Michael M. Zavlanos

Michael M. Zavlanos is the Yoh Family Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University's Pratt School of Engineering. He also holds secondary appointments in the Department of Computer Science and the Department of Electrical and Computer Engineering. Currently serving as the Director of the Healthcare Systems Optimization program with Duke AI Health and as an Amazon Scholar with Amazon Robotics, his academic career spans control theory, optimization, and artificial intelligence with applications across multiple domains.

Dr. Zavlanos received his educational foundation from prestigious institutions:

• Diploma in Mechanical Engineering from the National Technical University of Athens (NTUA), Greece (2002)
• M.S.E. in Electrical and Systems Engineering from the University of Pennsylvania (2005)
• Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2008)

His research program spans multiple interconnected domains, with a strong foundation in control theory, optimization, and learning methodologies. This theoretical work directly enables applications in robotics and autonomous systems, where his team develops algorithms for multi-robot coordination, motion planning under complex constraints, and network connectivity maintenance. A significant portion of his work addresses networked and distributed control systems, focusing on how multiple agents can coordinate effectively with limited communication. More recently, he has expanded his research into cyber-physical systems with healthcare applications, leveraging his expertise to optimize healthcare delivery systems through the Duke AI Health initiative.

Dr. Zavlanos' work demonstrates a consistent trajectory from theoretical foundations to real-world applications. His early work established fundamental principles for maintaining connectivity in mobile robot networks, which evolved into more sophisticated approaches for temporal task planning and risk-averse decision making in uncertain environments. The most recent phase of his research integrates machine learning with traditional control theory to address complex healthcare system optimization problems.

His significant contributions to the field have been recognized through prestigious awards:

• Office of Naval Research Young Investigator Program (YIP) Award (2014)
• National Science Foundation Faculty Early Career Development (CAREER) Award (2012)
• National Science Foundation Faculty Early Career Development (CAREER) Award (2011)
• Duke University Distinguished Faculty Rank (2019)
• Duke University Distinguished Professor designation (2018)

As an educator, Dr. Zavlanos has taught courses including ME 627: Linear System Theory, ME 592: Research Independent Study, ECE 391/291: Projects in Electrical and Computer Engineering, and CEE 627: Linear System Theory. His research program has been supported by multiple grants from the National Science Foundation and the Office of Naval Research, enabling him to mentor numerous graduate students and postdoctoral researchers in the development of cutting-edge control and optimization algorithms.

Dr. Zavlanos leads research efforts at the intersection of control theory, optimization, and artificial intelligence, with particular focus on translating theoretical advances into practical applications. His recent work with Duke AI Health represents a strategic expansion of his research portfolio into healthcare systems optimization, where he applies his expertise in algorithmic decision making to improve patient scheduling, resource allocation, and operational efficiency in medical settings. Through his Amazon Scholar role, he also contributes to advancing robotics technologies for real-world applications.