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Events for the 1st week of April
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ECE Seminar: Label-free Optical Imaging of Living Biological Systems
Mon, Mar 30, 2020 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Sixian You, PhD, Bioengineering, UIUC
Talk Title: Label-free Optical Imaging of Living Biological Systems
Abstract: Label-free optical imaging of living biological systems offers rich information that can be of immense value in biomedical tasks such as diagnosing cancer or assessing the tumor microenvironment. Despite the exceptional theoretical potential, current label-free nonlinear microscopy platforms are challenging for real-world clinical and biological applications. The major obstacles include the lack of flexible laser sources, limited contrast, and lack of molecular specificity for diseases.
In this talk, I will present a new optical imaging platform and methodology that will address these challenges. By generating and tailoring coherent supercontinuum from photonic crystal fibers, single-source single-shot metabolic and structural imaging can be achieved, enabling Simultaneous Label-free Auto-fluorescence Multi-harmonic (SLAM) contrast in living cells and tissues. These capabilities further motivate development of analytical tools for tissue assessment and diagnosis, showing broad potential of this label-free imaging technology in discovering new metabolic biomarkers and enabling real-time point-of-procedure applications.
Biography: Sixian You received her Ph.D. in 2019 from the University of Illinois, Urbana-Champaign (UIUC), under the guidance of Prof. Stephen A. Boppart. Her primary research interest is in developing innovative optical imaging solutions for biomedicine. She is particularly interested in developing next-generation label-free multiphoton imaging technologies to study the tumor microenvironment. Sixian was awarded the Microscopy Innovation Award by the Microscopy Society of America and McGinnis Medical Innovation Graduate Fellowship by UIUC.
Host: Justin Haldar, jhaldar@usc.edu
Webcast: https://usc.zoom.us/j/402440976WebCast Link: https://usc.zoom.us/j/402440976
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Seminar will be exclusively online (no in-room presentation) - CS Colloquium: Alan Liu (Carnegie Mellon University) - Enabling Future-Proof Telemetry for Networked Systems
Tue, Mar 31, 2020 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Alan Liu, Carnegie Mellon University
Talk Title: Enabling Future-Proof Telemetry for Networked Systems
Series: CS Colloquium
Abstract: Today's networked systems, such as data center, cellular, and sensor networks, face increasing demands on security, performance, and reliability. To fulfill these demands, we first need to obtain timely and accurate telemetry information about what is happening in the system. For instance, understanding the volume and the number of distinct network connections can help detect and mitigate network attacks. In storage systems, identifying hot items can help balance the server load. Unfortunately, existing telemetry tools cannot robustly handle multiple telemetry tasks with diverse workloads and resource constraints.
In this talk, I will present my research that focuses on building telemetry systems that are future-proof for current and unforeseen telemetry tasks, diverse workloads, and heterogeneous platforms. I will discuss the efficient algorithms and implementations that realize this future-proof vision in network monitoring for hardware and software platforms. I will describe how bridging theory and practice with sketching and sampling algorithms can significantly reduce memory footprints and speedup computations while providing robust results. Finally, I will end the talk with new directions in obtaining future-proof analytics for other types of networked systems, such as low-power sensors and mobile devices, while enhancing energy efficiency and data privacy.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Alan (Zaoxing) Liu is a postdoctoral researcher at Carnegie Mellon University. His research interests are in networked and distributed systems with a recent focus on efficient system and algorithmic design for telemetry, big-data analytics, and privacy. His research papers have been published in venues such as ACM SIGCOMM, USENIX FAST, and OSDI. He is a recipient of the best paper award at USENIX FAST'19 for his work on large-scale distributed load balancing. His work received multiples recognitions, including ACM STOC "Best-of-Theory" plenary talk and USENIX ATC "Best-of-Rest". Prior to CMU, he obtained his Ph.D. in Computer Science from Johns Hopkins University.
Host: Ramesh Govindan
Location: Seminar will be exclusively online (no in-room presentation)
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Undergraduate Admission Virtual Information Session
Tue, Mar 31, 2020 @ 02:00 PM - 03:00 PM
Viterbi School of Engineering Undergraduate Admission
Workshops & Infosessions
Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for prospective first-year students and their family members to learn more about the USC Viterbi undergraduate experience.Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life.Guests will be able to ask questions and engage in further discussion toward the end of the session.
Please register here!Audiences: Everyone Is Invited
Contact: Viterbi Admission
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**CANCELED** ISE 651 - Epstein Seminar
Tue, Mar 31, 2020 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Daniel W. Apley, Professor, Northwestern University
Talk Title: TBD
Host: Dr. Qiang Huang
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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Seminar will be exclusively online (no in-room presentation) - CS Colloquium: Baharan Mirzasoleiman (Stanford University) - Efficient Machine Learning via Data Summarization
Tue, Mar 31, 2020 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Baharan Mirzasoleiman, Stanford University
Talk Title: Efficient Machine Learning via Data Summarization
Series: CS Colloquium
Abstract: Large datasets have been crucial to the success of modern machine learning models. However, training on massive data has two major limitations. First, it is contingent on exceptionally large and expensive computational resources, and incurs a substantial cost due to the significant energy consumption.
Second, in many real-world applications such as medical diagnosis and self-driving cars, big data contains highly imbalanced classes and noisy labels. In such cases, training on the entire data does not result in a high-quality model. In this talk, I will argue that we can address the above limitations by developing techniques that can identify and extract the representative subsets from massive datasets. Training on representative subsets not only reduces the substantial costs of learning from big data, but also improves their accuracy and robustness against noisy labels. I will present two key aspects to achieve this goal: (1) extracting the representative data points by summarizing massive datasets; and (2) developing efficient optimization methods to learn from the extracted summaries. I will discuss how we can develop theoretically rigorous techniques that provide strong guarantees for the quality of the extracted summaries, and the learned models' quality and robustness against noisy labels. I will also show the applications of these techniques to several problems, including summarizing massive image collections, online video summarization, and speeding up training machine learning models.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Baharan Mirzasoleiman is a Postdoctoral Research Scholar in Computer Science Department at Stanford University, where she works with Prof. Jure Leskovec. Baharan's research focuses on developing new methods that enable efficient exploration and learning from massive datasets. She received her PhD from ETH Zurich, working with Prof. Andreas Krause. She has also spent two summers as an intern at Google Research. She was awarded an ETH medal for Outstanding Doctoral Dissertation, and a Google Anita Borg Memorial Scholarship. She was also selected as a Rising Star in EECS from MIT.
Host: Bistra Dilkina
Location: Seminar will be exclusively online (no in-room presentation)
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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ECE Seminar: Joint Wireless Communication and Sensing in mmWave and Terahertz Spectrum
Wed, Apr 01, 2020 @ 01:15 PM - 02:15 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yasaman Ghasempour, Ph.D. Candidate, ECE, Rice University
Talk Title: Joint Wireless Communication and Sensing in mmWave and Terahertz Spectrum
Abstract: Millimeter-wave and terahertz bands are emerging as the most promising spectrum to meet the data-rate and latency demands of future wireless applications, including virtual reality and autonomous cars. Moreover, large spectral availability and mm-scale wavelength provide the possibility for ubiquitous and high-resolution sensing. My research builds a foundation for joint communication and sensing in such high-frequency regimes. This perspective yields a paradigm shift in the design and development of future wireless systems. In this talk, I will present the world's first single-shot and single-antenna motion sensing system in THz bands. We demonstrate a novel node architecture exploiting a single leaky wave antenna, which is primarily used for beam steering in THz networks. I will show how we leverage this device's spatial-spectral characteristics in new ways to enable motion sensing functionalities with a single THz pulse transmission. I will then discuss the opportunities offered by this platform to enhance next-generation communication in unprecedented ways. In particular, we tackle the mobility, blockage, and scalability challenges of highly directional THz networks by efficiently adapting steering direction for mobile users. Finally, I will share several research directions that I would like to pursue in the future.
Biography: Yasaman Ghasempour is currently a Ph.D. Candidate in Electrical and Computer Engineering at Rice University. She received her Master's degree in Electrical and Computer Engineering from Rice University and her Bachelor's degree in Electrical Engineering from Sharif University of Technology in Iran. Her research interests include wireless communication and sensing, with a focus on emerging millimeter-wave and terahertz spectrum. She has published in top-tier IEEE and ACM conferences and journals and has been named an EECS rising star in 2019. She is also the recipient of Texas Instruments Distinguished Fellowship among multiple IEEE/ACM societies awards.
Host: Urbashi Mitra (ubli@usc.edu) and Konstantinos Psounis (kpsounis@usc.edu)
Webcast: https://usc.zoom.us/j/873150824WebCast Link: https://usc.zoom.us/j/873150824
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Virtual Alumni Spotlight Panel Aerospace, Mechanical, and Astronautical Engineering
Wed, Apr 01, 2020 @ 07:00 PM - 08:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Come hear Viterbi Alumni share about their Aerospace, Mechanical, and Astronautical Engineering experience during this Virtual Spotlight Panel!
This panel will take place online.
RSVP Here: https://usc.qualtrics.com/jfe/form/SV_cvV9pSBoTKkfuYtLocation: Virtual
Audiences: Undergrad
Contact: RTH 218 Viterbi Career Connections
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ECE Seminar: Safe and Data-efficient Learning for Robotics
Thu, Apr 02, 2020 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Somil Bansal, PhD Candidate, Department of EECS, University of California, Berkeley
Talk Title: Safe and Data-efficient Learning for Robotics
Abstract: Machine learning has led to tremendous progress in domains such as computer vision, speech recognition, and natural language processing. Fueled by these advances, machine learning approaches are now being explored to develop intelligent physical systems that can operate reliably in unpredictable environments. These include not only robotic systems such as autonomous cars and drones, but also large-scale cyberphysical systems such as transportation and energy systems. However, learning techniques widely used today are extremely data inefficient, making it challenging to apply them to real-world physical systems. Moreover, they lack the necessary mathematical framework to provide guarantees on correctness, causing safety concerns as data-driven physical systems are integrated in our society. We combine tools from robust optimal control theory with machine learning and computer vision to develop data-efficient and provably safe learning-based control algorithms for physical robotic systems. In particular, we design modular architectures that combine system dynamics models with modern learning-based perception approaches to solve challenging perception and control problems in a priori unknown environments in a data-efficient fashion. Moreover, due to their modularity, these architectures are amenable to simulation-to-real transfer, and can be used for different robotic systems without any retraining. Crucially, we use models not only for faster learning, but also to monitor and recognize the learning system's failures, and to provide online corrective safe actions when necessary. This allows us to provide safety assurances for learning-enabled systems in unknown and human-centric environments, which has remained a challenge to date.
Biography: Somil Bansal completed his B.Tech. in Electrical Engineering from Indian Institute of Technology, Kanpur in 2012, and an M.S. in Electrical Engineering and Computer Sciences from UC Berkeley in 2014. Since 2015, he is pursuing a PhD degree in Electrical Engineering and Computer Sciences at UC Berkeley, under the supervision of Prof. Claire Tomlin in the Hybrid Systems Laboratory. His research interests are in exploring how machine learning tools can be combined with the control theoretic frameworks to develop data-efficient and safe learning-based control algorithms for physical robotic systems, especially when the system is operating in an uncertain environment. During his PhD, he has also worked closely with companies like Skydio, Google, Boeing, as well as NASA Ames. Somil has received several awards, most notably the outstanding graduate student instructor award at UC Berkeley and the academic excellence award at IIT Kanpur.
Host: Ashutosh Nayyar, ashutosn@usc.edu
Webcast: https://usc.zoom.us/j/811254572WebCast Link: https://usc.zoom.us/j/811254572
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Seminar will be exclusively online (no in-room presentation) - CS Colloquium: Tegan Brennan (University of California, Santa Barbara) - Software Side Channels
Thu, Apr 02, 2020 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Tegan Brennan, University of California, Santa Barbara
Talk Title: Software Side Channels
Series: CS Colloquium
Abstract: Side channels in software are a class of information leaks where non-functional side effects of software systems (such as execution time, memory usage or power consumption) can leak information about sensitive data. In this talk, I present my research on a new class of side-channel vulnerabilities: JIT-induced side channels. In contrast to side channels introduced at the source code level, JIT-induced side channels arise at runtime due to the behavior of just-in-time (JIT) compilation. I show the existence of this class of side channels across multiple runtimes, and I demonstrate JIT-induced timing channels in large, open source projects large enough in magnitude to be detected over the public internet. I also present an automated approach to inducing this type of side channel in programs. In evaluating my automated technique, I show that programs classified as side-channel free by four state-of-the-art side channel analysis tools are, in fact, vulnerable to JIT-induced side channels. Finally, I discuss my contributions towards scalable quantification of side-channel vulnerabilities through a caching framework for model-counting queries.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Tegan Brennan is a PhD candidate in Computer Science at the University of California, Santa Barbara. Her research is in software engineering, formal verification and computer security. She has worked extensively on side-channel vulnerabilities in software. Tegan is a recipient of an IGERT Fellowship in Network Science, an NCWIT Collegiate Award Honorable Mention in 2018 and an invited participant of the 2019 Rising Stars workshop. She has also interned twice with Amazon's Automated Reasoning Group.
Host: Chao Wang
Location: Seminar will be exclusively online (no in-room presentation)
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Building Software for Social Impact - Product Design Workshop
Thu, Apr 02, 2020 @ 12:00 PM - 01:00 PM
Thomas Lord Department of Computer Science
Workshops & Infosessions
Learn the phases of software development starting with ideation! We will walk participants through a problem presented to us by a non-profit and how we've designed and built a software solution.
This event will be hosted by Jessica Au and Bryan Huang on behalf of the student organization: Code the Change.
Learn more about Code the Change!
Code the Change is an organization dedicated to building software for nonprofits. We are a team of developers, designers, and product managers; our unique skill sets allow us to build fully functional projects throughout the course of a school year.
Website: http://www.ctcusc.com/
Contact: ctcusc@gmail.com
The Zoom meeting link will be sent to CS undergraduates directly by email.Location: Online - Zoom
WebCast Link: Sent Directly to CS Undergraduates
Audiences: Undergrad
Contact: Ryan Rozan
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Undergraduate Admission Virtual Information Session
Thu, Apr 02, 2020 @ 02:00 PM - 03:00 PM
Viterbi School of Engineering Undergraduate Admission
Workshops & Infosessions
Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for prospective first-year students and their family members to learn more about the USC Viterbi undergraduate experience.Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life.Guests will be able to ask questions and engage in further discussion toward the end of the session.
Please register here!Audiences: Everyone Is Invited
Contact: Viterbi Admission
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Grammar TutoringFri, Apr 03, 2020 @ 10:00 AM - 12:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
INDIVIDUAL GRAMMAR TUTORIALS
Need help refining your grammar skills in your academic and professional writing? Meet one-on-one with professors from the Engineering Writing Program, work together on your grammar skills, and take your writing to the next level!
ALL VITERBI UNDERGRADUATE AND GRADUATE STUDENTS WELCOME!
Sign up here: http://bit.ly/grammaratUSC
All sessions will be via Zoom.
Questions? Contact helenhch@usc.eduLocation: ZOOM
Audiences: Graduate and Undergraduate Students
Contact: Helen Choi
