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• Conferences, Lectures, & Seminars (23)
• Workshops & Infosessions (43)
• University Calendar (9)
• Receptions & Special Events (7)
• Student Activity (5)

University Calendar
Events for September

• Sep

  11

  PhD Thesis Proposal - Jiao Sun

  Mon, Sep 11, 2023 @ 03:30 PM - 05:30 PM

  Thomas Lord Department of Computer Science

  University Calendar

  
  

  PhD Thesis Proposal - Jiao Sun
  
  Committee Members: Xuezhe (Max) Ma (Chair), Nanyun (Violet) Peng, Johnathan May, Cyrus Shahabi, O Leary Dan
  
  Title: Discovering and Addressing the Pitfalls in Practical Text Generation
  
  Abstract: Research in language models has advanced at an incredible place, to the point where it is making its way to our daily lives of not only NLP researchers but also outsiders. However, there are some pitfalls that will hurt the utility of generative models and even exclude certain groups from using such models. In this talk, I will do a thorough reality check for each stage of text generation: data, model, and evaluation, and ask three main questions: how will data quality impact the utility of generation? Are we building the right models for the right purpose? How reliable are current evaluation metrics? I will ground these questions by my recent works on investigating the utility of free form rationales, a brand new set up for pun generation and dialect robust evaluation of generated text. In final remarks, I will discuss some future research perspectives on building trustworthy generation systems.
  

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

  Audiences: Everyone Is Invited

  Contact: Melissa Ochoa

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

  

• Sep

  14

  DREAM Industry Mentorship speaker series

  Thu, Sep 14, 2023 @ 12:30 PM - 01:30 PM

  USC Viterbi School of Engineering

  University Calendar

  
  

  DREAM (Direct Response to Engineers Aspirations from Mentors) is a mentorship initiative which connects students to high profile industry professionals relevant to students dream pitches, an original leadership communication assignment where students create a vision for their future selves, align their careers around purpose, and build character in the context of growth, reinvention, and constant change. Industry mentors serve as moral exemplars as they discuss how professional challenges present opportunities for character and leadership development. Guest speakers and panels feature experienced professionals from a variety of tech and destination companies.
  
  This event will feature talks with Viterbi M.S. alumni Justene Karimi and Edgar Vargas about how they found career purpose in aerospace and their evolving roles as engineers and managers at Boeing.
  
  

  Location: Michelson Center for Convergent Bioscience (MCB) - 102

  Audiences: Everyone Is Invited

  Contact: Elisabeth Arnold Weiss

  Event Link: https://cglink.me/2nB/r391353

  

• Sep

  18

  PhD Thesis Proposal - Sabyasachee Baruah

  Mon, Sep 18, 2023 @ 02:00 PM - 04:00 PM

  Thomas Lord Department of Computer Science

  University Calendar

  
  

  PhD Thesis Proposal - Sabyasachee Baruah
  
  Committee Members: Shrikanth Narayanan (Chair), Jonathan May, Kristina Lerman, Emilio Ferrara, and Morteza Dehghani
  
  Title: Character Resolution and Attribution for Narrative Understanding
  
  Abstract: Narrativity is a mechanism through which human beings try to understand the world. We computationally define it as characters interacting with each other during events that relate in time and space. We propose a computational pipeline for narrative understanding and discuss its character centric aspects, resolution and attribution of characters. We contribute computational models and novel datasets towards these tasks in both the textual and visual domain. We work towards scaling our methods to long and multimodal narratives, and analyze how they portray characters across different attribute types and time.

  Location: Ronald Tutor Hall of Engineering (RTH) - 320

  Audiences: Everyone Is Invited

  Contact: Melissa Ochoa

  Event Link: https://usc.zoom.us/j/97813866730?pwd=dHRBUG1aQjJJTmxsS1d5ajhNMFZCQT09

  

• Sep

  19

  PHD Thesis Proposal (Shushan Arakelyan)

  Tue, Sep 19, 2023 @ 02:00 PM - 03:30 PM

  Thomas Lord Department of Computer Science

  University Calendar

  
  

  Thesis proposal committee members:
  Xiang Ren (Chair)
  Nenad Medvidovic
  Weihang Wang
  Jay Kuo
  Mukund Raghothaman
  
  Abstract:
  Successful deployment of any AI model requires generalization to previously unseen, real-world scenarios. Lack of generalization in models can lead to outcomes ranging from reduced performance to potential legal liabilities. In this thesis, I explore generalization challenges in large language models for code processing. I will discuss three different generalization concerns that language models for code processing can exhibit, and present my progress in building models that can overcome those. 1) I will start by discussing compositional generalization issues, where models must adapt to previously unseen instruction combinations 2) Next I will talk about out-of-domain generalization, and how distribution shifts within single projects or corporations can affect model performance, and how to overcome it. 3) Finally, I will talk about generalization of advanced models to programming languages with fewer resources.
  
  

  Location: https://usc.zoom.us/j/7195147935?pwd=SU5oS1MrQUxURC9tSFd2Y3VuUDdoQT09

  Audiences: Everyone Is Invited

  Contact: Asiroh Cham

  Event Link: https://usc.zoom.us/j/7195147935?pwd=SU5oS1MrQUxURC9tSFd2Y3VuUDdoQT09

  

• Sep

  20

  PhD Dissertation Defense - Ali S. Alotaibi

  Wed, Sep 20, 2023 @ 01:00 PM - 03:00 PM

  Thomas Lord Department of Computer Science

  University Calendar

  
  

  PhD Dissertation Defense - Ali S. Alotaibi
  
  Committee Members: William GJ Halfond (chair), Nenad Medvidovic, Mukund Raghothaman, Gisele Ragusa, and Chao Wang
  
  Title: Automated Repair of Layout Accessibility Issues in Mobile Applications
  
  Abstract: An increasing number of people are now dependent on mobile devices to access data and complete essential tasks. For people with disabilities, mobile apps that violate accessibility guidelines can prevent them from carrying out these activities. Layout accessibility issues are among the top accessibility issues in mobile applications. These issues impact the accessibility of mobile apps and make them difficult to use, especially for older people and people with disabilities. Unfortunately, existing techniques are limited in helping developers debug these issues. These techniques are only capable of detecting the issues but cannot help to repair them. Therefore, the repair of layout accessibility issues remains a manual process, which is both labor intensive and error prone.
  
  Automated repair of layout accessibility issues is complicated by several challenges. First, a repair must account for multiple issues holistically in order to preserve the relative consistency of the original app design. Second, due to the complex relationship between UI components, there is no clear way of identifying the set of elements and properties that need to be modified for a given issue. Third, assuming the relevant views and properties can be identified, the number of possible changes that need to be considered grows exponentially as more elements and properties need to be considered. Finally,
  a change in one element can create cascading changes that lead to further problems in other areas of the UI. Together, these challenges make a seemingly simple repair difficult to achieve. In this dissertation, I present an automated framework for repairing layout accessibility issues in mobile applications. To evaluate the effectiveness of this framework, I instantiated it to repair the different types of layout accessibility issues. I assessed the effectiveness of these instantiations by using them to repair issues detected in real world mobile apps. In addition, I conducted user studies to evaluate the impact of the repairs on the quality of the UIs of mobile apps. The results from these evaluations show that these techniques are effective in improving the accessibility of mobile apps without negatively impacting or distorting the UIs.
  
  

  Location: Charles Lee Powell Hall (PHE) - 325

  Audiences: Everyone Is Invited

  Contact: Melissa Ochoa

  

• Sep

  26

  PhD Dissertation Defense - Setareh Nasihati Gilani

  Tue, Sep 26, 2023 @ 03:00 PM - 05:00 PM

  Thomas Lord Department of Computer Science

  University Calendar

  
  

  PhD Dissertation Defense - Setareh Nasihati Gilani
  
  Committee Members: David Traum (Chair), Maja Mataric, Peter Kim, Kallirroi Georgila, Mohammad Soleymani
  
  Title: Understanding and Generating Multimodal Feedback in Human Machine Story Telling
  
  Abstract: People use feedback, verbal or nonverbal, from their interlocutors to guide their own behavior and alter the flow of conversation. In this thesis, we focus on human machine interactions that involve storytelling and investigate the role of understanding and providing feedback from the machines perspective. We explored the characteristics of stories that machines should use to increase rapport. We developed machine storytellers and listeners that can provide feedback and adapt their stories based on perceived multimodal feedback from their users. Finally, we investigated how machines can use real time predictions based on user feedback to further adapt the dialogue management policies of the system for better overall performance.

  Audiences: Everyone Is Invited

  Contact: Melissa Ochoa

  Event Link: https://usc.zoom.us/j/93206733633

  

• Sep

  26

  PhD Thesis Proposal - Taoan Huang

  Tue, Sep 26, 2023 @ 03:30 PM - 05:00 PM

  Thomas Lord Department of Computer Science

  University Calendar

  
  

  PhD Thesis Proposal - Taoan Huang
  
  Committee Members: Sven Koenig (co chair), Bistra Dilkina (co chair), Jyotirmoy Deshmukh, Stefanos Nikolaidis, John Carlsson, Peter Stuckey from Monash University
  
  Title: Improving Decision Makings in Search Algorithms with Machine Learning for Combinatorial Optimizations
  
  Abstract: Designing algorithms for combinatorial optimization problems (COP) are important and challenging tasks since it concerns a wide range of real world problems, such as vehicle routing, path planning and resource allocation problems. Most COPs are NP hard to solve and many research algorithms have been developed for them in the past few decades. Decision makings such as partitioning or pruning the search space and prioritizing exploration in the search space, are crucial to the efficiency and effectiveness of the search algorithms. Many of those heavily rely on domain expertise and human designed strategies.
  
  In this thesis, we hypothesize that one can leverage machine learning to improve human designed decision making strategies in different categories of search algorithms for combinatorial optimization problems. We validate the hypothesis on the problems of multiagent path finding and solving mixed integer linear programs, introducing different machine learning techniques to advance a few state of the art optimal and heuristic search algorithms for the two problems.
  
  

  Audiences: Everyone Is Invited

  Contact: Melissa Ochoa

  Event Link: https://usc.zoom.us/j/92825821724?pwd=a2RFY0x0QzV0S3hqYmkxakJvQUpYZz09

  

• Sep

  28

  PhD Thesis Proposal - Alan Romano

  Thu, Sep 28, 2023 @ 09:30 AM - 11:00 AM

  Thomas Lord Department of Computer Science

  University Calendar

  
  

  PhD Thesis Proposal - Alan Romano
  
  Committee Members: Weihang Wang (chair), William Halfond, Nenad Medvidovic, Pierluigi Nuzzo, Chao Wang
  
  Title: WAF: A Multi Purpose Static Program Analysis Framework for WebAssembly
  
  Abstract: WebAssembly is a recent standard for the web that aims to enable high performance web applications that can run at near native speeds. The standard has gained attention in both academia and industry for its ability to speed up existing user facing web applications. However, we have encountered several limitations in the static program analysis tools of the current WebAssembly ecosystem. We find that current program optimizations applied on WebAssembly modules may lead to diminished performance. We also identify a lack of tools that help developers understand WebAssembly modules through robust binary decompilation. Finally, we find a gap in the ability to analyze cross language WebAssembly applications across the two languages they are typically implemented in, i.e., WebAssembly and JavaScript.
  
  In this thesis, we present a novel WebAssembly Analysis Framework, or WAF. WAF is a static program analysis framework for WebAssembly modules that consists of multiple intermediate representations. Inspired by frameworks made for Java, the core of our framework lies in our three intermediate representations that each model the WebAssembly module at a different semantic level. This structure enables WAF to serve in multiple use cases, including program optimizations, binary decompilation, cross language program analysis, and malware detection. We aim to show that our framework can improve static program analysis in the areas that the WebAssembly ecosystem is lacking
  

  Audiences: Everyone Is Invited

  Contact: Melissa Ochoa

  Event Link: https://usc.zoom.us/j/98399405992?pwd=ME1ENk9QL2V1bmdXZld2K0psV2p6Zz09

  

• Sep

  29

  PhD Dissertation Defense - David Millard

  Fri, Sep 29, 2023 @ 03:00 PM - 05:00 PM

  Thomas Lord Department of Computer Science

  University Calendar

  
  

  PhD Dissertation Defense - David Millard
  
  Committee Members: Gaurav Sukhatme (chair), Lars Lindemann, Somil Bansal
  
  Title: Augmented Simulation Techniques for Robotic Manipulation
  
  Abstract: The development and application of capable robot manipulators require advances in world modeling and simulation. This thesis provides a comprehensive overview of our work in simulation methodologies, covering diverse physical phenomena and target applications, and with augmented structures that make associated computation tractable. First, we present work in rigid body dynamics with contact, augmented for fast computation of first and second order derivatives and probability distributions, and present applications to parameter estimation and control. We then present work in soft object modeling and control, first by using differentiable solid mechanics for constrained parameter estimation and then by machine learning based predictive control. Finally, we present work on GPU accelerated parallel Discrete Element Methods DEM and their applicability to the challenges of robotic sampling and excavation. To demonstrate the translational utility of this work, we present results from several methodologies from real data or on real hardware

  Location: Ronald Tutor Hall of Engineering (RTH) - 105

  Audiences: Everyone Is Invited

  Contact: Melissa Ochoa

  Event Link: https://usc.zoom.us/j/97228913263?pwd=aitNWjk3VjlmcnBEcmlERURDY1kwUT09