Oliver Rübel
Staff Scientist
Machine Learning & Analytics Group
Computational Biosciences Group
Computational Research Division
Lawrence Berkeley National Laboratory
Phone: +1 510 486 4064
Email: oruebel@lbl.gov
Wep: http://dav.lbl.gov/~oruebel/
Address: 1 Cyclotron Road, Mail Stop 50F1650, Berkeley, CA 94720

Education< and Employment

  • June 2019 – present: Staff Scientist, Machine Learning & Analytics Group, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
  • October 2014 – June 2019: Computer Research Scientist, Data Analytics and Visualization Group, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
  • March 2011 – 2014: Computer Systems Engineer, Visualization Group, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
  • March 2010 – March 2011: Post-doctoral researcher, Data Analysis Group, Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA, USA.
  • November 2009: Dr. rer. nat (equivalent Ph.D), Department of Computer Science, University of Kaiserslautern, Germany. Thesis title: "Linking Automated Data Analysis and Visualziation with Applications in Developmental Biology and High-energy Physics" Advisors: Dr. Hans Hagen (University of Kaiserslautern, Germany), Dr. Bernd Hamann (University of California, Davis, CA, U.S.A.), and Dr. Gunther H. Weber (Lawrence Berkeley National Laboratory, Berkeley, CA, U.S.A.). During my Ph.D. I was a fellow of the International Research Training Group 1131 of the University of Kaiserslautern and Student Assistant at the Visualization Group at the Lawrence Berkeley National Laboratory.
  • Janurary 2006: Diploma (equivalent M.S.), Computer Science, Department of Computer Science, University of Kaiserslautern, Germany. Thesis title: "Integrating Data Analysis and Visualization for the Exploration of Three-dimensional Gene Expression Data." Advisors: Dr. Hans Hagen (University of Kaiserslautern, Germany), Dr. Bernd Hamann (University of California, Davis, CA, U.S.A.), and Dr. Gunther H. Weber (University of California, Davis, CA, U.S.A; currently at LBNL). Received award by the Sparkassenstiftung for outstanding work in diploma thesis (Kaiserslautern, Germany, 2007).

Research Interests

My research is in the area of high-performance data science in support of the DOE mission in computational and experimental science. Overwhelmed by the rapid growth in data size, complexity and diversity, application sciences today are looking towards data sciences for end-to-end data solutions for analysis, management, knowledge-discovery, and sharing of data throughout the project and data life-cycle. Ultimately the goal of my research is to enable science teams to effectively utilize their data to gain new scientific insights not possible otherwise. I envision a future in which researchers will be able to seamlessly integrate and utilize data from multiple user facilities, online data archives, simulations, and other sources to seamlessly create complex, next- generation data-driven experiments in familiar, unified analysis and collaboration environments. Users will be able to discover new knowledge and detailed understanding of complex natural systems through the ability to investigate structure and dynamics across temporal and spatial scales guided by smart visualization and analysis solutions.

To address these challenges, my research to date has focused on the intersection of visualization, machine learning, and data management for large-scale and high-dimensional data, with a particular focus on (a) machine learning, visualization, and analytics algorithms and methods for large-scale, high-dimensional, and multi-modal data, (b) data modeling and management for efficient data sharing and integration, and (c) data and analysis systems to enable data sharing, reuse, and analysis.

My research interests more broadly are in the areas of: 1) FAIR data science, 2) machine learning and feature detection, 3) computational topological, 4) high-performance computing, 5) scientific/information visualization, 6) data management and I/O, 7) bioinformatics, 8) end-to-end data systems, 9) data analysis solutions for applications in bioscience (e.g., biomaging and neuroscience), high-energy physics (e.g, particle accelerator modeling), climate and others.