With a broad interest in the academic field of artificial intelligence and data science, Karmaker's primary focus lies at the intersection of natural language processing and information retrieval. More specifically, his research is primarily driven by the following broad research question: “How can we make AI and data science more accessible and useful to the end users in order to democratize AI to a broader audience?”

Karmaker completed his Ph.D. in computer science from the University of Illinois Urbana Champaign and was then a postdoctoral research associate in the Laboratory for Information and Decision Systems at the Massachusetts Institute of Technology. During his Ph.D., he also worked as a summer research intern at Microsoft Research, Yahoo Research, and Walmart Labs. As a researcher, Karmaker has published more than 30 peer-reviewed research articles at premier venues, including ACL, EMNLP, SIGIR, WWW, TMLR, COLING, CoNLL, AACL, CIKM, IUI, ACM TIST, and ACM Computing Surveys. To support his research, Karmaker has brought more than $1.4 million in total grants as the Lead PI from multiple funding agencies, including the National Science Foundation, Air Force Office of Scientific Research, Army Research Office and U.S. Department of Agriculture.

He serves as an action editor for the ACL Rolling Review Initiative (ARR) and also as the communication chair of the ARR initiative. Karmaker also served as the tutorial chair for CIKM 2022. He has served regularly as a program committee member for ACL and SIGIR-sponsored conferences for the last five years.

• [EMNLP 2024 (Findings)]. Yash Mahajan, Naman Bansal, Eduardo Blanco, Santu Karmaker." ALIGN-SIM: A Task-Free Test Bed for Evaluating and Interpreting Sentence Embeddings through Semantic Similarity Alignment." In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 7393–7428, Miami, Florida, USA.


• [TMLR 2024] Md. Mahadi Hassan, Alex Knipper, Shubhra Kanti Karmaker Santu. "Introducing 'Forecast Utterance' for Conversational Data Science." In Transactions on Machine Learning Research, 2024.


• [FSE 2024]: Md. Mahadi Hassan, John Salvador, Shubhra Kanti Karmaker Santu, Akond Rahman. “State Reconciliation Defects in Infrastructure as Code”. In ACM International Conference on the Foundations of Software Engineering (FSE), 2024 (To appear).


• [ICPE 2024]: Souvika Sarkar, Mohammad Fakhruddin Babar, Md. Mahadi Hassan, Monowar Hasan, Shubhra Kanti Karmaker Santu. “Processing Natural Language on Embedded Devices: How Well Do Modern Models Perform?” In International Conference on Performance Engineering (ICPE), 2024 (To appear).


• [EMNLP 2023]: Souvika Sarkar, Dongji Feng and Shubhra Kanti Karmaker Santu. “Zero-Shot Multi-Label Topic Inference with Sentence Encoders”. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: 16218-16233. (Acceptance Rate: 1047/4909 = 21.3%).


• [EMNLP 2023]: Mousumi Akter, Souvika Sarkar and Shubhra Kanti Karmaker Santu. “On Evaluation of Bangla Word Analogies”. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: 13121-13127. (Acceptance Rate: 1047/4909 = 21.3%).


• [EMNLP 2023]: Shubhra Kanti Karmaker Santu and Dongji Feng. “TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks”. In Findings of the 2023 Conference on Empirical Methods in Natural Language Processing: 14197-14203.(Acceptance Rate: 1060/3862 = 27.4%).


• [TIST 2023]: S. Sarkar, B. S. Bijoy, S. J. Saba, D. Feng, Y. Mahajan, S. R. Islam, Md. R. Amin and Shubhra Kanti Karmaker Santu. “Ad-Hoc Monitoring of COVID-19 Global Research Trends for Well-Informed Policy Making”. ACM Transactions on Intelligent Systems and Technology, 14(2), pp.1-28. (Impact Factor: 10.5).


• [ACL 2022]: M. Akter, N. Bansal, Shubhra Kanti Karmaker Santu. “Revisiting Automatic Evaluation of Extractive Summarization Task: Can We Do Better than ROUGE?” In Findings of the Association for Computational Linguistics: ACL 2022 (pp. 1547-1560). (Acceptance Rate: 361/2677 = 13.49%).


• [CSUR 2022]: Shubhra Kanti Karmaker Santu, Md. Mahadi Hassan, Micah J. Smith, Lei Xu, ChengXiang Zhai, Kalyan Veeramachaneni. “AutoML to Date and Beyond: Challenges and Opportunities”. ACM Computing Surveys (CSUR), 54(8), pp.1-36. (Impact Factor: 16.6)