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ABOUT THE SPEAKER:
Nima holds a Ph.D. in Systems and Industrial Engineering with a strong foundation in Applied Mathematics. He completed a postdoctoral fellowship at the C-MORE Lab (Center for Maintenance Optimization & Reliability Engineering) at the University of Toronto, where he worked on machine learning and operations research (ML/OR) projects in close collaboration with industry and service-sector partners.
He was part of the Maintenance Support and Planning Department at Bombardier Aerospace, applying ML/OR methodologies to reliability and survival analysis, maintenance optimization, and airline operations planning.
Nima is currently a Senior Data Scientist within the Corporate Functions Analytics team at Scotiabank in Toronto, Canada. His research and applied work span machine learning, optimization, and large-scale decision-making systems. He has authored over 40 peer-reviewed journal articles and book chapters in leading venues and holds one granted patent. His work has been featured at major machine learning and AI conferences, including NeurIPS, ICML, NVIDIA GTC, GRAPH+AI, and TMLS.
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ABSTRACT:
Causal discovery algorithms infer directed acyclic graphs (DAGs) from observational data but are highly sensitive to hyperparameters, structural constraints, and assumptions that are difficult to identify from data alone. We propose an LLM-guided causal discovery framework in which a large language model (LLM) acts as a domain-aware expert to inform the calibration of causal models. The LLM encodes prior knowledge about plausible causal directions, temporal ordering, lag structures, and exclusion constraints, which are translated into structured priors and tuning parameters for time-lagged causal discovery algorithms.
We apply the proposed approach to macroeconomic systems, where variables exhibit delayed and interdependent causal relationships. Empirical results show that LLM-guided calibration yields more stable and interpretable causal graphs and improves out-of-sample prediction of macroeconomic indicators compared to purely data-driven baselines. This work demonstrates how LLMs can bridge expert knowledge and statistical causal inference in complex dynamical systems.
WHAT YOU’LL LEARN:
Business Leaders: C-Level Executives, Project Managers, and Product Owners will get to explore best practices, methodologies, principles, and practices for achieving ROI.
Engineers, Researchers, Data Practitioners: Will get a better understanding of the challenges, solutions, and ideas being offered via breakouts & workshops on Natural Language Processing, Neural Nets, Reinforcement Learning, Generative Adversarial Networks (GANs), Evolution Strategies, AutoML, and more.
Job Seekers: Will have the opportunity to network virtually and meet over 30+ Top Al Companies.
Ignite what is an Ignite Talk?
Ignite is an innovative and fast-paced style used to deliver a concise presentation.
During an Ignite Talk, presenters discuss their research using 20 image-centric slides which automatically advance every 15 seconds.
The result is a fun and engaging five-minute presentation.
You can see all our speakers and full agenda here