Abid Ali
Currently teaching:
Database Systems and Data Preparation
Foundations of Data Engineering
Capstone Project
Abid Ali is currently working in Capgemini as portfolio manager in the global insights and data practice supporting financial services group across banking and insurance. He has an undergraduate degree in computer sciences, a master's in business intelligence and data warehousing, another master's in information and knowledge strategy, an EMBA, and a Ph.D. in organizational leadership. He has various industry certifications such as Teradata certified master, SAFe agilist, Celonis sales professional, and Azure fundamentals. He has several years of international consulting experience leading, designing, and delivering large scale data and reporting solutions in the U.S., Great Britain, Europe, and Asia Pacific. He is experienced in various industries such as retail, insurance, finance, banking, telecom, and travel. Ali specializes in data architecture design and implementation, data warehousing and business intelligence, and advanced analytics. His research interests include data analytics, organizational leadership, diversity and inclusion, and social and industrial/organizational psychology. Ali delivers guest lectures in different universities and has also taught in Columbia University, NYC in their master's of applied analytics program.
Edward Arroyo has worked for both on-ground and online universities in a variety of capacities during the last fifteen years. During that period he has taught undergraduate and graduate mathematics courses as well as undergraduate programming and theoretical computer science courses. In addition, he worked for several years outside of academia as a test engineer for a digital media company. His research interests and publications are in the areas of combinatorics and image reconstruction algorithms in computerized and discrete tomography. He holds a Ph.D. in pure mathematics from the Graduate School and University Center of CUNY.
Lynd Bacon is an applied scientist. He is an Adjunct Associate Professor in the Division of Epidemiology, Department of Internal Medicine, the University of Utah. At Utah he teaches graduate and post-graduate courses in data science, machine learning, statistics, and research methods. He does research about health services delivery, product and service design, and genomic testing. His prior teaching experience includes having taught at The David Eccles School of Business at the University of Utah, Notre Dame’s Mendoza School of Business, The University of Chicago’s Booth School of Business, Rush University, The University of Illinois at Urbana-Champaign, and the University of Illinois at Chicago. Bacon has founded two software companies, and has held senior management and chief research officer positions in venture-funded start-up companies and in global business intelligence enterprises. Bacon has a PhD and an MA in cognitive and physiological experimental Psychology from the University of Illinois at Chicago, and an MBA with specializations in marketing, econometrics, and healthcare management from the Booth School of Business. He completed a two year postdoctoral fellowship in neuropsychology at Rush University in Chicago, where he did neuroscience research on healthy and patient populations.
Atef Bader has been working in the IT industry for many years and currently is a member of the technical staff at Lucent Technologies. He is a recognized expert in object-oriented technology, automated software testing, concurrent software systems and software architecture. His technical papers have been presented at numerous conferences and published in technical journals. Bader holds a PhD in computer science from Illinois Institute of Technology.
Nathan Bastian
Currently teaching:
Artificial Intelligence and Deep Learning
Decision Analytics
Nathan Bastian is a leader, practitioner, researcher, and educator of mathematical, computational, analytical, data-driven, and decision-centric methods to support the improvement and enhancement of decision-making in cyber security, national defense, military operations, human resources and manpower, healthcare, logistics, energy and finance. As a decision analytics professional, his expertise lies in the scientific discovery and translation of actionable insights into effective decisions using algorithms, techniques, tools and technologies from operations research, data science, artificial intelligence, systems engineering, and economics to design, develop, deploy and operationalize intelligent decision-support systems and models for descriptive, predictive and prescriptive analytics. Bastian 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. His primary research interest is computational stochastic optimization and learning for making inferences and decisions under uncertainty, where he conducts research in the areas of autonomous cyber decision-support systems, cyber modeling and simulation, and AI system assurance (security, robustness, reliability, and resilience). Bastian has authored over 50 refereed journal articles and conference proceedings, several book chapters, and one textbook. He is the recipient of numerous academic awards and honors to include a Fulbright Scholarship and National Science Foundation Graduate Research Fellowship, as well as multiple research grants. Prof. Bastian serves as an Associate Editor for five journals, as well as Referee for over 20 journals. He is an active member of MORS, INFORMS, ACM, IEEE, SIAM, AAAI and AAAS.
Daniel Baumgartner is an independent management consultant with more than 30 years of business process management and information technology experience. He has held leadership roles in management and information technology consulting services, was a partner in several large management consulting firms and has expertise in improving business processes, developing IT strategies and plans, leading the implementation of IT solutions to address business issues and selecting and implementing enterprise-wide software applications. Baumgartner has managed client business analysts and functional managers. Baumgartner has a master’s degree from Northwestern’s Kellogg School of Management.
Shreenidhi Bharadwaj is an educator, data enthusiast and an analytics practitioner. Currently, as M&A COE lead, he advises Private Equity/Venture Capital firms and C-level Executives on value creation, post-close synergies along with data & analytics(BI/AI) strategies focused on business outcomes. In his previous executive leadership role at Syndigo, Bharadwaj led the data strategy, data science, next-gen platform and M&A integrations. His expertise revolves around driving innovation, standardization, development & operationalizing machine learning models, data engineering at scale using on premise & cloud platforms, effective data visualizations, model-driven design & algorithmic thinking. He was elected to the Global Standards Architecture Board at GS1, where he worked with global industry leaders to develop standards, road maps, governance & compliance requirements relating to food services, healthcare, retail, supply chain & CPG/FMCG verticals. His experience spans multiple verticals such as Healthcare, Retail, MarTech, AdTech, EdTech, FinTech, Telecom and public safety with companies operating as startups to fortune 100. Bharadwaj is a graduate of the MS Analytics program from The University of Chicago and BE, Electronics and Communications from Manipal Institute of Technology, India. His interests include Data Engineering, Intelligent Systems and Robotics, Machine Learning at scale, Data Visualization & Knowledge Engineering
Chad Bhatti
Currently teaching:
Supervised Learning Methods
Advanced Modeling Techniques
Capstone Project
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.
Moses Boudourides
Currently teaching:
Math for Data Scientists
Python for Data Science
Web and Network Data Science
Moses Boudourides was Professor of Mathematics at the University of Patras in Greece (from 1998 until September 2017, when he retired). Previously he was Associate Professor at the Department of Electrical and Computer Engineering of the Democritus University of Thrace in Xanthi, Greece (1982-1998). In addition he was Visiting Professor at the Department of Mathematics of the University of California Irvine (1990-1991). His PhD is from the Johns Hopkins University (1978) in Baltimore. His undergraduate studies were at the National Technical University of Athens in Greece, from where he graduated with a Diploma in Chemical Engineering (1973). His research interests and publications are on dynamical systems, social network analysis, social media data analysis, digital humanities and computational social science. Boudourides was recently awarded a Robert K Merton Visiting Research Fellowship from the Institute for Analytical Sociology (IAS) at Linköping University in Sweden.
Candice Bradley
Currently teaching:
Introduction to Data Science
Data Visualization
Data Governance, Ethics, and Law
Candice Bradley is a quantitative social scientist with over two decades experience teaching statistics and research methods at the graduate level. Trained at UC Irvine’s interdisciplinary Social Relations program, Bradley followed her doctoral studies with a Fulbright Fellowship to Africa, engaging in fieldwork documenting the start of Kenya’s fertility decline. As a quantitative anthropologist, a rather rare specialization in a discipline not known for number crunching, Bradley has published research using multivariate analyses in such areas as demography, academic advancement of women, cross-cultural methodology, deep ecology, and time allocation. Bradley earned her PhD and masters from UC Irvine in social sciences.
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.
Anil Chaturvedi
Currently teaching:
Unsupervised Learning Methods
Practical Machine Learning
Marketing Analytics
Anil Chaturvedi has over 25 years of professional experience at companies such as AT&T Bell Labs, Kraft Foods, Capital One, and Accenture. He has provided consulting services to Bank of America, Fannie Mae, Johnson & Johnson, and Proctor & Gamble. Chaturvedi's general research interests include the areas of multivariate analysis – multi-linear models, information mining, and business insights. He co-authored a book, Mathematical Tools for Applied Multivariate Analysis, with the late Professor Paul Green (University of Pennsylvania) and Professor J. Douglas Carroll (Rutgers University). Chaturvedi has patented and published analytical methods for direct marketing, information mining, market segmentation, new product development, product positioning, customer loyalty, consumer promotion mix optimization, and brand btrategy. He earned his PhD from Rutgers University and PGDM from IIM Ahmedabad, India.
Kimberly Chulis
Currently teaching:
Analytics Entrepreneurship
Business Leadership and Communications
Data Science in Practice
Data Governance, Ethics, and Law
Kimberly Chulis is cofounder and CEO of Core Analytics, LLC. With over 20 years of professional advanced analytics experience, Chulis demonstrated analytic expertise on projects at numerous companies and industries, including Microsoft, IBM, Dell, WellPoint, HCSC, UHG, Great West, Accenture, Ogilvy, MSN, Sprint/Nextel, Commonwealth Edison, TXU, Eloyalty, SPSS, General Motors, Allstate, Cendant, and others in the financial, telecommunications, healthcare, energy, nonprofit, retail, and educational sectors. Her current research focus is on Social Media Behavioral Applications, Big Data, Applied and Predictive Analytics. Her secondary focus is social media as a platform to drive positive public health, disease-related community support, early detection and intervention for healthcare outcomes. Chulis holds a PhD from Purdue University's Health and Human Sciences Consumer Behavior program, and a masters degree in economics with a focus on health economics and econometrics from the University of Illinois at Chicago. She is faculty in the MS Predictive Analytics program at Northwestern University since 2014, and served in the role of Director, Business Analytics Certificate Program at University of Chicago Graham School.
Kathryn Daugherty
Currently teaching:
Data Visualization
Accounting and Finance for Analytics Managers
Kathryn Daugherty is a manager, revenue management and data science, at Universal Parks & Resorts. Her work includes modeling, visualization, pricing strategy, data governance and ad hoc analysis. Previously, she held several positions at Disney Parks & Resorts, where she worked in analytics, revenue management, pricing and finance roles. Daugherty has also held finance positions at Lockheed Martin, banking positions at JPMorgan Chase and US Bank. 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).
John Derwent
Currently teaching:
Introduction to Data Science
Capstone Project
Data Science in Practice
John Derwent is an experienced learning professional with an analytical bent and is currently adjunct faculty and program advisor for the Certificate in Predictive Business Analytics program and adjunct faculty with the Masters of Science in Predictive Analytics program at Northwestern University. After completing his graduate work, he moved into the world of corporate education and development practicing mainly in the software and technology industries. Most recently, Derwent led content and offering strategy for education services in the business analytics area with SPSS, Inc and IBM. His main professional goal is to help people understand how mathematics can improve businesses and the world. Derwent earned his PhD at Northwestern University in applied mathematics.
Sharon Dill
Currently teaching:
Database Systems and Data Preparation
Python for Data Science
Sharon Dill is an Information Technology professional with 35 years’ experience. She recently retired from the Professional Association of Diving Instructors (PADI) where she was the Chief Information Officer and the Chief Data Officer. Throughout all the technology implementations over Sharon’s 28 years at PADI, data was always at the center and a focus. Sharon created an enterprise data rich environment that allowed for strategic planning and business decisions to be made based on data-driven insights. She also has vast knowledge on global data requirements, third party integrations data sharing and implementing self service business intelligence with data warehouse implementation. Sharon has a doctorate in computer science from Colorado Technical University with an emphasis in Enterprise Information Systems and she is a graduate of Northwestern’s M.S. in Predictive Analytics program.
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.
Lawrence Fulton
Currently teaching:
Applied Statistics with R
Practical Machine Learning
Lawrence Van Fulton is an associate professor at Texas State University. His research interests include the application of machine learning, simulation, optimization, statistics, and decision science to aeromedical evacuation, sustainability, and healthcare in general. Currently, he is applying machine learning techniques for classification of brain imagery. He has published in journals including Multivariate Behavioral Research, Pain, Interfaces, Simulation, IIE Transactions on Healthcare Systems Engineering, Journal of Healthcare Management, Journal of Nursing Administration, and many others. 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.
Noah Gift
Currently teaching:
Practical Machine Learning
Capstone Project
Analytics Application Engineering
Computer Vision
Noah Gift is lecturer and consultant at both UC Davis Graduate School of Management MSBA program and the Graduate Data Science program, MSDS, at Northwestern . He is teaching and designing graduate machine learning, AI, Data Science courses and consulting on Machine Learning and Cloud Architecture for students and faculty. These responsibilities including leading a multi-cloud certification initiative for students. He has published close to 100 technical publications including two books on subjects ranging from Cloud Machine Learning to DevOps. Gift received an MBA from UC Davis, a M.S. in Computer Information Systems from Cal State Los Angeles, and a B.S. in Nutritional Science from Cal Poly San Luis Obispo. Currently he is consulting startups and other companies, on Machine Learning, Cloud Architecture and CTO level consulting as the founder of Pragmatic AI Labs. His most recent book is Pragmatic AI: An introduction to Cloud-Based Machine Learning (Pearson, 2018).
Qung Go has taught project management in the MS in Data Science Program since 2011. Previously Qung designed and developed the Project Management for Professionals course in the Professional Development Program and taught the course. Qung also previously taught the capstone course in the MS in Information Technology Program at Northwestern’s Robert R. McCormick School of Engineering and Applied Science and was a member of the Industry Advisory Board for the program. Prior to teaching, Qung worked as a senior executive for a large global information consulting company for more than 27 years. He managed a wide range of projects involving hundreds of personnel resources and implemented leading-edge projects for major telecommunications, cable, and high-tech clients. Qung is a certified project management professional PMP® and has an MBA from Northwestern’s Kellogg School of Management.
Phillip Goldfeder
Currently teaching:
Math for Data Scientists
Data Governance, Ethics, and Law
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.
Lance Levenson is a Principal Data Scientist and Analytics Team Lead at Walmart Labs where he is responsible for architecting, designing, and implementing enterprise level machine learning and analytics products/services for various internal business groups across the globe. Prior to Walmart, he was an analytics consultant working with various trading firms and hedge funds in the Chicago and New York areas, building and implementing quantitative and predictive models using massive financial datasets. He is an experienced instructor of quantitative disciplines at both the undergraduate and graduate levels, having also held teaching positions in the fields of physics, astronomy, and mathematics. Levenson also teaches the Advanced Modeling and Risk Analytics courses in the Predictive Business Analytics Program, where he received the 2015 Distinguished Teaching Award. He holds bachelor’s degrees in Astrophysics and Mathematics from Arizona State University and a master’s degree in Financial/Applied Mathematics from the University of Chicago.
Alianna Maren
Currently teaching:
Natural Language Processing
Artificial Intelligence and Deep Learning
Capstone Project
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.
William Mickelson
Currently teaching:
Applied Statistics with R
Supervised Learning Methods
Unsupervised Learning Methods
Decision Analytics
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.
Thomas Miller
Currently teaching:
Unsupervised Learning Methods
Natural Language Processing
Knowledge Engineering
Analytics Consulting
Tom Miller is faculty director of the data science program at Northwestern University. He started with the program when it was called predictive analytics and for the past ten years has been responsible for growing the curriculum, introducing specializations, and designing numerous distance learning courses. During the 2020-21 academic year, he will be teaching natural language processing, knowledge engineering, and unsupervised learning methods. Tom is the author of six textbooks about data science published by Pearson Education. He also owns Research Publishers LLC, a California company that, in addition to publishing books and periodicals, provides research and consulting, measurement services, event forecasting, and natural language processing solutions. Most recently, Tom has been developing a new journal, Data Science Quarterly, promoting data science as a discipline and showing its relevance to social and political discourse. The online version is available at https://www.data-science-quarterly.com.
Ciju Nair is Global Marketing Science Lead at Kellogg Corp. and an adjunct faculty at Northwestern University in Chicago. He oversees cross functional teams on Business Drivers & Experience analytics and partners with Enterprise IT and integrated marketing. He also undertakes independent consulting projects on select topics of interest. Prior to joining Kellogg Corp, Nair led the Marketing Effectiveness & ROI practice at Kantar Analytics and has a unique background that spans 20+ years in building empirical econometric models, marketing mix modeling, business effectiveness consulting and academic research. He has extensive experience in traditional and digital media and thinks of solutions that are holistic and adds strategic value to clients. He has hands on experience delivering actionable consumer insights to drive profits and a track record of reversing declining trends in both ecommerce and retail marketing environments. Nair has also held leadership positions at Starcom MediaVest Group, Morningstar Inc. and Procter & Gamble. He has extensive experience integrating enterprise IT and advanced modeling solutions using onsite/offshore teams & vendors to deliver custom global analytic solutions for Fortune 50 advertisers across industry verticals. Nair received a Ph. D in Marketing from the Olin Business School, Washington University in St. Louis. He also holds a MBA from the S.P. Jain Institute of Management & Research, Mumbai, and a graduate degree in Mechanical Engineering from the Coimbatore Institute of Technology.
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
Currently teaching:
Applied Statistics with R
Time Series Analysis and Forecasting
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.
Nethra Sambamoorthi
Currently teaching:
Introduction to Data Science
Database Systems and Data Preparation
Capstone Project
Nethra Sambamoorthi is an adviser for CRMportals, a global analytics consulting company. In addition to delivering client solutions, he specializes in building best practices in data capture, designing innovative solutions and creating thought leadership in consumer dynamics and marketing intelligence. Sambamoorthi has produced many of the best practices in predictive modeling across multiple industries—credit cards, auto insurance, investment services, retail, CPG and pharmaceutical marketing. He previously was an entrepreneur, focusing on developing and delivering real-time email marketing systems and ROI trends. He has also worked with XLmarketing Corporation, Acxiom Corporation, Schering Plough Corporation, American Express and Prudential, using marketing analytics to address regulatory aspects while optimizing for return on investment and for consumer engagement. Sambamoorthi received a PhD from the University of Pittsburgh, specializing in applied multivariate statistics, and an MA in mathematics and an MSc in statistics from the University of Madras.
Dipyaman Sanyal
Currently teaching:
Accounting and Finance for Analytics Managers
A CFA charter holder and ex-hedge fund quant, Dipyaman Sanyal is the founder of dono consulting, a boutique quantitative research and financial modeling firm. A Commonwealth scholar who is recognized as one of the top data science academicians in India (2017 and 2018, Analytics India Magazine), Deep has also led three of the top-ranked analytics programs in India. Along with teaching at Northwestern SPS, he is the Program Director for the Postgraduate Program in Data Science and Machine Learning (PGPDM) offered jointly by the University of Chicago, IBM and Jigsaw Academy in India.
Deep spent most of his corporate life in New York as a quantitative and financial analyst for companies like Dow Jones Indexes, The Blackstone Group, Sorin Capital, and Thomson Reuters. He has a MS in Applied Economics from University of Texas at Dallas and a MA and BA in Economics from Jadavpur University, Kolkata. He is a PhD candidate in one of India’s leading economics programs at Jawaharlal Nehru University.
Marianne Seiler
Currently teaching:
Introduction to Data Science
Analytics Consulting
Marianne Seiler is a senior principal in Accenture’s customer analytics practice. She works with global marketing, sales and customer service leaders to drive growth and shareholder value through the application of analytics. She specializes in helping clients restructure organizations, reengineer processes, implement enabling technologies and establish performance metrics to convert analytic insights into market actions. Her research and writing is focused on data-driven marketing, high-performance customer management and analytics application in customer-facing functions. Seiler previously held executive positions in marketing and business development at Viacom, Harte Hanks and Dow Jones. She has a PhD in management from Claremont Graduate University and an MBA from the University of Texas at Austin.
Dr. Jennifer Sleeman has over 11 years of machine learning experience and over 21 years of software engineering experience, in both academic and government/industry settings. Her research is primarily centered around generative models, natural language processing, semantic representation and deep learning. Sleeman earned her Ph.D. in Computer Science from the University of Maryland, Baltimore County (UMBC) working under the expert direction of Dr. Tim Finin and Dr. Milton Halem in 2017. Her early work was published in AI Magazine in 2015. She formalized how one would apply generative topic models for graphs and introduced this work at the International Semantic Web conference in 2015. Following her novel contribution of generative topic modeling for graphs, she used generative topic models to discover cross-domain influence and relatedness among scientific research papers. Her Ph.D. thesis adapted the theory of data assimilation, which is based on theoretical approaches for temporal integration of physical observations with dynamic simulation models, and applied it to a large, multi-sourced, scientific text collection. This methodology provided a new approach for multi-source data integration and trend prediction using an innovative method for filtering noise and accounting for missing model data. Her work was awarded a Microsoft AI for Earth resource grant in 2017 and 2018 and also won the best paper award in the Semantic Web for Social Good Workshop presented at International Semantic Web Conference in 2018. Sleeman was recognized as a top data scientist in the Washington D.C. area in 2017 (FemTech) and was recognized as a 2019 CSEE Rising Star. She also teaches Introduction to Artificial Intelligence at the University of Maryland, Baltimore County (UMBC).
Bradley Smith
Currently teaching:
Sports Performance Analytics
Sports Management Analytics
Brad Smith currently works for a professional baseball organization. Previously, he served as the assistant director of sports analytics and a research assistant in the Brain, Behavior, and Emotion Lab at the University of Nebraska-Lincoln. Prior to these positions, Smith worked for 5 years as an assistant baseball coach at UNL, and an actuary for nearly 3 years. In 2006, he received the Associate of the Society of Actuaries designation before returning to graduate school. Smith received a BS and MS in mathematics from Pittsburg State University, an MS in statistics from the University of Nebraska and is completing a PhD in educational psychology with a specialization in qualitative, quantitative and psychometric methods from the University of Nebraska.
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.
Irene Tsapara
Currently teaching:
Applied Statistics with R
Introduction to Data Science
Decision Analytics
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.
Donald Wedding
Currently teaching:
Applied Statistics with R
Practical Machine Learning
Capstone Project
Donald Wedding has over twenty years of experience in the field of Data Science. He has worked in a variety of industries including Banking, Insurance, and Telecom. He has also spent time as a freelance consultant and spent over 10 years working at the SAS Institute. He currently works as a Customer Facing Data Scientist for Data Robot. Dr. Wedding joined the faculty of Northwestern in 2013 and has developed and taught a variety of courses. He is a frequent presenter on analytic topics at conferences, user groups and industry meetings. He is particularly interested in segmentation and cluster analysis and machine learning algorithms. He received his PhD in Engineering Systems from the University of Toledo, where he specialized in machine learning and expert systems. He has an MS in Engineering from the University of Toledo, an MS in Management from the University of Akron and an MS in Data Mining from Central Connecticut State University. He is also a graduate of the Stonier Graduate School of Banking.
Joe Wilck
Currently teaching:
Supervised Learning Methods
Unsupervised Learning Methods
Decision Analytics
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.