Predictive Analytics

Faculty

Nathan Bastian

Nathan Bastian is a leader, practitioner, researcher, and educator of quantitative, economic, computational, analytic, data-driven, and decision-centric methods to support the improvement of operational processes, decision-making, performance, and management in healthcare delivery systems, human resources, military operations, and humanitarian logistics. He is an experienced operations researcher, data scientist, decision analyst and industrial engineer who discovers and translates data-driven, actionable insights into effective decisions using mathematics, statistics, engineering, economics, and computational science to develop decision-support models for descriptive, predictive and prescriptive analytics. Nathan holds a Ph.D. degree in industrial engineering and operations research from the Pennsylvania State University, M.Eng. degree in industrial engineering from Penn State, M.S. degree in econometrics and operations research from Maastricht University, and B.S. degree in engineering management (electrical engineering) with honors from the U.S. Military Academy at West Point.

Currently teaching:Decision Analytics

Chad Bhatti

Chad R. Bhatti is vice president and senior statistician in the mortgage banking division of J. P. Morgan Chase. He previously was a National Science Foundation VIGRE (vertical integration of research and education) postdoctoral researcher in mathematics at Tulane University. His professional and research interests are in financial econometrics and statistical computing. Bhatti is trained in applied mathematics and statistics and has a PhD in statistics from Rice University.

Currently teaching:Risk Analytics
Advanced Modeling Techniques

Ernest Chan

Ernest Chan is the managing member of QTS Capital Management, LLC. His career has focused on the development of statistical models and advanced computer algorithms to find patterns and trends in large quantities of data. Chan has applied his expertise in statistical pattern recognition to projects ranging from textual retrieval at IBM Research, mining customer relationship data at Morgan Stanley, and statistical arbitrage trading strategy research at Credit Suisse First Boston, Mapleridge Capital Management, Millennium Partners, MANE Fund Management, EXP Capital Management. He is the author of Quantitative Trading: How to Build Your Own Algorithmic Trading Business and Algorithmic Trading: Winning Strategies and Their Rationale. He is also a popular financial blogger at epchan.blogspot.com. Chan has MS and PhD degrees in theoretical physics from Cornell University and an BSc from University of Toronto.

Currently teaching:Risk Analytics

Kathryn Daugherty

Kathryn Daugherty is a senior analyst, revenue management, on the business analytics and industrial engineering team at Universal Orlando Resort. Over the last several years, her work has focused on modeling, optimization, visualization, business strategy, reporting, data governance and general analysis. Previously, she held several positions at Walt Disney Parks & Resorts, where she worked in business analyst, revenue management, pricing and finance roles. Daugherty has also held finance positions at Lockheed Martin, banking positions at JPMorgan Chase and US Bank and served as a graduate teaching assistant at both Northwestern University and Rollins College. She received her MS in predictive analytics from Northwestern University, her MBA with specializations in finance and management from Rollins College and a BA in economics from the University of Colorado at Boulder. Daugherty holds a lean six sigma green belt certification, a six sigma green belt certification, and is a certified project management professional (PMP). Additionally, she volunteers with the Central Florida Project Management Institute chapter.

Currently teaching:Data Visualization

Lawrence Fulton

Lawrence Van Fulton is an assistant professor in the Rawls College of Business at Texas Tech University. His research interests include the application of simulation, optimization, statistics, and decision science to aeromedical evacuation, sustainability, and healthcare in general. Prior to his arrival at Texas Tech University, Fulton was an assistant professor in the CIS&QM Department of the McCoy School of Business, Texas State University. He has published in journals including Interfaces, Simulation, IIE Transactions on Healthcare Systems Engineering, Journal of Healthcare Management, Journal of Nursing Administration, as well as many others. Fulton serves on the Editorial Board for Military Medicine and previously for the Journal of Defense Modeling & Simulation. He is currently a co-PI on a $500,000 medical research grant sponsored by the Department of Defense. Prior to his work in academia, Dr. Fulton served a quarter of a century in the U.S. Army Medical Department, attaining the rank of colonel prior to his retirement. His earned PhD in management science is from the University of Texas at Austin, and he holds five separate masters degrees, including a master of science in statistics from UT Austin.

Currently teaching:Advanced Modeling Techniques

Don Graham

Don Graham has worked for nearly 20 years in engineering and management for Lockheed Martin, AVIS, nad the NYC Board of Education. Graham has taught in higher education on topics ranging from college math, logistics systems, and supply chain optimization. Graham earned his PhD in Transportation Systems Optimization from the University of Central Florida.

Currently teaching:Special Topics in Predictive Analytics

Sunil Kakade

Sunil Kakade has over 18 years' experience in various technical and leadership roles focused on lT transformation initiatives, analytics driven software development and big data technologies. Sunil is a big data and data science evangelist with hands-on expertise in implementing Hadoop and NoSQL technologies. Sunil leads the architecture and delivery of business systems for a fortune 100 company. His areas of interests include machine learning, data science, legacy systems modernization and open source technologies. His research papers have been presented at multiple international conferences and published in technical journals. He has patents pending in areas of IT and retail processes. Kakade has a MS in information technology and management from the Illinois Institute of Technology at Chicago and a MS in mechanical engineering from the Indian Institute of Technology, Madras, researching applications of artificial intelligence to manufacturing processes.

Currently teaching:Big Data Management and Analytics

Alianna Maren

Dr. Alianna J. Maren is a researcher and thought-leader in predictive analytics, with four patents, ranging from predictive analytics to knowledge discovery to sensor fusion (the latter was named as Patent of the Week by the New York Times shortly after it was awarded). Her expertise ranges from statistical thermodynamics to model patterns and rapidly-changing systems to text analytics in knowledge discovery to neural networks. As cofounder and chief scientist with EagleForce Associates (later Viziant Corporation), she was key in supporting DIA's panels on knowledge discovery and predictive analytics. Her innovative work at Viziant led to  three of her four patent awards and two rounds of investment funding. As senior scientist with Accurate Automation Corporation, Maren's innovations in neural networks, sensor fusion, and system identification were the basis for receiving over $3.4M in federally-funded SBIR and STTR contracts. She introduced and taught the first course in Knowledge Discovery at Georgetown University, and also Cloud Computing at both George Mason and Marymount Universities Her first book, the “Handbook of Neural Computing Applications” (Academic, 1990), became a landmark in the emerging neural network arena. Maren holds a Ph.D. in physical chemistry from Arizona State University and a bachelor's degree in mathematics from the University of North Dakota.

Currently teaching:Text Analytics
Special Topics in Predictive Analytics

Thomas Miller

Thomas W. Miller is faculty director of the Predictive Analytics program. He has helped to design courses for the program, including Marketing Analytics, Advanced Modeling Techniques, Data Visualization, Database Systems and Data Preparation, Web and Network Data Science, Sports Management Analytics, and the capstone course. He has taught extensively in the program, offering courses in modeling methods, machine learning, and web analytics. Before joining the faculty at Northwestern, Miller spent fifteen years in business IT in the computer and transportation industries. He also directed the A. C. Nielsen Center for Marketing Research and taught market research and business strategy at the University of Wisconsin-Madison. Miller is consulting editor to Pearson Education in practical data science. His books with Pearson include Data and Text Mining, Modeling Methods in Predictive Analytics with Python and R, Web and Network Data Science, Marketing Data Science, and Sports Analytics and Data Science. Miller is also owner of Research Publishers LLC and its ToutBay division, a technical publisher and provider of data science services. He has consulted widely in the areas of retail site selection, product positioning, segmentation, and pricing in competitive markets, and has worked with predictive models for more than thirty years. Miller holds a PhD in psychology (psychometrics) and a master's degree in statistics from the University of Minnesota, and an MBA and master's degree in economics from the University of Oregon.

Currently teaching:Web and Network Data Science
Data Visualization

Thomas Robinson

Tom Robinson is the Director of Football Research for the Dallas Cowboys. In his role he heads up all analytic efforts related to football, and works closely with the team’s scouts, coaches, and executives. Prior to joining the Cowboys, Tom worked for almost 10 years as a consultant assisting clients with project management, business intelligence, analytics, and data warehousing. Tom graduated from UT-Austin (McCombs School of Business) in 2000 with a BBA (Bachelors of Business Administration) in Management Information Systems. Tom also is a 2014 graduate of the MSPA (Predictive Analytics) program at Northwestern University.

Currently teaching:Sports Performance Analytics
Sports Management Analytics

Syamala Srinivasan

Syamala Srinivasan has over 30 years of industrial and academic experience in analytics applied to solve business problems. Currently, Syamala is leading the business analytics practice at CGN Global, Inc. She also teaches at National Louis University in the department of mathematics. Prior to CGN, she worked at Caterpillar Inc., at 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, focusing on developing and deploying analytical solutions to complex business problems in the entire value chain of product development, manufacturing, supply chain, marketing, and sales. 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. Her prior academic appointments include Bradley University, Chicago State University, Northern Illinois University, and Colorado State University. Srinivasan has a PhD in statistics and a MS in statistics from Colorado State University. She has several management and leadership certificates from Wharton & Kellogg’s school of business.

Currently teaching:Marketing Analytics

Jennifer Wightman

Jennifer Wightman is a principal research scientist at Battelle, the world’s largest nonprofit research and development organization. She works in a data analysis group, working on problems ranging from health to national security, and specializes in machine learning applications. Wightman was previously an assistant professor of mathematics at Coastal Carolina University. She earned a PhD in computational and applied mathematics from Rice University.

Currently teaching:Advanced Modeling Techniques