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DTSTART:20260308T030000
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DTSTART:20251102T010000
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DTSTAMP:20251128T011449Z
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DTSTART;TZID=America/New_York:20251121T120000
DTEND;TZID=America/New_York:20251121T133000
DESCRIPTION:AI for Behavioral Health: Machine Learning Approaches to Alcoho
 l and Marijuana Intoxication Detection\nArtificial intelligence is opening
  new frontiers in understanding human behavior through data. This talk exp
 lores how advances in machine learning and sensor analytics are transformi
 ng behavioral health assessment\, with a focus on detecting alcohol and ma
 rijuana intoxication from motion and physiological data.\n\nDrawing from i
 nterdisciplinary research across data science\, healthcare\, and behaviora
 l studies\, the session will walk through real-world AI pipelines—from d
 ata preprocessing and feature engineering to deep learning models like CNN
 s and Vision Transformers. Attendees will also gain insights into ethical 
 and regulatory considerations\, model interpretability\, and deployment ch
 allenges.\n\nThe talk will highlight how AI-driven behavioral analytics ca
 n inform clinical care\, improve community safety\, and expand our broader
  understanding of human cognition and behavior.\n\n[]\n\nSpeaker(s): Samue
 l\, \n\nVirtual: https://events.vtools.ieee.org/m/506597
LOCATION:Virtual: https://events.vtools.ieee.org/m/506597
ORGANIZER:aalmadani@wpi.edu
SEQUENCE:19
SUMMARY:AI for Behavioral Health – Intoxication Detection
URL;VALUE=URI:https://events.vtools.ieee.org/m/506597
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-bottom: .0
 001pt\;&quot;&gt;&lt;strong&gt;AI for Behavioral Health: Machine Learning Approaches to 
 Alcohol and Marijuana Intoxication Detection&lt;/strong&gt;&lt;br&gt;Artificial intell
 igence is opening new frontiers in understanding human behavior through da
 ta. This talk explores how advances in machine learning and sensor analyti
 cs are transforming behavioral health assessment\, with a focus on detecti
 ng alcohol and marijuana intoxication from motion and physiological data.&lt;
 /p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-bottom: .0001pt\;&quot;&gt;Drawing from in
 terdisciplinary research across data science\, healthcare\, and behavioral
  studies\, the session will walk through real-world AI pipelines&amp;mdash\;fr
 om data preprocessing and feature engineering to deep learning models like
  CNNs and Vision Transformers. Attendees will also gain insights into ethi
 cal and regulatory considerations\, model interpretability\, and deploymen
 t challenges.&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-bottom: .0001pt\;&quot;&gt;T
 he talk will highlight how AI-driven behavioral analytics can inform clini
 cal care\, improve community safety\, and expand our broader understanding
  of human cognition and behavior.&lt;/p&gt;\n&lt;p&gt;&lt;img src=&quot;https://events.vtools.
 ieee.org/vtools_ui/media/display/b42d604e-4b6d-4995-a6d0-8af6afd16bbf&quot; alt
 =&quot;&quot; width=&quot;679&quot; height=&quot;679&quot;&gt;&lt;/p&gt;
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