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</p>Urgent Decision-Making refers to the process of swiftly selecting an appropriate course of action under conditions of intense time pressure, high stakes, and often incomplete, fragmented, or unreliable information. These scenarios demand not only speed but also precision. Effective decision-making in such contexts hinges on the rapid synthesis of diverse and sometimes conflicting data sources into reliable, holistic summaries that support timely and informed responses. Technically, this task presents multiple challenges: information may arrive in real time from heterogeneous sources (e.g., news reports, social media, radio dispatches), its credibility may be uncertain, and the operational environment may evolve faster than systems can adapt. While recent advances in AI have shown remarkable capabilities in general-purpose summarization, current methods fall short in urgent contexts, where the integrity of available information is often under question, and latency, even by a few minutes, can result in irreversible consequences. To address these limitations, our group is currently focusing on the following two core research problems.
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</p>AI assurance is vital to ensure systems act reliably and ethically, especially in this generative AI era. As AI gains autonomy in creating text, images, and decisions, assurance provides confidence that models behave as intended, respect societal norms, and avoid misinformation or bias. It safeguards against misuse, ensures transparency and accountability, and verifies that generative systems uphold accuracy, fairness, and trustworthiness—protecting both users and institutions in an increasingly AI-driven world. To address these challenges, our lab focuses on three distinct themes under AI Assurance.
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</p>On the alignment front, our group has focused on developing innovative methods to improve AI alignment without requiring deep technical expertise. One such idea is Alignment via Conversation, where users can engage in a natural dialogue with an AI agent to explain their alignment goals, and the agent takes care of the rest, including fine-tuning, prompt engineering, etc. Also, we introduced a standardized taxonomy called TELeR for designing and categorizing prompts in LLM benchmarking, enabling consistent comparisons across studies and enhancing understanding of how prompt design affects AI performance on complex tasks.
Below is a summary of my professional experiences.
Published in IJCNN, 2014
Md. Mustafizur Rahman, Shubhra Kanti Karmaker Santu, Md. Monirul Islam, Kazuyuki Murase
Published in IEEE Congress on Evolutionary Computation, 2014
Shubhra Kanti Karmaker Santu, Md. Mustafizur Rahman, Md. Monirul Islam, Kazuyuki Murase
Published in ACM CIKM, 2016
Shubhra Kanti Karmaker Santu, Parikshit Sondhi, ChengXiang Zhai
Published in WWW, 2017
Shubhra Kanti Karmaker Santu, Liangda Li, Dae Hoon Park, Yi Chang, ChengXiang Zhai
Published in ACM SIGIR, 2017
Shubhra Kanti Karmaker Santu, Parikshit Sondhi, ChengXiang Zhai
Published in ACM CIKM [Short Paper] , 2017
Yiren Wang, Dominic Seyler, Shubhra Kanti Karmaker Santu, ChengXiang Zhai
Published in ACM CIKM, 2018
Shubhra Kanti Karmaker Santu, Liangda Li, Yi Chang, ChengXiang Zhai
Published in WPES@ACM CCS, 2018
Shubhra Kanti Karmaker Santu, Vincent Bindschaedler, ChengXiang Zhai, Carl A. Gunter
Published in ACM SIGKDD Explorations [Position Paper] , 2018
Shubhra Kanti Karmaker Santu, C. Geigle, D. C. Ferguson, W. Cope, M. Kalantzis, D. Searsmith, Chengxiang Zhai
Published in Arxiv [Preprint] , 2019
Lei Xu, Shubhra Kanti Karmaker Santu, Kalyan Veeramachaneni
Published in ACM CIKM, 2019
Saar Kuzi, Sahiti Labhishetty, Shubhra Kanti Karmaker Santu, Prasad Pradip Joshi and ChengXiang Zhai
Published in ACL SIGNLL CoNLL, 2019
Shubhra Kanti Karmaker Santu, Kalyan Veeramachaneni, ChengXiang Zhai
Published in ICWSM [To Appear], 2019
Naeemul Hassan, Amrit Poudel, Jason Hale, Claire Hubacek, Khandakar Tasnim Huq, Shubhra Kanti Karmaker Santu, Syed Ishtiaque Ahmed
Published in ACM CIKM, 2020
Shubhra Kanti Karmaker Santu, Parikshit Sondhi, ChengXiang Zhai
Undergraduate course, Bangladesh University of Engineering and Technology, 2012
</p>I have taught 3 theory course and more than 10 Lab courses at BUET CSE department at the undergraduate level.
Graduate course, University of Illinois Urbana Champaign, 2017
</p>I worked as one of the teaching assistants of CS 510 (Advanced Information Retrieval) Course in this semester under Prof. Chengxiang Zhai. The details of the course schedule can be found here
Graduate course, Auburn University, 2022
</p>I am teaching “Natural Language Processing” as a special topic CS/ Data Science course during Spring 2022 at Auburn University. I introduced this course at Auburn University. I have designed the course syllabus, assignments, exams and projects for this course can be found here.
Undergraduate course, Auburn University, 2023
</p>I am teaching “Machine Learning” CS undergraduate course during Fall 2023 at Auburn University.
Graduate course, Auburn University, 2023
</p>I am teaching Information Retrieval as a special topic CS/ Data Science course during Fall 2023 at Auburn University. I introduced this course at Auburn University. I have designed the course syllabus, assignments, exams and projects for this course can be found here.