Ashwin Kumar, a PhD candidate at Washington University in St Louis, working on fair resource allocation and explainable AI, speaks with Brent Phillips, Pitt HexAI podcast producer, about the intricacies of fairness in AI systems and about balancing fairness and efficiency in complex systems like ride-sharing and healthcare resource distribution. The discussion dives into AI transparency, human-machine interaction, multi-agent reinforcement learning, and the evolving challenges in ensuring equitable outcomes in AI decision-making. The conversation connects AI's technical workings with real-world ethical concerns.

John Carbone, Adjunct Professor of Applied AI and Applied Data Science at Baylor University speaks with Zoe Menezes, a student at the University of Pittsburgh, and Brent Phillips, Pitt HexAI podcast producer about the future of education in the AI age and how universities are adapting to the emergence of applications like ChatGPT. John elaborates on how universities like Baylor are embracing change, trying new things and and working towards keeping up with advances in technology. John also elaborates on a number of his AI connected academic papers and, on digital security in the AI age and offers advice for students and fellow faculty members on education and acquiring new skills.

Jordan Gass-Poore’, Pitt HexAI host, delves into the cutting-edge world of bioinformatics and cancer research with Dr. Aristotelis Tsirigos, a Professor of Medicine and Pathology and the Co-Director of Precision Medicine at NYU Grossman School of Medicine. Dr. Tsirigos shares his journey from computer science and electrical engineering to becoming a leading figure in pathology and bioinformatics.

In this episode, they explore the latest advancements in self-supervised deep learning and its transformative impact on cancer diagnosis and management. Dr. Tsirigos discusses his former work at IBM and his current research at NYU Langone Health's Perlmutter Cancer Center, including the innovative study "Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides." This study, conducted in collaboration with the University of Glasgow, leverages AI to identify distinct cancer phenotypes, offering new insights into cancer subtypes and potential therapeutic strategies.

Tune in to learn about the intersection of AI, molecular information, and healthcare, and how these advancements are paving the way for more precise and personalized cancer treatments

Fei Wang, Professor of Population Health Sciences and Emergency Medicine at the Weill Cornell Medical College and Founding Director of the WCM Institute of AI for Digital Health (AIDH) speaks with Pitt HexAI guest host Brent Phillips about Dr. Wang’s work at Cornell and about Cornell’s new Institute of AI for Digital Health. Dr. Wang and Brent also discuss new and existing data sources for health AI research, the evolution and power of complex machine learning models and their impact on informatics and computational health fields, education in the AI age and about explainable AI and advice for private sector companies interested in collaborating with academic institutions.

Adolph J. Yates Jr., MD, Chief of Orthopedics at the University of Pittsburgh Medical Center Shadyside, speaks with Pitt HexAI guest host Jamie Gramz, Senior Director of Digital Strategy and Business Development with Siemens Healthineers, about Dr. Yate's work specializing in hip and knee replacement as well as knee arthroscopy and his work as a professor and vice chairman for quality management within the UPMC's Department of Orthopedic Surgery. Jamie and Dr. Yates discuss in detail artificial intelligence and the future of orthopedics, how AI and machine learning are being used to support hip and knee replacements, the availability and adoption of machine learning models, collaboration between universities and hospitals around applications of AI and about Dr. Yates views on explainable AI.

Elizabeth Skidmore, a Professor of Occupational Therapy at the University of Pittsburgh and Associate Dean for Research at Pitt’s School of Health and Rehabilitation Sciences, speaks with Pitt HexAI podcast host Jordan Gass-Pooré about Beth’s work as an occupational therapist and rehabilitation scientist with expertise in neurological rehabilitation. Beth and Jordan discuss rehabilitation sciences, intervention program design, program implementation, fidelity, applications of artificial intelligence and the training of occupational therapists in the AI age.

Dr. Sarah Wheeler, Associate Professor of Pathology at the University of Pittsburgh, speaks with Pitt HexAI guest host Jamie Gramz, Senior Director of Digital Strategy and Business Development with Siemens Healthineers. Sarah and Jamie discuss Sarah’s work and expertise in clinical pathology and laboratory medicine and about the use of artificial intelligence for clinical diagnosis, robotics and automation, explainable and trustworthy AI, lab developed testing and the future of laboratory medicine.

Dr. Dana Tudorascu, Associate Professor of Psychiatry and Biostatistics at the University of Pittsburgh speaks with Pitt HexAI guest host Jamie Gramz, Senior Director of Digital Strategy and Business Development with Siemens Healthineers.

Jamie and Dr. Tudorascu discuss how neuroimaging statistics and artificial intelligence are being used in the fight against neurodegenerative diseases like Alzheimers. Dr. Tudorascu is recognized for her expertise in neuroimaging data analysis and imaging techniques and is currently conducting research in key areas including improving brain tissue classification, imaging data harmonization and reproducibility of neuroimaging studies on neurodegenerative diseases.

Jamie and Dr. Tudorascu touch on the importance of collaborating across disciplines like psychiatry, neurology and radiology, training others in biostatistical methods and study design and the roles they play in clinical research, multi-modal imaging studies, data harmonization across modalities, applications of artificial intelligence and machine learning in research, and on explainable AI and trustworthy AI in healthcare.