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Symposium Speakers

The Artificial Intelligence and Machine Learning Symposium offers three days of programming with each day offering something unqiue for attendees.:

  • DAY ONE will cover programs and analytical tools tutorials taught by University of Oklahoma graduate students.  
  • DAY TWO is dedicated to facilitating conversations between the campus community and representatives from companies who provide AI/ML/DS services or use these services as a component of their business.  
  • DAY THREE  is dedicated to facilitating conversations among faculty, researchers, staff and students on the OU campuses and affiliated research organizations. 

Wednesday, Sept. 25, 2019 - Tutorial Day

Thursday, Sept. 26, 2019: Industry Day

Michael Agarwal

Michael Agarwal

Topic: How cloud computing will change big data, AI and Ml

Michael Agarwal has 22 years of IT experience. He has done full stack development, database administration, project management, ETL, built data warehouses, etc. More recently he has been leading digital transformation and cloud computing, which includes automation, DevOps, CI/CD, modern data platforms, AI/ML, IoT, Cloud Center of Excellence, etc.

Agarwal is an alumni of the University of Oklahoma, where he earned his Master of Science degree in Computer Science. He is certified in PMP, SCRUM Master, AWS Solution Developer Associate, AWS Solution Architect Associate, AWS Sysops Administrator Associate and ITIL Foundation certifications.

Abstract:
In this 30 minute presentation, Agarwal will introduce Big data, AI and ML services available in AWS. He will also demo how easy it is to use these AWS services.

David Clausen

David Clausen

Topic: AM | AI is a Team Sport 
PM | A Day in the Life of Data” (Nested Life Cycles and Data Quality

David Clausen is a thought leader in the Cloud and data spaces and is the lead data architect for Enable Midstream Partners where he leads major data platform initiatives. His most recent projects include the design and implementation of a big data pipeline/modern data warehouse and a self-service analytics and reporting platform on Azure. 

Clausen’s 25 plus year IT career started when he was tasked with programming automated welding systems for the DOD, and he has the paper tape to prove it.  Over the years, Clausen has worked in all aspects of IT, from tech support to DBA to VP of IT.  He is an early adopter of Cloud platforms. In early 2008, he designed and deployed a 100% serverless architecture for a national insurance program management group. He is also recognized as a pioneer in the insurance industry for his innovative public data collection methods, that makes unique strategic marketing efforts possible. Clausen has experience in a number of different industries, including oil and gas, retail, manufacturing, supply chain, logistics, and health and financial services.  

However, his greatest life time achievement has been working alongside his wife to raise two outstanding young men who now lead IT and commercial real estate efforts in Oklahoma City and Denver.  His spare time is spent on their hobby farm and working with various local charity organizations.

Kelley France

Daniel M. Farrell

Topic: ML with Graph for a Recommendation Engine

Daniel M. Farrell is a 5 time published author on topics including analytics, streaming analytics, programming and modeling. A user of apache spark, and Apache Gremlin for many years, Daniel is expert on many data and analytics platforms, and has designed and delivered many world-wide, leading OLAP and OLTP enterprise application systems.

Kelley France

Kelley France

Topic: Got Milk? Modeling a Dairy Allergy: Oral Immunotherapy and the Immune Response

Kelley France is an Oklahoma native with a love of math. She began her career in the insurance industry where she gained an appreciation for detail, and subsequently spent six years in a fast-paced office where she ran all aspects of the business independently.

Recently, France graduated with a B.S. in Mathematics from University of Central Oklahoma. Her research has been focused on Biomathematics, studying the dynamics of both food allergy and HIV treatments. She is working as the data technologist at American Fidelity Corporation where she solves interesting business problems using data and data science tools.

Graham Ganssle

Graham Ganssle

Dr. Graham Ganssle is a specialist in the field of deep learning. His education focused on digital signal processing and optimization of recursively dependent nonlinear physical systems. His expertise is focused on autonomous generative systems and modern methodologies for time series analysis and forecasting.

Graham and his team architect and build machine learning systems to leverage the huge amount of data collected in the IOT and engineering domains. Recently, his team has designed and deployed an autonomous agent to optimally route vehicular deliveries to most efficiently utilize the company’s assets and drive costs down. His team is now building a suite of tools to examine the nonlinear relationships between features in connected car data and build rich predictive applications from these relationships.

Kelley France

David Gillen

Topic: AutoML: What It Can and Can't Do to Prove Value

David Gillen is the director of data science for Precocity where he is responsible for solution delivery, business development, and growth of the advanced analytics practice. He guides teams of machine learning engineers, visualization experts, data engineers and AI ops specialists to deliver real value to clients through data science. Gillen empathizes with executives and key stakeholders to deeply appreciate their problems and iterate on the right formulation of them before implementing a solution. Having consulted across many verticals, he has implemented mathematical solutions such as such as fraud detection, predictive maintenance, recommendation engines, lookalike modeling, anomaly detection, job schedule optimization and churn modeling. He holds a Master’s Degree in Mathematics from Clemson University, and has enjoyed applying machine learning and optimization techniques to solve business problems for over 20 years. 

Pavan Vankadaru

Vikram Jayaram

Topic: Predictive Analytics in Well Operations

Vikram Jayaram has over 16 years of experience in advancing the field of artificial intelligence (AI) and machine learning (ML) both in industry and academia. His interests are in developing AI powered production systems for advanced E&P analytics. Before joining Pioneer, he was a principal research scientist for Sabre Corporation, where he architected the next generation revenue management systems prototype utilizing AI/Game Theory models. He has also worked as a sr. research geophysicist with Global Geophysical Services and also served as a research faculty/scientist with the University of Oklahoma.

Jayaram did his postdoctoral work at the University of Texas M.D. Anderson Cancer Center, working on image reconstruction algorithms in nuclear imaging. He has a Ph.D. and M.S. in Electrical Engineering and was awarded the prestigious NASA doctoral fellowship. He also interned at the Eastman Kodak Research Labs, National Aerospace Labs, Motorola Research and Xerox Palo Alto Research Center.  

He has authored over 30 peer-reviewed publications in international conferences and journals. He is a senior member of IEEE Signal Processing Society, SPE, AGU, SEG and technical reviewer/chair for several international conferences and journals in signal processing, machine learning and geosciences. He is also an associate editor for SEG/AAPG Journal of Interpretation.

At Pioneer Natural Resources, Jayaram is a senior manager for Data Science and Advanced Analytics.

Jessica Lee

Jessica Lee

Topic: Buy till you die: Predicting attrition in non-contractual customers

Jessica Lee is the lead data scientist for Sonic Drive-In where she uses her passion for technology and all things data to address real-world business needs with data-driven solutions. Over the past 2.5 years she has been leading the first data science function within Sonic in order to help create a competitive advantage in the QSR industry through data science. 

Before joining Sonic in September 2015, Lee spent five years working as an analyst, database administrator, and software developer. In 2013, she received her Data Science Certification from EMC² and it's there where she began her journey into big data and machine learning. She has served on the leadership board for OKC Big Data/Data Science user group for the past three years and believes in the power of learning through collaboration.

Jessica Lee

Trey Lowe

TopicTaking Baseball’s Lead to Oil and Gas - Data Analytics is Changing the Game for the Oil Field

Trey Lowe is the vice president of technology at Devon Energy, a company known for its leading technology advancement in the exploration and production industry. Lowe’s petroleum engineering background provides perspective to help focus on technologies that quickly improve business value. 

Lowe has spent more than 20 years working on completion and production operations in various countries around the globe. Prior to joining Devon, he held various field, management and technical positions with Schlumberger Oilfield Services. Lowe has a Bachelor of Science in Chemical Engineering from Oklahoma State University.

Mark Nance

Mark Nance

Topic: Closing the Gap: Discovering the Art in Data Science

Mark Nance, a native Oklahoman, serves as a vice president and chief data officer for American Fidelity. As CDO, he has primary responsibility for ensuring that data is leveraged as an asset, delivering both business value and competitive advantage. 

Focusing on grass roots data initiatives centered on colleague engagement, actionable insight and using data to fuel the journey, Nance has championed successful legacy data conversions and data governance implementations as well as business intelligence and big data initiatives.

Nance holds a B.S. in Accounting from the University of Central Oklahoma.  He is a founding member of the International Society of Chief Data Officers, a member of the Gartner CDO Circle Program, a governing body chair of the Evanta CDO Inner Circle and the Oklahoma State University MADM Advisory Board.

Megan Oftedal

Dr. Megan Oftedal

Topic: How to Solve Problems: Lessons Learned from being a Data Scientist

Dr. Megan Oftedal is a senior data scientist at American Fidelity, a privately owned life and health insurance company that provides voluntary supplemental health insurance products and annuities. She is responsible for developing and implementing several projects using machine learning, artificial intelligence and Big Data.

Oftedal believes great data science is a team sport and works closely with colleagues across several divisions to ensure the quality and usefulness of her team’s work. Oftedal holds a B.A. in Economics from Loyola University, Chicago, and a Ph.D. in Policy Analysis from the RAND Pardee Graduate School.

Prior to her role at American Fidelity, Oftedal was a strategic data fellow at the Center for Education Policy Research at Harvard University, where she helped education leaders and policymakers leverage data and analytics to improve student outcomes.

Sean Owen

Sean Owen

Topic: Detecting Bias with SHAP: What do Developer Salaries Tell us about the Gender Pay Gap?

Sean Owen is the field data science lead at Databricks. He is an Apache Spark committer and PMC member, and co-author Advanced Analytics with Spark.

He started the Oryx project from his startup, Myrrix. Previously, he was director of Data Science at Cloudera and an engineer at Google.

Sree Ram

Sree Ram

Topic: Democratizing AI - A Developers Perspective 

Sree Ram is an accomplished IT leader with experience in solution architecture and the development of data warehousing, Advanced Analytics, IoT and AI systems that provide actionable insights, eliminate development redundancy and use best in-class data ingestion and storage mechanisms. He is the solution architect in the data and AI space at Microsoft, helping customers gain insights on data using ML services, cutting-edge data engineering pipelines and fast data ingestion mechanisms.

David Robinson

David Robinson

Topic: Classifying Thin Section Cuttings using Deep Learning

David Robinson is the subsurface technology data scientist at Devon Energy. He earned a Bachelor of Science in Petroleum Engineering in 2017 and Master of Science in Data Science and Analytics in 2019, both from the University of Oklahoma.

Robinson is currently focused on integrating data science with the oil and gas industry to deliver technical solutions. Most notably, he is applying machine learning for predicting failures of field equipment and classifying subsurface rock types using Cloud-based technologies.

Keith Tarter

Keith Tarter

Keith Tarter graduated from the OU School of Business in 1981. He lives in Dallas with his wife of 30 years and their two sons. Since graduating OU, Tarter has worked with global organizations applying software and technical services that improve operational efficiencies and generate improved revenue and profits. Today he is helping organizations achieve their digital transformations by applying AI to new business models in the Cloud.    

Pavan Vankadaru

Pavan K. Vankadaru

Topic: Leveraging Artificial Intelligence to Improve Farm Productivity and Profitability

Pavan K. Vankadaru is the chief information officer of RandomTrees and an AI Consultant with more than eighteen years of experience in providing leadership to technology enabled business process transformations. He has prior work experience with Big 4 consulting companies and delivered various IT/ERP projects to Fortune 500 companies across Industrial manufacturing, oil and gas, and life sciences.  

Vankadaru specializes in embedding artificial intelligence (AI) in various enterprise business processes to enable intelligent decision making. He works with Industry executives to create an AI mindset and helps them to approach AI both from strategic and tactical perspective. He provides frameworks that help drive the adoption of AI enabled systems and processes. 

Vankadaru is the portfolio owner of AURA, AI model suite developed by RandomTrees, an Enterprise AI company.  Aura provides architected domain specific AI models to augment business users of supply chain, customer service and HR functions with AI capabilities. He has published white papers on various topics such as AI enabled demand management for life sciences, AI powered dynamic pricing for retail and cognitive maintenance for oil and gas Industries. Vankadaru leads teams of data scientists, big data engineers, UX experts and NLP engineers to innovate with AI.  He has successfully integrated various machine-learning applications with ERP and other enterprise systems. 

Friday, Sept. 27, 2019: Research Day

Dr. Amy McGovern

Amy McGovern

Dr. Amy McGovern is a professor in the School of Computer Science at the University of Oklahoma and an adjunct professor in the School of Meteorology at the University of Oklahoma. McGovern is an NSF CAREER award winner and her research focuses on developing novel spatiotemporal data mining method for real-world applications, particularly focusing on severe weather.

McGovern received her PhD in Computer Science from the University of Massachusetts Amherst in 2002 and was a senior postdoctoral research associate at the University of Massachusetts until joining the University of Oklahoma in January, 2005. She received her MS from the University of Massachusetts Amherst (1998) and her BS (honors) from Carnegie Mellon University (1996).