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Kerstin Kleese van Dam

Director, Computational Science Initiative

Expertise

Kerstin Kleese van Dam leads Brookhaven Lab's Computational Science Initiative, which leverages computational science expertise and investments across multiple programs—including the flagship facilities that attract thousands of scientific users each year— to tackle the big data challenges at the frontiers of scientific discovery. Key partners in this endeavor include nearby universities like Stony Brook, Columbia, Cornell, NYU, and Yale, and companies such as IBM Research. 

With more than 25 years' experience in the field, Kleese van Dam comes to Brookhaven from Pacific Northwest National Lab (PNNL) in Richland, WA, where she most recently served as Associate Division Director of the Computational Science and Mathematics Division, as well as Chief Scientist and Lead of Data Services. Her responsibilities included a wide range of data management research and development projects in applied computer science research. She also led the creation of a data analysis thrust area in PNNL's Chemical Imaging Initiative.

She has co-authored more than 100 publications and is a member of the DOE Advanced Scientific Computing Research Advisory Committee's standing subcommittee on Science, Technology and Information. Her primary research interests are in the areas of scientific data management, curation, and exploitation using metadata and semantic technologies. 

Computational Sciences Research Activities

Extreme Scale Data Management, Metadata, Provenance and Data Curation

Education

BS and MS In Computer Science, Technical University Berlin, Germany

Professional Appointments

2015-Present Director, Computational Science Initative, Brookhaven National Laboratory, Upton, NY. Building a comprehensive research program in data driven discovery.

2010-2015 Associate Division Director & Group Leader Scientific Data Management, Pacific Northwest National Laboratory, Richland, WA. Research efforts included: 1) Development of new capabilities for in-situ analysis and interpretation of streaming data, DOE ASCR Integrated End-to-End Performance Prediction and Diagnosis for Extreme Scientific Workflows (Co-PI), DOE ASCR/BER Interoperable Design of Extreme-scale Application Software (task lead), PNNL Analysis in Motion initiative (co-lead), High Speed Data Acquisition in chemical Imaging (project lead); 2) development of data management and analysis techniques for extreme scale environments for DOE BER Climate Science for a Sustainable Energy Future and Accelerated Climate Modeling for Energy (task lead provenance), DOE BER Advanced Simulation Capability for Environmental Management (ASCEM), DOE HEP Belle II (task lead DB), Chemical Imaging Initiative (software lead – real time data analysis), Platform for Regional Integrated Modeling and Analysis (project lead), Future Power Grid (project lead – streaming feature detection); 

2009 Director Computing, Biomedical Sciences Faculty, University College London, UK

2001-2008 Group Leader, Scientific Data Management Group, Science and Technology Facilities Council, UK. Development of data management, sharing and curation infrastructures: ICAT (central data management infrastructure) for DIAMOND Light Source, ISIS Neutron Source and CLF Laser Facility, today also deployed at international facilities in Germany, France, Australia, Canada and the USA (e.g. SNS); Natural Environmental Research Council (NERC) Data Grid; Ecological DataGrid; e-Materials; e-Minerals; Integrated Systems Biology Centre; Integrative Biology; BBSRC Institutes Data Archival Service; UK Digital Curation Centre

1997-2001 Senior Research Scientist, High Performance Computing, Science and Technology Facilities Council, UK

1994-1997 Software Developer, German Climate Computing Centre (DKRZ), Germany

1989-1994 Software Developer / System Administration (HPC), INPRO, Germany

Selected Publications & Research Highlights

  1. Kleese van Dam, K., Stephan, E., Raju, B., Altintas, I., Elsethagen, T., Krishnamoorthy, S. 2015. Enabling Structured Exploration of Workflow Performance Variability in Extreme Scale Environments. SC15, MTAGS15 workshop, November 2015.
  2. Kleese van Dam, K., LaMothe, R., Vishnu, A., Smith, W., Thomas, M., Sharma, P., Zarzhitsky, D., Stephan, E., Elsethagen. 2015. T. Building the Analysis in Motion Infrastructure. PNNL-2340 Report.
  3. Thomas, M., Kleese-van Dam, K., Marshall, M., Kuprat, A., JCarson, J., Lansing, C., Guillen, Z., Miller, E., Lanekoff, I., Laskin, J. 2015. Towards Adaptive, Streaming Analysis of X-ray Tomography Data, Synchrotron Radiation News, 28:2, 10-14
  4. Kraucunas, I., L. Clarke, J. Dirks, J. Hathaway, M. Hejazi, K. Hibbard, M. Huang, C. Jin, M. Kintner-Meyer, K. Kleese van Dam, R. Leung, H.Li, R. Moss, M. Peterson, J. Rice, M. Scott, A. Thomson, N. Voisin, T. West. 2014. Investigating the nexus of climate, energy, water, and land at decision-relevant scales: the Platform for Regional Integrated Modeling and Analysis (PRIMA). Climate Change, p1-16, Springer, February 2014.
  5. Kleese van Dam, K., C. Lansing, T. Elsethagen, J. Hathaway, Z. Guillen, J. Dirks, D. Skorski, E. Stephan, W. Gorrissen, I. Gorton, Y. Liu. 2014. Nationwide Buildings Energy Research enabled through an integrated Data Intensive Scientific Workflow and Advanced Analysis Environment. Building Simulation; An International Journal, p 1-9, January 2014.
  6. Hafen, R., T. D. Gibson, K. Kleese van Dam, and T. Critchlow. 2013. Large-scale exploratory analysis, cleaning, and modeling for event detection in real-world power systems data. In Proceedings of the 3rd International Workshop on High Performance Computing, Networking and Analytics for the Power Grid (HiPCNA-PG '13). ACM, New York, NY, USA, Article 4, 9 pages. November 2013.
  7. Critchlow, T. and K. Kleese van Dam, Editors, 2013: Data Intensive Science. Boca Raton: Taylor and Francis, May 2013.
  8. Stephan EG, P Pinheiro da Silva, and K Kleese van Dam.  2013. Bridging the Gap between Scientific Data Producers and Consumers: A Provenance Approach. Chapter 12, p279-299, In Data Intensive Science Critchlow, Data Intensive Science. Boca Raton: Taylor and Francis, May 2013.
  9. Gorton, I., Y. Liu, C. Lansing, T. Elsethagen and K. Kleese van Dam, 2013: Build Less Code, Deliver More Science: An Experience Report on Composing Scientific Environments using Component-based and Commodity Software Platforms. Proceedings The 16th International ACM Sigsoft Symposium on Component-Based Software Engineering, June 2013. 
  10. Kleese van Dam, K, J. P. Carson, A. L. Corrigan, D. R. Einstein, Z. C. Guillen, B. S. Heath, A. P. Kuprat, I. T. Lanekoff, C. S. Lansing, J. Laskin, D. Li, Y. Liu, M. J. Marshall, E. A. Miller, G. Orr, P. Pinheiro da Silva, S. Ryu, C. J. Szymanski, and M. Thomas, 2012: Velo and REXAN - Integrated Data Management and High Speed Analysis for Experimental Facilities . In 8th IEEE International Conference on EScience.