Paul Grigas

With a visionary perspective, Paul Grigas addresses the complexities of optimization, machine learning, and data-driven decision-making as an Assistant Professor of Industrial Engineering and Operations Research at the University of California, Berkeley. His academic journey began with a B.S. in Operations Research and Information Engineering from Cornell University in 2011, followed by a Ph.D. in Operations Research from MIT in 2016. These formative experiences have equipped him with a robust foundation to tackle some of the most pressing challenges in his field. Professor Grigas's research is at the forefront of developing innovative machine learning models and optimization algorithms, with a particular focus on applications in online advertising. His work is characterized by a deep commitment to advancing the theoretical underpinnings of optimization and machine learning while simultaneously addressing practical, real-world problems. This dual focus allows him to contribute significantly to both academic discourse and industry practices. Throughout his career, Grigas has been recognized for his contributions to the field. In 2020, he was honored with the INFORMS Junior Faculty Interest Group Paper Competition award, a testament to his impactful research and thought leadership. Earlier, in 2018, he received the NSF CISE Research Initiation Initiative Award, which underscored his potential to drive innovation in computational and information sciences. His academic excellence was also acknowledged during his student years with the INFORMS Optimization Society Student Paper Prize in 2015. Beyond his research, Professor Grigas is deeply committed to education and mentorship. He is known for his engaging teaching style and his ability to inspire students to explore the intersections of theory and practice. His courses often emphasize the importance of leveraging data-driven insights to make informed decisions, preparing students to become leaders in the rapidly evolving landscape of industrial engineering and operations research. In addition to his academic pursuits, Grigas actively collaborates with industry partners, bridging the gap between theoretical research and practical applications. His work in online advertising, for instance, not only advances academic understanding but also provides tangible solutions that enhance the effectiveness and efficiency of advertising strategies. Paul Grigas continues to push the boundaries of what is possible in optimization and machine learning, driven by a passion for discovery and a dedication to making a meaningful impact on both academia and industry. His contributions are paving the way for future innovations in data-driven decision-making, ensuring that his work remains at the cutting edge of technological advancement.