Fall 2021 GRASP Seminar: Heng Yang, "Certifiable Outlier-Robust Geometric Perception: Robots that See through the Clutter with Confidence"
2021-12-15 14:00 - 15:00 (America/New_York) | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
*This will be a HYBRID Event with in-person attendance for Dr. Malikopoulos’ in-person talk in Wu & Chen Auditorium and Virtual attendance via Zoom Webinar here.
Geometric perception is the task of estimating geometric models (e.g., object pose and 3D structure) from sensor measurements and priors (e.g., point clouds and neural network detections). Geometric perception is a fundamental building block for robotics applications ranging from intelligent transportation to space autonomy. The ubiquitous existence of outliers —measurements that tell no or little information about the models to be estimated— makes it theoretically intractable to perform estimation with guaranteed optimality. Despite this theoretical intractability, safety-critical robotics applications still demand trustworthiness and performance guarantees on perception algorithms. In this talk, I present certifiable outlier-robust geometric perception, a new paradigm to design tractable algorithms that enjoy rigorous performance guarantees, i.e., they return an optimal estimate with a certificate of optimality for a majority of problem instances, but declare failure and provide a measure of suboptimality for worst-case instances. Particularly, I present two general-purpose algorithms in the certifiable perception toolbox: (i) an estimator that uses graph theory to prune gross outliers and leverages graduated non-convexity to compute the optimal model estimate with high probability of success, and (ii) a certifier that employs sparse semidefinite programming (SDP) relaxation and a novel SDP solver to endow the estimator with an optimality certificate or escape local minima otherwise. The estimator is fast and robust against up to 99% random outliers in practical perception applications, and the certifier can compute high-accuracy optimality certificates for large-scale problems beyond the reach of existing SDP solvers. I showcase certifiable outlier-robust perception on robotics applications such as scan matching, satellite pose estimation, and vehicle pose and shape estimation. I conclude by remarking opportunities for integrating certifiable perception with big data, machine learning, and safe control towards trustworthy autonomy.

ESE Fall Colloquium - "Harnessing Piezoelectricity in Novel Microsystems for Classical and Quantum Information Processing"
2021-12-16 11:00 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Piezoelectricity is the intrinsic coupling between electric fields and strains in materials. While piezoelectric sensors, actuators, and RF filters are ubiquitous and important components of existing microsystems, their potential is still largely underutilized in many application spaces. In this talk, I will discuss the physics, engineering, and applications of two novel classes of piezoelectrically enabled microsystems developed by the MEMS-Enabled Quantum Photonics group at Sandia National Labs: (1) piezoelectrically modulated photonic, phononic, and optomechanical integrated circuits and 2) piezoelectric-semiconductor hybrids for all-acoustic radio frequency signal processing and acoustoelectrically enhanced optomechanics. I will discuss these systems in detail and show how they are being used to make significant advances in both classical and quantum information processing applications.

BE Doctoral Dissertation Defense: "Computational imaging biomarkers for precision medicine: characterizing heterogeneity in breast cancer" (Rhea Chitalia)
2021-12-16 13:15 - 15:15 | BRB 253
The Department of Bioengineering at the University of Pennsylvania and Dr. Despina Kontos are pleased to announce the Doctoral Dissertation Defense of Rhea Chitalia.

Title: “Computational imaging biomarkers for precision medicine: characterizing heterogeneity in breast cancer”
Date: Thursday, December 16, 2021
Time: 1:15 PM
Location: BRB 0253 and Zoom.

Zoom information:

RHEA CHITALIA is inviting you to a scheduled Zoom meeting.
Topic: Doctoral Dissertation Defense of Rhea Chitalia
Time: Dec 16, 2021 01:15 PM Eastern Time (US and Canada)
Join Zoom Meeting
https://upenn.zoom.us/j/91285150761?pwd=[REDACTED]
Meeting ID: 912 8515 0761
Passcode: [REDACTED]
One tap mobile
+13017158592,,91285150761# US (Washington DC)
+13126266799,,91285150761# US (Chicago)
Dial by your location
+1 301 715 8592 US (Washington DC)
+1 312 626 6799 US (Chicago)
+1 646 558 8656 US (New York)
+1 253 215 8782 US (Tacoma)
+1 346 248 7799 US (Houston)
+1 669 900 9128 US (San Jose)
Meeting ID: 912 8515 0761
Find your local number: https://upenn.zoom.us/u/abWUD7gr1Z

BE Seminar: "Neural Engineering and the Primate Brain: Working at the Electrical and Optical Interface" (Bijan Pesaran)
2021-12-16 15:30 - 16:30 | Moore 216, 200 S. 33rd Street, Philadelphia, PA, 19104, United States
This seminar will be held live and broadcast on zoom – link coming soon.
Neural engineering is enjoying an era of transformative growth. Classical methods that dominated the neurosciences for decades are being replaced by powerful new technologies. In this talk, I will discuss how to engineer electrical and optical interfaces to the primate brain. I will first present work on electrode interfaces that stimulate and record at the surface of and within the brain. I will show how simultaneously measuring and manipulating neurons immediately beneath electrode contacts during behavior delivers ground-truth data. The results have implications for electrode interface design and new generations of implantable biomedical devices. I will then turn to optical neural interfaces. Two-photon fluorescence microscopy images the activity of neurons expressing genetically-encoded calcium indicators and is most often performed in small animal models, such as the mouse, worm and fly. I will present a cellular-resolution robotic imaging platform to investigate the non-human primate brain at scale. I will finish by discussing potential applications of this technology to a range of scientific and clinical goals.

CIS Seminar: "Encrypted Computation"
2022-01-11 13:00 - 14:15 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Can we use cryptography to protect data while simultaneously enabling computation over it? Such cryptosystems have the potential to enable all of the amazing benefits of cloud computing without forcing users to give up control over their private data. The breakthrough development of fully homomorphic encryption (FHE) took the first step in this direction by showing how to perform arbitrary computations over encrypted data, without revealing anything about the data itself. In this talk, I will survey some of my work that goes beyond FHE to enable computation over cryptographically protected data in many other scenarios. We will discuss Fully Homomorphic Signatures, Multi-Key FHE, Laconic Function Evaluation, and Program Obfuscation.

BE Seminar: "Self-Assembling Nanotechnologies for Precision Biomaterials" (Santiago Correa, Stanford)
2022-01-17 12:00 - 13:00
Self-assembled materials with defined nanoscale architectures can engage with biological systems in fundamentally new ways, providing unprecedented biomedical opportunities. In particular, the ability to more precisely control both the location and timing of drug release makes these biomaterials especially useful for delivering potent or sensitive cargo, which has major implications for cancer therapy and immuno-engineering. Whether it is drug delivery, gene therapy, or tissue regeneration, these materials are pushing the boundaries for engineering our own biology.
In this seminar, I will discuss how self-assembled biomaterials are used to manipulate the tumor microenvironment to achieve gene silencing, non-invasive tumor detection, and localized immunostimulation in vivo. By leveraging non-covalent interactions to build composite nanomaterials, we constructed multi-functional biomaterials capable of dynamic stimuli-responsive behaviors. I will detail the development of nanoparticle coatings that target ovarian tumors and preferentially traffic to specific subcellular compartments. These coatings enabled the development of both theranostic and immunostimulatory nanomedicines that required specific subcellular trafficking to carry out their functions. I will also discuss the development of nanoparticle-based, supramolecular hydrogels, which provide injectable, clinically relevant strategies for localized cancer immunotherapy. Together, these applications demonstrate the ability for self-assembled biomaterials to accomplish complex goals in vivo and mediate highly specific biomedical functions.

MEAM Seminar: "Assembly Engineering of Patchy Particles into Complex Structures, and Beyond"
2022-01-18 10:00 - 11:30 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
The ability to predict, design and make the perfect material with just the right properties to do what we want, how we want, and when we want is the holy grail of materials research. Such “materials on demand” require control over thermodynamics, kinetics, nonequilibrium behavior, and structure across many length and time scales. With continuing advances in computer simulation capabilities, we have never been closer to the goal, but many challenges – and opportunities – remain. Many of those are at the boundaries of the subfields of materials research, where ideas from one area spur advances in others, and where computational tools and concepts are transferable across domains and scales. At the same time, foundational understanding at one scale can help understand new discoveries at different scales, regardless of the nature of the material and the forces holding it together. In this seminar, we show how atomic and molecular crystal structures – made possible by chemical bonds – can be realized for nanoparticles and colloids via enthalpic and entropic bonds. We show how these bonds can be designed and engineered for self-assembly.

CEMB Future Leaders: "The place of plant chromatin in sensing mechanical stress"
2022-01-19 11:00 - 12:00 | https://upenn.zoom.us/j/96715197752
Launched in May 2021, the Future Leaders in Mechanobiology is a monthly seminar series featuring up-and-coming leaders in mechanobiology–PhD students and postdocs from a wide range of fields, backgrounds, and institutions. By providing an international stage to share one’s work and opportunities to interact with researchers at all career stages, we aim to create an inclusive and valuable series for early-stage researchers and the mechanobiology community as a whole.
Register HERE for access to the Zoom link and visit the CEMB website for more information.

CBE Seminar: "Tackling Disease-Associated Biomolecules: From RNA Therapeutics to Single-Molecule Detection"
2022-01-19 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract
The functionality versatility of polymeric and nucleic acid materials provides diverse engineering opportunities for the detection and therapeutic targeting of disease-associated biomolecules. My work has encompassed nanoscale and microscale technologies to address challenges in cancer therapeutics and diagnostics, from targeting the expression of dysregulated genes to probing rare protein biomarkers. I will first describe the design of a highly potent small interfering RNA nanoparticle delivery system that leverages approaches in nucleic acid engineering and polymer chemistry. Towards overcoming analytical barriers in clinical diagnostics, I will then describe the development of ultrasensitive single-molecule detection technologies to measure low abundance proteins. By utilizing powerful molecular amplification methods and simple signal readouts, these digital enzyme-linked immunosorbent assay (ELISA) platforms enable the detection of low attomolar (10-18 M) protein concentrations, with over four orders-of-magnitude improvements in sensitivity over conventional protein detection methods. Importantly, these methods democratize ultrasensitive protein detection, enabling high-throughput, high-multiplex digital measurements to be carried out with common laboratory instrumentation.
Finally, I will highlight the application of digital ELISA technology towards the development of an ultrasensitive blood test for a retrotransposon-encoded protein as a highly specific multi-cancer biomarker. Overall, the development of these technologies can pave the way towards the future integration of materials and biomolecular engineering approaches with ultrasensitive bioanalytical tools for accelerating biomarker signature discovery and precision medicine.

ESE Ph.D. Thesis Defense: "Statistical Learning for System Identification, Prediction, and Control"
2022-01-20 10:00 - 12:00 | Zoom – Meeting ID 949 5950 4530
Despite the recent widespread success of machine learning, we still do not fully understand its fundamental limitations. Going forward, it is crucial to better understand learning complexity, especially in critical decision making applications, where a wrong decision can lead to catastrophic consequences. In this thesis, we focus on the statistical complexity of learning unknown linear dynamical systems, with focus on the tasks of system identification, prediction, and control. We are interested in sample complexity, i.e. the minimum number of samples required to achieve satisfactory learning performance. Our goal is to provide finite-sample learning guarantees, explicitly highlighting how the learning objective depends on the number of samples. A fundamental question we are trying to answer is how system theoretic properties of the underlying process can affect sample complexity.
Using recent advances in statistical learning, high-dimensional statistics, and mini-max theory, we provide finite-sample guarantees in the following settings. i) System Identification. We provide the first finite-sample guarantees for identifying a stochastic partially-observed system; this problem is also known as the stochastic system identification problem. ii) Prediction. We provide the first end-to-end guarantees for learning the Kalman Filter, i.e. for learning to predict, in an offline learning architecture. We also provide the first logarithmic regret guarantees for the problem of learning the Kalman Filter in an online learning architecture, where the data are revealed sequentially. iii) Difficulty of System Identification and Control. Focusing on fully-observed systems, we investigate when learning linear systems is statistically easy or hard, in the finite sample regime. Statistically easy to learn linear system classes have sample complexity that is polynomial with the system dimension. Statistically hard to learn linear system classes have worst-case sample complexity that is at least exponential with the system dimension. We show that there actually exist classes of linear systems which are hard to learn. Such classes include indirectly excited systems with a large degree of indirect excitation. Similar conclusions hold for both the problem of system identification and the problem of learning to control.

MSE Seminar: "How do tissues fracture and repair across length scales?"
2022-01-20 10:30 - 11:30 | https://upenn.zoom.us/j/96715197752
In structural materials engineering, we often aim to create materials that are simultaneously strong, tough and lightweight- a combination classically considered mutually exclusive. Biogenic composite materials such as bone exhibit a combination of these properties exceeding that of their constituents, a feat generally credited to their hierarchal structure, down to the nanoscale. In this talk, we will demonstrate the use of micro and nanoscales site-specific microstructural characterization and mechanical experiments to probe the strength, deformation, and fracture of human bone. We will demonstrate an in situ SEM/nanoindentor methodology, that enables 3-point bending fracture experiments with observation and measurement of crack growth and toughening behavior at nano and micrometer scales. We will discuss how expanding these nanoscale tissue experiments can enable future efforts in fundamental understanding of tissue regeneration. We will use generalized lessons learned about biogenic materials to discuss additive manufacturing of metals and explore how to leverage the unique tunability of nanoparticle functionalization and nanostructuring feedstock in metal additive manufacturing to expand the library of currently “printable” materials for potential applications ranging from biomedical to thermal.

BE Seminar: "Engineering New Tools to Understand Airway Mucosal Barrier Function" (Gregg Duncan)
2022-01-20 15:30 - 16:30 | Moore 216, 200 S. 33rd Street, Philadelphia, PA, 19104, United States
This seminar will be held in person and on zoom (the link will be sent out via email).
Mucus is a biological gel within the lung designed to behave like an “escalator” with the ability to capture potentially harmful inhaled materials (e.g. pathogens, particulates) and carry these materials via mucociliary clearance up to the throat to be swallowed and sterilized. MUC5B and MUC5AC are large, gel-forming mucins that assemble to form airway mucus gels. In individuals with asthma, biochemical analysis of mucus produced by cough revealed mucin composition is altered as a function of disease severity, with a shift from MUC5B to MUC5AC as the predominant secreted mucin. However due to the lack of appropriate models, it is not yet fully understood how imbalance in ratio of MUC5B to MUC5AC contributes to the biological function of mucus. Recently, we have engineered a mucin-based biomaterial with native-like viscoelasticity, here termed ‘synthetic mucus’. To understand mucus dysfunction in asthma, we customized synthetic mucus to mimic the biochemical and biophysical properties of airway mucus in health and in asthma. In addition, we characterized the rheological properties and transport function of mucus in differentiated human airway tissue cultures genetically engineered to secrete either MUC5B or MUC5AC. These bioengineered models provide new key insights on how MUC5B and MUC5AC work in concert to enable host mucosal barrier function providing a highly valuable means to understand their roles in health and disease.

BE Seminar: "Visualizing the Unseen: Enabling Precision Oncology Through Microenvironment-Triggered Diagnostics and Therapeutics" (Liangliang Hao)
2022-01-24 12:00 - 13:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
The successful integration of precision diagnostics with new personalized therapies opens numerous doors to improve the management of a variety of diseases. In cancer, tissue-environmental features of tumor progression and invasion, including aberrant extracellular matrix remodeling, stromal composition changes, and immune cell engagement, create engineering opportunities for use in developing novel biomarkers and therapeutic targets. In this seminar, I will focus on an emerging paradigm in precision diagnostics: synthetic biomarkers. These bioengineered sensors harness microenvironment-dependent activation mechanisms to generate molecular reporters that can be read out in biofluids. To overcome the limitations often associated with molecular disease biomarkers (cross-reactivity with healthy tissues, dilution in biofluids below detectable levels, and rapid degradation of released material), I have engineered next-generation synthetic biomarker platforms with enhanced specificity and clinical actionability by 1) developing CRISPR-Cas-amplifiable urinary reporters to detect and differentiate disease states at the point-of-care; 2) advancing theranostic technologies to precisely target the hallmarks of cancer metastasis in specific tissues; 3) improving noninvasive in vivo imaging capabilities to allow for rapid assessment of disease status and interventional efficacy over time. Collectively, these studies highlight the use of chemical tools with built-in cancer-reactive modules, embracing a vision for precision health through integrated strategies: identification/monitoring, imaging, and intervention in a personalized manner.

MEAM Seminar: "'Tiny-but-tough' GaN- and Graphene-based Nanoelectronics for Extreme Harsh Environments"
2022-01-25 10:00 - 12:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
Gallium nitride (GaN) nanoelectronics have operated at temperatures as high as 1000°C making it a viable platform for robust space-grade (“tiny-but-tough”) electronics and nano-satellites. Even with these major technological breakthroughs, we have just begun the “GaN revolution.” New communities are adopting this nanoelectronic platform for a multitude of emerging device applications including the following: sensing, energy harvesting, actuation, and communication. In this talk, we will review and discuss the benefits of GaN’s two-dimensional electron gas (2DEG) over silicon’s p-n junction for space exploration applications (e.g., radiation-hardened, temperature-tolerant Venus probes). In addition, we will discuss the use of 2D materials such as graphene in space exploration applications, as well as the potential for synthesis of graphene mesostructures in prolonged microgravity environments on the International Space Station (ISS).

MEAM PhD Thesis Defense: "Accelerated Design of Architected Materials with Geometric Heterogeneity for Enhanced Failure Characteristics"
2022-01-25 13:00 - 14:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
Nature provides countless examples of the use of material heterogeneity to enhance the failure properties of materials. Many biological materials, such as bone, marine shells, and fish scales, are extremely resilient to fracture and failure. These often consist of regions that are highly mineralized and stiff and regions of biopolymers that are extremely soft. In practice, combining such disparate materials in synthetic systems is fraught with difficulties, such as poor interfacial adhesion. However, we will show, geometric heterogeneity can lead to similar enhancements to failure characteristics, including distribution of voids (inspired by bamboo) and spatial variations in fiber orientation (inspired by many materials, such as aorta). With the nearly arbitrary arrangements of materials that is enabled by 3D printing, it is possible to produce systems with bioinspired, spatially-varying microstructures that results in large improvements to failure properties.
In this dissertation, I will discuss two types of geometric heterogeneities that can be easily introduced to architected materials enhancing their failure characteristics. First, inspired by the microstructure of the dactyl club of the Mantis shrimp, we show how geometric defects that are intrinsic to extrusion-based additive processes (voids and weak interfaces) can be spatially arranged in a helical (Bouligand) pattern to produce complex crack patterns and enhanced energy absorption. Next, we show how spatial variations in fiber orientation (inspired by aorta) can be produced using direct ink writing (DIW), leading to soft composites with high toughness and fatigue threshold.
Such geometric heterogeneities in architected materials, and the 3D printing processes used to create them, introduce a large number of parameters into the material design process, such as infill layer angle, fiber orientation, void placement, etc. Bio-inspiration provides a starting point and some basic intuition about how to design heterogeneous materials for improved failure properties, but it cannot guarantee optimal failure properties. I will therefore conclude the talk with a discussion of the use of Bayesian optimization for the acceleration of the design of architected heterogeneous materials with optimal failure properties. We will introduce a multi-fidelity Bayesian optimization approach to accelerate the design of heterogeneous triangular lattices with maximal energy absorption during compressive loading.

Spring 2022 GRASP SFI: Paloma Sodhi, Carnegie Mellon University, "Learning in factor graphs for tactile perception"
2022-01-26 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
Factor graphs offer a flexible and powerful framework for solving large-scale, nonlinear inference problems as encountered in robot perception. Typically these methods rely on handcrafted models that are efficient to optimize. However, robots often perceive the world through complex, high-dimensional sensor observations. For instance, consider a robot manipulating an object in-hand and receiving high-dimensional tactile observations from which it must infer latent object poses. Can we learn models for such observations directly from sensor data?
In this talk, I will discuss algorithms and representations for learning observation models end-to-end with optimizers in the loop. I will present a novel approach, LEO, that casts the problem of learning observation models as cost function learning that makes no assumptions on the differentiability of the underlying optimizer. I will also discuss different feature representations for extracting salient information from tactile image observations. We will evaluate these approaches on a real-world application of tactile perception for robot manipulation where we demonstrate reliable object tracking in hundreds of trials across planar pushing and in-hand manipulation tasks.

CBE Seminar: "Revealing the Unknown Dynamics of High-Energy Density Lithium-Metal Batteries"
2022-01-26 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract
High-energy density batteries will play a remarkable role in hurdling global climate change. My research focuses on the fundamental understandings of their electrochemical reaction mechanisms and the design of materials, protocols, and characterization tools to enable their safe operations over long-term use. First, I will discuss about the previously overlooked dynamics of detached lithium metal filaments during battery operations. This discovery leads to the recovery of lost capacities in lithium-metal batteries and enables fast charging in lithium-ion batteries. Next, I will introduce a characterization tool for the on-board monitoring of battery health based on pressure evolutions. In addition to capturing the early signs of battery failure, this pressure sensing system offers new insights into the battery degradation process. Overall, the combination of fundamental study and the rational design of materials/protocols/characterization tools opens broad opportunities toward a clean energy future.

MSE Seminar: "Engineering Organoid Models for Understanding Human Neurodevelopment and neurological disorders"
2022-01-27 10:30 - 11:30 | https://upenn.zoom.us/j/96715197752
Human Induced pluripotent stem cells (hiPSCs) has the potential to generate all cell types of a human body under 2D culture conditions or form organ like structures-organoids, under 3D culture conditions. Brain organoid cultures from human iPSCs have been recently developed to recapitulate the cellular composition and the cytoarchitecture of the developing brain. These hiPSC based organoid model systems offer unique advantages in understanding molecular and cellular mechanisms governing embryonic neural development and in modeling neurodevelopmental and neurological disorders. I will discuss our recent work in developing brain region specific organoid systems and apply them to understand human brain development and neurotropism of SARS-CoV-2.

ESE Spring Colloquium - "The One Learning Algorithm Hypothesis-- Towards Universal Machine Learning Models and Architectures"
2022-01-27 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
We revisit the “One Learning Algorithm Hypothesis” of Andrew Ng (Google Brain) according to which the brain of higher-level animals and of humans processes and perceives sensory data (vision, sound, haptics) with the same abstract algorithmic architecture. We develop models, based on our earlier work on automatic target recognition with radar and other sensors, face recognition and image classification, which employ a multi-resolution preprocessor, followed by a group-invariance based feature extractor, followed by a machine learning (ML) module that employs the two fundamental algorithms of Kohonen Learning Vector Quantization (LVQ), for supervised learning, and Self-Organizing Map (SOM), for unsupervised learning. In addition the model and algorithms utilize a “global” feedback from the output of the overall system back to the feature extractor and to the multiresolution preprocessor. We first summarize briefly our older results with such algorithms and their remarkable, domain agnostic, performance on various applications. We then provide our recent results on the mathematical analysis of the resulting Tree Structure Learning Vector Quantization (TSLVQ) ML architecture and algorithms. We introduce and integrate Deterministic Annealing (DA) with our older architecture and demonstrate the resulting tremendous reduction in data required for learning and application. The new algorithms allow even on-line progressive learning. We utilize Bregman divergences as dissimilarity measures, which allows us to provide direct transition from “dissimilarity distance” to probability of error, which cannot be achieved with the commonly used metric-based dissimilarity measures. We show that many deep learning network architectures can be mapped to this “universal” architecture. We show that the integrated algorithm converges to the true Bayes decision surface, albeit with variable resolution at various parts of it, as required. The latter brings a tight connection to integrated hypothesis testing with compressed data. We demonstrate the results in various applications and close with future directions and extensions.

BE Seminar: "Orchestrating Cellular Regeneration at Organ Scale" (Yvon Woappi)
2022-01-27 15:30 - 16:30
Large scale tissue damage, such as organ failure and burn injury, is a leading cause of morbidity and death. However, the mechanisms underlying full regeneration of organs remain poorly understood. As the largest organ system in the body, the integumentary system is a composite tissue assembly evolutionarily adapted for healing. Consequently, its complex physiology requires multifaceted cooperation between several distinct cell populations and cell lineages of embryologically distinct origins. Equally integrated within this dynamic process is local immune response that produces mitogenic and inhibitory signals throughout the restoration procedure. There remains a significant gap in understanding how these processes are orchestrated, and how various skin cell populations from distinct developmental lineages functionally cooperate to regenerate tissue at organ scale. My research seeks to characterize the molecular language of tissue healing and to harness this malleable dialect for the regeneration of mammalian tissues. Through the development of organoid models of wound regeneration, and the coupling of these systems with novel gene-editing approaches, my work is enabling the functional understanding of the multifaceted cellular events executed throughout restorative healing. This seminar will describe these high throughput technologies and will illustrate their utility in identifying novel regulators of tissue healing.

CIS Seminar: "Social Reinforcement Learning"
2022-01-27 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Social learning helps humans and animals rapidly adapt to new circumstances, coordinate with others, and drives the emergence of complex learned behaviors. What if it could do the same for AI? This talk describes how Social Reinforcement Learning in multi-agent and human-AI interactions can improve coordination, learning, generalization, and lead to the development of agents better able to anticipate and serve human needs. I propose a unified method for improving coordination and communication based on causal social influence. Beyond coordination, I demonstrate how multi-agent training can be a useful tool for improving learning and generalization even in the single-agent setting. I present PAIRED, in which an adversary learns to construct training environments to maximize regret between a pair of learners, leading to the generation of a complex curriculum of environments that improve both learning and zero-shot generalization. Ultimately, the goal of my research is to create intelligent agents that can assist humans with everyday tasks; this means interacting effectively with humans, and learning from human-AI interactions. I show that learning from human social and affective cues scales more effectively than learning from manual feedback. However, it depends on accurate recognition of such cues. Therefore I will discuss how to dramatically enhance the accuracy of affect detection models using personalized multi-task learning to account for inter-individual variability. Together, this work argues that Social RL is a valuable approach for developing more general, sophisticated, and cooperative AI, which is ultimately better able to serve human needs.

GRASP on Robotics: Andreas Malikopoulos, University of Delaware, “Separation of Learning and Control for Cyber-Physical Systems”
2022-01-28 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
*This will be a VIRTUAL Event with attendance via Zoom Webinar here.
Cyber-physical systems (CPS), in most instances, represent systems of subsystems with an informationally decentralized structure. To derive optimal control strategies for such systems, we typically assume an ideal model, e.g., controlled transition kernel. Such model-based control approaches cannot effectively facilitate optimal solutions with performance guarantees due to the discrepancy between the model and the actual CPS. On the other hand, in most CPS there is a large volume of data with a dynamic nature which is added to the system gradually in real time and not altogether in advance. Thus, traditional supervised learning approaches cannot always facilitate robust solutions using data derived offline. By contrast, applying reinforcement learning approaches directly to the actual CPS might impose significant implications on safety and robust operation of the system. In this talk, I will present a theoretical framework founded at the intersection of control theory and learning that circumvents these challenges in deriving optimal strategies for CPS. In this framework, we aim at identifying a sufficient information state for the CPS that takes values in a time-invariant space, and use this information state to derive separated control strategies. Separated control strategies are related to the concept of separation between the estimation of the information state and control of the system. By establishing separated control strategies, we can derive offline the optimal control strategy of the system with respect to the information state, which might not be precisely known due to model uncertainties or complexity of the system, and then use learning methods to learn the information state online while data are added gradually to the system in real time. This approach could effectively facilitate optimal solutions with performance guarantees in a wide range of CPS applications such as emerging mobility systems, networked control systems, smart power grids, cooperative cyber-physical networks, cooperation of robots, and internet of things.

PSOC@Penn Seminar: "Unravelling the differences of copy number variations (CNV) and loss of heterozygosity (LOH) between solid and liquid tumors" (Mai Wang)
2022-01-31 12:00 - 13:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Physical Sciences in Oncology Center PSOC@Penn
Spring 2022 Hybrid-Seminar Series
Towne 225 / Raisler Lounge @ Noon (EST)
For Zoom link , please contact manu@seas.upenn.edu

MEAM Seminar: "Mechanistic Insights into Crystalline Interfaces via Thermal Fluctuations"
2022-02-01 10:00 - 11:30 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
Interfaces such as grain boundaries are ubiquitous in crystalline materials and have provided a fertile area of research over decades. Their importance stems from the numerous critical phenomena associated with them, such as grain boundary sliding, migration, and interaction with other defects that govern the mechanical properties of materials. Although these crystalline interfaces exhibit small out-of-plane fluctuations, statistical thermodynamics of membranes has been effectively used to extract relevant physical quantities such as the interface free energy, grain boundary stiffness, and interfacial mobility.
In this talk, I will put forward the viewpoint that monitoring thermal fluctuations of crystalline interfaces by way of molecular dynamics can serve as a computational microscope for gaining insights into the thermodynamic and kinetic properties of grain boundaries and present a rich source of future study. In particular, the talk will present how we use thermal fluctuations to estimate the grain boundary stiffness and mobility of grain boundaries by modeling them as Brownian particles.

CIS Seminar: "Trustworthy Machine Learning Systems via PAC Uncertainty Quantification"
2022-02-01 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Machine learning models are increasingly being incorporated into real-world systems, targeting domains such as robotics, healthcare, and software systems. A key challenge is ensuring that such systems are trustworthy. I will describe a novel strategy for composing machine learning models while providing provable correctness guarantees. First, we show how to quantify the uncertainty of any given model in a way that satisfies PAC correctness guarantees. Second, we show how to compose guarantees for individual models to obtain a guarantee for the overall system. Then, I will discuss applications to ensuring safety in reinforcement learning from visual inputs, and to speeding up inference time of deep neural networks. I will conclude with ongoing work on preserving correctness guarantees in the face of distribution shift.

Spring 2022 GRASP SFI: Anthony Bisulco, University of Pennsylvania, “A Tutorial on Neuromorphic Vision for High-Speed Perception”
2022-02-02 15:00 - 16:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This seminar will be held in person in Levine 307 as well as via Zoom.
Mobile robot systems operate in dynamic environments, such as forests, caves, or roadways, where they must perceive and react to incoming stimuli. Each of these environments presents unique challenges such as perception in low-light conditions, high-speed understanding of objects, and low power requirements. Neuromorphic Vision Systems such as event-based cameras have appealing properties for the above challenges such as high-temporal resolution, low-power footprint, and high-dynamic-range. As opposed to conventional frame-based imagers, event-based cameras output a stream of asynchronous events consisting of spatial illumination changes. This presents an expansive new processing model for event-based computer vision applications. In this talk, I will begin with a tutorial on event-based cameras, review processing techniques for the event-stream, and highlight our recent work on developing high-speed perception action systems for object catching.

CBE Seminar: "From Trash to Treasure: Advancing the Heterogeneous Catalytic Deconstruction and Upcycling of Waste Polyolefins"
2022-02-02 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract
The rapid global consumption of single-use plastics has caused an unsustainable accumulation of plastic waste in landfills and the environment. Unfortunately, current mechanical recycling methods are expensive and produce lower-quality products. New strategies in targeted chemical upcycling of waste plastics offer unique opportunities for selective depolymerization of polyolefins to higher value chemicals under milder conditions than thermal deconstruction or pyrolysis. Inspired by recent developments in the depolymerization of lignin, we turned to the method of hydrogenolysis to break the strong C-C bonds in polyolefins. This talk will cover our efforts in identifying a class of ruthenium-based materials as active and selective heterogeneous catalysts for the depolymerization of polyolefin waste, catalyst support modification strategies to further improve selectivity towards processible liquid alkanes, and new frameworks for the chemical upcycling of waste plastics and complex mixed waste streams to enable a circular carbon economy.

MSE Seminar: "Thermodynamic contributions to glass fragility"
2022-02-03 10:30 - 11:30 | https://upenn.zoom.us/j/96715197752
Liquid fragility characterizes the rate of drop off in viscosity of a glass-forming liquid as its temperature increases above the glass transition. This property is fundamental to glass physics and of critical importance for engineering applications, yet its origin is unclear. Competing theories suggest that vibrational properties may play a role in fragility by contributing entropy to the large excess entropy of the undercooled liquid over the crystal above the glass transition. In this talk, we will demonstrate the use of in situ inelastic neutron scattering to probe the vibrational properties of a series of glasses with varying fragilities. We will demonstrate that event-based neutron scattering allows collection of the phonon density of states in as little as two minutes, enabling capture of vibrational dynamics in the narrow stable window of the undercooled liquid. We will discuss the correlation between excess vibrational entropy in the undercooled liquid and liquid fragility. Finally, we will discuss these new results in the context of a universal understanding of fragility and vibrational contributions to the thermodynamics of the glass transition.

BE Seminar: "From Bench to Pet-Side: Intratumoral Immunotherapy Principles and Practice" (Noor Momin)
2022-02-03 15:30 - 16:30 | 216 Moore Building
Immunotherapies harness the body’s immune system to fight disease. Such therapies can help unleash an immune attack against diseased tissues but can inadvertently instigate an attack on healthy tissues. As a result, many promising immunotherapies face major toxicities, limiting their clinical use. By employing an iterative process that entails measuring, making, and modeling to manipulate immunity, we can develop effective immunotherapies for any ailment – from cancer to cardiovascular disease.
In this seminar, I will describe our effort to develop a novel intratumorally-injected treatment to fight cancer safely and effectively. Cytokines are promising cancer immunotherapies plagued by life-threatening toxicity. Injecting cytokines directly into tumors could provide a method of confining its benefits to the cancerous tissue and away from healthy tissues, but previous attempts to do this have resulted in the cytokines rapidly leaking out of the tumor and ravaging healthy tissues. To this end, we first engineered a strategy to retain cytokines injected in a tumor, thereby safely exerting their anti-tumor activity. Then, we generated a computational framework that outlines the pharmacokinetic underpinnings of an effective tumor localized immunotherapy. Lastly, we commenced a clinical trial in companion (i.e., pet) dogs with naturally-occurring cancer aimed at generating guidelines for the administration of tumor localized cytokines in humans. Together, this work powers safe and effective local immunotherapies for cancer treatment.

CIS Seminar: "Sublinear Algorithms for Massive Datasets"
2022-02-03 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The influx of massive data systems poses a unique challenge for algorithms research. In this modern era, the classical theory of algorithms is often insufficient, and our goal is to develop the theory of sublinear computation.
This talk will cover various scenarios where the resources used by an algorithm (running time, memory, number of measurements, … etc) should be significantly smaller than the input size. We will spend most of the time on sublinear time algorithms for similarity search in high-dimensional spaces and sublinear space algorithms for the optimal transport problem, where we will present new algorithmic and analytical techniques for tackling these questions. A central theme throughout the talk is the notion of randomized space partitions, and how they lead to algorithms which are simple, have provable guarantees, and are extremely useful.
