Adam Zemla
About Adam Zemla
Adam Zemla is a Senior Computer Scientist at Lawrence Livermore National Laboratory (LLNL), where he has worked since 1999. He specializes in developing algorithms for protein structure analysis and has contributed to several significant projects in the field.
Work at Lawrence Livermore National Laboratory
Adam Zemla has been employed at Lawrence Livermore National Laboratory (LLNL) since 1999, serving as a Senior Computer Scientist for 25 years. In this role, he has contributed to various projects that focus on the development of algorithms specifically aimed at protein structure analysis. His extensive experience at LLNL has positioned him as a key figure in the field of computational biology.
Education and Expertise
Adam Zemla holds a Doctor of Philosophy (Ph.D.) in Computational Mathematics and Physics from Lomonosov Moscow State University, where he studied from 1982 to 1985. He also earned a Master of Science (M.Sc.) in Numerical Analysis from the University of Warsaw, completing his studies from 1977 to 1981. His academic background provides a strong foundation for his expertise in computational methods applied to biological problems.
Research Contributions
Zemla has served as Principal Investigator on multiple projects at LLNL, focusing on the development of novel algorithms for protein structure analysis. He developed the AS2TS system, which enables fully automated protein structure modeling. Additionally, he played a significant role in designing and developing the ACE system, which is integral to the Critical Assessment of Techniques for Protein Structure Prediction (CASP) process.
Collaborations and Systems Development
Throughout his career, Adam Zemla has collaborated with prominent scientists, including John Moult, Tim Hubbard, Ceslovas Venclovas, and Krzysztof Fidelis. These collaborations have led to the development of software and infrastructure for the CASP initiative. He has also created the StralCP system for protein structure classification and the StralSV system for detecting protein distant homology and local sequence-structure variabilities.