Industrial engineering, operations research, and systems engineering are fields of study intended for individuals who are interested in analyzing and formulating abstract models of complex systems with the intention of improving system performance. Unlike traditional disciplines in engineering and the mathematical sciences, the fields address the role of the human decision-maker as key contributor to the inherent complexity of systems and primary benefactor of the analyses.
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Alejandro Carderera is a 1st-year Ph.D. student in Machine Learning at the H. Milton Stewart School of Industrial and Systems Engineering department. He received a B.S. in Industrial Engineering from the Universidad Politécnica de Madrid with a concentration in Energy Systems, and a M.S. in Applied and Engineering Physics from Cornell University, where he wrote his thesis on computational fluid electrohydrodynamics and received the Henry S. Sack Memorial Award.
After completing his M.S. degree he worked for two years as a Research Engineer at HP's 3D and Large Format Printing R&D center.
He is currently working with Prof. Sebastian Pokutta in the acceleration of projection-free first-order optimization algorithms. His broader interests lie in convex optimization, causal inference and theoretical machine learning.