Professor Jonathan Eckstein
Jonathan Eckstein is a Professor in the department of Management Science and Information Systems at Rutgers University. His principle research interests are in numerical optimization algorithms, both continuous and discrete, and especially their implementation on parallel computing platforms. Areas of particular focus include augmented Lagrangian/proximal methods, branch-and-bound algorithms, and stochastic programming. He has also worked on risk-averse optimization modeling and on applying O.R. techniques to managing information systems. He completed his Ph.D. in Operations Research at M.I.T. in 1989, and then taught at Harvard Business School for two years. He then spent four years in the Mathematical Sciences Research Group of Thinking Machines, Inc. before joining Rutgers. At Rutgers, he led an effort establishing a new undergraduate major in Business Analytics and Information Technology (“BAIT”). In 2014, he was elected a fellow of INFORMS (the Institute for Operations Research and Managment Science).
Website: http://rutcor.rutgers.edu/~jeckstei
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About the topic:
“Artificial intelligence”, which today largely means an area of computing called “Machine Learning” is currently one of the hottest topics in the business world, often ascribed nearly mystical powers. This presentation will discuss classification and regression problems, and attempt to demystify terms like “loss functions”, “regularizers”, “LASSO”, “neural nets” and “deep learning”.
