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Events for November 30, 2022
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PhD Thesis Proposal - Zhaoheng Zheng
Wed, Nov 30, 2022 @ 08:30 AM - 10:00 AM
Thomas Lord Department of Computer Science
University Calendar
Ph.D. Candidate: Zhaoheng Zheng
Topic: Incorporating Large-Scale Vision-Language Corpora in Visual Understanding
Committee Chair: Prof. Ram Nevatia
Committee Member: Prof. Keith Jenkins
Committee Member: Prof. Jesse Thomason
Committee Member: Prof. Greg Ver Steeg
Committee Member: Prof. Mohammad Soleymani
Abstract: Vision and language are key mediators through which humans interact with the external world or other members of society. One goal of artificial intelligence (AI) research is to create machines that can perceive the real world through multiple modalities. Previous research has shown remarkable progress in creating functional visual or linguistic perception systems with the help of deep neural networks. Recently, thanks to the advances of the Internet and social media, large-scale vision-language corpora can be easily accessed, motivating research that aims at creating large-scale Vision-Language Pre-training (VLP) models. Compared with previous methods, VLP models are stronger and more generalizable thanks to their data scale. In this thesis, we investigate the problem of how to leverage such data to boost existing visual understanding tasks. Particularly in FashionVLP, we propose to fine-tune a pre-trained VLP model for fashion image retrieval. More specifically, we fine-tune the model with customized input sequences containing various vision-language features, achieving significant improvements on multiple benchmarks. Moreover, we take a step further and explore better designs for VLP models to learn from large-scale corpora, resulting in our recent work, Fractional Intermediate Tower (FIT). FIT enhances the vision-language fusion process inside VLP models by encoding vision features from multiple vision layers before they are taken by the fusion encoder.
WebCast Link: https://usc.zoom.us/j/95655803815?pwd=d3RrOXNrU2dVVE1sTkZpYXU3NWxEUT09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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ServiceNow Open House (Virtual, External)
Wed, Nov 30, 2022 @ 10:00 AM - 11:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
At ServiceNow, our technology makes the world work for everyone, and our people make it possible. Our diverse team is changing the world with products that make a meaningful impact on people and communities. The more of 'you' you bring to work, the better. When you join ServiceNow, the world works.
Who is ServiceNow?
ServiceNow creates digital experiences that help organizations work smarter, faster, and better. Our purpose is to make the world work better for everyone.
ServiceNow Open Houses:
We are excited to announce our new Open Houses this Fall! These Open Houses are
available to anyone that would like to learn more about ServiceNow, our culture, and opportunities. Each open house will consist of an info session about ServiceNow and breakout rooms with recruiters and ServiceNow professionals. Join us and do not miss out on all the fun!
ServiceNow Workshops:
We are excited to announce that we are bringing back our career development
workshop series. These are free, virtual, career development workshops aimed to help those looking to jumpstart their careers in the tech industry. We'll be covering valuable topics that you won't want to miss!
ServiceNow Virtual Events
- Open House September 7th | 10 to 11 am
- Stand Out at Career Fairs and Conferences
Workshop September 14th| 10 to 11 am
- Open House September 22nd | 10 to 11 am
- Open House October 5th | 10 to 11 am
- Build Your Personal Brand and Give Your LinkedIn a Makeover Workshop October 12th |10 to 11 am
- Open House October 20th | 10 to 11 am
- Open House November 2nd |10 to 11 am
- How to Ace your In-Person and Virtual Interview Workshop November 9th 10:00 AM to 11:00 AM PDT
- Open House November 17th |10 to 11 am
- Open House November 30th | 10 to 11 am
- Overcoming Imposter Syndrome Workshop December 14th | 10 to 11 am
- Open House December 15th | 10:00 to 11:00 am
Check out all of our events and RSVP HERE
External employer-hosted events and activities are not affiliated with the USC Viterbi Career Connections Office. They are posted on Viterbi Career Connections because they may be of interest to members of the Viterbi community. Inclusion of any activity does not indicate USC sponsorship or endorsement of that activity or event. It is the participants responsibility to apply due diligence, exercise caution when participating, and report concerns to vcareers@usc.edu
Location: online
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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Virtual Efficient Estimation of Treatment Effect in Online Experiments
Wed, Nov 30, 2022 @ 10:00 AM - 11:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Congshan Zhang, Meta , Core Data Science Team at Meta
Talk Title: Efficient Estimation of Treatment Effect in Online Experiments
Abstract: Randomized controlled trials are commonly used by tech companies to draw causal conclusions on various product changes. The confidence intervals from these experiments, however, are usually too large due to reasons such as limited number of users, heavy-tailed outcome variables and small treatment effects. Improving estimation efficiency for randomized controlled trials is not only a scientifically interesting but also a practically relevant area of research. In this talk, I will go over a few prominent techniques in statistics to improve estimation efficiency. Basic techniques such as CUPED and more advanced methodologies based on ML and synthetic controls will be introduced.
Biography: Congshan Zhang is a research scientist on Core Data Science Team at Meta. Congshan is interested in various topics in statistics and econometrics including causal inference, machine learning and time series. Congshan holds Ph.D. in economics from Duke University. Before joining Meta, Congshan did research on financial econometrics, with a focus on nonparametric and semi-parametric inference using high-frequency data and on testing models of financial markets. His work appears in top journals of econometrics such as Journal of Econometrics and Annals of Applied Probability.
Host: Urbashi Mitra
More Info: https://usc.zoom.us/j/96927080167?pwd=Vk9MOEpOSUx3V1hlZFc3U0tmOTNsUT09 Meeting ID: 969 2708 0167 Passcode: 586135
More Information: ECE Seminar Announcement_Nov21.docx
Location: https://usc.zoom.us/j/96927080167?pwd=Vk9MOEpOSUx3V1hlZFc3U0tmOTNsUT09 Meeting ID: 969 2708 0167 P
Audiences: Everyone Is Invited
Contact: Susan Wiedem
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Computer Science General Faculty Meeting
Wed, Nov 30, 2022 @ 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 only. Event details emailed directly to attendees.
Location: Ronald Tutor Hall of Engineering (RTH) - 526 - Hybrid
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
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Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Wed, Nov 30, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Vikas Sindhwani, Google Brain
Talk Title: Foundation Models for Robotics
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Trained on internet-scale datasets, large language and vision models demonstrate breakthrough capabilities which until recently were thought to still be decades away in technological feasibility. Does this imply a paradigm shift in Robotics as well? If so, what is the bridge from symbols and tokens on the internet to actions in the physical world? Through a few illustrative vignettes of robotic manipulation and navigation research at Google, I will propose speculative paths towards making robots useful in human-centric spaces.
Biography: Vikas Sindhwani is Senior Staff Research Scientist in the Google Brain team in New York where he leads a research group focused on solving a range of planning, perception, learning, and control problems arising in Robotics. His interests are broadly in core mathematical foundations of statistical learning, and in end-to-end design aspects of building large-scale, robust machine intelligence systems. He received the best paper award at Uncertainty in Artificial Intelligence (UAI) 2013, the IBM Pat Goldberg Memorial Award in 2014, and was finalist for Outstanding Planning Paper Award at ICRA-2022. He serves on the editorial board of Transactions on Machine Learning Research (TMLR) and IEEE Transactions on Pattern Analysis and Machine Intelligence; he has been area chair and senior program committee member for NeurIPS, International Conference on Learning Representations (ICLR) and Knowedge Discovery and Data Mining (KDD). He previously led a team of researchers in the Machine Learning group at IBM Research, NY. He has a PhD in Computer Science from the University of Chicago and a B.Tech in Engineering Physics from Indian Institute of Technology (IIT) Mumbai. His publications are available at: http://vikas.sindhwani.org/.
Host: Somil Bansal, somilban@usc.edu
Webcast: https://usc.zoom.us/webinar/register/WN_ySGInGwKRKKHX7NHJwTk3QLocation: Online
WebCast Link: https://usc.zoom.us/webinar/register/WN_ySGInGwKRKKHX7NHJwTk3Q
Audiences: Everyone Is Invited
Contact: Talyia White
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AME Seminar (Virtual)
Wed, Nov 30, 2022 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Sebastian Pattinson, University of Cambridge
Talk Title: Generalisable 3D Printing Error Detection and Correction via Neural Networks
Abstract: Material extrusion is the most widespread additive manufacturing method but its application in end-use products is limited by vulnerability to errors. Humans can detect errors but cannot provide continuous monitoring or real-time correction. Existing automated approaches are not generalisable across different parts, materials, and printing systems. In this talk I will discuss recent work in our lab where we train a multi-head neural network using images automatically labelled by deviation from optimal printing parameters. The automation of data acquisition and labelling allows the generation of a large and varied extrusion 3D printing dataset, containing 1.2 million images from 192 different parts labelled with printing parameters. The thus trained neural network, alongside a control loop, enables real-time detection and rapid correction of diverse errors that is effective across many different 2D and 3D geometries, materials, printers, toolpaths, and even extrusion methods.
Biography: Sebastian Pattinson is an Assistant Professor in the Department of Engineering at the University of Cambridge. His group develops 3D printers that learn how to make things better and uses these to make better medical devices. Before joining the Cambridge, Sebastian was a postdoctoral fellow in the Department of Mechanical Engineering at MIT focusing on 3D printed materials and devices. He received Ph.D. and Masters degrees in the Department of Materials Science & Metallurgy at the University of Cambridge, where he developed nanomaterial synthesis methods. His awards include a UK Academy of Medical Sciences Springboard award; US National Science Foundation postdoctoral fellowship; UK Engineering and Physical Sciences Research Council Doctoral Training Grant; MIT Translational Fellowship; and a (Google) X Moonshot Fellowship.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09WebCast Link: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
