Logo: University of Southern California

Events Calendar



Select a calendar:



Filter March Events by Event Type:



Events for the 2nd week of March

  • Repeating EventAthenaHacks

    Sun, Mar 07, 2021

    Computer Science

    Student Activity


    Happening for a fifth year, from March 6-7 virtually, AthenaHacks is open to all levels of experience, majors, and backgrounds (undergraduate and graduate students both welcome).

    At AthenaHacks you'll have the opportunity to learn, network, and build through project building, technical and professional workshops, and speaker series.

    Everything is free! Our sponsors at the event will include Microsoft, Zynga, Facebook, Disney, and Bloomberg and we'll have thousands of dollars worth of prizes to compete for!


    Email any questions to AthenaHacks@gmail.com, join the event page: https://tinyurl.com/athenahacks21, and find us on Instagram on @athena_hacks.

    Applications are due Saturday 2/27/21 at 11:59pm PST!
    Apply at http://www.athenahacks.com/.

    Location: Online

    Audiences: Undergraduate and Graduate Students

    View All Dates

    Contact: AthenaHacks

    OutlookiCal
  • CS Colloquium: Mariya Toneva (Carnegie Mellon University) - Data-Driven Transfer of Insight between Brains and AI Systems

    Mon, Mar 08, 2021 @ 09:00 AM - 10:00 AM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Mariya Toneva, Carnegie Mellon University

    Talk Title: Data-Driven Transfer of Insight between Brains and AI Systems

    Series: CS Colloquium

    Abstract: Several major innovations in artificial intelligence (AI) (e.g. convolutional neural networks, experience replay) are based on findings about the brain. However, the underlying brain findings took many years to first consolidate and many more to transfer to AI. Moreover, these findings were made using invasive methods in non-human species. For cognitive functions that are uniquely human, such as natural language processing, there is no suitable model organism and a mechanistic understanding is that much farther away.

    In this talk, I will present my research program that circumvents these limitations by establishing a direct connection between the human brain and AI systems with two main goals: 1) to improve the generalization performance of AI systems and 2) to improve our mechanistic understanding of cognitive functions. Lastly, I will discuss future directions that build on these approaches to investigate the role of memory in meaning composition, both in the brain and AI. This investigation will lead to methods that can be applied to a wide range of AI domains, in which it is important to adapt to new data distributions, continually learn to perform new tasks, and learn from few samples.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Mariya Toneva is a Ph.D. candidate in a joint program between Machine Learning and Neural Computation at Carnegie Mellon University, where she is advised by Tom Mitchell and Leila Wehbe. She received a B.S. in Computer Science and Cognitive Science from Yale University. Her research is at the intersection of Artificial Intelligence, Machine Learning, and Neuroscience. Mariya works on bridging language in machines with language in the brain, with a focus on building computational models of language processing in the brain that can also improve natural language processing systems.

    Host: Yan Liu

    Audiences: By invitation only.

    Contact: Assistant to CS chair

    OutlookiCal
  • Central Intelligence Agency Office Hours | Pre-Signup Required

    Mon, Mar 08, 2021 @ 11:00 AM - 12:00 PM

    Viterbi School of Engineering Career Connections

    University Calendar


    Sign Up For a Time Slot: Link Coming soon!

    Schedule a one-on-one phone call with a recruiter to discuss career opportunities at the Central Intelligence Agency!

    All degree levels and Viterbi majors welcome.
    NOTE: The Central Intelligence Agency cannot sponsor international candidates.
    All times are in local Los Angeles time (PST)

    Talk one-on-one with a CIA Representative to explore career and paid internship opportunities and life at the CIA. These are informal sessions--not job interviews--but please be prepared with questions about positions found on cia.gov/careers.

    If you sign up for a "waitlist" slot, you will be called if the first student signed up for that time does not answer their phone.

    We recommend going to the website, taking the short survey through the website's Job Fit Tool and having your results for our conversation. We can discuss resume style, cover letters, preparing for any interview, or anything else regarding working at the CIA. Undergraduates and Graduates of all academic disciplines are encouraged to attend. All CIA positions require US citizenship and relocation to the Washington, DC metropolitan area.

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

    OutlookiCal
  • CS Colloquium: Saiph Savage (University of Washington) - The Future of A.I. for Social Good

    Mon, Mar 08, 2021 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Saiph Savage, University of Washington

    Talk Title: The Future of A.I. for Social Good

    Series: CS Colloquium

    Abstract: The A.I. industry has powered a futuristic reality of self-driving cars and voice assistants to help us with almost any need. However, the A.I. Industry has also created systematic challenges. For instance, while it has led to platforms where workers label data to improve machine learning algorithms, my research has uncovered that these workers earn less than minimum wage. We are also seeing the surge of A.I. algorithms that privilege certain populations and racially exclude others. If we were able to fix these challenges we could create greater societal justice and enable A.I. that better addresses people's needs, especially groups we have traditionally excluded.

    In this talk, I will discuss some of these urgent global problems that my research has uncovered from the A.I. Industry. I will present how we can start to address these problems through my proposed "A.I. For Good" framework. My framework uses value sensitive design to understand people's values and rectify harm. I will present case-studies where I use this framework to design A.I. systems that improve the labor conditions of the workers operating behind the scenes in our A.I. industry; as well as how we can use this framework to safeguard our democracies. I conclude by presenting a research agenda for studying the impact of A.I. in society; and researching effective socio-technical solutions in favor of the future of work and countering techno-authoritarianism.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Saiph Savage conducts research in the intersection of Human Computer Interaction, A.I., and Civic Technology. She is one of the 35 Innovators under 35 by the MIT Technology Review, a Google Anita Borg Scholarship recipient, and a fellow at the Center for Democracy & Technology. Her work has been covered in the BBC, Deutsche Welle, and the New York Times, as well as published in top venues such as ACM CHI, CSCW, and AAAI ICWSM, where she has also won honorable mention awards. Dr. Savage has been awarded grants from the National Science Foundation, the United Nations, industry, and has also formalized new collaborations with Federal and local Governments where she is driving them to adopt Human Centered Design and A.I. to deliver better experiences and government services to citizens. Dr. Savage has opened the research area of Human Computer Interaction at West Virginia University, and Saiph's students have obtained fellowships and internships in industry (Facebook Research, Twitch Research, and Microsoft Research) as well as academia (Oxford Internet Institute). Saiph holds a bachelor's degree in Computer Engineering from the National Autonomous University of Mexico (UNAM), and a master's and Ph.D. in Computer Science from the University of California, Santa Barbara (UCSB). Dr. Savage currently works at the University of Washington; previously she was a Visiting Professor at Carnegie Mellon University (CMU). Additionally, Dr. Savage has been a tech worker at Microsoft Bing, Intel Labs, and a crowd research worker at Stanford.

    Host: Bistra Dilkina

    Audiences: By invitation only.

    Contact: Assistant to CS chair

    OutlookiCal
  • CS Colloquium: Sanghamitra Dutta (Carnegie Mellon University) - Reliable Machine Learning for High-Stakes Applications: Approaches Using Information Theory

    Mon, Mar 08, 2021 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Sanghamitra Dutta , Carnegie Mellon University

    Talk Title: Reliable Machine Learning for High-Stakes Applications: Approaches Using Information Theory

    Series: CS Colloquium

    Abstract: How do we make machine learning (ML) algorithms not only ethical, but also intelligible, explainable, and reliable? This is particularly important today as ML enters high-stakes applications such as hiring and education, often adversely affecting people's lives with respect to gender, race, etc. Identifying bias/disparity in a model's decision is often insufficient. We really need to dig deeper and bring in an understanding of anti-discrimination laws. For instance, Title VII of the US Civil Rights Act includes a subtle and important aspect that has implications for the ML models being used today: Disparities in hiring that can be explained by a business necessity are exempt. E.g., disparity arising due to code-writing skills may be deemed exempt for a software engineering job, but the disparity due to an aptitude test may not be (e.g. Griggs v. Duke Power '71). This leads us to a question that bridges the fields of fairness, explainability, and law: How can we identify and explain the sources of disparity in ML models, e.g., did the disparity arise due to the critical business necessities or not? In this talk, I propose the first systematic measure of "non-exempt disparity," i.e., the illegal bias which cannot be explained by business necessities. To arrive at a measure for the non-exempt disparity, I adopt a rigorous axiomatic approach that brings together concepts in information theory, in particular, an emerging body of work called Partial Information Decomposition, with causal inference tools. This quantification allows one to audit a firm's hiring practices, to check if they are compliant with the law. This may also allow one to correct the disparity by better explaining the source of the bias, also providing insights into accuracy-bias tradeoffs.

    My research bridges reliability in learning with reliability in computing, which has led to an emerging interdisciplinary area called "coded computing". Towards the end of this talk, I will also provide an overview of some of my results on coded reliable computing that addresses long-standing computational challenges in large-scale distributed machine learning (namely, stragglers, faults, failures) using tools from coding theory, optimization, and queueing.

    This lecture satisfies requirements for CSCI 591: Research Colloquium


    Biography: Sanghamitra Dutta (B. Tech. IIT Kharagpur) is a Ph.D. candidate at Carnegie Mellon University, USA. Her research interests revolve around machine learning, information theory, and statistics. She is currently focused on addressing the emerging reliability issues in machine learning concerning fairness, explainability, and law with recent publications at AAAI'20, ICML'20 (also featured in New Scientist and CMU Engineering News). In her prior work, she has also examined problems in reliable computing, proposing novel algorithmic solutions for large-scale distributed machine learning in the presence of faults and failures, using tools from coding theory (an emerging area called "coded computing"). Her results on coded computing address problems that have been open for several decades and have received substantial attention from across communities (published at IEEE Transactions on Information Theory'19,'20, NeurIPS'16, AISTATS'18, IEEE BigData'18, ICML Workshop Spotlight'19, ISIT'17,'18, Proceedings of IEEE'20 along with two pending patents). She is a recipient of the 2020 Cylab Presidential Fellowship, 2019 K&L Gates Presidential Fellowship, 2019 Axel Berny Presidential Graduate Fellowship, 2017 Tan Endowed Graduate Fellowship, 2016 Prabhu and Poonam Goel Graduate Fellowship, the 2015 Best Undergraduate Project Award at IIT Kharagpur, and the 2014 HONDA Young Engineer and Scientist Award. She has also pursued research internships at IBM Research and Dataminr.

    Host: Bistra Dilkina

    Audiences: By invitation only.

    Contact: Assistant to CS chair

    OutlookiCal
  • ACM Front End Web Dev Workshop

    Mon, Mar 08, 2021 @ 07:00 PM - 08:00 PM

    Computer Science

    Student Activity


    Want to learn more about front-end Web Development? Join ACM on Monday, March 8, from 7-8 PM for a web dev workshop.

    During this workshop, ACM will teach you about HTML, CSS, and JavaScript. If you're not familiar with those terms or if you need to brush up on your front-end web developing skills, this workshop is your calling!

    Learn more at https://www.facebook.com/events/758195448435348/

    Location: Online - Zoom

    Audiences: Undergraduate and Graduate Students

    Contact: ACM

    OutlookiCal
  • 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

    OutlookiCal
  • CS Colloquium: Dani Yogatama (DeepMind) - Learning General Language Processing Agents

    Tue, Mar 09, 2021 @ 09:00 AM - 10:00 AM

    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

    OutlookiCal
  • 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

    OutlookiCal
  • CS Colloquium: Ranjay Krishna (Stanford University) - Visual Intelligence from Human Learning

    Tue, Mar 09, 2021 @ 11:00 AM - 12:00 PM

    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

    OutlookiCal
  • 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-RJhPtPdN6OnOpDDqr5pMFKqSMrphkOSDWXsqwwOzUSQVNHM4w

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

    OutlookiCal
  • 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

    OutlookiCal
  • 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

    OutlookiCal
  • Repeating EventUndergraduate Advisement Drop-in Hours

    Tue, Mar 09, 2021 @ 01:30 PM - 02:30 PM

    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

    View All Dates

    Contact: USC Computer Science

    OutlookiCal
  • 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

    OutlookiCal
  • 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

    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_UD7zYBdETsCyLBOiv2DoLw

    Location: Online Zoom Webinar

    WebCast Link: https://usc.zoom.us/webinar/register/WN_UD7zYBdETsCyLBOiv2DoLw

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

    OutlookiCal
  • 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

    OutlookiCal
  • CS Colloquium: Hongyang Zhang (Toyota Technological Institute) - New Advances in (Adversarially) Robust and Secure Machine Learning

    Wed, Mar 10, 2021 @ 09:00 AM - 10:00 AM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Hongyang Zhang , Toyota Technological Institute

    Talk Title: New Advances in (Adversarially) Robust and Secure Machine Learning

    Series: CS Colloquium

    Abstract: Deep learning models are often vulnerable to adversarial examples. In this talk, we will focus on robustness and security of machine learning against adversarial examples. There are two types of defenses against such attacks: 1) empirical and 2) certified adversarial robustness.

    In the first part of the talk, we will see the foundation of our winning system, TRADES, in the NeurIPS'18 Adversarial Vision Challenge in which we won 1st place out of 400 teams and 3,000 submissions. Our study is motivated by an intrinsic trade-off between robustness and accuracy: we provide a differentiable and tight surrogate loss for the trade-off using the theory of classification-calibrated loss. TRADES has record-breaking performance in various standard benchmarks and challenges, including the adversarial benchmark RobustBench, the NLP benchmark GLUE, the Unrestricted Adversarial Examples Challenge hosted by Google, and has motivated many new attacking methods powered by our TRADES benchmark.

    In the second part of the talk, to equip empirical robustness with certification, we study certified adversarial robustness by random smoothing. On one hand, we show that random smoothing on the TRADES-trained classifier achieves SOTA certified robustness when the perturbation radius is small. On the other hand, when the perturbation is large, i.e., independent of inverse of input dimension, we show that random smoothing is provably unable to certify L_infty robustness for arbitrary random noise distribution. The intuition behind our theory reveals an intrinsic difficulty of achieving certified robustness by "random noise based methods", and inspires new directions as potential future work.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Hongyang Zhang is a Postdoc fellow at Toyota Technological Institute at Chicago, hosted by Avrim Blum and Greg Shakhnarovich. He obtained his Ph.D. from CMU Machine Learning Department in 2019, advised by Maria-Florina Balcan and David P. Woodruff. His research interests lie in the intersection between theory and practice of machine learning, robustness and AI security. His methods won the championship or ranked top in various competitions such as the NeurIPS'18 Adversarial Vision Challenge (all three tracks), the Unrestricted Adversarial Examples Challenge hosted by Google, and the NeurIPS'20 Challenge on Predicting Generalization of Deep Learning. He also authored a book in 2017.

    Host: David Kempe

    Audiences: By invitation only.

    Contact: Assistant to CS chair

    OutlookiCal
  • DEN@Viterbi: How to Apply Virtual Info Session

    Wed, Mar 10, 2021 @ 12:00 PM - 01:00 PM

    Distance Education Network, Viterbi School of Engineering Graduate Admission

    Workshops & Infosessions


    Join USC Viterbi representatives for a step-by-step guide and tips for how to apply for formal admission into a Master's degree or Graduate Certificate program. The session is intended for individuals who wish to pursue a graduate degree program completely online via USC Viterbi's flexible online DEN@Viterbi delivery method.

    Attendees will have the opportunity to connect directly with USC Viterbi representatives and ask questions about the admission process throughout the session.

    Register Now!

    WebCast Link: https://uscviterbi.webex.com/uscviterbi/onstage/g.php?MTID=e25087b2d448ff393d7feb8469c8e8e30

    Audiences: Everyone Is Invited

    Contact: Corporate & Professional Programs

    OutlookiCal
  • CANCELLED - Computer Science General Faculty Meeting

    Wed, Mar 10, 2021 @ 12:00 PM - 02:00 PM

    Computer Science

    Receptions & Special Events


    Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.

    Location: TBD

    Audiences: Invited Faculty Only

    Contact: Assistant to CS chair

    OutlookiCal
  • Repeating EventUndergraduate Advisement Drop-in Hours

    Wed, Mar 10, 2021 @ 01:30 PM - 02:30 PM

    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

    View All Dates

    Contact: USC Computer Science

    OutlookiCal
  • Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar

    Wed, Mar 10, 2021 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Somil Bansal, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley

    Talk Title: Safe and Data-efficient Learning for Robotics

    Series: Center for Cyber-Physical Systems and Internet of Things

    Abstract: For successful integration of autonomous systems such as drones and self-driving cars in our day-to-day life, they must be able to quickly adapt to ever-changing environments, and actively reason about their safety and that of other users and autonomous systems around them. Even though control-theoretic approaches have been used for decades now for the control and safety analysis of autonomous systems, these approaches typically operate under the assumption of a known system dynamics model and the environment in which the system is operating. To overcome these challenges, machine learning approaches have been explored to operate autonomous systems intelligently and reliably in unpredictable environments based on prior data. However, learning techniques widely used today are extremely data inefficient, making it challenging to apply them to real-world physical systems. Moreover, they lack the necessary mathematical framework to provide guarantees on correctness, causing safety concerns as data-driven physical systems are integrated in our society.

    In this talk, we will present a toolbox of methods combining robust optimal control with data-driven techniques inspired by machine learning, to enable performance improvement while maintaining safety. In particular, we design modular architectures that combine system dynamics models with modern learning-based perception approaches to solve challenging perception and control problems in a priori unknown environments in a data-efficient fashion. These approaches are demonstrated on a variety of ground robots navigating in unknown buildings around humans based only on onboard visual sensors. Next, we discuss how we can use optimal control methods not only for data-efficient learning, but also to monitor and recognize the learning system's failures, and to provide online corrective safe actions when necessary. This allows us to provide safety assurances for learning-enabled systems in unknown and human-centric environments, which has remained a challenge to date.

    Biography: Somil Bansal completed his MS and PhD in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley in 2014 and 2020 respectively, and received his B.Tech. in Electrical Engineering from Indian Institute of Technology, Kanpur in 2012. He is currently spending a year as a research scientist at Waymo. In Fall 2021, he will join as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Southern California, Los Angeles. His research interests include developing mathematical tools and algorithms for control and analysis of autonomous systems, with a focus on bridging learning and control-theoretic approaches for safety-critical autonomous systems. Somil has received several awards, most notably the Eli Jury award and the outstanding graduate student instructor award at UC Berkeley, and the academic excellence award at IIT Kanpur.

    Host: Pierluigi Nuzzo, nuzzo@usc.edu

    Webcast: https://usc.zoom.us/webinar/register/WN_Qk4-7AthThudso7LXs2OiA

    Location: Online

    WebCast Link: https://usc.zoom.us/webinar/register/WN_Qk4-7AthThudso7LXs2OiA

    Audiences: Everyone Is Invited

    Contact: Talyia White

    OutlookiCal
  • AME Seminar

    Wed, Mar 10, 2021 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: George Park, University of Pennsylvania

    Talk Title: Toward Predictive Yet Affordable Computations of Practical Wall-Bounded Turbulent Flows

    Abstract: Kinetic energy of turbulence is generated at large scales controlled by boundary conditions, but it is dissipated into heat at the smallest scales. The ratio of these two length scales increases rapidly with Reynolds number. Solid walls add another dimension in this scale landscape, where the scale separation gets progressively less pronounced toward the wall. This has significant ramifications on the cost of scale-resolving simulation of practical engineering flows, such as those found in aircraft, wind turbines, and ship hydrodynamics. Direct approaches with full resolution of length and times scales close to the wall are still infeasible with current computing power. The demand for superior designs at reduced cost has led researchers to explore alternative computational approaches that have potential to be predictive yet affordable. Large-eddy simulation (LES) is one such approach where only the energy-containing scales are resolved directly, and the effect of the unresolved motions are modeled. In practical LES calculations, subgrid-scale (SGS) models are used in conjunction with wall models to augment the turbulent shear stress, which otherwise is underpredicted on coarse grids and leads to inaccurate prediction of mean and turbulence quantities.
    In this talk, I will discuss the research in my group on this wall-modeled LES approach. Widely used wall-modeling techniques will be discussed with their applications to canonical and complex wall-bounded flows. Challenges in robust and efficient implementation of the models in flow solvers for handling practical geometries will be discussed. I will also highlight recent work to predict flow over realistic aircraft geometries at flight conditions and a boundary layer with mean three dimensionality.

    Biography: George Park is an Assistant Professor of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania. He received his Ph.D. and M.S. in Mechanical Engineering (ME) from Stanford University in 2014 and 2011, respectively, and his B.S. in ME from Seoul National University, South Korea in 2009. He worked as a postdoctoral fellow and an engineering research associate at the Center for Turbulence Research (Stanford) prior to joining UPenn as a faculty member in 2018. His research interests include high-fidelity numerical simulation of complex wall-bounded turbulent flows, computational methods with unstructured grids, non-equilibrium turbulent boundary layers, and fluid-structure interaction.

    Host: AME Department

    More Info: https://usc.zoom.us/j/97491401429

    Webcast: https://usc.zoom.us/j/97491401429

    Location: Online event

    WebCast Link: https://usc.zoom.us/j/97491401429

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

    OutlookiCal
  • CS Colloquium: Vered Shwartz (University of Washington) - Commonsense Knowledge and Reasoning in Natural Language

    Wed, Mar 10, 2021 @ 04:00 PM - 05:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Vered Shwartz, University of Washington

    Talk Title: Commonsense Knowledge and Reasoning in Natural Language

    Series: CS Colloquium

    Abstract: Natural language understanding models are trained on a sample of the situations they may encounter. Commonsense and world knowledge, and language understanding and reasoning abilities can help them address unknown situations sensibly. This talk will discuss several lines of work addressing commonsense knowledge and reasoning in natural language. First, I will introduce a new paradigm for commonsense reasoning tasks with introspective knowledge discovery through a process of self-asking information seeking questions ("what is the definition of...") and answering them. Second, I will present work on nonmonotonic reasoning in natural language, a core human reasoning ability that has been studied in classical AI but mostly overlooked in modern NLP, including abductive reasoning (reasoning about plausible explanations), counterfactual reasoning (what if?) and defeasible reasoning (updating beliefs given additional information). Next, I will discuss how generalizing existing knowledge can help language understanding, and demonstrate it for noun compound paraphrasing (e.g. olive oil is "oil made of olives"). I will conclude with open problems and future directions in language, knowledge, and reasoning.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.

    Biography: Vered Shwartz is a postdoctoral researcher at the Allen Institute for AI (AI2) and the Paul G. Allen School of Computer Science & Engineering at the University of Washington, working with Yejin Choi. Vered's research interests are in NLP, AI, and machine learning, particularly focusing on commonsense knowledge and reasoning, computational semantics, discourse and pragmatics. Previously, Vered completed her PhD in Computer Science from Bar-Ilan University, under the supervision of Ido Dagan. Vered's work has been recognized with several awards, including The Eric and Wendy Schmidt Postdoctoral Award for Women in Mathematical and Computing Sciences, the Clore Foundation Scholarship, and an ACL 2016 outstanding paper award.

    Host: Xiang Ren

    Audiences: By invitation only.

    Contact: Assistant to CS chair

    OutlookiCal
  • Key Virtual Job Search Tips with Northrop Grumman

    Wed, Mar 10, 2021 @ 04:00 PM - 04:30 PM

    Viterbi School of Engineering Career Connections

    University Calendar


    Hear from Northrop Grumman recruiter (and USC alumni) Anjali Chopra about how to navigate your job search in a virtual world. This workshop will include tips for virtual networking events/job fairs, security tips, virtual interviewing preparation, how to succeed in an online interview.

    RSVP through Viterbi Career Gateway > Events > Workshops

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

    OutlookiCal
  • CS Colloquium: Gedas Bertasius (Facebook AI) - Designing Video Models for Human Behavior Understanding

    Thu, Mar 11, 2021 @ 09:00 AM - 10:00 AM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Gedas Bertasius, Facebook AI

    Talk Title: Designing Video Models for Human Behavior Understanding

    Series: CS Colloquium

    Abstract: Many modern computer vision applications require extracting core attributes of human behavior such as attention, action, or intention. Extracting such behavioral attributes requires powerful video models that can reason about human behavior directly from raw video data. To design such models we need to answer the following three questions: how do we (1) model videos, (2) learn from videos, and lastly, (3) use videos to predict human behavior?

    In this talk I will present a series of methods to answer each of these questions. First, I will introduce TimeSformer, the first convolution-free architecture for video modeling built exclusively with self-attention. It achieves the best reported numbers on major action recognition benchmarks while also being more efficient than state-of-the-art 3D CNNs. Afterwards, I will present COBE, a new large-scale framework for learning contextualized object representations in settings involving human-object interactions. Our approach exploits automatically-transcribed speech narrations from instructional YouTube videos, and it does not require manual annotations. Lastly, I will introduce a self-supervised learning approach for predicting a basketball player's future motion trajectory from an unlabeled collection of first-person basketball videos.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Gedas Bertasius is a postdoctoral researcher at Facebook AI working on computer vision and machine learning problems. His current research focuses on topics of video understanding, first-person vision, and multi-modal deep learning. He received his Bachelors Degree in Computer Science from Dartmouth College, and a Ph.D. in Computer Science from the University of Pennsylvania. His recent work was nominated for the CPVR 2020 best paper award.

    Host: Ramakant Nevatia

    Audiences: By invitation only.

    Contact: Assistant to CS chair

    OutlookiCal
  • Repeating EventVirtual First-Year Admission Information Session

    Thu, Mar 11, 2021 @ 09:00 AM - 10:00 AM

    Viterbi School of Engineering Undergraduate Admission

    Workshops & Infosessions


    Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for high school students and their family members to learn more about the USC Viterbi undergraduate experience. Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life. Guests will be able to ask questions and engage in further discussion toward the end of the session.

    Please Register Here!

    Audiences: Everyone Is Invited

    View All Dates

    Contact: Viterbi Admission

    OutlookiCal
  • CS Colloquium: Jiaming Song (Stanford University) - Beyond Function Approximation: Compression, Inference, and Generation via Supervised Learning

    Thu, Mar 11, 2021 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Jiaming Song, Stanford University

    Talk Title: Beyond Function Approximation: Compression, Inference, and Generation via Supervised Learning

    Series: CS Colloquium

    Abstract: Supervised learning methods have advanced considerably thanks to deep function approximators. However, important problems such as compression, probabilistic inference, and generative modeling cannot be directly addressed by supervised learning. At the core, these problems involve estimating (and optimizing) a suitable notion of distance between two probability distributions, which is challenging in high-dimensional spaces. In this talk, I will propose techniques to estimate and optimize divergences more effectively by leveraging advances in supervised learning. I will describe an algorithm for estimating mutual information that approaches a fundamental limit of all valid lower bound estimators and can empirically compress neural networks by up to 70% without losing accuracy. I will also show how these techniques can be used to accelerate probabilistic inference algorithms that have been developed for decades by nearly 10x, improve generative modeling and infer suitable rewards for sequential decision making.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Jiaming Song is a fifth-year Ph.D. candidate in the Computer Science Department at Stanford University, advised by Stefano Ermon. His research focuses on learning and inference algorithms for deep probabilistic models with applications in unsupervised representation learning, generative modeling, and inverse reinforcement learning. He received his B.Eng degree in Computer Science from Tsinghua University in 2016. He was a recipient of the Qualcomm Innovation Fellowship.

    Host: Bistra Dilkina

    Audiences: By invitation only.

    Contact: Assistant to CS chair

    OutlookiCal
  • Repeating EventUndergraduate Advisement Drop-in Hours

    Thu, Mar 11, 2021 @ 01:30 PM - 02:30 PM

    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

    View All Dates

    Contact: USC Computer Science

    OutlookiCal
  • Sage Corps Summer Program Info Session: Intern with Global Startup

    Thu, Mar 11, 2021 @ 03:00 PM - 04:00 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions


    Join our Founder and CEO, Matt Meltzer for an info session to learn about Sage Corps' unique program that has helped over 900 top college students accelerate their career development. 93% of Sage Corps alumni land full-time jobs within 3 months of graduating from college.

    In our program, you'll:

    - Complete 50+ hours of professional skill training in a selected vertical (marketing, business development & analysis; UX/UI & graphic design; software development; data analytics)
    - Intern with a global startup for 12 weeks this summer
    - Attend professional networking events
    - Connect with some of our 900+ alumni now at top global companies like Nike, Google, IBM, Accenture, and JP Morgan

    Sage Corps accepts all majors and any year in school, as well as non-US citizens. All are welcome to attend our info session.

    For more information about Sage Corps, visit www.sagecorps.com. For real-time updates about Sage Corps programs, job opportunities, and other relevant news, follow Sage Corps on Instagram (@sagecorps) or LinkedIn.

    To RSVP: Viterbi Career Gateway > Events > Information Sessions

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

    OutlookiCal
  • Career Conversations: How to Impress Employers

    Thu, Mar 11, 2021 @ 04:00 PM - 04:30 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions


    Will your skill set stand out to employers? Join our 2-part series of Career Conversations to gain an inside look at employer feedback for Viterbi students. During this session, learn practices to develop the key leadership and problem-solving skills employers want to see more of.

    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: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

    OutlookiCal
  • CS Colloquium: Swabha Swayamdipta (Allen Institute for AI) - Addressing Biases for Robust, Generalizable AI

    Thu, Mar 11, 2021 @ 04:00 PM - 05:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Swabha Swayamdipta, Allen Institute for AI

    Talk Title: Addressing Biases for Robust, Generalizable AI

    Series: CS Colloquium

    Abstract: Artificial Intelligence has made unprecedented progress in the past decade. However, there still remains a large gap between the decision-making capabilities of humans and machines. In this talk, I will investigate two factors to explain why. First, I will discuss the presence of undesirable biases in datasets, which ultimately hurt generalization. I will then present bias mitigation algorithms that boost the ability of AI models to generalize to unseen data. Second, I will explore task-specific prior knowledge which aids robust generalization, but is often ignored when training modern AI architectures. Throughout this discussion, I will focus my attention on language applications, and will show how certain underlying structures can provide useful inductive biases for inferring meaning in natural language. I will conclude with a discussion of how the broader framework of dataset and model biases will play a critical role in the societal impact of AI, going forward.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Swabha Swayamdipta is a postdoctoral investigator at the Allen Institute for AI, working with Yejin Choi. Her research focuses on natural language processing, where she explores dataset and linguistic structural biases, and model interpretability. Swabha received her Ph.D. from Carnegie Mellon University, under the supervision of Noah A. Smith and Chris Dyer. During most of her Ph.D. she was a visiting student at the University of Washington. She holds a Masters degree from Columbia University, where she was advised by Owen Rambow. Her research has been published at leading NLP and machine learning conferences, and has received an honorable mention for the best paper at ACL 2020.

    Host: Xiang Ren

    Audiences: By invitation only.

    Contact: Assistant to CS chair

    OutlookiCal
  • ACM Social Game Night

    Thu, Mar 11, 2021 @ 07:00 PM - 08:00 PM

    Computer Science

    Student Activity


    Stressed about midterms? Want to meet some of your classmates? Join ACM on Thursday, March 11, from 7-8 PM for our second social of the semester. We will be playing Codenames!

    Find out if you have what it takes to be the ultimate -spymaster-.

    Learn more at https://www.facebook.com/events/281047960107761/

    Location: Online - Zoom

    Audiences: Undergraduate and Graduate Students

    Contact: ACM

    OutlookiCal
  • AME PhD Student Seminar

    Fri, Mar 12, 2021 @ 03:00 PM - 04:00 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Samuel Goldman, USC AME PhD Student

    Talk Title: A Case Study of the Failure of a Compression Spring in a Lunar Percussion Mechanism

    Abstract: The Regolith and Ice Drill for the Exploration of New Terrains (TRIDENT) is a rotary-percussive drill being used on several upcoming Lunar exploration programs. Life testing of this drill resulted in the unexpected early failure of a critical compression spring, which cannot be explained by quasi-static analysis. The purpose of this work is to determine if transient dynamic behavior resulting from percussion can explain this failure. An experiment is conducted comparing the effect of various types of spacers, and it is found that a neoprene spacer allows the spring to survive more than twice as many cycles compared to metallic spacers. Additionally, the dynamic response of this system to impact is modeled using the Distributed Transfer Function Method (DTFM), and is compared to FEA and discrete element techniques. It is found that DTFM is capable of bounding the response as computed by FEA, while the discrete element model underestimates peak shear stress by more than 25% in boundary coils. FEA and DTFM both show that wave propagation within the spring could result in peak shear stresses in boundary coils that are over 20% higher than middle coils. These results suggest that percussive wave propagation can explain the early failure of this spring.

    Biography: Sam Goldman is a Ph.D. student under Dr. Flashner. His research focus is primarily in modeling and experimentation of percussion mechanisms used in extraterrestrial geotechnical tools. Sam has a B.S. in Biomedical Engineering from The Ohio State University, and an M.S. in Aerospace & Mechanical Engineering from USC.


    Host: AME Department

    More Info: https://usc.zoom.us/s/96549200347

    Audiences: Everyone Is Invited

    Contact: Christine Franks

    OutlookiCal
  • laytest Open Alpha's Game! --- OPE 2

    Sat, Mar 13, 2021 @ 10:00 AM - 12:00 PM

    Computer Science

    Student Activity


    Hello!

    Open Alpha has THE SECOND PROTOTYPE of our newest game! Come playtest our prototype this Saturday March 13th, 10am-12pm PST!

    To sign up for a time slot, please RSVP by Friday 3/12! There's more information about the event at bit.ly/oaplaytest, but here's the gist of it:
    -On Friday, if you RSVPed, we'll let you know your time slot. Let us know if your plans change and you can't make it.
    -When you show up, we'll set you up in a breakout room with an OA member who will be running your playtest. It'll take about 20 minutes.
    -This playtest event is OPEN, meaning invite your friends! We want to get feedback from as many people as possible!
    See you Saturday 10am-12pm! For more information, check out our website here!

    RSVP form: https://forms.gle/RHhxwxxvgfBwtJ39A
    Zoom link: https://usc.zoom.us/j/8745829211

    Best,
    The OA Team

    Location: Online - Zoom

    Audiences: Everyone Is Invited

    Contact: Open Alpha

    OutlookiCal