Research Interests

• Machine Learning: Graphical models, message passing algorithms
• Optimization: Efficient algorithms for machine learning
• Statistics: High-dimensional data analysis, regularization and shrinkage

 Biography

With a background in engineering, Murat has a keen interest in applying theory to solve real-world problems. His primary interest is in designing optimization algorithms for machine learning models. Using efficient algorithms, model training time can be reduced significantly, allowing researchers to efficiently test and select the best model for the problem at hand, be it recommender systems or image denoising. Murat completed his PhD in the Department of Statistics at Stanford University. He holds a Master’s in Computer Science from Stanford and Bachelor’s degrees in Electrical Engineering and Mathematics from Bogazici University in Turkey. Murat is currently a postdoctoral researcher at Microsoft Research – New England. Murat regularly publishes at the top-rated machine learning conference NIPS, and has journal papers in the Annals of Statistics and JMLR.

Research Activity and News

• M.A. Erdogdu, Y. Deshpande, A. Montanari, Inference in Graphical Models via SDP Hierarchies, NIPS 2017
• M.A. Erdogdu, M. Bayati, L.H. Dicker Scaled Least Squares Estimator for GLMs in Large-Scale Problems, NIPS 2016
• M.A. Erdogdu, Newton-Stein Method: An optimization method for GLMs via Stein’s lemma, NIPS 2015
• M.A. Erdogdu and A. Montanari, Convergence rates of sub-sampled Newton methods, NIPS 2015
• M. Bayati, M.A. Erdogdu, A. Montanari, Estimating Lasso risk and noise level, NIPS 2013