Penn Engineering Events

PSOC Seminar: “Mechanical waves identify the amputation position during wound healing in the amputated zebrafish tailfin” (Keng-hui Lin, Institute of Physics)
2022-11-28 13:00 - 14:00 (America/New_York) | 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

PSOC Seminar: "Mechanical Control of T Cell Function" (Morgan Huse, The Sloan Kettering Institute)
2022-11-21 13:00 - 14:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States

Dissertation Defense: Jingyu Wu
2022-11-21 10:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
LARGE-SCALE MICROFLUIDIC MANUFACTURING OF GRANULAR HYDROGELS AND MULTIPLE EMULSIONS
Abstract:
Droplet microfluidics have made tremendous progress in the last two decades in the generation of micrometer- and nanometer-scale materials. One of the major developments lies in the incorporation of multiple microfluidic devices onto single chips enabling emulsion generation at clinical and industrial-relevant scale. However, most demonstrated successes have focused on producing simple emulsions; production upscaling of complex emulsion and materials have not been realized. In this thesis, I will demonstrate new approaches and some early successes to fill this gap between lab-scale and industrially relevant scale production of complex emulsion and drop-based on-chip material synthesis. First, a silicon-and-glass based microchip is developed for ultrahigh throughput on-chip photopolymerization of microgels. We demonstrate that the mechanical properties of microgels can be modulated by tuning the UV dosage and a massive parallelization of 4080 microfluidic synthesis lines on a 4-inch wafer. Second, a new wettability patterning strategy, along with the fabrication process, is developed to pattern the hydrophobicity of a silicon-based microfluidic chip. We demonstrate wettability patterning with micrometer resolution and generation of both W/O/W and O/W/O double emulsions. Further, the scalability of this approach is demonstrated with chips that incorporate 50 parallelized double-emulsion generating devices, producing double emulsions in a high throughput manner (26.5 kHz double emulsions). Lastly, we extend the above approach and develop a novel approach to perform surface wettability patterning via polymer enrichment and replica molding (SUPER), controlling the wettability of microfluidic channels made of a solvent-resistant PFPE-PEG copolymer network. We demonstrate the utility of this approach by fabricating a PFPE-PEG based microfluidic chip, with hydrophobic/hydrophilic patterned microchannels, to generate double emulsions.

BE Doctoral Dissertation Defense: "High Throughput Microfluidics for Ultrasensitive Blood-Based Diagnostics" (Nishal Shah)
2022-11-18 14:00 - 16:00 | towne 217
The Department of Bioengineering at the University of Pennsylvania and Dr. David Issadore are pleased to announce the Doctoral Dissertation Defense of Nishal Shah.

Title: High Throughput Microfluidics for Ultrasensitive Blood-Based Diagnostics.
Date: November 18, 2022
Time: 2:00 pm
Location: Towne 217 ALC
or via Zoom, link below

https://upenn.zoom.us/j/93933189939

The public is welcome to attend.

MSE Special Seminar: “From sustainable Flooring to Sustainable Business: What you Don’t Learn in School”
2022-11-18 13:45 - 15:15 | David Rittenhouse Lab, A4, 209 South 33rd Street, Philadelphia, PA, 19104, United States
In this open conversation, he will share his experience as a glass-ceiling breaking executive who has successfully built the HMTX from a small retailer to a business of $800M revenue focusing on Manufacturing Efficiency, Durability, and Sustainability.

ESE Ph.D. Thesis Defense - "Modeling and Control of Dynamic Behavior of Spreading Processes on Networks"
2022-11-18 11:30 - 13:30 | Greenberg Lounge (Room 114), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
Epidemiological spreading processes constitute the core of a large number of disparate networks. In some, faster spread is desirable, in others containing the spread is critically important. We focus on understanding the spatio-temporal spread of epidemics over contact networks with the goal of facilitating or containing the spread as the case may be. In this study, we choose specific systems as exemplars of instances where the spread is desirable (e.g., Vehicle-to-Everything; V2X) and others where the spread is harmful (e.g., spread of infectious diseases).
In transportation systems, vehicles are expected to exchange messages with each other and with bikers, pedestrians, wheelchairs (Vehicle-to-Vehicle; V2V) and with signaling infrastructure on the roadways (Infrastructure-to-Vehicle; I2V) (together, V2X). We seek to qualitatively understand and subsequently quantitatively model the impact of complex, various interdependencies between wireless communication (spread of information through evolving links) and vehicular mobility (spatial movements of nodes). Towards this end, we introduce epidemiological modeling into transportation systems, a novel concept geared towards V2X, in which the computations remain tractable even for large, complex transportation networks. We additionally accommodate arbitrary traffic synchronization patterns corresponding for example to the presence of an arbitrary number of traffic signals. Furthermore, numerical computations using our mathematical framework reveal several questions that influence the practice of V2X network design and security.
Next, suppressing spread of infectious diseases is clearly of critical importance. Recently COVID-19 pandemic has wrecked havoc on lives and livelihoods worldwide. We explore the costs and benefits of a new tracing and testing concept towards containing COVID-19. We propose to preemptively identify the contact chain by testing primary, secondary, tertiary or further-off contacts of those who test positive, more specifically, the k-hop contacts for a parameter k of choice. We evaluate the costs and benefits of this novel multi-hop testing strategy for various reported disease parameters and on diverse human contact patterns, and see if the cost-benefit tradeoffs may be substantially enhanced through the deployment of the strategy. Furthermore, we propose an analytical methodology for evaluating multi-hop contact tracing strategy by combining the multi-hop contact tracing dynamics and the virus transmission mechanism in a single framework using microscopic Markov Chain approach.

Fall 2022 GRASP on Robotics: Julie Adams, Oregon State University, "Towards Adaptive Human-Robot Teams: Workload Estimation"
2022-11-18 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.

ABSTRACT
The ability for robots, be it a single robot, multiple robots or a robot swarm, to adapt to the humans with which they are teamed requires algorithms that allow robots to detect human performance in real time. The multi-dimensional workload algorithm incorporates physiological metrics to estimate overall workload and its components (i.e., cognitive, speech, auditory, visual and physical). The algorithm is sensitive to changes in a human’s individual workload components and overall workload across domains, human-robot teaming relationships (i.e., supervisory, peer-based), and individual differences. The algorithm has also been demonstrated to detect shifts in workload in real-time in order to adapt the robot’s interaction with the human and autonomously change task responsibilities when the human’s workload is over- or underloaded. Recently, the algorithm was used to post-hoc analyze the resulting workload for a single human deploying a heterogeneous robot swarm in an urban environment. Current efforts are focusing on predicting the human’s future workload, recognizing the human’s current tasks, and estimating workload for previously unseen tasks.

ESE Ph.D. Thesis Defense - "Robustness of Temporal Logics with Applications to Safe Autonomy"
2022-11-18 10:00 - 12:00 | Zoom – Meeting ID 564 482 9525
Signal Temporal Logic (STL) is a common way to express a broad range of real-time constraints that can be imposed on control systems. Spatial robustness of STL specifications, quantifying permissible spatial perturbations, has been widely studied in the literature. However, despite the importance of various time-critical systems, temporal robustness of STL has not yet been studied in depth nor has been used for control design.
In the first part of this thesis, we establish a comprehensive theoretical framework for temporal robustness of STL. We define synchronous and asynchronous temporal robustness and show that these notions quantify the robustness with respect to synchronous and asynchronous time shifts in the predicates of the underlying signal temporal logic specification. We further prove that synchronous temporal robustness upper bounds asynchronous temporal robustness. Moreover, we show under which conditions these two robustness notions are equivalent. Introduced synchronous and asynchronous notions are directional and consider either left or right perturbations. Due to this reason we additionally define and study the combined temporal robustness which simultaneously considers left and right time shifts.
In the second part of this thesis, we focus on applications of various robustness functions to robust planning and control design questions. We first propose solutions to the temporally-robust control synthesis problem by presenting Mixed-Integer Linear Programming (MILP) encodings for derived temporal robustness notions. Second, we solve the spatially-robust control synthesis problem and show how to adapt the smooth operator for space robustness maximization. Furthermore, we propose possible distributed solutions to centralized multi-agent planning problems. Through various simulations, as well as experiments on actual robotic systems, we show that our presented solutions are computationally efficient as well as can be used in a wide variety of applications.

A Celebration of the Life of Dr. Max Mintz
2022-11-17 15:30 - 17:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The CIS Department and GRASP Lab invite you to please join us on Thursday, November 17th, at 3:30pm as we celebrate the life and legacy of Dr. Max Mintz, Professor of Computer and Information Science.
Max joined Penn as an assistant professor of Systems Engineering (now part of ESE) in 1974. He changed his primary appointment to Computer and Information Science in 1986, and he was an important part of CIS undergraduate education and advising for 36 years. His research in Penn’s GRASP Lab focused on developing robust algorithms for decision-making under uncertainty with applications to machine perception and robotics. Max was a legendary teacher, an extraordinary advisor, and an inspiration to his students, encouraging them to go well beyond their comfort level in their academic preparation and homework. He won numerous awards including the S. Reid Warren, Jr. Award, the University’s Lindback Award, and the Ford Motor Company Award for Distinguished Advising.
A memorial celebration will be held in the Wu and Chen Auditorium of Levine Hall, followed by a reception. We hope you can attend.
*If you would like to attend, please RSVP here!
*If you’d like to submit photos of Dr. Mintz to be shared during the event, please upload them here!
*To attend virtually, please join via Zoom here.
*Full details on the schedule and event may be found here.

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ABOUT Dr. Max Mintz:
Max Mintz was born on September 4th, 1942, and grew up outside of New York City. He received bachelor’s and master’s degrees in Electrical Engineering at Cornell University and then completed a Ph.D. in Systems Theory in 1968. He then joined Yale University, first as a post-doc and then as an Assistant Professor in Control Theory and Electrical Engineering. He subsequently taught at the University of Illinois for two years before joining the Systems Engineering department (now part of Electrical and Systems Engineering) at the University of Pennsylvania in 1974. In 1984, at the invitation of Ruzena Bajcsy, he joined the GRASP Lab, and in 1986 he switched his primary affiliation to the Department of Computer and Information Science.
As an integral member of the GRASP Lab, Dr. Mintz’s research focused on control theory with stochastic systems. He developed robust algorithms for decision-making under uncertainty with applications to machine perception and robotics, combining ideas from mathematics, physics, and game theory. Later in his career, he turned his attention to quantum computing, and especially how to teach it in a way that was accessible to undergraduates.
Dr. Mintz was a legendary teacher, an extraordinary advisor, and an inspiration to his students, encouraging them to go well beyond their comfort level in their academic preparation and homework. He played an important part of CIS undergraduate education and advising for 36 years, winning numerous awards for these efforts, including the S. Reid Warren, Jr. Award, the University’s Lindback Award, and the Ford Motor Company Award for Distinguished Advising.
*Full details on the schedule and event may be found here.

BE Seminar: "Developments in Stem Cell-Derived Islets for Diabetes Cell Replacement Theory" (Jeffrey R. Millman, Washington University School of Medicine)
2022-11-17 15:30 - 16:30 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
This is a hybrid seminar which will be held in Glandt Forum (Singh Center) and via Zoom (link coming soon).
“Developments in Stem Cell-Derived Islets for Diabetes Cell Replacement Theory”
Cellular and tissue engineering promises new therapeutic options for people suffering from a wide range of diseases. Differentiation of stem cells is a powerful renewable source of these functional replacement cells and tissues that can be grown in the laboratory. Diabetes is cause by the death or dysfunction of insulin-secreting islets, which are a tissue type found in the pancreas that contain β cells and other endocrine cell types. We have recently developed approaches combining modulating the actin cytoskeleton and signal transduction pathways during differentiation to produce stem cell-derived islets (SC-islets) capable of undergoing glucose-stimulated insulin secretion, their primary function. We have further expanded this approach to make SC-islets from patients with diabetes and used CRISPR-Cas9 to correct their diabetes-causing mutations. Upon transplantation into mice with severe pre-existing diabetes, these SC-islets rapidly restore normoglycemia and can maintain this functional cure for a year. Our hope is that one day this technology can be used to replace unhealthy islets in patients for therapy and provide a better disease-in-a-dish model to discover new drugs to prevent, stop, or reverse diabetes progression.

ESE Ph.D. Thesis Defense - "Accelerating HLS Autotuning of Large, Highly-Parameterized Reconfigurable SoC Mappings"
2022-11-17 15:00 - 17:00 | Room 35, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
High-level synthesis has accelerated the adoption of autotuners to explore design spaces. Design-space size increases exponentially in the number of design parameters, and synthesizing a single configuration for a device-scale application easily consumes hours, so existing autotuners are frequently demonstrated with small kernels and few configurations to render the problem tractable. This dissertation shows that exploration of applications with more than 25 parameters mapped on reconfigurable SoCs exceeding 200K LUTs becomes feasible using the model-based approach we refine. We explore various techniques to reduce the tuning time. At the heart of our tuner are multi-fidelity models, which enable discontinuation of unpromising builds in multi-stage CAD flows. We rearranged the build resources in a pipeline to improve the tuning performance and increase the utilization of build resources. Build failures and underperforming configurations are avoided using latency, area, critical path delay, congestion, error probability, and build time prediction models. Next, we introduce a novel model that combines the preexisting hierarchical and multi-fidelity models. The hierarchical model enables the decomposition of design spaces into exponentially smaller subspaces that can be explored faster. Also novel is the use of out-of-context compilation to lower the build time further. A tree-based model alleviates the long training times of Gaussian process models. To validate our approach, we injected 29 – 44 parameters, varying from compiler pragmas to CAD tool parameters, in the Rosetta benchmarks. Compared to non-pipelined multi-fidelity Bayesian optimization, our tuner succeeds 20% more often at finding mappings on the ZCU102, and tuning runs are on average at least 2.2x shorter. Moreover, it locates 3.6x faster solutions within 24 hours.

ESE Fall Colloquium - "Micro- and Nanoscale Electro-fluidics: From Basic Research to Translational Medicine"
2022-11-17 12:30 - 13:30 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
In this talk, I will discuss my group’s work on fabricating micro- and nanosensing platforms for health monitoring. My group has developed novel electronic sensing modalities and has demonstrated their use for both in vitro with human clinical samples and in vivo in animals. In the first part of my talk, I will discuss sensor fabrication, characterization, and benchmarking against the gold standard, along with the role that these sensors can play in monitoring various chronic and acute inflammatory conditions. I will also discuss a new class of wirelessly powered nanobiosensors for immune monitoring. Finally, I will discuss challenges in translation of biosensing and microfluidic technologies beyond the lab, my personal experience, and potential solutions.

MSE Seminar: “Advanced Microscopy Techniques for Understanding Dislocation Interactions & Damage in Complex Microstructures”
2022-11-17 10:30 - 23:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Microstructurally and compositionally complex alloys (MCCA) such as Nickel-Aluminum-Bronze (NAB) are important to Navy and maritime applications due to their high strength, toughness, and fatigue resistance, as well as excellent corrosion resistance. NAB’s are widely used in many naval applications including ship propellers, underwater fasteners, pumps, and valves. Traditional sand cast NAB alloys tend to have a large amount of waste material, and reduced complexity in component geometry due to the limitations of the casting processing. As a result, NAB alloys are emerging as a viable alloy for additive manufacturing (AM) and therefore provides a new space to establish fundamental relationships between AM processing, structure and properties. Of the additive processes, wire arc additive manufacturing (WAAM) is an evolving technology for fabricating large-scale, near net shape NAB components. It is understood that the high cooling rates achieved in WAAM prevent the precipitation of coarse rosette-like kI phase which usually form during the latter stages of solidification during the casting process. In this work, the dislocation interactions with interfaces such as grain boundaries and precipitates will be used to understand fatigue life and crack initiation and growth behavior in these alloys during low-cycle and high-cycle fatigue.

ASSET Seminar: Building Safe Autonomous Systems, Rahul Mangharam (University of Pennsylvania)
2022-11-16 12:00 - 13:30 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
ABSTRACT:
Balancing performance and safety are crucial to deploying autonomous vehicles in multi-agent environments. In particular, autonomous racing is a domain that penalizes safe but conservative policies, highlighting the need for robust, adaptive strategies. Current approaches either make simplifying assumptions about other agents or lack robust mechanisms for online adaptation. In this talk we will explore research themes on perception, planning and control at the limits of performance. We explore (1) How to generate the most competitive agents who dynamically balance safety and assertiveness by using distributionally robust online adaptation; (2) How to build the most efficient autonomous racecar with Multi-domain optimization across vehicle design, planning and control; (3) How to combine previous system designs to auto-complete new designs with new requirements, and (4) Understand the value of Cooperation in Multi-Agent Games. We realize all our research in the https://f1tenth.org autonomous racecar platform that is 10th the size, but 10x the fun! The main takeaway from this talk is how you can get involved in very exciting research on safe autonomous systems. I will also present projects on AV Gokart that we are doing in the Autoware Center of Excellence for Autonomous Driving at Pennovation.

Fall 2022 GRASP SFI: Yasuo Kuniyoshi, University of Tokyo, "Behavior and Cognition Emerge and Develop From Embodiment - A Constructive Study of Human Fetus/Infant"
2022-11-16 10:00 - 11:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
This is a hybrid event with in-person attendance in Raisler Lounge (Towne 225) and virtual attendance via Zoom. This talk will NOT be recorded, please make sure to arrive on time.
ABSTRACT
In this talk, I will first show that physics of human-like body in action already provide certain information structure which can set the natural basis of categorization and meaning.
Then, I will show a principle of autonomous exploration that reveals the embodied information structure, aka. body affordances. Coordinated motor patterns consistent with the embodiment emerge from multiple chaotic elements coupled through body-environment physics.
In humans, the above principle may drive early motor development. And the resulting sensory-motor information can be captured by self-organizing neural circuits, forming the basis of cognitive structures.
In order to investigate this hypothetical scenario, we constructed a simulation model of a human fetus. It consists of a musculo-skeletal body, whole body cutaneous receptors (tactile), uterus and amniotic fluid, neuronal model of spine and medulla, and a whole neocortex model with self-organizing neural network.
With very little “innate” functional neural circuits, the model acquired various behavior patterns that comply with its embodiment, and the neural model self organizes to capture the embodied information structure. It exhibits spontaneous motor development and sensory-motor map organization comparable to human data. Also, by changing the model parameters, we can simulate “atypical” development.
Our series of experiments shows that sensory-motor experiences in the fetal period can be crucial to the formation of body representations and multi-modal sensory integration, which are significantly affected under “preterm birth” conditions, providing new insights about the developmental origins of social cognition and autism spectrum disorders.
Implications for the next generation AI/robotics will also be discussed if time allows.

CIS Seminar: "Generative multitask learning mitigates target-causing confounding"
2022-11-15 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
We propose a simple and scalable approach to causal representation learning for multitask learning. Our approach requires minimal modification to existing ML systems, and improves robustness to prior probability shift. The improvement comes from mitigating unobserved confounders that cause the targets, but not the input. We refer to them as target-causing confounders. These confounders induce spurious dependencies between the input and targets. This poses a problem for the conventional approach to multitask learning, due to its assumption that the targets are conditionally independent given the input. Our proposed approach takes into account the dependency between the targets in order to alleviate target-causing confounding. All that is required in addition to usual practice is to estimate the joint distribution of the targets to switch from discriminative to generative classification, and to predict all targets jointly. Our results on the Attributes of People and Taskonomy datasets reflect the conceptual improvement in robustness to prior probability shift.

ESE Ph.D. Thesis Defense - "Learning and Control of Network Phenomena"
2022-11-15 12:00 - 13:30 | Room 401B, 3401 Walnut, 3401 Walnut Street, Philadelphia, PA, 19104, United States
The intersection of dynamical systems and networks are used to model a huge variety of phenomena such as the spread of disease, multi-agent systems, opinions in social networks, and more. Many properties of these network phenomena can be understood by examining the eigenvalue spectrum of a matrix representation of the underlying graph. Using this intuition, this thesis explores the learning and control of network phenomena. First, I present techniques for matching individuals across correlated networks and learning the spectra of a graph matrix using only the sparse output measurements of a networked dynamical system with periodic inputs. Next, I present a data-driven framework for multi-task learning and non-linear control of epidemics. Finally, I propose a new architecture for signal processing on higher-order graphs, along with a new transferability bound on the performance of graph neural networks via spectral similarity. This transferability result is valid for arbitrary graphs regardless of their structure, resulting in the first bound on the transferability of a machine learning approach for higher-order graphs.

ESE Fall Colloquium - "Using Information Geometry to Find Simple Models of Complex Processes"
2022-11-15 10:30 - 11:30 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Effective theories play a fundamental role in how we reason about the world. Although real physical processes are very complicated, useful models abstract away the irrelevant degrees of freedom to give parsimonious representations. In contrast, overly complex models can be difficult to evaluate, suffer from numerical instabilities, and may overfit data. They also obscure useful insights into the relationship among different physical systems. I use information geometry to explore the role of simplicity in scientific explanation. I interpret a multi-parameter model as a manifold embedded in the space of all possible data, with a metric induced by statistical distance. These manifolds are often bounded and very thin, so they are well-approximated by a low-dimensional, simple model. For many types of models, there is a hierarchy of natural approximations that reside on the manifold’s boundary. These approximations are not black-boxes. They remain expressed in terms of the relevant combinations of mechanistic parameters and reflect the physical principles on which the complicated model was built. They can also be constructed systematically using computational differential geometry, as I illustrate with examples from physics and systems biology

MEAM Seminar: "Cell Packings and Tissue Flows in Developing Embryos"
2022-11-15 10:00 - 11:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
During embryonic development, groups of cells reorganize into functional tissues with complex form and structure. Tissue reorganization can be rapid and dramatic, often occurring through striking embryo-scale flows that are mediated by the coordinated actions of hundreds or thousands of cells. In Drosophila, cell rearrangements in the embryonic epithelium rapidly narrow and elongate the tissue, producing a tissue flow that doubles the length of the body axis in just 30 minutes. These types of tissue movements can be driven by internal forces generated by the cells themselves or by external forces. While much is known about the molecules involved in these cell and tissue movements, it is not yet clear how these molecules work together to coordinate cell behaviors, give rise to emergent tissue mechanics, and generate coherent flows at the embryo scale. To gain mechanistic insight into this problem, my lab combines genetic and biophysical approaches with emerging optogenetic technologies for manipulating molecular and mechanical activities inside cells with high precision. I will discuss some of our recent findings on how cellular properties and mechanical forces are regulated in the Drosophila embryo to allow (or prevent) rapid cell rearrangements and tissue flows during specific events in embryonic development.

MEAM Ph.D. Thesis Defense: "Enabling Ultra-Low Viscosity Lubricants Through Fundamental Understanding of ZDDPs Anti-Wear Additives and their Tribofilm Growth Mechanisms: An In-Situ Study"
2022-11-14 15:00 - 16:00 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Lubricants with low viscosity have the potential to improve fuel efficiency in engines due to friction reduction. However, a reduction in viscosity increases the likelihood of wear. Zinc dialkyldithiophosphate (ZDDP), the most widely used antiwear additive in engine oils, has been extensively studied over the last few decades. ZDDP forms surface-bound tribofilms at sliding contacts that prevent surface wear. Recent studies reveal that mechanochemical reactions drive tribofilm growth via stress and temperature. However, the individual effects of shear stress, compressive stress, and temperature on tribofilm growth are not yet fully understood.
In this study, we investigate the kinetic of ZDDP tribofilm formation on both microscale and nanoscale. In the microscale, we studied the driving factors separately by using different compositions of a high-viscosity, high-traction fluid mixed with a polyalphaolefin (PAO) basestock with a ball-on-disc tribometer in the elastohydrodynamic lubrication regime. In addition, we presented a new approach, the strip analysis method, to deconvolute the mechanisms under one single contact more effectively and efficiently. In the nanoscale, we used an atomic force microscope (AFM) to simulate a single asperity contact sliding on an iron oxide surface with droplets containing alkylated naphthalene (AN), PAO, and ZDDP. This enables us to investigate the mechanochemistry of ZDDP tribofilm formation in the boundary lubrication regime with a well-controlled contact.

ESE Ph.D. Thesis Defense: "Analysis and Control of Neural Network Dynamical Systems"
2022-11-14 10:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Integrating machine learning and control systems has achieved remarkable success in controlling complex dynamical systems such as autonomous vehicles. However, the resulting controlled system often has a neural network (NN) in the loop which represents the system dynamics, control policy, or perception. The nonlinearity and large scale of NNs make it challenging to provide formal safety or stability guarantees for such learning-enabled systems. This thesis focuses on developing specialized numerical tools for efficiently analyzing NN dynamical systems and a novel robust model predictive control (MPC) framework that is promising for controlling NN dynamical systems with safety guarantees. In the first part of the thesis, I demonstrate how to build a hierarchy of verification methods for isolated output range analysis of NNs, closed-loop reachability analysis, and closed-loop stability analysis of NN dynamical systems. In the second part, I present a novel robust MPC method for uncertain linear dynamical systems with significantly reduced conservatism compared with existing baselines and discuss the possibility of combining NN verification tools and robust MPC for safe control of complex dynamical systems.

BE Doctoral Dissertation Defense: "Multibreath Hyperpolarized Gas Imaging of Lung Ventilation and Gas Exchange in Humans for Diagnosis and Treatment Response Monitoring" (Hooman Hamedani)
2022-11-11 13:00 - 15:00 | Donner-Grice Auditorium
The Department of Bioengineering at the University of Pennsylvania and Dr. Rahim Rizi are pleased to announce the Doctoral Dissertation Defense of Hooman Hamedani.

Title: Multibreath Hyperpolarized Gas Imaging of Lung Ventilation and Gas Exchange in Humans for Diagnosis and Treatment Response Monitoring

Date: November 11th, 2022
Time: 1:00 PM-3:00 PM

Location: Donner – Grice Auditorium, 2 Dulles

The public is welcome to attend.

Fall 2022 GRASP on Robotics: Radhika Nagpal, Princeton University, "Towards Collective Artificial Intelligence"
2022-11-11 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.
ABSTRACT
In nature, groups of thousands of individuals cooperate to create complex structure purely through local interactions — from cells that form complex organisms, to social insects like termites and ants that build nests and self-assemble bridges, to the complex and mesmerizing motion of fish schools and bird flocks. What makes these systems so fascinating to scientists and engineers alike, is that even though each individual has limited ability, as a collective they achieve tremendous complexity. What would it take to create our own artificial collectives of the scale and complexity that nature achieves? In this talk I will discuss several ongoing projects that use inspiration from biological self-assembly to create robotic systems: The Kilobot swarm inspired by cells, the Termes and EcitonR robots inspired by the 3D assembly of termites and army ants, and the BlueSwarm project inspired by fish schools. There are many challenges for both building and programming robot swarms, and we use these systems to explore decentralized algorithms, embodied intelligence, and methods for synthesizing complex global behavior. Our theme is the same: can we create simple robots that cooperate to achieve collective complexity?

ESE Ph.D. Thesis Defense: "Resilient, Information Theoretic, Active Exploration for Multi-Robot Teams"
2022-11-10 15:00 - 16:30 | Room 313, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Over the past decades we have seen robots move from constrained and heavily designed industrial environments out into the world. Along with this shift there is a need for smaller, safer, and less expensive robots which can complete tasks autonomously in teams, sometimes covering large areas. Multi-robot teams can expand the capabilities of a single robot, however working with teams presents its own set of challenges. Robots must be able to reach a shared understanding of their task as well as gracefully recover from single member failures, often while having no centralized coordination.
In many task assignment and coordination applications, it is assumed that the robots begin with a shared understanding of the environment, often in the form of a map. In small application spaces, such maps could be made by hand, but in the case of large or potentially hazardous environments, it will be necessary to have the robots themselves create the map. To this end, in this thesis we present two map representations designed specifically for autonomous mapping by robots with high-noise sensors. For each method, we develop an information theoretic value function which can be used to autonomously maximize the information gained about the map. This is a principled approach which accounts for both information gained by exploring new areas, as well as information gained by further inspection of the existing map to account for sensor uncertainty.
Additionally, in applications where inexpensive robots are operating autonomously in large and potentially hazardous areas, the likelihood increases that a robot on the team can become non-cooperative, either through unintentional damage or through tampering. To be able to autonomously cooperate as a team it is important that the system can recover gracefully from such failures. Consensus algorithms are ubiquitous in distributed systems as they provide a mechanism for reaching agreement within a team using only local communications. We extend a highly distributed approach to resilient consensus for static networks to applications with multi-robot teams. This approach has been largely limited to small static networks because verification that the network is sufficiently connected is formally hard. First we develop a method which can be used for teams with time-varying range-based communication which is suitable for tasks where robots are not required to spread out in the environment. We then present a method that is well suited to mapping and coverage applications which uses a well known communication structure to guarantee successful resilient consensus.
The methods presented in this thesis lay the groundwork for a class of resilient active information gathering algorithms which can be used for low cost teams. Such algorithms have wide ranging applicability, from persistent monitoring tasks such as those found in agriculture to time critical tasks such as search and rescue.

ESE Fall Colloquium - "Phase Transitions, Symmetry, and Reed-Muller Codes on BMS Channels"
2022-11-10 12:30 - 13:30 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
This talk will begin by discussing phase transitions in high-dimensional statistical inference problems. Some effort will be made to distinguish between problems with random structure (e.g., random codes and sparse PCA) and problems with deterministic structure (e.g., highly symmetric codes such as Reed-Muller codes). For problems with deterministic structure, we will observe that symmetry can sometimes play a key role in characterizing their phase transitions. In particular, I will describe my recent work with Galen Reeves that proves Reed-Muller (RM) codes achieve capacity on binary memoryless symmetric (BMS) channels with respect to bit-error rate. This result resolves a long-standing open problem that connects information theory and error-correcting codes. Our approach generalizes some elements of an earlier proof for the binary erasure channel but also derives new tools to avoid previous steps that do not generalize. The new idea is to combine a nesting property of RM codes with new information inequalities relating the derivative of the conditional entropy (as a function of the channel parameter) with minimum mean-squared error estimation.

MSE Seminar: “Understanding Degradation in Metal Halide Perovskite Solar Cells and Modules”
2022-11-10 10:30 - 11:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Halide perovskite solar cells (PSCs) have been the focus of much research in recent years due to their extremely high photoconversion efficiencies and their ability to be synthesized by solution processing. These materials crystallize in the perovskite, AMX3 crystal structure where A is a monovalent cation (e.g. methyl ammonium, MA, formamidinium, FA, and/or Cs), M is the metal cation and X is the halide anion. While great strides have been made to optimize the device performance of PSCs, there remain open questions as to the long-term field performance of these materials. While constant improvement in lab scale performance stability is being demonstrated a fundamental understanding of degradation mechanisms can still provide key insight into performance improvements. In addition, to fundamental lab scale studies field performance will be required to de-risk this technology for commercialization. In this talk, I will cover examples of both lab scale and field studies to improve our understanding of PSCs degradation mechanisms. First, I will discuss an application of X-ray scattering methods to probe the nanoscale heterogeneity of PSC absorber layers and couple these results to device level stability studies to understand the role heterogeneity plays in device performance. Finally, I will present a brief overview of an initial field demonstration of PSC modules as part of the Perovskite PV Accelerator for Commercializing Technologies, PACT, program. Together this work aims to improve our confidence in real world PSC module performance.

CBE Seminar Series: "Targeted Delivery of Macromolecular Therapeutics" (Christopher A. Alabi, Cornell University)
2022-11-09 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States

Fall 2022 GRASP SFI: Roberto Calandra, Meta AI, "Perceiving, Understanding, and Interacting through Touch"
2022-11-09 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.
ABSTRACT
Touch is a crucial sensor modality in both humans and robots. Recent advances in tactile sensing hardware have resulted — for the first time — in the availability of mass-produced, high-resolution, inexpensive, and reliable tactile sensors. In this talk, I will argue for the importance of creating a new computational field of “Touch processing” dedicated to the processing and understanding of touch, similarly to what computer vision is for vision. This new field will present significant challenges both in terms of research and engineering. To start addressing some of these challenges, I will introduce our open-source ecosystem dedicated to touch sensing research. Finally, I will present some applications of touch in robotics and discuss other future applications.

ASSET Seminar: Decision-Aware Learning for Global Health Supply Chains, Osbert Bastani (University of Pennsylvania)
2022-11-09 12:00 - 13:30 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
ABSTRACT:
Machine learning algorithms are increasingly used in conjunction with optimization to guide decision making. A key challenge is aligning the machine learning loss with the decision-making loss. Existing solutions have limited flexibility and/or scale poorly to large datasets. We propose a principled decision-aware learning algorithm that uses a Taylor expansion of the optimal decision loss to derive the machine learning loss. Importantly, our approach only requires a simple re-weighting of the training data, allowing it to easily and scalably be incorporated into complex modern data science pipelines while producing sizable efficiency gains. We apply our framework to optimize the distribution of essential medicines in Sierra Leone in collaboration with their National Medical Supplies Agency. Out-of-sample results demonstrate that our end-to-end approach significantly reduces unmet demand across 1000+ health facilities throughout Sierra Leone.

ESE Ph.D. Thesis Defense: "Design of Low-power CMOS Integrated Systems: from Biomedical Applications to Optical Links"
2022-11-09 10:30 - 12:30 | Levine 512
Electronic and photonic microsystems realized in the form of integrated circuits (IC) has been revolutionizing numerous fields that traditionally exploit bulky implementations. The advantages stemming from device miniaturization have opened up wide and growing opportunities to design for unprecedented functionality and enhanced performance. Leveraging novel CMOS and silicon photonic IC designs, this thesis presents four energy-efficient systems focusing on sensing and communication techniques: first, a somatosensory feedback system, composed of wireless body channel transceivers and implantable sensor nodes, is proposed to restore a sense of touch to a paralyzed hand; second, an analog SoC is implemented for neural signal recording and processing, achieving accurate and unsupervised classification of action potentials; third, a monolithically integrated, power and area efficient PAM4 optical receiver is developed for a wavelength-division multiplexing (WDM) system achieving 1 Tb/s aggregate data-rate; finally, an electronic-photonic lab-on-chip with optical power and data transmission is proposed to utilize the long-reach and low-cost fiber interconnects.
