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SUNMONTUEWEDTHUFRISAT
Events for the 3rd week of June
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Meet USC: Admission Presentation, Campus Tour, and Engineering TalkMon, Jun 10, 2019
Viterbi School of Engineering Undergraduate Admission
Workshops & Infosessions
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!
RSVPLocation: Ronald Tutor Campus Center (TCC) - USC Admission Office
Audiences: Everyone Is Invited
Contact: Viterbi Admission
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Jonathan Habif, Monday, June 10th at 2pm in EEB 248
Mon, Jun 10, 2019 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jonathan Habif, Research Scientist at ISI Waltham
Talk Title: The Laboratory for Quantum-Limited Information: Optical Sensing at the Quantum Limit
Abstract: In this talk I will begin by introducing the Laboratory for Quantum-Limited Information (QLIlab), part of the USC Information Sciences Institute. QLIlab is focused on understanding the fundamental limits for extracting information from physical signals in photon-starved sensing and communications settings and demonstrating the capability to achieve these limits with novel designs for optical receivers. As an example, I will present our theoretical and experimental work on quantum-limited discrimination of photon-starved classical states. In this work the fundamental quantum limit for discriminating between thermal and coherent (laser) light is calculated, along with the error-probability for discrimination that can be achieved by using traditional optical receivers. The Generalized-Kennedy (GK) receiver is shown to have near optimal performance for this discrimination task. The GK receiver has been constructed in the laboratory and shown to out-perform theoretical limits from direct and coherent detection optical receivers. Finally, I will describe how this work will be extended to broader classes of sensing and communications challenges, including passive imaging and weak coherent signals buried in bright noise. A key goal for this talk is to engage with the audience to identify new optical and opto-electronic technologies that can help to realize optical receivers operating at the quantum limit of sensitivity, and ultimately miniaturize the systems so they can be transitioned up the technology readiness level scale.
Biography: Dr. Jonathan L. Habif is an experimental physicist and research lead at the University of Southern California Information Sciences Institute (ISI). His research has focused on photon-starved, classical communication and imaging, quantum-secured optical communications in free-space and fiber, and integrated nano-photonics for both classical and non-classical applications. Prior to joining ISI, Dr. Habif was with BBN technologies where he served as principal investigator for a number of DARPA-sponsored research programs, partnering with university collaborators to demonstrate revolutionary optical technologies impacting traditional communications, sensing and computation systems.
Dr. Habif earned a Ph.D. from the University of Rochester in the field of superconducting quantum computing and continued this course of research as a postdoctoral associate at MIT.
Host: ECE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Meet USC: Admission Presentation, Campus Tour, and Engineering Talk
Wed, Jun 12, 2019
Viterbi School of Engineering Undergraduate Admission
Workshops & Infosessions
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!
RSVPLocation: Ronald Tutor Campus Center (TCC) - USC Admission Office
Audiences: Everyone Is Invited
Contact: Viterbi Admission
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Meet USC: Admission Presentation, Campus Tour, and Engineering Talk
Fri, Jun 14, 2019
Viterbi School of Engineering Undergraduate Admission
Workshops & Infosessions
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!
RSVPLocation: Ronald Tutor Campus Center (TCC) - USC Admission Office
Audiences: Everyone Is Invited
Contact: Viterbi Admission
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Stochastic Regularizer for High Dimensional Small-Sampled Data and Online learning for Time Series Forecasting
Fri, Jun 14, 2019 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Sergül Aydöre, Department of Electrical and Computer Engineering of Stevens Institute of Technology
Talk Title: Stochastic Regularizer for High Dimensional Small-Sampled Data and Online learning for Time Series Forecasting
Abstract: In this talk, I will focus on developing efficient Machine Learning algorithms for two situations. One situation is high dimensional, small sampled and noisy data situations as in neuroscience, biology or geology where data collection is expensive. This phenomenon is known as curse of dimensionality that causes overfitting which is often an obstacle for using machine learning techniques. We formulated a structured stochastic regularization that relies on feature grouping. Using a fast clustering algorithm, we define a family of groups of features that capture feature covariations. Inside a stochastic gradient descent loop, we then randomly average these features. Experiments on two real-world datasets demonstrate that our approach produces models that generalize better than those trained with conventional regularizers, and also improves convergence speed, and has a linear computational cost. Another challenging situation that I will talk about is updating a machine learning model with streaming data without iterating through previously seen data. This is also known as "online learning" and one application is forecasting time series. The performance of online learning algorithms is typically evaluated by the regret. We introduce a new local regret for non-convex models in dynamic environments. We present an update rule incurring a cost, according to our proposed regret, which is sublinear in time T. Our update uses time-smoothed gradients. Using a real-world dataset we show that our time-smoothed approach yields several benefits when compared with state-of-the-art competitors: results are more stable against new data; training is more robust to hyperparameter selection; and our approach is more computationally efficient than the alternatives.
Biography: Sergul Aydore has been an Assistant Professor at the Department of Electrical and Computer Engineering of Stevens Insitute of Technology in August 2018. Before joining Stevens, Sergul was a Machine Learning Scientist at Amazon's demand forecasting where she built neural network models to predict the demands of millions of products to enable better in-stock positions. She is also an associate member of Parietal team at Inria, Saclay. She received her PhD degree from the Signal and Image Processing Institute at the University of Southern California in 2014. Her PhD work was on developing robust connectivity measures for neuroimaging data. Prior to Amazon, Sergul was a postdoctoral researcher at Columbia University where she implemented machine learning models for EEG data. She then spent a year as a Data Scientist at JP Morgan. She received her B.S. and M.S. degrees in Electrical and Electronics Engineering from Bogazici University, Istanbul where she developed signal processing techniques to investigate biomedical signals. She was a recipient of the Viterbi School of Engineering Doctoral fellowship and was recognized as a 2014 USC Ming Hsieh Institute Ph.D. Scholar.
Host: Professor Richard Leahy
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Talyia White
