IEEE Photonics Society

Boston Photonics Society Chapter

Boston Chapter of the IEEE Photonics Society

Machine Learning and Optical Systems
-- The workshop is being postponed to October due to COVID-19 --
PDF

Wednesday, April 8, 15, 22, 29 and May 6, 2020, 7:00-9:30 PM
Located at MIT Lincoln Laboratory – 3 Forbes Road, Lexington, MA, 02420, USA

Wednesday
April 22, 2020
7 PM
 

-- The workshop is being postponed due to COVID-19 --: Model-Based Machine Learning for Fiber-Optic Communication Systems"/>Add to Calendar Add to Calendar

Model-Based Machine Learning for Fiber-Optic Communication Systems

Prof. Henry D. Pfister, Duke University, Durham, NC

 

Prof. Henry D. Pfister, Duke University, Durham, NC

Abstract:  In this work, we propose a new machine-learning approach for fiber-optic systems where signal propagation is governed by the nonlinear Schrödinger equation (NLSE). Our main idea is to exploit the fact that the popular split-step method for numerically solving the NLSE has essentially the same functional form as a “deep” multi-layer neural network: in both cases, one alternates linear steps and pointwise nonlinearities. We demonstrate that this connection allows for a principled machine-learning approach by appropriately parameterizing the split-step method. This has several key advantages when compared to conventional “black-box” function approximation.  In particular, it allows us to examine the learned solution and understand why it performs well.

 

Biography:  Henry D. Pfister received his Ph.D. in electrical engineering in 2003 from the University of California, San Diego and is currently a professor in the Electrical and Computer Engineering Department of Duke University with a secondary appointment in Mathematics.  Prior to that, he was an associate professor at Texas A&M University (2006-2014), a post-doctoral fellow at the École Polytechnique Fédérale de Lausanne (2005-2006), and a senior engineer at Qualcomm Corporate R&D in San Diego (2003-2004).  His current research interests include information theory, error-correcting codes, quantum computing, and machine learning.


He received the NSF Career Award in 2008 and a Texas A&M ECE Department Outstanding Professor Award in 2010.  He is a coauthor of the 2007 IEEE COMSOC best paper in Signal Processing and Coding for Data Storage and a coauthor of a 2016 Symposium on the Theory of Computing (STOC) best paper.  He has served the IEEE Information Theory Society as a member of the Board of Governors (2019-2022), an Associate Editor for the IEEE Transactions on Information Theory (2013-2016), and a Distinguished Lecturer (2015-2016).  He was also the General Chair of the 2016 North American School of Information Theory.

 

Advance registration and fee required (Open to all IEEE members as well as non-members)

$50/$60 (IEEE Member/Non-Member) early registration fee for ten 1-hour talks over five nights; cost includes coffee and cookies each night, as well as downloadable copies of speakers slides. Early registration deadline March 18th, 2020. Post deadline fee $60/$70 (IEEE Member/Non-Member).


For more information on the technical content of the workshop, contact either:
1) Keisuke Kojima, (kojima@merl.com), Chair
2) Ajay Garg, (ajay.sinclair.garg@ieee.org), Co-Chair
3) Dean Tsang, (tsang@ieee.org), Co-Chair
4) Bill Nelson, (w.nelson@ieee.org), Co-Chair