A Deep Learning Framework for Ensuring Responsible Play in Skill-based Cash Gaming. - Deepanshi Seth; Sharanya Eswaran; Tridib Mukherjee; Mridul Sachdeva (ICMLA, 2020)

Game Action Modeling for Fine Grained Analyses of Player Behavior in Multi-player Card Games (Rummy as Case Study). - Sharanya Eswaran; Mridul Sachdeva; Vikram Vimal; Deepanshi Seth; Suhaas Kalpam; Sanjay Agarwal; Tridib Mukherjee; Samrat Dattagupta (KDD, 2020)

GAIM: Game Action Information Mining Framework for Multiplayer Online Card Games (Rummy as Case Study). - Sharanya Eswaran; Vikram Vimal; Deepanshi Seth; Tridib Mukherjee (PAKDD (2), 2020)

AI Based Information Retrieval System for Identifying Harmful Online Gaming Patterns. - Deepanshi Seth; Rukma Talwadker; Tridib Mukherjee; Usama Chitapure; Nagesh Adiga; Avantika Gupta (SIGIR, 2021)

ComParE: A User Behavior Centric Framework for Personalized Recommendations in Skill Gaming Platforms. - Koyel Mukherjee; Deepanshi Seth; Prachi Mittal; Nuthi S. Gowtham; Tridib Mukherjee; Dattatreya Biswas; Sanjay Agrawal 005 (COMAD/CODS, 2022)

EFfECT-RL: Enabling Framework for Establishing Causality and Triggering engagement through RL. - Debanjan Sadhukhan; Deepanshi Seth; Sanjay Agrawal 005; Tridib Mukherjee (CIKM, 2024)

Generalized Experiment Conclusion Framework for Extremely Volatile Data. - Satyam Anand; Deepanshi Seth; Sanjay Agrawal 005; Debanjan Sadhukhan; Tridib Mukherjee (COMAD/CODS (December), 2024)