Dean Abbott is President of Abbott Analytics and currently is the Bodily Bicentennial Professor in Analytics at UVA Darden School of Business. He is an internationally recognized thought leader and innovator in data science and predictive analytics with more than three decades of experience solving a wide range of private and public sector problems. Mr. Abbott is the author of Applied Predictive Analytics (Wiley, 2014) and coauthor of The IBM SPSS Modeler Cookbook (Packt Publishing, 2013).

Rohit is the Chief Data Officer at Bizom (Mobisy Technologies Pvt Ltd), where he leads a dynamic team of data scientists and software engineers. His team focuses on advancing retail analytics through cutting-edge machine learning and deep learning techniques, with his insights regularly featured in India's top financial publications. Rohit previously spent 11 years at GE, tackling complex data challenges in healthcare and life sciences. 

An active Manning Reviewer, he has reviewed numerous books and live projects on deep learning and NLP. Rohit is also an accomplished speaker, having presented at international conferences such as the Predictive Analytics Conference in Munich (2019,2024), Berlin (2020, online), and Las Vegas (2022, 2023), Data Engineering Summit 2024 (India). Rohit holds a master’s degree in Information Technology and a bachelor’s degree in Computer Science

Anthony is a lead software engineer at Genesys where he is working on several AI and ML projects related to customer experience. He has over 20 years experience in designing and building scalable software. Anthony holds a Ph.D. degree in Electrical Engineering with specialization in Intelligent Robotics Software and has worked on various problems in the areas of human-AI interaction and predictive analytics for SaaS business optimization.

Eitan is the Chief Data Scientist at Bill.com and has many years of experience as a researcher. His recent focus is in machine learning, deep learning, applied statistics and software engineering. Before, he was a Postdoctoral Scholar at Lawrence Berkeley National Lab, received his PhD in Physics from Boston University and his B.S. in Astrophysics from University of California Santa Cruz. He holds 4 patents and 11 publications to date and has spoken about data at various conferences around the world.

Vladimir Barash is Director Graphika Labs. He has received his Ph.D. from Cornell University, where he studied Information Science and wrote his thesis on the flow of rumors and virally marketed products through social networks. At Graphika, Vladimir's research focuses on deep learning applications of network analysis, detection and deterrence of disinformation operations on networks, and causal mechanisms of large-scale social behavior.

In addition to his research duties, Vladimir has a decade's experience working with big data, from scientific computing (Matlab, scipy) to parallel processing technologies (Hadoop / Hive) to data storage and pipelining (Redis, mongodb, MYSQL) at the terabyte scale. At Graphika, Vladimir has co-designed and implemented systems that process tens of millions every six hours to deliver timely information on influencers and conversation leaders in online communities tailored to client interests. Vladimir is proficient in over a dozen programming languages and frameworks and has designed production-ready systems for every stage of big data analysis, from collection to client-facing presentation via web, spreadsheet or graphic visualization.

Vladimir has been active in the Social Media Research Foundation (SMRF) and the NodeXL project, helping build a network analysis package that brings relational data analysis at scale to the fingertips of any interested user, without requiring specialized knowledge or technical training beyond familiarity with Microsoft Excel. NodeXL has enabled users in academia, industry and the general public to analyze tens of thousands of social networks, from networks of politicians voting on bills to networks of motorcycle enthusiasts working together. As part of his work with SMRF and the NodeXL team, Vladimir has contributed a chapter on Twitter analysis to Analyzing Social Media Networks with NodeXL: Insights from a Connected World.

Vladimir's work has received awards at the International Conference for Weblogs in Social Media and Bits on Our Minds. He has presented his research at academic and industrial campuses all over North America and Europe, including: Xerox/PARC, Microsoft, Colgate University, Northeastern University, UMCP and Oxford University (Oxford Internet Institute). He currently resides in Somerville, MA.

Clinton Brownley, Ph.D., is a data scientist at WhatsApp, where he’s responsible for a variety of analytics projects designed to improve messaging and VoIP calling performance and reliability.  Before WhatsApp, Clinton was a data scientist at Facebook, working on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions.  As an avid student and teacher of modern analytics techniques, Clinton is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis,” and also teaches Python programming and data science courses at Facebook and in the Bay Area. Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.

Siva Chamarti is an inspiring leader with in-depth knowledge in developing and deployment of AI on Edge with special interest in computer vision. At Shell, he is leading AI/ML Engineer team with specialist skills in scaling up AI products. At Shell, he helped in building Shell.AI platform which helps in accelerating AI application development and scaling up the projects.

Aaron is currently the Vice President of Data Science and Solutions at dotData. As a data science practitioner with 14 years of research and industrial experience, he has held various leadership positions in spearheading new product development in the fields of data science and business intelligence. At dotData, Aaron leads the data science team in working directly with clients and solving their most challenging problems.

Prior to joining dotData, he was a Data Science Principle Manager with Accenture Digital, responsible for architecting data science solutions and delivering business values for the tech industry on the West Coast. He was instrumental in the strategic expansion of Accenture Digital’s footprint in the data science market in North America.

Aaron received his Ph.D. degree in Applied Physics from Northwestern University.

Tom is the founding partner of At One Ventures, which backs early-stage (Seed, Series A) companies using disruptive deep tech to upend the unit economics of established industries while dramatically reducing their planetary footprint.

Previous to founding At One, Tom was a founding member of Google X where he led the teams that created self-driving cars, deep learning artificial intelligence, wearable augmented reality and internet connectivity expansion. He played a significant role in established projects with global reach including Microsoft (Outlook) and Yahoo (Search, Answers). 

He has also spent time in the developing world and via social entrepreneurship accelerators, mentoring 200+ entrepreneurs working on global development issues such as access to clean water, electricity, education, health care, and employment.

Tom has spoken at TEDx, Aspen Institute, YPO, SuperVenture, IDEO, Unreasonable and Summit at Sea. He  is also a lifelong inventor, with 75 patents across hardware, software, design, and mechanical systems.

Hamza has over a decade of experience in leading Data Science/ Machine Learning teams. His tenure spans over three continents and seven countries across APAC, Middle East and Africa and North America and multiple industries, namely, Tech, Telecommunications, Finance and Retail. He is currently working at Google as a Research Scientist and is also serving as an Adjunct Professor at University of Minnesota.

Bala Ganesh leads the Operation Technology, Advanced Technology & Technology Support Groups at UPS. He joined UPS in 2012 as a Program Manager for UPS’s customer technology group. Thereafter he had stints in Global Retail Strategy and the Advanced Analytics & Revenue Management groups.

Prior to joining UPS, Bala was a consultant with McKinsey & Co.  He has also worked as an Aerospace Engineering researcher at the Georgia Institute of Technology.  Early in his career, Ganesh served as a pilot in the Indian Air Force. 

Bala earned a PhD in Aerospace Engineering with a minor in Math along with an MBA from the Georgia Institute of Technology. He graduated from the Indian National Defense Academy and Air Force Academy with his undergraduate degree.

Siddha Ganju, whom Forbes featured in their 30 under 30 list, leads AI innovation in LLM and Guardrails along with the deployment of Medical Instruments for Nvidia partners at Nvidia. Siddha previously worked in the self-driving teams for simulation, perception, scalable training, and inference along with global automotive partnerships and go-to-market strategies.

Vladyslav Ivanov is a Quantitative Researcher with extensive experience in leading quantitative trading strategies research and applying statistical learning to problems in quantitative finance. Vladyslav's work at Outremont Technology is focused on quantitative trading strategies research and technological development of the hedge fund. Prior to joining Outremont, Vladyslav worked at a Chicago proprietary trading firm, where he conducted alternative data strategies research and was a product owner of the research framework. He also worked in the quantitative research group at a leading New York hedge fund, where he designed and implemented a market regimes analysis system, collaborated with the portfolio manager on strategies development, and built large-scale data processing systems.

Vladyslav holds a degree in Financial Economics and Data Science from Claremont McKenna College. He also received Computer Science and Financial Engineering training during his time at Stanford University, where he carried out research at the Institute for Computational & Mathematical Engineering, and Department of Statistics.

Jared P. Lander is Chief Data Scientist of Lander Analytics, the Organizer of the New York Open Statistical Programming Meetup and the New York and Government R Conferences, an Adjunct Professor at Columbia Business School, and a Visiting Lecturer at Princeton University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. Jared oversees the long-term direction of the company and acts as Lead Data Scientist, researching the best strategy, models and algorithms for modern data needs. This is in addition to his client-facing consulting and training. He specializes in data management, multilevel models, machine learning, generalized linear models, data management, visualization and statistical computing. He is the author of R for Everyone (now in its second edition), a book about R Programming geared toward Data Scientists and Non-Statisticians alike. The book is available from Amazon, Barnes & Noble and InformIT. The material is drawn from the classes he teaches at Columbia and is incorporated into his corporate training. Very active in the data community, Jared is a frequent speaker at conferences, universities and meetups around the world.

Venugopal Mani is a Senior Data Scientist 2 from the Search & Recommendations team at Shipt. He has a Master's degree in Computer Science from the University of Minnesota and his research focuses on applying Machine Learning techniques to generate large scale recommendations. Prior to Shipt, Venugopal worked for nearly 4 years at Walmart. In his years at Walmart, he has led many initiatives including smart substitutions for Walmart's popular online grocery platform and has also participated in research involving responsible machine learning through design of differentially private recommenders. His current work at Shipt involves translating abstract methods to large scale , production systems while considering various real-world challenges like the need to maintain low latency while maintaining high accuracy.

James McCaffrey, who annually leads the full-day deep learning workshop at Machine Learning Week, works for Microsoft Research in Redmond, Wash. James has a PhD in cognitive psychology and computational statistics from the University of Southern California, a BA in psychology, a BA in applied mathematics, and an MS in computer science. James learned to speak to the public while working at Disneyland as a college student, and he can still recite the entire Jungle Cruise ride narration from memory.

Ehsan Mousavi is a Principal Machine Learning Engineer at Apple. He has led teams of engineers and scientists to develop various machine learning-based products in Apple, Uber, and Target. He received his Ph.D. from Stanford University in Operation Research. Dr. Mousavi specializes in deep learning, reinforcement learning, optimization and statistical modeling, and their synergistic application to the problems arising in E-commerce, revenue management, and marketing. 

Taesik Na is a senior machine learning engineer at Instacart where he focuses on search relevance and ranking models. Prior to Instacart, he worked on efficient ML training algorithms and optimization techniques at Microsoft. He also worked on computer aided design at Samsung. Taesik received his Ph.D. in Electrical and Computer Engineering from Georgia Tech, where his research focused on energy efficient, noise robust and secure deep learning system design.

Sharada Narayanan is a Senior Data Scientist at dotData. Currently working in the ML and Feature Engineering automation technologies, Sharada has worked on multiple projects with large volume and variety of data which generate value for organizations in the Healthcare, Supply chain and Customer Analytics space. She has a passion for educating organizations new to data science to facilitate strategic and quick success in driving data driven decisions.

Kumaran Ponnambalam is a technology leader with 20+ years of experience in AI/ML, Data and Analytics. His focus is on creating robust, scalable Gen AI models and services to drive effective business solutions. He is currently leading Generative AI initiatives at Outshift by Cisco, building next-generation AI innovations and products to help enterprises. In his previous roles, he has built conversational bots, ML platforms, data pipelines and cloud services. A frequent speaker at technology conferences, he has also authored several courses on the LinkedIn Learning Platform  in AI/ML and Big Data.

Working within an internal AI consultancy, David spearheads the end to end design and implementation of python apps incorporating AI. David channels his petroleum engineering roots to meet with experts from the business and turn their needs into programmable inputs and outputs, going from PoC or MVP to deployment with a handoff plan for IT. David is accomplished in the predictive maintenance, subsurface, and retail fuels pricing domains complete with working and used deployments of both applications and entire predictive systems. For fun, David delves into IoT devices, lifting weights, arcade cabinet repair, and PCB design.

Eric Siegel, Ph.D., is a former Columbia University professor who helps companies deploy machine learning. He is the cofounder and CEO of Gooder AI, the founder of the long-running Machine Learning Week conference series, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at hundreds of universities, as well as The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching the graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. A Forbes contributor, Eric publishes op-eds on analytics and social justice.

Eric has appeared on Bloomberg TV and Radio, BNN (Canada), Israel National Radio, National Geographic Breakthrough, NPR Marketplace, Radio National (Australia), and TheStreet. A Forbes contributor, Eric and his books have been featured in BBC, Big Think, Businessweek, CBS MoneyWatch, Contagious Magazine, The European Business Review, Fast Company, The Financial Times, Fortune, GQ, Harvard Business Review, The Huffington Post, The Los Angeles Times, Luckbox Magazine, MIT Sloan Management Review, The New York Review of Books, The New York Times, Newsweek, Quartz, Salon, The San Francisco Chronicle, Scientific American, The Seattle Post-Intelligencer, Trailblazers with Walter Isaacson, The Wall Street Journal, The Washington Post, and WSJ MarketWatch.

Dr. Marc A. Smith is a sociologist specializing in the social organization of online communities and computer mediated interaction. Smith leads the Connected Action consulting group. Smith co-founded the Social Media Research Foundation (http://www.smrfoundation.org/), a non-profit devoted to open tools, data, and scholarship related to social media research. He contributes to the open and free NodeXL project (http://nodexl.codeplex.com) that adds social network analysis features to the familiar Excel spreadsheet. NodeXL enables social network analysis of email, Twitter, Flickr, WWW, Facebook and other network data sets. Along with Derek Hansen and Ben Shneiderman, he is the co-author and editor of Analyzing Social Media Networks with NodeXL: Insights from a connected world, from Morgan-Kaufmann which is a guide to mapping connections created through computer-mediated interactions. Smith has published research on social media extensively, providing a map to the landscape of connected communities on the Internet.

Rama is currently a Senior Machine Learning Engineer in the Ads Quality team at Instacart. In his previous roles at Quora and Walmart, he built and deployed scalable ML models to generate opportunities for ad targeting, model different user journeys, and serve personalized recommendations to customers, thus boosting clicks, conversions, and revenue.

Rama obtained an M.S. in EECS (Electrical Engineering and Computer Science) from UC Berkeley and a B.Tech (Hons.) in Electrical Engineering from IIT Madras. He has experience in collaborative intelligence, neural machine translation, speaker identification, audio compression, applications of biomedical signal processing and ML in wearable devices, etc. and has published in reputable conferences/workshops (IEEE ICASSP, IEEE Big Data, WWW 2021, etc). He has also filed 5+ patents.

When he's not working, he loves playing the flute. He's a traveler at heart and loves exploring new places and meeting new people.

Vinesh Sukumar currently serves as Senior Director – Head of AI/ML product management at Qualcomm Technologies, Inc (QTI). In this role, he leads AI product definition, strategy and solution deployment across multiple business units.

He has about 20 years of industry experience spread across research, engineering and application deployment. He currently holds a doctorate degree specializing in imaging and vision systems while also completing a business degree focused on strategy and marketing. He is a regular speaker in many AI industry forums and has authored several journal papers and two technical books.

James Taylor is Executive Partner at Blue Polaris and is a leading expert in how to use business rules and analytic technology to build decision management systems. He is passionate about using decision management systems to help companies improve decision-making and develop an agile, analytic and adaptive business. He provides strategic consulting to companies of all sizes, working with clients in all sectors to adopt decision-making technology. James is an expert member of the International Institute for Analytics and is the author of multiple books and articles on decision management, decision modeling, predictive analytics and business rules, and writes a regular blog at JT on EDM. James also delivers webinars, workshops and training. He is a regular keynote speaker at conferences around the world.

Yentai Wan is the Director of Network Planning & Optimization in UPS Corporate Industrial Engineering group. His primary responsibilities are to (a) improve network planning processes, (b) generate network efficiencies and (c) support strategic initiatives across the enterprise. He joined UPS in 2007 as an Enterprise Network Planning Manager in Corporate Transportation.

Yentai was born and raised in Taipei City, Taiwan. He came to US in 2000 for advanced education and received his PhD of Industrial Engineering and Operations Research from Georgia Institute of Technology. He is passionate about applying Operations Research and Machine Learning with real world applications. Yentai is a Industry Advisory Board member of The Georgia Tech Supply Chain and Logistics Institute. 

Evan is the Director of Analytics for PyMC Labs, helping to marry the exciting things that analytics can do with the myriad of challenges that organizations are facing. Evan almost always has a smile on his face, and that smile is the widest when he is helping organizations use data in new ways to solve unique problems.

Since 2012, Evan has worked in data and analytics, beginning in finance with roles in the Military and Investment Banking, before expanding into analytics consulting.

He holds a BS in Management from Georgia Tech, an MS in Economics from East Carolina University, and an MS in Analytics from the Institute for Advanced Analytics at NC State University.

He loves teaching and learning and can often be found exploring the outdoors with his wife and children.

Dr. Chengwei Zhai has been designing and developing simulation and machine-learning-based risk models for complex infrastructure systems. Currently, he serves as Staff Data Scientist at One Concern, a Menlo Park-based Resilience as a Service solution provider that brings disaster science together with machine learning, for better decision-making. In his role, he leads resilience model development, data analytics, verification, and validation of the company’s infrastructure system models.