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Events for October 02, 2019

  • PhD Defense - Anil Ramakrishna

    Wed, Oct 02, 2019 @ 12:00 PM - 02:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Candidate: Anil Ramakrishna

    Shri Narayanan (chair)
    Aiichiro Nakano
    Morteza Dehghani

    Location: RTH 320
    Time: October 2nd, 12 pm.

    Title: Computational Models for Multidimensional Annotations of Affect

    Abstract: Affect is an integral aspect of human psychology, it acts as a regulator for all our interactions with external stimuli. Affect includes several related concepts such as sentiment, emotion as well as as higher order constructs such as mood and humor. By its nature, it is highly subjective, with different stimuli leading to different responses in people due to varying personal and cultural artifacts. For example, a specific image or audio clip may evoke different emotions in people depending on their personality. Computational modeling of affective dimensions is an important problem in Artificial Intelligence (AI). It covers a variety of tasks such as sentiment analysis, emotion recognition and opinion mining, which often involve supervised training of models using a large number of labeled data instances. However, training labels are difficult to obtain due to the inherent subjectivity of these constructs. Typical approaches to obtain the training labels include collecting opinions from expert or naive annotators, followed by a suitable aggregation.
    In this dissertation, we will present our contributions towards building computational models for noisy annotations of affect, specifically in the aggregation of multidimensional annotations. We propose latent variable models to capture annotator behaviors using additive Gaussian noise and matrix factorization, leading to more accurate estimates of the underlying ground truth. We then apply the joint matrix factorization model to the task of sentence level estimation of psycholinguistic normatives. Finally, we highlight our ongoing efforts in estimating agreement on multidimensional annotations.

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

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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  • Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar

    Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar

    Wed, Oct 02, 2019 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars

    Speaker: Sanjay Shakkottai, The University of Texas at Austin

    Talk Title: Hyper-parameter Tuning for ML Models: A Monte-Carlo Tree Search (MCTS) Approach

    Series: Center for Cyber-Physical Systems and Internet of Things

    Abstract: We study the application of online learning techniques in the context of hyper-parameter tuning, which is of growing importance in general machine learning. Modern neural networks have several tunable parameters, where training for even one such parameter configuration can take several hours to days. We first cast hyper-parameter tuning as optimizing a multi-fidelity black-box function (which is noise-less) and propose a multi-fidelity tree search algorithm for the same. We then present extensions of our model and algorithm, so that they can function even in the presence of noise. We show that our tree-search based algorithms can outperform state of the art hyper-parameter tuning algorithms on several benchmark data-sets.

    Biography: Sanjay Shakkottai received his Ph.D. from the ECE Department at the University of Illinois at Urbana-Champaign in 2002. He is with The University of Texas at Austin, where he is currently the Temple Foundation Endowed Professor No. 3, and a Professor in the Department of Electrical and Computer Engineering. He received the NSF CAREER award in 2004, and was elected as an IEEE Fellow in 2014. His research interests lie at the intersection of algorithms for resource allocation, statistical learning and networks, with applications to wireless communication networks and online platforms.

    Host: Paul Bogdan

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

    Audiences: Everyone Is Invited

    Contact: Talyia White

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  • AME Seminar

    Wed, Oct 02, 2019 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars

    Speaker: Mimi Koehl, University of California, Berkeley

    Talk Title: Navigating in a Turbulent Environment

    Abstract: When organisms locomote and interact in nature, they must navigate through complex habitats that vary on many spatial scales, and they are buffeted by turbulent wind or water currents and waves that also vary on a range of spatial and temporal scales. We have been using the microscopic larvae of bottom dwelling marine animals to study how the interaction between the swimming or crawling by an organism and the turbulent water flow around them determines how they move through the environment. Many bottom dwelling marine animals produce microscopic larvae that are dispersed to new sites by ambient water currents, and then must land and stay put on surfaces in suitable habitats. Field and laboratory measurements enabled us to quantify the fine scale, rapidly changing patterns of water velocity vectors and of chemical cue concentrations near coral reefs and along fouling communities (organisms growing on docks and ships). We also measured the swimming behavior of larvae of reef dwelling and fouling community animals, and their responses to chemical and mechanical cues. We used these data to design agent based models of larval behavior. By putting model larvae into our real world flow and chemical data, which varied on spatial and temporal scales experienced by microscopic larvae, we could explore how different responses by larvae affected their transport and their recruitment into reefs or fouling communities. The most effective strategy for recruitment depends on habitat.

    Biography: Mimi Koehl, a Professor of the Graduate School in the Department of Integrative Biology at the University of California, Berkeley, earned her Ph.D. in Zoology at Duke University. She studies the physics of how organisms interact with their environments, focusing on how microscopic creatures swim and capture food in turbulent water flow, how organisms glide in turbulent wind, how wave battered marine organisms avoid being washed away, and how olfactory antennae catch odors from water or air moving around them.

    Professor Koehl is a member of the National Academy of Sciences and the American Academy of Arts and Sciences, and is a Fellow of the American Physical Society. Her awards include a MacArthur genius grant, a Presidential Young Investigator Award, a Guggenheim Fellowship, the John Martin Award (Association for the Sciences of Limnology and Oceanography, for research that created a paradigm shift in an area of aquatic sciences), the Borelli Award (American Society of Biomechanics, for outstanding career accomplishment), the Rachel Carson Award (American Geophysical Union, for cutting-edge ocean science), and the Muybridge Award (International Society of Biomechanics highest honor).

    Host: AME Department

    More Info: https://ame.usc.edu/seminars/

    Location: John Stauffer Science Lecture Hall (SLH) - 102

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

    Event Link: https://ame.usc.edu/seminars/

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  • Astani Civil and Environmental Engineering Seminar

    Wed, Oct 02, 2019 @ 04:00 PM - 05:00 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars

    Speaker: Dr. Kostas Papakonstantinou, Penn State University

    Talk Title: Computational decision-making under uncertainty in engineering systems: Linking UQ to the action space

    Abstract: At the core of every engineering problem lies a decision-making quest, either directly or indirectly. Sophisticated UQ methods are essentially providing decision support through efficient quantification of selected metrics and quantities of interest, and sensitivity analysis. Nonetheless, despite significant progress in UQ methods and techniques, the actual decision-making process is still largely dependent on the static and rather limited traditional cost-benefit analysis framework, and dedicated rigorous computational methodologies for engineering decisions under uncertainty are practically elusive. In this talk, an approach for a seamless integration of stochastic models and data with computational decision-making, able to directly and autonomously offer optimal actions to decision-makers/agents is analyzed. As shown, challenging sequential decision-making problems in nonstationary dynamic environments can be efficiently formulated along the premises of optimal stochastic control, through Markov Decision Processes (MDPs), Partially Observable Markov Decision Processes (POMDPs), and mixed approaches thereof. In systems with relatively low dimensional state and action spaces, MDPs and POMDPs can be satisfactorily solved to global optimality through appropriate dynamic programming algorithms. However, optimal planning for large systems with multiple components is computationally hard and severely suffers from the curse of dimensionality. New developments on Deep Reinforcement Learning (DRL) methods and their capacity of addressing this problem are discussed, with emphasis on our developed DRL formulations and novel algorithmic schemes, specifically tailored to the needs of large engineering systems, able to solve otherwise intractable problems with immense state and action spaces. DRL relations to Artificial Intelligence and Machine Learning are also explained and techniques are demystified down to their fundamental mathematical attributes, underlying computational aspects and connections to engineering. The talk concludes with numerous ongoing efforts along these lines, from centralized/decentralized infrastructure management, to emergency response of cooperating agents, to autonomous robotic navigation and wildfire prevention.

    Biography: Dr. Kostas Papakonstantinou is an Assistant Professor of Civil Engineering at Penn State. He obtained a five year Diploma in Civil Engineering and a M.S. in Structural Engineering from the National Technical University of Athens, and M.S. and Ph.D. degrees at the University of California, Irvine. Prior to joining Penn State, he was an Associate Research Scientist at Columbia University. Dr. Papakonstantinou work focuses on probabilistic analysis and stochastic mechanics, decision-making under uncertainty, machine learning, optimization-inverse methods, and their integration with computational structural mechanics and engineering applications. His research has been funded by various programs and his work has received several awards, including the National Science Foundation CAREER award in 2018.

    Host: Dr. Roger Ghanem

    More Information: Abstract-Bio-K. Papakonstantinou.pdf

    Location: Ray R. Irani Hall (RRI) - 101

    Audiences: Everyone Is Invited

    Contact: Evangeline Reyes

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  • Computer Science & Data Science Progressive Degree Information Session

    Wed, Oct 02, 2019 @ 05:00 PM - 06:30 PM

    Thomas Lord Department of Computer Science

    Workshops & Infosessions

    Viterbi Graduate Admissions and the Computer Science Department will be presenting information about the progressive degree program (accelerated BS+MS) on Wednesday, October 2nd at 5pm in RTH-211. If you are a first year, sophomore, or junior interested in applying for the progressive degree program please join us! Advisors will be available to answer your questions after the presentations.

    PDP Fall 2019 Application Deadlines:
    Early Application Deadline: Friday September 27, 2019
    Final Application Deadline: Wednesday December 18, 2019


    Location: Ronald Tutor Hall of Engineering (RTH) - 211

    Audiences: Undergrad

    Contact: Ryan Rozan

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  • USC Graduate Engineering Info Session: Izmir

    Wed, Oct 02, 2019 @ 06:00 PM - 08:00 PM

    Viterbi School of Engineering Graduate Admission

    Workshops & Infosessions

    Candidates with a strong academic background and a Bachelor's degree (or those in the process of earning a Bachelor's degree) in engineering, computer science, applied mathematics, or physical science (such as physics, biology, or chemistry) are welcome to attend this session to learn more about applying to graduate engineering programs at the University of Southern California. Attendees will also receive an application fee waiver.

    Topics covered:

    Master's & Ph.D. Programs in engineering and computer science
    How to Apply
    Scholarships and Funding
    Student Life at USC and in Los Angeles
    Application Tips

    There will also be sufficient time for questions during the information session.
    Register Here

    Audiences: Everyone Is Invited

    Contact: USC Viterbi Graduate Programs

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