Shahram Farhadi

Built a multimodal AI agent combining sensor data with LLMs and generative data analytics workflows to deliver real-time outdoor intelligence. Designed a scalable data collection schema for high frequency data coverage in large areas, and recruited and led a cross-functional team to deliver rapid MVPs.

Led a global team of 30 scientists across four groups, establishing development processes for AI solutions from definition to delivery. Conducted detailed technical reviews for numerous client-based projects, generating $50MM in revenue, and led a $3MM research program with Caltech on theoretical machine learning.

Led hiring and team development for the data science team, defining projects with clients and leading solution design. Presented technology to investors, contributing to a $135MM Series C raise, and led high-profile AI-based product developments valued at $15MM and $5MM.

Applied causal reasoner from NASA JPL to solve complex problems in energy, medical, financial, and industrial domains. Was a key developer for Cognitive Reservoir Advisor, an end-to-end algorithmic pipeline that secured $20MM Series B investment and a follow-up $6MM MVP contract.

Developed a geologic feature extraction package for borehole images using computer vision, automating image analysis and yielding $3MM in cost savings. Initiated and implemented a pipeline for image-based characterization of subsurface rock samples, involving 3D image analysis and multiphase fluid flow simulations.

Focused on High Performance Computing and 3D Image Analysis for the doctoral thesis. Conducted advanced research in these areas.

Specialized in Computational Fluid Dynamics during master's studies. Gained expertise in numerical methods for fluid flow problems.