Daniel earned his Ph.D. in Mathematics from the University of California, San Diego. He previously served as a Postdoctoral Researcher at Oxford University, a Postdoctoral Researcher and Visiting Professor at Northwestern University, and most recently as an Assistant Professor at Johns Hopkins University.
Daniel works at the intersection of Mathematical Optimization and Data Science. He designs, analyzes, and implements algorithms for solving continuous optimization problems arising in applications for healthcare and computer vision.
His articles have appeared in top applied mathematics journals, such as Mathematical Programming and SIAM Journal on Optimization, as well as in top data science conference proceedings, such as the International Conference on Machine Learning and IEEE Conference on Computer Vision and Pattern Recognition. He is an Associate Editor for the reputed journals Computational Optimization and Applications and the Optimization Methods and Software.
Daniel was a founding member of Johns Hopkins' Mathematical Institute for Data Science (MINDS) and helped to establish JHU's Master of Science in Data Science program. He has (twice) received the Professor Joel Dean Award for Excellence in Teaching. He has been awarded numerous grants from the National Science Foundation and other funding agencies.
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