Award as well as best paper awards in machine learning, computer vision, controls, and medical robotics.

CIS Seminar: "Live Programming and Programming by Example: Better Together"
2022-09-20 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Live programming is a paradigm in which values from program execution are shown to the programmer through continual feedback. Programming by example is a paradigm in which code is synthesized from example values showing a desired behavior. This talk presents some of our recent research that combines these two paradigms in beneficial ways. I will walk through our ideas, explain our contributions, discuss what we learned and finally provide thoughts for the future.

MEAM Seminar: "Open Access Benchmark Datasets and Metamodels for Problems in Mechanics"
2022-09-20 10:00 - 11:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Metamodels, or models of models, map defined model inputs to defined model outputs. When metamodels are constructed to be computationally cheap, they are an invaluable tool for applications ranging from topology optimization, to uncertainty quantification, to real-time prediction, to multi-scale simulation. In particular, for heterogeneous materials, metamodels are useful for exploring the influence of the (potentially massive) heterogeneous material property parameter space. By nature, a given metamodel will be tailored to a specific dataset. However, the most pragmatic metamodel type and structure will often be general to larger classes of problems. At present, the most pragmatic metamodel selection for dealing with mechanical data — specifically simulations of heterogenous materials — has not been thoroughly explored. In this work, we draw inspiration from the benchmark datasets available to the computer vision research community. These benchmark datasets have both made it feasible to compare different methods for solving the same problem, and inspired new directions for method development. In response, we introduce benchmark datasets for engineering mechanics problems (for example, the Mechanical MNIST Collection https://open.bu.edu/handle/2144/39371 [1,2,3]). Then, we show some example problems that we are exploring with these datasets such as our methodology for constructing metamodels for predicting full field quantities of interest (e.g., full field displacements, stress, strain, or damage variable), and for leveraging information from multiple simulation fidelities, and for predicting out of distribution behavior. Looking forward, we anticipate that disseminating both these benchmark datasets and our computational methods will enable the broader community of researchers to develop improved techniques for understanding the behavior of spatially heterogeneous materials. We also hope to inspire others to use our datasets for educational and research purposes, and to disseminate datasets and metamodels specific to their own areas of interest (https://elejeune11.github.io/).
[1] Lejeune, E. (2020). Mechanical MNIST: A benchmark dataset for mechanical metamodels. Extreme Mechanics Letters, 36, 100659.
[2] Lejeune, E., & Zhao, B. (2020). Exploring the potential of transfer learning for metamodels of heterogeneous material deformation. Journal of the Mechanical Behavior of Biomedical Materials, 104276.
[3] Mohammadzadeh, S., & Lejeune, E. (2022). Predicting mechanically driven full-field quantities of interest with deep learning-based metamodels. Extreme Mechanics Letters, 50, 101566.

Theory Seminar- Aaron Roth (University of Pennsylvania)
2022-09-16 12:00 - 13:00 | Room 401B, 3401 Walnut, 3401 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
Dawid gives two conceptualizations for models of individual probabilities: “Group to Individual” and “Individual to Group”. A classical concern about the “Group to Individual” view of probability is the reference class problem: Given that we can empirically measure only averages over many individuals, which group or “reference class” do we choose to average over when estimating the probability for an individual? Machine Learning on the other hand operates in the “Individual to Group” conceptualization: models purport to assign probabilities to individuals, which can be aggregated over to obtain group probabilities. Multicalibration gives us a way to obtain models that predict individual probabilities that are consistent with an arbitrary number of reference classes. But (with finite data) it does not solve the “model multiplicity” problem: there may be multiple models that are multicalibrated that assign many people very different individual probabilities. How are we to adjudicate between such models? We argue that if two parties agree on the data distribution, then they cannot agree to disagree on (very many) individual probabilities, even given access only to a small number of samples from the distribution.
Joint work with Alexander Tolbert and Scott Weinstein

Fall 2022 GRASP Seminar: GRASP Research Overview - Day 2
2022-09-16 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
GRASP Lab faculty confirmed presentations (where appropriate their presenters)…
*This is a HYBRID Event with in-person attendance in Wu & Chen Auditorium and virtual attendance via Zoom.
Dr. Ani Hsieh (Welcome and introductions)
Dr. Nadia Figueroa
Dr. Daniel Koditschek
Dr. Vijay Kumar (presented by Kelsey Saulnier)
Dr. Nik Matni (presented by Thomas Zhang)
Dr. Marc Miskin
Dr. George Pappas (presented by Dr. Lars Lindenmann)
Dr. Cynthia Sung

BE/MEAM Seminar: "Synthetic Embryology for Constructing Human Embryo and Organ Models" (Jianping Fu, University of Michigan)
2022-09-15 15:30 - 16:30 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
This is a hybrid seminar held in Glandt Forum (Singh Center) and via Zoom. Check email for the zoom link or contact cd0318@seas.upenn.edu.
“Synthetic Embryology for Constructing Human Embryo and Organ Models”
Early human development remains mysterious and difficult to study. Recent advances in developmental biology, stem cell biology, and bioengineering have contributed to a significant interest in constructing stem cell-based models of human embryo and organs (embryoids / organoids). The controllability and reproducibility of these human development models, coupled with the ease of genetically modifying stem cell lines, the ability to manipulate culture conditions and the simplicity of live imaging, make them robust and attractive systems to disentangle cellular behaviors and signaling interactions that drive human development. In this talk, I will describe our effort in using human pluripotent stem cells (hPSCs) to develop controllable models of the peri-implantation embryonic development and neural development. The peri-implantation human embryoids recapitulate key early post-implantation developmental landmarks successively, including pro-amniotic cavity formation, amniotic ectoderm-epiblast patterning, primordial germ cell specification, and development of the primitive streak with controlled anteroposterior polarity. I will further discuss an hPSC-based neuroectoderm patterning model to recapitulate the formation of the neural plate and another more recently developed, patterned neural tube model with fully defined anterior-posterior and dorsal-ventral axes. Specifically, the patterned neural tube model recapitulates neural patterning along two orthogonal axes in a three-dimensional tubular geometry, offering in vivo-like tissue architecture and spatiotemporal tissue patterning, promising for studying human neurodevelopment and diseases.

MSE Seminar: "Bioinstructive Materials Regulate Inflammation in Cancer and Regeneration"
2022-09-15 10:30 - 11:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Fibrosis and remodeling of extracellular matrix are involved in many diseases, such as tumors, wound healing, and chronic inflammation. During fibrosis, tissues undergo changes in their viscoelastic properties, i.e., how they resist deformation like a solid and dissipate stress over time like a fluid. My research program determines the impact of viscoelasticity on inflammation in fibrotic tissues and develops new immune therapies in cancer and regeneration. I study the role of monocytes, which infiltrate into tissue and differentiate into phagocytic and antigen-presenting cells, such as macrophages and dendritic cells. I utilize an artificial extracellular matrix to dissect a mechanical checkpoint of monocyte fate and develop strategies to target monocytes in hematopoietic malignancies and head and neck cancer. Further, I develop biomaterials that interface with dental tissues to modulate inflammation and support regeneration. Overall, my long-term goal is to determine how extracellular matrix physically impacts inflammation and to develop therapies targeting immuno-mechanical signaling.

ESE Fall Colloquium - "Steampunk Data Science"
2022-09-15 10:30 - 11:30 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
How did scientists make sense of data before statistics and computing? This talk will explore this question by focusing on the discovery of vitamins, which occurred in the early 20th century just before the advent of modern statistical methodology. I will describe the varied practices in experimentation and reporting and highlight the sorts of insights required to uncover what “works.” Through this discussion, I will draw connections to contemporary data science tools to illustrate their pros and cons in facilitating discovery.

CBE Seminar Series: "A Molecular Scale Understanding of Electrified Separations and Catalysis" (Marta Hatzell, Georgia Institute of Technology)
2022-09-14 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States

Fall 2022 GRASP SFI: Herbert Tanner, University of Delaware, "Networked aerial detection of mobile radiation sources"
2022-09-14 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 via Zoom here…
Mobile robotic sensor networks promise cutting edge technological solutions to a broad collection of real-world observation, detection, and decision-making problems, in domains ranging from environmental monitoring, to emergency response, to national security. Nuclear nonproliferation is a particular domain international implications in the latter area, framing an active sensing application problem where robots are called to rapidly detect radiological material in transit. Our approach to tackling this engineering challenge is rooted in our conviction that robot control, estimation, and decision making are all intrinsically interlinked and necessitate co-design, where measurement statistics should directly inform robot control and navigation, and estimation should leverage robot motion planning. The approach led to the development of a networked active sensing architecture for groups of aerial vehicles equipped with commercial off-the-shelf radiation sensors which can achieve an order of magnitude improvement in detection performance over alternative solutions at that time. This success also highlights the importance of model-driven measurement and measurement-informed robot deployment strategies.

ASSET Seminar: When Will You Become the Best Reviewer of Your Own Papers? A Truthful Owner-Assisted Scoring Mechanism (Weijie Su, University of Pennsylvania)
2022-09-14 12:00 - 13:30 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Presentation Abstract:
Alice submits a number of papers to a machine learning conference and has knowledge of the quality of her papers. Given noisy grades provided by independent reviewers, can Bob obtain accurate estimates of the ground-truth quality of the papers by asking Alice a question about the ground truth? In this talk, we address this when the payoff of Alice is additive convex utility over all her papers. First, if Alice would truthfully answer the question because by doing so her payoff is maximized, we show that the questions must be formulated as pairwise comparisons between her papers. Moreover, if Alice is required to provide a ranking of her papers, which is the most fine-grained question via pairwise comparisons, we prove that she would be truth-telling. By incorporating the ground-truth ranking, we show that Bob can obtain an estimator with the optimal squared error in certain regimes based on any possible ways of truthful information elicitation. Moreover, the estimated grades are substantially more accurate than the raw grades when the number of papers is large and the raw grades are very noisy. Finally, we conclude the talk with several extensions and some refinements for practical considerations. This is based on arXiv:2206.08149 and arXiv:2110.14802.
Speaker Bio:
Weijie Su is an Associate Professor in the Wharton Statistics and Data Science Department and, by courtesy, in the Department of Computer and Information Science, at the University of Pennsylvania. He is a co-director of Penn Research in Machine Learning. Prior to joining Penn, he received his Ph.D. from Stanford University in 2016 and his bachelor’s degree from Peking University in 2011. His research interests span deep learning theory, privacy-preserving data analysis, optimization, and high-dimensional statistics. He is a recipient of the Stanford Theodore W. Anderson Dissertation Award in 2016, an NSF CAREER Award in 2019, an Alfred Sloan Research Fellowship in 2020, the SIAM Early Career Prize in Data Science in 2022, and the IMS Peter Gavin Hall Prize in 2022.

BE Doctoral Dissertation: "Biomechanical Evaluation of the Role of Decorin and Biglycan during Neonatal Tendon Development and Healing" (Zak Beach)
2022-09-13 14:00 - 15:00 | JMB Reunion Auditorium, 3620 Hamilton Walk, Philadelphia, PA, 19104, United States
The Department of Bioengineering at the University of Pennsylvania and Dr. Lou Soslowsky are pleased to announce the Doctoral Dissertation Defense of Zak Beach.
Title: Biomechanical Evaluation of the Role of Decorin and Biglycan during Neonatal Tendon Development and Healing
Date: Tuesday, September 13, 2022
Time: 2pm-3pm
Location: JMB Class of 62 Auditorium

MEAM Seminar: MEAM Faculty Research Overview
2022-09-13 10:00 - 11:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Please join us on Tuesday, September 13 for a series of short research talks by MEAM faculty. Five MEAM faculty will give “flash talks” to introduce their research activities and recent work. This is an excellent opportunity for current graduate students to learn about ongoing research in the Department.

Fall 2022 GRASP Seminar: GRASP Research Overview - Day 1
2022-09-09 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
GRASP Lab faculty confirmed presentations (where appropriate their presenters)…
*This is a HYBRID Event with in-person attendance in Wu & Chen Auditorium and virtual attendance via Zoom.
Dr. Mark Yim (Welcome & Introduction)
Dr. Pratik Chaudhari
Dr. Kostas Daniilidis
Dr. Eric Eaton
Dr. Dinesh Jayaraman
Dr. Michelle Johnson (presented by Francis Sowande)
Dr. Michael Posa
Dr. Jianbo Shi

MEAM Ph.D. Thesis Defense: "A Spin-Lattice Dynamics Model with Improved Energy and Angular Momentum Conservation"
2022-09-09 10:00 - 11:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
Magnetic materials are critically important in a wide range of application areas including data storage, medicine, energy harvesting, and refrigeration. Atomistic numerical simulations of magnetic materials can provide important insight in these applications because they offer the ability to track phenomena such as magnon-phonon interactions, ultrafast demagnetization processes, and magnetization and energy at time and length scales that can be difficult to observe experimentally. Spin-lattice dynamics, a classical atomistic simulation method that models atomic magnetic moments and atomic displacements simultaneously, is able to capture these phenomena. Unfortunately, energy stability can be a challenge in spin-lattice dynamics simulations and angular momentum artifacts are a known issue in atomistic models of periodic systems. Both of these problems can cause errors in the evolution of spin orientations and atomic positions, leading to unphysical predictions of temperature, magnetization, and thermal-magnetic coupling in magnetic materials. This dissertation presents an improved computational model for spin-lattice dynamics simulations developed to address the above challenges. The model offers superior energy and magnetization conservation and the ability to quantify lattice angular momentum changes generated by spin relaxation processes in bulk materials. The improvements made in this work advance spin-lattice dynamics as a computational tool for the design and analysis of magnetic materials.
