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VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VEVENT
DTSTAMP:20260124T050559Z
UID:652FE3CD-6BDF-46BC-886A-C0AAF5A475F5
DTSTART;TZID=Etc/UTC:20250619T120000
DTEND;TZID=Etc/UTC:20250619T130000
DESCRIPTION:[LLMs\, Optimization\, and Game Theory]\n\nSpecial Presentation
  by Dr. Marios Avgeris (U. of Amsterdam\, the Netherlands)\n\nHosted by th
 e Future Networks Artificial Intelligence &amp; Machine Learning (AIML) Workin
 g Group\n\nDate/Time: Thursday\, 19 June 2025 @ 12:00 UTC\n\nTopic:\n\nNet
 works for AI\n\nAbstract:\n\nAs AI workloads become increasingly distribut
 ed\, data-intensive\, and latency-sensitive\, the symbiotic relationship b
 etween networking and artificial intelligence is reshaping the design and 
 operation of modern communication infrastructures. This talk explores the 
 emerging paradigm of “Networks for AI”\, where networks are no longer 
 passive conduits but active enablers of scalable\, secure\, and efficient 
 AI deployment. We will touch upon how AI augments network performance but 
 mainly how modern network architectures are evolving to meet the stringent
  demands of AI training and inference. Emphasis will be placed on AI-nativ
 e networking principles\, high-performance fabrics for AI workloads\, and 
 in-network machine learning using programmable data planes. We also discus
 s key standardization efforts and recent research directions. Through this
  lens\, the talk highlights how telecom and AI co-evolve to unlock new cap
 abilities for both consumer and enterprise applications.\n\nSpeaker:\n\n[]
 \nMarios Avgeris : I am currently holding an Assistant Professor position 
 with the Informatics Institute (IvI) at the University of Amsterdam (UvA)\
 , carrying out work in the Multiscale Networked Systems (MNS) group. My re
 search focuses in next-generation network management and orchestration\, c
 ombining techniques from machine learning\, and control theory. Specifical
 ly\, I focus on designing intelligent\, self-adaptive network architecture
 s that leverage advanced AI techniques and/or rigorous control-theoretic p
 rinciples to optimize performance and formally guarantee reliability. Rece
 ntly\, I have also been exploring the development of goal-oriented\, seman
 tic communication frameworks and the integration of generative AI to enabl
 e proactive network optimization\, digital twinning\, and zero-touch servi
 ce management. I have received my diploma from the Department of Electrica
 l and Computer Engineering (ECE) at the National Technical University of A
 thens (NTUA)\, Greece\, in 2016\, and my PhD from the same department in 2
 021. All this time at NTUA I was embedded at the NETwork Management and Op
 timal DEsign (NETMODE) Lab. From 2022 to 2024\, I was a Postdoctoral Fello
 w with the Department of Systems and Computer Engineering\, Carleton Unive
 rsity\, Ottawa\, Canada and the Software and Information Technology Engine
 ering department at the École de Technologie Supérieure (ÉTS)\, Montrea
 l\, Canada. In parallel\, I worked at Ericsson Canada. During that time\, 
 I was also awarded the CU-PSAC Postdoctoral Fellow Research Award.\n\nCo-s
 ponsored by: Future Networks Artificial Intelligence &amp; Machine Learning (A
 IML) Working Group\n\nVirtual: https://events.vtools.ieee.org/m/485450
LOCATION:Virtual: https://events.vtools.ieee.org/m/485450
ORGANIZER:baw@ieee.org
SEQUENCE:47
SUMMARY:Networks for AI
URL;VALUE=URI:https://events.vtools.ieee.org/m/485450
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: .25in
 \;&quot;&gt;&lt;img src=&quot;https://events.vtools.ieee.org/vtools_ui/media/display/16dcf
 e5b-b87e-4f17-97c8-09562b53c34f&quot; alt=&quot;LLMs\, Optimization\, and Game Theor
 y&quot; width=&quot;750&quot; height=&quot;197&quot;&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: 
 12.0pt\;&quot;&gt;Special Presentation by&lt;strong&gt; Dr. Marios Avgeris (U. of Amster
 dam\, the Netherlands)&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-to
 p: 12.0pt\;&quot;&gt;Hosted by the Future Networks&lt;strong&gt; Artificial Intelligence
  &amp;amp\; Machine Learning (AIML) Working Group&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoN
 ormal&quot; style=&quot;margin-top: 12.0pt\;&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 14.0pt
 \; font-family: Copperplate\; mso-fareast-font-family: PMingLiU\; mso-fare
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 me-font: minor-bidi\; mso-ansi-language: EN-US\; mso-fareast-language: ZH-
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 t-size: 12.0pt\; font-family: &#39;Calibri&#39;\,sans-serif\; mso-ascii-theme-font
 : minor-latin\; mso-fareast-font-family: PMingLiU\; mso-fareast-theme-font
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 : Arial\; mso-bidi-theme-font: minor-bidi\; mso-ansi-language: EN-US\; mso
 -fareast-language: ZH-TW\; mso-bidi-language: AR-SA\;&quot;&gt;: &lt;strong&gt;Thursday\
 , 19 June 2025&lt;/strong&gt;&lt;strong&gt; @ 12:00 UTC&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=
 &quot;MsoNormal&quot; style=&quot;margin-top: .25in\;&quot;&gt;&lt;strong&gt;&lt;u&gt;&lt;span style=&quot;font-size:
  16.0pt\; font-family: Copperplate\;&quot;&gt;Topic&lt;/span&gt;&lt;/u&gt;&lt;/strong&gt;&lt;strong&gt;&lt;sp
 an style=&quot;font-size: 16.0pt\; font-family: Copperplate\;&quot;&gt;:&lt;/span&gt;&lt;/strong
 &gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 16pt\;&quot;&gt;Networ
 ks for AI&amp;nbsp\;&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-t
 op: .25in\;&quot;&gt;&lt;strong&gt;&lt;u&gt;&lt;span style=&quot;font-size: 16.0pt\; font-family: Copp
 erplate\;&quot;&gt;Abstract&lt;/span&gt;&lt;/u&gt;&lt;/strong&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 16.
 0pt\; font-family: Copperplate\;&quot;&gt;:&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNorm
 al&quot;&gt;As AI workloads become increasingly distributed\, data-intensive\, and
  latency-sensitive\, the symbiotic relationship between networking and art
 ificial intelligence is reshaping the design and operation of modern commu
 nication infrastructures. This talk explores the emerging paradigm of &amp;ldq
 uo\;Networks for AI&amp;rdquo\;\, where networks are no longer passive conduit
 s but active enablers of scalable\, secure\, and efficient AI deployment. 
 We will touch upon how AI augments network performance but mainly how mode
 rn network architectures are evolving to meet the stringent demands of AI 
 training and inference. Emphasis will be placed on AI-native networking pr
 inciples\, high-performance fabrics for AI workloads\, and in-network mach
 ine learning using programmable data planes. We also discuss key standardi
 zation efforts and recent research directions. Through this lens\, the tal
 k highlights how telecom and AI co-evolve to unlock new capabilities for b
 oth consumer and enterprise applications.&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;&lt;span style=&quot;fon
 t-size: 16.0pt\; font-family: Copperplate\;&quot;&gt;&lt;u&gt;Speaker&lt;/u&gt;:&lt;/span&gt;&lt;/stron
 g&gt;&lt;/p&gt;\n&lt;table style=&quot;border-collapse: collapse\; width: 100%\;&quot; border=&quot;1
 &quot;&gt;&lt;colgroup&gt;&lt;col style=&quot;width: 21.017274%\;&quot;&gt;&lt;col style=&quot;width: 78.886756%
 \;&quot;&gt;&lt;/colgroup&gt;\n&lt;tbody&gt;\n&lt;tr&gt;\n&lt;td&gt;&lt;img src=&quot;https://events.vtools.ieee.o
 rg/vtools_ui/media/display/a21ac998-5dd1-44fb-8a01-f6620cd32f4b&quot; alt=&quot;&quot; wi
 dth=&quot;240&quot; height=&quot;255&quot;&gt;&lt;/td&gt;\n&lt;td&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top
 : 6.0pt\;&quot;&gt;&lt;strong&gt;Marios Avgeris&lt;/strong&gt; : I am currently holding an Ass
 istant Professor position with the Informatics Institute (IvI) at the Univ
 ersity of Amsterdam (UvA)\, carrying out work in the Multiscale Networked 
 Systems (MNS) group. My research focuses in next-generation network manage
 ment and orchestration\, combining techniques from machine learning\, and 
 control theory. Specifically\, I focus on designing intelligent\, self-ada
 ptive network architectures that leverage advanced AI techniques and/or ri
 gorous control-theoretic principles to optimize performance and formally g
 uarantee reliability. Recently\, I have also been exploring the developmen
 t of goal-oriented\, semantic communication frameworks and the integration
  of generative AI to enable proactive network optimization\, digital twinn
 ing\, and zero-touch service management.&amp;nbsp\;I have received my diploma 
 from the Department of Electrical and Computer Engineering (ECE) at the Na
 tional Technical University of Athens (NTUA)\, Greece\, in 2016\, and my P
 hD from the same department in 2021. All this time at NTUA I was embedded 
 at the NETwork Management and Optimal DEsign (NETMODE) Lab. From 2022 to 2
 024\, I was a Postdoctoral Fellow with the Department of Systems and Compu
 ter Engineering\, Carleton University\, Ottawa\, Canada and the Software a
 nd Information Technology Engineering department at the &amp;Eacute\;cole de T
 echnologie Sup&amp;eacute\;rieure (&amp;Eacute\;TS)\, Montreal\, Canada. In parall
 el\, I worked at Ericsson Canada. During that time\, I was also awarded th
 e CU-PSAC Postdoctoral Fellow Research Award.&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; st
 yle=&quot;margin-top: 6.0pt\;&quot;&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;/tbody&gt;\n&lt;/table&gt;
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