Penn Engineering Events

SEAS Green Team Book Swap & E-Waste Collection
2022-04-19 10:00 - 14:00 (America/New_York) | Lobby and Mezzanine, Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States

Tedori-Callinan Distinguished Lecture: "Multiscale Hierarchical Modeling and Thermal Management of Electrification Technologies"
2022-04-19 10:00 - 11:30 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
Core electrification technologies require developments in battery packs, chargers, electric motors, and thermal management strategies to improve their performance, longevity, reliability and safety. The next battery technology leap relies on novel thermal management strategies and packaging architectures, realized as intelligent battery thermal management systems (BTMS), which can optimally control the thermo-electrochemical phenomena occurring inside the batteries to maximize performance, minimize degradation, enable fast-charging protocols, and accelerate a seamless transition of degraded electric vehicle (EV) batteries into less-demanding second-life stationary systems. This Lecture will present current engineering challenges and opportunities on EV thermal management, with a focus on our research on multiscale hierarchical design, modelling, and optimization approaches to overcome cooling and heating challenges across multiple physical domains and length scales, from battery electrodes, to battery cells, battery packs, to EV thermal management systems.

PSOC@Penn Seminar
2022-04-18 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

PICS Colloquium: "Transforming Healthcare from the Outside: the OBSERVER project"
2022-04-15 14:00 - 15:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
We live with a health care system that has evolved into an increasingly fragmented, primarily treatment-oriented, and now heavily burdened environment. More importantly, issues related to privacy, space, and disease transmissibility have limited access to non-medical personnel who might offer creative, evidence-based, radical change to the health care system, as proposed years ago by the National Research Council. One way to reimagine that access is through the use of multimodal digital data that summarize patient encounters and make them amenable to outside observers. This talk with summarize the rationale for this project and offer a framework for thinking through how the OBSERVER project could be realized.

BE Doctoral Dissertation Defense: "Leveraging Macrophage Immune Checkpoint Blockade to Link Innate and Adaptive Immunity Against Solid Tumors" (Jason Andrechak)
2022-04-15 11:00 - 13:00 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
The Department of Bioengineering at the University of Pennsylvania and Dr. Dennis Discher are pleased to announce the Doctoral Dissertation Defense of Jason Andrechak.

Title: Leveraging Macrophage Immune Checkpoint Blockade to Link Innate and Adaptive Immunity Against Solid Tumors

Date/time: Friday, 4/15 at 11am
Location: Glandt Forum
Singh Center for Nanotechnology
3205 Walnut St, Philadelphia, PA 19104

It will be hybrid:
Zoom link: https://upenn.zoom.us/j/96831196783?pwd=[REDACTED]

Password: [REDACTED]

The public is welcome to attend.

GRASP on Robotics: Vincent Sitzmann, Massachusetts Institute of Technology, “Self-supervised Scene Representation Learning for Robotics”
2022-04-15 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Given only a single picture, people are capable of inferring a mental representation that encodes rich information about the underlying 3D scene. We acquire this skill not through massive labeled datasets of 3D scenes, but through self-supervised observation and interaction. Building machines that can infer similarly rich neural scene representations is critical if they are to one day parallel people’s ability to understand, navigate, and interact with their surroundings. In this talk, I will demonstrate how we can equip neural networks with inductive biases that enable them to learn 3D geometry, appearance, and even semantic information, self-supervised only from posed images. I will show how this approach unlocks the learning of priors, enabling 3D reconstruction from only a single posed 2D image. I will then talk about a recent application of self-supervised scene representation learning in robotic manipulation, where it enables us to learn to manipulate classes of objects in unseen poses from only a handful of human demonstrations, as well as the application of neural rendering to learn latent spaces amenable to control. I will then discuss recent work on learning the neural rendering operator to make rendering and training fast, and how this speed-up enables us to learn object-centric neural scene representations, learning to decompose 3D scenes into objects, given only images. Finally, I will discuss how neural scene representations may offer a new angle to tackle challenges in robotics.

MEAM Seminar: "Exploiting Environmental Fields for Orienteering and Planning Problems"
2022-04-14 13:30 - 14:30 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
Robots are increasingly being used to complete tasks in complex environments. To complete these tasks, two of the most fundamental choices that the robot must make are (1) task planning: which tasks to complete and in what order, and (2) path planning: what path to take when navigating between those tasks. In this seminar, I will consider robots operating in environments where the vehicle dynamics are impacted by the dynamics of the environment (environments with flows) while they complete these tasks. I begin by exploring several limitations of existing strategies that tackle task and path planning in environments with flows as separate problems. First, I examine a problem that focuses on the high level problem of task planning, but abstracts away the environment and assumes that the low-level path planning work is solved independently. This helps simplify the problem, but neglects to include environmental information in the problem. In order to address this limitation, I consider a path planning algorithm in an environment where the flow field can be designed. In particular, I consider a scenario where a magnetic force field is used to manipulate the flow field. In this case, I show how a topological approach can be used to take advantage of the structures in the field. Finally, I examine a scenario where the flow field is determined by ocean currents and therefore cannot be controlled. I will present an integrated solution approach to task and path planning that unifies the two problems in the presence of ocean currents.

GRASP Special Seminar: Steven Ceron, Cornell University, “Swarms Across Length Scales with Local-to-Global Behaviors”
2022-04-14 10:30 - 11:30 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
*This will be a HYBRID Event with in-person attendance in Towne 337 and Virtual attendance via Zoom
Collectives in nature demonstrate behaviors that extend far beyond the capabilities of any single agent. Social slime mold, for example, has thousands of cells that aggregate and form mobile and immobile nutrient-searching structures as a function of chemical signals. This species embodies many features that swarm roboticists wish to incorporate in scalable, self-reconfigurable robot collectives: local-to-global behaviors, low-level communication, plasticity, and simple constituents. I argue that regardless of the length scale, we can implement some of the same principles and features to exploit robot morphology, physical interactions among agents, and low-level coordination mechanisms to enable diverse collective behaviors for useful functions in many fields. I present novel emergent collective behaviors at the macro-scale and micron scale and explain how each behavior arises as a function of agents interacting with other agents, agents reacting to their environment, and agents exploiting their environment to affect other agents. Specifically, I show cell-inspired, macro-scale soft robot collectives for distributed systems with coupled sensing and actuation, magnetic microrobot collectives with reconfigurable behaviors and functions for biomedical applications, and cross-scale coordination mechanisms through virtual swarming coupled oscillators for macro- and micro-scale collective control applications.

MSE Seminar: "Role of Solvation and Dynamics on Ion Transport in Polymer Electrolytes"
2022-04-14 10:30 - 11:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Polymer electrolytes are an important class of ion conducting materials critical to enabling various electrochemical systems. Fundamentally, ion-polymer coordination, inter-connectivity of solvation sites, and corresponding ion-solvating polymer dynamics are critical in understanding the limits of ionic conductivity. Here, the importance of these effects is highlighted in a series of combined experimental and computational studies on model lithium-ion conducting polymer electrolytes. First, we focus on graft polymer architectures of poly[(oligo ethylene oxide) methyl ether methacrylate] (POEM) varying oligo side-chain lengths. With addition of lithium bis(trifluoromethanesulfonyl)imide (LiTFSI), the observed differences in ionic conductivity between the POEM derivatives and linear poly(ethylene oxide) (PEO) cannot be adequately explained by differences in Tg. Instead, graft architecture of POEM leads to nonuniform, position-dependent relaxation and ion solvation behaviors along the side chain. Importantly, the limits of ionic conductivity are dictated by the segmental mobility of the ethylene oxide units that form effective solvation sites, rather than system-wide dynamics. Lastly, the talk will end with results from ongoing work focusing on solvation site formation and ion transport mechanisms in mixed polarity copolymers based on POEM and poly(glycerol carbonate methacrylate) (PGCMA).

CBE Seminar: John A. Quinn Distinguished Lecture - "Molecular and Colloidal Interactions in Water”
2022-04-13 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract
Life occurs in water, and water-mediated interactions are central to biomolecular assembly processes. Synthetic materials as common as soap and shampoo also rely on water-mediated interactions. This presentation will describe experiments that highlight our lack of understanding of interactions in water. The opportunity to formulate a refined set of design rules for self-assembly of functional materials in water will be discussed.

Spring 2022 GRASP SFI: Georgios Georgakis, University of Pennsylvania, “Cross-modal Map Learning for Vision and Language Navigation”
2022-04-13 15:00 - 16:00 | Levine 512
*This will be a HYBRID Event with in-person attendance in Levine 512 and Virtual attendance via Zoom
We consider the problem of Vision-and-Language Navigation (VLN) in previously unseen realistic indoor environments. Arguably, the biggest challenge in VLN is grounding the natural language to the visual input. The majority of current methods for VLN are trained end-to-end using either unstructured memory such as LSTM, or using cross-modal attention over the egocentric RGB-D observations of the agent. We are motivated by studies on navigation of biological systems that suggest humans build cognitive maps during such tasks. In contrast to other works, we argue that an egocentric map offers a more natural representation for this task. In this talk, we will explore a novel navigation system for the VLN task in continuous environments that learns a language-informed representation for both map and trajectory prediction. This approach semantically grounds the language through an egocentric map prediction task that learns to hallucinate information outside the field-of-view of the agent. This is followed by spatial grounding of the instruction by path prediction on the egocentric map. We experimentally test the basic hypothesis that language-driven navigation can be solved given a map, and then show competitive results on the full VLN-CE benchmark.

Cell Mechanics Discussion Group
2022-04-13 12:00 - 13:00 | https://upenn.zoom.us/j/96715197752

MEAM Ph.D. Thesis Defense: "Mechanical Properties of Fibrous Network Materials"
2022-04-12 13:00 - 14:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
We discuss mechanical behavior of specific fibrous network materials, including the evolution of tension in fibrin clots, compression of pulmonary emboli, and fracture of Whatman filter paper.
The first material, fibrin clots, consist of random networks of fibrin fibers. When clots form by polymerization they develop tensile pre-stresses. We construct a mathematical model for the evolution of tension in isotropic fibrin gels. As the fiber diameter grows over time, properties which depend on it, such as the stored energy per unit length of a single fiber, the force-stretch relation of a fiber, and therefore the tension in the network as a whole, also evolve over time.
The second fibrous network is pulmonary emboli, which consist of random networks of fibrin fibers with fluid-filled pores and red blood cells (RBCs). Stress-strain responses of human pulmonary emboli under cyclic compression were measured, revealing that emboli exhibit hysteretic stress-strain curves characteristic of foams. We describe the hysteretic response of emboli using a model of phase transitions, in which the compressed embolus is segregated into coexisting rarefied and densified phases whose fractions change as compression progresses. Our model takes into account RBC rupture in compressed emboli and stresses due to fluid flow through their small pores. The mechanical response of emboli is shown to vary depending on their RBC content.
The third fibrous network is Whatman filter paper. The effect of humidity on properties such as out-of-plane fracture toughness of Whatman filter paper is studied for a broad range of relative humidities. Crack growth is modeled using traction-separation laws, whose parameters are fitted to experiments. Additionally, a novel model is developed to capture the high peak and sudden drop in the experimental force measurement caused by the existence of an initiation region, an imperfect zone ahead of a nascent crack. The relative effect of each independent parameter is explored to better understand the humidity dependence of the traction-separation parameters.
The materials studied have biological, clinical, and industrial applications, and the methods described here are also applicable to other fibrous network materials.

MEAM Ph.D. Thesis Defense: "Metal and Air Generate Power for Robots"
2022-04-12 10:30 - 11:30 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
In many cases, the size and weight of energy storage technologies required to power robotic systems are too large or massive for a robot to carry, leading to limited operational times and long recharging times over which the robot remains unused. Computer-free autonomous decision making based on environmental cues provides exciting alternatives to classic control systems for robots and smart materials. Although this functionality has been studied in microswimmers and active colloids where energy in the surrounding liquid is prevalent, there are no devices that can provide sufficient power from environmental chemicals to move and steer larger scale robots and vehicles in dry environments.
In this talk, I will show a new approach for powering robots and electronics by electrochemically scavenging energy from metal surfaces. This approach overcomes energy storage scaling laws by allowing robots and electronics to extract energy from large volumes of energy dense material without having to carry the material on-board. Then, we characterized the evolution of the metal during discharging by using FIB and Micro-CT in our system. Next, we demonstrated an environmentally controlled voltage source that, when directly attached to electric motors on a vehicle, can increase the energy available to the vehicle and provide computer-free autonomous navigation toward chemical fuels in the environment and away from hazards. The voltage source uses electrochemistry to extract power from the chemical fuels, and the vehicle avoids hazards that reduce the output voltage of electrochemical kinetics.
These works present a novel technique to simultaneously steer and power vehicles and robots without computers by directly responding to a wide variety of chemical fields in their environment using electrochemistry.

MEAM Seminar: "The Statistical Mechanics of Granular Clogging"
2022-04-12 10:00 - 23:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The gravity-driven flow of grains from a hole in a hopper is an iconic granular phenomenon. It’s different from a fluid in that the rate is independent of fill height, and more spectacularly so in that it can suddenly and unexpectedly clog. How does the susceptibility to clogging decrease with increasing hole size, and is there a well-defined clogging transition above which the system never clogs? This problem is distinct from jamming due to the presence of boundaries and gradients. We show how the fraction F of flow configurations that cause a clog may be deduced from the average mass discharged between clogs. We construct a simple model to account for the observation that F decays exponentially in hole width to the power of dimensionality. Thus the clogging transition is not sharp but rather is defined by observation limits, similar to the glass transition. When the system is immersed in water, F barely changes. Therefore, grain momenta play only a secondary role in destabilizing weak incipient arches and the crucial microscopic variables are likely the grain positions. Work is now in progress to distinguish free-flowing versus clog-causing position microstates using machine learning. Altogether, this gives an entirely new way to think about clogging, beyond the usual approach of investigating the zoology of possible arches and their mechanical stability.

CBE PhD Dissertation Defense | “The role of clot structure and mechanics on hemodynamics and pharmacology”
2022-04-12 10:00 - 12:00 | Greenberg Lounge (Room 114), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
Abstract:
“Hemostatic clots have a core/shell hierarchy comprised of a P-selectin positive core surrounded by a P-selectin negative shell. A new human blood microfluidic assay utilizing a 2-stage assay was developed to interrogate core/shell mechanics. By first perfusing whole blood over a collagen/TF surface and then swapping out the blood for buffer, we were able to pause thrombus growth at a specific point in time. The shear rate was then drastically increased to interrogate platelet-platelet interactions. We found that thrombin inhibition, but not fibrin, led to greater erosion of the thrombus, and that the core region of the thrombus was stable with or without the presence of fibrin. Next, we tested the effects of ADP or TxA2 inhibitors on thrombus growth and stability. We found that inhibiting TxA2 production did not affect platelet deposition, but the platelets that did attach to the clot were more likely to shear off after being subjected to shear. Both P2Y1 and P2Y12 antagonism led to decreased initial platelet deposition and more platelet erosion. P2Y12 signaling had the most significant effect on deposition and stability suggesting that it might have an amplification effect on other signaling pathways. We demonstrated a new method to examine thrombus strength and stability and demonstrated the effect of thrombin on core size and the importance of secondary agonists on stability in the shell region.
We used an 8-channel microfluidic device to examine the efficacy of ex vivo apixaban on platelet and fibrin deposition in neonates with CHD. This patient population was shown to have significantly more activity blood after surgery We found that apixaban drastically lowers coagulation activity in neonatal blood before and after surgery and significantly prolongs occlusion time to a greater extent than adults. We also demonstrated that andexanet alfa reverses the inhibitory effect of apixaban in both neonates and adults. These results demonstrated that microfluidics are a useful way to examine pharmacological effects, especially in a population where only small quantities of blood can be drawn.“

CBE PhD Dissertation Defense | "Understanding the relationship between clot contraction and platelet biology under hemodynamic conditions"
2022-04-11 14:00 - 15:30 | Greenberg Lounge (Room 114), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
Abstract:
“As thrombosis proceeds, platelets in a clot can expose phosphatidylserine (PS), providing a negatively charged surface for thrombin generation. These PS+ platelets have been shown to sort to the perimeter of platelet masses via platelet contraction. However, it remains unclear how thrombin and fibrin affect PS+ platelet sorting within a clot. We used an 8-channel microfluidic device to perfuse blood over collagen/TF to evaluate temporal and spatial PS+ platelet sorting. We found that thrombin inhibition, fibrin polymerization inhibition, or fibrinolysis each increased clot contraction and PS sorting. Fibrin attenuated clot contraction and PS sorting. Clots without fibrin had a 3.6-times greater contraction than clots with fibrin. Based on these results, we wanted to study contraction further. We tested the effect of inhibitors of ADP and/or thromboxane A2 (TXA2) signaling on clot contraction. We developed two automated imaging methods to score fluorescent platelet percent contraction: (1) “global” measurement of clot length, and (2) “local” changes in surface area coverage of platelet aggregates within the clot. Total platelet FI and global aggregate contraction were highly correlated (R2 =0.87). Local aggregate contraction was more pronounced than global aggregate contraction across all inhibition conditions. Conditions with TXA2 inhibition were shown to significantly reduce local aggregate contraction relative to conditions without TXA2, unlike conditions with ADP inhibition. Lastly, we recently obtained a glycoprotein VI (GPVI) inhibitor. GPVI is a collagen receptor on platelets that drives platelet activation; however, its role at later stages in clotting remains unclear. We tested the effect of anti-GPVI Fab on PS exposure, which occurs at later stages of platelet activation. On collagen/TF, Fab present at t=0s reduced PS exposure, but had no effect when added 30 or 90 seconds later. Thrombin generated via PS exposure had an important role in driving platelet deposition when Fab was present, since inhibition of PS via annexin V binding in the presence of Fab significantly inhibited platelet deposition. Our results from these studies help elucidate the relationship between platelet activation, PS exposure, fibrin, and clot contraction.“

PSOC@Penn Seminar: Morgan Huse, PhD
2022-04-11 12:00 - 13:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States

MEAM Ph.D. Thesis Defense: "Relationships Between Structure, Dynamics, and Flow in Sheared Amorphous Materials"
2022-04-11 09:30 - 10:30 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
Amorphous solids, those composed of haphazardly arranged constituents, are found everywhere from our windows as silicate glass, in the ground and foundations as mud and concrete, and our grocery stores as granular piles of oranges. Even though they can be found over a huge range of length scales, it remains a challenge to systematically design their mechanical properties using knowledge of their microstructure. In this thesis, I investigate the link between the microstructure and the mechanical properties of a-thermal solids.
First, I probe the particle trajectories for chaotic signatures that relate to bulk rheology. Particles are confirmed to exhibit chaotic, Brownian like motion during cyclic shear, even though the particles are large enough that thermal motion is negligible. I also find that, the average area traced by returning particles is proportional to the amplitude of strain, which could be useful for emph{in situ} measurements in industrial, granular, mixing applications.
Next, I examine the interconnection between particle dynamics and the arrangements of the constituents. I calculate the characteristic time for particles to shift past each other, called relaxation time, and the configurational entropy of the system in excess of a reference ideal gas. I show that the relaxation time at any given instant is related to the excess entropy a quarter shear cycle later, which implies that the dynamics of particles shape the eventual structure. This means it is possible to take a snapshot of particle positions and infer its mechanical past.
Finally, I focus on the interplay between particle positions and bulk yield by using concepts from kinetics, thermodynamics, statistical mechanics, and shear transformation zone theory. I establish a relationship between excess entropy and energy dissipation and uncover a novel definition for the yield transition based on memory signatures within the microstructure. Using these observations, I derive a phenomenological model that links the microstructure to bulk rheology that is physically informed and whose parameters are all quantitatively measurable. This dissertation elucidates how the statistics of particle configurations and dynamics give rise to the macroscopic transition from elasticity to plasticity during yield of amorphous, a-thermal solids.

PICS Colloquium "The Dynamics of Gas-Particle Partitioning: Insights from Laboratory, Field, and Modeling studies"
2022-04-08 14:00 - 15:00 | Zoom – email kathom@seas.upenn.edu
Abstract: Ultrafine aerosols can significantly influence Earth’s climate if they are able to grow to sizes large enough to interact with the incoming solar radiation and nucleate cloud droplets. In clear air, aerosol growth occurs via gas-to-particle conversion of condensable trace gases, including sulfuric acid, nitric acid, hydrochloric acid, ammonia, and myriad oxidation products of many different volatile organic compounds. Nearly all aerosol models developed to date to simulate the aerosol growth assume instantaneous equilibrium between semivolatile gases and submicron-sized particles. This assumption effectively favors the growth of the largest pre-existing particles in accordance with Raoult’s law. In this talk, I will use a combination of laboratory, field, and modeling studies to show that the equilibrium assumption may not always hold, especially when aerosol growth occurs from condensation of supersaturated vapors and/or when the pre-existing aerosols are in a solid or semisolid phase state. In such cases, gas-particle partitioning must be treated as a dynamic process and size-resolved bulk diffusion limitation must be considered inside semisolid particles. I will outline a computationally efficient dynamic gas-particle partitioning treatment in the MOSAIC aerosol model to address this issue and discuss implications on the growth of ultrafine aerosols from semivolatile vapors.

ESE Spring Seminar - "End-to-end Learning for Robust Decision Making"
2022-04-08 11:00 - 12:00 | Heilmeier Hall (Room 100), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Because the physical world is complex, ambiguous, and unpredictable, autonomous agents must be engineered to exhibit a human-level degree of flexibility and generality — far beyond what we are capable of explicitly programming. Achieving such rich and intricate decision making requires rethinking the foundations of intelligence across all stages of the autonomous learning lifecycle.
In this talk, I will share new learning-based approaches towards dynamic, resilient, and robust decision making of autonomous systems. Such solutions are capable of not only reliably solving a particular problem, but also anticipating what could go wrong in order to strategize, adapt, and continuously learn. We advance robust decision making by (1) computationally designing rich synthetic environments of hard to collect, out-of-distribution edge-cases; (2) creating efficient, expressive, and interpretable learning models; and (3) developing adaptive, robust, and grounded learning algorithms, and exploiting their interdependence to realize generalizable decision making.

GRASP on Robotics: Jing Xiao, Worcester Polytechnic Institute, “Perception-Action Synergy in Uncertain Environments”
2022-04-08 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Many robotic applications require a robot to operate in an environment with unknowns or uncertainty, at least initially, before it gathers enough information about the environment. In such a case, a robot must rely on sensing and perception to feel its way around. Moreover, it has to couple sensing/perception and motion synergistically in real time, such that perception guides motion, while motion enables better perception. In this talk, I will introduce our research in combining perception and motion of a robot to achieve autonomous contact-rich assembly, object recognition, object modeling, and constrained manipulation in uncertain or unknown environments, under force/torque, RGBD, or touch sensing. I will also introduce our recent work on integrated semantic SLAM and accurate loop closure detection, SmSLAM+LCD.

MEAM Ph.D. Thesis Defense: "User-friendly, Low-cost, Microfluidic Devices with Capillary Circuits for Multiplexed, Isothermal, Point-of-care Nucleic Acid Amplification Tests"
2022-04-07 15:30 - 16:30 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
Rapid, sensitive, and specific detection of causative pathogens is key to personalized medicine and the prompt implementation of appropriate mitigation measures to reduce disease transmission, mortality, morbidity, and cost. Conventional molecular detection methods require trained personnel, sophisticated equipment, and specialized laboratories, which limits their use to centralized laboratories. To enable molecular diagnostics at the point of need and in resource-poor settings, inexpensive, simple devices that combine multiple unit operations and are capable of co-detecting endemic pathogens are needed.
In this dissertation, I have developed microfluidic devices with capillary circuits to automate liquid distribution, eliminating the need for expensive equipment, sophisticated laboratory facilities, and skilled personnel to enable molecular diagnostics at the point of need. Capillary valves with different sizes were developed and implemented to aliquot samples and reagents to multiple reaction chambers and to enable draining liquids from supply lines without affecting liquids in the various reaction chambers, enabling bubble-free operation. The sealing of my microfluidic devices to prevent evaporation during incubation is facilitated with phase-change materials and capillary-induced motion. When my microfluidic chip is heated to its incubation temperature, the phase change material melts and flows to seal ports of entry and air vent. Numerical simulations were carried out to assess the viability of on-chip, in-house developed, two-stage isothermal nucleic acid amplification in the presence of diffusion and advection. An Android-based smartphone application was developed to automate real-time signal monitoring, time series image analysis, and diagnostic result interpretation. Three different 3D-printed, portable, microfluidic devices with capillary circuits were designed, fabricated, and tested for single-stage and two-stage, isothermal nucleic acid amplification with either liquid reagents or ii pre-stored dry reagents that do not require a cold chain. All devices have proved successful for rapid, sensitive, and specific multiplexed detections of human and animal pathogens.

BE Seminar: "Analysis of High-content Genomic Screening Data with Large-scale Optical Pooled Screens" (Paul Blainey, MIT)
2022-04-07 15:30 - 16:30 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
This seminar will be held in person in Towne 337 and on zoom (check email for link or contact ksas@seas.upenn.edu).
Genetic screens are critical for the systematic identification of genes underlying cellular phenotypes. While pooling gene perturbations greatly increases screening throughput, this approach was not yet compatible with the high-content imaging of complex and dynamic cellular phenotypes. Our group recently developed optical pooled screening using in situ sequencing-by-synthesis to link pooled perturbations with their associated visual phenotypic outcomes at the single-cell level in mammalian cells. We have since established this approach for very large-scale screens where hundreds of phenotypic parameters are recorded for each cell. I will provide an overview of the applicability of this kind of approach and discuss in more detail two large-scale, high-content collaborative screening projects, one related to intracellular responses to viral infection, and another focused on human cell division phenotypes.

ESE Spring Seminar - "Minimally Invasive and Chronically Stable Brain-Machine Interface"
2022-04-07 11:00 - 12:00 | Zoom – Meeting ID 992 3585 3697
Stable chronic mapping of brain activities at the action potential level with high temporal resolution is essential for both fundamental neuroscience research and biomedical applications, including cognitive studies, memory encoding and retrieval, and neural prostheses. Conventional neural probes can provide high spatiotemporal-resolution brain signal recordings independent of probing depth, although they generally trigger foreign body response and tissue damage in the brain. As a result, they are usually unable to stably interface with the brain in a chronic manner, which substantially hinders their applications in brain-machine interface and neuroscience researches. In this seminar, I will present a new paradigm, mesh-like electronics, for minimally invasive and chronically stable brain-machine interface. The mesh-like electronics can seamlessly interface with mammal brains with significantly reduced foreign body response and can stably record brain signals with high spatiotemporal resolution for more than 8 months. I will then present the application of mesh-like electronics for chronic recording and modulations of spinal cord sensory and motor neurons in awake mice. In the end, I will present an alternative approach to designing minimally invasive neural electronics with hydrogel-based materials and the rapid fabrication of designed neural electronics with additive manufacturing. Both the mesh-like electronics and hydrogel electronics opened up new windows to stably communicating with the nervous system with minimum perturbation and foreign body responses.

MSE Seminar: "Hierarchically Ordered Block Copolymer Materials via Nonequilibrium Processing"
2022-04-07 10:30 - 11:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The diversity and vastness in the types of properties of living systems, including enhanced mechanical properties of skin and bone, or responsive optical properties derived from structural coloration, are a result of the multiscale, hierarchical structure of the materials. The field of materials chemistry has leveraged equilibrium concepts to create complex materials seen in nature, yet achieving the remarkable properties present in living systems requires moving beyond this formalism by utilizing nonequilibrium processes to create new and exciting materials. Here, the presentation will describe a new method to create hierarchically ordered, physically crosslinked hydrogels, and recent developments in further processing the hydrogel materials to create linear and rotary actuators. Specifically, we have explored a modified nonsolvent-induced phase separation method termed rapid injection processing to produce hierarchically ordered hydrogels with structures and mechanical properties resembling those of living biomaterials. The hydrogel fabrication process entails injecting a triblock copolymer, such as poly(styrene)-poly(ethylene oxide)-poly(styrene) (SOS), solution into a coagulating liquid (i.e., water), driving the hydrophobic polymer domains to organize at the nano and microscale and forming bulk hydrogels. We have established a universal and quantitative method for fabricating and controlling physically crosslinked hydrogels exhibiting hierarchical ordering by controlling the initial pre-injection triblock copolymer solution concentration and water-miscible organic solvent. Additionally, water-swollen hydrogel materials are easily processed to create high-performance linear and rotary actuators via strain-programmed hydrogel crystallization. The crystallized fibers display enhanced mechanical properties due to the aligned alternating amorphous and crystalline domains, and actuation is triggered using either water or heat. The work presented here highlights that by harnessing nonequilibrium methods, it is possible to create materials with tunable physical properties via controlling the structure from the nanometer to the micrometer.

CBE Seminar: "Processive-Cleavage and Functionalization-Cleavage for Deconstruction of Polyolefins”
2022-04-06 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract
The massive quantities of single-use plastics discarded each year requires new sustainable end-of-life solutions. Current technologies, such as melt-processing for recycling or incineration for partial energy recovery, are insufficient to deal with the crisis in its entirety. New methods involving chemical upcycling, by catalytic conversion of the used materials into higher value products, could provide molecules that make use of components of the existing catenated carbon starting materials. In addition, few existing catalytic methods can break carbon-carbon bonds in aliphatic hydrocarbons lacking directing groups and introduce new heteroatom functionality. In collaborative work, we have constructed and studied the first example of a hydrogenolysis catalyst that makes use of its 3D architecture to transform polyolefins into narrow distributions of shorter linear hydrocarbon chains. A second approach considers approaches to break carbon-carbon bonds in polyolefins and concurrently introduce reactive sites that could enable the use of products as chemical synthons. Early transition metal complexes are capable of breaking carbon-carbon bonds via β-alkyl elimination to form shorter carbon chains in oligomerylmetal species. Transmetalation to a main group element could afford reactive species to access fatty alcohols and fatty acids by oxidation or carboxylation, for example. We have developed examples of such transformations, based on organozirconium single-site catalysts and hydride-generating organoaluminum reagents.

Spring 2022 GRASP SFI: Jason Ma, University of Pennsylvania, “Beyond Expected Reward in Offline Reinforcement Learning”
2022-04-06 15:00 - 16:00 | Levine 512
*This will be a HYBRID Event with in-person attendance in Levine 512 and Virtual attendance via Zoom
Offline reinforcement learning (RL), which uses pre-collected, reusable offline data without further environment interactions, permits sample-efficient, scalable and practical decision-making; however, most of the existing literature (1) focuses on improving algorithms for maximizing the expected cumulative reward, and (2) assumes the reward function to be given. This limits the applicability of offline RL in many realistic settings — for instance, there are often safety or risk constraints that need to be satisfied, and the reward function is often difficult to specify. In this talk, we will explore how we can (1) train a broad class of risk-sensitive agents using purely risk-neutral offline data and provably prevent out-of-distribution extrapolations, and (2) bootstrap offline RL through flexible forms of expert demonstrations, significantly expanding the scope of valid supervision for offline policy learning. With these advances, we aim to bring offline RL closer to real-world applications.

MEAM Ph.D. Thesis Defense: "Deep Learning and Uncertainty Quantification: Methodologies and Applications"
2022-04-06 13:00 - 14:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
Uncertainty quantification is a recent emerging interdisciplinary area that leverages the power of statistical methods, machine learning models, numerical methods and data-driven approach to provide reliable inference for quantities of interest in natural science and engineering problems. In practice, the sources of uncertainty come from different aspects such as: aleatoric uncertainty where the uncertainty comes from the observations or is due to the stochastic nature of the problem; epistemic uncertainty where the uncertainty comes from inaccurate mathematical models, computational methods or model parametrization. Cope with the above different types of uncertainty, a successful and scalable model for uncertainty quantification requires prior knowledge in the problem, careful design of mathematical models, cautious selection of computational tools, etc. The fast growth in deep learning, probabilistic methods and the large volume of data available across different research areas enable researchers to take advantage of these recent advances to propose novel methodologies to solve scientific problems where uncertainty quantification plays important roles. The objective of this dissertation is to address the existing gaps and propose new methodologies for uncertainty quantification with deep learning methods and demonstrate their power in engineering applications.
On the methodology side, we first present a generative adversarial framework to model aleatoric uncertainty in stochastic systems. Secondly, we leverage the proposed generative model with recent advances in physics-informed deep learning to learn the uncertainty propagation in solutions of partial differential equations. Thirdly, we introduce a simple and effective approach for posterior uncertainty quantification for learning nonlinear operators. Fourthly, we consider inverse problems of physical systems on identifying unknown forms and parameters in dynamical systems via observed noisy data.
On the application side, we first propose an importance sampling approach for sequential decision making. Second, we propose a physics-informed neural network method to quantify the epistemic uncertainty in cardiac activation mapping modeling and conduct active learning. Third, we present an anto-encoder based framework for data augmentation and generation for data that is expensive to obtain such as single-cell RNA sequencing.

ESE Spring Seminar - "Emergent Active Photonic Platforms for Next-Generation Mid-Infrared and Ultrafast Photonics"
2022-04-05 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
As two basic properties of light, wavelength and timescale are central to numerous photonic applications. Compared to visible and near-infrared, the longer wavelength mid-infrared spectral regime contains unique thermal visual information and chemical fingerprints of the environment. On a different front, femtosecond light sources and systems can enable ultrafast information processing, sensing, and computing. Yet, current chip-scale photonic devices and systems are facing tremendous challenges in detecting, generating, and processing light of long wavelength and ultrashort timescale. Overcoming these challenges requires new materials and clever device architectures, and these technologies stand poised to revolutionize fields such as biomedical sensing, free-space communication, and photonic computing in both classical and quantum domains.
In this talk, I will show that by engineering the carrier and nonlinear dynamics in emergent active photonic materials, we can detect photons beyond the regimes accessible to conventional laser sources and detectors, and process information in an ultrafast manner. In the first half of my talk, I will first briefly introduce the discovery of black phosphorus (BP) mid-infrared photonics, highlighting the world’s first BP mid-infrared detectors with high internal gain, as well as BP’s electrically tunable spectral response due to its unique bandgap tunability. Then, I will discuss a new strategy for detecting longer wavelength mid-infrared radiations at 12 µm. This is achieved by harnessing the intrinsic mid-infrared plasmons in large-scale graphene.
The second half of my talk will cover my recent work on integrated lithium niobate (LN) ultrafast photonics in both classical and quantum domains. I will discuss the realization of ultra-strong nonlinear optical interactions and dynamics in dispersion-engineered and quasi-phase-matched integrated LN devices, which have enabled 100 dB/cm optical parametric amplification, ultra-wide bandwidth quantum squeezing, as well as femtosecond and femtojoule all-optical switching. Finally, I will outline promising pathways toward realizing chip-scale ultrafast light sources and microsystems for on-chip spectroscopic sensing, mid-infrared free-space communication, coherent all-optical computing, and next-generation thermal vision technologies.
