In this discussion with some of the region's most talented and promising AI-based founders, we discussed what makes an AI startup tick. How has AI enabled new capabilities that differentiate from traditional business models? How do you source and develop your team, technology, and funding? Is AI out in front with the customer, or behind the scenes?

Moderator - Travis Sheridan - President, CIC Venture Cafe Global Institute

Erica Barnell -Co-Founder & CSO, Geneoscopy

Jim Eberlin - CEO & Founder, TopOPPS

David Karandish - CEO & CoFounder, Jane.ai

Demand for resources that are collectively controlled or regulated by society, like social services or organs for transplantation, typically far outstrips supply. How should these scarce resources be allocated? Any approach to this question requires insights from computer science, economics, and beyond; we must define objectives, predict outcomes, and optimize allocations, while carefully considering agent preferences and incentives. In this talk, I will discuss our work on weighted matching and assignment in two domains, namely living donor kidney transplantation and provision of services to homeless households. My focus will be on how effective prediction of the outcomes of matches has the potential to dramatically improve social welfare both by allowing for richer mechanisms and by improving allocations. I will also discuss implications for equity and justice.

Sanmay Das is an associate professor in Computer Science and Engineering and the chair of the steering committee of the newly formed Division of Computational and Data Sciences at Washington University in St. Louis. He is vice-chair of the ACM Special Interest Group on Artificial Intelligence and a member of the board of directors of the International Foundation for Autonomous Agents and Multiagent Systems. Dr. Das has served as program co-chair of the AAMAS and AMMA conferences, and has been recognized with awards for research, service, and teaching, including an NSF CAREER Award and the Department Chair Award for Outstanding Teaching at Washington University.

In NASCAR, everything happens fast, whether it’s a lap, tire changes or refueling. Crews have to quickly recognize opportunities and be empowered to make real-time decisions. WWT is helping make this a reality for Richard Petty Motorsports through data analytics, software development, and AI.

Chris Infanti is a Senior Engagement Manager in World Wide Technology’s Business and Analytics Advisors group. He brings 12+ years of analytics consulting and delivery experience, working with a variety of Fortune 500, education, and government customers. Chris was formerly an engagement manager at Opera Solutions, a data science consulting firm focused on delivering business-outcome focused analytics for customers. Prior to that, he was a consultant at IBM Global Business Services, focusing on the implementation of large-scale data warehouses for state and local government customers, including data modeling, ETL and reporting. Chris has a Bachelor’s Degree from Georgetown University, with majors in Mathematics and English Literature.

In this panel discussion, we will explore how A.I. can help individuals more efficiently obtain, communicate, process, and understand health information and services to make appropriate health decisions. Technical and non-technical audiences welcome.

Moderator - Chris Miller - Founder & CEO, The Mission Center

Blake Marggraff - CEO, Epharmix

Dr. Catina O’Leary - President & CEO, Health Literacy Media

Michael Kehoe - Founder & CEO, Johego

This session will discuss how one gym software company leveraged AI/ML to transform how it delivered value to their fitness facility clients. It will introduce how the business case was created and how specific high value business use cases were identified to achieve the business objectives. The discussion will focus on how machine learning was leveraged to predict member and club churn to optimize the overall member experience and optimize gym operations. This includes how metrics were leveraged to measure success in both of those areas. The discussion will highlight lessons learned and challenges faced during this transformation on both a business and technical level including how UX and machine learning were brought together to deliver this solution to a non-technical fitness user community.

Andy Sweet is a technology leader with over 25 years of experience in organizations ranging from startups to mid-size public and Fortune 500 companies guiding their strategic business and technology initiatives. Has repeatedly built and run high-performing organizations that have delivered consistent business results and world-class customer experience. Expertise in delivering data-driven, mobile-first SaaS based solutions in a variety of industries. Sought after public speaker around enterprise transformations that leverage advanced data and mobile solutions. Currently serving as a Digital Strategist at Valorem.

Data science and intelligent automation are growing trends that are poised to dramatically disrupt the business world - reducing costs while creating strategic insights into the design of customer and employee experiences. The talk will focus on the place where data science intersects with design; how the two disciplines inform each other and why they are better together than apart.

Jeromey Farmer is a pioneer who can drive his vision to execution. Clients and consultants describe him as a leader, mentor, teacher, learner, inventor, scientist, creator, and trusted advisor. He is a proven strategic business leader and developer of talent, both as an educational practitioner and visionary leader at Slalom. Jeromey leads the Slalom Information Management and Analytics practice to deliver data solutions across the entire spectrum of big data: business intelligence strategy, data management, information governance, visual analytics, advanced analytics, machine learning, AI, and predictive modeling. His vision is to position St. Louis as the true gateway for the evolution of enhancing the human experience through technology.

We’re finally here! We’re living in the future we always dreamed about and that our parents never thought would ever come. We literally have machines deployed for us, representing us, and all around us … and they do stuff for us and bring us things! We trust our machines, too. We have built them to do intimate, human things: to connect us, to offer lifelines, to find us love, to understand our DNA, to make us healthier. We’re so lucky! BUT. As we use machines more and more to represent us, do we lose a portion of our own humanity? As the technologists that build these machines, do we lose more of our humanity in the delivery? Do we build machines to represent the people that we are, or do we impersonate machines? In “How to Build a Nicer Machine: The Movement to Humanize Data and Interactions” we’ll reflect upon where we are as a society of technical consumers and builders to explore the challenge of building machines more like us and to consciously, actively choose to become less like robots. We’ll explore a humanization framework that includes tangible ways to humanize the data we use to deliver meaningful digital interactions.

Christina Vallery joined Express Scripts in the summer of 2016 with a purpose-driven focus on applying a design thinking mentality to the problems facing healthcare and with a mission to do work that contributes to the broader world community. In her time at Express Scripts, she’s engaged in a long-term build of the design system of the future: one that applies human feeling in its expression.

Danielle Smith joined Express Scripts in 2016 because building a team tasked with keeping part of the healthcare experience grounded in the values, needs, and abilities of patients sounded like the kind of crazy-ambitious-noble mission that she loves. Over the past 3 years, that mission has helped to move Express Scripts that much closer to providing a service that feels human. Danielle uses her training as a social scientist coupled with her prior experiences in product market research consulting and technology development to build a cohesive approach to experience insights – uncovering and combining qualitative and quantitative insights in ways that help shape digital experiences that deliver care.

This session will explore how AI will change the consumer experience; from big data, personalized services, always-on predictions, and more. We'll also discuss the implications for privacy, regulation, ethics, and human-machine interaction.

Moderator - Matt Sebek, Vice President of Digital, World Wide Technology

Dheeraj Patri - Co-Founder & CTO at Label Insight

Tony Sardella - CEO, Evolve24

Michael Wojcik - VP, Enterprise Technology Services, Panera

Semantics is linguistics and natural language processing to help make sense and meaning of the world around us. In visual analytics, using principles of semantics can help improve the meaning and overall cognition of a visualization to the user. In this talk, we will discuss how we are thinking of adding semantics to help enrich various aspects of the visual analytics workflow. By incorporating and inferring general concepts to empowering users to add domain-specific knowledge, semantic governance can bring more meaning to the analytical experience.

Vidya Setlur is a manager on the Natural Language Team at Tableau Software, and works out of the Palo Alto office. Previously, she was a staff research scientist on the research team and prior to joining Tableau, worked as a principal research scientist at the Nokia Research Center for 7 years. She earned her doctorate in Computer Graphics in 2005 at Northwestern University. Her research interests lie at the intersection of natural language processing (NLP) and computer graphics, around semantics and visual interfaces. At Tableau, Vidya works on natural language interfaces to help enable analytical conversations with data.

In this talk, Kay Apperson will highlight the values of Smart Manufacturing which is one of the cutting-edge pillars that enable the Industry 4.0 time that we are in. In addition to honing in on the Vision AI used in a Fiber Optic Cable Defect Detection project in collaboration with MIT, she will walk through key real-world use cases in Smart Manufacturing. Those use cases will center around two AI solutions -- Predictive Maintenance and Defect Detection. She will discuss in-depth the Big Data enterprise-grade architecture necessary for these solutions. For the Vision AI application that is powered by Microsoft Azure Cognitive Services – Custom Vision, she will walk you through the actual application that was built for the MIT project. Since the fiber optic problem is a more challenging problem to solve using Vision AI due to the nature of the object that is a very thin line, the demo of the application will underscore the level of success that AI could achieve to date. A goal of the Vision AI application is to categorize in real-time on the plant floor with high accuracy whether a fiber optic cable is produced correctly or it is a defect.

Kay Apperson, PhD is a Data Solution Architect and Data Scientist at Microsoft U.S. Manufacturing. She’s worked with many U.S. manufacturing and automotive companies to successfully transform their manufacturing decision making using a data driven IIoT approach. Some of the benefits the companies gain are such as preventing machine downtime, reducing or eliminating unnecessary maintenance, improving safety, improving production planning, and reducing scraps and wastes. In 2008, Kay received a PhD in Computer Engineering and Computer Science with a dissertation in data mining. During the research time, she won 1st prize winner awards from a couple of knowledge mining competitions. Some of her past employers are such as Centene, Monsanto, Harvard University, and Massachusetts General Hospital.

Tony has applied an arsenal of data science and behavioral research techniques to tackle complex, nonmarket issues at clients like IBM, Airbnb, Ford, and Nestle. In a series of fascinating case studies and data exploration, he will examine the societal shifts in perception and behavior that have led to the rise of AI, what may impact the acceptance or rejection of this technology and what business leaders need to know in addressing societal concerns. What are the trends, what are the fads and where will AI have the greatest impact?

Tony Sardella is the Vice-Chairman and Founder of evolve24, a decision sciences and predictive analytics company that drives greater certainty in strategy decisions. His 30-year career includes serving as a Director of market analytics for a global biotech company and leading strategy as the Chief Innovation Officer for a major sales and marketing services organization. In addition to his role with evolve24, he serves on the faculty of the Olin Business School at Washington University in St. Louis, where he lectures on applying big data, AI, and data sciences to make enhanced strategy decisions in the face of disruptive externalities shaping a business or the societal environment in which it operates. In building evolve24, Tony has steadily grown and developed the company into an industry leader in expert statistical data research, analytical strategy initiatives, and behavioral and social science methodologies.

This is an exciting time in health care because of Machine Learning and Artificial Intelligence (AI). Cloud services and modern processing power have helped technology catch up to the research and theory behind AI. Health care organizations can now turn to the most advanced Machine Learning tools and techniques to streamline patient care workflow, identify new treatment plans, and automate diagnoses. Health care organizations and their data scientists are often well-positioned to leverage their data to create insights, ultimately, to inform and automate health care decisions. The revolutionary potential of Machine Learning and AI makes many organizations eager to put them into practice. However, the haste to apply Machine Learning can be challenged by a number of factors. These challenges can arise, not because of limitations of the technology, but from issues surrounding the project itself. To help illustrate these issues, we will walk attendees through a case study from work 1904Labs is doing with Mercy Virtual. This case study involves building a solution to predict one of the questions every health care company wants to know: the likelihood of hospitalization. Drawing on real world experience applying Machine Learning with leading global enterprises, we will explore some of the factors that challenge Machine Learning, discuss how to recognize when you "out-science" your problem, and what to do when you are faced with this realization. Join us as we explain the challenges we faced in training a machine to recognize high, at-risk patients, the hurdles encountered along the way, and how we moved forward.

Mark Bennetts - Product Management, Mercy Virtual

Brandon Fischer - Practice Lead, 1904 Labs

Matt Pitlyk - Technical Lead, 1904 Labs

Women hold only about a quarter of data jobs in the United States. Why is this number so low? What challenges and obstacles stand in the way of women in high tech, and what is being done about it? We will hear from talented women that live and breath these questions in the primary and higher educational spaces, in a high tech startup, and in a large enterprise.

Moderator - Tara Nesbitt - Experience Design Lead at Slalom Consulting

Katrina Brundage - Data Scientist & Legal Analyst, Juristat

Sherea Dunlap - Executive Director, Create a Loop

Eshe Hawash - Targeted Training Manager, Launch Code

Vicki Sauter - Professor of Information Systems, UMSL

Vidya Setlur - Manager of Natural Language Team, Tableau Software

AI and machine learning are technical fields, which can mean they are siloed to data scientists and engineers. This limits the awesome potential of AI. If we instead view AI as a new tool for everyone to use, it will lead to an explosion of creativity in AI’s uses. In this session, we will explore how companies can expand their thinking around AI’s uses and how they can activate their whole staff to be AI advocates.

Jesse Wolfersberger is the Chief Data Officer for Maritz Motivation Solutions. He leads Maritz's Decision Sciences team, a group of data scientists who specialize in merging the fields of behavioral science and artificial intelligence. Jesse's career started as a sports writer, before jumping into marketing as Director of Consumer Insights for GroupM's digital media think tank. In his free time, Jesse uses his data skills in the baseball world, where he consults for a Major League team.

Over the past 2 years the evolution of Machine Learning has made monumental advances and the changes are only accelerating. Some of these advances are due to the ability to automate the selection of the correct algorithms, which enables faster time-to-value for solutions. We will explore these ideas in the context of the Big Squid SaaS platform, which automates much of the workflow involved in training machine learning models and putting them in production.

Jorge Zuloaga got his masters in mathematics from the University of Waterloo in Canada and his PhD in computational physics from Rice University in Houston, TX. His work in computational mathematics and physics has spanned several fields, from particle physics to healthcare, and beyond.

The currency of tomorrow isn’t what you think: It’s not cold hard cash, precious metals, land or even cryptocurrency – it’s data. In the very near future, every company in the world will either buy or sell data as this corporate asset continues to gain value. But, it’s not enough to have access to vast amounts of data, you need to understand it and use it. Transforming data to business value is harder than many companies thought it would be, requiring deeper resources, more expertise and harder work than expected. This session will detail how Intel builds AI systems that can access and analyze large datasets so businesses can take advantage of the explosion of data as the fuel powering digital transformation. Melvin Greer, Intel Chief Data Scientist will describe how artificial intelligence and machine learning help transform data effectively and deliver experiences people have never seen before or imagined.

Melvin Greer is Chief Data Scientist, Americas, Intel Corporation. He is responsible for building Intel’s data science platform through graph analytics, machine learning and cognitive computing to accelerate transformation of data into a strategic asset for Public Sector and commercial enterprises. Mr. Greer has been named a 2018 AI Executive of the Year by Washington Executive. He has been awarded the 2017 BDPA Lifetime Achievement Award and the 2012 BEYA Technologist of the Year Award. Melvin is also a Professor in the Data Science program at Southern Methodist University (SMU) and Adjunct Faculty, Advanced Academic Program at Johns Hopkins University. He functions as a principal investigator in advanced research studies, including Nanotechnology, Additive Manufacturing and Gamification.

This panel of leaders in the technology investment space will delve into the impact that AI is having on new ventures. Investors are expecting large returns from AI enabled business models, but how and why? And what are the pitfalls and challenges that one should keep in mind?

Moderator - Cindy Teasdale - Marketing Director, Prepare.ai

Emily Lohse-Busch - Executive Director, Arch Grants

Brian Hopcraft - Managing Director, Lewis & Clark Ventures

John True - General Partner, Cultivation Capital

The AI for Earth program applies machine learning and data science to hard challenges in agriculture, water, climate, and biodiversity. In this talk, we will discuss how the AI for Earth team, Microsoft Research, and AI for Earth grant recipients are using machine learning to enable precision agriculture, to predict outbreaks of disease, to detect poachers in real time, and to classify animals for conservation. Finally, we will briefly provide details on the AI for Earth grant program to obtain resources for everyone to work on these challenges.

Jennifer Marsman is the Principal Engineer of Microsoft’s “AI for Earth” group, where she uses data science, machine learning, and artificial intelligence to aid with clean water, agriculture, biodiversity, and climate change. Jennifer is a frequent speaker at software development conferences around the world. Since 2016, Jennifer was recognized as one of the “top 100 most influential individuals in artificial intelligence and machine learning” by Onalytica, reaching the #2 slot in 2018. She has been featured in Bloomberg for her work using EEG and machine learning to perform lie detection. In 2009, Jennifer was chosen as "Techie whose innovation will have the biggest impact" by X-OLOGY for her work with GiveCamps, a weekend-long event where developers code for charity. She has also received many honors from Microsoft, including the “Best in Role” award for Technical Evangelism, Central Region Top Contributor Award, Heartland District Top Contributor Award, DPE Community Evangelist Award, CPE Champion Award, MSUS Diversity & Inclusion Award, Gold Club, and Platinum Club. Previously, Jennifer was a software developer in Microsoft’s Natural Interactive Services division. In this role, she earned multiple patents for her work in search and data mining algorithms. Jennifer has also held positions with Ford Motor Company, National Instruments, and Soar Technology. Jennifer holds a Bachelor’s Degree in Computer Engineering and Master’s Degree in Computer Science and Engineering from the University of Michigan in Ann Arbor. Her graduate work specialized in artificial intelligence and computational theory.