GRASP/MEAM Seminar: “Towards Safe and Efficient Learning and Control for Physical Human Robot Interaction”
2021-03-26 11:00 - 12:00 (America/New_York) | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
From factories to households, we envision a future where robots can work safely and efficiently alongside humans. For robots to truly be adopted in such dynamic environments, we must i) minimize human effort while communicating and transferring tasks to robots; ii) endow robots with the capabilities of adapting to changes in the environment, in the task objectives and human intentions; and iii) ensure safety for both the human and the robot. However, combining these objectives is challenging as providing a single optimal solution can be intractable and even infeasible due to problem complexity and contradicting goals. In my research, I seek to unify robot learning and control strategies to provide safe and fluid physical human-robot-interaction (pHRI) while theoretically guaranteeing task success and stability. To achieve this, I devise techniques that step over traditional disciplinary boundaries, seamlessly blending concepts from control theory, robotics, and machine learning. In this talk, I will present contributions that leverage Bayesian non-parametrics with dynamical system (DS) theory, solving challenging open problems in the Learning from Demonstration (LfD) and pHRI domains. By formulating and learning motion policies as DS with convergence guarantees, a single motion policy (or sequence of) can be used to solve a myriad of robotics problems. I will present novel DS formulations and efficient learning schemes that are capable of executing i) continuous complex motions, such as pick-and-place and trajectory following tasks; ii) sequential household manipulation tasks, such as rolling dough or peeling vegetables; iii) and more dynamic scenarios, such as object hand-overs from humans and catching objects in flight. Finally, I will show how these techniques scale to more complex scenarios and domains such as navigation and co-manipulation with humanoid robots.

PICS Colloquium: "Workflows, Datasets and Models for Active Discovery in Catalysis"
2021-03-26 14:00 - 15:00 | PA
Abstract: Machine learning accelerated catalyst discovery efforts has seen much progress in the last few years. Datasets of computational calculations have improved, models to connect surface structure with electronic structure or adsorption energies have gotten more sophisticated, and active learning exploration strategies are becoming routine in discovery efforts. However, there are several large challenges that remain: to date, models have had trouble generalizing to new materials or reaction intermediates and applying these methods requires significant training. I will review and discuss methods in my lab for high-throughput catalyst screening and on-line discovery of interesting materials, resulting in an optimized Cu-Al catalyst for CO2-to-ethylene conversion. I will then introduce the Open Catalyst Project and the Open Catalyst 2020 dataset, a collaborative project to span surface composition, structure, and chemistry and enable a new generation of deep machine learning models for catalysis, with initial results for state-of-the-art deep graph convolutional models. Finally, I will discuss on-going work to develop small ML models to accelerate routine calculations without requiring expert intervention.

ODEI Spotlight: Queer in Grad School - A Roundtable Discussion
2021-03-26 14:00 - 15:00 | PA
Join Panelists from Penn Law, Wharton MBA, Perelman SOM, & Annenberg for the Queer in Grad School: Roundtable discussion.

Penn Law Lambda, Wharton out for Business, Penn Med Pride, and the Annenberg School of Communication will be speaking about being queer in grad school.

The event will take place on Friday March 26th from 2:00 – 3:00 pm:
https://vpul-upenn.zoom.us/meeting/register/tJwsfuCrrD0tHdZmCFxSPGxiwRO8Puh-XlvQ

CBE PhD Dissertation Defense | Establishing the Transient Mass Balance of Thrombosis Under Venous Flow: From A Microfluidic Approach To A Reduced Model
2021-03-26 15:00 - 17:00 | Zoom – Email CBE for link
Abstract:
Coagulation kinetics are well established in well plates assays in which human plasma clots isotropically. However, less is known about thrombin kinetics and transport within clots formed under hemodynamic flow. Using microfluidic perfusion of Factor XIIa-inhibited human whole blood over a 250-micron long patch of collagen/tissue factor and immunoassays of the effluent for fragment 1.2, thrombin-antithrombin, and D-dimer (post-endpoint plasmin digest), we sought to establish the transient mass balance for clotting under venous flow. Based upon these measurements under flow conditions, we have developed a highly reduced extrinsic pathway coagulation model (7 ODEs) under flow considering a thin 15-micron platelet layer where transport limitations were largely negligible (except for fibrinogen) and where cofactors (FVIIa, FV, FVIII) were not rate-limiting. By including thrombin feedback activation of FXI and the antithrombin-I activities of fibrin, the model accurately simulated measured fibrin formation and thrombin fluxes. The model required free thrombin in the clot (~100 nM) to have an elution half-life of ~2 sec, consistent with measured albumin elution, with most thrombin being fibrin-bound. Thrombin-feedback activation of FXIa became prominent and reached 5 pM at >500 sec in the simulation, consistent with anti-FXIa experiments. Further, we did a sensitivity analysis by conducting 10,000 Monte Carlo simulations for ±50% variation of 5 plasma zymogens and 2 fibrin binding sites for thrombin. A sensitivity analysis of zymogen concentrations indicated that FIX activity most influenced thrombin generation, a result expected from hemophilia A and B. Averaging all MC simulations confirmed both the mean and standard deviation of measured fibrin generation on 1 tissue factor molecule per µm2. Across all simulations, free thrombin in the layer ranged from 20 to 300 nM with a mean 50 nM. The model also suggested the antithrombotic potency of FXIa inhibitors may vary depending on normal ranges of zymogen concentrations. To sum up, our reduced model, which supported by experimental data, predicts thrombin and fibrin co-regulation during thrombosis under flow, gives insights into the dynamics of the species involved, and may be useful for multiscale simulation

Doctoral Dissertation: "Leveraging Neuroimmune Responses to Treat Pain Using Micellar Formulations Informed by Secretory Phospholipase A2 Responses in Neuropathy" (Sonia Kartha)
2021-03-26 15:30 - 17:30 | https://upenn.zoom.us/j/96715197752
The Department of Bioengineering at the University of Pennsylvania and Dr. Beth Winkelstein are pleased to announce the Doctoral Dissertation Defense of Sonia Kartha.
Title: Leveraging Neuroimmune Responses to Treat Pain Using Micellar Formulations Informed by Secretory Phospholipase A2 Responses in Neuropathy
Date: Friday March 26th at 3:30PM (EST)
You are invited to attend the virtual defense via the zoom link below:
https://upenn.zoom.us/j/94184088781
The public is welcome to attend.

Google Explore CS Research Event
2021-03-26 16:00 - 17:00 | https://upenn.zoom.us/j/96715197752
Are you interested in undergraduate research? Attend the Google Explore CS Research Event on Friday, March 26th at 4 to 5pm. Faculty will present their research problems and let you know how you can get involved. This event is exclusively for women and underrepresented students.
Participating faculty: Sebastian Angel, Chris Callison-Burch, Pratik Chaudhari, Anindya De, Benjamin Pierce, Dan Roth, Cynthia Sung
Questions? Email Rita Powell, rpowell@cis.upenn.edu
View a recording of the Google Explore event here:
https://upenn.zoom.us/rec/share/PPwhTqj4e_KG9qlQuMC_hIzrQE5rc677x1YUQ0Lpxu_6KT5apCBLpdosy9x0YDp1.WE72PeJ_p1EYjV1A
Passcode: [REDACTED]

ODEI Spotlight: Natives at Penn Virtual Powwow Experience
2021-03-27 13:00 - 15:00 | PA
Natives At Penn (NAP) has pulled together a small program with a few local singers and dancers for a virtual pow wow experience, including some helpful education. Come and listen to some live singing AND dance if you feel the urge. (:
You must register to get the Zoom link : http://bit.ly/NAPpowwow2021

ODEI Spotlight: Self-Care Sunday from CAPS x USABE
2021-03-28 15:00 - 16:00 | PA
CAPS x USABE: Self-Care Sunday
Date: Sunday, March 28th
Time: 3:00 – 4:00 PM EST
Want to enjoy a relaxing afternoon of self-care and wellness activities? Join USABE on Sunday, March 28th for a joint student-faculty wellness workshop! During this session led by a CAPS expert, students and faculty will engage in interactive activities related to mental health and an open dialogue around the different strategies we all use for self-care. Register and attend the workshop to be entered into a raffle to win a self-care kit!
Register: https://tinyurl.com/capsusabersvp

PSOC Webinar: "Unraveling the aneuploidy conundrum in eukaryotic cells” (Rong Li)
2021-03-29 12:00 - 13:00 | PA
Physical Sciences in Oncology Center PSOC@Penn
Spring 2021 Webinar Series Mondays at 12:00 noon (EST)
For webinar links, please contact manu@seas.upenn.edu.

MEAM Seminar: "Breaking Bones In situ: Multiscale Bioinspired Functional Materials"
2021-03-30 10:30 - 12:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
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 behavior 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 the crack (1 initiation and growth mediated underlying fibril microstructure (~50 nm) in bone and place it in the context of hierarchical toughening up to the macro/organ level (500+ um) fracture. We will use generalized lessons learned about biogenic materials to discuss additive manufacturing of metals. We will explore how we can leverage of the unique processing conditions and tunability of nanoparticle functionalized feedstock in metal additive manufacturing to control microstructure, as well as expand the library of currently “printable” materials for potential applications ranging from biomedical to thermal.

ESE Seminar: "Integrated Nonlinear and Quantum Photonic Devices"
2021-03-30 11:00 - 12:00 | Zoom – Email ESE for Link jbatter@seas.upenn.edu
Recent advances in nanofabrication technology have allowed for the realization of ultra-low loss nanophotonic waveguides and is opening up exciting opportunities for next-generation nonlinear photonic circuits with higher integration density, advanced functionalities, and ultralow energy consumption. Those features are critical for advancing photonic technologies in both classical and quantum domains.
In this talk, I will discuss silicon-based and thin-film lithium niobate (LN)-based photonic devices, which leverage the combination of Kerr and second-order nonlinearities, along with electro-optic (EO) and piezoelectric effects to achieve highly efficient and controllable light-matter interactions with optical, electrical, or mechanical waves at extremely compact footprints. I will talk about the first mode-locked Kerr frequency combs and ultrafast spectroscopic device in the mid-infrared regime, based on silicon microresonators. Furthermore, I will discuss a novel class of silicon-nitride-based optical parametric oscillators and its experimental realization for building a true quantum random number generator and a spatially multiplexed nanophotonic spin glasses system for coherent photonic computing. Thirdly, I will talk about the emerging LN optoelectronic platform and show the developments of several novel EO devices and circuits for optical non-reciprocity, femtosecond pulse lasers, frequency-shifters, and electro-optic frequency combs. Combination of multiple nonlinearities of LN also enables ultrabroadband optical spectral generation from ultraviolet to mid-infrared. Lastly, I will discuss the potential of nonlinear photonic platform for scaling up and accelerating classical and quantum technologies in single-photon sources and manipulation, photonic computing, communication networks, and information processing.

CIS & ESE Seminar: "Wireless Systems that See the Invisible with Machine Learning: Through-Wall Vision, Emotion Recognition, and Health Monitoring"
2021-03-30 15:00 - 16:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
Today, there is a huge interest in sensing technologies that can sense people and monitor their health. Yet, existing solutions require people to wear different sensors and devices on their bodies. In contrast, my research uses wireless signals to sense people without any physical contact. Wireless signals, which have been traditionally used for data communication, have great potential as a sensing modality. Specifically, wireless signals propagate in space, traverse walls and obstacles, reflect off human bodies, and get modulated by our movements, respiration, and even heartbeats. In this talk, I will demonstrate how we use custom machine learning to extract semantics from wireless signals despite their complex interactions with people and the environment. In particular, I will introduce how we develop wireless systems to detect humans through walls, track their movements, and recognize their actions, enabling a form of x-ray vision. I will also show how such systems can capture people’s physiological signals, monitor sleep stages, and recognize emotions without putting any sensors on their bodies. Finally, I will touch on how these new sensing technologies can help address unmet needs and reduce overhead in healthcare.

ODEI Spotlight: PAACH Restorative Practice Circle for Penn Undergraduate Students
2021-03-30 19:30 - 21:00 | PA
This event will be on Zoom.
Please click the link below to join the meeting:
https://upenn.zoom.us/j/96719299272

CIS Seminar: "Communication Complexity, Quantum Computing and Optimization: New Connections and Insights"
2021-03-31 14:00 - 15:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
How much information flows through a system? This fundamental question is at the heart of communication complexity. Techniques from this field have turned out to be immensely powerful and fairly universal tools to understand the power of different kinds of algorithms.

In this talk, I will describe new methods that I have developed to analyze communication which offer fresh insights into quantum computing and optimization. Using these techniques, I will answer a question of Aaronson and Ambainis regarding the maximal advantage that quantum algorithms can have over classical algorithms in the “blackbox” model, and another conjecture due to Rothvoss about optimal linear programs for approximately solving the matching problem.

Looking forward, I also propose new directions to explore further connections among these areas with the intention of answering key questions regarding quantum speedups and more powerful optimization approaches, such as semidefinite programming.

CBE Seminar: "Systems Biology of Glycosylation: Examples from Cancer Biology, Inflammatory Disease and SARS-CoV-2 Infection"
2021-03-31 15:00 - 16:00 | Zoom – Email CBE for link
Abstract
Glycosylation is a common and complex post-translational modification that is orchestrated in all mammalian cells. Glycan structures thus formed either absolutely control or fine-tune various biological processes. These include the half-life of biologics in circulation, the rates of leukocyte-endothelial cell adhesion interaction during inflammation, tumorigenesis and the kinetics of cancer metastasis. The pattern of glycans formed on individual cells during normal health and disease depends on a family of ~350 genes that are together called the “glycogenes.” The enzymes produced by these glycogenes either add saccharides, clip-off terminal sugar units, or participate in the metabolic synthesis of building blocks required for glycosylation reactions. My presentation will describe novel molecular tools and complementary computational strategies to advance the study of glycosylation from a systems perspective. These represent key enabling technologies that provide new insight into the progress of diverse human diseases: cancer biology, human inflammatory disease, and viral infection mediated by SARS-CoV-2. The emerging knowledge suggests potential diagnostic strategies and therapeutic avenues to combat these ailments.

Spring 2021 GRASP SFI: “Optimizing Orthoses: Challenges in predicting human performance”
2021-03-31 15:00 - 16:00 | https://upenn.zoom.us/j/96715197752
Abstract: Innovations in hardware and control have created a new class of orthoses or exoskeletons to augment and assist human movement. These designs enable forces and torques to be applied to nearly any segment of the body. However, predicting how an individual will adapt their movement in response to external assistance remains incredibly challenging. These predictions are challenging for unimpaired individuals, let alone for individuals after neurologic injury, such as in cerebral palsy or stroke. Even for a “simple” case such as single degree-of-freedom ankle foot orthoses, we often fail to predict how a given device will alter or improve an individual’s movement. In this seminar, we will discuss these challenges as well as new methods that may assist in optimizing orthoses after neurologic injury. In particular, I will discuss how machine learning can help us to learn from past prescriptions, while also using musculoskeletal modeling, muscle synergy analysis, and ultrasound imaging to quantify neuromuscular adaptations and inform orthotic design.
Click here to join the Zoom meeting

ODEI Spotlight: Cultivating Equity-Focused Institutional Change in University STEM Departments
2021-03-31 15:30 - 16:30 | PA
Discipline-based education research has made great strides in understanding how to foster more equitable and effective teaching in science, technology, engineering, and mathematics (STEM). However, there still has yet to be widespread adoption of reforms in university STEM departments. Our project seeks to understand how departments can implement sustainable, large-scale changes to undergraduate education. While these changes take many forms, they are grounded in six core principles, including upholding a commitment to equity, inclusion, and justice. We facilitate Departmental Action Teams (DATs), teams of faculty, students, and staff within a single STEM department working on some issue related to undergraduate education. During this talk, I will present an overview of the DAT model and the principles which guide our work. I will then present some empirical research discussing how to meaningfully develop student-staff-faculty partnerships while implementing departmental changes. Finally, I will discuss the multifaceted nature of complex equity-focused changes.
At 3:00 pm on Thursday, April 1, there will be an opportunity for grad students and post docs to join a discussion with Dr Quan. The discussion will use the same zoom link as the lecture.
Full zoom link: https://upenn.zoom.us/j/96987577706?pwd=[REDACTED]
Meeting ID: 969 8757 7706
Passcode: [REDACTED]
Please contact us, penn.dip@gmail.com with any questions.

MSE Seminar: "Reaction-Diffusion Driven Pattern Formation in Thermosetting Polymers"
2021-04-01 10:45 - 11:45 | PA
Reaction-diffusion processes are versatile, yet underexplored methods for manufacturing that provide unique opportunities to control the spatial properties of materials, achieving order through broken symmetry. The mathematical formalism and derivation of equations coupling reaction and diffusion were presented in the seminal paper by Alan Turing [Phil. Trans. R. Soc. Lond. B 237, 37,1952], which describes how random fluctuations can drive the emergence of pattern and structure from initial uniformity. Inspired by reaction-diffusion systems in nature, this talk will describe a new processing strategy predicated on the exploitation of an advancing polymerization front sustained through coupled reaction and thermal diffusion. The system uses the exothermic release of energy to provide a positive feedback to the reaction. In turn, this stimulates further exothermic energy release and a self-propagating reaction “front” that rapidly moves through the material – a process called frontal polymerization. We recently reported the frontal ring-opening metathesis polymerization (FROMP) of dicyclopentadiene (DCPD) that exhibits the high energy density, high reactivity, relatively long pot life, and low viscosity required for the synthesis of high-performance thermosetting polymers and composites [Robertson et al., Nature, 557 (2018)]. This talk will describe several novel methods to control thermal transport in this system, giving rise to symmetry breaking events that enable complex, emergent pattern formation and control over growth, topology, and shape.

ESE Seminar: "Engineering (Useful) Quantum Systems"
2021-04-01 11:00 - 12:00 | Zoom – Email ESE for Link jbatter@seas.upenn.edu
Quantum technologies have the potential to revolutionize sensing, communication, and computation. To realize this potential, it will be necessary to scale the size and complexity of engineered quantum systems by several orders of magnitude, without sacrificing coherence or fidelity.
Trapped ion qubits provide unparalleled coherence and are a leading platform for current small-scale quantum technology demonstrations. Optical addressing of individual ions with low crosstalk enables high-fidelity single and multi-qubit gates, and ions trapped in the same potential naturally allow for all-to-all connectivity. However, free-space control and routing of these optical control fields presents a scaling challenge. I will focus on technology requirements for a deployable trapped-ion quantum sensor and introduce an integrated photonics platform for parallel laser delivery which will increase stability, reduce size, and allow us to increase the number of sensors measured in parallel without sacrificing fidelity.
Finally, I will present a path towards a modular trapped-ion quantum processor with active integrated photonics for control within each module, and high fidelity physical and optical links between modules.

BE Seminar: "Deciphering the Dynamics of the Unconscious Brain Under General Anesthesia" (Emery Brown, MIT)
2021-04-01 15:00 - 16:00 | PA
This event will be held virtually on zoom – check email or contact ksas@seas.upenn.edu.
General anesthesia is a drug induced state that is critical for safely and humanely allowing a patient to undergo surgery or an invasive diagnostic procedure. During the last 10 years the study of the neuroscience of anesthetic drugs has been an active area of research. In this lecture we show how anesthetics create altered states of arousal by creating oscillation that impede how the various parts of the brain communicate. These oscillations, which are readily visible on the electroencephalogram (EEG), change systematically with anesthetic dose, anesthetic class and patient age. We will show how the EEG oscillations can be used to monitor the brain states of patients receiving general anesthesia, manage anesthetic delivery and learn about fundamental brain physiology.

CIS Seminar: "Expanding the Reach of Fuzzing"
2021-04-01 15:00 - 16:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
Software bugs are pervasive in modern software. As software is integrated into increasingly many aspects of our lives, these bugs have increasingly severe consequences, both from a security (e.g. Cloudbleed, Heartbleed, Shellshock) and cost standpoint. Fuzzing refers to a set of techniques that automatically find bug-triggering inputs by sending many random-looking inputs to the program under test. In this talk, I will discuss how, by identifying core under-generalized components of modern fuzzing algorithms, and building algorithms that generalize or tune these components, I have expanded the application domains of fuzzing. First, by building a general feedback-directed fuzzing algorithm, I enabled fuzzing to consistently find performance and resource consumption errors. Second, by developing techniques to maintain structure during mutation, I brought fuzzing exploration to “deeper” program states. Third, by decoupling the user-facing abstraction of random input generators from their sampling distributions, I built faster validity fuzzing and even tackled program synthesis. Finally, I will discuss the key research problems that must be tackled to make fuzzing readily-available and useful to all developers.

MEAM Seminar: "Lubrication and Tribology Trends and Challenges in Passenger Electric Vehicles"
2021-04-02 10:00 - 11:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
It is becoming more difficult and expensive to continue developing internal combustion engine (ICE) powered vehicles to meet the increasingly tight emissions goals addressing global climate change. Over the last few years, automobile manufacturers have been turning their attention toward electrification as the most practical and cost-effective way of providing personal transportation options that will meet future emission standards. Multiple sources are now predicting that over the next few decades, electric vehicles (EVs) will represent the fastest growing segment of passenger vehicle production.
The engine lubricants used in conventional ICE-powered personal vehicles represent one of the largest segments in the global lubricant market. It has become increasingly evident that the trend toward vehicle electrification will transform this market. However, EVs still require the use of lubricants and other fluids to function properly. It is likely that fluids specifically developed for EVs will play an even bigger part in enabling future EV technology than their earlier counterparts did during the development of ICE-powered vehicles.
This presentation will examine the trends in passenger EVs from the perspective of the unique lubrication, tribology and thermal management challenges they pose. It will also provide some insights gained from examining the layout and parts of three commercial EVs. While EV drivelines are mechanically simpler than their ICE vehicle counterparts, they expose the lubricant to a broader range of operating conditions. Also, the range anxiety concern expressed by consumers places an even bigger focus on friction reduction compared to ICE vehicles. Thermal management of different EV components has emerged as another technical challenge. Current fluids are not truly optimized to meet the competing requirements and there is room for future development of both the cooling systems and the fluids used in them.
We have now seen the first round of mass-produced EVs enter the market. Each manufacturer has developed their individual platforms and it is not clear if we have yet reached the most optimized vehicle architecture. Currently there are very few standards or even test methods to characterize the various fluids used in EVs. This will no doubt change over the next few years. We can also expect to uncover some unanticipated lubrication and tribology challenges that will require innovation and practical solutions from both vehicle manufacturers and lubricant suppliers.

PICS Colloquium: "Molecular Engineering of Ice Responsive Materials: Decoding Heterogeneous Ice Nucleation"
2021-04-02 14:00 - 15:00 | Zoom – email kathom@seas.upenn.edu
Abstract: The presence of particles such as dust and pollen affect cloud microphysics significantly through their effect on the state of water. These particles can hinder or accelerate the liquid-to-solid transition of water, and also affect the ice polymorph formed in the clouds. This indirectly cloud reflectivity, cloud lifetime, and precipitation rates. While a predominant phenomenon, the understanding of the surface factors that affect ice nucleation is minimal. In our research, we use molecular simulations to illuminate the pathways through which surface properties influence ice nucleation. Experiments cannot probe the length and time scales relevant to nucleation. While molecular simulations, in principle, can probe the length and time scales of nucleation, in practice nucleation is challenging to sample. Nucleation is often associated with large free energy barriers and thus, is difficult to sample in straightforward simulations. Advanced sampling techniques and other creative approaches are needed. In this talk, I will discuss the insights we have obtained on heterogeneous ice nucleation through studies of three surfaces – silver iodide, kaolinite and mica. I will also highlight the synergistic combination of experiments and simulations in understanding heterogeneous ice nucleation. I will introduce a recently developed method in our group facilitate computational studies of heterogeneous nucleation. I will conclude by providing a perspective on the broader implications of our studies on interfacial phenomena and surface design.

ESE Thesis Defense: "Accelerated Risk Assessment and Domain Adaptation for Autonomous Vehicles"
2021-04-02 14:00 - 16:00 | Zoom- Email mokelly@seas.upenn.edu for link
Autonomous vehicles (AVs) are already driving on public roads around the US; however, their rate of deployment far outpaces quality assurance and regulatory efforts. Consequently, even the most elementary tasks, such as automated lanekeeping, have not been certified for safety, and operations are constrained to narrow domains. First, due to the limitations of worst-case analysis techniques, we hypothesize that new methods must be developed to quantify and bound the risk of AVs. Counterintuitively, the better the performance of the AV under consideration, the harder it is to accurately estimate its risk as failures become rare and difficult to sample. This thesis presents a new estimation procedure and framework which can efficiently evaluate AV risk even in the rare event regime; we demonstrate the approach’s performance on a variety of AV software stacks. Second, given a framework for AV evaluation, we turn to a related question: how can AV software be efficiently adapted for new or expanded operating regimes? We hypothesize that stochastic search techniques can improve the naive trial-and-error approach commonly used today. One of the most challenging aspects of this task is that proficient driving requires making tradeoffs between performance and safety. Moreover, for novel scenarios or operational domains there may be little data which can be used to understand the behavior of other drivers. To study these challenges we create a low-cost scale platform, simulator, benchmarks, and baseline solutions. Using this testbed, we develop a new population-based self play method for creating dynamic actors and detail both offline and online procedures for adapting AV components to these conditions. Taken as a whole, this work represents a rigorous approach to the evaluation and improvement of AV software.

PSOC Webinar: "Cancer Cell Unjamming as Predictive Tumor Marker" (Josef A. Käs)
2021-04-05 12:00 - 13:00 | PA

MEAM Seminar: "Nonintrusive Reduced Order Models Using Physics Informed Neural Networks"
2021-04-06 10:30 - 12:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
The development of reduced order models for complex applications, offering the promise for rapid and accurate evaluation of the output of complex models under parameterized variation, remains a very active research area. Applications are found in problems which require many evaluations, sampled over a potentially large parameter space, such as in optimization, control, uncertainty quantification, and in applications where a near real-time response is needed.
However, many challenges remain unresolved to secure the flexibility, robustness, and efficiency needed for general large-scale applications, in particular for nonlinear and/or time-dependent problems.
After giving a brief general introduction to projection based reduced order models, we discuss the use of artificial feedforward neural networks to enable the development of fast and accurate nonintrusive models for complex problems. We demonstrate that this approach offers substantial flexibility and robustness for general nonlinear problems and enables the development of fast reduced order models for complex applications.
In the second part of the talk, we discuss how to use residual based neural networks in which knowledge of the governing equations is built into the network and show that this has advantages both for training and for the overall accuracy of the model.
Time permitting, we finally discuss the use of reduced order models in the context of prediction, i.e. to estimate solutions in regions of the parameter beyond that of the initial training. With an emphasis on the Mori-Zwansig formulation for time-dependent problems, we discuss how to accurately account for the effect of the unresolved and truncated scales on the long term dynamics and show that accounting for these through a memory term significantly improves the predictive accuracy of the reduced order model.
NB: This announcement has been updated with a new title and abstract.

ESE Seminar: "Harnessing Light-Matter Interaction for Photonic Quantum Technologies"
2021-04-06 11:00 - 12:00 | Zoom – Email ESE for Link jbatter@seas.upenn.edu
Photonic quantum technologies have a unique potential for applications such as large-scale quantum networks and quantum-enhanced sensing. Furthermore, photons provide new paradigms for quantum simulations and a testbed for benchmarking the advantage of quantum simulators over the classical ones. These applications demand novel resources such as efficient single-photon sources, large clusters of entangled photons, and nonlinear optical gates. A 1D-atom, a quantum emitter coupled to a single optical mode with high efficiency, can deliver these functionalities.
In this talk, I will introduce two realizations of an artificial 1D-atom based on a single quantum dot coupled to either a photonic-crystal waveguide or a tunable Fabry-Perot microcavity. To start, I will present a single-photon source with record efficiency, speed, and coherence. I will then go on to discuss the transmission properties of the 1D-atom and show that we can achieve optical nonlinearities at the single-photon level. I will also show that the interplay between the local electric field and the quantum dot can lead to interesting phenomena such as directional emission and nonreciprocal transport for photons.
At the end of my talk, I will present an overview of my feature research direction and my vision for quantum computing and quantum networking using photons.

CIS Seminar: "Bridging Learning and Decision Making"
2021-04-06 15:00 - 16:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
Machine learning is becoming widely used in decision making, in domains ranging from personalized medicine and mobile health to online education and recommendation systems. While (supervised) machine learning traditionally excels at prediction problems, decision making requires answering questions that are counterfactual in nature, and ignoring this mismatch leads to unreliable decisions. As a consequence, our understanding of the algorithmic foundations for data-driven decision making is limited, and efficient algorithms are typically developed on an ad hoc basis. Can we bridge this gap and make decision making as easy as machine learning?
Focusing on the contextual bandit, a core problem in data-driven decision making, we bridge the gap by providing the first optimal and efficient reduction to supervised machine learning. The algorithm allows users to seamlessly apply off-the-shelf supervised learning models and methods to make decisions on the fly, and has been implemented in widely-used, industry-standard tools for decision making.
Our results advance a broader program to develop a universal algorithm design paradigm for data-driven decision making. I will close the talk by discussing challenges and opportunities in building such a framework, including efforts to extend our developments to difficult reinforcement learning problems in large state spaces.

CBE Seminar: "Engineering Microsystems and Computational Pipelines to Understand the Brain"
2021-04-07 15:00 - 16:00 | Zoom – Email CBE for link
Abstract
My lab is interested in engineering micro systems and computational tools to address questions in systems neuroscience, developmental biology, and cell biology that are difficult to answer with conventional techniques. We are particularly interested in the questions of how the brain is assembled during development (and changes during aging) and information processed by brain circuits. We work with a powerful genetic system – the free-living soil nematode C. elegans. In this talk, I will introduce two sets of powerful mathematical and physics-based tools accessible by engineers to accelerate the biological understanding. I will talk about two recent developments in discrete microfluidic systems exploiting multiphase and dynamical behavior of the fluids and microswimmers (i.e. C. elegans). By designing the microfluidic system cleverly using appropriately chosen dimensionless numbers, we can have exquisite control of the samples and experimental conditions. I will also talk about a powerful graph-theory-based framework to build probabilistic models of brain atlases. This machine-learning approach greatly reduces bias, enables automated and robust cell identification, and will enable a variety of applications including gene-expression analysis, whole-brain imaging, and connectomics.

CIS Seminar: "Human-Centered Interactive Systems for Configuring, Extending, and Developing AI Applications"
2021-04-07 15:00 - 16:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
Artificial intelligence (AI) technologies are emerging and affecting our lives in many aspects. However, the majority of individuals are merely users of AI with little capability to adapt AI to their own long-tail tasks, preferences, and interests that are not covered by the existing AI solutions. Democratizing AI to empower the individuals to create, configure, and extend AI-powered systems requires a new human-centered approach to guide the users to provide more effective inputs to the AI systems, and to enable the systems to better understand and generalize from inputs in the user’s natural style. In this talk, I will present my research on designing, developing, and studying (1) a new smartphone interactive task learning agent that empowers end-users to teach new tasks, concepts, and automation rules using the combination of natural language instructions and demonstrations on existing third-party app GUIs; and (2) a new developer tool that allows developers without natural language processing expertise to bootstrap a task-oriented conversational bot from existing mobile apps using the task model extracted from the app interaction traces. These projects examplify my approach of combining human-centered methods with AI techniques to create new novel interfaces and system capabilities that facilitate effective human-AI collaboration, lowering the barriers to customizing, extending, and developing AI applications for end-users and non-expert developers.
