Dr. Bart Peintner serves as an Advisor and has served as Chief Technology Officer from 2012 to 2020. His extensive background in Artificial Intelligence and automated personalization was gained through his Ph.D. work at the University of Michigan and his seven years as a Senior Computer Scientist in the Artificial Intelligence Center at SRI International (founded as Stanford Research Institute) led by Vinay Chaudhri, one of the largest private research facilities in the United States with 2,300 scientists.

At the University of Michigan, Bart was the principal developer of the Autominder system, a pioneering cognitive orthotic deployed on the mobile robot Pearl as part of the multi-disciplinary, multi-university Nursebot project—the Initiative on Personal Robotic Assistants for the Elderly. Autominder was a robot-based cognitive aid designed to help older adults adapt to cognitive decline by analyzing their actions and adapting to their changing behaviors. Unlike existing systems that simply provided alarms at fixed times, Autominder used a range of AI techniques to model an individual's daily plans, observe and reason about the execution of those plans through robot sensors, and make intelligent decisions about whether and when to issue personalized reminders for activities of daily living. This groundbreaking work was tested at the Longwood Retirement Community in Oakmont, Pennsylvania, and generated new representations and techniques for reasoning about the behaviors and preferences of people. Bart's Ph.D. research was conducted under the supervision of Dr. Martha E. Pollack, a leading AI researcher who later became President of Cornell University.

At SRI International, Bart was a key scientist in the DARPA CALO project (Cognitive Assistant that Learns and Organizes), the largest government-funded AI project in U.S. history prior to the 2022 AI surge. CALO was a five-year, $150–200 million initiative led by SRI that brought together over 300 researchers from 25 of the top university and commercial research institutions—including Carnegie Mellon University, the University of Massachusetts, and the Institute for Human and Machine Cognition—with the goal of building a new generation of cognitive assistants that can reason, learn from experience, be told what to do, explain what they are doing, reflect on their experience, and respond robustly to surprise. The project was the first AI initiative worldwide that successfully integrated numerous AI technologies into one cohesive cognitive assistant. The PAL (Personalized Assistant that Learns) capabilities developed during CALO were also integrated into the U.S. Army's Command Post of the Future (CPOF) command and control system and deployed to Iraq in 2010.

During the CALO project, Bart worked at the intersection of machine learning, natural language processing, and preference reasoning to automatically identify models of user behavior and decision-making. He was instrumental in developing PTIME (Personalized Time Management), a user-adaptive personal assistant agent that helped busy knowledge workers with time management and meeting scheduling by learning their preferences and coordinating across distributed multi-agent environments. Bart deployed these research concepts in a production-level collaborative task management tool called Task Assistant that was used by the U.S. Navy, enabling users to automatically leverage web services in military environments.

Also at SRI, Bart co-led groundbreaking research analyzing the speech of older adults to find markers of cognitive diseases. One study proved that speech analysis methods using machine learning could predict the onset of Alzheimer's disease with 70% accuracy over ten years before official clinical diagnosis. Another study, presented at the Workshop on Computational Linguistics and Clinical Psychology at ACL 2014, demonstrated that machine-learned algorithms could reliably distinguish Alzheimer's disease from three variants of Frontotemporal Lobar Degeneration (semantic dementia, progressive nonfluent aphasia, and others) based on brief spontaneous speech samples of less than 10 minutes—a classification task that typically requires highly specialized doctors using expensive batteries of tests.
Bart has produced approximately 38 research publications and holds multiple U.S. patents in the areas of virtual personal assistants, intelligent automated assistants, semantic inference, machine learning, natural language processing, preference reasoning, automated scheduling and planning, constraint optimization, temporal constraint reasoning, and probabilistic reasoning. According to Google Scholar, his work has been cited in over 4,200 papers. His notable co-authors include Dr. Martha E. Pollack (President of Cornell University), Dr. Neil Yorke-Smith (Delft University of Technology), Dr. Karen Myers (SRI International), Dr. Melinda Gervasio (SRI International), Dr. Pauline Berry (SRI International), Dr. Kristen Brent Venable (Tulane University/IHMC), Dr. William Jarrold (SRI International), and other leading figures in AI research.

Bart holds a Ph.D. in Computer Science (2002–2005) and a Master of Science in Computer Science and Engineering (2000–2001) from the University of Michigan, and a Bachelor of Science in Electrical Engineering (1995–1999) from Kansas State University. Colleagues have described him as "one of the sharpest students in the AI program at the University of Michigan" and "exactly the type of person you want working on any machine learning/artificial intelligence type problem."