Select a calendar:
Filter April Events by Event Type:
SUNMONTUEWEDTHUFRISAT
Events for April 13, 2023
-
NL Seminar -Drinking From The Firehose of Science.
Thu, Apr 13, 2023 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Waleed Ammar, Allen Inst of AI (AI2)
Talk Title: Drinking From The Firehose of Science.
Series: NL Seminar
Abstract: REMINDER:
Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you are highly encouraged to use your USC account to sign into Zoom.
If you are an outside visitor, please inform us at nlg DASH seminar DASH host AT isi DOT edu beforehand so we will be aware of your attendance and let you in.
Five years ago, I visited ISI to talk about our progress taming the scientific literature in the Semantic Scholar team at the Allen Institute for Artificial Intelligence. In this talk, I will highlight some of the exciting developments in Semantic Scholar over the past few years, then share with you how we've enabled a wide variety of users and partners to "drink" from the firehose of scientific publications by interfacing with the Semantic Scholar APIs. I will end with an interactive discussion of how we can increase the participation of underrepresented groups in science.
Biography: Waleed Ammar currently leads the Semantic Scholar APIs effort at the Allen Institute for Artificial intelligence (AI2), which enables researchers, practitioners and decision makers to do various computations on the scientific literature in a wide variety of research fields. Before rejoining AI2 this year, Waleed was a senior research scientist at Google, where he helped develop transformer-based models for generating DNA sequences based on PacBio long-reads which significantly reduced variant-calling errors [Nature Biotech'22]. He also helped develop task-oriented dialog systems which are more robust to disfluencies, code-switching and user revisions [arXiv'23]. Prior to joining Google, Waleed led the Semantic Scholar research team's efforts to develop ML-based methods to facilitate access to the literature [e.g., NAACL 19], build a knowledge graph of the scientific literature [NAACL'18], and use this wealth of information to identify systemic social problems in science [JAMA'19]. He also occasionally teaches courses at UW linguistics as an affiliate faculty member. In 2016, Waleed received a Ph.D. degree in artificial intelligence from Carnegie Mellon University. Before pursuing the Ph.D., Waleed was a research engineer at Microsoft Research and a web developer at eSpace Technologies. Outside work, Waleed spends most of his time on the water or in dancing studios.
Host: Jon May and Justin Cho
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://www.youtube.com/watch?v=SsJNCkPEDu8Location: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://www.youtube.com/watch?v=SsJNCkPEDu8
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
-
CS Colloquium: Ibrahim Sabek (MIT) - Building Better Data-Intensive Systems Using Machine Learning
Thu, Apr 13, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ibrahim Sabek, MIT
Talk Title: Building Better Data-Intensive Systems Using Machine Learning
Series: CS Colloquium
Abstract: Database systems have traditionally relied on handcrafted approaches and rules to store large-scale data and process user queries over them. These well-tuned approaches and rules work well for the general-purpose case, but are seldom optimal for any actual application because they are not tailored for the specific application properties (e.g., user workload patterns). One possible solution is to build a specialized system from scratch, tailored for each use case. Although such a specialized system is able to get orders-of-magnitude better performance, building it is time-consuming and requires a huge manual effort. This pushes the need for automated solutions that abstract system-building complexities while getting as close as possible to the performance of specialized systems. In this talk, I will show how we leverage machine learning to instance-optimize the performance of query scheduling and execution operations in database systems. In particular, I will show how deep reinforcement learning can fully replace a traditional query scheduler. I will also show that-”in certain situations-”even simpler learned models, such as piece-wise linear models approximating the cumulative distribution function (CDF) of data, can help improve the performance of fundamental data structures and execution operations, such as hash tables and in-memory join algorithms.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Ibrahim Sabek is a postdoc at MIT and an NSF/CRA Computing Innovation Fellow. He is interested in building the next generation of machine learning-empowered data management, processing, and analysis systems. Before MIT, he received his Ph.D. from University of Minnesota, Twin Cities, where he studied machine learning techniques for spatial data management and analysis. His Ph.D. work received the University-wide Best Doctoral Dissertation Honorable Mention from University of Minnesota in 2021. He was also awarded the first place in the graduate student research competition (SRC) in ACM SIGSPATIAL 2019 and the best paper runner-up in ACM SIGSPATIAL 2018.
Host: Cyrus Shahabi
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
Semiconductors & Microelectronics Technology Seminar - Qiushi Guo, Thursday, April 13th at 11am in EEB 248
Thu, Apr 13, 2023 @ 11:00 AM - 12:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Qiushi Guo, CUNY Advanced Science Research Center (ASRC)
Talk Title: Lithium niobate integrated nonlinear photonics: new devices and systems on an old material
Series: Semiconductors & Microelectronics Technology
Abstract: Despite being an old material in optical and microwave technologies in its bulk form, thin-film lithium niobate (TFLN) has recently emerged as one of the most promising integrated photonic platforms owing to its strong electro-optic (EO) coefficient, quadratic optical nonlinearity, and broadband optical transparency ranging from 250 nm to 5 um. In this talk, I will first overview the basic optical properties of LN, and how LN nanophotonics can grant us new regimes of nonlinear photonics. Then I will present some of our recent experimental results on the realization and utilization of dispersion-engineered and quasi-phase-matched ultrafast photonic devices in both classical and quantum domains. I will discuss the realization of 100 dB/cm optical parametric amplification, 1.5-3 um widely tunable optical parametric oscillator (OPO), ultra-wide bandwidth quantum squeezing, femtosecond and femtojoule on chip all-optical switching, and the integrated mode-locked lasers based on TFLN with watt-level peak power.
Biography: Qiushi Guo is an assistant professor at the Advanced Science Research Center, City University of New York. Prior to joining the ASRC and the CUNY Graduate Center, Qiushi was a postdoctoral research associate at the California Institute of Technology. He received his Ph.D. in Electrical Engineering from Yale University in Dec. 2019, advised by Prof. Fengnian Xia. He received his M.S. degree in Electrical Engineering from the University of Pennsylvania in 2014, and his B.S. degree in Electrical Engineering from Xi'an Jiaotong University in 2012. Qiushi is the finalist of the 2022 Rising Star of Light, and the winner of the 2021 Henry Prentiss Becton Graduate Prize for his exceptional research achievements at Yale University. His research interests include integrated nonlinear and quantum photonics, mid-infrared photonics, and 2-D materials optoelectronics. He has published more than 40 peer-reviewed research papers in leading scientific journals with citations more than 300 times. He is serving on the editorial board of the journal Micromachines.
Host: J Yang, H Wang, C Zhou, S Cronin, W Wu
More Information: Qiushi Guo_2023-4-13.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
-
Biomedical Engineering Master's Programs w/ Prof. Wang
Thu, Apr 13, 2023 @ 11:00 AM - 12:00 PM
Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi School of Engineering for a webinar with Prof. Peter Yingxiao Wang who will highlight the Biomedical Engineering master's degree programs.
The webinar will discuss program details, USC Viterbi's DEN@Viterbi online delivery option, admission requirements and more!WebCast Link: https://uscviterbi.webex.com/weblink/register/r00f4fe97748670d8408f2c257b6343bc
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
-
Industrial & Systems Engineering Master's Programs w/ Prof. Dessouky
Thu, Apr 13, 2023 @ 02:00 PM - 03:00 PM
Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi School of Engineering for a webinar with Prof. Dessouky who will highlight the Master's degree programs in Industrial & Systems Engineering. The webinar will include program details, USC Viterbi's DEN@Viterbi online delivery option, admission requirements, a live Q&A session and more!
WebCast Link: https://uscviterbi.webex.com/weblink/register/r1bb001c4f4fd4e3ac264214ef504f256
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
-
CS Colloquium: Michael Oberst (MIT) - Rigorously Tested & Reliable Machine Learning for Health
Thu, Apr 13, 2023 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Michael Oberst, MIT
Talk Title: Rigorously Tested & Reliable Machine Learning for Health
Series: CS Colloquium
Abstract: How do we make machine learning as rigorously tested and reliable as any medication or diagnostic test?
Machine learning (ML) has the potential to improve decision-making in healthcare, from predicting treatment effectiveness to diagnosing disease. However, standard retrospective evaluations can give a misleading sense for how well models will perform in practice. Evaluation of ML-derived treatment policies can be biased when using observational data, and predictive models that perform well in one hospital may perform poorly in another.
In this talk, I will introduce methods I have developed to proactively assess and improve the reliability of machine learning models. A central theme will be the application of external knowledge, including guided review of patient records, incorporation of limited clinical trial data, and interpretable stress tests. Throughout, I will discuss how evaluation can directly inform model design.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Michael Oberst is a final-year PhD candidate in Computer Science at MIT. His research focuses on making sure that machine learning in healthcare is safe and effective, using tools from causal inference and statistics. His work has been published at a range of machine learning venues (NeurIPS / ICML / AISTATS / KDD), including work with clinical collaborators from Mass General Brigham, NYU Langone, and Beth Israel Deaconess Medical Center. He has also worked on clinical applications of machine learning, including work on learning effective antibiotic treatment policies (published in Science Translational Medicine). He earned his undergraduate degree in Statistics at Harvard.
Host: Yan Liu
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
The Knowledge Shop, Volunteer Info Session
Thu, Apr 13, 2023 @ 07:00 PM - 07:30 PM
USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
The KNOWLEDGE SHOP was created so that families in disadvantaged communities could get access to educational services that would assist in the empowerment and educational development of their children. Join the info session to learn how you can get involved by volunteering your time, knowledge, and skills.
Learn more about The Knowledge Shop by visiting:
https://www.theknowledgeshopla.com/
Location: Online Event
Audiences: Everyone Is Invited
Contact: Noe Mora
Event Link: https://engage.usc.edu/viterbi/rsvp?id=389323
