Bill Marino, PhD, is a distinguished expert and leader in the field of Artificial Intelligence and Machine Learning, with a strong track record of applying AI to solve complex, real-world scientific and industrial problems. He is a co-founder and serves as the Chief Data Scientist at KoBold Metals, a company pioneering the use of AI for mineral exploration, particularly for critical materials like cobalt, nickel, copper, and lithium necessary for the green energy transition. Prior to KoBold, Bill was the founding Director of Applied AI at the California Institute of Technology (Caltech), where he led initiatives to translate cutting-edge AI research into practical applications across various scientific disciplines. He holds a PhD in Computational Science and Engineering from Georgia Institute of Technology. His work emphasizes the intersection of sophisticated algorithms, large-scale data, and domain expertise to drive innovation and discovery.
Bill Marino's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Co-founded and leads the data science and AI strategy for KoBold Metals, developing machine learning models and data infrastructure to significantly improve the efficiency and success rate of discovering new deposits of critical minerals essential for electric vehicles and renewable energy.
Established and directed Caltech's applied AI program, fostering collaborations between AI researchers and domain scientists to accelerate scientific discovery and develop innovative AI-driven solutions in areas like seismology, climate science, and materials science.
Earned a doctorate from Georgia Institute of Technology, specializing in machine learning and high-performance computing, which laid the academic foundation for his impactful career in applied AI.
Frequently invited to speak at industry conferences and academic institutions on the practical applications of AI, data-driven discovery, and the future of machine learning in scientific research and resource exploration.
The Wharton School - Year 2023
The Wharton School - Year 2024
The Wharton School - Year 2024
The Wharton School - Year 2024
Penn State University - Year 2003
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