IEEE INFOCOM 2024

Program at a Glance

IEEE INFOCOM 2024

Session Opening: Opening, Awards, and Keynote

Session Break-1-1: Coffee Break Session Break-1-2: Coffee Break

Session A-1: A-1: Network Privacy Session A-2: A-2: Blockchains Session A-3: A-3: Video Streaming

Session B-1: B-1: Radio Access Networks Session B-2: B-2: MIMO and Beamforming Session B-3: B-3: Satellite networks

Session C-1: C-1: UAV networking Session C-2: C-2: Wireless Security Session C-3: C-3: Intrusion-Detection Systems

Session D-1: D-1: Federated Learning 1 Session D-2: D-2: Multi-Armed Bandits Session D-3: D-3: Federated Learning 2

Session E-1: E-1: Network Measurement Session E-2: E-2: Scheduling 1 Session E-3: E-3: Scheduling 2

Session F-1: F-1: Network Security 1 Session F-2: F-2: Network Security 2 Session F-3: F-3: Network Security 3

Session Lunch-1: Conference Lunch (for Registered Attendees)

Session Reception: Welcome Reception (for Registered Attendees)

Session Opening

Opening, Awards, and Keynote

Conference

8:00 AM — 10:00 AM PDT

Local

May 21 Tue, 11:00 AM — 1:00 PM EDT

Location

Regency C/D

Networking Research in the Age of AI/ML: More Science, Less Hubris

Dr. Walter Willinger (Chief Scientist at NIKSUN, Inc.)

The raison d’être of science is to understand cause, not just effect. However, for many empirically observed Internet phenomena, establishing cause typically requires great diligence and significant efforts and involves (i) conjecturing physical explanations of the observed phenomenon, (ii) empirically validating these explanations (typically with new data that didn’t figure in the original discovery of the phenomenon), and (iii) demonstrating their capability to withstand the scrutiny of domain experts. For each of the recent decades of Internet research, I will illustrate with a concrete example where this approach has either been successful or where challenges remain. These examples include the observed ``self-similar” nature of measured Internet traffic (from the 1990s), the purported ``scale-free” nature of measured Internet router topologies (from the 2000s), and the elusive nature of the Internet’s peering fabric or AS-level topology (from the 2010s). As for the current decade that is experiencing a boom in the use of AI/ML for networking, I will discuss why this area calls for a drastic course correction that does away with the existing unfettered pursuit of black-box modeling and instead extolls the virtues of new learning models that we can understand, explain, and ultimately trust.

Speaker Dr. Walter Willinger, Chief Scientist at NIKSUN, Inc.

Walter Willinger received the Dipl. Math. degree from the ETH Zurich and M.S. and Ph.D. degrees in Operations Research and Industrial Engineering from Cornell University. He worked at Bellcore Applied Research and AT&T Labs-Research and is currently Chief Scientist at NIKSUN, Inc., a Princeton-based company developing industry-leading real-time and forensics-based cybersecurity and network performance solutions. An internationally recognized expert in the field of Internet measurement, Dr. Willinger has made pioneering contributions to the understanding of the temporal nature of real-world Internet traffic and the spatial structure of the physical and logical topologies of today’s Internet. The resulting groundbreaking insights have informed generations of researchers and engineers to design their new protocols, applications, and systems around facets of Internet behavior that do not change amidst an otherwise highly dynamic and uncertain environment. His research garnered the 1995 IEEE Communications Society (ComSoc) W.R. Bennett Prize Paper Award, the 1996 IEEE W.R.G. Baker Prize Award, two ACM/SIGCOMM Test-of-Time Paper Awards (2005 & 2016), the 2023 Applied Networking Research Prize, and his paper “On the self-similar nature of Ethernet traffic” was featured in the 2007 IEEE ComSoc publication “The Best of the Best—Fifty Years of Communications and Networking Research”, as one of the most influential papers in communications and networking in the last half century. He is a Fellow of IEEE, ACM, AT&T, SIAM, and AAIA.

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Program at a Glance