I'm an assistant professor in the department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor.

Prior to joining University of Michigan, I was a Math+X postdoctoral fellow working with Emmanuel Candès at Stanford University. I received my Ph.D. in Electrical Engineering and Computer Science from UC Berkeley in 2016. My Ph.D. advisors were Laurent El Ghaoui and Martin Wainwright, and my studies were supported partially by a Microsoft Research PhD Fellowship. I obtained my B.S. and M.S. degrees in Electrical Engineering from Bilkent University, where I worked with Orhan Arikan and Erdal Arikan.

Research Interests: Large Scale Optimization, Machine Learning and Big Data, Signal Processing, Compressed Sensing, Information Theory and Polar Coding

Contact


E-mail:
pilanci[at]umich.edu
Address:
Electrical Eng Computer Sci Bl
1301 Beal Ave
Ann Arbor, MI 48109-2122

Teaching


Fall 2017

EECS 545 — Machine Learning

Winter/Fall 2018

EECS 351 — Digital Signal Processing

Winter 2019

EECS 598 — Large Scale Optimization for Machine Learning







Publications

2018


M. Pilanci and E. J. Candès
Randomized Direction Method of Multipliers
Preprint, 2018
randomized algorithms distributed optimization

M. Pilanci and E. J. Candès
Randomized Methods for Fitting Non-Convex Models
Preprint, 2018
non-convex optimization neural nets phase retrieval

2017


M. Pilanci and M. J. Wainwright
Newton Sketch: A Linear-time Optimization Algorithm with Linear-Quadratic Convergence
SIAM Journal of Optimization, SIAM J. Optim., 27(1), 205–245, 2017
randomized algorithms newton's method interior point method convex optimization linear program logistic regression
DOI PDF arXiv

2016


M. Pilanci and M. J. Wainwright
Iterative Hessian sketch: Fast and accurate solution approximation for constrained least-squares
Journal of Machine Learning Research (JMLR) 17, 2016
information-theoretic lower-bounds sketching l1 regularized least-squares nuclear norm regularization
PDF arXiv
M. Pilanci
Fast Randomized Algorithms for Convex Optimization and Statistical Estimation
PhD Thesis, University of California, Berkeley, 2016
machine learning convex optimization convex relaxations randomized algorithms
PDF

2015


M. Pilanci, M. J. Wainwright, and L. E. Ghaoui
Sparse learning via Boolean relaxations
Mathematical Programming, 151(1), 2015
sparse regression sparse classification randomized rounding
DOI PDF

M. Pilanci and M.J. Wainwright
Randomized Sketches of Convex Programs With Sharp Guarantees
IEEE Transactions on Information Theory, 61(9), 2015
random projection regression compressed sensing data privacy
DOI arXiv

Y. Yang, M. Pilanci, and M. J. Wainwright
Randomized sketches for kernels: Fast and optimal non-parametric regression
Annals of Statistics, Volume 45, Number 3 (2017), 991-1023. 2015
kernel regression smoothing random projection Rademacher complexity
DOI PDF arXiv

2014


M. Pilanci and M. J. Wainwright
Randomized sketches of convex programs with sharp guarantees
2014 IEEE International Symposium on Information Theory (ISIT), 2014
random projection convex optimization
DOI arXiv

2012


M. Pilanci, L. E. Ghaoui, and V. Chandrasekaran
Recovery of sparse probability measures via convex programming
Advances in Neural Information Processing Systems (NIPS), 2012
probability measures convex relaxation data clustering
PDF
A. C. Gurbuz, M. Pilanci, and O. Arikan
Expectation maximization based matching pursuit
Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, 2012
sparse approximation compressed sensing
DOI PDF

2011


M. Pilanci and O. Arikan
Recovery of sparse perturbations in least squares problems
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on,2011
robust optimization regression sparsity
DOI
A. C. Gurbuz, M. Pilanci, and O. Arikan
Sparse signal reconstruction with ellipsoid enlargement
Signal Processing and Communications Applications (SIU), 2011 IEEE 19th Conference on, 2011
sparsity compressed sensing signal processing
DOI

2010


M. Pilanci, O. Arikan, and M. C. Pinar
Structured least squares problems and robust estimators
IEEE Transactions on Signal Processing, 58(5), 2010
robust optimization robust estimation
DOI
M. Pilanci
Uncertain linear equations
MS Thesis, Bilkent University, 2010
robust optimization compressed sensing coding and information theory polar codes
PDF

M. Pilanci, O. Arikan, and E. Arikan
Polar compressive sampling: A novel technique using Polar codes
Signal Processing and Communications Applications Conference (SIU), 2010 IEEE 18th,2010
compressed sensing coding and information theory polar codes signal processing

B. Guldogan, M. Pilanci, and O. Arikan
Compressed sensing on ambiguity function domain for high resolution detection
Signal Processing and Communications Applications Conference (SIU), 2010 IEEE 18th, 2010
compressed sensing signal processing radar

M. Pilanci and O. Arikan
Compressive sampling and adaptive multipath estimation
Signal Processing and Communications Applications Conference (SIU), 2010 IEEE 18th, 2010
wireless communications compressed sensing

2009


M. Pilanci, O. Arikan, B. Oguz, and M. Pinar
A novel technique for a linear system of equations applied to channel equalization
Signal Processing and Communications Applications Conference, 2009. SIU 2009. IEEE 17th, 2009
wireless communications robust optimization
DOI
M. Pilanci, O. Arikan, B. Oguz, and M. Pinar
Structured least squares with bounded data uncertainties
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on, 2009
robust optimization regression statistical estimation
DOI