ESE Ph.D. Thesis Defense: “The Development of A Fingertip Implantable MEMS Tactile Sensing System”
2021-04-16 12:00 - 14:00 (America/New_York) | Zoom – email dulin@seas.upenn.edu for link
Abstract: This thesis explores the development of a fingertip implantable MEMS tactile sensing system to provide tactile sensing capabilities for paralyzed people using a brain machine interface (BMI) technology. With the BMI-controlled stimulation, paralyzed people are able to restore hand movement with their native hands without sensation. However, sensory feedback for these BMI-controlled stimulators is still missing. To overcome the barrier, development of a tactile sensing system is necessary. In previous studies, multiple approaches have been exploited to realize fingertip tactile recognition for wearable electronics, prosthetics, and robotics. Compared with these existing tactile sensors with wearable devices or robotic arms, we are interested in combining tactile sensing with implantable MEMS technologies. In this dissertation, we build an implantable tactile sensing system with wireless power and signal transmission capabilities for somatosensory feedback of BMI systems.
Toward these goals, a fused silica package with good hermeticity to moisture, biocompatibility, CMOS compatibility, as well as multiple feed-throughs for electronic access was developed and characterized. A localized fusion bonding technology by using CO2 laser-assisted machining was proposed to achieve simultaneous bonding and dicing of fused silica wafer stacks.
To demonstrate an implantable tactile sensor, a capacitive force-sensing technology based on the packaging technology was developed to satisfy hermetic and biocompatible requirements for implantation applications. The performance of this tactile sensor is investigated with quantitative static and dynamic loading measurements. In addition, both in vitro study with the sensor embedded under skin-phantom and implantation in a monkey hand are examined.
To further develop an implantable tactile sensing system with wireless communication capability, a multilayer fused silica structure incorporating a capacitive force sensor, an ASIC for wireless power and data communication, and the hermetic package technology for encapsulating the electronics is built. The development of an antenna coil integrated with the system was presented and the fabrication process of the system is discussed. To characterize the tactile sensing system, a customized experimental setup is employed and static loading measurement with dynamic loading analyzer is performed verifying that both of the pressure sensing and wireless transmission of the system are functional. This wafer-level technology will be very useful for other implantable pressure sensing and MOEMS applications.

Advisor: Mark G. Allen, Alfred Fitler Moore Professor of Electrical and Systems Engineering, University of Pennsylvania
Dissertation Committee:
Chair: Jan Van der Spiegel, Professor of Electrical and Systems Engineering, University of Pennsylvania
Member: Firooz Aflatouni, Associate Professor of Electrical and Systems Engineering, University of Pennsylvania
Member: Igor Bargatin, Associate Professor of Mechanical Engineering and Applied Mechanics, University of Pennsylvania
Member: Troy Olsson, Assistant Professor of Electrical and Systems Engineering, University of Pennsylvania

BE Doctoral Dissertation: "Silver sulfide nanoparticles for breast cancer imaging with dual energy mammography and other modalities"
2021-04-16 13:30 - 15:30 | https://upenn.zoom.us/j/96715197752
The Department of Bioengineering at the University of Pennsylvania and Dr. David Cormode are pleased to announce the Doctoral Dissertation Defense of Jessica Hsu.

Title: Silver sulfide nanoparticles for breast cancer imaging with dual energy mammography and other modalities

Date: April 16, 2021
Time: 1:30 pm (Eastern Time)

Please join via the zoom link below:
https://upenn.zoom.us/j/92408719412?pwd=[REDACTED]

The public is welcome to attend.

PICS Colloquium: "Modeling and simulations of protein conformational changes and virus entry"
2021-04-16 14:00 - 15:00 | Zoom – email kathom@seas.upenn.edu
Abstract: Virus infections remain major threats to human health worldwide. Viruses are intracellular parasites, and must enter host cells and deliver their genetic material to initiate infection. Virus entry is a highly complex process that may involve hundreds of trans-membrane and peripheral membrane proteins. This highly complex process is dictated by various events, such as virus motion, membrane deformation and merging as well as molecular scale protein-protein, protein- lipid interactions and drastic protein conformational changes, occurring at multiple stages and at multiple length and time scales. The question of how these biochemical and biomechanical events work together culminating in productive entry is not well understood but fundamentally important for development of vaccine candidates and identification of new targets for inhibitor design. Modeling and simulations of virus entry at different scales can provide mechanistic insights into this complex process. In this talk, we will present our recent simulation research on membrane deformation and protein conformational changes for virus entry. A mesoscale stochanstic membrane model has been implemented to investigate the membrane deformations during the entry process. We will also discuss our development of the coase-grained force field to capture the protein conforamtional changes, and the on-going work of machaine-learning facilitated sampling of protein structures.

ESE Thesis Defense: "A Microwell-Based Impedance Sensor in Microneedle Shape for Cytokine Detection"
2021-04-16 14:00 - 16:00 | Zoom – email naixins@seas.upenn.edu for link
Monitoring cytokine profiles plays a crucial role in predictive and early disease diagnosis, as well as in research in many biological fields, thus multiple approaches have been investigated. Despite the promise of these techniques, many still require specialized instrumentation or have not been shown in real-time applications, impeding their clinical adoption as point-of-care systems. Impedance-based protein detection sensors for point-of-care diagnostics require quantitative specificity as well as rapid or real-time operation. Furthermore, microfabrication of these sensors can lead to form factors suitable for in vivo operation.
Herein, we present microfabricated needle-shaped microwell impedance sensors for rapid sample-to-answer, label-free detection of cytokines. The sensor utilizes a micro-well array configuration at the microneedle tip to enable label-free detection while simultaneously maintaining the capability of high sensitivity detection, despite the high salt concentration of complex biological fluids. The microneedle form factor allows the sensors to be utilized in transcutaneous or transvascular sensing applications. In vitro experimental characterization confirmed sensor specificity and sensitivity to multiple proteins of interest. Mechanical characterization demonstrated sufficient microneedle robustness for transcutaneous insertion, as well as preserved sensor function post-insertion. We further utilized these sensors to carry out real-time in vivo quantification of human interleukin 8 (hIL8) concentration levels in the blood of transgenic mice that endogenously express hIL8. To assess sensor functionality, hIL8 concentration levels in serum samples from the same mice were quantified by ELISA. Excellent agreement between real-time in vivo sensor readouts in blood and subsequent ELISA serum assays was observed over multiple transgenic mice expressing hIL8 concentrations from 62 pg/mL to 539 ng/mL. Further, to reduce large mechanical mismatch between fused silica microneedle and surrounding soft tissue thus explore potential chronic applications, materials with lower Young’s modulus (e.g., Parylene materials) have been employed in the microneedle fabrication. Moreover, the relative sizes of microwell array and needle footprint offer the potential for multiple bioassay sensors on a single microneedle by functionalizing the surface of each sensor with distinct antibodies, forming a full sensor platform. Such multiplexed sensors could allow the real-time assessment of more complex diseases or conditions in vivo.
Advisor: Dr. Mark G. Allen, Alfred Fitler Moore Professor, Department of Electrical and Systems Engineering
Dissertation Committee:
Chair:
Dr. David Issadore, Associate Professor, Department of Bioengineering, University of Pennsylvania
Members:
Dr. A.T. Charlie Johnson, Rebecca W. Bushnell Professor, Department of Electrical and Systems Engineering, University of Pennsylvania
Dr. Flavia Vitale, Assistant Professor, Department of Bioengineering, University of Pennsylvania

MSE Senior Design Presentations
2021-04-16 14:30 - 17:00 | PA

CBE PhD Dissertation Defense | A Scalable, Point-of-Care Microfluidic Approach for Assessing Thrombosis and Hemostasis
2021-04-19 10:00 - 11:30 | Zoom – Email CBE for link
Abstract:
Coagulation testing is an important diagnostic tool for the detection of excessive bleeding risk or obstructive clot formation (thrombosis), using blood samples from patients. Microfluidic flow devices have been well established to provide insights on the impacts of shear rate, drug action, and disease state on coagulation and platelet biology. The bulk of the microfluidic devices and assays used in the past have relied upon manufactured using polydimethyl siloxane (PDMS), a material and construction method not well suited to use in a clinical setting. This thesis describes the design and testing of a single-use, storage stable evolution of previous PDMS microfluidic designs, manufactured via injection molding and pressure-sensitive adhesive bonding. Using this device, we demonstrate the ability to make consistent and repeatable measurements of platelet and fibrin fluorescence intensity in a clot forming under venous shear rate, using a benchtop LED microscope and physiologically consistent constant-pressure driven flow. We also demonstrate the ability to detect a strong, dose-dependent inhibition in the fibrin fluorescence intensity signal to in vitro spiking of direct oral anticoagulants (DOACs). Further, we showed the ability to reverse this inhibition, through small quantities of reversal agents to the anticoagulant drugs. In addition, the presence of the direct oral anticoagulants in the blood of patients on the medications was clearly detected as well. By comparing the response of a DOAC patient’s blood to reversal agent with the dose response established via spiking of healthy blood, quantitation of the current level of DOAC present in the patient’s blood was demonstrated as well. The field of coagulation testing has lacked a fast, reliable means of accurately assessing patient anticoagulation status. Taken together, the ability to identify the presence of and quantitate the amount of DOAC present in a patient’s blood using a single-use microfluidic chip approach as described in this thesis represents a potential promising new direction for coagulation testing at the bed-side.

BE Doctoral Dissertation: "Development of Nanoparticle-based Contrast Agents for Applications with Conventional and Photon-counting CT Imaging"
2021-04-19 13:30 - 15:30 | https://upenn.zoom.us/j/96715197752
The Department of Bioengineering at the University of Pennsylvania and Dr. David Cormode are pleased to announce the Doctoral Dissertation Defense of Johoon Kim.

Title: Development of nanoparticle-based contrast agents for applications with conventional and photon-counting CT imaging
Date/time: April 19, 2021
Time: 1:30pm

You are invited to attend via zoom:

Zoom link: https://upenn.zoom.us/j/94279939416

MEAM Seminar: "Micromechanics of Near-ideal Polymer Networks"
2021-04-20 10:30 - 12:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
Understanding the relationships between the structure of polymer networks and their mechanical properties remains a long-standing challenge in polymer physics. In recent years, a new paradigm for network formation has emerged, whereby near-ideal hydrogels are produced by the cross-coupling of branched macromolecules with well-defined chain length. Such near-ideal networks constitute an excellent model system to revisit this question, as well as a promising platform for the design of new materials with tuneable properties. In this work, we systematically investigate the relative contributions of various network parameters (chain length, crosslink coordination, second-order loops) to the elasticity of near-ideal polymer networks using a computational random network model. Numerical results are compared to classical estimates of rubber elasticity theory. Our results highlight the role of the chain pre-stretch on the mechanical response, as well as the importance of topological defects on the elastic properties. We also compare our results to experimental data for near-ideal tetra-arm PEG hydrogels.

BE Dissertation: "Large Scale Integration of Microengineered Tissue Models for High-content, High-throughput analysis of Complex Human Physiological Systems"
2021-04-20 14:00 - 15:00 | https://upenn.zoom.us/j/96715197752
The Department of Bioengineering at the University of Pennsylvania and Dr. Dan Huh are pleased to announce the Doctoral Dissertation Defense of Andrei Georgescu.

Title: Large scale integration of microengineered tissue models for high-content, high-throughput analysis of complex human physiological systems.

Date: Tuesday April 20, 2021
Time: 2:00pm

You are invited to attend via zoom:

Zoom link: https://upenn.zoom.us/j/93808399625?pwd=[REDACTED]

ESE Thesis Defense: "Scalable Learning in Distributed Robot Teams"
2021-04-21 11:00 - 13:00 | Zoom – Email kate.tolstaya@gmail.com for link
Mobile robots are already in use for mapping, agriculture, entertainment, and the delivery of goods and people. As robotic systems continue to become more affordable, large numbers of mobile robots may be deployed concurrently to accomplish tasks faster and more efficiently. Practical deployments of very large teams will require scalable algorithms to enable the distributed cooperation of autonomous agents. We focus on the three main algorithmic obstacles to the scalability of robot teams: coordination, control, and communication.
To address these challenges, we design graph-based abstractions that allow us to apply Graph Neural Networks (GNNs). First, a team of robots must continually coordinate to divide up mission requirements among all agents. We focus on the case studies of exploration and coverage to develop a spatial GNN controller that can coordinate a team of tens of agents as they visit thousands of landmarks. A routing problem of this size is intractable for existing optimization-based approaches.Second, a robot in a team must be able to execute the trajectory that will accomplish its given sub-task. In large teams with high densities of robots, planning and execution of safe, collision-free trajectories may require the joint optimization over all agent trajectories, which is impractical in large teams. We present a controller for the problem of flocking that uses delayed communication formalized via a GNN to allow aerial robots to avoid collisions and align velocities. Third, robot teams may need to operate in harsh environments without existing communication infrastructure, requiring the formation of ad-hoc networks to exchange information. Many algorithms for control of multi-robot teams operate under the assumption that low-latency, global state information necessary to coordinate agent actions can readily be disseminated among the team. Our approach leverages GNNs to control the connectivity within the ad-hoc network and to provide the data distribution infrastructure necessary for countless multi-robot algorithms.

Advisors:
Alejandro Ribeiro, Professor of Electrical and Systems Engineering
Vijay Kumar, Nemirovsky Family Dean of Penn Engineering and Professor of Mechanical Engineering and AppliedMechanics

Committee:
George Pappas, UPS Foundation Professor and Chair of Electrical and Systems Engineering
Raquel Urstasun, Professor of Computer Science, University of Toronto

Chair: Victor Preciado, Associate Professor and Graduate Chair of Electrical and Systems Engineering

BE Doctoral Dissertation: "Image Processing Techniques for Dual-Energy Contrast-Enhanced X-Ray Breast Imaging" (Kristen Lau)
2021-04-21 13:00 - 15:00 | https://upenn.zoom.us/j/96715197752
The Department of Bioengineering at the University of Pennsylvania and Dr. Andrew Maidment are pleased to announce the Doctoral Dissertation Defense of Kristen Lau.

Title: “Image Processing Techniques for Dual-Energy Contrast-Enhanced X-Ray Breast Imaging.”

Date: April 21, 2021
Time: 1:00pm

You are invited to attend via Zoom.

Join Zoom Meeting
https://upenn.zoom.us/j/97899385849?pwd=[REDACTED]
Meeting ID: 978 9938 5849
Passcode: [REDACTED]

CIS Seminar: "Learning with Label Noise: A Progressive Approach"
2021-04-21 15:00 - 16:00 | Zoom
The Machine Learning Research team at Morgan Stanley invites Penn students pursuing any degree type or major to participate in an interactive research talk by Dr. Yikai Zhang. The event will include a brief introduction to ML Research at Morgan Stanley by the Head of the Machine Learning Center of Excellence, Dr. Yuriy Nevmyvaka.

ABSTRACT:
Label noise is ubiquitous in real world data. There are several ways that noise can be introduced in data collection including through mistakes made by human/automatic annotators, ambiguity in the data/class, and the stochastic nature of the underlying process. Addressing noise in training set labels is an important problem in supervised learning. In practice, many heuristic approaches rely on a trained model to determine whether the label is faithful. However, there is a lack of understanding on why this type of approach works well and a general provably correct framework is missing.
In this presentation we will introduce a label correction algorithm which progressively identifies trustworthy data using confidence of a trained model. Under a general and natural noise pattern, the algorithm can asymptotically approach the Bayes optimal classifier with provable guarantees. The empirical results show the approach is robust to various noise types and outperforms SOTA baselines on multiple datasets.
Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Mayank Goswami, Chao Chen
Learning with feature dependent label noise: a progressive approach
(January 2021)

ESE Ph.D. Thesis Defense: "Cryptographic Foundations for Control and Optimization"
2021-04-22 09:00 - 11:00 | Zoom – email aandreea@seas.upenn.edu for link
Abstract: Advances in communication technologies and computational power have determined a technological shift in the data paradigm. The resulting architecture requires sensors to send local data to the cloud for global processing such as estimation, control, decision and learning, leading to both performance improvement and privacy concerns. This thesis explores the emerging field of private control for Internet of Things, where it bridges dynamical systems and computations on encrypted data, using applied cryptography and information-theoretic tools.
Our research contributions are privacy-preserving interactive protocols for cloud-outsourced decisions and data processing, as well as for aggregation over networks in multi-agent systems, both of which are essential in control theory and machine learning. In these settings, we guarantee privacy of the data providers’ local inputs over multiple time steps, as well as privacy of the cloud service provider’s proprietary information. Specifically, we focus on (i) private solutions to cloud-based constrained quadratic optimization problems from distributed private data; (ii) oblivious distributed weighted sum aggregation; (iii) linear and nonlinear cloud-based control on encrypted data; (iv) private evaluation of cloud-outsourced data-driven control policies with sparsity and low-complexity requirements. In these scenarios, we require computational privacy and stipulate that each participant is allowed to learn nothing more than its own result of the computation. Our protocols employ homomorphic encryption schemes and secure multi-party computation tools with the purpose of performing computations directly on encrypted data, such that leakage of private information at the computing entity is minimized. To this end, we co-design solutions with respect to both control performance and privacy specifications, and we streamline their implementation by exploiting the rich structure of the underlying private data.
Advisor: George J. Pappas, UPS Foundation Professor and Chair of the Department of Electrical and Systems Engineering
Dissertation Committee:
Chair: Manfred Morari, Practice Professor, Department of Electrical and Systems Engineering
Member: Tal Rabin, Professor, Department of Computer and Information Science
Member: Sebastian Angel, Raj and Neera Singh Term Assistant Professor, Department of Computer and Information Science

MSE Seminar: "Atomic-scale Insights into Electrocatalyst Structure and Function"
2021-04-22 10:45 - 11:45 | PA
The development of efficient renewable energy conversion and storage devices is one of the most important challenges of the 21st century. Fuel cell catalysis, CO2 reduction, etc. are interesting reactions that can transform our energy economy; however, many of these reactions are sluggish from unfavorable reaction kinetics and poor product selectivity. These renewable energy technologies are rooted in interfacial electrochemistry in which reactions are catalyzed at electrified metal-solution interfaces. Controlling the properties of both the metal and electrolyte solution is paramount for achieving high performance electrocatalysis. This talk will discuss our recent efforts on the synthesis, stability, and catalytic activity of atomically well-defined alloys (ordered intermetallic compounds) prepared by an electrochemically induced phase transformation (EIPT) at room temperature. We also discuss how the catalytic reactivity of materials can be tuned by controlling the identity of the electrolyte. Developing fundamental insights into electrified metal-solution interfaces can provide knowledge essential for ushering in the next generation of renewable devices.

BE Seminar: "Promoting Appendage/Limb Regeneration in Jellyfish, Drosophila, and Mouse" (Lea Goentoro)
2021-04-22 15:00 - 16:00 | https://upenn.zoom.us/j/96715197752
This seminar will be held virtually on Zoom – check email for link or contact ksas@seas.upenn.edu.
Can limb regeneration be induced? In this talk, I will discuss our work to promote regeneration in animals with limited regeneration capacity. I will present our recent discovery of a strategy for inducing regenerative response in appendages, which works across three species that span the animal phylogeny. In Cnidaria, the frequency of appendage regeneration in the moon jellyfish Aurelia was increased by feeding with the amino acid L-leucine and the growth hormone insulin. In insects, the same strategy induced tibia regeneration in adult Drosophila. Finally, in mammals, L-leucine and sucrose administration induced digit regeneration in adult mice, including dramatically from mid-phalangeal amputation. The conserved effect of L-leucine and insulin/sugar suggests a key role for energetic parameters in regeneration induction. The simplicity by which nutrient supplementation can induce appendage regeneration provides a testable hypothesis across animals.

CBE PhD Dissertation Defense | Hydrodeoxygenation of Biomass-Derived Model Compounds Over Bifunctional Catalysts
2021-04-23 11:00 - 12:30 | Zoom – Email CBE for link
Abstract:
The negative social and environmental impacts from burning of fossil fuels motivated the development of renewable and sustainable sources for the production of electricity such as wind and solar. Lignocellulosic biomass has emerged as a promising feedstock for carbon-based fuels and chemicals and much research effort has recently been directed at developing efficient catalytic pathways for the upgrading of lignin-derived oxygenate into useful products. This dissertation aimed to study the viability of utilizing bifunctional catalysts consisting of a transition metal either supported on or modified by a reducible metal oxide to promote the direct C-O bond cleavage in aromatic oxygenates (m-cresol). Surface science techniques such as HREELS, XPS and TPD were used to provide insights into the reaction pathway and mechanism of this reaction. In all studies featured in this thesis, transition metals such as Pt and Ni, exhibit moderate selectivity to desired product, toluene. It was found that the strong interaction between aromatic rings and metal surfaces facilitates the ring hydrogenation which forms an intermediate pool. While metal catalysts promoted by WOx and NbOx are extremely active and selective for the direct deoxygenation reaction. The interaction at the metal-metal oxide interfaces helps keep the metal oxide partially reduced and provide the active bonding sites for the adsorption of hydroxyl group. It was also found that the m-cresol tends to adsorb on oxygen vacancies while the aromatic rings is tilted away from the surface. This bonding configuration would promote the direct deoxygenation of hydroxyl group.

GRASP On Robotics: “Autonomous, Agile Micro Drones: Perception, Learning, and Control”
2021-04-23 11:00 - 12:30 | Zoom
Abstract: Autonomous quadrotors will soon play a major role in search-and-rescue, delivery, and inspection missions, where a fast response is crucial. However, their speed and maneuverability are still far from those of birds and human pilots. High speed is particularly important: since drone battery life is usually limited to 20-30 minutes, drones need to fly faster to cover longer distances. However, to do so, they need faster sensors and algorithms. Human pilots take years to learn the skills to navigate drones. What does it take to make drones navigate as good or even better than human pilots? Autonomous, agile navigation through unknown, GPS-denied environments poses several challenges for robotics research in terms of perception, planning, learning, and control. In this talk, I will show how the combination of both model-based and machine learning methods united with the power of new, low-latency sensors, such as event cameras, can allow drones to achieve unprecedented speed and robustness by relying solely on onboard computing.
Click here to join the Zoom Webinar

PICS Colloquium: "Network-Based Characterization, Modeling, and Control of Fluid Flows"
2021-04-23 14:00 - 15:00 | Zoom – email kathom@seas.upenn.edu
The network of interactions in a sea of vortices gives rise to the amazingly rich dynamics of fluid flows. To describe these interactions, we consider the use of mathematical tools from the emerging field of network science that is comprised of graph theory, dynamical systems, data science, and control theory. In this presentation, we discuss ways to describe unsteady fluid flows with vortical-interaction, modal-interaction, and probability-transition networks. The insights gained from these formulations are used to characterize, model, and control laminar and turbulent flows. We will also discuss some of the challenges of applying network based techniques to fluid flows and the prospects of addressing them through data-inspired techniques.

ODEI Town Hall Discussion of the Chauvin Trial Verdict
2021-04-23 16:00 - 17:00 | PA
This past week a jury in Minneapolis rendered its verdict in the trail of Derek Chauvin. While this verdict cannot undo the trauma that George Floyd’s family and the entire country experienced, it is one step towards building a society where justice and accountability are guaranteed for all citizens. In order to support the Penn SEAS community, the Office of Diversity, Equity and Inclusion will be hosting an informal Town Hall this Friday April 23 from 4-5PM EST for anyone interested in processing the events of this week and in building community so we emerge stronger.
Dr. Vincent Walford and Alaina Spiegel, PsyD from CAPS will be taking part in this discussion to lend their professional guidance and support.

Join Zoom Meeting:
https://upenn.zoom.us/j/94715341128?pwd=[REDACTED]
Meeting ID: 947 1534 1128
Passcode: [REDACTED]

ODEI Annual Banquet: Resiliency and Intentionality in Engineering
2021-04-23 19:00 - 20:00 | PA
Come celebrate graduating Penn Engineering students, network with alumni, socialize with current students, and play games for chances to win prizes! Penn Engineering alumna Faith Taliaferro will be a guest speaker. You don’t want to miss it!
Join Zoom: https://upenn.zoom.us/j/95920893362
Please RSVP here: https://forms.gle/QG3sAK3ikK7DjDqm7

PSOC Webinar: “Diet and metabolic therapeutics in cancer" (Jason Locasale)
2021-04-26 12:00 - 13:00 | PA
Title: “Diet and metabolic therapeutics in cancer”
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.

MSE Seminar: "Predicting and Controlling Scalable Quantum Systems"
2021-04-26 13:00 - 14:00 | PA
Quantum materials host spectacular excited-state and nonequilibrium effects, but many of these phenomena remain challenging to control and, consequently, technologically underexplored. My group’s research, therefore, focuses on how quantum systems behave, particularly away from equilibrium, and how we can harness these effects. By creating predictive theoretical and computational approaches to study dynamics, decoherence and correlations in quantum systems, our work could enable technologies that are inherently more powerful than their classical counterparts ranging from scalable quantum information processing and networks, to ultra-high efficiency optoelectronic and energy conversion systems. In this talk, I will present work from my research group on describing, from first principles, the microscopic dynamics, decoherence and optically-excited collective phenomena in quantum matter at finite temperature to quantitatively link predictions with 3D atomic-scale imaging and quantum spectroscopy. Capturing these dynamics poses unique theoretical and computational challenges. The simultaneous contribution of processes that occur on many time and length-scales have remained elusive for state-of-the-art calculations and model Hamiltonian approaches alike, necessitating the development of new methods. I will show selected examples of our approach in ab initio design of active defects in quantum materials, and control of collective phenomena to link these active defects. Building on this, in the second part of my seminar, I will present promising physical mechanisms and quantum device architectures for coupling to other qubit platforms via dipole-, phonon-, and magnon-mediated interactions. Finally, I will discuss ideas in directly emulating quantum information systems, particularly addressing the issues of model abstraction and scalability, and present an outlook on various co-design strategies with algorithms efforts underway.
Register for the Zoom link here.

ODEI Spotlight: "Understanding Asian Racialized Trauma"
2021-04-27 11:00 - 12:00 | PA
Understanding Asian Racialized Trauma

Date: Tuesday, April 27

Time: 11:00AM EDT

This presentation aims to help the Asian international student body of Penn and allies to start a conversation, centering Asian perspectives, about the rising anti-Asian hate we are witnessing nationally. We will discuss the unique ways in which Asians living in America are racialized as well as what our trauma responses can look like – in our history, to our health, and across our campuses.
Jessica C. Kim, LCSW is a Korean American licensed clinical social worker with 15 years of experience as a therapist working with youth, adults, and families. She received her master’s degree in social work from Columbia University and also completed a fellowship in child and adolescent mental health at Yale University Child Study Center. She is currently a PhD student at the School of Social Policy and Practice at Penn. Her research interests surround the intersection of Asian American youth & families and culturally reflexive mental health practice.

REGISTER HERE

Neural Networks for Machine Learning: A Symposium to Honor Kunihiko Fukushima, Recipient of the 2021 Bower Award and Prize for Achievement in Science
2021-04-28 09:00 - 13:00 | PA
The School of Engineering and Applied Science is honored to co-organize, with Drexel University and the Franklin Institute, a symposium to honor the Bower Awardee, Kunihiko Fukushima, the inventor of the Neocognitron. Dr. Fukushima joins a list of many distinguished laureates of the Franklin Institute – of these over 90 laureates have later received the Nobel Prize.
Dr. Fukushima has received the Bower Award “for his pioneering research that applied principles of neuroscience to engineering through his invention of the first deep convolutional neural network, “Neocognitron”—a key contribution to the development of artificial intelligence.”
https://www.fi.edu/awards
Register Here: https://tinyurl.com/BowerFukushima

ODEI Spotlight: FGLI Community Forum
2021-04-28 14:00 - 15:00 | PA
Wednesday, April 28 | 2:00PM EDT
Join Penn First Plus, PENNCAP and CAPS (Counseling and Psychological Services) for a community forum focusing on learning more about CAPS, its services, and ways in which FGLI students can utilize CAPS services during the semester and summer.
Zoom Link: P1P/CAPS/PENNCAP Community Forum »

ODEI Spotlight: PAACH & ASAM End-of-Year Celebration
2021-04-29 20:00 - 21:00 | PA
PAACH & ASAM
End-of-Year Celebration
Help us honor our 2020 and 2021 graduates, recognize our incredible community leadership, and reflect on our challenges and triumphs of this past year.
4pm-6pm: GRAB AND GO GIVEAWAY FOR 2020 & 2021 GRADUATES
Meet us at a table in front of the ARCH Building (36th and Locust)! Get your PAACH stole, T-shirts and other goodies!!
8pm-9pm: VIRTUAL END-OF-YEAR CELEBRATION W/ PAACH & ASAM @ GATHERTOWN
Join our virtual gathering to celebrate our diverse, resilient communities. Congratulate our graduating students and don’t miss a special guest speaker appearance at the beginning!!
All students (undergrad, grad & professional), faculty, staff, post-docs, alumni, and local community partners are welcome!
Register Here: bit.ly/paachasam

ODEI Spotlight: Fireside Chat with Former Ambassador Gary Locke on U.S.-China Relations and Stopping Anti-Asian Racism
2021-04-29 21:00 - 22:00 | PA
Thursday, April 29 | 9PM EDT (Friday, April 30 | 9AM Beijing Time)
Former Ambassador to China, Former Governor of Washington State, and Interim President at Bellevue College Gary Locke has been at the forefront of addressing and combating Anti-Asian Racism as U.S.-China relations deteriorate. As a representative of the Asian American community, he is bringing his diplomatic expertise to join Penn Law Professor Jacques deLisle in discussing U.S.-China relations and the ongoing social campaign against the surge of hatred, violence, and discrimination against Asians.

Register: Fireside Chat with Ambassador Locke »

MEAM Thesis Defense: "Inkjet Printed Neuromorphic Inference Circuits with Memristor-Based Neuron Network"
2021-04-30 10:30 - 11:30 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
The deployment of machine learning inference algorithms on Internet of Things (IoT) devices remains challenging. Despite the low fabrication cost, flexibility, and low power consumption of the printed electronics for IoT applications, there are not many demonstrations of printed electronics solving neural network tasks, mainly due to the poor electrical performance, low device yield, and large footprint of printed thin-film transistors. In this study, we design and fabricate an inkjet-printed all-passive neuromorphic circuit based on printable memristor neuron architecture. The fully inkjet printed memristor is based on Ag/Poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS)/Ag. It shows bipolar resistance switching with low switching voltage (0.1~0.2V), good stability (9 days), high cyclability (200 cycles), and a large ON-to-OFF resistance margin (~40). A write-once-read-many times (WORM) memory is also observed in the proposed memristor when a large voltage (~3V) is applied. To present the neuromorphic computing capability, we fabricated the circuit that can realize the XOR classification problem with 100% accuracy, 0.7 cm2 size, and power consumption of 0.68 mW, by integrating the inkjet printed memristors with inkjet printed silver resistors. After establishing the device model of the inkjet printed memristors, we demonstrate via SPICE circuit simulations how a shallow network of memristor-resistors-based passive neuron networks can solve the IRIS classification problem with 95% accuracy and 2 mW power consumption. By providing the fabrication and simulation of the proposed memristor-based shallow multilayer perceptron, this thesis paves the way for low-power and low-cost neuromorphic inference devices for future applications of IoT.

PSOC Webinar: “Revisiting the Reciprocal Mechanical Interactions Between Cancers and Tissue“ (Bo Sun)
2021-05-03 12:00 - 13:00 | https://upenn.zoom.us/j/96715197752
Title: “Revisiting the Reciprocal Mechanical Interactions between Cancers and Tissue”

Physical Sciences in Oncology Center PSOC@Penn
Spring 2021 Webinar Series Mondays at 12:00 noon (EDT)
For webinar links, please contact manu@seas.upenn.edu

ESE Seminar: "Demystifying the Efficiency of Reinforcement Learning: Two Recent Stories"
2021-05-03 15:00 - 16:00 | https://upenn.zoom.us/j/96715197752
Reinforcement learning (RL), which is frequently modeled as sequential learning and decision making in the face of uncertainty, is garnering growing interest in recent years due to its remarkable success in practice. In contemporary RL applications, it is increasingly more common to encounter environments with prohibitively large state and action space, thus imposing stringent requirements on the sample and computational efficiency of the RL algorithms in use. Despite the empirical success, however, the theoretical underpinnings for many popular RL algorithms remain highly inadequate even for the tabular setting.
In this talk, we present two vignettes regarding the effectiveness of RL algorithms. The first vignette demonstrates that a perturbed model-based RL approach is minimax optimal under a generative model, without suffering from a sample size barrier that was present in all past work. The second vignette covers policy optimization in reinforcement learning. On the one hand, we demonstrate that the popular softmax policy gradient method can take exponential time to converge; on the other hand, employing natural policy gradients and enforcing entropy regularization provably achieve fast global convergence. These results cover two distinctive RL paradigms, and might shed light on the efficacy of these algorithms in more complicated scenarios.
For the seminar link, email Danielle T. Kopicko at dtadros@seas.upenn.edu.
