Wei Wang: A Pioneer in Big Data Analytics and Computational Science
Wei Wang is a distinguished Chinese-born American computer scientist renowned for her groundbreaking contributions to big data analytics, bioinformatics, and computational biology. As a professor in the Computer Science Department at the UCLA Samueli School of Engineering, she assumed the role of department chair on July 1, 2025. She is the director of the Scalable Analytics Institute (ScAi) at the University of California, Los Angeles. She is also a member of the UCLA Jonsson Comprehensive Cancer Center, Institute for Quantitative and Computational Biology, and Bioinformatics Interdepartmental Graduate Program. Wang's research focuses on designing efficient algorithms for discovering and representing complex patterns in large-scale, heterogeneous data.
Education and Early Career
Wei Wang's academic journey began at Nankai University, where she pursued undergraduate studies in computer science from 1990 to 1993. She continued her education in the United States, earning a Master of Science degree in Systems Science from Binghamton University in 1995. In 1999, she completed her Ph.D. in computer science from the University of California, Los Angeles (UCLA). After earning her PhD degree in Computer Science from the University of California, Los Angeles in 1999. Between 1999 and 2002, Wang gained valuable experience as a research staff member at the IBM Thomas J. Watson Research Center. From 2002 to 2012 she was at the University of North Carolina at Chapel Hill.
Research Focus and Contributions
Dr. Wang's research interests include big data analytics, data mining, machine learning, natural language processing, bioinformatics and computational biology, computational medicine, and AI for science. Her expertise lies in developing scalable machine learning and data mining methods for massive datasets across diverse applications. At UCLA, Wang directs the Scalable Analytics Institute, which develops tools and techniques to analyze large datasets. Her research interests include big data analytics, data mining, machine learning, natural language processing, bioinformatics and computational biology, computational medicine and AI for science. Dr. Wang’s current research spans the areas of big data analytics, bioinformatics and computational biology, computational medicine, database systems, data mining, machine learning, and natural language processing with a focus on designing efficient algorithms for discovering and representing complex patterns in large-scale, heterogeneous data.
Wang's work has significantly impacted the fields of genomics and biomedicine. At the Carolina Center for Genome Sciences, she collaborated with scientists from the behavioral sciences/ psychiatry, biostatistics, genetics, and proteomics to develop analytic methods and computational platforms for better understanding the genetic factors of diseases and disorders.
Selected Publications
Dr. Wang has contributed significantly to the scientific literature. Some of her notable publications include:
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- Zhang X, Huang S, Sun W, Wang W. Rapid and robust resampling-based multiple-testing correction with application in a genome-wide expression quantitative trait loci study. Genetics. 2012 Apr;190(4):1511-20. doi: 10.1534/genetics.111.137737. Epub 2012 Jan 31. This paper presents a method for improving the accuracy and efficiency of eQTL studies.
- Szatkiewicz JP, Wang W, Sullivan PF, Wang W, Sun W. Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation. Nucleic Acids Res. 2013 Feb 1;41(3):1519-32. doi: 10.1093/nar/gks1363. Epub 2012 Dec 28. This work focuses on enhancing the detection of copy number variations in genomic data.
- Liu EY, Li M, Wang W, Li Y. MaCH-admix: genotype imputation for admixed populations. Genet Epidemiol. 2013 Jan;37(1):25-37. doi: 10.1002/gepi.21690. Epub 2012 Oct 16. This publication introduces a novel approach to genotype imputation in admixed populations.
- Xia K, Shabalin AA, Huang S, Madar V, Zhou YH, Wang W, Zou F, Sun W, Sullivan PF, Wright FA. seeQTL: a searchable database for human eQTLs. Bioinformatics. 2012 Feb 1;28(3):451-2. doi: 10.1093/bioinformatics/btr678. This paper describes the development of seeQTL, a database for human eQTLs.
Awards and Recognition
Dr. Wang's exceptional contributions to the field have been recognized with numerous prestigious awards and honors. She received the IBM Invention Achievement Awards in 2000 and 2001, acknowledging her innovative work at IBM. In 2005, she was named a Microsoft Research New Faculty Fellow, a testament to her potential as a rising star in computer science. She was also the recipient of a UNC Junior Faculty Development Award in 2003 and an NSF Faculty Early Career Development (CAREER) Award in 2005. Further accolades include the 2007 Phillip and Ruth Hettleman Prize for Artistic and Scholarly Achievement at UNC, the IEEE ICDM Outstanding Service Award in 2012, an Okawa Foundation Research Award in 2013, and an ACM SIGKDD Service Award in 2016. She has filed seven patents; was recognized with a National Science Foundation Faculty Early Career Development (CAREER) award; a Microsoft Research Faculty Fellowship, and an Okawa Foundation Research Award, among others.
Dr. Wang is a fellow of both the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE), recognizing her significant and lasting impact on the field. She has won numerous awards and has been recognized for her leadership in the data analytics field by both the IEEE International Conference on Data Mining and the ACM Special Interest Group on Knowledge Discovery and Data Mining, where she currently serves as chair. She is the Chair of the ACM Special Interest Group on Knowledge Discovery in Data (SIGKDD).
Leadership and Vision
Dr. Wang's leadership extends beyond her research. As the founding Director of the UCLA Scalable Analytics Institute and a core faculty member of the University of California, Institute for Prediction Technology, she is shaping the future of data science research and education. As a professor in the Computer Science Department and serves as its chair, effective July 1, 2025, at the UCLA Samueli School of Engineering.
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