Data Science Symposium
Data Science Symposium 2022: November 8
The Data Science Symposium 2022 will be held on November 8, 2022 in the Lindner College of Business at the University of Cincinnati. This in-person, all-day event will include three featured speakers, two sixty-minute Tech Talk track sessions with four presentations in each track (eight total), and a post event reception. We are currently accepting applications for sponsorships and speakers.
Download the Data Science Symposium 2022 Sponsor sheet here
We are also inviting applications for Technical Talks that can range from tutorials on various data and analytics tools to applying data science, analytics or cloud technology in organizations.
Please contact Larry Porter at firstname.lastname@example.org or 513-556-4742
See below for descriptions and videos from past Data Science Symposiums
Paul Bessire, VP of Data, Coterie Insurance
Title: “Data Science and the Business of Sports”
Abstract: Through various examples spanning everything sports related - from tickets, to hiring, to strategy, to gambling - learn why sports
is both the ultimate catalyst and proving ground for innovation in data science and what this means for all business.
Bio: Paul Bessire has been in predictive analytics and data science for 15+ years. He has used data and technology to become one of the world's foremost disruptors in sports analytics and is now focused on doing something similar in insurtech.
Paul recently joined Coterie after four years in consulting where he and team help middle-market clients, nonprofits and sports teams make more efficient decisions using information and technology.
Dwitiya Sawant, Senior Manager, Global Data Science, McDonald's
Mathew Zettinger, Data Scientist, McDonald's
Title: "Understanding Delivery Order Inaccuracies"
Abstract: The explosion of third-party food delivery is quickly changing the way restaurants serve their customers. But this new channel also brings challenges, as an incorrect order can leave a customer frustrated without an easy resolution. McDonald’s is turning to data and analytics to better understand why inaccurate orders happen, building tools to help identify the factors that contribute to inaccuracies, improve the accuracy of delivery orders, and enhance the customer experience. In this talk we will explore how McDonald’s uses modeling, optimization, and engineering tools to uncover insights on delivery order inaccuracies.:
Bio: Dwitiya Sawant is Senior Data Science Manager at McDonald’s Corporation leading Data Science work in the Operations and Delivery domain. Dwitiya has worked in the field of data science for over a decade and is currently leading a team of Data Scientists and Data Engineers at McDonald’s. Through her career she has helped companies in diverse domains including CPG, Finance, Communications and Insurance solve their business problems with modern Data Science solutions. Prior to McDonald’s she worked for data science teams at Nielsen, PwC and Intrado. Dwitiya is passionate about the work she does day to day at McDonald’s and is a strong supporter of Women in Tech and Women in Leadership roles. Other than Data Science, Dwitiya is trained in Indian classical dance, loves to spend time refining her dance skills and feels blessed to be mom of a little boy.:
Bio: Matt Zettinger is a Data Scientist on the Global Data and Analytics Hub at McDonald’s, where he constructs models and analyses to drive value for a variety of business units. He holds a bachelor’s degree in engineering from the University of Illinois at Urbana-Champaign. Prior to McDonald’s he spent time building credit models in the financial technology space and forecasting demand in the retail/consumer packaged goods industry.
Denise White, Assistant Professor-Educator, University of Cincinnati
Title: "Simulating Large Systems: Challenges and Approaches to the Design of a Whole Hospital Model"
Abstract: Hospitals differ from most systems in the complexity of operations and the unparalleled variability and uncertainty of demand.
How do you model a system where virtually every entity has a uniquely defined path and set of needs? Using simulation modeling, we explore how data analysis techniques, appropriate definition of stochastic and probabilistic elements, and streamlined design can be used to develop a complete model of patient flow through a large hospital. We explore unique business challenges in managing the operations and demonstrate how to incorporate decision making based on hospital status into a simulation model.
Bio: Denise L. White, PhD, MBA, is an Assistant Professor – Educator at the University of Cincinnati where she is the Director of the Master of Science in Business Analytics and teaches courses in analytics, operations management and healthcare. She also holds a position as an Assistant Professor- Research at the James M. Anderson Center for Health Systems Excellence at Cincinnati Children’s Hospital Medical Center. Before joining UC as a full-time faculty member, she was the Director of Quality and Transformation Analytics at Cincinnati Children’s where she was responsible for overseeing analytic staff members supporting hospital-wide improvement efforts
Data Science Symposium 2020 Virtual Event Speakers
Jason Pontin: Contributing Writer To Wired And Senior Advisor At Flagship Pioneering
"Future Impacts of Technology"
Abstract: Jason Pontin will provide a lively tour of the future of data science, including emerging techniques in machine learning and quantum computing, and the revolution they are driving in healthcare, energy, and other fields. Along the way, he'll discuss the opportunities and challenges for any organization that seeks to harness the revolution in data science—revealing his experiences in investing and creating companies that directly use data science to solve big problems and create extraordinary value for shareholders and society.
Jason Pontin is a Senior Advisor at venture capital firm Flagship Pioneering, which conceives, creates, resources and grows first-in-category life sciences companies. He is also an expert on innovation, and a renowned journalist. Jason addresses audiences around the world with speech topics on both how we can all think like a futurist, as well as the future of work, encompassing AI, Robotics and Big Data. He has a regular column at WIRED magazine on disruptive technologies. Formerly, he was editor and publisher of the MIT Technology Review, where he was responsible for the publication’s editorial direction, media platforms and business strategy.
Marshall Fisher: UPS Professor, The Wharton School
"Sell what you’ve made vs. make what’s selling, Where is the world headed?"
Abstract: Footwear and apparel retailers can choose three paths: 1) get fast, so they can make what’s selling (as exemplified by Zara), 2) develop accurate forecasts so they can produce in advance to the sales quantities well matched to demand, or 3) live with the excess inventory and markdowns that result from long lead times and inaccurate forecasts. We compare these approaches and describe a forecasting method that leverages social media to enhance preseason forecast accuracy for footwear and apparel.
Marshall Fisher is the UPS Professor of Operations, Information and Decisions at the Wharton School of the University of Pennsylvania and co-director of the Fishman-Davidson Center for Service and Operations Management. He holds an SB degree in electrical engineering, an MBA, and a PhD in operations research, all from MIT. Dr. Fisher joined the faculty of the Wharton School in 1975. Prior to that he was a systems engineer in the Boston Manufacturing and Distribution Sales office of IBM and on the faculty of the University of Chicago Graduate School of Business.
Dr. Fisher’s research during his 35-year career has focused on supply chain management, with a varying emphasis that has included private truck fleet scheduling, supply chain management for hard to predict fashion products and a scientific approach to retailing. Dr. Fisher has been a consultant to many Fortune 500 companies, including Advance Auto Parts, Air Products and Chemicals, Albert Heijn, Bertelsmann Music Group, Bulgari, Campbell Soup, Dupont, Experticity, Exxon, Frito Lay, General Motors, IBM, Jo-Ann Fabric and Craft Stores, Kronos, Lutron, Motorola, Nike, Nokia, Scott Paper, Spencer Gifts and Spiegel, Inc., to name a few.
Radhika Kulkarni, PhD: VP (Retired), Advanced Analytics R&D, SAS Institute, Inc.
"Building Analytics Teams for Success: A Perspective from Two Sides"
Abstract: You have chosen a career in Data Science, Analytics and Operations Research: Do you wonder if algorithmic innovation is enough to succeed? As you advance in your career and assume leadership positions in Analytics, what are the lessons you need to build and grow a successful team? In this talk, you can hear some of the experiences from both perspectives: Lessons learned as a young professional and also as a leader of a large Analytics R&D organization.
Radhika Kulkarni retired as VP, Advanced Analytics R&D at SAS Institute Inc. where she was responsible for the world’s leading Analytics Software products portfolio. She holds a Ph.D. in Operations Research from Cornell University. Under her leadership, OR gained recognition as a key contributor to scalability and performance of algorithms in statistics, machine learning, forecasting, data mining, econometrics, etc. She serves on many academic advisory boards. Kulkarni is an INFORMS Fellow and WORMS Award winner.
Jude A Schramm: Exec VP/CIO/Head:Information Technology, Fifth Third Bancorp
"Adopting Data and Analytics into the Fiber of a Company"
Abstract: Data science and analytics technologies, skills, and jobs are rapidly growing. The proliferation of enterprise data offices, advanced technologies in data science and analytics, and a generation of digital first workers are enabling a new way of working led by data driven decisions. What are some key areas of focus and interest companies should consider as they continue the journey?
Jude Schramm serves as Chief Information Officer. He leads teams that provide innovative solutions in support of the Bank’s strategy, including information technology, Agile software development, infrastructure and cloud solutions, information security and enterprise data management.
Before joining the Bank, Jude served as Chief Information Officer for GE Aviation, where he was responsible for leading IT strategy and digital transformation. In previous roles there, he led IT organizations for the services, digital industrial and commercial divisions. He also led digital and enterprise data transformation for the GE IT Corp. He began his career at GE in 2001 as a developer and had roles of increasing responsibilities over his 17 year career. Jude began his career as a senior consultant with Ernst & Young LLP and also worked as a project manager with Whittman-Hart.
Matt Brems: Managing Partner, Distinguished Faculty at General Assembly, Betavector,
"Good, Fast, Cheap: Doing Data Science with Missing Data"
Abstract: If you've never heard of the "good, fast, cheap" dilemma, it goes something like this: You can have something good and fast, but it won't be cheap. You can have something good and cheap, but it won't be fast. You can have something fast and cheap, but it won't be good. In short, you can pick two of the three but you can't have all three. If you've done a data science problem before, I can all but guarantee that you've run into missing data. How do we handle it? Well, we can avoid, ignore, or try to account for missing data. The problem is, none of these strategies are good, fast, *and* cheap. We'll walk through practical tips for working with missing data and recommendations for integrating it with your workflow that allow you to make better, more informed decisions when doing data science with missing data.
- Matt Brems is currently Managing Partner and Principal Data Scientist at BetaVector. His full-time professional data work spans finance, education, consumer-packaged goods, and politics and he earned General Assembly's 2019 "Distinguished Faculty Member of the Year" award. Matt earned his Master's degree in statistics from Ohio State. Matt is passionate about responsibly putting the power of machine learning into the hands of as many people as possible and mentoring folx in data and tech careers. Matt also volunteers with Statistics Without Borders.
Pavan Chundi: Senior Analyst, Cincinnati Children's Hospital
"Improving Patient Family Experience: A Machine Learning Approach"
Abstract: Effective communication has always been a great intervention to improve patient family experience. Being able to accurately predict clinic visit length (including wait time) and making this part of the communication can improve overall experience. Explore this opportunity on how we make use of machine learning model to predict visit lengths by incorporating a range of variables like date, time, schedule, patient flow and others.
- Pavan K Chundi is a Senior Analyst at the James M Anderson Center for Health System Excellence, Cincinnati Children’s Hospital Medical Center. He graduated from University of Cincinnati with a Master’s degree in Business Analytics and holds a Master’s degree in Biomedical Engineering from University of Surrey, UK. His areas of specialization are Healthcare Analytics, Healthcare Outcomes and Improvement, Data Visualization and Business Intelligence. He has been instrumental in the development of the analytical framework and interactive dashboards that have helped clinical divisions to provide patient centered care. His expertise on time series modeling is evident from his recent publication in JAMA Pediatrics.
James Lee, PhD: Associate Vice Provost for Digital Scholarship and Associate Dean of Libraries,
Lindsay Nickels, PhD: Program Coordinator, Digital Scholarship Center, University of Cincinnati
"Sentiment Analysis 2.0"
Abstract: In this talk, we will present our "Sentiment Analysis 2.0" project, which combines deep learning with linguistics expertise to define human behavior and meaning encoded in text of different forms - social media, articles, electronic health records - in n-dimensions determined by the language actually being used. We have developed our methods as a way to avoid assumptions or overly-narrow rules of positive or negative sentiment that we could be applied to the data. This approach allows the full complexity of the language data to be revealed.
- James Lee, PhD is the Associate Vice Provost for Digital Scholarship and Associate Dean of Libraries at the University of Cincinnati, where he also serves as the Director of the Digital Scholarship Center and is an Associate Professor of Digital Humanities. His research and teaching are in the areas of digital humanities, machine learning and text mining techniques on historical archives, social network analysis and data visualization. He works largely in early modern literature and culture. More recently, his research has branched out into fascinating collaborations applying digital humanities methods with partners in biomedical informatics, corpus linguistics, and law.
He received his PhD at the University of California, Berkeley. His research has been supported by the Andrew W. Mellon Foundation and the National Endowment for the Humanities. He is a PI of the Catalyst Model for Transdisciplinary Teams in Digital Scholarship project, supported by the Andrew W. Mellon Foundation.
- Lindsay Nickels, PhD is the Program Coordinator for the Digital Scholarship Center at the University of Cincinnati and works with transdisciplinary research teams to develop and execute innovative methodologies on large unstructured text datasets. Her research is situated in critical discourse studies and corpus approaches to discourse analysis.
Dan Shah SEI, Senior Consultant, Rob Dodd 84.51, Digital Customer Insights, Reid McCreary 84.51, Digital Customer Insights
"Understanding and Improving Kroger Customer Experience During COVID"
Abstract: To say COVID-19 impacted the way customers shop and the supply chains that deliver food to shelves is an understatement. Kroger Pickup and Home Delivery saw an unprecedented increase in demand which required improved data visibility and insight to customer behavior. Rob, Reid and Dan will talk about how 84.51’s Customer Insights and Kroger’s Digital Business Intelligence responded to this dynamic – specifically, through adopting a new approach to measuring causality and by modeling customer behavior to understand what’s important.
- Dan Shah is a Senior Consultant at Systems Evolution Consulting (SEI) where he helps companies convert data into insights. Before joining SEI, he worked at Procter & Gamble as a data scientist. He's an alum of UC’s MS Business Analytics Program.
- Rob Dodd partners with Kroger and 84.51° Product Management, Data Science, and Market Research teams to deliver actionable customer insights to improve Kroger’s digital customer experience and lower the cost to serve. He formerly was a data scientist with 84.51° and Asset Manager with Ergo Investment Partners. He earned a M.S. in Predictive Analytics from Northwestern University and a B.S. in Computer Information Systems and Operations Management from Indiana University.
- Reid McCreary is a consultant with 84.51°, with a focus on customer strategies and activations within Kroger’s ecommerce fulfillment space. Reid has 10 years of data analytics experience with 84.51° and dunnhumbyUSA. He earned a M.S. in Enterprise Integration and a B.S. in Management Information Systems, both from the University of Alabama.
Yangsu Chen: Amazon Web Services, Data Scientist
"Leveraging Forecasting Power of Classic Time Series Models and Deep Learning"
Abstract: Time series forecasting has always been an essential topic with many applications in the industry. Businesses can look backward to find trends and patterns in the historical data, but even more important is the ability to make accurate forecasts of business demands in the future. Combining historical patterns and future trends can provide a holistic story for business to make forward-looking strategies and actionable insights. In this talk, I will summarize several industry-standard time series forecasting techniques from classic models (e.g. Seasonal Naïve, ARIMA)), non-linear model (e.g. Prophet), to deep learning models (e.g. LSTM) and provide an overview of practical use cases.
- Yangsu Chen is a data scientist at Amazon Web Services (AWS). Before joining AWS, he was a data scientist at Amazon.com and a national leading non-profit organization. His work ranges from building machine learning models, time series/deep learning forecasting, market segmentation, causal experimental design, and inferential statistics. He holds his PhD from University of Nevada Las Vegas and MS from Purdue University. Before joining Amazon, he served as an adjunct professor at the University of Nevada Las Vegas.
"Future Impacts of Data Science"
- Jason Pontin: Moderator
- Jude Schramm: Fifth Third Bank, Executive Vice President, Chief Information Officer
- Craig Brabec: McDonald's, VP/Chief Data Analytics Officer.
Bio: As McDonald’s Chief Data Officer, Craig is helping to define and infuse data across the global enterprise. He is creating the roadmap to identify the team, technology, process and culture change required for enabling enterprise data transformation, establishing best-in-class data strategy and governance and evangelizing these changes throughout McDonald’s.
Most recently, Craig was the Director, Global Data Insights & Analytics within Ford Motor Company. In this role, Craig provided the executive office, product development, manufacturing, global supply chain, finance, human resources and business units with data insights to enable better decision making for the enterprise. Craig has more than 25 years of experience working in corporate strategy and data analytics as well as manufacturing, construction, IT, supply chain and management consulting. He has a proven track record of driving value creation, growth and business performance. Prior to Ford, Craig was the first Senior Vice President, Data Analytics at McKesson Corporation, leading analytics for the Fortune 5 company worldwide.
- Carlos Amesquita: CEA Advisory, Founder & President, Former CIO, The Hershey Company
Bio: Carlos' areas of expertise are IT and Shared Services Leadership and Strategy, Strategic Partnership Models, and Digital Transformation Strategies that weave Consumer Engagement and Marketing, with Supply Chain, Digital Commerce, and Retail Execution via a strong Analytics backbone. He is a business driven and results-oriented IT and Shared Services executive with extensive experience at the intersection of business and technology in Fortune 500 companies. . As Hershey's CIO, Carlos led one of the broadest digital programs in the industry, including a comprehensive organization transformation required to deliver and sustain its digitization plan. Before joining Hershey, Carlos spent 28 years at Procter & Gamble, in a broad and diverse set of roles across several countries. He was CIO and Shared Services Leader for North America in 2011, Global Household Care CIO in 2009, and Global Food & Beverage CIO in 2000. He was P&G Latin America CIO in the late 1990's. Carlos also led P&G's IT Innovation from 2005 to 2009.
Jaime Windeler: Associate Professor of Information Systems, Lindner College of Business
"Data Science Teams: Fundamentals and Future of Virtual Collaboration"
Video Not Available
Abstract: COVID-19 has pushed many data science teams to completely remote work. Some teams are getting a crash course in grappling with longstanding challenges of virtual teamwork: isolation, relationship management, and diffusion of responsibility. Other teams have been collaborating virtually for many years. These teams have figured out how to be productive and engaged but face new challenges now that everyone else is now virtual. To address this spectrum of virtual team experiences, I will share best practices and discuss the future of virtual teamwork.
- Jaime Windeler is an Associate Professor of Information Systems at the University of Cincinnati. Her research is published in premier IS journals and focuses on the management of distributed collaboration, as well as the attraction, motivation, and retention of IT professionals. She collaborates with several F500 companies on enhancing virtual team leadership and their IT workforce diversity and inclusion.
Ryan Kaplan: Director Data and Analytics, Kamal Raad: Lead Data Engineer, Marketing Analytics, Fifth Third Bank
"Data DevOps in the Financial Sector?"
Video Not Available
Abstract: Data DevOps in the Financial Sector - Is that even possible? Join Ryan Kaplan and Kamal Raad as they walk through Fifth Third Bank's journey of Data DevOps in the Enterprise Data Office (EDO) to drive customer outcomes.
- Ryan Kaplan is a Director of Data & Analytics in the Enterprise Data Office (EDO) at Fifth Third Bank. He leads software engineers within the EDO, is responsible for delivering platforms like Kafka, and supports cloud data movement. Ryan is a proud UC alumni, having earned a Master’s degree of Science in Information Technology.
- Kamal Raad is a Lindner alum and Data Engineer at Fifth Third Bank in the Marketing Analytics space. His agile team is focused on providing a 360 degree view of the bank's customers, as well as supporting Marketing campaigns and customer relationship outcomes. They collaborate with the Marketing, Sales, and Retail organizations of the bank.
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