Logo: University of Southern California

Events Calendar



Select a calendar:



Filter January Events by Event Type:



Events for January 24, 2024

  • Viterbi Employer Mock Interviews

    Wed, Jan 24, 2024 @ 10:00 AM - 04:00 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions


    Interested in scheduling a mock interview with an employer 1:1? You will have the opportunity to practice your interviewing skills!
     
    Viterbi\'s Mock Interview Day provides students with a valuable opportunity to connect with Viterbi Alumni and industry experts, gain insight into potential employers and industries, and receive essential career advice.
     
    Mock Interview Day is designed to enhance your skills, boost your confidence, and equip you with the necessary tools to excel in future job interviews.
     
    Location: On-Campus
     
    Audience: All Viterbi Students
     
    For more information:
     
    https://viterbicareers.usc.edu/students/events/mock-interviews-student/

    Audiences: All Viterbi

    Contact: RTH 218 Viterbi Career Connections

    Event Link: https://viterbicareers.usc.edu/students/events/mock-interviews-student/

    OutlookiCal
  • Repeating EventEiS Communications Hub Drop-In Hours

    Wed, Jan 24, 2024 @ 10:00 AM - 01:00 PM

    Viterbi School of Engineering Student Affairs

    Workshops & Infosessions


    Viterbi Ph.D. students are invited to stop by the EiS Communications Hub for one-on-one instruction for their academic and professional communications tasks. All instruction is provided by Viterbi faculty at the Engineering in Society Program.

    Location: Ronald Tutor Hall of Engineering (RTH) - 222A

    Audiences: Viterbi Ph.D. Students

    View All Dates

    Contact: Helen Choi

    Event Link: https://sites.google.com/usc.edu/eishub/home?authuser=0

    OutlookiCal
  • PhD Thesis Defense - Jiao Sun

    Wed, Jan 24, 2024 @ 11:00 AM - 01:30 PM

    Thomas Lord Department of Computer Science

    University Calendar


    PhD Thesis Defense - Jiao Sun  
     
    Committee members: Xuezhe (Max) Ma (Chair), Nanyun (Violet) Peng, Johnathan May, Emilio Ferrara, Dan O’Leary  
     
    Title: Emphasizing the Importance of Data and Evaluation in the Era of Large language Models  
     
    Abstract: Large-scale models have marked the beginning of a new era, significantly transforming language understanding, text and image generation, and complex decision-making tasks. For example, they may perpetuate stereotypes or produce misleading information. Nevertheless, due to the limitations of existing evaluation methods, these problems are often overlooked. My research highlights the imperative need for more careful and nuanced model evaluation and assessment. Upon investigation, large models may have generated biased or inappropriate content due to inadequacies in their training data. Identified through trustworthy evaluation methods, I address these challenges with a focus on aligning large models with human intentions from a data-centric perspective. 

    Location: Ronald Tutor Hall of Engineering (RTH) - 306

    Audiences: Everyone Is Invited

    Contact: CS Events

    Event Link: https://usc.zoom.us/my/jiaosun

    OutlookiCal
  • PhD Thesis Proposal - Haowen Lin

    Wed, Jan 24, 2024 @ 02:00 PM - 04:00 PM

    Thomas Lord Department of Computer Science

    University Calendar


    Committee: Cyrus Shahabi (Chair), Bistra Dilkina, Muhao Chen, Xiong Li, Marlon Boarnet
     
    Title: Accurate and controllable trajectory generation  
     
    Abstract : In various application domains like transportation, urban planning, and public health, analyzing human mobility, represented as a sequence of consecutive visits (aka trajectories), is crucial for uncovering essential mobility patterns. Due to privacy and commercial concerns, real-world trajectories are not readily available, giving rise to an important research area of generating synthetic but realistic trajectories. This thesis addresses the challenge of trajectory generation using data-driven approaches, integrating both explicit and implicit constraints within a continuous spatiotemporal domain. First, I present a framework based on generative adversarial imitation learning that synthesizes realistic trajectories that preserve moving behavior patterns (.g., work commute, shopping purpose) in real data. Next, I explore the hypothesis that grouping trajectories governed by similar dynamics into clusters before trajectory modeling could enhance modeling effectiveness. I present a framework that can simultaneously model trajectories in continuous space and time  while clustering them. Finally, we discuss the proposed work that will incorporate explicit spatial and temporal constraints that will potentially generate more representative and realistic trajectories.  
     
    Zoom link: https://usc.zoom.us/j/95828555243

    Location: https://usc.zoom.us/j/95828555243

    Audiences: Everyone Is Invited

    Contact: CS Events

    Event Link: zoom link: https://usc.zoom.us/j/95828555243

    OutlookiCal
  • AME Seminar

    Wed, Jan 24, 2024 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Grace Gao, Stanford University

    Talk Title: Robust Autonomous Vehicle Localization using GPS: from Tandem Drifting Cars to GPS on the Moon

    Abstract: Autonomous vehicles often operate in complex environments with various sensing uncertainties. On Earth, GPS signals can be blocked or reflected by buildings; and camera measurements are susceptible to lighting conditions. While having a variety of sensors is beneficial, including more sensing information can introduce more sensing failures as well as more computational load. For space applications, such as localization on the Moon, it can be even more challenging. In this talk, I will present our recent research efforts on robust vehicle localization under sensing uncertainties. We turn sensing noise and even absence of sensing into useful navigational signals. Inspired by cognitive attention in humans, we select a subset of “attention landmarks” from sensing measurements to reduce computation load and provide robust positioning. I will also show our localization techniques that enable various applications, from autonomous tandem drifting cars to a GPS-like system for the Moon.

    Biography: Grace X. Gao is an assistant professor in the Department of Aeronautics and Astronautics at Stanford University. She leads the Navigation and Autonomous Vehicles Laboratory (NAV Lab). Prof. Gao has won a number of awards, including the National Science Foundation CAREER Award, the Institute of Navigation Early Achievement Award and the RTCA William E. Jackson Award. Prof. Gao and her students won Best Presentation of the Session/Best Paper Awards 29 times at Institute of Navigation conferences over the past 17 years. She also won various teaching and advising awards, including the Illinois College of Engineering Everitt Award for Teaching Excellence, the Engineering Council Award for Excellence in Advising, AIAA Illinois Chapter’s Teacher of the Year, and most recently Advisor of the Year Award and Teacher of the Year Award by AIAA Stanford Chapter in 2022 and 2023, respectively.

    Host: AME Department

    More Info: https://ame.usc.edu/seminars/

    Webcast: https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09

    Location: James H. Zumberge Hall Of Science (ZHS) - 252

    WebCast Link: https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

    Event Link: https://ame.usc.edu/seminars/

    OutlookiCal