Penn Engineering Events

Google Explore CS Research Event
2021-03-26 16:00 - 17:00 (America/New_York) | 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.

Spring 2021 GRASP SFI: “Robotic Caregivers—Sensing, Simulation, and Physical Human-Robot Interaction”
2021-04-07 15:00 - 16:00 | Zoom
Abstract: Autonomous robots have the potential to serve as versatile caregivers that improve quality of life for millions of people with disabilities worldwide. Yet, physical robotic assistance presents several challenges, including risks associated with physical human-robot interaction, difficulty sensing the human body, and a lack of tools for benchmarking and training physically assistive robots. In this talk, I will present techniques towards addressing each of these core challenges in robotic caregiving. First, I will introduce a method inspired by human perspective-taking which allow assistive robots to predict how their future actions will apply forces to a person’s body. I will then describe capacitive servoing, a new sensing technique for robots to sense the human body and track trajectories along the body. Finally, I will show how we can develop intelligent robotic caregivers via simulation and virtual reality, and I will introduce Assistive Gym, the first physics simulation framework for benchmarking and training physically assistive robots.
Click here to join the Zoom meeting

MSE Seminar: "Bio-like Structural Hydrogels with Life-like Intelligence"
2021-04-08 10:45 - 11:45 | PA
From the cellular level up to the body system level, living organisms present elegant designs and strategies to realize the desirable structures, properties and functions. For example, tendons and muscles are tough but soft, owing to highly complex hierarchical structures rarely found in synthetic materials. Plants can automatically track the sun and our body can self-regulate motions adaptively to environment, presenting superior intelligence also lacking in manmade systems. Hydrogels, as a class of crosslinked polymers, not only have tissue-like water-rich porous networks and can also change their volume and physical properties in response to environmental cues including temperature, light, and specific molecules. At UCLA He lab, we exploit fundamental material processing-structure-property-function studies of hydrogels and their derivatives, to create (i) ‘bio-like’ structures and properties and (ii) ‘life-like’ intelligence in functional soft materials for applications in robotics, biomedicine, energy and environment. In this talk, I will start with showcasing how the stimuli-responsiveness of hydrogels can unify sensing-diagnosis-actuation process to create ‘synthetic intelligence’ with built-in feedback loop, such as sunflower-like light tracking for solar harvesting (Nat. Nanotech. 2019) and self-sensing actuators for autonomous soft robotics (Sci. Robotics 2019, 2021; Matter 2021). Driven by the remaining challenges revealed in these function developments, I will then present our structural material approaches to breaking the fundamental limits in mechanical, diffusion and electrical properties. I will discuss the mechanics and general principles to design extreme properties, including simultaneously high toughness and stretchability (Nature 2021; Adv. Mater. 2021; Sci. Adv. 2020), tunable porosity and diffusivity (Adv. Mater. 2021; EcoMat 2021) and stretchable conductive soft materials for flexible (bio)electronics (Matter, 2020; Adv. Funct. Mater. 2020; Adv. Mater. 2019). I will conclude my talk with a perspective on future human-machine convergence enabled by soft materials.

BE Seminar: "Reaction-Coupled Solid-State Nanopore Digital Counting: Towards Sensitive, Selective and Fast Nucleic Acid Testing" (Weihua Guan)
2021-04-08 15:00 - 16:00 | PA
This seminar will be held virtually on Zoom (details coming soon).
Due to their conceptual simplicity, the nanopore sensors have attracted intense research interest in electronic single molecule detection. While considerable success has been achieved, the solid-state nanopores still face three significant challenges, including repeatable nanopore size control, introduction sensing specificity, and prolonged sensor response time at low concentrations. In this talk, I will discuss a calibration-free solid-state nanopore counting method and two representative applications in nucleic acid testing. One is an isothermal amplification-coupled nanopore counting for malaria analysis. The other is the CRISPR-cas12a-coupled nanopore counting for HIV analysis. Finally, I will also discuss how we can develop a fully integrated ‘sample-to-result’ nucleic acid testing device using the solid-state counting strategy. I believe the reaction-coupled solid-state nanopore digital counting could open a new avenue towards compact, robust, low-cost electronic nucleic acid testing at the point of care.

CIS Seminar: "AI for Population Health: Melding Data and Algorithms on Networks"
2021-04-08 15:00 - 16:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
As exemplified by the COVID-19 pandemic, our health and wellbeing depend on a difficult-to-measure web of societal factors and individual behaviors. My research aims to build AI which can impact such social challenges, advancing health and equity on a population level. This effort requires new algorithmic and data-driven paradigms which span the full process of gathering costly data, developing machine learning models to understand and predict interactions, and optimizing the use of limited resources in interventions. In response to these needs, I will present methodological developments at the intersection of machine learning, optimization, and social networks which are motivated by on-the-ground collaborations on HIV prevention, tuberculosis treatment, and the COVID-19 response. These projects have produced deployed applications and policy impact. For example, I will present the development of an AI-augmented intervention for HIV prevention among homeless youth. This system was evaluated in a field test enrolling over 700 youth and found to significantly reduce key risk behaviors for HIV.

GRASP On Robotics: “Perspectives on Machine Learning for Adaptive Robotic Systems”
2021-04-09 11:00 - 12:30 | https://upenn.zoom.us/j/96715197752
Abstract: Recent advances in machine learning are leading to new tools for designing intelligent robots: functions relied on to govern a robot’s behavior can be learned from a robot’s interaction with its environment rather than hand-designed by an engineer. Many machine learning methods assume little prior knowledge and are extremely flexible, they can model almost anything! But, this flexibility comes at a cost. The same algorithms are often notoriously data hungry and computationally expensive, two problems that can be debilitating for robotics. In this talk I’ll discuss how machine learning can be combined with prior knowledge and structure to build effective solutions to robotics problems. I’ll introduce an online learning perspective on robot adaptation that unifies well-known algorithms and suggests new approaches. I’ll discuss how structure and simulation can augment learning and how imperfect models, simple policies, and hierarchical abstractions can help to build adaptive, resilient systems. I will also show how we have applied some of these ideas to several robotics tasks that require impressive sensing, speed, and agility to complete.
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