Ishansh Gupta

WORK HISTORY

EDUCATION

SKILLS

ABOUT ISHANSH GUPTA

Dr. Ishansh Gupta, is a globally recognized expert in Causal Machine Learning, currently leading AI strategy and digitalisation at the BMW Group HQ and leading & teaching Causal AI initiatives at Constructor University. He works at the intersection of industry and academia, bridging real-world decision-making with cutting-edge research in explainable and responsible AI.Ishansh was selected as one of the top young economists worldwide to attend the prestigious Lindau Nobel Laureate Meeting in Economic Sciences, spending a week with 30+ Nobel Prize winners, exploring digitalization, AI policy, and causal inference. His research focuses on causal discovery, policy simulation, and integrating tribal knowledge into causal models, using tools like DAGs, LLMs, and econometric methods such as difference-in-differences.With multiple Q1 journal publications and multiple filed patents in causal AI, Ishansh has built a reputation for turning complex ideas into actionable insights. He has spoken at leading conferences in New York, Peru and Europe, appeared on top Causal AI podcasts and serves as a driving force in educating and mentoring the next generation of data scientists.He has supervised 100+ students (bachelor’s, master’s, PhDs) from institutions & companies like MIT, TUM, Constructor University, BMW Group and has contributed to hiring over 50 professionals across industry and academia. His work connects high-stakes industrial challenges with academic rigor especially in the automotive, supply chain, and AI policy domains.For the fun side: when he’s not building causal graphs or simulating counterfactuals, Ishansh enjoys working out, hiking the Bavarian Alps, and is currently obsessed with Padel. He may have a PhD in Causal AI, but let’s just say his padel level on Playtomic might not pass the significance test… yet. Feel free to reach out if you’d like to collaborate, nerd out about causal inference, or just trade stories about research, startups or how to win a game of Padel using instrumental variables