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Events for March 09, 2021
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Internship/Job Search Open Forum
Tue, Mar 09, 2021 @ 08:00 AM - 08:30 AM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Increase your career and internship knowledge on the job/internship search by attending this professional development Q&A moderated by Viterbi Career Connections staff or Viterbi employer partners.
To access this workshop:
Log into Viterbi Career Gateway>> Events>>Workshops: https://shibboleth-viterbi-usc-csm.symplicity.com/sso/
For more information about Labs & Open Forums, please visit viterbicareers.usc.edu/workshops.
Location: Online
Audiences: All Viterbi Students
Contact: RTH 218 Viterbi Career Connections
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CS Colloquium: Dani Yogatama (DeepMind) - Learning General Language Processing Agents
Tue, Mar 09, 2021 @ 09:00 AM - 10:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dani Yogatama, DeepMind
Talk Title: Learning General Language Processing Agents
Series: CS Colloquium
Abstract: The ability to continuously learn and generalize to new problems quickly is a hallmark of general intelligence. Existing machine learning models work well when optimized for a particular benchmark, but they require many in-domain training examples (i.e., input-output pairs that are often costly to annotate), overfit to the idiosyncrasies of the benchmark, and do not generalize to out-of-domain examples. In contrast, humans are able to accumulate task-agnostic knowledge from multiple modalities to facilitate faster learning of new skills.
In this talk, I will argue that obtaining such an ability for a language model requires significant advances in how we acquire, represent, and store knowledge in artificial systems. I will present two approaches in this direction: (i) an information theoretic framework that unifies several representation learning methods used in many domains (e.g., natural language processing, computer vision, audio processing) and allows principled constructions of new training objectives to learn better language representations; and (ii) a language model architecture that separates computation (information processing) in a large neural network and memory storage in a key-value database. I will conclude by briefly discussing a series of future research programs toward building a general linguistically intelligent agent.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dani Yogatama is a staff research scientist at DeepMind. His research interests are in machine learning and natural language processing. He received his PhD from Carnegie Mellon University in 2015. He grew up in Indonesia and was a Monbukagakusho scholar in Japan prior to studying at CMU.
Host: Xiang Ren
Audiences: By invitation only.
Contact: Assistant to CS chair
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Astani Department of Civil and Environmental Engineering Seminar
Tue, Mar 09, 2021 @ 11:00 AM - 12:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Arghavan Louhghalam, Assistant Professor, University of Massachusetts Dartmouth
Talk Title: Physics-based and Data-driven Modeling from eco-friendly roadway network to infrastructure resilience analytics
Abstract: Development of sustainable and resilient infrastructure systems requires novel frameworks that leverage the explosion of data available through advances in sensors, internet, mobility as well as computational models to design for and respond to the challenges of 21st century. In this talk, I will showcase how physics-constrained data-driven modeling enables development of quantitative platforms for identification, monitoring and projection of infrastructure performance. In the first part of the presentation I will describe a citizen-enabled framework to monitor, in real-time, road surface condition, vehicle excess energy consumption, and the related environmental impact at network scale. Unlike the widely used approaches for road infrastructure monitoring that rely solely on data and empirical models, this framework integrates physics-compatible models of road-vehicle interaction with crowdsourced data to characterize the parameters of system. The proposed data-centric platform has the potential to not only help transportation authorities make optimal decisions in the allocation of resources to road maintenance but also guide route selection by individual drivers or fleet owners. This will be a key player in a rapidly evolving world where an accelerating climate change is pressing for dramatic measures to reduce carbon footprint and GHG emissions. The second part of this talk will be focused on modeling damage using an energy-based formulation of lattice element method (LEM). I will describe the potential of mean force (PMF) approach, widely used in statistical physics and introduce a hybrid PMF formulation of LEM to efficiently model fracture and crack growth in heterogenous media. The framework is validated and utilized for meso-scale simulations to estimate the effective fracture properties of heterogeneous materials. The hybrid approach is shown to be a viable choice due to its flexibility in modeling discontinuity and its computational efficiency and reliable results. Finally, I will discuss our efforts to leverage the versatility of this framework and adapt the formulation as a means for efficient characterization of failure and damage in structural systems to establish an efficient quantitative tool for resilience analytics.
Biography: Arghavan Louhghalam is an assistant professor in the department of Civil and Environmental Engineering with a joint appointment in Mechanical Engineering Department at University of Massachusetts, Dartmouth. She also holds a research affiliate position in the department of Civil and Environmental Engineering at MIT. Prior to that she was a postdoctoral research associate at Massachusetts Institute of Technology. She earned her PhD in Engineering Mechanics from the Department of Civil Engineering at the Johns Hopkins University. Her research interests lie in the area of engineering mechanics, physics-constrained data-driven modeling, and applied statistics with particular emphasis on development of smart solutions for resilient and sustainable built environment. Dr Louhghalam is a recipient of NSF early CAREER award and her research on citizen-enabled crowdsourced monitoring of transportation infrastructure has been recognized nationally and featured in media outlets such as the New York Times.
Host: Dr. Roger Ghanem
Location: Zoom: https://usc.zoom.us/j/97228056404; Meeting ID: 972 2805 6404: Passcode: 864779
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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CS Colloquium: Ranjay Krishna (Stanford University) - Visual Intelligence from Human Learning
Tue, Mar 09, 2021 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ranjay Krishna , Stanford University
Talk Title: Visual Intelligence from Human Learning
Series: CS Colloquium
Abstract: At the core of human development is the ability to adapt to new, previously unseen stimuli. We comprehend new situations as a composition of previously seen information and ask one another for clarification when we encounter new concepts. Yet, this ability to go beyond the confounds of their training data remains an open challenge for artificial intelligence agents. My research designs visual intelligence to reason over new compositions and acquire new concepts by interacting with people. My talk will explore these challenges and present the two following lines of work:
First, I will introduce scene graphs, a cognitively-grounded, compositional visual representation. I will discuss how to integrate scene graphs into a variety of computer vision tasks, enabling models to generalize to novel compositions from a few training examples. Since our introduction of scene graphs, the Computer Vision community has developed hundreds of scene graph models and utilized scene graphs to achieve state-of-the-art results across multiple core tasks, including object localization, captioning, image generation, question answering, 3D understanding, and spatio-temporal action recognition.
Second, I will introduce a framework for socially situated learning. This framework pushes agents beyond traditional computer vision training paradigms and enables learning from human interactions in online social environments. I will showcase a real-world deployment of our agent, which learned to acquire new visual concepts by asking people targeted questions on social media. By interacting with over 230K people over 8 months, our agent learned to recognize hundreds of new concepts. This work demonstrates the possibility for agents to adapt and self-improve in real-world social environments.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Ranjay Krishna is a 5th-year Ph.D. candidate at Stanford University, where he is co-advised by Fei-Fei Li and Michael Bernstein. His research lies at the intersection of computer vision and human-computer interaction; it draws on ideas from behavioral and social sciences to improve visual intelligence. His work has been recognized by the Christofer Stephenson Memorial award, as an Accell Innovation Scholar and by two Brown Institute for Media Innovation grants. His work has also been featured in Forbes magazine and in a PBS NOVA documentary. During his Ph.D., he re-designed Stanford's undergraduate Computer Vision course and currently also instructs the graduate Computer Vision course, Stanford's second largest course. He has a M.Sc. from Stanford University. Before that, he conferred a B.Sc. with a double major in Electrical Engineering and in Computer Science from Cornell University. In the past, he has interned at Google AI, Facebook AI Research, and Yahoo Research.
Host: Ramakant Nevatia
Audiences: By invitation only.
Contact: Assistant to CS chair
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Oracle: NetSuite Diversity Lunch & Learn
Tue, Mar 09, 2021 @ 12:00 PM - 01:00 PM
Viterbi School of Engineering Career Connections
University Calendar
Please join the Oracle NetSuite Diversity Team at one of our upcoming virtual open houses to learn more about NetSuite and explore a career in sales or consulting within the Tech industry.
The sessions will discuss the following:
- Our commitment to diversity & inclusion in the workplace
- Available full-time opportunities
- Q&A with sales and consulting business leaders
Register Here: https://apexapps.oracle.com/pls/apex/f?p=10412:1::::RP,1:P1_EVENT_ID:DSLBELYBMT&cs=1GOoqZbXxpvWGcUP20T8rOOzikdq1kk0ISQS8-RJhPtPdN6OnOpDDqr5pMFKqSMrphkOSDWXsqwwOzUSQVNHM4wAudiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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Amazon SDE 101
Tue, Mar 09, 2021 @ 01:00 PM - 02:00 PM
Viterbi School of Engineering Career Connections
University Calendar
*This is an external event hosted by Amazon*
For people who like to invent, there's no better place to explore opportunities than at Amazon! Come learn more about our Software Development Engineer (SDE) full-time and internship opportunities, our culture, the recruitment process and interview tips.
Please register for our upcoming info session and submit your questions in advance. (we will select the most frequent pre-submitted questions to answer at the end of the session).
Register through Viterbi Career Gateway > Events > Information Sessions
Join our team and help us build the future!Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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Optomechanical Manipulation Enabled by Photonic Metasurfaces
Tue, Mar 09, 2021 @ 01:00 PM - 02:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Ognjen Ilic, Professor University of Minnesota
Talk Title: Optomechanical Manipulation Enabled by Photonic Metasurfaces
Series: Photonics Seminar
Host: Electrical and Computer Engineering: Wade Hsu, Mercedeh Khajavikhan, Michelle Povinelli, Constantine Sideris, and Wei Wu
More Info: https://usc.zoom.us/meeting/register/tJEqcuuprD4oE9ZVf6lwC_KIX9-3i55nMAMV
More Information: Photonics Seminar _Ognjen Ilic 3-9-21.png
Audiences: Everyone Is Invited
Contact: Jennifer Ramos/Electrophysics
Event Link: https://usc.zoom.us/meeting/register/tJEqcuuprD4oE9ZVf6lwC_KIX9-3i55nMAMV
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Undergraduate Advisement Drop-in HoursTue, Mar 09, 2021 @ 01:30 PM - 02:30 PM
Thomas Lord Department of Computer Science
Workshops & Infosessions
Do you have a quick question? The CS advisement team will be available for drop-in live chat advisement for declared undergraduate students in our four majors during the spring semester on Tuesdays, Wednesdays, and Thursdays from 1:30pm to 2:30pm Pacific Time. Access the live chat on our website at: https://www.cs.usc.edu/chat/
Location: Online
Audiences: Undergrad
Contact: USC Computer Science
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ISE 651 - Epstein Seminar
Tue, Mar 09, 2021 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Junyi Liu, Postdoctoral Associate, Epstein Dept. of Industrial & Systems Engineering, USC
Talk Title: Nonconvex and Nonsmooth Stochastic Optimization With Modern Applications
Host: Prof. Jong-Shi Pang
More Information: March 9, 2021.pdf
Location: Online/Zoom
Audiences: Everyone Is Invited
Contact: Grace Owh
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CS Distinguished Lecture: Jure Leskovec (Stanford University) - Mobility Networks for Modeling the Spread of COVID-19: Explaining Inequities and Informing Reopening
Tue, Mar 09, 2021 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jure Leskovec, Stanford University
Talk Title: Mobility Networks for Modeling the Spread of COVID-19: Explaining Inequities and Informing Reopening
Series: Computer Science Distinguished Lecture Series
Abstract: The COVID-19 pandemic dramatically changed human mobility patterns, necessitating epidemiological models which capture the effects of changes in mobility on virus spread. We introduce a metapopulation SEIR model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in 10 of the largest US metropolitan statistical areas. Derived from cell phone data, our mobility networks map the hourly movements of 98 million people from neighborhoods (census block groups, or CBGs) to points of interest (POIs) such as restaurants and religious establishments, connecting 57k CBGs to 553k POIs with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in population behavior over time. Our model predicts that a small minority of "superspreader" POIs account for a large majority of infections and that restricting maximum occupancy at each POI is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups solely from differences in mobility: we find that disadvantaged groups have not been able to reduce mobility as sharply, and that the POIs they visit are more crowded and therefore higher-risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more effective and equitable policy responses to COVID-19.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_UD7zYBdETsCyLBOiv2DoLw
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Jure Leskovec is Associate Professor of Computer Science at Stanford University, Chief Scientist at Pinterest, and investigator at Chan Zuckerberg Biohub. Dr. Leskovec was the co-founder of a machine learning startup Kosei, which was later acquired by Pinterest. His research focuses on machine learning and data mining large social, information, and biological networks. Computation over massive data is at the heart of his research and has applications in computer science, social sciences, marketing, and biomedicine. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, and numerous best paper and test of time awards. It has also been featured in popular press outlets such as the New York Times and the Wall Street Journal. Leskovec received his bachelor's degree in computer science from University of Ljubljana, Slovenia, PhD in machine learning from Carnegie Mellon University and postdoctoral training at Cornell University. You can follow him on Twitter at @jure.
Host: Xiang Ren
Webcast: https://usc.zoom.us/webinar/register/WN_UD7zYBdETsCyLBOiv2DoLwLocation: Online Zoom Webinar
WebCast Link: https://usc.zoom.us/webinar/register/WN_UD7zYBdETsCyLBOiv2DoLw
Audiences: Everyone Is Invited
Contact: Computer Science Department
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Mork Family Department Spring Virtual Seminars - Jiefei Zhang
Tue, Mar 09, 2021 @ 04:00 PM - 05:20 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Jiefei Zhang, University of Southern California
Talk Title: A NEW PARADIGM FOR ON-CHIP SCALABLE QUANTUM PHOTONICS
Abstract: ZOOM MEETING INFO:
https://usc.zoom.us/j/98225952695?pwd=d0NMenhCNkliR1ZIR1lBamRpZHh1UT09
Meeting ID: 982 2595 2695 • Passcode: 322435
Host: Andrea Hodge
More Info: https://usc.zoom.us/j/98225952695?pwd=d0NMenhCNkliR1ZIR1lBamRpZHh1UT09
Audiences: Everyone Is Invited
Contact: Greta Harrison
Event Link: https://usc.zoom.us/j/98225952695?pwd=d0NMenhCNkliR1ZIR1lBamRpZHh1UT09
