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Events for the 5th week of March

  • Repeating EventHack IoT

    Sun, Mar 25, 2018

    Viterbi School of Engineering Student Organizations

    University Calendar


    Calling all hackers! We will be having our first hackathon, Hack IoT , on March 24th and 25th. This 24-hour event is centered around the Internet of Things, which is a field of growing interest in industry today! We invite USC students of all skill levels to sign up and have some fun making cool stuff! Even if you know nothing about IoT, we will have workshops to teach you all you need to know to make something amazing!

    Applications are NOW AVAILABLE via our website , so gather a team and sign up below!

    Event Day: March 24th and 25th
    Event Location: Kings Hall, USC
    Facebook Event: https://www.facebook.com/events/807358462799299/
    Website: http://hack-iot.org

    Location: Frank L. King Olympic Hall Of Champions (KOH) - Kings Hall - Main Room

    Audiences: Everyone Is Invited

    View All Dates

    Contact: Institute of Electrical and Electronics Engineers

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  • Meet USC: Admission Presentation, Campus Tour, and Engineering Talk

    Mon, Mar 26, 2018

    Viterbi School of Engineering Undergraduate Admission

    University Calendar


    This half day program is designed for prospective freshmen (HS juniors and younger) and family members. Meet USC includes an information session on the University and the Admission process, a student led walking tour of campus, and a meeting with us in the Viterbi School. During the engineering session we will discuss the curriculum, research opportunities, hands-on projects, entrepreneurial support programs, and other aspects of the engineering school. Meet USC is designed to answer all of your questions about USC, the application process, and financial aid.

    Reservations are required for Meet USC. This program occurs twice, once at 8:30 a.m. and again at 12:30 p.m.

    Please make sure to check availability and register online for the session you wish to attend. Also, remember to list an Engineering major as your "intended major" on the webform!

    RSVP

    Location: Ronald Tutor Campus Center (TCC) - USC Admission Office

    Audiences: Prospective Freshmen (HS Juniors and Younger) & Family Members

    Contact: Viterbi Admission

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  • CS Colloquium: Himabindu Lakkaraju (Stanford University) Human-Centric Machine Learning: Enabling Machine Learning for High-Stakes Decision-Making

    Mon, Mar 26, 2018 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Himabindu Lakkaraju, Stanford University

    Talk Title: Human-Centric Machine Learning: Enabling Machine Learning for High-Stakes Decision-Making

    Series: CS Colloquium

    Abstract: Domains such as law, healthcare, and public policy often involve highly consequential decisions which are predominantly made by human decision-makers. The growing availability of data pertaining to such decisions offers an unprecedented opportunity to develop machine learning models which can aid human decision-makers in making better decisions. However, the applicability of machine learning to the aforementioned domains is limited by certain fundamental challenges:
    1) The data is selectively labeled i.e., we only observe the outcomes of the decisions made by human decision-makers and not the counterfactuals.
    2) The data is prone to a variety of selection biases and confounding effects.
    3) The successful adoption of the models that we develop depends on how well decision-makers can understand and trust their functionality, however, most of the existing machine learning models are primarily optimized for predictive accuracy and are not very interpretable.

    In this talk, I will describe novel computational frameworks which address the aforementioned challenges, thus, paving the way for large-scale deployment of machine learning models to address problems of significant societal impact. First, I will discuss how to build interpretable predictive models and explanations of complex black box models which can be readily understood and consequently trusted by human decision-makers. I will then outline efficient and provably near-optimal approximation algorithms to solve these problems. Next, I will present a novel evaluation framework which allows us to reliably compare the quality of decisions made by human decision-makers and machine learning models amidst challenges such as missing counterfactuals and presence of unmeasured confounders (unobservables). Lastly, I will provide a brief overview of my research on diagnosing and characterizing biases (systematic errors) in human decisions and predictions of machine learning models.

    I will conclude the talk by sketching future directions which enable effective and efficient collaboration between humans and machine learning models to address problems of societal impact.

    This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity, seats will be first come first serve.


    Biography: Hima Lakkaraju is a Ph.D. candidate in Computer Science at Stanford University. Her research focuses on enabling machine learning models to complement human decision making in high-stakes settings such as law, healthcare, public policy, and education. At the core of her research lie rigorous computational techniques leading to algorithmic contributions in machine learning, data mining, and econometrics. Hima has received several fellowships and awards including the Robert Bosch Stanford graduate fellowship, Microsoft research dissertation grant, Google Anita Borg scholarship, IBM eminence and excellence award, and best paper awards at SIAM International Conference on Data Mining (SDM) and INFORMS. Her research has been covered by various media outlets such as the New York Times, MIT Tech Review, Harvard Business Review, TIME, Forbes, Business Insider, and Bloomberg.

    Host: Aleksandra Korolova

    Location: Ronald Tutor Hall of Engineering (RTH) - 115

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

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  • EE-EP Faculty Candidate, Wei Bao, Monday, March 26th @12pm in EEB 132

    Mon, Mar 26, 2018 @ 12:00 PM - 01:30 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Wei Bao, University of California, Berkeley

    Talk Title: Interacting Light with Semiconductor at the Nanoscale

    Abstract: The ability to probe and control light-matter interaction at the nanometer scale not only advances frontiers of fundamental science, but also is a critical prerequisite to device applications in electronics, sensing, catalysis, energy harvesting, and more. Exploiting and enhancing the originally weak light-matter interactions via nanofabricated photonic structures; we will be able to sense chemical species at single molecule levels, to devise better imaging and manufacturing tools, to transfer data more efficiently at higher speed.

    In this talk, I will first describe a simple and general nano-optical device developed during my Ph.D., called campanile probe, which lay groundwork for generally-applicable nano-optical studies. Two examples will be discussed, where we cross the boundary from insufficient to sufficient resolution beyond optical diffraction limit and perform optical hyperspectral imaging of luminescence heterogeneity along InP nanowires and synthetic monolayer MoS2, providing spectral information distinct from diffraction limited micro-PL spectral imaging. Following this, I will discuss the recent works using cavities to further enhance the strength of light-matter interaction into the strong coupling regime. The formation of coherently coupled cavity exciton-polariton in two-dimensional monolayer WS2 and the inorganic perovskite CsPbBr3 as well as the ultralow threshold optically pumped polariton lasing in perovskite cavities will be shown. Finally, I will conclude by presenting my vision of how these devices can enable a wide range of capabilities with relevance to multidimensional spectroscopy imaging, efficient solid-state lighting and even beyond.


    Biography: Dr. Wei Bao is a postdoctoral researcher in Prof. Xiang Zhang's lab at the University of California, Berkeley. Previously he earned his B.A. in Physics (minor in Chemistry) at Peking University in 2009, and his M.S. in Mechanical Engineering (minor in Electrical Engineering) at UCLA in 2010. Wei then received his Ph.D. in Materials Science and Engineering (minor in Electrical Engineering) at University of California, Berkeley under the supervision of Prof. Miquel Salmeron and Prof. P. James Schuck in 2015. His Ph.D. work in nanoscale spectroscopic investigations of optoelectronic has led to several awards including: MRS Graduate Student Gold Award, Dorothy M. and Earl S. Hoffman Scholarships, Ross N. Tucker Memorial Award, as well as a R&D 100 Award 2013. His postdoc research currently focuses on polaritonics lasing devices, a scientific direction at the interface between low-dimensional semiconductor nanophotonics and quantum physics.

    Host: EE-Electrophysics

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

    Audiences: Everyone Is Invited

    Contact: Marilyn Poplawski

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  • Biomedical Engineering Seminars

    Mon, Mar 26, 2018 @ 12:30 PM - 01:50 PM

    Biomedical Engineering

    Conferences, Lectures, & Seminars


    Talk Title: TBA

    Host: Professor Qifa Zhou

    Location: Olin Hall of Engineering (OHE) - 122

    Audiences: Everyone Is Invited

    Contact: Mischalgrace Diasanta

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  • Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering

    Mon, Mar 26, 2018 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: B. Ross Barmish, University of Wisconsin, Madison

    Talk Title: From the Kelly-Shannon Collaboration to Stock Trading Based on Feedback Control

    Series: Joint CSC@USC/CommNetS-MHI Seminar Series

    Abstract: This talk begins with a description of some ideas related to gambling which originated at Bell Labs in the 1950s by John Kelly and Claude Shannon. With their work serving as motivation for this talk, I will provide an overview of my research on the development of new stock-trading algorithms. The most salient feature of my approach is that no model of any sort is used for the underlying stock-price dynamics. Instead, in the spirit of technical analysis, the size of the time-varying stock position is determined using some simple ideas involving the adaptive power of feedback control loops. This approach is said to be "reactive" rather than predictive and amounts to assigning high priority to sound money management. After the key ideas driving this research are explained, the back-testing of the trading algorithms using historical data will be addressed with attention paid to practical considerations such as transaction costs, leverage and margin. It is interesting to note that sometimes the simulations lead to unexpected results which were not contemplated during the course of the research.

    Biography: B. Ross Barmish is Professor of Electrical and Computer Engineering at the University of Wisconsin, Madison. Prior to joining UW in 1984, he held faculty positions at Yale University and the University of Rochester. From 2001-2003, he served as Chair of the EECS Department at Case Western Reserve while holding the Nord endowed professorship. He received his Bachelor's degree in EE from McGill University and the M.S. and Ph.D. degrees, also in EE, from Cornell University.

    Throughout his career, he has served the IEEE Control Systems Society in many capacities and has been a consultant for a number of companies. Professor Barmish is the author of the textbook ``New Tools for Robustness of Linear Systems'' and is a Fellow of both the IEEE and IFAC for his contributions to robust control. He received two Best Journal Publication awards, each covering a three-year period, from the International Federation of Automatic Control and has given a number of keynotes and plenary lectures at major conferences. In~2013, he received the IEEE Control Systems Society Bode Prize.

    While his earlier work concentrated on robustness of dynamical systems, his current university research involves building a bridge between feedback control theory and trading in complex financial markets. In addition to this academic pursuit, in his capacity as CEO of Robust Trading Solutions, his work involves transition of stock-trading algorithms from theory to practice and government sponsored research on the NASDAQ Limit Order Book.

    Host: Petros Ioannou, ioannou@usc.edu

    More Information: barmish.jpg (JPEG Image, 411 × 568 pixels).pdf

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

    Audiences: Everyone Is Invited

    Contact: Gerrielyn Ramos

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  • EE Seminar: Enabling Optical Methods for Next-Generation Neural Prostheses

    Mon, Mar 26, 2018 @ 03:00 PM - 04:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Andrea Giovannucci, Research Scientist, Flatiron Institute, Simons Foundation

    Talk Title: Enabling Optical Methods for Next-Generation Neural Prostheses

    Abstract: Optical methods present interesting new opportunities for brain computer interfaces (BCIs) and closed-loop experiments because of their capability to densely monitor and stimulate in-vivo large neural populations across weeks with single cell resolution. For instance, combining optical methods for recording (two-photon imaging of calcium indicators) and perturbing (optogenetics) neural ensembles opens the door to exciting closed-loop experiments, where the stimulation pattern can be determined based on the recorded activity and/or the behavioral state. However, the adoption of such tools for BCIs is currently hindered by the lack of algorithms that track neural activity in real-time. In a typical closed-loop experiment, the monitored/perturbed regions of interest (ROIs) have been preselected by analyzing offline a previous dataset from the same field of view. Monitoring the activity of a ROI, which usually corresponds to a soma, typically entails averaging the fluorescence over the corresponding ROI, resulting in a signal that is only a proxy for the actual neural activity and which can be sensitive to motion artifacts and drifts, as well as spatially overlapping sources, background/neuropil contamination, and noise. Furthermore, by preselecting the ROIs, the experimenter is unable to detect and incorporate new sources that become active later during the experiment or track changes in neuronal morphology, which prevents the execution of truly closed-loop experiments.

    In the first portion of this talk I will present an Online, single-pass, algorithmic framework for the Analysis of Calcium Imaging Data (OnACID). The framework is highly scalable with minimal memory requirements, as it processes the data in a streaming fashion one frame at a time, while keeping in memory a set of low dimensional sufficient statistics and a small minibatch of the last data frames. Every frame is processed in four sequential steps: i) The frame is registered against the previous denoised (and registered) frame to correct for motion artifacts. ii) The fluorescence activity of the already detected sources is tracked. iii) Newly appearing neurons are detected and incorporated to the set of existing sources. iv) The fluorescence trace of each source is denoised and deconvolved to provide an estimate of the underlying spiking activity. I will present the results of applying OnACID to several large-scale (90-350GB) mouse and zebrafish larvae in-vivo datasets. OnAcid can find and track tens of thousands of neurons faster than real-time, and outperforms state of the art algorithms benchmarked on multiple manual annotations using a precision-recall framework.

    In the second portion of the talk, I will present an application of brain optical imaging to unveil coding properties and feedback mechanisms implemented by neurons in the cerebellum, a brain area implied in motor control and in the production of agile movement sequences. By monitoring across days the same neuronal populations of mice undergoing associative learning I will show that a predictive signal about the upcoming movement is widely available at the input stage of the cerebellar cortex, as required by forward models of cerebellar control.

    In the last section of the talk, I will discuss my plans to develop all-optical neural prostheses interfacing with the cerebellum to recover lost motor function in the central nervous system because of injury or disease.

    Biography: Andrea Giovannucci has a Ph.D. in computer science from Universitat Autònoma de Barcelona in Spain and a B.S. in electrical engineering from Politecnico di Milano in Italy. From 2008 to 2010 he was a postdoctoral fellow at Pompeu Fabra University (Barcelona), where he developed signal processing algorithms and circuit models for neuroprosthetic applications. From 2010 to 2015 he completed a postdoctoral fellowship at the Princeton Neuroscience Institute (PNI), Princeton University. At PNI, he pioneered the use of genetically encoded calcium indicators to image neurons in the cerebellum of awake learning mice, and applied them to investigate coding properties of cerebellar neurons during motor learning. Since 2015 Andrea Giovannucci is a research scientist at the Flatiron Institute, Simons Foundation, where he develops algorithms for the analysis of calcium imaging data, general-purpose neural networks and data-intensive computing projects. Dr. Giovannucci was the recipient of the First Prize for the Best Agent Service or Application in the Agent Technology Competition (IST Agentcities.net) in 2003, was shortlisted for the best Ph.D. thesis in artificial intelligence (ECCAI), and was the recipient of the prestigious Juan de La Cierva (Spain) and New Jersey Commission on Brain Injury Research (USA) fellowships. Andrea Giovannucci is the leader developer of the CaImAn open source software platform for calcium imaging analysis, currently used by hundreds of research laboratories worldwide.

    Host: Maryam Shanechi, shanechi@usc.edu

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • EE Seminar: Statistical and Formal Methods in Hardware Security

    Tue, Mar 27, 2018 @ 10:30 AM - 11:45 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Yiorgos Makris, Professor, ECE Department, The University of Texas at Dallas

    Talk Title: Statistical and Formal Methods in Hardware Security

    Abstract: Partly because of design outsourcing and migration of fabrication to low-cost areas around the globe, and partly because of increased reliance on third-party intellectual property, the integrated circuit (IC) supply chain is now considered far more vulnerable than ever before. With electronics ubiquitously deployed in sensitive domains and critical infrastructure, such as wireless communications, industrial environments, as well as health, financial and military applications, understanding the corresponding risks and developing appropriate remedies have become paramount. To this end, in this presentation I will discuss the role that statistical and formal methods can play in ensuring security and trustworthiness of ICs and the systems wherein they are deployed, and I will introduce two solutions that my research group has contributed to the area of hardware security.

    The first contribution, known as Statistical Side-Channel Fingerprinting, is a statistical method for assessing whether an integrated circuit originates from a known distribution or not, based on parametric measurements such as delay, power, electromagnetic emanations, temperature, etc. Effectiveness of this method in detecting ICs which have been subjected to malicious modifications (a.k.a. hardware Trojans) will be demonstrated using silicon measurements from a custom-designed wireless cryptographic IC. Solutions to the main challenges of statistical side-channel fingerprinting, namely the availability of a statistically significant trusted population and the detection of hardware Trojans which are activated after deployment, will also be discussed and demonstrated in silicon.

    The second contribution, known as Proof-Carrying Hardware Intellectual Property, is a formal method for proving compliance of an electronic design acquired from a third-party vendor with a set of security properties. These properties, which are expressed as theorems with corresponding proofs in a formal proof management system (i.e., Coq) and which can be automatically checked by the consumer, outline the boundaries of trusted operation without necessarily specifying the exact functionality of the design. Effectiveness of this method in certifying secure instruction execution will be demonstrated on a popular microcontroller and its utility for data secrecy protection through fully-automated information flow tracking will be demonstrated on a cryptographic core.

    I will conclude by revisiting the modus operandi of the hardware security research area as it enters its second decade of activity and I will emphasize the need for (i) intensified efforts towards statistical and formal methods which can offer risk bounds and provable security, and (ii) synergy platforms whereby hardware security can be seamlessly integrated with software security, network security and cryptography, towards developing holistic system-level solutions for both contemporary and emerging applications. In this context, I will also briefly review our recent efforts in mixed-signal and system-level proof-carrying hardware, covert wireless communications, machine learning-based malware detection and workload forensics, as well as in establishing an NSF Industry/University Cooperative Research Center on Hardware and Embedded System Security and Trust (CHEST).

    Biography: Yiorgos is a professor of Electrical and Computer Engineering at The University of Texas at Dallas, where he leads the Trusted and RELiable Architectures (TRELA) Research Laboratory. Prior to joining UT Dallas in 2011, he spent a decade as a faculty of Electrical Engineering and of Computer Science at Yale University. He holds a Ph.D. (2001) and an M.S. (1997) in Computer Engineering from the University of California, San Diego, and a Diploma of Computer Engineering and Informatics (1995) from the University of Patras, Greece. His main research interests are in the application of formal and machine learning-based methods in the design of trusted and reliable integrated circuits and systems, with particular emphasis in the analog/RF domain. He is also investigating hardware-based malware detection, forensics and reliability methods in modern microprocessors, as well as on-die learning and novel computational modalities using emerging technologies. His research activities have been supported by NSF, SRC, ARO, AFRL, DARPA, Boeing, IBM, LSI, Intel, Advantest, AMS and TI. Yiorgos is as an associate editor of the IEEE Transactions on Information Forensics and Security, the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, the IEEE Design & Test periodical and the Springer Journal of Electronic Testing: Theory and Applications. He served as the 2016-2017 general chair and the 2013-2014 program chair of the IEEE VLSI Test Symposium, and as a topic coordinator and/or program committee member for several IEEE and ACM conferences. He is a Senior Member of the IEEE, a recipient of the 2006 Sheffield Distinguished Teaching Award and a recipient of the Best Paper Award from the 2013 Design Automation and Test in Europe (DATE'13) conference and the 2015 VLSI Test Symposium (VTS'15).

    Host: Peter Beerel, beerel@usc.edu

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • CS Colloquium: Stefanos Nikolaidis (Carnegie Mellon University) - Mathematical Models of Adaptation in Human-Robot Collaboration

    Tue, Mar 27, 2018 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Stefanos Nikolaidis, Carnegie Mellon University

    Talk Title: Mathematical Models of Adaptation in Human-Robot Collaboration

    Series: CS Colloquium

    Abstract: The goal of my research is to improve human-robot collaboration by integrating mathematical models of human behavior into robot decision making. I develop game-theoretic algorithms and probabilistic planning techniques that reason over the uncertainty in the human internal state and its dynamics, enabling autonomous systems to act optimally in a variety of real-world collaborative settings.

    While much work in human-robot interaction has focused on leader-assistant teamwork models, the recent advancement of robotic systems that have access to vast amounts of information suggests the need for robots that take into account the quality of the human decision making and actively guide people towards better ways of doing their task. In this talk, I propose an equal partners model, where human and robot engage in a dance of inference and action, and I focus on one particular instance of this dance: the robot adapts its own actions via estimating the probability of the human adapting to the robot. I start with a bounded memory model of human adaptation parameterized by the human adaptability - the probability of the human switching towards a strategy newly demonstrated by the robot. I then propose data-driven models that capture subtler forms of adaptation, where the human teammate updates their expectations of the robot's capabilities through interaction. Integrating these models into robot decision making allows for human-robot mutual adaptation, where coordination strategies, informative actions and trustworthy behavior are not explicitly modeled, but naturally emerge out of optimization processes. Human subjects experiments in a variety of collaboration and shared autonomy settings show that mutual adaptation significantly improves human-robot team performance, compared to one-way robot adaptation to the human.



    This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity, seats will be first come first serve.

    Biography: Stefanos Nikolaidis completed his PhD at Carnegie Mellon's Robotics Institute in December 2017 and he is currently a research associate at the University of Washington, Computer Science & Engineering. His research lies at the intersection of human-robot interaction, algorithmic game-theory and planning under uncertainty. Stefanos develops decision making algorithms that leverage mathematical models of human behavior to support deployed robotic systems in real-world collaborative settings. He has a MS from MIT, a MEng from the University of Tokyo and a BS from the National Technical University of Athens. He has additionally worked as a research specialist at MIT and as a researcher at Square Enix in Tokyo. He has received a Best Enabling Technologies Paper Award from the IEEE/ACM International Conference on Human-Robot Interaction, has a best paper nomination from the same conference this year and was a best paper award finalist in the International Symposium on Robotics.

    Host: Joseph Lim

    Location: Olin Hall of Engineering (OHE) - 100D

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

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  • Lunch and Learn: Doctoral Seminar Series

    Tue, Mar 27, 2018 @ 12:00 PM - 01:30 PM

    Viterbi School of Engineering Doctoral Programs

    Workshops & Infosessions


    This monthly series provides PhD students with a forum to improve communication skills and discuss scientific topics of societal significance in a friendly, peer-to-peer manner. Each month, one student will introduce a new topic and lead the group discussion over lunch. Come hungry and ready to engage others! Lunch is provided.

    Tuesday, March 27, 2018 at 12:00 PM

    For more details on speaking or attending Lunch and Learn, please contact Prof. Mojarad (mojarad@usc.edu). One-on-one presentation coaching is offered to all students who lead lunch discussions.

    Audiences: PhD Students only.

    Contact: Jennifer Gerson

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  • Epstein Institute Seminar, ISE 651

    Tue, Mar 27, 2018 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Fernando Ordonez, Professor, University of Chile

    Talk Title: Solving Stackelberg Equilibrium in Stochastic Games

    Host: Prof. Maged Dessouky

    More Information: March 27, 2018.pdf

    Location: Ethel Percy Andrus Gerontology Center (GER) - 206

    Audiences: Everyone Is Invited

    Contact: Grace Owh

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  • Tips to Getting an Internship or a Job

    Tue, Mar 27, 2018 @ 04:00 PM - 05:30 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions


    Virtually everything you think you know about getting a job/internship is wrong -- especially in the real-world after graduation.

    Discover how to cope with competition and avoid pitfalls in your job hunt, learn what employers are actually looking for during an interview and get tips on how to negotiate your salary.

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

    Audiences: All Viterbi

    Contact: RTH 218 Viterbi Career Connections

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  • Undergraduate Fall Registration Begins

    Wed, Mar 28, 2018

    Viterbi School of Engineering Student Affairs

    University Calendar


    Audiences: Everyone Is Invited

    Contact: Sheryl Koutsis

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  • Meet USC: Admission Presentation, Campus Tour, and Engineering Talk

    Wed, Mar 28, 2018

    Viterbi School of Engineering Undergraduate Admission

    University Calendar


    This half day program is designed for prospective freshmen (HS juniors and younger) and family members. Meet USC includes an information session on the University and the Admission process, a student led walking tour of campus, and a meeting with us in the Viterbi School. During the engineering session we will discuss the curriculum, research opportunities, hands-on projects, entrepreneurial support programs, and other aspects of the engineering school. Meet USC is designed to answer all of your questions about USC, the application process, and financial aid.

    Reservations are required for Meet USC. This program occurs twice, once at 8:30 a.m. and again at 12:30 p.m.

    Please make sure to check availability and register online for the session you wish to attend. Also, remember to list an Engineering major as your "intended major" on the webform!

    RSVP

    Location: Ronald Tutor Campus Center (TCC) - USC Admission Office

    Audiences: Prospective Freshmen (HS Juniors and Younger) & Family Members

    Contact: Viterbi Admission

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  • EE Seminar: Statistical Interference of Properties of Distribution: Theory, Algorithms, and Applications

    Wed, Mar 28, 2018 @ 10:30 AM - 11:30 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Jiantao Jiao, Stanford University

    Talk Title: Statistical Interference of Properties of Distribution: Theory, Algorithms, and Applications

    Abstract: Modern data science applications - ranging from graphical model learning to image registration to inference of gene regulatory networks - frequently involve pipelines of exploratory analysis requiring accurate inference of a property of the distribution governing the data rather than the distribution itself. Notable examples of properties include Shannon entropy, mutual information, Kullback-Leibler divergence, and total variation distance, among others.

    This talk will focus on recent progress in the performance, structure, and deployment of near-minimax-optimal estimators for a large variety of properties in high-dimensional and nonparametric settings. We present general methods for constructing information theoretically near-optimal estimators, and identify the corresponding limits in terms of the parameter dimension, the mixing rate (for processes with memory), and smoothness of the underlying density (in the nonparametric setting). We employ our schemes on the Google 1 Billion Word Dataset to estimate the fundamental limit of perplexity in language modeling, and to improve graphical model and classification tree learning. The estimators are efficiently computable and exhibit a "sample size boosting" phenomenon, i.e., they attain with n samples what prior methods would have needed n log(n) samples to achieve.

    Biography: Jiantao Jiao is a Ph.D. student in the Department of Electrical Engineering at Stanford University. He received the B.Eng. degree in Electronic Engineering from Tsinghua University, Beijing, China in 2012, and the M.Eng. degree in Electrical Engineering from Stanford University in 2014. He is a recipient of the Presidential Award of Tsinghua University and the Stanford Graduate Fellowship. He was a semi-plenary speaker at ISIT 2015 and a co-recipient of the ISITA 2016 Student Paper Award. He co-designed and co-taught the graduate course EE378A (Statistical Signal Processing) at Stanford University in 2016 and 2017, with his advisor Tsachy Weissman. His research interests are in statistical machine learning, high-dimensional and nonparametric statistics, information theory, and their applications in medical imaging, genomics, and natural language processing. He is a co-founder of Qingfan (www.qingfan.com), an online platform that democratizes technical training and job opportunities for anyone with access to the internet.

    Host: Salman Avestimehr, avestimehr@gmail.com

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • Computer Science General Faculty Meeting

    Wed, Mar 28, 2018 @ 12:00 PM - 02:00 PM

    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

    Audiences: Invited Faculty Only

    Contact: Assistant to CS chair

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  • Aerospace and Mechanical Engineering Seminar

    Wed, Mar 28, 2018 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: David Lentink, Assistant Professor, Department of Mechanical Engineering, Stanford University

    Talk Title: Avian Inspired Design

    Abstract: Many organisms fly in order to survive and reproduce. My lab focusses on understanding bird flight to improve flying robots - because birds fly further, longer, and more reliable in complex visual and wind environments. I use this multidisciplinary lens that integrates biomechanics, aerodynamics, and robotics to advance our understanding of the evolution of flight more generally across birds, bats, insects, and autorotating seeds. The development of flying organisms as an individual and their evolution as a species are shaped by the physical interaction between organism and surrounding air. The organism's architecture is tuned for propelling itself and controlling its motion. Flying animals and plants maximize performance by generating and manipulating vortices. These vortices are created close to the body as it is driven by the action of muscles or gravity, then are 'shed' to form a wake (a trackway left behind in the fluid). I study how the organism's architecture is tuned to utilize these and other aeromechanical principles to compare the function of bird wings to that of bat, insect, and maple seed wings. The experimental approaches range from making robotic models to training birds to fly in a custom-designed wind tunnel as well as in visual flight arenas - and inventing methods to 3D scan birds and measure the aerodynamic force they generate - nonintrusively - with a novel aerodynamic force platform. The studies reveal that animals and plants have converged upon the same solution for generating high lift: A strong vortex that runs parallel to the leading edge of the wing, which it sucks upward. Why this vortex remains stably attached to flapping animal and spinning plant wings is elucidated and linked to kinematics and wing morphology. While wing morphology is quite rigid in insects and maple seeds, it is extremely fluid in birds. I will show how such 'wing morphing' significantly expands the performance envelope of birds during flight, and will dissect the mechanisms that enable birds to morph better than any aircraft can. Finally, I will show how these findings have inspired my students to design new flapping and morphing aerial robots.

    Biography: Professor Lentink's multidisciplinary lab studies biological flight, in particular bird flight, as an inspiration for engineering design. http://lentinklab.stanford.edu He has a BS and MS in Aerospace Engineering (Aerodynamics, Delft University of Technology) and a PhD in Experimental Zoology cum laude (Wageningen University). During his PhD he visited the California institute of Technology for 9 months to study insect flight. His postdoctoral training at Harvard was focused on studying birds. Publications range from technical journals to cover publications in Nature and Science. He is an alumnus of the Young Academy of the Royal Netherlands Academy of Arts and Sciences, recipient of the Dutch Academic Year Prize, the NSF CAREER award and he has been recognized in 2013 as one of 40 scientists under 40 by the World Economic Forum.

    Host: Department of Aerospace and Mechanical Engineering

    Location: Seaver Science Library (SSL) - 150

    Audiences: Everyone Is Invited

    Contact: Ashleen Knutsen

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  • CAIS Seminar: Dr. Mayank Kejriwal (USC Information Sciences Institute) - Building Knowledge Graphs for Social Good

    Wed, Mar 28, 2018 @ 04:00 PM - 05:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Dr. Mayank Kejriwal, USC Information Sciences Institute

    Talk Title: Building Knowledge Graphs for Social Good

    Series: USC Center for Artificial Intelligence in Society (CAIS) Seminar Series

    Abstract: Illicit activities like human trafficking and narcotics have a significant Web footprint. In this talk, I will introduce and talk about building knowledge graphs (KG), a powerful means of representing and reasoning over knowledge using intelligent algorithms, to combat such problems for social good. I will also introduce a KG-centric system called DIG, developed in our group, that is currently being used by more than 100 US law enforcement agencies to combat human trafficking.

    This lecture satisfies requirements for CSCI 591: Research Colloquium


    Biography: Dr. Mayank Kejriwal is a researcher at the USC Information Sciences Institute. His research on knowledge graphs, currently funded under both DARPA and IARPA, has been published in multiple interdisciplinary ACM, IEEE, Springer and Elsevier venues. He is authoring a textbook on knowledge graphs (MIT Press) with Pedro Szekely and Craig Knoblock.


    Host: Milind Tambe

    Location: Mark Taper Hall Of Humanities (THH) - 102

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • CS Colloquium: Junier Oliva (Carnegie Mellon University) Scalable Learning Over Distributions

    Thu, Mar 29, 2018 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Junier Oliva, Carnegie Mellon University

    Talk Title: Scalable Learning Over Distributions

    Series: CS Colloquium

    Abstract: A great deal of attention has been applied to studying new and better ways to perform learning tasks involving static finite vectors. Indeed, over the past century the fields of statistics and machine learning have amassed a vast understanding of various learning tasks like clustering, classification, and regression using simple real valued vectors. However, we do not live in a world of simple objects. From the contact lists we keep, the sound waves we hear, and the distribution of cells we have, complex objects such as sets, distributions, sequences, and functions are all around us. Furthermore, with ever-increasing data collection capacities at our disposal, not only are we collecting more data, but richer and more bountiful complex data are becoming the norm.

    In this presentation we analyze regression problems where input covariates, and possibly output responses, are probability distribution functions from a nonparametric function class. Such problems cover a large range of interesting applications including learning the dynamics of cosmological particles and general tasks like parameter estimation.

    However, previous nonparametric estimators for functional regression problems scale badly computationally with the number of input/output pairs in a data-set. Yet, given the complexity of distributional data it may be necessary to consider large data-sets in order to achieve a low estimation risk.

    To address this issue, we present two novel scalable nonparametric estimators: the Double-Basis Estimator (2BE) for distribution-to-real regression problems; and the Triple-Basis Estimator (3BE) for distribution-to-distribution regression problems. Both the 2BE and 3BE can scale to massive data-sets. We show an improvement of several orders of magnitude in terms of prediction speed and a reduction in error over previous estimators in various synthetic and real-world data-sets.


    This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity, seats will be first come first serve.

    Biography: Junier Oliva is a Ph.D. candidate in the Machine Learning Department at the School of Computer Science, Carnegie Mellon University. His main research interest is to build algorithms that understand data at an aggregate, holistic level. Currently, he is working to push machine learning past the realm of operating over static finite vectors, and start reasoning ubiquitously with complex, dynamic collections like sets and sequences. Moreover, he is interested in exporting concepts from learning on distributional and functional inputs to modern techniques in deep learning, and vice-versa. He is also developing methods for analyzing massive datasets, both in terms of instances and covariates. Prior to beginning his Ph.D. program, he received his B.S. and M.S. in Computer Science from Carnegie Mellon University. He also spent a year as a software engineer for Yahoo!, and a summer as a machine learning intern at Uber ATG.

    Host: Fei Sha

    Location: Olin Hall of Engineering (OHE) - 100D

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

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  • EE Seminar: Innovating Secure IoT Solutions for Extreme Environments

    Thu, Mar 29, 2018 @ 02:30 PM - 03:30 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Rabia Yazicigil, Massachusetts Institute of Technology

    Talk Title: Innovating Secure IoT Solutions for Extreme Environments

    Abstract: The Internet of Things (IoT) is redefining how we interact with the world by supplying a global view based not only on human-provided data but also human-device connected data. For example, in Health Care, IoT will bring decreased costs, improved treatment results, and better disease management. However, the connectivity-in-everything model brings heightened security concerns. Additionally, the projected growth of connected nodes not only increases security concerns, it also leads to a 1000-fold increase in wireless data traffic in the near future. This data storm results in a spectrum scarcity thereby driving the urgent need for shared spectrum access technologies. These security deficiencies and the wireless spectrum crunch require innovative system-level secure and scalable solutions.

    This talk will introduce energy-efficient and application-driven system-level solutions for secure and spectrum-aware wireless communications. I will present a novel ultra-fast bit-level frequency-hopping scheme for physical-layer security. This scheme utilizes the frequency agility of devices in combination with novel radio frequency architectures and protocols to achieve secure wireless communications. To address the wireless spectrum crunch, future smart radio systems will evaluate the spectrum usage dynamically and opportunistically use the underutilized spectrum; this will require spectrum sensing for interferer avoidance. I will discuss a system-level approach using band-pass sparse signal processing for rapid interferer detection in a wideband spectrum to convert the abstract improvements promised by sparse signal processing theory, e.g., fewer measurements, to concrete improvements in time and energy efficiency.

    The tightly-coupled system solutions derived at the intersection of electronics, security, signal processing, and communications extend in applications beyond the examples provided here, enabling innovative IoT solutions for extreme environments.

    Biography: Rabia Yazicigil is currently a Postdoctoral Associate at MIT. She received her PhD degree in Electrical Engineering from Columbia University in 2016. She received the B.S. degree in Electronics Engineering from Sabanci University, Istanbul, Turkey in 2009, and the M.S. degree in Electrical and Electronics Engineering from École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland in 2011.

    Her research interest lies at the interface of electronics, security, signal processing and communication to innovate system-level solutions for future energy-constrained Internet of Things applications. She has been a recipient of a number of awards, including the "Electrical Engineering Collaborative Research Award" for her PhD research on Compressive Sampling Applications in Rapid RF Spectrum Sensing (2016), the second place at the Bell Labs Future X Days Student Research Competition (2015), Analog Devices Inc. outstanding student designer award (2015) and 2014 Millman Teaching Assistant Award of Columbia University. She was selected among the top 61 female graduate students and postdoctoral scholars invited to participate and present her research work in the 2015 MIT Rising Stars in Electrical Engineering Computer Science.

    Host: Peter Beerel, pabeerel@usc.edu

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • CS Distinguished Lecture: Satinder Singh (University of Michigan) – Reinforcement Learning: From Vision to Action and Back

    Thu, Mar 29, 2018 @ 04:00 PM - 05:20 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Satinder Singh, University of Michigan

    Talk Title: Reinforcement Learning: From Vision to Action and Back

    Series: Computer Science Distinguished Lecture Series

    Abstract: Stemming in part from the great successes of other areas of Machine Learning, in particular the recent success of Deep Learning, there is renewed hope and interest in Reinforcement Learning (RL) from the wider applications communities. Indeed, there is a recent burst of new and exciting progress in both theory and practice of RL. I will describe some theoretical results from my own group on a simple new connection between planning horizon and overfitting in RL, as well as some results on combining RL with Deep Learning in Minecraft, and Zero-Shot Generalization across compositional tasks. I will conclude with some lookahead at what we can do, both as theoreticians and those that collect data, to accelerate the impact of RL.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Satinder Singh is a Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor. He has been the Chief Scientist at Syntek Capital, a venture capital company, a Principal Research Scientist at AT&T Labs, an Assistant Professor of Computer Science at the University of Colorado, Boulder, and a Postdoctoral Fellow at MIT's Brain and Cognitive Science department. His research focus is on developing the theory, algorithms and practice of building artificial agents that can learn from interaction in complex, dynamic, and uncertain environments, including environments with other agents in them. His main contributions have been to the areas of reinforcement learning, multi-agent learning, and more recently to applications in cognitive science and healthcare. He is a Fellow of the AAAI (Association for the Advancement of Artificial Intelligence) and has coauthored more than 150 refereed papers in journals and conferences and has served on many program committee's. He was Program-CoChair of AAAI 2017, and in 2013 helped cofound RLDM (Reinforcement Learning and Decision Making), a biennial multidisciplinary meeting that brings together computer scientists, psychologists, neuroscientists, roboticists, control theorists, and others interested in animal and artificial decision making.


    Host: Haipeng Luo

    Location: Henry Salvatori Computer Science Center (SAL) - 101

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • Meet USC: Admission Presentation, Campus Tour, and Engineering Talk

    Fri, Mar 30, 2018

    Viterbi School of Engineering Undergraduate Admission

    University Calendar


    This half day program is designed for prospective freshmen (HS juniors and younger) and family members. Meet USC includes an information session on the University and the Admission process, a student led walking tour of campus, and a meeting with us in the Viterbi School. During the engineering session we will discuss the curriculum, research opportunities, hands-on projects, entrepreneurial support programs, and other aspects of the engineering school. Meet USC is designed to answer all of your questions about USC, the application process, and financial aid.

    Reservations are required for Meet USC. This program occurs twice, once at 8:30 a.m. and again at 12:30 p.m.

    Please make sure to check availability and register online for the session you wish to attend. Also, remember to list an Engineering major as your "intended major" on the webform!

    RSVP

    Location: Ronald Tutor Campus Center (TCC) - USC Admission Office

    Audiences: Everyone Is Invited

    Contact: Viterbi Admission

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  • W.V.T. RUSCH ENGINEERING HONORS COLLOQUIUM

    Fri, Mar 30, 2018 @ 01:00 PM - 01:50 PM

    USC Viterbi School of Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Lily Lai, Associate Clinical Professor of Surgery, City of Hope

    Talk Title: Utilization of Engineering in Cancer Care

    Host: Dr. Prata & EHP

    Location: Henry Salvatori Computer Science Center (SAL) - 101

    Audiences: Everyone Is Invited

    Contact: Su Stevens

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  • EE-EP Faculty Candidate - Mercedeh Khajavikhan, Friday, March 30th @ 2pm in EEB 132

    Fri, Mar 30, 2018 @ 02:00 PM - 03:30 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Mercedeh Khajavikhan, University of Central Florida

    Talk Title: Non-Hermitian Photonics: Optics at an Exceptional Point

    Abstract: In recent years, non-Hermitian degeneracies, also known as exceptional points (EPs), have emerged as a new paradigm for engineering the response of optical systems. At such points, an N-dimensional space can be represented by a single eigenvalue and an eigenvector. As a result, these points are associated with abrupt phase transition in parameter space. Among many different non-conservative photonic configurations, parity-time (PT) symmetric systems are of particular interest since they provide a powerful platform to explore and consequently utilize the physics of exceptional points in a systematic manner. In this talk, I will review some of our recent works in the area of non-Hermitian (mainly PT-symmetric) active photonics. For example, in a series of works, we have demonstrated how the generation and judicial utilization of these points in laser systems can result in unexpected dynamics, unusual linewidth behavior, and improved modal response. On the other hand, biasing a photonic system at an exceptional point can lead to orders of magnitude enhancement in sensitivity- an effect that may enable a new generation of ultrasensitive optical sensors on chip. Non-Hermiticity can also be used as a means to promote or single out an edge mode in photonic topological insulator lattices. This effect has been recently utilized to demonstrate the first magnetic free topological insulator laser. In this talk, I will also discuss other topological behaviors in non-Hermitian systems, especially those associated with encircling an exceptional point in parameter space.

    Biography: Mercedeh Khajavikhan received her Ph.D. in Electrical Engineering from the University of Minnesota in 2009. Her dissertation was on coherent beam combining for high power laser applications. In 2009, she joined the University of California in San Diego as a postdoctoral researcher where she worked on the design and development of nanolasers, plasmonic devices, and silicon photonics components. Since August 2012, she is an assistant professor in the College of Optics and Photonics (CREOL) at the University of Central Florida (UCF), working primarily on novel phenomena in active photonic systems. She received the NSF Early CAREER Award in 2015, the ONR Young Investigator Award in 2016, and the University of central Florida Reach for the Stars Award in 2017.

    Host: EE-Electrophysics

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

    Audiences: Everyone Is Invited

    Contact: Marilyn Poplawski

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  • Astani Civil and Environmental Engineering Seminar

    Fri, Mar 30, 2018 @ 03:00 PM - 04:00 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Farimah Shirmohammadi, Astani CEE Ph.D. Student

    Talk Title: TBA

    Abstract: TBA

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

    Audiences: Everyone Is Invited

    Contact: Evangeline Reyes

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  • NL Seminar-Generating Adversarial Examples with Syntactically Controlled Paraphrase Networks

    Fri, Mar 30, 2018 @ 03:00 PM - 04:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Mohit Iyyer , AI2, UMass Amherst)

    Talk Title: Generating Adversarial Examples with Syntactically Controlled Paraphrase Networks

    Series: Natural Language Seminar

    Abstract: Many datasets for natural language processing problems lack linguistic variation, which hurts generalization of models trained on them. Recent research has shown that it is possible to break many learned models by evaluating them on adversarial examples, which are generated by manually introducing lexical, pragmatic, and syntactic variation to existing held-out examples from the data. Automating this process is challenging, as input semantics must be preserved in the face of potentially large sentence modifications. In this talk, I will focus specifically on syntactic variation in discussing our recent work on syntactically controlled paraphrase networks SCPN for adversarial example generation.
    Given a sentence and a target syntactic form e.g., a constituency parse, an SCPN is trained to produce a paraphrase of the sentence with the desired syntax. We show it is possible to create training data for this task by first doing back translation at a very large scale, and then using a parser to label the syntactic transformations that naturally occur during this process. Such data allows us to train a neural encoder decoder model with extra inputs to specify the target syntax. A combination of automated and human evaluations show that SCPNs generate paraphrases that almost always follow their target specifications without decreasing paraphrase quality when compared to baseline uncontrolled paraphrase systems. Furthermore, they are more capable of generating syntactically adversarial examples that both 1. Fool pretrained models and 2. improve the robustness of these models to syntactic variation when used for data augmentation.



    Biography: Mohit Iyyer will be joining UMass Amherst as an assistant professor in Fall 2018. Currently, he is a Young Investigator at the Allen Institute of Artificial Intelligence; prior to that, he received a Ph.D. from the Department of Computer Science at the University of Maryland, College Park, advised by Jordan Boyd Graber and Hal Daume III. His research interests lie at the intersection of natural language processing and machine learning. More specifically, he focuses on designing deep neural networks for both traditional NLP tasks e.g., question answering, language generation and new problems that involve creative language e.g., understanding narratives in novels). He has interned at MetaMind and Microsoft Research, and his research has won a best paper award at NAACL 2016 and a best demonstration award at NIPS 2015.



    Host: Nanyun Peng

    More Info: http://nlg.isi.edu/nl-seminar/

    Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey

    Audiences: Everyone Is Invited

    Contact: Peter Zamar

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