Chad R. Bhatti is a Principal Data Scientist at Citizens Bank. He has worked in data science and modeling in both front-end (marketing) and back-end (risk) in the banking industry for over fifteen years. Prior to working in the banking industry he was a National Science Foundation VIGRE postdoctoral researcher in the Department of Mathematics at Tulane University. He has taught graduate and undergraduate courses in the traditional areas of statistics, e.g. mathematical statistics, linear regression, and generalized linear models, and in their applications, e.g. introductory and advanced modeling courses and topics courses like financial modeling. Dr. Bhatti is trained in applied mathematics and statistics and has a PhD in Computational Statistics from Rice University.
Carrie Dugan has held a variety of positions instructing and writing curriculum for all undergraduate levels of online mathematics courses. In addition to her teaching experience, she has numerous years of actuarial experience working with both defined benefit pension plans and self-funded health care plans. Some of her current research interests include character theory, graph theory, and game theory. She holds a PhD in pure mathematics from Kent State University and a BS in mathematics with a minor in computer science from Marshall University.
Philip Goldfeder has worked as a business consultant with Booz & Company (formerly Booz Allen and Hamilton) and as an educator for both traditional and online universities. He is currently a faculty member at several online universities. His consulting specialization is mergers and acquisitions, primarily in the spirits and automobile industries. He earned a PhD in applied mathematics from Northwestern’s Robert R. McCormick School of Engineering and Applied Science.
William Mickelson
Currently teaching:
Applied Statistics with R
Supervised Learning Methods
William T. Mickelson is an applied statistician with more than 25 years of statistical consulting, measurement, evaluation and survey research experience in both academia and industry. Mickelson was on the faculties of the University of Idaho, the University of Nebraska–Lincoln, and the University of Wisconsin–Whitewater. He has also worked with the RAND Corporation as a policy analyst and with Chamberlain Research Consultants of Madison, Wisconsin, as a senior consultant and division director. Mickelson has taught across the entire spectrum of research and statistical topics, including applied statistics, probability, modern robust and nonparametric methods, multivariate methods, regression, experimental design and ANOVA and mathematical and statistical modeling. His current research interests include the robustness of commonly used statistical tests and predictive models, the use of modern resampling and nonparametric statistical methods and the teaching and learning of statistical reasoning, thinking and literacy. Mickelson received his PhD in educational psychology in quantitative methods from the University of Wisconsin–Madison and a master’s degree in statistics from Michigan State University.
Melvin Ott is owner and president of Melvin Ott & Associates, LLC, a statistical and economic consulting firm. He has made presentations for private companies on predictive modeling in healthcare, banking, and market research. Ott has worked with predictive models for more than thirty years. His consulting practice focuses on applications in healthcare payment methodologies and expert witness testimony plus adjunct teaching at several universities in statistics, mathematics, and market research. Previously, Ott was director of research and database for Ingenix, Inc., director for data management at a large medical center (responsible for SAS online access to the medical center data and performed the survival analysis for the kidney transplant program) and was a director for reimbursement for a Blue Shield plan. He is a SAS programmer and Visual Basic programmer with online logistic regression and simulation applications. He holds a PhD in statistics from Oregon State University and a master's degree in mathematics from Utah State University.
Jamie Riggs works with the Statistics for Physical and Engineering Science Institute and specializes in statistical methods used in astronomy, physics, and engineering. She has worked with researchers from the Adler Planetarium, the American Association of Variable Star Observers, the Laboratory for Atmospheric and Space Physics, and the National Radio Astronomy Observatory on numerous projects. Riggs was with Sun Microsystems as a senior staff statistician, where she was co-issued three patents, each involving statistical methodologies. She worked on warranty cost estimating using general mixed models, tape library reliability and survivability, spare parts logistics time and location forecasting, and a number of designed experiments. Riggs worked as a mathematical scientist at The Boeing Company, having worked with W. Edwards Deming and his associates. Riggs worked with the Forecast Systems Laboratory of the National Oceanic and Atmospheric Administration on meteorological forecasting. Riggs earned her PhD in applied statistics and research methods from the University of Northern Colorado and MS degrees in applied mathematics and statistics and physics from Wichita State University.
Syamala Srinivasan
Currently teaching:
Applied Statistics with R
Supervised Learning Methods
Syamala Srinivasan has been a faculty of the Data Science program at Northwestern University since 2013. She teaches courses in Deep Learning, NLP, Machine Learning and Modeling. She has over 35 years of industrial and academic experience in data science. She has applied data science methods to solve business problems. Currently, Syamala is the Chief Analytics Officer at Tada Cognitive solutions, Inc. She is integrating AIML business solutions with the Tada platform. Prior to Tada, she worked for the US Army in predictive maintenance of ground vehicles, for 4 years. Before that, she worked at Caterpillar Inc., in various technical and management positions for 22 years. She started and developed the Department of Analytics and was the director of analytics before retiring from Caterpillar. She focused on developing and deploying analytical solutions to complex business problems in the entire value chain of product development, manufacturing, supply chain, marketing, sales, and human resources. She developed innovative techniques when solving complex business problems and received two U.S. patents. One of those patents was used to spin off a new startup company, called Akoya. It was one of the first Data Science companies. Akoya was then integrated into Birla soft, part of Birla Technologies Inc.
Syamala has a Ph.D. and M.S. in Statistics from Colorado State University. She also received a M.S. and a B.S in Mathematics from India.
Her prior academic appointments include National Louis University, Bradley University, Chicago State University, Northern Illinois University, and Colorado State University. She has been teaching 401, 410, 422, 450, 453 and 458 at Northwestern University.
Irene Tsapara
Currently teaching:
Applied Statistics with R
Unsupervised Learning Methods
Irene Tsapara has worked in the financial industry for 5 years as a financial engineer, building financial algorithms for trading systems at Goldman Sachs/Hull Trading and Hedge Fund Research company. She has taught for over 20 years in universities across the US and Europe and speaks English and Greek fluently and she has a good knowledge of French and Spanish. Tsapara earned her PhD in mathematical computer science from University of Illinois with a focus in computational learning theory and universal algebra. She holds a masters in computer science from University of Illinois and a bachelors in mathematics from University of Patras.
Philip Waggoner is the Director of Data Science at YouGov America. Waggoner is also a research scholar at Columbia University, and an adjunct professor of data science at several schools. He has published widely, including two books with Cambridge University Press, on computational social science, responsible automated decision making, experimental design, and open-source statistical computing. Prior to entering industry as a data scientist, Waggoner was a member of the computational social science faculty at the University of Chicago and before that, the College of William & Mary. For more information, visit his site: https://pdwaggoner.github.io.
Joe Wilck has been teaching analytics, operations research, and systems engineering courses since 2006. His research specialization is in applied optimization, and his research has been funded by the National Science Foundation, the Department of Energy, and industry. He is a member and volunteer for a number of professional organizations, including INFORMS. He is a licensed and registered professional engineer. Wilck received his PhD in industrial engineering and operations research from Pennsylvania State University, and his MS and BS from Virginia Tech in industrial and systems engineering.