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Events for the 4th week of March

  • AI Seminar

    Mon, Mar 20, 2017 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Xiang Ren, Computer Science PhD candidate at University of Illinois at UrbanaChampaign

    Talk Title: EFFORT-LIGHT STRUCTMINE: TURNING MASSIVE CORPORA INTO STRUCTURES

    Series: Recruitng Seminar

    Abstract: The realworld data, though massive, are hard for machines to resolve as they are largely unstructured and in the form of natural-language text. One of the grand challenges is to turn such massive corpora into machine-actionable structures. Yet, most existing systems have heavy reliance on human effort in the process of structuring various corpora, slowing down the development of downstream applications.

    In this talk, I will introduce a data-driven framework, EffortLight StructMine, that extracts structured facts from massive corpora without explicit human labeling effort. In particular, I will discuss how to solve three structure mining tasks under Effort-Light StructMine framework: from identifying typed entities in text, to fine-grained entity typing, to extracting typed relationships between entities. Together, these three solutions form a clear roadmap for turning a massive corpus into a structured network to represent its factual knowledge. Finally, I will share some directions towards mining corpus-specific structured networks for knowledge discovery.


    Biography: Xiang Ren is a Computer Science PhD candidate at University of Illinois at Urbana-Champaign, working with Jiawei Han and the Data and Information System DAIS Research Lab. The research Xiang develops data-driven methods for turning unstructured text data into machine-actionable structures. More broadly, his research interests span data mining, machine learning, and natural language processing, with a focus on making sense of massive text corpora. His research has been recognized with several prestigious awards including a Google PhD Fellowship, a Yahoo!-DAIS Research Excellence Award, and a C. W. Gear Outstanding Graduate Student Award from UIUC Computer Science. Technologies he developed has been transferred to US Army Research Lab, NIH, Microsoft, Yelp and TripAdvisor


    Host: Craig Knoblock

    Webcast: http://webcastermshd.isi.edu/Mediasite/Play/6b83d48fc61f4e398d8d8bbdff0004e01

    Location: Information Science Institute (ISI) - 11th Floor Large CR #1135

    WebCast Link: http://webcastermshd.isi.edu/Mediasite/Play/6b83d48fc61f4e398d8d8bbdff0004e01d

    Audiences: Everyone Is Invited

    Posted By: Alma Nava / Information Sciences Institute

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  • CS Colloquium: Yuanjie Li (UCLA) - Stimulating Intelligence in the Mobile Networked Systems

    Mon, Mar 20, 2017 @ 11:00 AM - 12:20 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Yuanjie Li, UCLA

    Talk Title: Stimulating Intelligence in the Mobile Networked Systems

    Series: CS Colloquium

    Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.

    The mobile networked systems (4G and upcoming 5G) are at a critical stage of the technology revolution. Despite offering working solutions for billions of users, they are complex and closed: The infrastructure lacks guarantees for the right designs and operations, while the mobile client lacks the insights of the "black-box" network behaviors. Both fundamentally limit our understanding of why various problems could happen, and how to resolve them.

    In this talk, I describe primitives that stimulate more infrastructure and client intelligence. For the infrastructure, I present verification and state management techniques that enforce provably correct designs and operations. For the client, I show how a data-driven system design allows it to be more active in improving its performance, reliability, and security. These results suggest that the future systems (5G) should be equipped with more intelligence, and make themselves easy to understand and use.

    Biography: Yuanjie Li is a Ph.D. candidate in Computer Science at UCLA, advised by Professor Songwu Lu. His interests include the networked systems, mobile computing, and their security. He has won ACM MobiCom'16 Best Community Paper Award and UCLA Dissertation Year Fellowship in 2016. His work has resulted in an open-source community tool (MobileInsight) used by 130 universities and companies so far.

    Host: CS Department

    Location: Ronald Tutor Hall of Engineering (RTH) - 217

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • Seminars in Biomedical Engineering

    Mon, Mar 20, 2017 @ 12:30 PM - 01:50 PM

    Biomedical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Zhongping Chen , Professor of Biomedical Engineering, UC Irvine, Beckman Laser Institute

    Talk Title: Novel OCT for Biomedical Application

    Host: Qifa Zhou

    Location: Olin Hall of Engineering (OHE) - 122

    Audiences: Everyone Is Invited

    Posted By: Mischalgrace Diasanta

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  • Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute for Electrical Engineering Joint Seminar Series on Cyber-Physical Systems

    Mon, Mar 20, 2017 @ 02:00 PM - 03:30 PM

    Ming Hsieh Department of Electrical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Brian Munsky, Assistant Professor, Colorado State University

    Talk Title: Identification of stochastic models to predict single-cell gene regulation dynamics

    Abstract: Stochastic fluctuations can cause identical cells or individual molecules to exhibit wildly different behaviors. Often labeled "noise," these fluctuations are frequently considered a nuisance that compromises cellular responses, complicates modeling, makes predictive understanding and control all but impossible. However, if we computationally examine fluctuations more closely and carefully match them to discrete stochastic analyses, we discover virtually untapped, yet powerful sources of information and new opportunities. In this talk, I will present our collaborative endeavors to integrate single-cell and single-molecule experiments with precise stochastic analyses to gain new insight and quantitatively predictive understanding for signal-activated gene regulation. I will explain how we experimentally quantify transcription dynamics at high temporal and spatial resolutions; how we use precise computational analyses to model this data and efficiently infer biological mechanisms and parameters; how we predict and evaluate the extent to which model constraints (i.e., data) and uncertainty (i.e., model complexity) contribute to our understanding. We will examine how different data statistics (e.g., expectation values versus probability densities) contribute to model bias and uncertainty, and we will show how these affect predictive power. Finally, we will introduce a new approach to compute the Fisher Information Matrix, and we will illustrate its application for the improved design of single-cell experiments.

    Biography: Dr. Munsky received B.S. and M.S. degrees in Aerospace Engineering from the Pennsylvania State University in 2000 and 2002, respectively, and his Ph.D. in Mechanical Engineering from the University of California at Santa Barbara in 2008. Following his graduate studies, Dr. Munsky worked at the Los Alamos National Laboratory -” as a Director's Postdoctoral Fellow (2008-2010), as a Richard P. Feynman Distinguished Postdoctoral Fellow in Theory and Computing (2010-2013), and as a Staff Scientist (2013). In 2014, he joined the Colorado State University Department of Chemical and Biological Engineering and the School of Biomedical Engineering, in which he is now an Assistant Professor. Dr. Munsky is best known for his discovery of Finite State Projection algorithm, which has enabled the efficient study of probability distribution dynamics for stochastic gene regulatory networks. Dr. Munsky's research interests are in the integration of discrete stochastic models with single-cell experiments to identify predictive models of gene regulatory systems. Dr. Munsky was the recipient of the 2008 UCSB Department of Mechanical Engineering best Ph.D. Dissertation award, the 2010 Leon Heller Postdoctoral Publication Prize, and the 2012 LANL Postdoc Distinguished Performance Award for his work in this topic. Dr. Munsky became a Keck Scholar in 2016. Dr. Munsky is the contact organizer of the internationally recognized, NIH-funded q-bio Summer School (q-bio.org), where he runs a course on single-cell stochastic gene regulation.

    Host: Paul Bogdan

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

    Audiences: Everyone Is Invited

    Posted By: Estela Lopez

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  • Repeating EventUSC Stem Cell Seminar: Flora Vaccarino, Yale University

    Tue, Mar 21, 2017 @ 11:00 AM - 12:00 PM

    Biomedical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Flora Vaccarino, Yale University

    Talk Title: TBD

    Series: Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC Distinguished Speakers Series

    Host: USC Stem Cell

    More Info: http://stemcell.usc.edu/events
    Webcast: http://keckmedia.usc.edu/stem-cell-semina

    Location: Eli & Edythe Broad CIRM Center for Regenerative Medicine & Stem Cell Resch. (BCC) - First Floor Conference Room

    WebCast Link: http://keckmedia.usc.edu/stem-cell-seminar

    Audiences: Everyone Is Invited

    View All Dates

    Posted By: Cristy Lytal/USC Stem Cell

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  • CS Colloquium: Justin Cheng (Stanford) - Antisocial Computing: Explaining and Predicting Negative Behavior Online

    Tue, Mar 21, 2017 @ 11:00 AM - 12:20 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Justin Cheng, Stanford University

    Talk Title: Antisocial Computing: Explaining and Predicting Negative Behavior Online

    Series: CS Colloquium

    Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.

    Antisocial behavior and misinformation are increasingly prevalent online. As users interact with one another on social platforms, negative interactions can cascade, resulting in complex changes in behavior that are difficult to predict. My research introduces computational methods for explaining the causes of such negative behavior and for predicting its spread in online communities. It complements data mining with crowdsourcing, which enables both large-scale analysis that is ecologically valid and experiments that establish causality. First, in contrast to past literature which has characterized trolling as confined to a vocal, antisocial minority, I instead demonstrate that ordinary individuals, under the right circumstances, can become trolls, and that this behavior can percolate and escalate through a community. Second, despite prior work arguing that such behavioral and informational cascades are fundamentally unpredictable, I demonstrate how their future growth can be reliably predicted. Through revealing the mechanisms of antisocial behavior online, my work explores a future where systems can better mediate interpersonal interactions and instead promote the spread of positive norms in communities.

    Biography: Justin Cheng is a PhD candidate in the Computer Science Department at Stanford University, where he is advised by Jure Leskovec and Michael Bernstein. His research lies at the intersection of data science and human-computer interaction, and focuses on cascading behavior in social networks. This work has received a best paper award, as well as several best paper nominations at CHI, CSCW, and ICWSM. He is also a recipient of a Microsoft Research PhD Fellowship and a Stanford Graduate Fellowship.

    Host: CS Department

    Location: Ronald Tutor Hall of Engineering (RTH) - 217

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • Improved Myocardial Arterial Spin Labeled Perfusion Imaging

    Tue, Mar 21, 2017 @ 01:00 PM - 02:00 PM

    Ming Hsieh Department of Electrical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Hung Phi Do, Department of Physics and Astronomy, University of Southern California

    Talk Title: Improved Myocardial Arterial Spin Labeled Perfusion Imaging

    Series: Medical Imaging Seminar Series

    Abstract: Coronary artery disease (CAD) affects more than 15.5 million Americans and causes approximately 310,000 deaths per year. Several different diagnostic tests are performed to diagnose and manage this disease. One of the most common is perfusion stress testing, primarily performed using single photon emission computed tomography (SPECT) or first-pass cardiovascular magnetic resonance (CMR). These methods require the use of ionizing radiation or exogenous contrast agents that carry associated risks to patients, especially those who require frequent assessment or have kidney dysfunction. Myocardial arterial spin labeling (ASL) is a promising MRI-based perfusion imaging method that can quantitatively measure myocardial tissue perfusion without the use of ionizing radiation or exogenous contrast agents. Its feasibility has been previously demonstrated by our lab, however several challenges remain, including low sensitivity, coarse spatial resolution, and limited spatial coverage. The contributions of this dissertation are (1) improving sensitivity, (2) exploring clinical applications, and (3) developing a new and advantageous labeling method for myocardial ASL.



    Biography: Hung Phi Do is a Physics Ph.D. student working under the supervision of Prof. Nayak at the Magnetic Resonance Engineering Laboratory. His research focuses are MR physics and MR pulse sequence development for quantitative cardiovascular magnetic resonance. He received an M.S. in Electrical Engineering from the University of Southern California in 2014, a Diploma in Physics from the International Center for Theoretical Physics in 2009, and a B.S. in Physics from the Hanoi National University of Education in 2007.



    Host: Prof. Krishna Nayak

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - PHE223

    Audiences: Everyone Is Invited

    Posted By: Talyia White

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  • MHI Seminar Series - Visitor Program

    Tue, Mar 21, 2017 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Magnús Már Halldórsson, Professor at Reykjavik University's School of Computer Science

    Talk Title: Algorithms and Models for the Capacity of Arbitrary Wireless Networks

    Abstract: At the heart of wireless network operation is the fundamental question of their capacity: How much communication can be achieved in a network, utilizing all the tools and diversity available: power control, scheduling, routing, channel assignment and rate adjustment?

    The obvious aims of obtaining general purpose algorithms to solve this question run into two (walls) challenges:
    - How to model communication and interference faithfully, and
    - How to reason algorithmically in the more accurate models, which are also more intricate and harder to analyze.

    We overview recent progress in developing algorithms for capacity and scheduling in the physical (or SINR) model with good performance guarantees on arbitrary networks. In particular, we indicate how many of the complications of the physical models can be abstracted away, at a small cost in performance. We also outline various efforts to add additional realism to the models, while maintaining generality and algorithmic tractability. We conclude with open questions and challenges.

    This is based on joint work with Tigran Tonoyan

    Biography: Prof. Magnús Már Halldórsson from Reyjkjavik University in Iceland will visit USC in late March 2017. He is a leading expert in algorithms for distributed computing and wireless networks. He has been the Chair of top conferences in the area including PODC 2014 and ICALP 2015. In 2017 he is leading the organization a Dagstuhl conference on "Foundations of Wireless Networking" together with Profs. C. Fragouli (UCLA), K. Jamieson (Princeton) and B. Krishnamachari (USC).


    Host: Bhaskar Krishnamachari

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

    Audiences: Everyone Is Invited

    Posted By: Cathy

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  • CS Colloquium: Nihar Shah (UC Berkeley) - Learning from People

    Tue, Mar 21, 2017 @ 04:00 PM - 05:20 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Nihar Shah, UC Berkeley

    Talk Title: Learning from People

    Series: CS Colloquium

    Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.

    Learning from people represents a new and expanding frontier for data science. Two critical challenges in this domain are of developing algorithms for robust learning and designing incentive mechanisms for eliciting high-quality data. In this talk, I describe progress on these challenges in the context of two canonical settings, namely those of ranking and classification. In addressing the first challenge, I introduce a class of "permutation-based" models that are considerably richer than classical models, and present algorithms for estimation that are both rate-optimal and significantly more robust than prior state-of-the-art methods. I also discuss how these estimators automatically adapt and are simultaneously also rate-optimal over the classical models, thereby enjoying a surprising a win-win in the bias-variance tradeoff. As for the second challenge, I present a class of "multiplicative" incentive mechanisms, and show that they are the unique mechanisms that can guarantee honest responses. Extensive experiments on a popular crowdsourcing platform reveal that the theoretical guarantees of robustness and efficiency indeed translate to practice, yielding several-fold improvements over prior art.

    Biography: Nihar B. Shah is a PhD candidate in the EECS department at the University of California, Berkeley. He is the recipient of the Microsoft Research PhD Fellowship 2014-16, the Berkeley Fellowship 2011-13, the IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012, and the SVC Aiya Medal from the Indian Institute of Science for the best master's thesis in the department. His research interests include statistics and machine learning, with a current focus on applications to learning from people.

    Host: CS Department

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

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • MHI CommNetS

    Wed, Mar 22, 2017 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Corey Baker, UC San Diego

    Talk Title: When Disaster Strikes: Supplementing Centralized Infrastructure with Opportunistic Communication

    Series: CommNetS

    Abstract: Reliance on Internet connectivity is detrimental where modern networking technology is lacking, power outages are frequent, or network connectivity is sparse or non-existent (i.e., developing countries, natural disasters, and in-field military scenarios). Realization of the limitations resulting from reliance on Internet and cellular connectivity were prevalent in Hurricane Matthew (2016), which killed over 1000 people and destroyed cellular infrastructure. As an alternative, deploying resilient networking technology can facilitate the flow of information in resource-deprived environments to disseminate life saving data. In addition, leveraging opportunistic communication can supplement cellular networks to assist with keeping communication channels open during high-use and extreme situations. This talk will discuss the progress of a research platform and middleware that enables opportunistic communication and in vivo evaluation of delay tolerant routing schemes when the Internet is interrupted or unavailable by leveraging node relationships to create a delay tolerant social network. The solutions discussed in this talk further include applications related to IoT, mobile healthcare, and smart city environments.

    Biography: Corey E. Baker, Ph.D., is a University of California President's Postdoctoral Fellow in the Electrical and Computer Engineering Department at the University of California, San Diego and is mentored by Professor Ramesh Rao. Dr. Baker's research interests are in the area of cyber physical systems specializing in opportunistic wireless communication for the Internet of Things (IoT), smart cities, smart homes, and mobile health environments. Particularly, Dr. Baker is interested in pragmatic applications and the fundamental issues related to real-world resource availability in today's operating systems for opportunistic wireless communication. Dr. Baker received a B.S. degree in Computer Engineering from San Jose State University, a M.S. in Electrical and Computer Engineering from California State University, Los Angeles, and M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University of Florida where he was advised by Professor Janise McNair. Corey has served on the board of directors of the National Society of Black Engineers (NSBE) numerous times as a two term National Treasurer and CFO, two term National Treasurer Emeritus, and as the Region 6 Chairperson. Dr. Baker is currently a NSBE Region 6 Finance Zone Advisor. Formerly, Dr. Baker was the official blogger for GEM and blogged about topics to promote success amongst STEM graduate students which included securing graduate school funding, navigating Ph.D. programs, and publishing.

    Host: Prof. Ashutosh Nayyar

    Location: 248

    Audiences: Everyone Is Invited

    Posted By: Annie Yu

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  • Neuro-Gastroenterologic Engineering

    Wed, Mar 22, 2017 @ 10:45 AM - 11:45 AM

    Ming Hsieh Department of Electrical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Todd P. Coleman, Associate Professor/UCSD

    Talk Title: Neuro-Gastroenterologic Engineering

    Abstract: The discoordination between the central and autonomic nervous systems is increasingly being identified as playing a key role in affecting neurological, psychiatric, and gastroenterologic problems; the causal role that the enteric nervous system may play in Parkinson's disease serves as an example. However, traditionally, the brain and GI system have been studied scientifically and treated clinically, separately. There is a dearth of approaches to use engineering perspectives to better measure, characterize, and provide actionable insight about the GI system as well as its interplay with the brain. In this talk, we will discuss our recent contributions to address this unmet need. Specifically, we will discuss our recent development of novel methods to assess the GI system with high-resolution multi-electrode surface potential recordings, an approach that non-invasively characterizes propagation velocity and propagation patterns consistent with gastric serosal slow wave myoelectric activity, which had not been accomplished until now. We will also highlight novel applied probability methods to interpret these classes of dynamic multi-channel physiologic datasets, including directed information graphs, a new class of probabilistic graphical models that provides minimal descriptions of causal relationships in multiple time series. To enable the recording of multiple physiologic time series simultaneously and unobtrusively, we will lastly discuss our development of multi-electrode arrays embedded within skin-mounted adhesives for ambulatory monitoring. We will highlight how all of these methods and technologies are being used within the context of neuro-gastroenterologic engineering and how there is transformational potential to improve health, reduce healthcare costs, and advance science.

    Biography: Todd P. Coleman received B.S. degrees in electrical engineering (summa cum laude), as well as computer engineering (summa cum laude) from the University of Michigan. He received M.S. and Ph.D. degrees from MIT in electrical engineering, and did postdoctoral studies at MIT in neuroscience. He is currently an Associate Professor in Bioengineering at UCSD, where he directs the Neural Interaction Laboratory. Dr. Coleman's research has been featured on CNN, BBC, and the New York Times. Dr. Coleman has been selected as a National Academy of Engineering Gilbreth Lecturer and a TEDMED speaker.

    Host: Dr. Sandeep Gupta

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

    Audiences: Everyone Is Invited

    Posted By: Mayumi Thrasher

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  • Computer Science General Faculty Meeting

    Wed, Mar 22, 2017 @ 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

    Posted By: Assistant to CS chair

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  • Seminar Announcement: Soft Robots for Delicate and Effective Interactions with Humans: Multi-Scale Soft Biomedical Robots

    Wed, Mar 22, 2017 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Tomasso Ranzani, Postdoctoral Fellow, Harvard University

    Talk Title: Soft Robots for Delicate and Effective Interactions with Humans: Multi-Scale Soft Biomedical Robots

    Abstract: Soft robots are constructed from compliant materials, resulting in machines that can safely interact with the natural environments. Given their inherent compliance, they are particularly suitable for exploring and interacting with unstructured environments, and manipulating soft, delicate, and irregular objects. These properties make soft robots particularly promising for biomedical applications, such as wearable and medical devices, given the highly compliant and delicate structures of the body. On the other hand, the compliance of soft robots limits their ability to effectively apply forces on objects whose stiffness is comparable to the one of the robot itself, leading to the challenge of matching the compliance of soft devices with the environment or objects they will encounter. During this talk, I will describe progress in soft robotics and its potential for revolutionizing biomedical devices. I will introduce a soft manipulator inspired by the structure and the manipulation capabilities of the octopus tentacle, which is able to selectively tune its stiffness to address the challenge of impedance matching. I will also introduce the potential of soft robotics at the millimeter and micrometer scales, addressing the challenge of manufacturing complex meso-scale three-dimensional soft structures using two-dimensional processes involving laser machining, lamination, and soft lithography. These manufacturing processes could pave the way for soft microrobots as well as a new class of deployable, small, and safe medical devices.

    Biography: Tommaso Ranzani received the Master's degree in biomedical engineering from the University of Pisa, Pisa, Italy, in 2010 and the Ph.D. degree in BioRobotics in 2014 at the BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy. During his Ph.D., he explored soft robotic technologies to develop a bioinspired manipulator, which integrates design principles from biological systems for performing advanced procedures in minimally invasive surgery. He is currently a postdoctoral fellow at the Harvard Microrobotics Laboratory and at the Harvard Biodesign Laboratory working on different manufacturing paradigms, materials, and actuation technologies to develop novel mm-scale robotic tools and structures able to overcome current challenges in medicine and surgery. His research interests include soft and bioinspired robotics, medical robotics and advanced manufacturing.

    Host: Department of Aerospace and Mechanical Engineering

    More Info: http://ame-www.usc.edu/seminars/3-22-17-ranzani.shtml

    Location: Seaver Science Library (SSL) - 150

    Audiences: Everyone Is Invited

    Posted By: Ashleen Knutsen

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  • Film Screening of She Started It - featuring BS Computer Science Alumna

    Wed, Mar 22, 2017 @ 07:00 PM - 10:00 PM

    Computer Science

    Receptions & Special Events


    The Computer Science Department and USC School of Cinema are pleased to bring you a special screening of She Started It.

    RSVP via: http://cinema.usc.edu/SheStartedIt

    She Started It gives a new face to the popular image of the tech entrepreneur: a female face.

    Following five women over two years as they pitch VCs, build teams, bring products to market, fail and start again, She Started It takes viewers on a global roller coaster ride from San Francisco to Mississippi, France and Vietnam. Along the way, it weaves in big-picture perspectives from women like investor Joanne Wilson; White House CTO Megan Smith; GoldieBlox CEO Debbie Sterling; and Ruchi Sanghvi, the first female engineer at Facebook.

    Through intimate, action-driven storytelling, She Started It explores the cultural roots of female underrepresentation in entrepreneurship. including pervasive self-doubt, fear of failure, and risk aversion among young women. It exposes, too, the structural realities women face as they become entrepreneurs, including lack of female role models and investors, and the persistent dearth of venture capital funding made available to women-led companies.

    Directed by Nora Poggi
    Co-Directed by Insiyah Saeed
    Produced by Nora Poggi and Insiyah Saeed

    Followed by a Q&A with Nora Poggi and USC B.S. Computer Science 2009 Alumna Thuy Truong

    7:00 P.M. on Wednesday, March 22nd, 2017
    The Ray Stark Family Theatre, SCA 108
    900 W. 34th Street, Los Angeles, CA 90007

    FREE ADMISSION. OPEN TO THE PUBLIC. RSVPs REQUIRED.

    RSVP via: http://cinema.usc.edu/SheStartedIt

    Location: School Of Cinematic Arts (SCA) - 108

    Audiences: Everyone Is Invited

    Posted By: Ryan Rozan

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  • CS Colloquium: Long Lu (Stony Brook University) - New OS and Programming Support for Securing Mobile and IoT Platforms

    Thu, Mar 23, 2017 @ 11:00 AM - 12:20 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Long Lu, Stony Brook University

    Talk Title: New OS and Programming Support for Securing Mobile and IoT Platforms

    Series: CS Colloquium

    Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.

    Software running on mobile and IoT platforms increasingly falls victim to new attacks, which cause device compromises and privacy leaks that are often more severe than their counterparts on conventional computers. My research finds that new attacks on these platforms are possible primarily due to a gap between the evolving security needs of software and the legacy security support provided by operating systems and programming tools.

    In this talk, I will first overview my recent works that aim to bridge this gap by rethinking the principles and designs of security mechanisms in operating systems, compilation toolchains, and TEEs (Trusted Execution Environments). I will then present two systems that address a critical yet previously unmet security need of today's apps, namely in-app isolation. The first system introduces a new OS-managed code execution unit, called shred, to compensate thread and process. A shred is a segment of a thread execution. Code inside a shred can access, in addition to the regular virtual memory, a private memory region. Using shreds, programmers can now protect sensitive in-memory code and data against untrusted code running in the same process or thread. The second system enables comprehensive security policy enforcement at the sub-app granularities, preventing mutually distrusting app modules from abusing each other's resources and privileges. In the final part of the talk, I will discuss my ongoing and future works on laying the system foundation for securing IoT platforms.

    Biography: Long Lu is an Assistant Professor of Computer Science and the director of RiS3 Lab at Stony Brook University. Long's research spans the broad area of systems and software security. His recent work is focused on application and operating system security for emerging platforms, such as mobile and IoT/CPS devices. He designs code and data protection mechanisms, program analysis techniques, and user-facing software tools to prevent real attacks. He constantly publishes in the top-tier computer security conferences and is frequently invited to serve on their program committees. His research outcomes have been adopted by IBM, Microsoft, NEC, and Samsung. His work is currently funded by NSF, ONR, ARO, and AFRL. Long is a recipient of the NSF CAREER Award and the Air Force Faculty Fellowship. He holds a Ph.D. in Computer Science from Georgia Tech.

    Host: CS Department

    Location: Ronald Tutor Hall of Engineering (RTH) - 217

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • CS Colloquium: Matthew Brown (UCLA) -Typed Self-Applicable Meta-Programming

    Thu, Mar 23, 2017 @ 04:00 PM - 05:20 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Matthew Brown, UCLA

    Talk Title: Typed Self-Applicable Meta-Programming

    Series: CS Colloquium

    Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.

    Meta-programming is a fundamental technique in computer science. It allows high levels of abstraction to be utilized with low cost. Meta-programs like compilers, interpreters, and program optimizers make high-level programming languages efficient, providing increased programmer productivity and performance comparable to lower-level languages. Self-applicable meta-programming makes meta-programming first-class, enabling many powerful
    techniques. However, meta-programming and particularly self-applicable meta-programming is often complex, error-prone and difficult to debug. For these reasons it has untapped potential to provide benefits in many areas. Typed meta-programming uses modern techniques for type checking meta-programs to make them less error-prone and easier to understand and debug. It also brings the power of self-applicable meta-programming to statically-typed languages, ending a long-persisting trade-off between static and dynamic type checking. In this talk I discuss foundational results in typed self-applicable meta-programming.

    Biography: Matt Brown is PhD candidate at UCLA, working in the compilers lab under Jens Palsberg. He holds a Bachelor's degree from UC Santa Cruz and a Master's from UCLA. His research focus is typed self-applicable meta-programming, which uses typed program representation techniques to ensure correctness properties of self-applicable meta-programs like self-interpreters. Other research interests include type systems, program verification, concurrency, and functional programming languages. He was recently a part-time lecturer at Loyola Marymount University and has six years of industry experience.

    Host: CS Department

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

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • Civil and Environmental Engineering Seminar

    Fri, Mar 24, 2017 @ 11:00 AM - 12:00 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Susan Hubbard, University of California, Berkeley

    Talk Title: Geophysical Approaches for Quantifying Watershed Structure and Function

    Host: Dr. Felipe de Barros

    More Information: Hubbard Seminar Announcement.pdf

    Location: 102

    Audiences: Everyone Is Invited

    Posted By: Kaela Berry

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  • AI Seminar: EVOLUTION OF NEURAL NETWORKS

    Fri, Mar 24, 2017 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Risto Miikkulainen, Univ. of Texas

    Talk Title: EVOLUTION OF NEURAL NETWORKS

    Series: Artificial Intelligence Seminar

    Abstract: Evolution of artificial neural networks has recently emerged as a powerful technique both in deep networks and reinforcementlearning. While the performance of deep learning networks depends crucially on the network architecture; with neuroevolution, it ispossible to discover such architectures automatically. While reinforcement learning works well when the environment is fully observable, neuroevolution makes it possible to disambiguate hidden state through memory. In this tutorial, I will review 1 neuroevolution methods that evolve fixed-topology networks, network topologies, and network construction processes, 2 ways of combining gradient-based training with evolutionary methods, and 3 applications of neuroevolution to control, robotics, artificial life, and games.





    Biography: Risto Miikkulainen is a Professor of Computer Science at the University of Texas at Austin and a Research Fellow at Sentient Technologies, Inc. He received an M.S. in Engineering from the Helsinki University of Technology, Finland, in 1986, and a Ph.D. in Computer Science from UCLA in 1990. His current research focuses on methods and applications of neuroevolution, as well as neural network models of natural language processing, and self-organization of the visual cortex; he is an author of over 370 articles in these research areas. He is an IEEE Fellow, member of the Board of Governors of the Neural Network Society, and an action editor of Cognitive Systems Research and IEEE Transactions on Computational Intelligence and AI in Games.

    Host: Mayank Kejriwal

    Webcast: http://webcastermshd.isi.edu/Mediasite/Play/f0024e2d2140457586ec2ed6a78026b01

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

    WebCast Link: http://webcastermshd.isi.edu/Mediasite/Play/f0024e2d2140457586ec2ed6a78026b01d

    Audiences: Everyone Is Invited

    Posted By: Peter Zamar

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  • W.V.T. Rusch Engineering Honors Program Colloquium

    Fri, Mar 24, 2017 @ 01:00 PM - 01:50 PM

    USC Viterbi School of Engineering

    University Calendar


    Join us for a presentation by Dr. Vikram Ravi, Millikan Fellow in Astronomy, Cahill Center for Astronomy and Physics at the California Institute of Technology, titled "The Hottest Explosions of the Universe."

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

    Audiences: Everyone Is Invited

    Posted By: Ramon Borunda/Academic Services

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  • Ming Hsieh Institute Seminar Series on Integrated Systems

    Fri, Mar 24, 2017 @ 02:30 PM - 04:30 PM

    Ming Hsieh Department of Electrical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Aydin Babakhani, Assistant Professor, Rice University

    Talk Title: Silicon-based Integrated Sensors and Systems with On-chip Antennas From Picosecond Pulse Radiators to Miniaturized Spectrometers

    Host: Profs. Hossein Hashemi, Mike Chen, Dina El-Damak, and Mahta Moghaddam

    More Information: MHI Seminar Series IS - Aydin Babakhani.pdf

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

    Audiences: Everyone Is Invited

    Posted By: Jenny Lin

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  • Seminars in Biomedical Engineering

    Fri, Mar 24, 2017 @ 02:30 PM - 04:30 PM

    Biomedical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Aditya Rajagopal, PhD, Founder & CTO, Chromacode; Visiting Associate (Visiting Faculty), Caltech

    Talk Title: Engineering Methods for Biological Measurement

    Series: Seminars in BME (Lab Rotations)

    Host: Brent Liu, PhD

    Location: Corwin D. Denney Research Center (DRB) - 146

    Audiences: Everyone Is Invited

    Posted By: Mischalgrace Diasanta

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  • NL Seminar - Intuitive Interactions with Black-box Machine Learning

    Fri, Mar 24, 2017 @ 03:00 PM - 04:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Sameer Singh, UCI

    Talk Title: Intuitive Interactions with Black-box Machine Learning

    Series: Natural Language Seminar

    Abstract: Machine learning is at the forefront of many recent advances in natural language processing, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned. It is incredibly difficult to understand, predict, or "fix" the behavior of NLP models that have been deployed. In this talk, I propose interpretable representations that allow users and machine learning models to interact with each other: enabling machine learning models to provided explanations as to why a specific prediction was made and enabling users to inject domain knowledge into machine learning. The first part of the talk introduces an approach to estimate local, interpretable explanations for black-box classifiers and describes an approach to summarize the behavior of the classifier by selecting which explanations to show to the user. I will also briefly describe work on "closing the loop", i.e. allowing users to provide feedback on the explanations to improve the model, for the task of relation extraction, an important subtask of natural language processing. In particular, we introduce approaches to both explain the relation extractor using logical statements and to inject symbolic domain knowledge into relational embeddings to improve the predictions. I present experiments to demonstrate that an interactive interface is effective in providing users an understanding of, and an ability to improve, complex black-box machine learning systems.


    Biography: Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interactive machine learning applied to information extraction and natural language processing. Till recently, Sameer was a Postdoctoral Research Associate at the University of Washington. He received his PhD from the University of Massachusetts, Amherst in 2014, during which he also interned at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was selected as a DARPA Riser, was awarded the Adobe Research Data Science Award, won the grand prize in the Yelp dataset challenge, has been awarded the Yahoo! Key Scientific Challenges fellowship, and was a finalist for the Facebook PhD fellowship. Sameer has published more than 30 peer-reviewed papers at top-tier machine learning and natural language processing conferences and workshops.

    Host: Marjan Ghazvininejad and Kevin Knight

    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

    Posted By: Peter Zamar

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