Carlos Guestrin: AI Innovator and Entrepreneur | Generated by AI
Biography
Carlos Guestrin is a Chilean-American computer scientist and entrepreneur, born in 1973. He is a professor at Carnegie Mellon University (CMU), holding joint appointments in the Machine Learning Department, Computer Science Department, and Heinz College. He earned his PhD in computer science from Stanford University in 2003, where he worked on probabilistic models and machine learning. Guestrin is known for his foundational contributions to structured prediction, online learning, and scalable machine learning systems.
Career Highlights
- Academic Roles: At CMU, he leads the Machine Learning for Humanity Lab and has mentored numerous PhD students. He previously taught at the University of Washington.
- Industry Experience: He served as Apple’s first VP of Machine Learning, leading the AI/Perceptron team (2013–2016). He co-founded GraphLab Inc. (later acquired by Apple and rebranded as Dato) and helped develop the GraphLab system for large-scale machine learning. In 2019, he co-founded the AI company Oso, focused on optimization in AI. As of 2023, he is the CEO of Together AI, a startup developing open-source AI models.
- Key Research: Pioneered techniques in probabilistic graphical models, submodular optimization for sensor networks, and reinforcement learning for decision-making. His work has influenced applications in autonomous systems, healthcare, and energy management.
Achievements and Awards
- Elected a Fellow of the Association for Computing Machinery (ACM) in 2020 and the Association for the Advancement of Artificial Intelligence (AAAI) in 2016 for his contributions to machine learning.
- Received the 2009 ACM SIGKDD Innovation Award and multiple ICML Test-of-Time Awards (e.g., for his 2001 paper on conditional random fields and 2010 for ensemble methods).
- Authored over 150 publications in top venues like NeurIPS, ICML, and JMLR, and co-authored textbooks like “Elements of Statistical Learning.”
Public Recognition
Guestrin is influential in the AI community, having spoken at events like TED and contributed to AI ethics discussions. For instance, his work on interpretable AI has been highlighted in media such as Wired and MIT Technology Review. His approximated 27,000 citations on Google Scholar as of late 2023 reflect his impact in the field. For more details, refer to his CMU faculty page or Google Scholar profile.