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Events for March 05, 2020
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CS Colloquium: Emma Pierson (Stanford) - Data Science Methods to Reduce Inequality and Improve Healthcare
Thu, Mar 05, 2020 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Emma Pierson, Stanford University
Talk Title: Data Science Methods to Reduce Inequality and Improve Healthcare
Series: CS Colloquium
Abstract: I will describe how to use data science methods to understand and reduce inequality in two domains: criminal justice and healthcare. First, I will discuss how to use Bayesian modeling to detect racial discrimination in policing. Second, I will describe how to use machine learning to explain racial and socioeconomic inequality in pain.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Emma Pierson is a PhD student in Computer Science at Stanford, supported by Hertz and NDSEG Fellowships. Previously, she completed a master's degree in statistics at Oxford on a Rhodes Scholarship. She develops statistical and machine learning methods to study two deeply entwined problems: reducing inequality and improving healthcare. She also writes about these topics for broader audiences in publications including The New York Times, The Washington Post, FiveThirtyEight, and Wired. Her work has been recognized by best paper (AISTATS 2018), best poster (ICML Workshop on Computational Biology), and best talk (ISMB High Throughput Sequencing Workshop) awards, and she has been named a Rising Star in EECS and Forbes 30 Under 30 in Science.
Host: Bistra Dilkina
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Xiaonan Hui - ECE-EP Seminar, Thursday, March 5th at 11am in EEB 248
Thu, Mar 05, 2020 @ 11:00 AM - 12:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Xiaonan Hui, Cornell University
Talk Title: Harmonic RF sensing from indoor localization to vital signs monitoring
Abstract: When wireless is perfectly applied, the whole earth will be converted into a huge brain, which in fact it is all things being particles of a real and rhythmic whole." For almost a century, electrical engineers are endeavoring to approach what Nikola Tesla predicted in 1926 for a "World Wireless System". However, as of today, many hurdles remain when we think of all things connected rhythmically with interaction and links between the cyber and the physical worlds, because sensing of the "things", especially "living things", is still heavily constrained. The location and shape of objects, as well as the vital signs of people and animals are critical information to the overall systems. In this talk, I will first highlight our solutions of highly reliable and accurate indoor RF ranging, localization and imaging. The demonstrated radio frequency (RF) localization method bypasses the Uncertainty-Principle mathematical model commonly seen in the radar-like system, so that the high temporal (kHz) and spatial (microns) resolutions can be achieved simultaneously with ~915 MHz signals which have deep penetration to many dielectrics of interests such as building materials and living tissues. Vital-sign monitoring is the second part of the talk, including the heartbeat dynamics, respiration, and blood pressures of both central and pulmonary circulations, with the new near-field coherent sensing (NCS) approach, which not only provides unparalleled RF vital-sign signal quality and sensing capability, but also does not require skin touch or motion restraint to greatly improve the applicability to people and animals. The systems in this talk can be implemented in the applications of high precision indoor locating, assisted living, RF bio-tomography, biometrics for security, wearable sensors, and clinical researches. The talk will include the supporting RF theory, the design methods and the hardware/software experimental system, but its content will be aimed for the general audience in engineering.
Biography: Xiaonan Hui is a Ph.D. candidate in the School of Electrical and Computer Engineering at Cornell University. He works with Prof. Edwin Kan and focuses on radio-frequency systems for Cyber Physical System (CPS) and Internet of Things (IoT) applications. His recent works on vital-sign acquisition for people as well as animals were published on high-impact journals and conferences, attracting not only more than 30 news agencies, but also generating broad industrial interests for automotive, medical, pharmaceutical, and digital agricultural applications. Moreover, his high-precision indoor localization works provide an innovative method for IoT tracking, robotic localization, and civil structure integrity monitoring. He is the principal investigator of Cornell Scale-up and Prototype Grants, the winner of Cornell ECE Outstanding Ph.D. Thesis Research Award, and other 3 fellowships. He also serves as the reviewer for Springer Nature, IEEE journals and conferences in the areas of electromagnetic systems, vital-sign sensing and wireless communications. More of his academic information can be found in his website: www.xiaonanhui.com
Host: ECE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar
Thu, Mar 05, 2020 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Frederic Sala, Stanford Computer Science Department
Talk Title: Structure to the Rescue: Breaking Data Barriers in Machine Lear
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: The current machine learning zeitgeist is that models are only as good as the data they are fed, so that limitations in the data---and especially mismatches with the ML algorithm---present fundamental barriers to model performance. However, for ML to continue its growth and be safely and widely deployed across domains with significant societal impact, such limitations must be minimized. In this talk, I will describe two ways to exploit structure in data to overcome apparent obstacles, with theoretical guarantees.
First, I will argue that geometry is a barrier to producing quality representations used by models. The root cause is a mismatch between the geometric structure of the data and the geometry of the model---but the issue can be resolved by adopting matching non-Euclidean geometries, relying on, for example, hyperbolic geometry for hierarchical data. Next, motivated by the fact that labeling large datasets is a major bottleneck in supervised learning, I will discuss a weak supervision framework for automating the process of labeling, overcoming the lack of hand-labeled data. This is done by encapsulating different aspects of manual labeling into heuristics whose structure is characterized by learnable accuracies and correlations. I will describe extensions of this framework to handle multitask, time-series, and other forms of structured data. This framework is widely used in industry, helping drive applications used by millions daily.
Biography: Frederic Sala is a postdoctoral scholar in the Stanford Computer Science Department, advised by Chris Ré. His research interests include machine learning, data-driven systems, and information and coding theory, and in particular problems related to the analysis and design of algorithms that operate on diverse and challenging forms of data. He received the Ph.D. and M.S. degrees in Electrical Engineering from UCLA, where he received the Distinguished Ph.D. Dissertation in Signals & Systems Award from the UCLA Electrical Engineering Department, the NSF graduate fellowship, and the Edward K. Rice Outstanding Master's Student Award.
Host: Paul Bogdan, pbogdan@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Talyia White
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ECE Seminar: Collaborative Perception and Learning Between Robots and the Cloud
Thu, Mar 05, 2020 @ 02:15 PM - 03:15 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Sandeep Chinchali, PhD Candidate, Dept of CS, Stanford University
Talk Title: Collaborative Perception and Learning Between Robots and the Cloud
Abstract: Augmenting robotic intelligence with cloud connectivity is considered one of the most promising solutions to cope with growing volumes of rich robotic sensory data and increasingly complex perception and decision-making tasks. While the benefits of cloud robotics have been envisioned long before, there is still a lack of flexible methods to trade-off the benefits of cloud computing with end-to-end systems costs of network delay, cloud storage, human annotation time, and cloud-computing time. To address this need, I will introduce decision-theoretic algorithms that allow robots to significantly transcend their on-board perception capabilities by using cloud computing, but in a low-cost, fault-tolerant manner.
Specifically, for compute-and-power-limited robots, I will present a lightweight model selection algorithm that learns when a robot should exploit low-latency on-board computation, or, when highly uncertain, query a more accurate cloud model. Then, I will present a collaborative learning algorithm that allows a diversity of robots to mine their real-time sensory streams for valuable training examples to send to the cloud for model improvement. The utility of these algorithms will be demonstrated on months of field data and experiments on state-of-the-art embedded deep learning hardware. I will conclude this talk by outlining a number of future research directions on the systems and theoretical aspects of networked system control, some of which extend beyond cloud robotics.
Biography: Sandeep Chinchali is a computer science PhD candidate at Stanford, advised by Sachin Katti and Marco Pavone. Previously, he was the first principal data scientist at Uhana, a Stanford startup working on data-driven optimization of cellular networks, now acquired by VMWare. His research on networked control has led to proof-of-concept trials with major cellular network operators and was a finalist for best student paper at Robotics: Science and Systems 2019. Prior to Stanford, he graduated from Caltech, where he worked on robotics at NASA's Jet Propulsion Lab (JPL). He is a recipient of the Stanford Graduate Fellowship and National Science Foundation (NSF) fellowships.
Host: Host: Professor Konstantinos Psounis
Location: Michelson Center for Convergent Bioscience (MCB) - 102
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Sonny Astani Civil and Environmental Engineering Seminar
Thu, Mar 05, 2020 @ 04:00 PM - 05:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Francesca Boso, Stanford University
Talk Title: Data and probabilistic forecasting in environmental applications
Abstract: Mathematical models expressing conservation of certain quantities (e.g. mass) are ubiquitous in the environmental sciences. A common challenge is often the lack of enough observations to inform these models, either because data collection is costly or impractical/impossible at the required level of spatial and temporal refinement. We propose a computational tool to treat the parametric uncertainty of these models, leveraging the inherent physical constraints, and combining them with data. Specifically, we quantify the impact of parametric uncertainty by deriving model-dependent deterministic equations for the probability distribution (Probability Density Function, PDF, or Cumulative Distribution Function, CDF) of the model solution. These equations can be derived in exact form for a class of nonlinear hyperbolic governing laws (e.g. advection-dominated transport in heterogeneous flows), whereas in general they require the development of ad-hoc closures. I will be presenting an overview of strategies to obtain workable PDF-CDF equations for specific conservation problems, and some recent work on how to combine them with available data to eventually reduce uncertainty.
Biography: Francesca is a senior research scientist in the Energy Resources Engineering Department at Stanford University, following her postdoc at the University of California, San Diego. She received her PhD in Environmental Engineering from the University of Trento, Italy, specializing in hydrology. She has been investigating uncertainty quantification for environmental applications.
Host: Dr. Felipe de Barros
Location: Michelson Center for Convergent Bioscience (MCB) - 102
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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Careers in Patent Law - Information Session
Thu, Mar 05, 2020 @ 04:00 PM - 05:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
If you have (or will soon have) a degree in engineering, computer sciences, or the hard sciences (chemistry, biology, physics and related disciplines), you can take the Patent Office's Registration Exam to become a Patent Agent. Patent Agents can make up to $20,000 a year more than similarly situated engineers and scientists. The Exam is available virtually on-demand via computer. Becoming a patent agent can also be a step toward the even more lucrative career of becoming a Patent Attorney.
If this sounds intriguing, come join Practising Law Institute (PLI) for a presentation by Mark Dighton, PLI's Director of Law School Relations and a Director of their Patent Office Exam Course. Topics for discussion include:
- Careers for patent agents (what the work is like and where you find it),
- The qualities and skills that will make you happy and successful in this field (e.g., communication skills), and
- The latest info on the Patent Office's Registration Exam (who can take it, how it works, and recommendations on how to study for this very difficult Exam).
Date: Thursday, March 5th
Time: 4pm
Location: EEB 132
Please visit us on the Web for a wealth of information on the resources we offer:
www.PatentOfficeExamCourse.com www.pli.eduLocation: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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Boeing Freshman Design Challenge
Thu, Mar 05, 2020 @ 05:00 PM - 09:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Hands-on design challenge for Freshman only. Contest will be judged by Boeing representatives and offer prizes to top participants.
Familiarize USC freshman with The Boeing Company, its products and values, and its recruitment schedule. Get questions answered by Boeing representatives during an extensive Q&A and foster excitement about a degree/career in engineering through a technical challenge.
Target audience: freshman engineers only
Majors: all engineering majorsLocation: Seeley G. Mudd Building (SGM) - 101
Audiences: Viterbi Freshmen and Sophomores
Contact: RTH 218 Viterbi Career Connections
