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Analytics Lecturers

Sema Barlas, PhD, MScA Program Director
Courses: Introduction to Statistical Concepts, Research Design for Business Applications

Dr. Barlas is the Director of MSc in Analytics Program. Her expertise is in the areas of applied marketing models, statistics, research methodology, and consumer behavior. Barlas’s research has been published in journals such as Organizational Behavior and Human Decision Processes and Applied Journal of Econometrics with over 500 citations. Prior to the University of Chicago, Barlas had taught at Argosy University in Chicago, McGill University in Canada, and University of Groningen in Netherlands and worked in industry for several years. At Sears, Roebuck, and Co., she developed credit risk, credit revenue, and line-of-credit models to manage acquisitions and portfolios of existing accounts. At Experian, she led projects in all aspects of database marketing. Barlas earned her PhD from the University of Chicago in Quantitative Psychology and Research Methodology and Master’s degree from University of Illinois at Urbana/Champaign in Applied Statistics.

Shaddy Abado, PhD
Courses: Linear Algebra & Matrix Analysis

Shaddy Abado is a Staff Data Scientist at General Electric Digital, where he researches and develops data analytics solutions for equipment predictive maintenance, condition based monitoring and Industrial Internet of Things. He also has five years of experience instructing in a graduate professional studies setting. Prior to joining GE, he researched light-fluid interactions and developed different sensors and numerical methods to analyze these interactions. He has presented research at national conferences and published papers in peer-reviewed journals and conference proceedings. He received his MSc and PhD in aerospace and mechanical engineering from the University of Notre Dame.

Arnie Aronoff, PhD
Courses: Leadership Skills

Arnie Aronoff is formerly the senior director for human resources at Princeton University and the University of Chicago, Arnie Aronoff has brought his professional expertise to educational, nonprofit, social service, and other organizations as a consultant, coach, and trainer for over fifteen years. Mr. Aronoff is an experienced instructor who has been teaching for the Professional Development Program at SSA since 1996. He earned his doctorate from the University of Chicago and pursued advanced training in organizational development from the National Training Laboratories in Applied Behavioral Science (NTL) and the Gestalt Institute of Cleveland.

Francisco Azeredo, PhD
Courses: Linear and Nonlinear Models for Business Application, Statistical Analysis, Optimization for Analytics

Francisco Azeredo is a financial economist and data scientist with eight years of experience working for leading financial institutions and consulting firms in the Chicago area. He is presently a vice-president and specialist at Northern Trust, leading analytics projects in FX high-frequency trading and digital client experience. He also consulted on credit risk, operational risk, and macroeconomic modeling. Prior to Northern Trust, he consulted on a wide range of high profile cases involving large financial institutions and multinational corporations. His research in financial economics was recognized by leading economists in the Handbook of the Equity Risk Premium, Intermediate Financial Theory, and an encyclopedia of behavior economics. He currently teaches statistical analysis, linear and non-linear statistical modeling and optimization in the MSc. Analytics at the University of Chicago Graham School. He holds a Ph.D. in Economics from the University of California, Santa Barbara.

Yuri Balasanov, PhD
Courses: Statistical Analysis, Linear and Nonlinear Models for Business Applications, Financial Analytics, Real Time Analytics, Machine Learning and Predictive Analytics

Dr. Balasanov earned his Master’s degree in Applied Mathematics and Ph. D. in Probability Theory and Mathematical Statistics from the Moscow State University, Russia. His primary expertise and research interests are in the area of stochastic modeling and advanced data analysis with applications in many fields including finance and economics, marketing, biology, medical studies, etc. Yuri has led many research teams running data analysis projects and developing well known statistical software. He has published multiple articles with his academic and applied research and presented at scientific conferences across the world. Yuri spent significant part of his career in major institutions of financial industry as quantitative researcher, quantitative trader and head of trading research, Chief Investment Officer and risk manager. He is the founder and the President of Research Software International. He combines his work in the industry with years of teaching experience at the Moscow State University, Department of Mathematical Statistics and the University of Chicago, Graduate Program on Financial Mathematics and Graduate Program on Analytics.

Mark Bennett, PhD
Courses: Financial Analytics

Dr. Bennett is a computer scientist who has been working in large datasets and real-time high performance computing for over two decades. His early work experience was in applied mathematics at Argonne National Laboratory and as research scientist at Unisys Corporation. Later he was a member of technical staff at AT&T Bell Laboratories, senior technical advisor and engineering manager at Northrop Grumman aerospace, senior technology specialist at XR Trading LLC and quantitative trader at Transcend Investments, LLC . He has published his findings in industry conferences. In addition to his appointment at the University of Chicago, Mark is currently director in the field of financial analytics at Bank of America Merrill Lynch. In 15 years there, he has managed groups of software engineers, performed live-trade economic analysis , and designed statistical forecasting algorithms . Mark holds a BS (Cum Laude ) from the University of Iowa, an MS from the University of Southern California, and a PhD from UCLA, all in computer science.

Shree Bharadwaj
Courses: Database Design & Implementation

Shree Bharadwaj is the Director of Emerging Products at IRI, a market research company that provides clients with Market Performance and Strategy, Consumer and Shopper Intelligence, and In-Market Execution. In this role he is responsible for Innovation, Product Ownership of solutions revolving around Consumer and Media science such as Segmentation, Targeting and Activation, Campaign Measurement using Big Data, Predictive and Prescriptive Analytics, and Hyper Personalization. He has over 15 years of experience in the Telecommunications, Finance, Healthcare, Retail/CPG, and the Public Safety domains. During his early years he was part of the core team that built products from scratch that currently serve 60 percent wireless 911 calls in the United States. In addition to having filed for a couple of patents, he has an extensive background in formulating product launch strategies that have helped over a dozen products move from inception to market. Shree has a Master of Science in Analytics degree from The University of Chicago and a Bachelor of Science in Electronics and Communications Engineering from Manipal Institute of Technology. When not catching up on the latest developments in AI, Digital & Media or IoT, he spends his time with family and playing volleyball.

Arnab Bose, PhD
Courses: Time Series and Forecasting, Health Analytics

Arnab Bose is the Managing Director at Abzooba, a data analytics company. He is a 20 year veteran who enjoys working with data and has extensive analytics experience in healthcare and finance. Arnab is also a consultant for analytics in automated vehicle control. He focuses on natural language processing and machine learning models for unstructured and structured data. Arnab serves on the board of the financial engineering graduate program at University of Southern California. Arnab holds MS and PhD degrees in electrical engineering from University of Southern California, and a BS in electrical engineering from the Indian Institute of Technology in Kharagpur, India

Anil Chaturvedi, PhD
Courses: Data Mining Principles, Marketing Analytics, Research Design for Business Applications

Dr. 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.  His general research interests include the areas of multivariate analysis methods for Big Data – multi-linear models, information mining, and business insights.  He has 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). He has patented and published analytical methods for Direct Marketing and Information Mining, Market Segmentation, New Product Development, Product Positioning, Customer Loyalty, Consumer Promotion Mix optimization, and Brand Strategy. He completed his PhD from Rutgers University, and PGDM from IIM Ahmedabad, India.  

Michelangelo D'Agostino, PhD
Courses: Machine Learning and Predictive Analytics

Dr. D'Agostino is a reformed particle physicist turned data scientist. He is currently the lead data scientist for Braintree, a Chicago-based online payments company that was recently acquired by PayPal. Prior to Braintree, Michelangelo was a senior analyst with the 2012 Obama re-election campaign. As a member of the campaign's highly successful analytics effort, he used data and statistical models to effectively raise money, recruit volunteers, and turn out voters. He holds a PhD in particle astrophysics from the University of California, Berkeley, where he got his start with machine learning through large scale data analysis of neutrino physics data from the IceCube experiment located at the South Pole. He has published extensively in scientific journals and has covered science and technology for The Economist.

Cris Doloc, PhD
Courses: Linear Algebra and Matrix Analysis

Cris Doloc, PhD is a Computational Scientist and an accomplished technology leader with more than 25 years of experience in Enterprise Software Architecture, Machine Learning & High Performance Computing. Cris holds a PhD in Computational Physics and he brings a wealth of scientific and technology expertise by working in both academia and the industry. He has spent the last 16 years in the field of computational finance where he has architected enterprise systems and developed pattern detection machine learning algorithms for several top-tier financial firms. Cris has been the Chief Technology Officer of Terra-Nova Financial, a self-clearing Broker dealer in US Equity and Options, and the Founder and Principal of Quantras Research Ltd., a boutique Research & Development firm specialized in providing expertise in the area of Computational Finance, Financial Engineering, and Quantitative Trading. He is also the founder of 3 tech startups.

Dafna Gabel
Courses: Marketing Analytics

Dafna Gabel is Executive Director, Global Marketing Performance at Catalina Marketing, a global personalized digital media company connecting shoppers to the brands they want. Dafna has over twenty years of experience in the field of Marketing Science Analytics in the CPG industry. Her career spans Media, Food & Beverage companies as well as Analytic Consulting. At Catalina, she is focused on efforts to improve in-market performance of both in-store and digital media activation. Prior to Catalina, Dafna spent a number of years at Nestlé USA, most recently as the leader of the Analytics Center of Excellence. Earlier, Dafna held a global role leading efforts in the areas of digital media and integrated marketing communication measurement at Kraft Foods, and before that she led the Chicago Analytic Insights team for Information Resources Inc. Dafna earned a Bachelor’s degree in Statistics and Geography from Haifa University -Israel, and a Master’s degree in Statistics from Northwestern University in Evanston Illinois.

Jan Gollins
Courses: Marketing Analytics

Jan Gollins is principal and founder of the Delta Modelling Group. Delta provides predictive models, forecasting, trade promotion analytics, advanced data analysis, and consumer research services to leading CPG and pharmaceutical companies. He is a marketing science executive recognized for pioneering many of the widely accepted analytical techniques used by CPG companies for analyzing electronic POS scanning and consumer household purchasing data. His research in the application and use of moment-to-moment affect trace methodology has been used in nationally televised presidential debates, television pilot testing, TV ad copy testing, and virtual focus groups. Gollins’s international experience includes living in the United Kingdom and working extensively in Europe, Asia, and South Africa. His work experience includes several years in senior-level positions at Nielsen, Information Resources, Inc., and DiscoverWhy. In addition to his appointment at the University of Chicago, Gollins is an adjunct faculty member at DePaul University’s Kellstadt Graduate School of Business and the College of Computing and Digital Media. He was the recipient of the 2013 Kellstadt Marketing Center Professional Educator Award, and currently serves as President of DePaul’s Marketing Advisory Council. Gollins holds an MBA from Lehigh University.

Jim Guangjin Xiao
Courses: Credit and Insurance Risk Analytics

Jim Guangjin Xiao is an AVP Analytics at CNA Insurance Companies. He received his Master of Science in Applied Statistics from Bowling Green State University in Ohio. Mr. Xiao is a fellow actuary (FCAS) practicing in the property and casualty insurance industry, and is an active member of Casualty Actuarial Society and American Academy of Actuaries. Mr. Xiao has ten years of research and modeling experience in predictive analytics for both personal and commercial lines focusing on insurance product pricing, customer segmentation, and risk management. 

Kevin Hartman
Courses: Digital Marketing Analytics in Theory and Practice

Kevin Hartman is a devoted and artistic practitioner of data. He believes data must do more than simply provide insights that motivate creative thought. Rather, data must express insights in ways that are just as creative as the ideas they inspire. As Head of Industry at Google, Kevin and his team partner with major advertisers, creative agencies, and media companies to develop digital solutions that build businesses and brands. His approach mixes science and art to deliver inventive, fact-based strategies that reduce uncertainty and increase effectiveness in the marketing and advertising programs they create. Kevin’s roster of client work is long and varied, and includes brand names such as Nestle, General Mills, Kellogg's, Anheuser-Busch, MillerCoors, Motorola, the United States Postal Service, Taco Bell, Kentucky Fried Chicken, Del Monte, Boeing, Qualcomm, Wagner USA, ABN Amro, Bank of America, and the Chicago Board of Trade, among others. Kevin holds a BA from the University of Notre Dame, a MPP from the University of Chicago's Harris School, and an MBA with honors from the University of Chicago's Graduate School of Business. He teaches extensively on topics related to marketing, digital analytics, and data management and is on the adjunct teaching staffs at the University of Chicago, the University of Notre Dame, and the University of Illinois.

Joon-Ku Im, PhD
Courses: Credit and Insurance Risk Analytics

Joon-Ku Im is a statistical modeler at one of the leading U.S. consumer financial service companies. His experience includes development of loss forecasting models for consumer loan portfolio as well as development of scorecard models for consumer loan underwriting. He enjoys translating analytical findings into business insights. Joon-Ku earned his PhD in Industrial Engineering and Management Sciences from Northwestern University and his dissertation discusses methodologies for identification of nonlinear variation sources. He also earned MS in Mathematics from The Ohio State University and BS in Mathematics from Seoul National University (South Korea). While studying in The Ohio State University, he worked as a lecturer for 2 years and found it both interesting and challenging to deliver technical knowledge to students.

Nick Kadochnikov
Courses: Big Data and Text Analytics, Data Visualization Techniques

Nick Kadochnikov is a Principal Data Scientist at IBM Chief Analytics Office. Nick’s focus is in the areas of predictive analytics, text analytics/social media analytics and change management. He is equally experienced in both “doing” analytics as well as “consulting” on analytics, which includes integrating analytical projects into the business processes to improve their effectiveness and efficiency. In his professional career Nick has been applying analytics to various areas of the business, such as Marketing, Sales, Fraud Prevention, Product Development and Financial Optimization, utilizing advanced data mining techniques, building econometric models, and working with Big Data, including: Client Wallet estimates, Segmentation, ROI for Smart, Propensity to Buy, Development Productivity and Social Business. He holds an MS in Economics from St. Petersburg State University and MS in Global Marketing Management from Virginia Commonwealth University.

Ming-Long Lam, PhD
Courses: Data Mining Principles, Statistical Analysis

Dr. Lam is a data scientist with over twenty years of practicing predictive analytics in software development, property and casualty insurance, and retail financial service industries. Ming-Long has strong expertise in developing statistical algorithms, customizing solutions for analyzing person data, and implementing solutions in major statistical software. Ming-Long is currently a Senior Manager of Advanced Analytics R&D at the SAS Institute. Prior to the SAS Institute, Ming-Long had a rewarding career in directing development of analytical features in SPSS. In between these two major statistical software vendors, Ming-Long developed rating plans for Allstate to set insurance premiums and assessment models for Chase to recruit their retail financial staff. Ming-Long has co-published the book Using Data Analysis to Improve Student Learning: Toward 100% Proficiency. Ming-Long holds a PhD in Statistics from the University of Chicago.

Katie Malone, PhD
Courses: Machine Learning and Predictive Analytics

Katie Malone is a data scientist with experience in data science consulting, software development, management, and machine learning education. She currently is the Director of Data Science Research and Development at Civis Analytics, a Chicago-based data science software and consulting firm, where she manages a team of data scientists who specialize in causal inference, supervised and unsupervised machine learning, and predicting individual-level attitudes and behavior. Her previous experience includes spending two years in Switzerland doing Higgs boson research at CERN, and also teaching the Introduction to Machine Learning course for the online education startup Udacity. She holds a PhD in experimental particle physics from Stanford University and hosts Linear Digressions, a podcast on data science and machine learning.

Angelo Mancini, PhD
Courses: Machine Learning and Predictive Analytics

Angelo is a Senior Applied Data Scientist at Civis Analytics, where he primarily focuses on machine learning applications in the media and finance sectors. Examples include building a recommender model to help a media client gauge audience interest in upcoming premieres, deploying a decision application that clusters all television programs using raw viewership data, and the ongoing development of a tool that combines optimization with predictive analytics to help clients allocate marketing spend. He holds a PhD in Management Science and an MBA from the University of Chicago Booth School of Business, where his research focused on stochastic dynamic programming. Prior to his time at Booth, Angelo worked in macroeconomic research at the Federal Reserve Bank of Chicago, and at a Chicago-area software company focused on helping banks understand their exposure to interest rate risk. He also holds a BA in Mathematics and Economics from Northwestern University.

Roger Moore
Courses: Leadership Skills

Roger Moore is a Senior Principal at Sagence, a management advisory firm dedicated to helping clients maximize the value of their data assets – from thinking to doing. Roger assists clients in the acquisition, evaluation, development and maintenance of their data assets, and in application of analytics to drive new insights and better decisions. Roger’s previous experience includes Executive Advisor at Booz & Company, Director in PwC's Diamond Advisory Services where he was a core member of Diamond’s Information and Analytics Center, Principal at the Boston Consulting Group (BCG), Director of Technical Services at Sawtooth Software (a provider of market research and statistical analysis software). His experience at these firms spanned industries including financial services, insurance, consumer, retail, industrial goods, healthcare and travel. He has worked in North America, South America, Europe, Asia and Australia. Roger holds an MBA from the University of Chicago’s Booth School of Business and received a BS in Electrical and Computer Engineering from the University of Wisconsin - Madison. Roger is the founder and chair of the Chicago Booth Big Data & Analytics Roundtable that meets at Gleacher Center – typically on the first Thursday of the month.

Sridhar Ramaswamy, PhD
Courses: Machine Learning & Predictive Analytics

Dr. Sridhar Ramaswamy (Sri) earned his PhD in Statistics, from the Indian Statistical Institute, India, and has further post doctorates in Optimization and Operations Management from the University of New Brunswick and the University of Toronto, Canada. Sri currently works as an Analytics Advisor for Caterpillar Inc., leading a global team of data scientists. Sri has more than twenty years of experience in advanced data analytics with applications to marketing, sales, manufacturing, and finance. Sri has helped businesses and mentored as an analytics director in his prior roles. He is often an invited speaker in conferences. Sri teaches time series, predictive analytics, machine learning, simulation & optimization, and data science topics using analytics software such as R, Python, Gurobi, SAS, Minitab and Matlab. He brings excellent case studies from his industry experience into his teaching curriculum, which helps students develop a deeper understanding of the courses. Sri professionally guides and conducts training programs in business analytics using big data.

Vadim Sokolov, PhD
Courses: Optimization and Simulation Methods for Analytics, Time Series Analysis and Forecasting

Dr. Sokolov is a principal computational scientist at ArgonneNational Laboratory, and a fellow at the Computation Institute at the University of Chicago. His research is focused on statistical and mathematical modelling of large scale complex systems, such as transportation networks. During his eight years at Argonne he has worked on a variety of projects, ranging from the analysis of evacuation plans for military installations, to modelling transportation systems of megacities, such as Chicago. His responsibilities include developing algorithms for modelling the performance of highway systems and travelers’ behavior, as well as, writing software to implement those models. His research has been published in leading mathematical and engineering journals. In addition, he is interested in Bayesian analysis of state-space and time-varying parameter models with applications in finance and engineering. He holds a PhD in computational mathematics from Northern Illinois University, and pursued graduate studies in statistics at the University of Chicago, while working at Argonne.