Headshot of Héctor García Martín
Our People

Héctor García Martín

  • Research Affiliate, ICSI
  • Director of Data Science and Modeling, Biofuels and Bioproducts Division, Joint BioEnergy Institute (JBEI)
  • Staff Scientist, Lawrence Berkeley National Laboratory (LBNL)
  • External Scientific Member, Basque Center for Applied Mathematics (BCAM)

Biography

Héctor García Martín, PhD, is a Research Affiliate at ICSI. He also serves as the Director of Data Science and Modeling at Biofuels and Bioproducts Division at the Joint BioEnergy Institute (JBEI), and a Staff Scientist at Lawrence Berkeley National Laboratory where he founded the Quantitative Metabolic Modeling Group.

A leader in predictive biology and metabolic engineering, Dr. García Martín integrates mechanistic modeling, automation, microfluidics, synthetic biology, and artificial intelligence to create predictive frameworks for biological design. His research focuses on the intersection of synthetic biology, machine learning, and robotics, so as to produce predictive models able to guide the design of biological systems for biomanufacturing (e.g., producing performance-enhanced biofuels and bioproducts). His work has produced some of the first practical demonstrations of predictive synthetic biology.

Dr. García Martín studied physics at the University of the Basque Country before earning his PhD in condensed matter physics from the University of Illinois at Urbana-Champaign. During his doctoral work, he explored Bose-Einstein condensates and scaling laws in ecology, sparking his interest in applying theoretical physics tools to biological systems. He later joined the Department of Energy Joint Genome Institute as a postdoctoral fellow, where he studied microbial communities using metagenomics. Seeking to improve predictive models in biology, he shifted his focus to engineering microbial cells and became a group lead at Berkeley Lab in 2007.

Publications

Areas of Expertise

  • Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
  • Planning, Decision-Making, and Intelligent Agents
  • Robotics, Autonomous Systems, and Control
  • AI in Healthcare, Biomedicine, and Life Sciences
  • Statistical Modeling, Bayesian Methods, and Inference
  • Mathematical Optimization, Operations Research, and Decision Science
  • Computational Biology, Genomics, and Bioinformatics

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