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<p class="MsoNormal" style="line-height:115%"><span style="font-size:13.5pt;line-height:115%;font-family:"Arial",sans-serif;color:black"><img width="600" height="171" style="width:6.25in;height:1.7812in" id="Picture_x0020_1" src="cid:image001.png@01DB7373.82065D10" alt="Dissertation Defense Announcement at the Cullen College of Engineering"></span><span style="font-size:13.5pt;line-height:115%;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="font-size:18.0pt;font-family:"Times New Roman",serif;color:#C8102E;mso-ligatures:none">Adaptive, Federated and Resilient Health Monitoring via Novel Online Collaborative Learning Algorithms
</span></b><span style="font-size:12.0pt;font-family:"Times New Roman",serif;mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="font-size:13.5pt;font-family:"Times New Roman",serif;color:black;mso-ligatures:none">Tanapol Kosolwattana</span></b><span style="font-size:13.5pt;font-family:"Times New Roman",serif;mso-ligatures:none"><o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">February 5, 2025; 2:30 p.m. to 4:30 p.m. (CST)<br>
Location: Mechanical Engineering Conference Room 202</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Microsoft Teams Link:
</span><span style="color:black;mso-ligatures:none;mso-fareast-language:ZH-TW"><a href="https://urldefense.com/v3/__https://teams.microsoft.com/l/meetup-join/19*3ameeting_Mjk0Njk2MzYtZTJiZi00ZmE0LWIxYjItMGE0NzI3ZDc0Njdk*40thread.v2/0?context=*7b*22Tid*22*3a*22170bbabd-a2f0-4c90-ad4b-0e8f0f0c4259*22*2c*22Oid*22*3a*2258acac18-0128-4198-b4a0-29a441635034*22*7d__;JSUlJSUlJSUlJSUlJSUl!!LkSTlj0I!AVfuOS0BXr0HeuBpFvCjRsiZxQ6wU0UuVAw0zg20pl588xDRw3XJ8X6SqG5satFrFtn2aDaXLTb0nN9CI1B-M5jeKb8$"><b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:blue;mso-fareast-language:EN-US">Join
the meeting</span></b></a></span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Committee Chair:</span></b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">
<br>
Ying Lin, Ph.D.</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Committee Members:</span></b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><br>
Gino Lim, Ph.D. | Qianmei (May) Feng, Ph.D. | Renjie Hu, Ph.D. | Huazheng Wang, Ph.D.
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<b><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#C8102E;mso-ligatures:none">Abstract</span></b><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#C8102E;mso-ligatures:none"><o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Adaptive monitoring of a large population of dynamic processes is critical for the timely detection of abnormal events under limited resources. This issue is pervasive
in various sectors, including healthcare and engineering systems, due to the disparity between available monitoring resources and the large population of units, and the uncertain and heterogeneous dynamics of unit progression. To effectively address this problem,
in this dissertation, we introduce advanced methodologies for designing adaptive monitoring strategies. We first develop an online collaborative learning framework that efficiently models and monitors a population of dependent units under resource constraints.
We then develop a decentralized online collaborative framework that enables online modeling and monitoring of units with latent dynamics while preserving data privacy. Finally, we develop a novel online robust collaborative algorithm designed to capture latent
structures inherent in the population from sequentially observed data corrupted by adversarial attacks. We have demonstrated the effectiveness of the proposed methods through rigorously proven theoretical analysis and experiments, including simulation studies
and real-world world applications including cognitive degradation monitoring in Alzheimer’s Disease (AD) and battery degradation monitoring.</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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