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DTSTART;TZID=America/Chicago:20260515T120000
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DESCRIPTION:Abstract: Artificial Intelligence has quickly become a powerful
  tool for addressing complex systems problems\, and networking has been no
  exception. Yet\, the journey from promising idea to practical impact ofte
 n reveals unexpected challenges and deeper insights. This reflects lessons
  learned from applying AI to networking problems\, drawing on experiences 
 from our Maestro and Wixor projects. These efforts tackled issues such as 
 Wi-Fi QoE orchestration and cellular (5G) resource scheduling\, showing bo
 th the promise and the limits of AI in networked systems. Several themes e
 merged across these projects: the critical role of formulating the right l
 earning problem\, the tradeoffs between model complexity and system usabil
 ity\, and the value of embedding domain knowledge into AI-driven solutions
 . Along the way\, we also discovered that applying AI to networking is not
  just about better algorithms—it can reshape how we think about networks
  themselves. This talk will take a step back to distill these experiences 
 into broader reflections: when does AI truly help in networking\, what pit
 falls should we anticipate\, and how can we design systems that are both i
 ntelligent and trustworthy?\n\nCo-sponsored by: Rice University ECE and IE
 EE Student Branch\n\nSpeaker(s): Puneet Sharma\n\nAgenda: \nPresentation a
 t 12:00 to 1:00 pm CDT at O'Connor Building for Engineering and Science\, 
 Room 406 at Rice University\n\nRoom: 406\, Bldg: O'Connor Building for Eng
 ineering and Science\, Rice University\, 6100 Main Street\, Houston\, Texa
 s\, United States\, 77005
LOCATION:Room: 406\, Bldg: O'Connor Building for Engineering and Science\, 
 Rice University\, 6100 Main Street\, Houston\, Texas\, United States\, 770
 05
ORGANIZER:cavallar@rice.edu
SEQUENCE:37
SUMMARY:Networking and Distributed Systems at HPE Labs
URL;VALUE=URI:https://events.vtools.ieee.org/m/559386
X-ALT-DESC:Description: <br /><p>Abstract: Artificial Intelligence has quic
 kly become a powerful tool for addressing complex systems problems\, and n
 etworking has been no exception. Yet\, the journey from promising idea to 
 practical impact often reveals unexpected challenges and deeper insights. 
 This reflects lessons learned from applying AI to networking problems\, dr
 awing on experiences from our Maestro and Wixor projects. These efforts ta
 ckled issues such as Wi-Fi QoE orchestration and cellular (5G) resource sc
 heduling\, showing both the promise and the limits of AI in networked syst
 ems. Several themes emerged across these projects: the critical role of fo
 rmulating the right learning problem\, the tradeoffs between model complex
 ity and system usability\, and the value of embedding domain knowledge int
 o AI-driven solutions. Along the way\, we also discovered that applying AI
  to networking is not just about better algorithms&mdash\;it can reshape h
 ow we think about networks themselves. This talk will take a step back to 
 distill these experiences into broader reflections: when does AI truly hel
 p in networking\, what pitfalls should we anticipate\, and how can we desi
 gn systems that are both intelligent and trustworthy?</p>\n<p><span style=
 "font-size: 11.0pt\; font-family: 'Aptos'\,sans-serif\; mso-fareast-font-f
 amily: Aptos\; mso-fareast-theme-font: minor-latin\; mso-bidi-font-family:
  Aptos\; mso-ansi-language: EN-US\; mso-fareast-language: EN-US\; mso-bidi
 -language: AR-SA\;"><br style="mso-special-character: line-break\;"><!-- [
 if !supportLineBreakNewLine]--><br style="mso-special-character: line-brea
 k\;"><!--[endif]--></span></p><br /><br />Agenda: <br /><p>Presentation at
  12:00 to 1:00 <span class="lw_end_time">pm</span>&nbsp\;<span class="lw_c
 al_tz_abbrv tz_editable">CDT at O'Connor Building for Engineering and Scie
 nce\, Room 406 at Rice University</span></p>
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