Nina Taherimakhsousi is a seasoned Chief Data Scientist at AutoChem Mettler Toledo, where she has spearheaded innovative machine learning and AI-driven solutions for over 3.5 years. Her work focuses on developing advanced systems for real-time monitoring and control in chemical and pharmaceutical processes, integrating cutting-edge technologies like computer vision and deep reinforcement learning. Notably, she co-authored a publication on a generalizable computer vision and machine learning model for automated real-time monitoring and control of workup processes, demonstrating its application across various experimental techniques .
In addition to her industry role, Nina serves as an Adjunct Professor in the Department of Electrical and Computer Engineering at the University of Victoria. Her academic expertise includes deep learning, reinforcement learning, computer vision, and artificial intelligence. Nina earned her Ph.D. in Computer Science from the University of Victoria, specializing in machine learning, AI, and self-adaptive software. Her doctoral research, conducted in collaboration with the Canada Centre for Advanced Studies, explored reinforcement learning for AI-based self-adaptive software. She later completed postdoctoral research at the University of British Columbia in the Chemistry Department, focusing on to accelerate the discovery and optimization of advanced clean energy materials by developing self-driving laboratories that combine flexible automation with machine learning and computational screening.
With over a decade of experience in machine learning, Nina bridges academia and industry, establishing herself as a thought leader in applying AI and machine learning to chemical and pharmaceutical innovation.