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Conferences, Lectures, & Seminars
Events for October
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CS Colloquium: Robert Sedgewick (Princeton University) - A 21st Century Model for Disseminating Knowledge
Tue, Oct 10, 2017 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Robert Sedgewick, Princeton
Talk Title: A 21st Century Model for Disseminating Knowledge
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
In the early years of the third millenium, most professors are still teaching in virtually the same way they were taught and their teachers were taught, stretching back centuries. This situation is likely to change, relatively soon. Technology is transforming (if not threatening to overwhelm) higher education, as MOOCs and online content become widely available. University students seeking to learn a topic who now have little if any choice are about to be presented with a vast array of choices. What student would not want to swap a tired professor writing slowly on a chalkboard for a well-produced series of videos and associated content, given by a world leader in the field? We are on the verge of a transformation on the scale of the transformation wrought by Gutenburg. This imminent change raises a host of fascinating and far-reaching questions.
In this talk, we describe a scalable model for teaching and learning based on a combination of studio-produced video lectures, innovative online content and assessment mechanisms, and an authoritative classic textbook. We initially proved this approach effective for teaching algorithms and data structures, the analysis of algorithms, and analytic combinatorics. More recently, we have published a new textbook in computer science, new studio-produced lectures, and online content that teachers and learners can use for a first-year course sequence in computer science that can stand alongside traditional first courses in physics, chemistry, economics, and other disciplines. Our model enables us to reach millions of students and programmers around the world.
Biography: Robert Sedgewick is the founding chair and the William O. Baker Professor in the Department of Computer Science at Princeton and served for many years as a member of the board of directors of Adobe Systems. He has held visiting research positions at Xerox PARC, IDA, INRIA, and Bell Laboratories.
Prof. Sedgewick's research interests include analytic combinatorics, algorithm design, the scientific analysis of algorithms, curriculum development, and innovations in the dissemination of knowledge. He has published widely in these areas and is the author of twenty books, which have sold nearly one million copies. He has also published extensive online content (including studio-produced video lectures) on analysis of algorithms and analytic combinatorics and (with Kevin Wayne) algorithms and computer science. Their MOOC on algorithms has been named one of the "top 10 MOOCs of all time."
Host: Computer Science Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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.
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CS Colloquium: Max Levchin (Affirm) - Fireside Chat with Max Levchin, CEO of Affirm
Tue, Oct 17, 2017 @ 06:30 PM - 08:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Max Levchin, Affirm
Talk Title: Fireside Chat with Max Levchin, CEO of Affirm
Series: CS Colloquium
Abstract: Fireside Chat with Max Levchin from 6:30 PM - 7:30 PM followed by networking from 7:30 PM - 8:00 PM. Coffee and cookies will be served.
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity in GFS 106, seats will be first come first serve.
Biography: Max Levchin is the founder and CEO of Affirm, a financial services technology company, cofounder and Chairman of Glow, a women's reproductive and sexual health company, and cofounder and general partner at SciFi VC, a private venture capital firm. All three companies were created and launched from his San Francisco based innovation lab, HVF (Hard, Valuable, Fun). Max was one of the original cofounders of PayPal where he served as the CTO until its acquisition by Ebay in 2002. In 2002, he was named to the Technology Review TR100 as one of the top 100 innovators in the world as well as Innovator of the Year. In 2004, he founded Slide, a personal media-sharing service for social networking sites such as MySpace and Facebook which he sold to Google in August 2010. Also in 2004, he helped start Yelp, where he was the first investor in and Chairman of the Board from 2004 until 2015. He has served on several boards such as Yahoo!, Yelp, Evernote and currently serves on the Consumer Advisory Board of the U.S. Consumer Financial Protection Bureau. Max is a serial entrepreneur, computer scientist, philanthropist and active investor in more than 100 startups.
Host: Computer Science Department
Location: Grace Ford Salvatori Hall Of Letters, Arts & Sciences (GFS) - 106
Audiences: Everyone Is Invited
Contact: Computer Science Department
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: Henny Admoni (Carnegie Mellon University) - Toward Natural Interactions With Assistive Robots
Wed, Oct 18, 2017 @ 12:00 PM - 01:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Henny Admoni, Carnegie Mellon University
Talk Title: Toward Natural Interactions With Assistive Robots
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Research Colloquium.
Robots can help people live better lives by assisting them with the complex tasks involved in everyday activities. This is especially impactful for people with disabilities, who can benefit from robotic assistance to increase their independence. For example, physically assistive robots can collaborate with people in preparing a meal, enabling people with motor impairments to be self sufficient in cooking and eating. Socially assistive robots can act as tutors, coaches, and partners, to help people with social or learning deficits practice the skills they have learned in class or therapy. Developing effective human-robot interactions in these cases requires a multidisciplinary approach that involves fundamental robotics algorithms, insights from human psychology, and techniques from artificial intelligence and machine learning.
In this talk, I will describe my vision for robots that collaborate with and assist humans on complex tasks. I will explain how we can leverage our understanding of natural, intuitive human behaviors to detect when and how people need assistance, and then apply robotics algorithms to produce effective human-robot interactions. I explain how models of human attention, drawn from cognitive science, can help select robot behaviors that improve human performance on a collaborative task. I detail my work on algorithms that predict people's mental states based on their eye gaze and provide assistance in response to those predictions. And I show how breaking the seamlessness of an interaction can make robots appear smarter. Throughout the talk, I will describe how techniques and knowledge from cognitive science help us develop robot algorithms that lead to more effective interactions between people and their robot partners.
Biography: Henny Admoni is an Assistant Professor in the Robotics Institute at Carnegie Mellon University, where she works on assistive robotics and human-robot interaction. Henny develops and studies intelligent robots that improve people's lives by providing assistance through social and physical interactions. She studies how nonverbal communication, such as eye gaze and pointing, can improve assistive interactions by revealing underlying human intentions and increasing human-robot communication. Previously, Henny was a postdoctoral fellow at CMU with Siddhartha Srinivasa in the Personal Robotics Lab. Henny completed her PhD in Computer Science at Yale University with Professor Brian Scassellati. Henny holds an MS in Computer Science from Yale University, and a BA/MA joint degree in Computer Science from Wesleyan University. Henny's scholarship has been recognized with awards such as the NSF Graduate Research Fellowship, the Google Anita Borg Memorial Scholarship, and the Palantir Women in Technology Scholarship.
Host: Computer Science Department
Location: Seeley G. Mudd Building (SGM) - 123
Audiences: Everyone Is Invited
Contact: Computer Science Department
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: Phillip Isola (UC Berkeley) - Learning to See Without a Teacher
Thu, Oct 19, 2017 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Phillip Isola, UC Berkeley
Talk Title: Learning to See Without a Teacher
Series: Visa Research Machine Learning Seminar Series hosted by USC Machine Learning Center
Abstract: This lecture satisfies requirements for CSCI 591: Research Colloquium.
Over the past decade, learning-based methods have driven rapid progress in computer vision. However, most such methods still require a human "teacher" in the loop. Humans provide labeled examples of target behavior, and also define the objective that the learner tries to satisfy. The way learning plays out in nature is rather different: ecological scenarios involve huge quantities of unlabeled data and only a few supervised lessons provided by a teacher (e.g., a parent). I will present two directions toward computer vision algorithms that learn more like ecological agents. The first involves learning from unlabeled data. I will show how objects and semantics can emerge as a natural consequence of predicting raw data, rather than labels. The second is an approach to data prediction where we not only learn to make predictions, but also learn the objective function that scores the predictions. In effect, the algorithm learns not just how to solve a problem, but also what exactly needs to be solved in order to generate realistic outputs. Finally, I will talk about my ongoing efforts toward sensorimotor systems that not only learn from provided data but also act to sample more data on their own.
Biography: Phillip Isola is currently a Fellow at OpenAI, and he will be starting as an Assistant Professor in EECS at MIT in 2018. He received his Ph.D. in the Brain & Cognitive Sciences department at MIT, and spent two years as a postdoc in the EECS department at UC Berkeley. He studies visual intelligence from the perspective of both minds and machines.
Host: Joseph Lim
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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: Rachel Greenstadt (Drexel University) - Using Stylometry to Attribute Programmers and Writers
Tue, Oct 24, 2017 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Rachel Greenstadt, Drexel University
Talk Title: Using Stylometry to Attribute Programmers and Writers
Abstract: This lecture satisfies requirements for CSCI 591: Research Colloquium.
In this talk, I will discuss my lab's work in the emerging field of adversarial stylometry and machine learning. Machine learning algorithms are increasingly being used in security and privacy domains, in areas that go beyond intrusion or spam detection. For example, in digital forensics, questions often arise about the authors of documents: their identity, demographic background, and whether they can be linked to other documents. The field of stylometry uses linguistic features and machine learning techniques to answer these questions. We have applied stylometry to difficult domains such as underground hacker forums, open source projects (code), and tweets. I will discuss our Doppelgänger Finder algorithm, which enables us to group Sybil accounts on underground forums and detect blogs from Twitter feeds and reddit comments. In addition, I will discuss our work attributing unknown source code and binaries.
Biography: Dr. Rachel Greenstadt is an Associate Professor of Computer Science at Drexel University where she teaches graduate-level courses in computer security, privacy, and machine learning. She founded the Privacy, Security, and Automation Laboratory at Drexel University in 2008. She has attracted a research team of PhD students and undergraduates with interests and expertise in information extraction, machine learning, agents, privacy, trust, and security.
Dr. Greenstadt's scholarship has been recognized by the privacy research community. She is an alum of the DARPA Computer Science Study Group and a recipient of the NSF CAREER Award. Her work has received the PET Award for Outstanding Research in Privacy Enhancing Technologies and the Andreas Pfitzmann Best Student Paper Award. She currently serves as co-editor-in-chief of the journal Proceedings on Privacy Enhancing Technologies (PoPETs). Her research has been featured in the New York Times, the New Republic, Der Spiegel, and other local and international media outlets.
Host: Aleksandra Korolova
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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. -
CAIS Seminar: Dr. Sze-Chuan Suen (University of Southern California) - A POMDP Model for Drug Resistant Tuberculosis Screening
Thu, Oct 26, 2017 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Sze-Chuan Suen, University of Southern California
Talk Title: A POMDP Model for Drug Resistant Tuberculosis Screening
Series: Center for AI in Society (CAIS) Seminar Series
Abstract: This lecture satisfies requirements for CSCI 591: Research Colloquium.
Patients with drug resistant disease may need different treatment than those with drug-sensitive disease. However, identifying these patients may be difficult since tests to determine disease strain may be time consuming or costly. In this project, we develop a model using POMDP and simulation techniques to identify when and which first-line tuberculosis patients are most likely at risk for drug resistance and should be screened to reduce costs and increase health outcomes.
Biography: Sze-Chuan Suen received her PhD in the department of Management Science and Engineering from Stanford University in 2016. Her research interests include developing applied mathematical models to identify epidemiological trends and evaluate health policies to support informed decision-making. Her research draws from techniques in simulation, dynamic systems modeling, optimization, and decision analysis.
Host: Milind Tambe
Location: Seeley Wintersmith Mudd Memorial Hall (of Philosophy) (MHP) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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.
