Gaurav Gaonkar - From Academia Research to Applied ML Lessons from Academia & Industry

In this talk, Gaurav will share his journey from conducting cognitive science and computer vision research at Brown University to building applied machine learning systems in industry. He will talk about how cognitive science and AI complement each other, and how he balanced core AI research with applied projects, and what he learned along the way. He will also highlight why applied AI projects are important, the value they bring in bridging theory and real-world impact. Finally, he’ll share some insights on navigating ML careers, including his experience as a Machine Learning Engineer, where research thinking helps him ask the right questions before solving a problem. He’ll reflect on lessons learned from both successes and failures across academia and industry. Gaurav Gaonkar is a Machine Learning Engineer with experience building large-scale personalization and recommendation systems in AdTech. He holds a Master’s degree in Computer Science from Brown University, where his research focused on computer vision and cognitive science, exploring how human visual perception can help us improve AI models. At Brown, he also served as a Teaching Assistant for courses in Deep Learning, Computer Vision. Gaurav’s work bridges research and engineering, with a strong focus on building reproducible, well-structured ML systems. He’s passionate about building systems that connect cutting-edge research to meaningful, real-world impact. This session is brought to you by the Cohere Labs Open Science Community - a space where ML researchers, engineers, linguists, social scientists, and lifelong learners connect and collaborate with each other. We'd like to extend a special thank you to Arya Suneesh and Prahitha Movva Leads of our ML Industry group for their dedication in organizing this event. If you’re interested in sharing your work, we welcome you to join us! Simply fill out the form at https://forms.gle/ALND9i6KouEEpCnz6 to express your interest in becoming a speaker. Joi