BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Hong_Kong BEGIN:STANDARD DTSTART:19791021T023000 TZOFFSETFROM:+0900 TZOFFSETTO:+0800 TZNAME:HKT END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260707T131751Z UID:CCDCB176-3498-4C31-B8D3-AE630C229DF0 DTSTART;TZID=Asia/Hong_Kong:20260707T153000 DTEND;TZID=Asia/Hong_Kong:20260707T163000 DESCRIPTION:Seminar: Minimum Entropy Probability Couplings\n\nSpeaker: Prof . Yanina Shkel\, École Polytechnique Fédérale de Lausanne\, Switzerland \n\nDate: 7 July 2026 (Tuesday)\n\nTime: 3:30pm - 4:30pm\n\nVenue: Rm 513\ , 5/F\, William MW Mong Engineering Building\, CUHK\n\nJointly organized b y CUHK Department of Information Engineering\, CUHK The Institute of Theor etical Computer Science and Communications\, and IEEE Information Theory S ociety Hong Kong Chapter.\n\nAbstract\n\nThe minimum entropy probability c oupling problem aims to couple m probability mass functions (PMFs) in a wa y that minimizes the entropy of the joint PMF. This problem has applicatio ns in information theory\, causal inference\, transportation polytopes\, r andomness generation\, and many more related areas. However\, the computat ion of the minimum entropy coupling is known to be NP-hard. In this talk\, we describe a number of known lower bounds on the minimum achievable entr opy using tools like majorization and information spectrum. We also discus s several approximation algorithms and their optimality gaps.\n\nBiography \n\nYanina Shkel is an Assistant Professor with École Polytechnique Féd érale de Lausanne\, where she is affiliated with the Information Processi ng Group in the School of Computer and Communication Sciences. She receive d the B.S. degree in mathematics and computer science and the M.S. and Ph. D. degrees in electrical and computer engineering from the University of W isconsin–Madison. Before graduate school\, she was a Database Developer with Morningstar Inc. During graduate school\, she spent her time with 3M Corporate Research\, as an Intern. After completing her PhD in 2014\, she was a Post-Doctoral Researcher with Princeton University and University of Illinois at Urbana–Champaign. She joined EPFL as a Scientist in 2019\, and as an Assistant Professor in 2023. Her research interests include theo retical aspects of data science: in particular\, statistics\, cryptography \, as well as information\, learning\, and coding theory. She was a recipi ent of the 2015 NSF Center for Science of Information (CSoI) Post-Doctoral Fellowship and the 2022 Swiss NSF Starting Grant.\n\nCo-sponsored by: CUH K Department of Information Engineering\, CUHK The Institute of Theoretica l Computer Science and Communications\n\nSpeaker(s): Prof. Yanina Shkel\, \n\nRoom: ERB 513\, Bldg: William M.W. Mong Engineering Building\, The Chi nese University of Hong Kong\, Hong Kong SAR of China\, Hong Kong\, Hong K ong LOCATION:Room: ERB 513\, Bldg: William M.W. Mong Engineering Building\, The Chinese University of Hong Kong\, Hong Kong SAR of China\, Hong Kong\, Ho ng Kong ORGANIZER:ctli@ie.cuhk.edu.hk SEQUENCE:13 SUMMARY:Seminar by Prof. Yanina Shkel: Minimum Entropy Probability Coupling s URL;VALUE=URI:https://events.vtools.ieee.org/m/566687 X-ALT-DESC:Description: <br /><p>Seminar: Minimum Entropy Probability Coupl ings</p>\n<p>Speaker: Prof. Yanina Shkel\, &Eacute\;cole Polytechnique F&e acute\;d&eacute\;rale de Lausanne\, Switzerland</p>\n<p>Date: 7 July 2026 (Tuesday)</p>\n<p>Time: 3:30pm - 4:30pm</p>\n<p>Venue: Rm 513\, 5/F\, Will iam MW Mong Engineering Building\, CUHK</p>\n<p>Jointly organized by CUHK Department of Information Engineering\, CUHK The Institute of Theoretical Computer Science and Communications\, and IEEE Information Theory Society Hong Kong Chapter.</p>\n<p><br>Abstract</p>\n<p>The minimum entropy probab ility coupling problem aims to couple m probability mass functions (PMFs) in a way that minimizes the entropy of the joint PMF. This problem has app lications in information theory\, causal inference\, transportation polyto pes\, randomness generation\, and many more related areas. However\, the c omputation of the minimum entropy coupling is known to be NP-hard. In this talk\, we describe a number of known lower bounds on the minimum achievab le entropy using tools like majorization and information spectrum. We also discuss several approximation algorithms and their optimality gaps.</p>\n <p><br>Biography</p>\n<p>Yanina Shkel is an Assistant Professor with &Eacu te\;cole Polytechnique F&eacute\;d&eacute\;rale de Lausanne\, where she is affiliated with the Information Processing Group in the School of Compute r and Communication Sciences. She received the B.S. degree in mathematics and computer science and the M.S. and Ph.D. degrees in electrical and comp uter engineering from the University of Wisconsin&ndash\;Madison. Before g raduate school\, she was a Database Developer with Morningstar Inc. During graduate school\, she spent her time with 3M Corporate Research\, as an I ntern. After completing her PhD in 2014\, she was a Post-Doctoral Research er with Princeton University and University of Illinois at Urbana&ndash\;C hampaign. She joined EPFL as a Scientist in 2019\, and as an Assistant Pro fessor in 2023. Her research interests include theoretical aspects of data science: in particular\, statistics\, cryptography\, as well as informati on\, learning\, and coding theory. She was a recipient of the 2015 NSF Cen ter for Science of Information (CSoI) Post-Doctoral Fellowship and the 202 2 Swiss NSF Starting Grant.</p> END:VEVENT END:VCALENDAR