AIoTCoE Panel – Creating business value through advanced analytics and AI, And Delivering ROI From IOT investments


It is with great enthusiasm that we are kicking off the AI Center of Excellence (CoE) at the June 2022 IoT Slam during this panel discussion with executives from SAS, Ford, Otis Elevator, SoftServe, Red Hat, Equinor and Amazon. This new CoE, focused on the potential of AI in industry, will harvest thought leadership from the experience and expertise of its Chair and Pillar Leads to help IoT Community members better address the various analytical/AI needs of IoT use cases in sectors such as Manufacturing, Transportation, Oil & Gas, Health & Life Sciences and other industrial settings. Our ultimate goal is to build a CoE that will continuously highlight the challenges, approaches, and practices emerging as analytics/AI are used in creating business value from IoT data.


SASGul Ege is the Senior Director in SAS IoT R&D leading the advanced analytical components innovation.  She has BS, MS and PhD degrees in Industrial and System Engineering.  She received her Ph.D. from Industrial and Systems Engineering at NC State University.  She is a registered Professional Engineer in North Carolina. She has been in SAS Research and Development for 31 years. Over the years, she has worked on financial analysis, financial risk, forecasting and solutions for retail and manufacturing verticals. She has received the SAS CEO Award of Excellence and the NCSU- Industrial and System Engineering Distinguished Alumni award in 2011.

Michael Cavaretta is an Analytics Executive at Ford, having had multiple roles in Global, Insights, Data and Analytics (GDIA). Since joining in the organization 2015, he’s worked in connected vehicles, analytics infrastructure, customer data and manufacturing and the Industrial Internet of Things. In addition to leading analytics teams, he has managed large IT projects of $40M / year with 100+ direct reports. Before joining GDIA, he spent over 15 years applying data analytics to business problems as part of Research and Advanced Engineering. While there, he led multiple analytic projects across all areas of Ford, including sales and marketing, warranty and quality, manufacturing, and HR, saving the company hundreds of millions of dollars. Michael received his Ph.D. in Computer Science with a concentration on Artificial Intelligence and Machine Learning from Wayne State University.

Pooja Dewan is the Vice President and Chief Data & Analytics Officer for Otis Elevator Company. In this critical role, she leads the company’s data science and analytics capabilities, identifying opportunities to accelerate growth and efficiency. She is responsible for driving the data and analytics vision, strategy, and execution. She owns the Otis data management roadmap driving sustainable business growth and profitability, as well as internal efficiencies through improved data structures, constant data cleanliness and insight, and efficient data governance and processes. Prior to this role, Pooja spent more than 20 years at BNSF Railway where she served as the Chief Data Scientist. There she led the Operations Research and Advanced Analytics group for 16 years. Her team received international recognition through an INFORMS award as the Best Advanced Analytics Team in 2019. Pooja has been a member of INFORMS (Operations Research Society) since 1993. During this time, she led several initiatives offered by INFORMS, including the Edelman competition, and she has also been instrumental in championing activities that help bridge the gap between academia and real-world application. Pooja earned a master’s and doctorate in industrial engineering from Pennsylvania State University. She is also the author of several research publications in various scientific journals.

Ivan Oliveira leads Surface Research Science at Amazon Transportation Services, where his team develops methods, models, and algorithms to solve large industrial  logistics and transportation problems. His team develops AI/ML and operations research tools to tackle problems in vehicle routing, planning, safety, and  sustainability. Ivan started his career at Intel Corp., where he led development of optimization algorithms for VLSI circuit design. Ivan then moved to SAS Institute, where he founded the AIML COE, for solving business optimization and machine learning problems with specialized technical consulting and research for several Fortune 100 companies in the areas of inventory optimization, pricing and revenue management, logistics, and applications of computer vision and object detection. During this period, Ivan also served as Adjunct Assistant Professor at Elon University’s School of Business, where he taught in optimization and operations research. Ivan currently serves on the INFORMS Analytics Collections (IAC) Board, and previously served as INFORMS Prize Judge. Ivan holds patents in areas of analytics, inventory optimization, object tracking, and vehicle routing.

Sebastian Santibanez, is an Associate Director in the Center for Intelligent Enterprise at SoftServe. His work enables clients in manufacture to become faster, leaner, and more reliable by leveraging AI, Big Data, IoT, and other emerging technologies. Over the past 15 years and across three continents, Seba has held positions in manufacturing, startups, academia, research, and consulting, among others.

Audrey Reznik is a Sr. Principal Software Engineer in the Red Hat Cloud Services – OpenShift Data Science team focusing on managed services, AI/ML workloads and next-generation platforms. She has been working in the IT Industry for over 20 years in full stack development to data science roles. As a former technical advisor and data scientist Audrey has been instrumental in educating data scientists and developers about what the OpenShift platform is and how to use OpenShift containers (images) to organize, develop, train and deploy intelligent applications using MLOps. She is passionate about Data Science and in particular the current opportunities with ML and Open Source technologies.

Duncan Irving, is responsible for creating new business models from conventional, emerging and digital technologies as Equinor evolves into a full-spectrum energy company. Many of the business models are data-driven, and AI forms a key part of them as either a tactical enabler, or a strategic driver for new capabilities. His portfolio contains the scalability roadmaps that ensure long-term sustainability of analytics and AI in new business models. Before this role, Duncan spent twelve years in analytics and data consulting: nine years initiating and leading the EMEA oil and gas consulting capabilities for Teradata, where he worked in, led, and scaled data engineering and data science projects; and then three years at TietoEVRY in Norway where he headed energy sector business consulting, and was also the principle consultant for data, analytics and AI. He has delivered and led analytics and data science projects in several energy companies, often in the scientific domains, and typically with strong focus on data management and enterprise governance. He has created data and analytics strategies for several organisations, and served as the interim data officer of a smaller UK energy company during its digital transformation. Duncan has a degree in Geophysics (Edinburgh) and a PhD in geotechnical modelling of frozen ground (Cardiff and ETH Zurich). As a post-doctoral researcher he has variously worked on supercomputing problems, deep ocean surveying, and modelling glacial environments, moving into business development and the commercialization of academic projects. He has co-authored several papers and a book on the practicalities of becoming data-driven in the energy sector.

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