I am a post-doctoral research scholar in the Electrical Engineering Department at Stanford University, working with Prof. David Tse. I received my Ph.D. and M.Sc. in Electrical Engineering and Computer Science, with a minor degree in Mathematics, at MIT

I work in the area of Machine Learning and Statistical Inference. My research focuses on developing and analyzing methods for nonlinear and high-dimensional Inference problems.

Here is my CV.

Contact: sfeizi at stanford dot edu


Selected Publications

Nonlinear Learning Models


Understanding GANs: the LQG Setting
Soheil Feizi, Changho Suh, Fei Xia and David Tse
Resources: [paper]
Under review, 2017

Maximally Correlated Principal Component Analysis
Soheil Feizi and David Tse
Resources: [paper][code]
Under review at Journal of Machine Learning Research (JMLR)
Presented at Information Theory and Applications Workshop, San Diego, 2017

Porcupine Neural Networks: (Almost) All Local Optima Are Global
Soheil Feizi, Hamid Javadi, Jesse Zhang, David Tse
Resources: [paper] [code]
A short version presented at Allerton Conference, 2017

Network Maximal Correlation
Soheil Feizi*, Ali Makhdoumi*, Ken Duffy, Manolis Kellis, Muriel Medard
*: authors with equal contributions
Resources: [paper]
Accepted for Publication at IEEE Transactions on Network Science and Engineering, 2017 

High-Dimensional Inference Models


Tensor Biclustering
Soheil Feizi, Hamid Javadi and David Tse
Resources: [paper] [code]
Accepted for Publication at NIPS'17: Advances in Neural Information Processing Systems Foundation, 2017

Network Infusion to Infer Information Sources in Networks
Soheil Feizi, Ken Duffy, Manolis Kellis and Muriel Medard
Resources: [paper]
Under review at IEEE Transactions on Network Science and Engineering
Presented at IC2S2'15: International Conference on Computational Social Sciences, 2015

Spectral Alignment of Graphs
Soheil Feizi, Gerald Quon, Mariana Mendoza, Muriel Medard, Manolis Kellis, and Ali Jadbabaie
Resources: [paper] [code]
Under review at IEEE Transactions on Network Science and Engineering
Presented at CSHL'16: Cold Spring Harbor Laboratory Conference on Networks

Maximum Likelihood Latent Space Embedding of Logistic Random Dot Product Graphs
Luke O'Connor*, Muriel Medard and Soheil Feizi*
*: authors with equal contributions
Resources: [paper] [code]
Under review at IEEE Transactions on Pattern Analysis and Machine Intelligence

Biclustering Using Message Passing
Luke O'Connor* and Soheil Feizi*
*: authors with equal contributions
Resources: [paper] [code]
Published at NIPS'14: Advances in Neural Information Processing Systems Foundation, 2014

Network Deconvolution as a General Method to Distinguish Direct Dependencies in Networks
Soheil Feizi, Daniel Marbach , Muriel Medard and Manolis Kellis
Resources: [paper] [code]
Published at Nature Biotechnology 31, pp. 726-733, 2013

Other Publications

Signal Processing and Information Theory


On Network Functional Compression
Soheil Feizi and Muriel Medard
Resources: [paper]
Published at IEEE Transactions on Information Theory, Vol. 60, No. 9, 2014

Backward Adaptation for Power Efficient Sampling
Soheil Feizi, Georgios Angelopoulos, Vivek K Goyal and Muriel Medard
Resources: [paper]
Published at IEEE Transactions on Signal Processing, Vol. 62, No. 16, 2014

Time-Stampless Adaptive Nonuniform Sampling for Stochastic Signals
Soheil Feizi, Vivek K Goyal and Muriel Medard
Resources: [paper]
Published at IEEE Transactions on Signal Processing, Vol. 60, No. 10, 2012

A Power Efficient Sensing/Communication Scheme: Joint Source-Channel-Network Coding by Using Compressive Sensing
Soheil Feizi and Muriel Medard
Resources: [paper]
Published at Allerton Conference on Communication, Control, and Computing, 2011

Compressive Sensing Over Networks
Soheil Feizi, Muriel Medard and Michelle Effros
Resources: [paper]
Published at Allerton Conference on Communication, Control, and Computing, 2010

Impulsive Noise Cancellation Based on Soft Decision and Recursion
Sina Zahedpour, Soheil Feizi, Arash Amini and Farrokh Marvasti
Resources: [paper]
Published at IEEE Transactions on Instrumentation and Measurement, Vol. 58, No. 8, 2780-2790, 2009

Robust Audio Data Hiding Using Correlated Quantization With Histogram-Based Detector
Ali Akhaee, Mohammad Saberian, Soheil Feizi and Farrokh Marvasti
Resources: [paper]
Published at IEEE Transactions on Multimedia, Vol. 51, No. 6, 2009

Data Mining


Microfluidic neurite guidance to study structure-function relationships in topologically-complex population-based neural networks
Thibault Honegger, Moritz I Thielen, Soheil Feizi, Neville E Sanjana and Joel Voldman
Resources: [paper]
Published in Scientific Reports, Vol. 6, 2016

Integrative Analysis of 111 Reference Human Epigenomes
Roadmap Epigenomics Consortium, et al.
Resources: [paper]
Published in Nature, 518, pp. 317-330, 2015

Systematic Dissection and Optimization of Inducible Enhancers in Human Cells Using a Massively Parallel Reporter Assay
A. Melnikov, A. Murugan, X. Zhang, T. Tesileanu, L. Wang, P. Rogov, Soheil Feizi, A. Gnirke, C. G Callan Jr, J. B Kinney, M. Kellis, E. S Lander and T. S Mikkelsen
Resources: [paper]
Published in Nature Biotechnology, 30, pp. 271-277, 2012

Selected Talks


Advisees

Junior Ph.D. and Master Students

  • Evan Huang, First-Year Rotation Ph.D. Student, Stanford University, Co-Supervised with Prof. David Tse, Fall 2017
    Project: Understanding Generative Adversarial Networks (GANs)
  • Luke O'Connor, Ph.D. Student at Harvard University, Informally Supervised, 2013-2016
    Project: Biclustering and Clustering in Graphs
    Joint Publication in: Advances in Neural Information Processing Systems Foundation (NIPS), 2014
  • Amy Zhang, Master of Engineering Student, MIT, Co-Supervised with Prof. Medard Medard, 2012-2013
    Project: A Network Flow Framework in Cloud Computing
    Joint Publication in: Information Sciences and Systems (CISS), 2012
  • Muriel L. Rambeloarison, Master of Engineering Student, MIT, Co-Supervised with Prof. Medard Medard, 2012-2013
    Project: Rate-Distortion Analysis of Compressive Sensing
    Joint Publication in: Asilomar Conference on Signals, Systems, and Computers, 2012

Undergraduate Students

  • Anthony Degleris, Electrical Engineering Department, Stanford, Summer of 2017
    Project: Biclustering in a Matrix with Categorical Values
    For more details, see the REU program at Stanford here
  • Logan Spear, Electrical Engineering Department, Stanford, Spring and Summer of 2017
    Project: PCA and Biclustering Analysis of Time-Series Metabolic Data
    For more details, see the REU program at Stanford here
  • Aritro Biswas, EECS, MIT, 2014
    Project: Source Inference in Social Networks
  • Eric Mazumdar, EECS, MIT, Spring 2014
    Project: Gene Expression Analysis Using Hidden Markov Models
  • Liz Deny, EECS, MIT, 2013
    Project: Graph Coloring Schemes for Network Functional Compression
  • Sebastian Palacios, EECS, MIT, 2013
    Project: High-Resolution Characterization and Classification of Neural Activities
  • Eric Soderstrom, EECS, MIT, 2011
    Project: Motif Discovery Using Random Projections

High-School Students

  • Allison Paul, MIT PRIMES Program, 2015-2016
    Project: Spectral Inference of DAGs using Pairwise Similarities of Leaves
    Allison's project has been selected as a semifinalist in the Intel Science Talent Search 2016. For more details, see the PRIMES program at MIT here
  • Michael Colavita, MIT PRIMES Program, 2013-2015
    Project: Identifying Disease-Related Modules over Human Gene Regulatory Networks
    For more details, see the PRIMES program at MIT here
  • Anna Matilde Tanga, International School of Boston, Summer of 2014
    Project: Applications of Statistics and Machine Learning in Computational Biology


Awards