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Events for April 12, 2018
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Explore USC – Admitted Student Day
Thu, Apr 12, 2018
Viterbi School of Engineering Undergraduate Admission
University Calendar
Explore USC is the most comprehensive campus visit program for admitted students. It is a full-day program that allows you to interact with dozens of our current students, tour the campus, learn more about financial aid, gives you opportunities to sit in on classes, and start the morning with the Viterbi School of Engineering.
Your time with us in the Viterbi School will take you through an informative session on our academic programs. We will arrange a meeting with faculty from the major you are interested in as well as engineering facility tours of that same area. For lunch we will have you hanging out with some of our engineering students for a few hours, eating in the dinning facilities, seeing the residence halls, but most importantly experiencing the full USC atmosphere.
RSVP
Location: USC Admission Office
Audiences: Admitted Students and Their Families
Contact: Viterbi Admission
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CS Colloquium: Mikael Henaff (New York University) - Learning Models of the Environment for Sample-Efficient Planning
Thu, Apr 12, 2018 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Mikael Henaff, New York University
Talk Title: Learning Models of the Environment for Sample-Efficient Planning
Series: CS Colloquium
Abstract: Learning to predict how an environment will evolve and the consequences of one's actions is an important ability for autonomous agents, and can enable planning with relatively few interactions with the environment which may be slow or costly. However, learning an accurate predictive model is made difficult due to several challenges, such as partial observability, long-term dependencies and inherent uncertainty in the environment. In this talk, I will present my work on architectures designed to address some of these challenges, as well as work focused on better understanding recurrent network memory over long timescales. I will then present some recent work applying learned environment models for planning, using a simple gradient-based approach which can be used in both discrete and continuous action spaces. This approach is able to match or outperform model-free methods while requiring fewer environment interactions and still enabling real-time performance.
This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity, seats will be first come first serve.
Biography: Mikael Henaff is a fifth-year PhD student in computer science at New York University, advised by Yann LeCun. His current research interests are centered around learning predictive models of the environment, model-based reinforcement learning and memory-augmented neural networks. Prior to his Ph.D studies, he worked at the NYU Langone Medical Center and has interned several times at Facebook AI Research. He holds a B.S in mathematics from the University of Texas at Austin and an M.S in mathematics from New York University.
Host: Fei Sha
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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PhD Defense - Amulya Yavdav
Thu, Apr 12, 2018 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Amulya Yadav
Committee: Milind Tambe (Chair), Kristina Lerman, Aram Galstyan, Eric Rice, Dana Goldman
Title: Artificial Intelligence for Low Resource Communities: Influence Maximization in an Uncertain World
Time: April 12 (Thursday) 1:00-3:00 PM
Location: KAP 209
Abstract:
The potential of Artificial Intelligence (AI) to tackle challenging problems that afflict society is enormous, particularly in the areas of healthcare, conservation and public safety and security. Many problems in these domains involve harnessing social networks of under-served communities to enable positive change, e.g., using social networks of homeless youth to raise awareness about Human Immunodeficiency Virus (HIV) and other STDs. Unfortunately, most of these real-world problems are characterized by uncertainties about social network structure and influence models, and previous research in AI fails to sufficiently address these uncertainties, as they make several unrealistic simplifying assumptions for these domains.
This thesis addresses these shortcomings by advancing the state-of-the-art to a new generation of algorithms for interventions in social networks. In particular, this thesis describes the design and development of new influence maximization algorithms which can handle various uncertainties that commonly exist in real-world social networks (e.g., uncertainty in social network structure, evolving network state, and availability of nodes to get influenced). These algorithms utilize techniques from sequential planning problems and social network theory to develop new kinds of AI algorithms. Further, this thesis also demonstrates the real-world impact of these algorithms by describing their deployment in three pilot studies to spread awareness about HIV among actual homeless youth in Los Angeles. This represents one of the first-ever deployments of computer science based influence maximization algorithms in this domain. Our results show that our AI algorithms improved upon the state-of-the-art by 160% in the real-world. We discuss research and implementation challenges faced in deploying these algorithms, and lessons that can be gleaned for future deployment of such algorithms. The positive results from these deployments illustrate the enormous potential of AI in addressing societally relevant problems.
Location: Kaprielian Hall (KAP) - 209
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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EE Seminar: Towards Generalizable Imitation in Robotics
Thu, Apr 12, 2018 @ 01:30 PM - 02:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Animesh Garg, Postdoctoral Researcher, Stanford University AI lab
Talk Title: Towards Generalizable Imitation in Robotics
Abstract: Robotics and AI are experiencing radical growth, fueled by innovations in data-driven learning paradigms coupled with novel device design, in applications such as healthcare, manufacturing and service robotics. And in our quest for general purpose autonomy, we need abstractions and algorithms for efficient generalization.
Data-driven methods such as reinforcement learning circumvent hand-tuned feature engineering, albeit lack guarantees and often incur a massive computational expense: training these models frequently takes weeks in addition to months of task-specific data-collection on physical systems. Further such ab initio methods often do not scale to complex sequential tasks. In contrast, biological agents can often learn faster not only through self-supervision but also through imitation. My research aims to bridge this gap and enable generalizable imitation for robot autonomy. We need to build systems that can capture semantic task structures that promote sample efficiency and can generalize to new task instances across visual, dynamical or semantic variations. And this involves designing algorithms that unify in reinforcement learning, control theoretic planning, semantic scene & video understanding, and design.
In this talk, I will discuss two aspects of Generalizable Imitation: Task Imitation, and Generalization in both Visual and Kinematic spaces. First, I will describe how we can move away from hand-designed finite state machines by unsupervised structure learning for complex multi-step sequential tasks. Then I will discuss techniques for robust policy learning to handle generalization across unseen dynamics. I will revisit structure learning for task-level understanding for generalization to visual semantics.
And lastly, I will present a program synthesis based method for generalization across task semantics with a single example with unseen task structure, topology or length. The algorithms and techniques introduced are applicable across domains in robotics; in this talk, I will exemplify these ideas through my work on medical and personal robotics.
Biography: Animesh is a Postdoctoral Researcher at Stanford University AI lab. Animesh is interested in problems at the intersection of optimization, machine learning, and design. He studies the interaction of data-driven Learning for autonomy and Design for automation for human skill-augmentation and decision support. Animesh received his Ph.D. from the University of California, Berkeley where he was a part of the Berkeley AI Research center and the Automation Science Lab. His research has been recognized with Best Applications Paper Award at IEEE CASE, Best Video at Hamlyn Symposium on Surgical Robotics, and Best Paper Nomination at IEEE ICRA 2015. And his work has also featured in press outlets such as New York Times, UC Health, UC CITRIS News, and BBC Click.
Host: Pierluigi Nuzzo, nuzzo@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Epstein Department Seminar
Thu, Apr 12, 2018 @ 02:00 PM - 03:30 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Michael Yu Wang, Profesor, Hong Kong University of Science and Technology
Talk Title: Architectured Meso-Scale Cellular Materials and Structures: Topology Optimization for Additive Manufacturing
Host: Dr. Yong Chen
More Information: Michael Yu Wang_flyer.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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2018 Viterbi Keynote Lecture
Thu, Apr 12, 2018 @ 04:00 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: David Tse, Thomas Kailath and Guanghan Xu Professor, Stanford University
Talk Title: Maximum likelihood Genome Sequencing
Series: Viterbi Lecture
Abstract: Genome sequencing is one of the biggest breakthroughs in science in the past two decades. Modern sequencing methods use linking data at multiple scales to reconstruct pertinent information about the genome. Many such reconstruction problems can be formulated as maximum likelihood sequence decoding from noisy linking data. We discuss two in this talk: haplotype phasing, the problem of sequencing genomic variations on each of the maternal and paternal chromosomes, and genome scaffolding, the problem of finishing genome assembly using long-range 3D contact data. While maximum likelihood sequence decoding is NP-hard in both of these problems, spectral and linear programming relaxations yield efficient approximation algorithms that can provably achieve the information theoretic limits and perform well on real data. These results parallel the biggest success of information theory: efficiently achieving the fundamental limits of communication.
Biography: David Tse received the B.A.Sc. degree in systems design engineering from University of Waterloo in 1989, and the M.S. and Ph.D. degrees in electrical engineering from Massachusetts Institute of Technology in 1991 and 1994 respectively. From 1995 to 2014, he was on the faculty of the University of California at Berkeley. He received the Claude E. Shannon Award in 2017 and was elected member of the U.S. National Academy of Engineering in 2018. Previously, he received a NSF CAREER award in 1998, the Erlang Prize from the INFORMS Applied Probability Society in 2000 and the Frederick Emmons Terman Award from the American Society for Engineering Education in 2009. He received multiple best paper awards, and is the inventor of the proportional-fair scheduling algorithm used in all third and fourth-generation cellular systems.
Host: Sandeep Gupta, sandeep@usc.edu
More Info: https://minghsiehee.usc.edu/viterbi-lecture/
Webcast: https://bluejeans.com/401381224/More Information: 20180412 Tse Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
WebCast Link: https://bluejeans.com/401381224/
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
Event Link: https://minghsiehee.usc.edu/viterbi-lecture/
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Washington D.C. - Admitted Student Program
Thu, Apr 12, 2018 @ 07:00 PM - 09:00 PM
Viterbi School of Engineering Undergraduate Admission
University Calendar
These Admitted Student Programs, hosted by the Undergraduate Admission Office, provide admitted students and their families an opportunity to meet admission counselors, representatives from academic departments, alumni, and you will have the opportunity to meet other admitted students from your local area. Viterbi and University Admission counselors will be there to answer any questions you might have, tell you more about campus life and your specific academic program, and welcome you to the Trojan Family. The program will last approximately two hours.
We love seeing our newly admitted students in person! if you live in or near a city we will be visiting, we encourage you to join us!
RSVP
Location: Key Bridge Marriott, 1401 Lee Highway
Audiences: Admitted Students and Their Families
Contact: Viterbi Admission
