המרכז לפיתוח קריירה של אוניברסיטת תל אביב מזמינים אתכם להתניע את הקריירה שלכם!

מפגש עם עשרות חברות מובילות במשק הישראלי, בתחומי הטכנולוגיה וההייטק, המגיעות לקמפוס על מנת להכיר אתכם – הסטודנטים האיכותיים ביותר מתחומי ההנדסה והמדעים המדויקים.

8.1.18 יריד תעסוקה טכנולוגי, 17:00-10:00, אולם סמולרש, אוניברסיטת תל אביב

 (The talk will be given in English)

Speaker:     Dr. Lior Weizman
                    Faculty of EE, Technion and the FMRIB research facility in Oxford University

Monday, January 8^th, 2018
15:00 - 16:00

Room 011, Kitot Bldg., Faculty of Engineering

Breaking the barriers of MRI by exploiting signals structure

Abstract

Magnetic Resonance Imaging (MRI) is an interdisciplinary field involving physics, engineering, chemistry, mathematics, and neuroscience. It is the best imaging modality for soft tissues and is considered safe as there is no exposure to ionizing radiation. However, it is still highly limited by the physics, in terms of slow scanning time, limited resolution and lack of quantitative information. Recent advances in signal processing theories have enabled breaking those barriers.

I will start the talk by presenting the physical principles of MRI and the conventional solutions to improve MRI by undersampling. I will describe several MRI applications, including structural, functional and quantitative MRI, each involves a unique structure of acquired data. I will show how the methods we developed, that rely on exploiting redundant information within and between MRI scans via sparse-based reconstruction and low-rank modeling, can significantly improve each of those applications. I will show that the theoretical bounds we developed for our methods outperform bounds developed for existing approaches and support the results obtained.  

Bio
Lior Weizman is currently a research associate in the Faculty of EE, Technion and the FMRIB research facility in Oxford University. He holds a Ph.D. from the Computer Science and Engineering School at the Hebrew University and M.Sc. and B.Sc. from the EE department at Ben-Gurion University.His research interests include signal processing, magnetic resonance imaging (MRI) and applications of compressed sensing for medical imaging. He previously worked as a signal-processing algorithms developer in various companies in Israel, including RAFAEL, and has served as an advisor for medical imaging and algorithmic solution companies. He is the recipient of the British-Technion Society Fellowship for postdoctoral studies in the UK, the Eshkol and National Foundation for scientific research and engineering Fellowships and the Viterbi Fellowship for postdoctoral studies.

(The talk will be given in English)

Speaker:     Dr. Lele Wang
                   Stanford University

Monday, January 1^st, 2018
15:00 - 16:00

Room 011, Kitot Bldg., Faculty of Engineering

Structure and Complexity Aspects of Communication and Detection

Abstract

Seven decades ago, Shannon established the fundamental limits of point-to-point communication. Since then, designing explicit code structures with low encoding/decoding complexity for practical communication systems has been an active research area. Recently, two classes of low-complexity codes--polar codes and spatially coupled LDPC codes--were shown to achieve the theoretical limits of point-to-point communication. However, in various other communication and detection scenarios, despite decades of research, there is still a gap between theory and practice. Advanced coding schemes in Shannon theory, which are shown to significantly boost the theoretical performance, typically incur a prohibitive computational complexity. Consequently, designing low-complexity implementable coding schemes that achieve optimal theoretical rates is a key challenge for practical system design.

In this talk, I will showcase how to design appropriate code structures in various distinct communication and detection problems, including interference management, universal channel coding, and phase detection for positioning systems. Leveraging tools from information theory, coding theory, communication theory, graph theory and combinatorics, these schemes achieve optimal theoretical rates while maintaining an implementable encoding/decoding complexity. I will conclude by briefly  discussing future research directions in emerging data science applications through the lens of structure and complexity. 
 

Bio
Lele Wang is a postdoctoral researcher in Electrical Engineering at Stanford University. She spent one year at Tel Aviv University before joining Stanford, also as a postdoctoral researcher. She received the Ph.D. degree in Communication Theory and Systems at the University of California, San Diego. Her research interests include information theory, coding theory, communication theory, graph theory and combinatorics. She is a recipient of the 2013 UCSD Shannon Memorial Fellowship, the 2013-2014 Qualcomm Innovation Fellowship, and the 2017 Center for Science of Information (CSoI) Postdoctoral Fellowship. Her Ph.D. thesis "Channel coding techniques for network communication" won the 2017 IEEE Information Theory Society Thomas M. Cover Dissertation Award.