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Conferences, Lectures, & Seminars
Events for November
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Astani Department of Civil and Environmental Engineering Seminar
Mon, Nov 02, 2020 @ 04:00 PM - 05:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Larry Rosen, Professor Emeritus of Psychology, California State University, Dominguez Hills
Talk Title: WE ARE FACING AN ATTENTION CRISIS: WHAT IS DRIVING OUR DISTRACTED MINDS?
Abstract: Please see attached Abstract-Bio and Zoom Meeting info.
Host: Dr. Burcin Becerik-Gerber
More Information: L. Rosen_Abstract_Bio.pdf
Location: Join Zoom Meeting: https://usc.zoom.us/j/98766114432 Meeting Id #98766114432 Passcode: 175729
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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Professional Enhancement Seminar
Tue, Nov 03, 2020 @ 04:00 AM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: TBD, TBD
Talk Title: TBD
Abstract: This bi-monthly seminar brings industry professionals from fields within electrical and computer engineering to share advice and answer questions about what students can do to improve their professional experience.
Meeting ID: 974 2555 7004
Passcode: 494632
Host: Mihailo Jovanovic
Webcast: https://usc.zoom.us/j/97425557004?pwd=T29UWER0emdmRllVMVFiT3pRNlk5QT09WebCast Link: https://usc.zoom.us/j/97425557004?pwd=T29UWER0emdmRllVMVFiT3pRNlk5QT09
Audiences: Everyone Is Invited
Contact: Benjamin Paul
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ISE 651 - Epstein Seminar
Tue, Nov 03, 2020 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Pitu Mirchandani, Professor, Department of Computing, Informatics, and Decision Systems Engr, ASU
Talk Title: Managing Hurricane Evacuation with Stochastic Dynamic Networks
Host: Prof. Suvrajeet Sen
More Information: November 3, 2020.pdf
Location: Online/Zoom
Audiences: Everyone Is Invited
Contact: Grace Owh
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CS Colloquium: Yuanzhi Li (CMU) - Multi-player Multi-armed Bandit: Can We Collaborate Without "Zoom"?
Tue, Nov 03, 2020 @ 03:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Yuanzhi Li, Carnegie Mellon University
Talk Title: Multi-player Multi-armed Bandit: Can We Collaborate Without "Zoom"?
Series: Computer Science Colloquium
Abstract: Multi-armed bandit is a well-established area in online decision making, where one player makes sequential decisions in a non-stationary environment to maximize his/her accumulative rewards. The traditional multi-armed bandit problem becomes significantly more challenging when there are multiple players in the same environment, while only one piece of reward is presented at a time for each arm. In this setting, if two players pick the same arm at the same round, they are only able to get one piece of reward instead of two. When the rewards are non-negative, to maximize the total accumulative rewards by all players, they need to collaborate to avoid "collision" -- i.e. the players need to make sure that they do not all rush to the same arm (even if it has the highest reward) at the same round. We focus on the setting where communications between players are completely disabled: e.g. they are separated in different places of the world without any "Zoom". We show that low-regret can still be obtained in this setting: Players can actually collaborate to maximize total rewards by avoiding collision in a non-stationary environment, even when they do not communicate at all during the entire sequence of decisions.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_kVp5jz5qSIKAZIphNGWaWw
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Yuanzhi Li is an assistant professor at CMU, Machine Learning Department. He did his Ph.D. at Princeton, under the advice of Sanjeev Arora (2014-2018) as well as a one-year postdoc at Stanford. His wife is Yandi Jin.
Host: Haipeng Luo
More Info: https://usc.zoom.us/webinar/register/WN_kVp5jz5qSIKAZIphNGWaWw
Location: Online Zoom Webinar
Audiences: Everyone Is Invited
Contact: Computer Science Department
Event Link: https://usc.zoom.us/webinar/register/WN_kVp5jz5qSIKAZIphNGWaWw
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WiE's Negotiation Seminar with Tahl Raz, 11/4 at 1pm
Wed, Nov 04, 2020 @ 01:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Tahl Raz, Co-Author of Never Split the Difference
Talk Title: Negotiation Seminar
Abstract: Our event is back on! We're happy that our speaker is feeling better and are looking forward to seeing you all soon! The Graduate Committee of Women in Engineering is excited to host Tahl Raz, New York Times bestselling author, award-winning journalist, and co-author of the nation's leading publication on negotiation, Never Split the Difference, in our Negotiation Seminar on Wednesday, November 4th at 1pm PST.
RSVP to attend!
Learn more about Tahl: https://www.linkedin.com/in/tahlraz
Zoom Link: https://usc.zoom.us/j/98308499819?pwd=bVZHeDJRODcrSlFpN3hGZ1dyczU2UT09
RSVP Form: https://forms.gle/7dHxaaMwyq7faceb8
Biography: Learn more about Tahl: https://www.linkedin.com/in/tahlraz
Host: The Graduate Committee of Women in Engineering
More Info: https://forms.gle/7dHxaaMwyq7faceb8
Webcast: https://usc.zoom.us/j/98308499819?pwd=bVZHeDJRODcrSlFpN3hGZ1dyczU2UT09Location: Zoom
WebCast Link: https://usc.zoom.us/j/98308499819?pwd=bVZHeDJRODcrSlFpN3hGZ1dyczU2UT09
Audiences: Everyone Is Invited
Contact: USC Computer Science
Event Link: https://forms.gle/7dHxaaMwyq7faceb8
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar
Wed, Nov 04, 2020 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Nick Gravish, Mechanical & Aerospace Engineering University of California, San Diego
Talk Title: The Hard Parts of Soft Robots
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: The form and shape of modern robots are rapidly changing from rigid, stiff, but precise machines to more compliant, adaptable, but inherently underactuated systems; often called soft robotics. The emergence of soft robots is in part motivated by the need for safe robotic technologies when human interaction is frequent. However, another motivation for designing soft robotic systems is to exploit the compliant mechanics and high degree of freedom of these systems for adaptability, actuation, and sensing. The majority of efforts to build soft robots utilize a standard toolkit of silicone elastomer casting, pneumatic actuation, and stretchable conducting elements. In this talk I will present our efforts to design and build robots capable of compliance control, reconfiguration, and adaptability using laminate and 3D printing techniques, where "softness" is derived from the configuration of rigid constituent materials. This will focus on three research efforts: compliance control through sliding-layer laminates, insect-inspired 3D printing for "flexoskeleton" robots, and shape changing robot feet for improved mobility of legged robots. While these efforts focus primarily on the mechanical domain of soft robots I will highlight opportunities for sensor and electronics integration through these fabrication approaches.
Biography: Dr. Nick Gravish received his PhD from Georgia Tech where he worked on understanding the locomotion of ants within their nest. Gravish used robots as physical models to motivate and study aspects of biological locomotion. During his post-doc Gravish worked in the microrobotics lab of Rob Wood at Harvard, where he gained expertise in designing and studying insect-scale robots. Gravish is an assistant professor at UC San Diego in the Mechanical and Aerospace Engineering department. His lab focuses on developing new bio-inspired robotic technologies to improve the adaptability and resilience of mobile robots.
Host: Feifei Qian, feifeiqi@usc.edu
More Info:
Webcast: https://usc.zoom.us/webinar/register/WN_YSl0DRVOQJetWGNAACPOYQLocation: Online
WebCast Link: https://usc.zoom.us/webinar/register/WN_YSl0DRVOQJetWGNAACPOYQ
Audiences: Everyone Is Invited
Contact: Talyia White
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AME Seminar
Wed, Nov 04, 2020 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Faisal Amlani, USC
Talk Title: Novel High-Performance Numerical Methods for Problems in Solids, Fluids and Their Interactions: Predictions and Insights into the Underlying Physics
Abstract: This talk discusses efforts to study wave-like phenomena in realistic applications through the development of new high-order methodologies for the numerical analysis of the partial differential equations (PDEs) that govern both linear and nonlinear behavior. These techniques include new Fourier-based methods in the time-domain as well as adaptive boundary element methods in frequency-space, where the ultimate goal is to provide fast, stable and physically-faithful resolution of the underlying mechanical dynamics. With an eye towards mutual validation of both simulation and experiment, these tools will be demonstrated through some of the collaborative scientific problems that have inspired them, including those in materials science (ultrasonic non-destructive testing), cardiovascular medicine (hemodynamic waves) and geophysics (supershear ruptures and tsunami generation).
Biography: Faisal Amlani received his BA from Rice University and his PhD from Caltech, both in applied mathematics. His doctoral work was awarded the Caltech W.P. Carey Prize and the Caltech Demetriades Prize for the most outstanding dissertation in mathematics and seismo-engineering, respectively. After some years working as an experimentalist and engineer at an R&D aerospace startup in Los Angeles, he returned to academia by way of France through postdocs at Sorbonne University and the Institut Polytechnique de Paris. He is currently a Postdoctoral Scholar-Research Associate in the Department of Aerospace & Mechanical Engineering at USC.
Host: AME Department
More Info: https://usc.zoom.us/j/98031374607
Webcast: https://usc.zoom.us/j/98031374607Location: Online event
WebCast Link: https://usc.zoom.us/j/98031374607
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://usc.zoom.us/j/98031374607
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AME PhD Student Seminar
Fri, Nov 06, 2020 @ 03:00 AM - 04:00 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Shilpa Vijay, USC AME PhD Student
Talk Title: Interfacial thermal transport in partially porous channel flow at turbulent flow regimes
Abstract: We investigate interfacial thermal transport in a partially porous channel via laboratory experiments to evaluate the effect of porous medium microstructure at varying Reynolds numbers. Previous direct numerical simulations for partially porous channel flow indicate that large vortex structures enhance turbulent heat transfer at the porous medium-unobstructed
flow interface. Commercially-available Aluminum foams with nominal pore sizes 10 ppi and 40 ppi are attached to a heater block and placed in a forced convection arrangement adjacent to an unobstructed channel. Measurements of pressure drop and temperatures are made across the porous section for bulk Reynolds number varying from 500 to 1500 to characterize friction factors and Nusselt numbers. Heat transfer efficiency with respect
to pumping power requirements is evaluated. Particle Image Velocimetry (PIV) measurements made at a subset of these Reynolds numbers are being analyzed to test for the emergence of interfacial vortex structures, and quantify their effect on interfacial thermal transport.
Biography: Shilpa Vijay is a Ph.D. student under Professor Mitul Luhar. Her research focuses on characterizing thermal transport over porous interfaces in turbulent regimes. Shilpa has a B.S. in Civil Engineering from College of Engineering Pune in India (2016), and an M.S. in Mechanical Engineering from USC (2018).
Host: AME Department
More Info: https://usc.zoom.us/j/92144809085
Audiences: Everyone Is Invited
Contact: Christine Franks
Event Link: https://usc.zoom.us/j/92144809085
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Advanced Manufacturing Seminar Series
Fri, Nov 06, 2020 @ 11:30 AM - 01:00 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Ajay Malshe, Purdue
Talk Title: Nature's Tool Box for Smart Manufacturing Enterprise
Abstract: Speaker will discuss simplicity, elegance, and robustness as foundations for innovations for equity in a smart and inclusive enterprise. This talk will discuss functions, structures, and resilience demonstrated by nature in a sustainable ecosystem. Speaker will share multiple examples to illustrate lessons materials and manufacturing disciplines can learn and apply to advance state of the art for better multifunctionality,
adaptability, survivability, and sustainability.
Biography: Please see attached flyer.
More Info: https://usc.zoom.us/webinar/register/WN_Og2AM47xQPmuDYgkAP-3NA
Webcast: https://usc.zoom.us/webinar/register/WN_Og2AM47xQPmuDYgkAP-3NAMore Information: Adv Mfg Seminar Fall 2020_Ajay Malshe.pdf
Location: Online event
WebCast Link: https://usc.zoom.us/webinar/register/WN_Og2AM47xQPmuDYgkAP-3NA
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://usc.zoom.us/webinar/register/WN_Og2AM47xQPmuDYgkAP-3NA
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Astani Department of Civil and Environmental Engineering Seminar
Mon, Nov 09, 2020 @ 04:00 PM - 05:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Henry Burton, Englekirk Presidential Chair in Structural Engineering, University of California, Los Angeles
Talk Title: Seismic Risk and Resilience Modeling of Water Distribution Systems
Abstract: Water distribution systems are critical to the well-being of communities since they contribute to the functionality of all other infrastructure. Earthquakes and other natural hazards can cause damage to the components of a water distribution system, causing far-reaching socioeconomic consequences. This presentation will discuss some recent advancements in seismic risk and resilience modeling of water distribution systems. First, an end-to-end simulation framework to evaluate post-earthquake functional loss and restoration of a water system is developed, which encompasses seismic hazard characterization, component damage, hydraulic performance and network restoration modeling. The modeling framework is validated using data from the 2014 South Napa Earthquake and extended to a hypothetical scenario. To deal with the temporal complexities that are embedded in the post-earthquake restoration process, a general dynamic updating framework is developed to reduce uncertainties in the outcomes of post-event recovery forecasts using Bayesian Inferencing, by exploiting real-time data. The specific example of updating post-earthquake functional recovery forecasts is presented and validated on a real pipe network (Napa water system) and event (2014 earthquake and recovery). The end-to-end framework is then extended to enable stochastic event set assessments of the water network using the UCERF2 earthquake rupture forecast model. Given that evaluating a large set of events with end-to-end simulation modeling is computationally expensive, a framework that uses active learning to select a subset of ground motion maps and associated occurrence rates that reasonably estimates the water network risk is also developed.
Biography: Dr. Henry V. Burton is an Associate Professor and the Englekirk Presidential Chair in Structural Engineering in the Department of Civil and Environmental Engineering at the University of California, Los Angeles. His research is directed towards understanding and modeling the relationship between the performance of infrastructure systems within the built environment, and the ability of communities to minimize the extent of socioeconomic disruption following extreme events. Dr. Burton is a registered structural engineer in the state of California. Prior to obtaining his PhD in Civil and Environmental Engineering at Stanford University, he spent six years in practice at Degenkolb Engineers, where he worked on numerous projects involving design of new buildings and seismic evaluation and retrofit of existing buildings. He is a recipient of the National Science Foundation Next Generation of Disaster Researchers Fellowship (2014) and the National Science Foundation CAREER Award (2016).
Host: Dr. Bora Gencturk and Dr. Roger Ghanem
Location: Zoom Meeting: https://usc.zoom.us/j/98766114432 Meeting ID#98766114432 Passcode:175729
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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CS Colloquium: Xuezhe Ma (USC ISI) - Towards Structured-Infused and Disentangled Representation Learning
Tue, Nov 10, 2020 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Xuezhe Ma, USC
Talk Title: Towards Structured-Infused and Disentangled Representation Learning
Abstract: One of the keys to the empirical successes of deep neural networks in many domains, such as natural language processing and computer vision, is their ability to automatically extract salient features for downstream tasks via the end-to-end learning paradigm.
In this talk, I will present two of our recent work. First, I will introduce how to encode structured dependencies into learned representations to achieve efficient non-autoregressive machine translation models. Second, I will present our work on learning representations to decouple global and local information from/for image generation. I will conclude by laying out future research directions towards interpretable and controllable representation learning.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Join Zoom Meeting
https://usc.zoom.us/j/91743613540?pwd=S0hPWEk5MHFSTVdoSmVidkxLVmlwQT09
Meeting ID: 917 4361 3540
Passcode: 296867
Biography: Xuezhe Ma joined ISI as a computer scientist in Fall 2020.
Xuezhe received his PhD degree in Language Technologies Institute at Carnegie Mellon University, advised by Eduard Hovy.
Before that, he received his B.E and M.S from Shanghai Jiao Tong University. His research interests fall in areas of natural language processing and machine learning, particularly in deep learning and representation learning with applications to linguistic structured prediction and deep generative models. Xuezhe has interned at Allen Institute for Artificial Intelligence (AI2) and earned the AI2 Outstanding Intern award. His research has been recognized with outstanding paper award at ACL 2016 and best demo paper nomination at ACL 2019.
Host: Xiang Ren
Audiences: Everyone Is Invited
Contact: Cherie Carter
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***NO ISE 651, Epstein Seminar - Week of INFORMS***
Tue, Nov 10, 2020 @ 03:00 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Audiences: Everyone Is Invited
Contact: Grace Owh
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CS Distinguished Lecture: Steve Easterbrook (University of Toronto) - Computing the Climate: Building the Software for Understanding Climate Change
Tue, Nov 10, 2020 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Steve Easterbrook, University of Toronto
Talk Title: Computing the Climate: Building the Software for Understanding Climate Change
Series: Computer Science Distinguished Lecture Series
Abstract: The history of climate science is closely tied to the history of computing. Climate scientists have always pushed the limits of computational modelling, from the first computational weather forecasts developed by von Neumann and Charney to run on ENIAC, to the earth system models used to produce projections of future climate change for the most recent IPCC reports. Along the way, climate scientists have developed a sophisticated set of software development practices tailored to the needs of a science in which virtual experiments are essential for understanding the relationships between human activity and the global climate system. In this talk, I will first explain what climate models do, via a quick tour of the history of climate modelling. I will then show how a core set of software development practices are used to support a culture of scientific experimentation which provides robust answers to societally important questions. I will end the talk with a brief overview of the current generation of climate model experiments. These address critically important questions such as whether there are still viable pathways to deliver the UN's commitment to constrain global warming to no more than +2*C, and whether geo-engineering can buy us more time to address the underlying causes of climate change.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_0sw0PJhSTFuyqKxoQie5Gw
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Steve Easterbrook is the Director of the School of the Environment and Professor of Computer Science at the University of Toronto. He received his Ph.D. (1991) in Computing from Imperial College in London (UK), and joined the faculty at the School of Cognitive and Computing Science, University of Sussex. From 1995-99, he was lead scientist at NASA's Independent Verification and Validation (IV&V) Facility in West Virginia, where he investigated software verification on the Space Shuttle Flight Software, the International Space Station, and the Earth Observation System. He moved to the University of Toronto in 1999. His research interests range from modelling and analysis of complex adaptive systems to the socio-cognitive aspects of team interaction. His current research is in climate informatics, where he studies how climate scientists develop computational models to improve their understanding of earth systems and climate change, and the broader question of how that knowledge is shared with other communities. He has been a visiting scientist at the UK Met Office Hadley Centre, in Exeter, the National Centre for Atmospheric Research in Boulder, Colorado; the Max-Planck Institute for Meteorology, in Hamburg, and the Institute Pierre Simon Laplace in Paris.
Host: Heather Culbertson
More Info: https://usc.zoom.us/webinar/register/WN_0sw0PJhSTFuyqKxoQie5Gw
Location: Online Zoom Webinar
Audiences: Everyone Is Invited
Contact: Computer Science Department
Event Link: https://usc.zoom.us/webinar/register/WN_0sw0PJhSTFuyqKxoQie5Gw
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Lyman L. Handy Colloquia - Nick Birbillis
Tue, Nov 10, 2020 @ 04:00 PM - 05:20 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Nick Birbillis, Australian National University
Talk Title: CAUSE WE ARE LIVING IN A MATERIAL WORLD
Abstract: https://usc.zoom.us/j/93139729396?pwd=UmNqVmVac1BGcEZoVEgxaGNnRzVaUT09
Meeting ID: 931 3972 9396
Passcode: 514283
Host: Andrea Hodge
Audiences: Everyone Is Invited
Contact: Greta Harrison
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AME Seminar
Wed, Nov 11, 2020 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Mark Hodes, Tufts University
Talk Title: Asymptotic Nusselt Numbers for Internal Flow in the Cassie State and Their Application to Thermal Management of Electronics
Abstract: We consider laminar, fully-developed, Poiseuille flows of liquid in the Cassie state through diabatic, parallel-plate microchannels symmetrically textured with isoflux ridges. Through the use of matched asymptotic expansions we analytically develop expressions for dimensionless (apparent hydrodynamic) slip lengths and variously-defined Nusselt numbers. Our small parameter (ε) is the pitch of the ridges divided by the height of the microchannel. When the ridges are oriented parallel to the (fully developed) flow, we quantify the error in the Nusselt number expressions in the literature and we provide a new closed-form result. The latter is accurate to O(ε2) and valid for any solid (ridge) fraction, whereas those in the current literature are accurate to O(ε) and break down in the important limit when solid fraction approaches zero. When the ridges are oriented transverse to the (periodically fully-developed) flow, the error associated with neglecting inertial effects to find the slip length is shown to be O(ε3Re) where Re is the channel-scale Reynolds number based on its hydraulic diameter. The corresponding Nusselt number expressions are new and their accuracy is shown to be dependent on Reynolds number, Peclet number and Prandtl number in addition to ε. They're compared to numerical results from the literature. In concluding this talk, we will show how the results can be used to design enhanced liquid-metal cooling solutions for microelectronics.
Biography: Marc Hodes earned his BS, MS, and PhD degrees in Mechanical Engineering from the University of Pittsburgh, the University of Minnesota and the Massachusetts Institute of Technology, respectively. He spent 10 years at Bell Labs Research (Murray Hill, NJ) and has spent extended periods in residence at the National Institute of Standards and Technologies (NIST), the University of Limerick and Imperial College London. He joined the Department of Mechanical Engineering at Tufts University in 2008 where he is a Professor and the Director of Graduate Studies. His Groups research there has been funded by government agencies, e.g., NSF, DARPA and DoE, and industry, e.g., Huawei and Google. Research interests are in Transport Phenomena and, over the course of his career, four thematic areas have been addressed: 1) the thermal management of electronics, 2) mass transfer in supercritical fluids, 3) analysis of thermoelectric modules, and 4) momentum, heat, mass and charge transport in the presence of apparent slip. Professor Hodes is the sole- or co-author of 50 papers in archival journals on these subjects. He is also a co-inventor on 15 issued US patents. His current research lies in three areas. First, analytical solutions for Poiseuille and Nusselt numbers for liquid flows over diabatic structured surfaces that capture, e.g., the effects of curvature, thermocapillary stress and/or evaporation and condensation along menisci, are being developed. This thread is in the context of the Red Lotus Project, a collaboration with applied mathematicians at Imperial College London. Secondly, a series of experiments to measure densities, molecular and Soret diffusion coefficients and mass transfer rates in alcohol-carbon dioxide solutions at supercritical conditions relevant to the drying of aerogels are being conducted. Thirdly, a numerical method for the optimization of heat sinks is under development. The latter was recently spun out of Tufts University as a software product by a start-up company, Transport Phenomena Technologies, LLC, co-founded by Professor Hodes, per NSF SBIR funding.
Host: AME Department
More Info: https://usc.zoom.us/j/94808927541
Webcast: https://usc.zoom.us/j/94808927541Location: Online event
WebCast Link: https://usc.zoom.us/j/94808927541
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://usc.zoom.us/j/94808927541
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NL Seminar-The Unreasonable Syntactic Expressivity of RNNs
Thu, Nov 12, 2020 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: John Hewitt, Stanford University
Talk Title: The Unreasonable Syntactic Expressivity of RNNs
Series: NL Seminar
Abstract: In 2015, Andrej Karpathy posted a now famous blog post on The Unreasonable Effectiveness of Recurrent Neural Networks. To summarize this sense of wonder, Karpathy emphasized We will train RNNs to generate text character by character and ponder the question how is that even possible? RNNs empirically generate natural language with high syntactic fidelity, but their success is not well understood theoretically. In this talk, I will provide theoretical insight into this success, proving in a finite precision setting that RNNs can efficiently generate bounded hierarchical languages that reflect the scaffolding of natural language syntax. I will introduce Dyck k,m, the language of well nested brackets of k types and m bounded nesting depth, reflecting the bounded memory needs and long distance dependencies of natural language syntax. The best previously known results use Ok m 2 memory hidden units to generate these languages. I will prove that an RNN with O m log k hidden units suffices, an exponential reduction in memory, by an explicit construction. Finally, I will show that no algorithm, even with unbounded computation, can suffice with o m log k hidden units.
Biography: John is a 3rd year PhD student in computer science at Stanford University, advised by Chris Manning and Percy Liang. He works on understanding and improving how unsupervised neural networks learn and process human languages. He is supported by a National Science Foundation Graduate Research Fellowship, and is the recipient of an EMNLP Runner Up Best Paper award.
Host: Jon May and Mozhdeh Gheini
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://usc.zoom.us/j/95584315616Location: Information Science Institute (ISI) - Virtual Only
WebCast Link: https://usc.zoom.us/j/95584315616
Audiences: Everyone Is Invited
Contact: Petet Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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CS Colloquium: Muhao Chen (USC ISI) - Knowledge Acquisition with Transferable Representation Learning
Thu, Nov 12, 2020 @ 03:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Muhao Chen, USC
Talk Title: Knowledge Acquisition with Transferable Representation Learning
Abstract: Multi-relational data provide structural and actionable knowledge representations for various AI systems. As constructing such structural knowledge is often costly and has relied on extensive human effort, there is a pressing need for approaches to automate knowledge acquisition. In this talk, I will summarize two lines of my research to accomplish this mission: (i) transferable representation learning, and (ii) constrained and indirect supervision. Transferable representation learning can automatically capture the association of knowledge across different data sources with minimal supervision, therefore holds the promise of creating a universal representation scheme to support the synchronization of knowledge. Meanwhile, constrained and indirect supervision methods could develop more reliable learning systems for knowledge acquisition from unstructured data, particularly in cases without sufficient training labels. Based on these two lines of research, I will also discuss several applications for a wide range of tasks in areas of knowledge base construction, natural language understanding and computational biology.
This talk satisfies requirements for CSCI 591: Research Colloquium
Join Zoom Meeting
https://usc.zoom.us/j/96706950791?pwd=cXp3TWlhRmo5ZDB0bnA0a0lOQ1VVdz09
Meeting ID: 967 0695 0791
Passcode: 808248
Biography: Muhao Chen joined as a computer scientist at USC ISI in Fall 2020. Prior to that, he was a postdoctoral fellow at UPenn, hosted by Dan Roth. He received his Ph.D. in Computer Science from UCLA in 2019, and B.S. in Computer Science from Fudan University in 2014. His research focuses on data-driven machine learning approaches for processing structured data, and knowledge acquisition from unstructured data. Particularly, he is interested in developing knowledge-aware learning systems with generalizability and requiring minimal supervision, and with concrete applications to natural language understanding, knowledge base construction, computational biology and medicine. Muhao has published over 40 papers in leading AI, NLP and Comp. Bio/med venues. His work has received a best student paper award at ACM BCB, and best paper award nomination at CoNLL. Additional information is available at https://muhaochen.github.io/
Host: CS Department
Audiences: Everyone Is Invited
Contact: Cherie Carter
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AME PhD Student Seminar
Fri, Nov 13, 2020 @ 03:00 PM - 04:00 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Mark Hermes, USC AME PhD Student
Talk Title: Pentaradial sea stars generate downforce
Abstract: Intertidal sea stars often function in environments with extreme hydrodynamic loads that can compromise their ability to remain attached to surfaces. While behavioral responses such as burrowing into sand or sheltering in rock crevices can help minimize hydrodynamic loads, previous work shows that sea stars also alter body shape in response to flow conditions. This morphological plasticity suggests that sea star body shape may play an important hydrodynamic role. In this study, we measured the fluid forces acting on surface-mounted sea star and spherical dome models in water channel tests. All sea star models created downforce, i.e., the fluid pushed the body towards the surface. In contrast, the spherical dome generated lift. We also used Particle Image Velocimetry (PIV) to measure the midplane flow field around the models. Control volume analyses based on the PIV data show that downforce arises because the sea star bodies serve as ramps that divert fluid away from the surface. These observations are further rationalized using force predictions and flow visualizations from numerical simulations. The discovery of downforce generation could explain why sea stars are shaped as they are: the pentaradial geometry aids attachment to surfaces in the presence of high hydrodynamic loads.
Biography: Mark Hermes is a Ph.D. student advised by Dr. Mitul Luhar working in the Fluid-Structure Interactions Lab at University of Southern California (USC). His research explores the intersection of underwater crawling and hydrodynamic shape optimization for surface-attached bodies. Mark received his B.S. in Mechanical Engineering at the University of Texas at Austin and his M.S. at USC.
Host: AME Department
More Info: https://usc.zoom.us/j/92144809085
Audiences: Everyone Is Invited
Contact: Christine Franks
Event Link: https://usc.zoom.us/j/92144809085
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Astani Department of Civil and Environmental Engineering Seminar
Mon, Nov 16, 2020 @ 04:00 PM - 05:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Jose-Luis Jimenez, University of Colorado at Boulder
Talk Title: The Modes of Transmission of SARS-CoV-2, and How to Protect Ourselves: What We Know Now
Abstract: See attached Abstract and Bio.
Host: Dr. George Ban-Weiss
More Information: J. Jimenez- Abstract-Bio.pdf
Location: Zoom Meeting
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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CS Colloquium: Mohammad Rostami (USC ISI) - Learning Efficiently in Data-Scarce Regimes
Tue, Nov 17, 2020 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Mohammad Rostami, USC
Talk Title: Learning Efficiently in Data-Scarce Regimes
Abstract: The unprecedented processing demand, posed by the explosion of big data, challenges researchers to design efficient and adaptive machine learning algorithms that do not require persistent retraining and avoid learning redundant information. Inspired from learning techniques of intelligent biological agents, identifying transferable knowledge across learning problems has been a significant research focus to improve machine learning algorithms. In this talk, we explain how the challenges of knowledge transfer can be addressed through embedding spaces that capture and store hierarchical knowledge.
We first focus on the problem of cross-domain knowledge transfer. We explore the problem of zero-shot image classification, where the goal is to identify images from unseen classes using semantic descriptions of these classes. We train two coupled dictionaries that align visual and semantic domains via an intermediate embedding space. We then extend this idea by training deep networks that match data distributions of two visual domains in a shared cross-domain embedding space.
We then investigate the problem of cross-task knowledge transfer in sequential learning settings. Here, the goal is to identify relations and similarities of multiple machine learning tasks to improve performance across the tasks. We first address the problem of zero-shot learning in a lifelong machine learning setting, where the goal is to learn tasks with no data using high-level task descriptions. Our idea is to relate high-level task descriptors to the optimal task parameters through an embedding space. We then develop a method to overcome the problem of catastrophic forgetting within a continual learning setting of deep neural networks by enforcing the tasks to share the same distribution in the embedding space.
Finally, we focus on current research directions to expand the past progress and plans for the future research directions. Through this talk, we demonstrate that despite major differences, problems within the above learning scenarios can be tackled using a unifying strategy that allows transferring knowledge effectively.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Join Zoom Meeting
https://usc.zoom.us/j/91954313931?pwd=U3JmUWR4WVZ6aDEyMUs0dEk0akZ5QT09
Meeting ID: 919 5431 3931
Passcode: 299776
Biography: Mohammad Rostami is a computer scientist at USC Information Sciences Institute. He received Ph.D. degree in Electrical and Systems Engineering from the University of Pennsylvania in August 2019. He also received an M.S. degree in Robotics and M.A. degree in Philosophy at Penn. Before Penn, he obtained an M.Sc. degree in Electrical and Computer Engineering from University of Waterloo, and B.Sc. degree in Electrical Engineering and B.Sc. degree in Mathematics from the Sharif University of Technology. His current research area is learning in time-dependent and data-scarce regimes within machine learning.
Host: CS Department
Audiences: Everyone Is Invited
Contact: Cherie Carter
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ISE 651 - Epstein Seminar
Tue, Nov 17, 2020 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Siqian Shen, Associate Professor, Industrial & Operations Engineering, University of Michigan
Talk Title: Multistage Distributionally Robust Mixed-Integer Programming with Decision-Dependent Moment-Based Ambiguity Sets
Host: Prof. Suvrajeet Sen
More Information: November 17, 2020.pdf
Location: Online/Zoom
Audiences: Everyone Is Invited
Contact: Grace Owh
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CS Distinguished Lecture: Jennifer Rexford (Princeton University) - Securing Internet Applications From Routing Attacks
Tue, Nov 17, 2020 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jennifer Rexford, Princeton University
Talk Title: Securing Internet Applications From Routing Attacks
Series: Computer Science Distinguished Lecture Series
Abstract: The Internet is a "network of networks" that interconnects tens of thousands of separately administered networks. Yet, the Border Gateway Protocol (BGP), the glue that holds the disparate parts of the Internet together, is notoriously vulnerable to misconfiguration and attack. The consequences range from making destinations unreachable, to misdirecting traffic through unexpected intermediaries, to impersonating legitimate services. Attacks on Internet routing are typically viewed through the lens of availability and confidentiality, assuming an adversary that either discards traffic or performs eavesdropping. Yet, a strategic adversary can use routing attacks to compromise the security of critical Internet applications like Tor, certificate authorities, and the bitcoin network. In this talk, we survey such application-specific routing attacks and argue that both application-layer and network-layer defenses are essential and urgently needed. While application-layer defenses are easier to deploy in the short term, we hope that greater awareness of strategic attacks on important applications can provide much needed momentum for the deployment of network-layer defenses like secure routing protocols.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_uiLYEP8mRR2_UIQ4oJn5ug
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Jennifer Rexford is the Gordon Y.S. Wu Professor of Engineering and the Chair of Computer Science at Princeton University. Before joining Princeton in 2005, she worked for nine years at AT&T Labs--Research. Jennifer received her BSE degree in electrical engineering from Princeton University in 1991, and her PhD degree in electrical engineering and computer science from the University of Michigan in 1996. She is co-author of the book "Web Protocols and Practice" (Addison-Wesley, 2001). She served as the chair of ACM SIGCOMM from 2003 to 2007. Jennifer received ACM's Grace Murray Hopper Award for outstanding young computer professional, the ACM Athena Lecturer Award, the NCWIT Harrold and Notkin Research and Graduate Mentoring Award, the ACM SIGCOMM award for lifetime contributions, and the IEEE Internet Award. She is an ACM Fellow, an IEEE Fellow, and a member of the American Academy of Arts and Sciences, the National Academy of Engineering, and the National Academy of Sciences.
Host: Heather Culbertson
More Info: https://usc.zoom.us/webinar/register/WN_uiLYEP8mRR2_UIQ4oJn5ug
Location: Online Zoom Webinar
Audiences: Everyone Is Invited
Contact: Computer Science Department
Event Link: https://usc.zoom.us/webinar/register/WN_uiLYEP8mRR2_UIQ4oJn5ug
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar
Wed, Nov 18, 2020 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Taylor T. Johnson, Electrical Engineering and Computer Science, Vanderbilt University
Talk Title: Verifying Deep Neural Networks in Autonomous Cyber-Physical Systems
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: The ongoing renaissance in artificial intelligence (AI) has led to the advent of machine learning (ML) methods deployed within components for sensing, actuation, and control in safety-critical cyber-physical systems (CPS). While such learning-enabled components (LECs) are enabling autonomy in systems like autonomous vehicles, swarm robots, and other CPS, as demonstrated in part through recent accidents in semi-autonomous/autonomous CPS and by adversarial ML attacks, ensuring such components operate reliably in all scenarios is extraordinarily challenging. We will discuss methods for assuring safety and security specifications in autonomous CPS using our NNV (Neural Network Verification) software tool (https://github.com/verivital/nnv), which has been applied to verify specifications for adaptive cruise control (ACC) and autonomous emergency braking (AEB) systems in motor vehicles. Next, we will present recent results on using NNV to prove robustness of neural networks used for perception tasks, such as image classification, applied to the VGG16/VGG19 networks that achieve high accuracy on ImageNet, as well as recent work on robustness of semantic segmentation. We will conclude with some architectural solutions to provide safety assurance in autonomous CPS at runtime, building on supervisory control with the Simplex architecture using real-time reachability, and will discuss future research directions for establishing trustworthy AI within CPS that we are exploring in a DARPA Assured Autonomy project.
Biography: Dr. Taylor T. Johnson, PE, is an Assistant Professor of Computer Engineering (CmpE), Computer Science (CS), and Electrical Engineering (EE) in the Department of Electrical Engineering and Computer Science (EECS) in the School of Engineering (VUSE) at Vanderbilt University (since August 2016), where he directs the Verification and Validation for Intelligent and Trustworthy Autonomy Laboratory (VeriVITAL) and is a Senior Research Scientist in the Institute for Software Integrated Systems (ISIS). Dr. Johnson was previously an Assistant Professor of Computer Science and Engineering (CSE) at the University of Texas at Arlington (September 2013 to August 2016). Dr. Johnson earned a PhD in Electrical and Computer Engineering (ECE) from the University of Illinois at Urbana-Champaign in 2013, where he worked in the Coordinated Science Laboratory with Prof. Sayan Mitra, and earlier earned an MSc in ECE at Illinois in 2010 and a BSEE from Rice University in 2008. Dr. Johnson has published over 90 papers on formal methods and their applications across cyber-physical systems (CPS) domains, such as power and energy, aerospace, automotive, transportation, biotechnology, and robotics, one of which was awarded an ACM Best Software Repeatability Award. Dr. Johnson is a 2018 and 2016 recipient of the AFOSR Young Investigator Program (YIP) award, a 2015 recipient of the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Research Initiation Initiative (CRII), and his research is / has been supported by AFOSR, ARO, AFRL, DARPA, NSA, NSF, the MathWorks, NVIDIA, ONR, Toyota, and USDOT. Dr. Johnson is a member of AAAI, AAAS, ACM, AIAA, IEEE, and SAE, and is a Professional Engineer (PE) in Tennessee.
Host: Pierluigi Nuzzo, nuzzo@usc.edu
Webcast: https://usc.zoom.us/webinar/register/WN_YSl0DRVOQJetWGNAACPOYQLocation: Online
WebCast Link: https://usc.zoom.us/webinar/register/WN_YSl0DRVOQJetWGNAACPOYQ
Audiences: Everyone Is Invited
Contact: Talyia White
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CS Colloquium: Matthew Gombolay (Georgia Institute of Technology) - Democratizing Robot Learning for Safe, Efficient Human-Robot Interaction
Thu, Nov 19, 2020 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Matthew Gombolay, Georgia Institute of Technology
Talk Title: Democratizing Robot Learning for Safe, Efficient Human-Robot Interaction
Series: Computer Science Colloquium
Abstract: Robotic technology offers the promise of performing at-home care tasks, revitalizing manufacturing, and even scaling the power of earth-bound scientists in autonomous space exploration. However, each new robot deployment today requires an ad hoc army of consultants and vast computing resources operating on black box, sample-inefficient models. To unlock the potential of robotics, we need to democratize machine learning and put the power of these tools in the hands of the end user. In this talk, I will present exciting, novel work in my lab that enables to safely and efficiently learn from human teachers and interactions with their environments. I will demonstrate how we can 1) enable robots to learn new skills from heterogeneous human teachers, 2) balance the need to actively learn more about their environment while remaining safe in proximity to humans, and 3) and convey their knowledge to human teachers and teammates through interpretable machine learning representations.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_6Ti2CLNuS7SqIcROZ7FJ6Q
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Dr. Matthew Gombolay is an Assistant Professor of Interactive Computing at the Georgia Institute of Technology. He received a B.S. in Mechanical Engineering from the Johns Hopkins University in 2011, an S.M. in Aeronautics and Astronautics from MIT in 2013, and a Ph.D. in Autonomous Systems from MIT in 2017. Gombolay's research interests span robotics, AI/ML, human-robot interaction, and operations research. Between defending his dissertation and joining the faculty at Georgia Tech, Dr. Gombolay served as a technical staff member at MIT Lincoln Laboratory, transitioning his research to the U.S. Navy, earning him an R&D 100 Award. His publication record includes a best paper award from American Institute for Aeronautics and Astronautics, a best student paper from the American Controls Conference, and he was selected as a DARPA Riser in 2018. He was also awarded a NASA Early Career Fellowship for his work increasing science autonomy in space.
Host: Stefanos Nikolaidis
More Info: https://usc.zoom.us/webinar/register/WN_6Ti2CLNuS7SqIcROZ7FJ6Q
Location: Online Zoom Webinar
Audiences: Everyone Is Invited
Contact: Computer Science Department
Event Link: https://usc.zoom.us/webinar/register/WN_6Ti2CLNuS7SqIcROZ7FJ6Q
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Advanced Manufacturing Seminar
Fri, Nov 20, 2020 @ 10:00 AM - 11:30 AM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Hangbo Zhao, USC
Talk Title: Unconventional Manufacturing of 3D Micro- and Mesostructures: From Strain-Engineered Growth to Mechanically Guided Assembly
Abstract: The growing availability of methods for 3D manufacturing has implications across diverse areas ranging from energy systems to microelectronics, yet few techniques offer the necessary capabilities in geometric complexity, materials compatibility and design versatility. In this talk, I will discuss two novel manufacturing approaches to creating 3D functional material systems that are not feasible by conventional manufacturing methods: 1) strain-engineered growth of complex 3D carbon nanotube microarchitectures, and 2) mechanically guided 3D assembly of a broad range of functional materials and electronics. I will show how strain-engineered growth of carbon nanotubes, in combination with conformal coatings, enables direct formation of hierarchically structured surfaces with tailorable mechanical and interfacial properties for controlling liquid wetting and adhesion. Next, I will describe novel manufacturing technologies that exploit structural buckling and local twisting to create morphable 3D micro- and mesoscale structures, and show their applications as optical metamaterials and as electronic scaffolds in tissue-on-chip systems.
Biography: Dr. Hangbo Zhao is an assistant professor in the Department of Aerospace and Mechanical Engineering at USC. His focus areas include micro/nano manufacturing, bio-integrated electronics, engineered surfaces, and active/smart materials. Prior to joining USC, he was a postdoctoral researcher in the Center for Bio-Integrated Electronics at Northwestern University, where he worked on multifunctional 3D materials systems and bio-integrated electronics for applications in tissue engineering and healthcare. He received his Ph.D. degree in the Department of Mechanical Engineering at MIT in 2017 on developing engineered, hierarchical surfaces for controlling liquid wetting and adhesion. His research has been published in journals including Advanced Materials, Proceedings of the National Academy of Sciences (PNAS), and Nano Today, and highlighted by Nature Nanotechnology and PNAS, His awards include the Materials Research Society (MRS) Best Poster Award (2014) and Outstanding Poster Award for the International Conference of the Polymer Processing Society (2015).
Host: AME Department
More Info: https://usc.zoom.us/webinar/register/WN_I7Rzv2KHQXeWKqDmB83P-g
Webcast: https://usc.zoom.us/webinar/register/WN_I7Rzv2KHQXeWKqDmB83P-gLocation: Online event
WebCast Link: https://usc.zoom.us/webinar/register/WN_I7Rzv2KHQXeWKqDmB83P-g
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://usc.zoom.us/webinar/register/WN_I7Rzv2KHQXeWKqDmB83P-g
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AME PhD Student Seminar
Fri, Nov 20, 2020 @ 03:00 PM - 04:00 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Vamsikrishna Chinta, USC AME PhD Student
Talk Title: Reconstructing the time evolution of wall-bounded turbulent flows from non-time-resolved PIV measurements
Abstract: Particle image velocimetry (PIV) systems are often limited in their ability to fully resolve the spatiotemporal fluctuations inherent in turbulent flows due to hardware constraints. In this study, we develop models based on rapid distortion theory (RDT) and Taylor's hypothesis (TH) to reconstruct the time evolution of a turbulent flow field in the intermediate period between consecutive PIV snapshots obtained using a non-time resolved system. The linear governing equations are evolved forward and backward in time using the PIV snapshots as initial conditions. The flow field in the intervening period is then reconstructed by taking a weighted sum of the forward and backward estimates. This spatiotemporal weighting function is designed to account for the advective nature of the RDT and TH equations. Reconstruction accuracy is evaluated as a function of spatial resolution and reconstruction time horizon using direct numerical simulation data for turbulent channel flow from the Johns Hopkins Turbulence Database. This method reconstructs single-point turbulence statistics well and resolves velocity spectra at frequencies higher than the temporal Nyquist limit of the acquisition system. Reconstructions obtained using a characteristics-based evolution of the flow field under TH prove to be more accurate compared to reconstructions obtained from numerical integration of the discretized forms of RDT and TH. The effect of measurement noise on reconstruction error is also evaluated.
Biography: Vamsikrishna Chinta is a PhD student working with Prof. Mitul Luhar. His research focuses on turbulent flow reconstruction using physics-based models. Prior to joining USC as a PhD student, Vamsikrishna received his masters from Indian Institute of Science (IISc) Bangalore, and bachelors from National Institute of Technology (NIT) Calicut, both in Mechanical Engineering.
Host: AME Department
More Info: https://usc.zoom.us/j/92144809085
Audiences: Everyone Is Invited
Contact: Christine Franks
Event Link: https://usc.zoom.us/j/92144809085
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***NO ISE 651, Epstein Seminar - Thanksgiving Recess***
Tue, Nov 24, 2020 @ 03:00 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Audiences: Everyone Is Invited
Contact: Grace Owh
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Mork Family Department Fall Virtual Seminars - Rafael Verduzco
Tue, Nov 24, 2020 @ 04:00 PM - 05:20 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Rafael Verduzco, Rice University
Talk Title: EFFICIENT AND MECHANICALLY ROBUST ORGANIC PHOTOVOLTAICS THROUGH SELF-ASSEMBLY
Abstract: https://usc.zoom.us/j/93139729396?pwd=UmNqVmVac1BGcEZoVEgxaGNnRzVaUT09
Meeting ID: 931 3972 9396
Passcode: 514283
Host: Nicholas Graham
Audiences: Everyone Is Invited
Contact: Greta Harrison
