Dr. Spall is a member of the principal professional staff at the JHU Applied Physics Lab and serves as co-chair of the data science program and chair of the applied and computational mathematics program at Johns Hopkins University. He has published extensively in the areas of control and statistics and holds two patents in the US for inventions in control systems. After completing his SM (i.e., master’s of science) in technology and policy at MIT, he obtained his PhD in systems engineering at the University of Virginia. He has written three books on systems and control and currently teaches courses on Monte Carlo methods, system identification and likelihood methods, and stochastic search and optimization, among others. He is an esteemed member of several professional groups, including the IEEE, the American Statistical Association, Sigma Xi, and Tau Beta Pi.
Dr. Mulligan is an associate professor at UC Berkeley’s School of Information. She has taught several courses such as technology and design, information law and policy, and the Federal Trade Commission and online privacy, among others. Additionally, she serves as a faculty director for the Berkeley Center for Law & Technology. She has more than 15 reputable publications to her name, and her research interests include cybersecurity, technology and governance, and exploring users’ conceptions of privacy in the online environment. Notably, she’s also a founding member of the Global Network Initiative and a standing committee member for the AI 100 Project.
Dr. Leake is a professor of computer science at Indiana University, Bloomington, where he also serves as the executive dean. He focuses his research on artificial intelligence, cognitive science, and data science, among other subfields of the discipline. He’s affiliated with the Data to Insight Center and the Center for Data and Search Informatics. Additionally, he was the editor of AI Magazine and served on the editorial boards of the Journal of the Learning Sciences and the International Journal of Expert Systems Research and Applications. He has published three books related to data science.
At the University of Wisconsin, Dr. Bansal serves as a professor of management information systems and statistics. His research interests include information security and privacy, trust, and data-mining. He currently works on the editorial board of the Journal of the Midwest Association for Information Systems, and his research has been nominated for the Best Paper Award at the International Conference on Information Systems. He previously was the conference co-chair for the 7th Midwest AIS Conference (2012). Also, before starting his academic career, he worked as a quality-assurance executive for General Motors India and Daewoo Motors India.
Dr. Aragon is a professor in the Department of Human-Centered Design and Engineering at the University of Washington. She focuses her work on visual analytics, data science and big data, emotion in informal text communication, and other fields. She serves as the director of the Human-Centered Data Science Lab and is a senior data science fellow at the eScience Institute. She has been the principal investigator (or co-PI) for projects comprising over $27 million in grants from the National Science Foundation and others. Additionally, she’s associated with the Deep Sky Project and the Particle Data Group. Notably, she received the Presidential Early Career Award for Scientists and Engineers (PECASE) for her work in collaborative data-intensive science in 2008.
Dr. Allen serves as a professor of computer science at Columbia University, where he’s affiliated with the data science institute. He’s the recipient of many honors, including the Presidential Young Investigator award from the National Science Foundation; a CBS Foundation Fellowship from the University of Pennsylvania; the Army Research Office Fellowship; and the Rubinoff Award for innovative uses of computers. He has innumerable publications to his name and teaches courses on humanoid robots, computation aspects of robots, and data structures and algorithms.
Dr. Silva is a professor in NYU’s Tandon School of Engineering, as well as the interim director of the Center for Data Science. He also serves as the head of disciplines at the Center for Urban Science & Progress. His research interests include big data and urban systems, geometry processing, visualization and data analysis, and sports analytics and visualization. He has previously taught courses on principles of urban informatics and scientific visualization. According to Google Scholar, he has over 10,000 research citations; has published more than 220 journal and conference papers; and is an inventor with 12 US patents, among other accomplishments.
Dr. Donoho is a professor in Stanford University’s Department of Statistics. His research interests include data visualization, harmonic analysis, and signal processing. He was awarded the Shaw Prize in Mathematical Sciences in 2013, as well as the Wiener Prize in Applied Mathematics in 2010. According to this faculty page, his algorithms have contributed significantly to our understanding of the maximum entropy principle, the structure of robust procedures, and sparse data description.
At Southern Methodist University, Mr. Greenberg is a professor in the Department of Computer Science and Engineering. He teaches visualization of information and creative coding, and serves as the director of the Center of Creative Computation. He’s perhaps best known for writing the first major reference on the processing program language titled Processing: Creative Coding and Computational Art (Berkeley, CA 2007). In addition to data visualization, he’s also an accomplished painter and animator and has been affiliated with with the Flywheel Gallery (Piermont, New York) and the Bowery Gallery (New York City).
Dr. Klump is a professor at Lewis University who focuses his research on scientific visualization, numerical methods, and programming languages, among other areas. He also serves as the chair of the Department of Computer Sciences and Mathematics at the university, where he began his career in 2001. He has published several influential papers on the field of computer applications for power systems. In addition, he works with PowerWorld, where he is the principal developer of their Retriever product.
Dr. Rege of the University of St. Thomas has taught a number of classes at the undergraduate and graduate level, and was nominated by students for the Rochester Institute of Technology’s (RIT) Eisenhart Award for Teaching Excellence. He has been published in a number of prestigious journals, including Transactions on Knowledge and Data Engineering, as well as Data Mining & Knowledge Discovery. He also serves on the editorial review board of the Journal of Computer Information Systems.
Dr. Argamon is a professor of computer science at Illinois Institute of Technology and the founding director of the school’s master of data science program. His research focuses on computational analysis of natural language style, specifically on discovering relationships between linguistic structures, individual reasoning, and social context. He works on applications in intelligence analysis, forensic linguistics, biomedical informatics, and humanities scholarship. He is a fellow of the British Computer Society.
Dr. Shen, a professor at the University of Minnesota’s Department of Statistics, has received numerous awards, including the 2012 John Black Johnston Distinguished Professorship; the 2012 Best Paper Award from the International Biometric Society; and a 2011 fellowship with the American Association for the Advancement of Science. He has taught courses on statistical learning and data mining. Additionally, he served as the chair-elect in 2011 for the American Statistical Association’s group on Statistical Learning and Data Mining, as well as the program co-chair in 2009 for the Institute of Mathematical Statistics.
Dr. Freund is a professor of computer science and engineering at UC San Diego, where he also serves as the faculty co-director for the data science and engineering program. He is an internationally known researcher in the field of machine learning, and he received the 2003 Gödel Prize in theoretical computer science, as well as the 2004 Kanellakis Prize. His work focuses on applications of machine learning algorithms in bioinformatics, computer vision, finance, network routing, and high-performance computing.
Dr. Alvarado is a professor at the University of Virginia who focuses his professional interests on data visualization, ontology, databases, iconography, text datasets, and transduction, among other areas. He is the associate director of SHANTI (i.e., the Sciences, Humanities & Arts Network of Technological Initiatives) and teaches courses on dataesthetics, the internet, introduction to digital liberal arts, and a myriad others. He has authored various impactful publications, including “Figuring the Data in a Database of Figures” in Dame Philology’s Charrette: Approaching Medieval Textuality through Chretien’s Lancelot (2012).
In addition to working as a professor at UCI’s Department of Computer Science, Dr. Smyth also serves as the director of the UCI Data Science Initiative, as well as the associate director of the Center for Machine Learning and Intelligent Systems. He has published a number of recent papers, including “Content Coding of Psychotherapy Transcripts Using Labeled Topic Models” in the IEEE Journal of Biomedical and Health Informatics (2015). In the winter of 2017, he will be teaching courses on projects in AI, probabilistic learning, and a seminar in data science.
Dr. Bosl of the University of San Francisco has many prominent publications to his name, including articles for the International Journal of Medical Research, Epilepsy and Behavior, and BMC Medicine. His research foci are nonlinear signal processing and machine learning in healthcare; the early detection of neurodevelopmental disorders using nonlinear EEG analysis; and cognitive phenotypes, consciousness, and electrophysiology, among others. He works at the intersection of healthcare and advanced technology, serving as the director of the master of science in health informatics program, and previously worked as a computational physicist before joining the Boston Children’s Hospital Informatics Program as a faculty member with Harvard Medical School.
Roger Eastman is a professor of computer science at Loyola University Maryland, where he teaches courses such as computer science; GUI design and implementation; theory of computation; and human-computer interaction, among others. His research interests include computing and the arts, as well as image registration (i.e., the alignment of images taken in different applications). He is the co-author of Image Registration for Remote Sensing (Oct. 2010) and he has started research initiatives on advanced sensors in manufacturing robots.
Dr. Rundensteiner is a professor of computer science at Worcester Polytechnic Institute, where she also serves as the director of the data science program. Her research focuses on how to make use of data and information in an effective manner in order to achieve goals in business, scientific discovery, health services, or personal endeavors. She’s affiliated with both IEEE and the Association for Computing Machinery. She was the 2011 recipient of the EPTS Innovative Principles Award and the 2007 Sigma Xi Outstanding Senior Faculty Researcher Award.
Dr. Kautz is the Robin and Tim Wentworth Director of the Goergen Institute for Data Science, as well as a professor in the Department of Computer Science at the University of Rochester. He teaches courses on methods in data-enabled research, computer models and limitations, pervasive computing, and artificial intelligence, among many others. He has been honored as a fellow by the American Association for the Advancement of Science, the Association for Computing Machinery, and the Association for Advancement of Artificial Intelligence. Notably, he received the IJCAI Computers & Thought Award; the Ubicomp 10-Year Impact Award; the AAAI Classic Paper Award; and the IAAI Deployed Application Award.
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