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<p class="MsoNormal"><span style="font-size:11.0pt">RESEARCH PROPOSAL SEMINAR NOTICE<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">GEOSPATIAL COMPUTER SCIENCE PROGRAM<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">SCHOOL OF ENGINEERING AND COMPUTING SCIENCES<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">TEXAS A&M UNIVERSITY-CORPUS CHRISTI<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">SUBJECT: Augmenting Reliability in Connected Autonomous Vehicles Through Multimodal Learning Methods<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">SPEAKER: Abdolazim Rezaei<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">DATE: 12/18/2025<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">TIME: 9 AM – 11 AM<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">PLACE: CI-122<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-bottom:12.0pt"><span style="font-size:10.5pt;font-family:"Segoe UI",sans-serif;color:black">Join Zoom Meeting<br>
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<p class="MsoNormal"><span style="font-size:11.0pt">ABSTRACT<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Connected and Autonomous Vehicles (CAVs) are a key part of future Intelligent Transportation Systems (ITS). They use multiple sensors and communication links to perceive the environment, make decisions, and
cooperate with vehicles and infrastructure. They must operate safely in dynamic and uncertain conditions, so reliability is essential. Current CAV pipelines still face major reliability limits. Sensors can fail or degrade in bad weather or due to hardware
issues. Communication can become unstable because of congestion or limited coverage. Privacy rules can also restrict how visual data are stored and used. Many existing models assume ideal sensors and stable networks, and they often require high compute and
are hard to adapt. Multimodal learning can improve reliability by combining information from LiDAR, radar, cameras, GNSS, and other signals. Parameter-efficient methods can also help deployment on edge devices. This proposal introduces three contributions:
PEFT-DML for robust 3D detection, RL-MoE for privacy-preserving vision-to-text, and PEHT for multimodal network traffic prediction.<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:10.5pt;font-family:"Segoe UI",sans-serif;color:black">Abdolazim Rezaei is inviting you to a scheduled Zoom meeting.<br>
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