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Events for December 14, 2022
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ServiceNow Open House (Virtual, External)
Wed, Dec 14, 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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Computer Science General Faculty Meeting
Wed, Dec 14, 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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PhD Defense -Sara Mohammadinejad
Wed, Dec 14, 2022 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Sara Mohammadinejad
Title: Learning logical abstractions from sequential data
Committee: Jyotirmy Deshmukh, Chao Wang, Jesse Thomason, Mukund Raghothaman, Paul Bogdan
Sequential data refers to data where order between successive data-points is important. Time-series data and spatio-temporal data are examples of sequential data. Cyber-physical system applications such as autonomous vehicles, wearable devices, and avionic systems generate a large volume of time-series data. The Internet-of-Things, complex sensor networks, multi-agent cyber-physical systems are all examples of spatially distributed systems that continuously evolve in time, and such systems generate huge amounts of spatio-temporal data. Designers often look for tools to extract high-level information from such data. Traditional machine learning (ML) techniques for sequential data offer several solutions to solve these problems; however, the artifacts trained by these algorithms often lack interpretability. A definition of interpretability by Biran and Cotton is: Models are interpretable if their decisions can be understood by humans.
Formal parametric logic, such as Signal Temporal Logic (STL) and Spatio-temporal Reach and Escape Logic (STREL) are seeing increasing adoption in the formal methods and industrial communities as go-to specification languages for sequential data. Formal parametric logic are machine-checkable, and human-understandable abstractions for sequential data, and they can be used to tackle a variety of learning problems that include but are not limited to classification, clustering and active learning. The use of formal parametric logic in the context of machine learning tasks has seen considerable amount of interest in recent years. We make several significant contributions to this growing body of literature. This dissertation makes five key contributions towards learning formal parametric logic from sequential data. (1) We develop a new technique for learning STL-based classifiers from time-series data and provide a way to systematically explore the space of all STL formulas. (2) We conduct a user study to investigate whether STL formulas are indeed understandable to humans. (3) As an application of our STL-based learning framework, we investigate the problem of mining environment assumptions for cyber-physical system models. (4) We develop the first set of algorithms for logical unsupervised learning of spatio-temporal data and show that our method generates STREL formulas of bounded description complexity. (5) We design an explainable and interactive learning approach to learn from natural language and demonstrations using STL. Finally, we showcase the effectiveness of our approaches on case studies that include but are not limited to urban transportation, automotive, robotics and air quality monitoring.
WebCast Link: https://usc.zoom.us/j/94145108434?pwd=R3M0Smh0ZVp6UkR4S2hiamdhdjlMUT09
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
