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Events for April 09, 2025
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EiS Communications Hub - Tutoring for Engineering Ph.D. StudentsWed, Apr 09, 2025 @ 10:00 AM - 12:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
Viterbi Ph.D. students are invited to drop by the Hub for instruction on their writing and speaking tasks! All tutoring is one-on-one and conducted by Viterbi faculty.
Location: Ronald Tutor Hall of Engineering (RTH) - 222A
Audiences: Viterbi Ph.D. Students
Contact: Helen Choi
Event Link: https://sites.google.com/usc.edu/eishub/home
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
CS Colloquium: Xia (Ben) Hu (Rice University) - Efficient LLM Serving via Lossy Computation
Wed, Apr 09, 2025 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Xia (Ben) Hu, Rice University
Talk Title: Efficient LLM Serving via Lossy Computation
Series: Computer Science Colloquium
Abstract: Large language models (LLMs) have exhibited human-like conversational abilities. Yet, scaling LLMs to longer contexts, such as extracting information from lengthy articles, one of the most fundamental tasks in healthcare applications, poses significant challenges. The primary issues are their inability to handle contexts beyond pre-training lengths and system constraints that make deployment difficult, as memory requirements for inference increase with context length. The key idea to overcome these challenges is that LLMs are extremely robust to noise from lossy computation, such as low-precision computation. Following this insight, we will discuss recent advancements in serving LLMs at scale, particularly in handling longer contexts. To address the algorithmic challenge, I will share our recent work on extending LLM context length to at least 8× longer by coarsening the positional information of distant tokens. To address the system challenge, I will discuss our recent efforts in quantizing the intermediate states of past tokens to 2-bit numbers, leading to a 8x memory efficiency and 3.5x wall-clock time speedup without harming performance. Finally, I will highlight our latest projects applying LLMs in healthcare, particularly how we utilize retrieval techniques for long contexts to mitigate the hallucination problem in healthcare chatbots. This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dr. Xia “Ben” Hu is an Associate Professor at Rice University in the Department of Computer Science. Dr. Hu has published over 200 papers in several major academic venues, including NeurIPS, ICLR, ICML, KDD, IJCAI, etc. An open-source package developed by his group, namely AutoKeras, has become the most used automated deep learning system on GitHub (with over 9,000 stars and 1,000 forks). Additionally, his work on LLM efficiency, deep collaborative filtering, anomaly detection, knowledge graphs, and fast interpretation has been incorporated into production systems at Hugging Face, TensorFlow, Apple, Bing, and Meta, respectively. His papers have received several Best Paper (Candidate) awards from venues such as ICML, WWW, WSDM, ICDM, AMIA, and INFORMS. He is the recipient of the NSF CAREER Award and the ACM SIGKDD Rising Star Award. His work has been cited more than 30,000 times with an h-index of 76. He served as General Co-Chair for WSDM 2020 and ICHI 2023, as well as Program Co-Chair for AIHC 2024 and CHASE 2025.
Host: Yan Liu
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone (USC) is invited
Contact: CS Faculty Affairs
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Computer Science General Faculty Meeting
Wed, Apr 09, 2025 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty and staff only. Event details emailed directly to attendees.
Location: Ginsburg Hall (GCS) - 107
Audiences: Invited Faculty Only
Contact: Julia Mittenberg-Beirao
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
PhD Thesis Proposal - Arash Hajisafi
Wed, Apr 09, 2025 @ 12:30 PM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
Presentation Title: Dynamic GNNs for Accurate and Efficient Modeling of Instant and Lagged Dependencies in Multivariate Time Series
Date and Time: Wednesday, April 9th, 2025 - 12:30p - 2:00p
Location: GCS 502C
Committee Members: Cyrus Shahabi (Chair), Ibrahim Sabek, Viktor Prasanna, Ruishan Liu, John P. Wilson (External)
Abstract: Graph Neural Networks (GNNs) have shown great success in modeling complex dependencies within multivariate time series by explicitly capturing intra-series (within individual series) and inter-series (across different series) relationships. However, existing methods often struggle to represent evolving correlations, particularly when multiple contexts and lagged interactions are involved. My previous research has developed GNN-based prediction models addressing instant dependencies across various contexts, incorporating both static and dynamic relationship aspects, and achieving significant improvements in forecasting accuracy and efficiency. Despite these advancements, real-world time series, such as those found in financial markets, frequently exhibit lagged dependencies, where changes in one series influence others after varying delays. Building on my prior contributions, my dissertation proposes developing a novel dynamic GNN method explicitly designed to capture these lagged dependencies, aiming to further enhance the prediction accuracy in applications like stock forecasting.Location: Ginsburg Hall (GCS) - 502C
Audiences: Everyone Is Invited
Contact: Arash Hajisafi
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
AME Seminar
Wed, Apr 09, 2025 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Pedro Paredes, NASA Langley Research Center
Talk Title: Hypersonic Boundary Layer Transition over Blunt Cones
Abstract: The linear amplification of modal Mack-mode disturbances that lead to boundary-layer transition in two-dimensional/axisymmetric hypersonic configurations is strongly reduced by the presence of a blunt nosetip. The mechanisms underlying the low Mack-mode N-factor values at the observed onset of transition over the cone frustum are currently unknown. As the nose bluntness is increased beyond the critical nose Reynolds number for transition reversal, the transition location rapidly moves upstream, and transition appears to depend on uncontrolled disturbances due to nosetip roughness. Linear nonmodal analysis has shown that both planar and oblique traveling disturbances that peak within the entropy layer experience appreciable energy amplification for moderate to large nosetip bluntness. Nonlinear nonmodal analysis shows that planar entropy-layer disturbances excited near the nose tip can excite the high frequency Mack-mode disturbances and hence, can lead to a reduction in the transition N-factor. Digital wind-tunnel simulations are conducted via direct numerical simulations (DNS) to understand the effects of freestream acoustic disturbances in transition over blunt cones during a conventional tunnel experiment. The results confirm the appearance of entropy-layer disturbances predicted by linear nonmodal analysis and the numerical schlieren contours show the inclined structures predicted by nonlinear nonmodal analysis and observed in experiments.
Biography: Pedro Paredes is a Research Scientist at the Computational AeroSciences Branch of the NASA Langley Research Center. Dr. Paredes earned his Ph.D. and M.Sc. in Aerospace Engineering from the Polytechnic University of Madrid, Spain. He was one of the recipients of the Air Force Office of Scientific Research Young Investigator Award in 2020 and has been awarded with two Office of Naval Research grants as the principal investigator. Dr. Paredes was honored with the American Institute of Aeronautics and Astronautics (AIAA) Associate Fellow distinction in 2024. The research activities of Dr. Paredes are related to boundary layer transition (BLT) prediction and physics-based development of technology concepts for BLT control across the flight speed regimes. He has developed and applied advanced, multidimensional stability-analysis methods for BLT prediction of high-speed flow configurations. With a prolific academic record, he has authored over 50 journal articles and 80 conference papers.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Location: James H. Zumberge Hall Of Science (ZHS) - 252
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Six Sigma Black Belt
Wed, Apr 09, 2025 @ 04:00 PM - 04:00 PM
Executive Education
Conferences, Lectures, & Seminars
Speaker: IISE Faculty, IISE Faculty
Talk Title: Six Sigma Black Belt
Abstract: USC Viterbi School of Engineering's Six Sigma Black Belt program, offered in partnership with the Institute of Industrial and Systems Engineers, enables professionals to learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Learn the advanced problem-solving skills you need to implement the principles, practices, and techniques of our Six Sigma Black Belt course in order to maximize performance and cost reductions in your organization. During this three-week practitioner course, you will learn how to measure a process, analyze the results, develop process improvements, and quantify the resulting savings. You will be required to complete a project demonstrating mastery of appropriate analytical methods and pass an examination to earn Six Sigma Black Belt Certification. This practitioner course for Six Sigma implementation provides extensive coverage of the Six Sigma process, as well as intensive exposure to the key analytical tools associated with Six Sigma, including project management, team skills, cost analysis, FMEA, basic statistics, inferential statistics, sampling, goodness of fit testing, regression and correlation analysis, reliability, design of experiments, statistical process control, measurement systems analysis, and simulation. Computer applications are emphasized.
Host: USC Viterbi Corporate and Professional Programs
More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
Audiences: Six Sigma Black Belt Students
Contact: VASE Executive Education
Event Link: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
