Abstract: The last two decades have seen significant acceptance and penetration of computing technologies in biomedicine and it is estimated we are only at the beginning of a transformational process. Building on strong theoretical foundations, I will illustrate how geometric optimization algorithms and data structures can be used to obtain feasible solutions for some difficult medical decision problems such as those arising in minimally invasive and robotic surgery. I will further illustrate how geometric features can be combined with machine learning methods for improved classification in medical applications.

Biography: Professor Ovidiu Daescu obtained his M.S. and Ph.D. degrees in Computer Science and Engineering from the University of Notre Dame, USA, in 1997 and 2000, respectively. He joined The University of Texas at Dallas in Fall 2000 and is currently Professor and Associate Head of the Computer Science Department. He was instrumental in the creation of the BS degree in Data Science, a joint degree between the Computer Science and Mathematics departments, and is the CS faculty liaison for that degree.

Professor Daescu’s current research focus is on geometric optimization algorithms with applications in biomedicine. He has been instrumental in developing efficient and sometimes optimal algorithms and data structures for various discrete and computational geometry problems and has successfully applied a combination of geometric and machine learning approaches for biomedical problems. His research has been published in renowned journals and conferences in computer science and biomedicine.

From 2014-2020 he has been UTD Site Director of iPerform, an NSF I/UCRC for Assistive Technologies to Improve Human Performance, promoting biomedical algorithms and applications to industry partners.