If you're interested in the applications of machine learning and artificial intelligence models to the healthcare domain and the applications of fairness in machine learning, join this tutorial at KDD 2020. The tutorial assumes some familiarity with the foundational concepts of machine learning but does not presuppose domain knowledge in the healthcare domain.
Ankur Teredesai is a Professor in the Department of Computer Science at University of Washington Tacoma, and founder and director of the Center for Data Science at University of Washington. He is also the founder and CTO of KenSci, a vertical machine learning/AI healthcare informatics company focused on risk prediction in healthcare. Professor Teredesai has published more than 70 research papers in top machine learning and data mining conferences like KDD, AAAI, CIKM, SDM, PKDD etc. He is also the information officer of KDD.
Muhammad Aurangzeb Ahmad is a Research Scientist and Principal Data Scientist at KenSci Inc. a Machine learning/AI healthcare informatics company focused on risk prediction in healthcare. He is also Affiliate Associate Professor in the Department of Computer Science at University of Washington Tacoma. He has had academic appointments at University of Washington, Center for Cognitive Science at University of Minnesota, Minnesota Population Center and the Indian Institute of Technology at Kanpur. He has published more than 50 research papers in top machine learning and data mining conferences KDD, AAAI, SDM, PKDD etc.
Dr. Arpit Patel is a General Surgeon who graduated from residency training at The Brooklyn Hospital Center in NY. He is currently finishing the Clinical Informatics fellowship training program in the Department of Bioinformatics and Medical Education at the University of Washington in Seattle. Throughout his medical career, he has taken an interest in various aspects of healthcare informatics, including developing documentation and clinical decision support tools in the EHR and the theory and clinical applications of machine learning.
Vikas Kumar is a Data Scientist working at KenSci. In this role, Vikas works with a team of data scientists and clinicians to build consumable and trustable machine learning solutions for healthcare. His focus is in building explainable models in healthcare and application of recommendation systems in clinical settings. Vikas holds a Ph.D. with a major in Computer Science and minor in Statistics from the University of Minnesota, Twin Cities. He has worked on modeling and application of recommendation systems in various domains, such as media, location, and healthcare. His focus has been to interpret the balance users seek between known (or familiarity) and unknown (or novel) items to build adaptive recommendations. Prior to his Ph.D., he completed his Bachelor's at the National Institute of Technology, India and worked as a software engineer in Microsoft India.
Dr. Carly Eckert MD, MPH, is the Medical Director of Clinical Informatics at KenSci Inc. In this role, Dr. Carly leads and works with doctors and data scientists to identify patterns in patient data to predict risk that can cost-effectively improve care outcomes. Prior to her role at KenSci, Dr. Carly was the associate medical director for catastrophic care at the Department of Labor and Industries for the state of Washington. Dr. Carly trained in General Surgery at Vanderbilt University Medical Center and in Occupational and Environmental Medicine and Preventive Medicine at the University of Washington (UW). She has also co-authored several publications on topics related to general surgery, occupational health, and occupational injury.
Responsible machine learning is central to driving adoption of machine learning in healthcare. While the focus of deployment of responsible machine learning system has largely been on robustness and interpretable machine learning, fairness is now becoming a pivotal issue in healthcare AI/ML. . Even though there is already a large and growing body of literature on fairness in machine learning in general, a focused emphasis on requirements for fair and unbiased systems deployed in healthcare settings is lacking. This tutorial is motivated by the need to comprehensively study fairness in the context of applied machine learning in healthcare.
Corresponding author: Muhammad Aurangzeb Ahmad, firstname.lastname@example.org