Ph.D. Candidate (2018)
I am a Ph.D. candidate in Computer Science at Columbia University. My research
advisor is Liam
Paninski. I work on machine learning and statistical
methods for neuroscience problems.
Before, I used to work at CCLS on smart cities
projects including forecast and optimization of energy
consumption in smart buildings.
My research interests include Bayesian probabilistic models,
deep learning, and reinforcement learning.
My resume and
CV (August 2017).
- Ph.D. Computer Science, Columbia University,
- M.S. Computer Science, Columbia University,
- B.S. Computer Engineering-Software Engineering,
Sharif University of Technology, 2007-2012
- Graduate Research Assistant, Columbia University,
- Software Engineering Intern, Google Inc.,
May 2017-Aug 2017
- Research Coordinator, Columbia University, Feb 2014-Aug 2014
- Di-BOSS - comprehensive building operating system that
aims to optimize energy consumption and increase security
using real-time data streams to make predictions and
provide recommendations for future operation.
- NYC public transport - Feasibility study for wireless
charging technology. The goal of the study was to discover
the optimal locations of charging stations for buses
powered by electricity using probabilistic modeling and heuristic search.
Natural Language Processing
- Omnimixture: Enhancing topic models with graph based representation
of textual data that leads to new features that are lowly correlated with topic model features.
Neural Data Analysis
- Processing dense multi-electrode array recording
of neural activity. We are developing a method and a software to efficiently and accurately
carry out spike sorting.
- Head Teaching Assistant, Discrete Math,
Columbia University, September-December 2015
- Instructor, Introduction to Calculus, Barnard
College, Columbia University, June-August 2015
- YASS: Yet Another Spike Sorter [PDF]
- JinHyung Lee, David Carlson, Hooshmand Shokri, Weichi Yao, Georges Goetz,
Espen Hagen, Eleanor Batty, EJ Chichilnisky, Gaute Einevoll, Liam Paninski.
- Omnimixture: Enriched Topic Modeling [Code]
- Lauren A. Hannah, Rebecca J.
Passonneau, Hooshmand Shokri Razaghi, Ruilin Zhong.
- Adaptive Stochastic Controller for Smart
- Roger N. Anderson, Albert
Boulanger, Promiti Dutta, and Ashish Gagneja, Hooshmand
Shokri Razaghi, New York Academy of Sciences ML Conference,
- Di-BOSS: Digital Building Operating System
- Roger N. Anderson, Albert
Boulanger, Vaibhav Bhandari, Jessica Forde, Ashwath Rajan,
Vivek Rathod, Hooshmand Shokri Razaghi, NIPS, 2013.
- An Efficient Simulated Annealing Approach to
Traveling Tournament Problem [PDF]
- Sevnaz Nourollahi, Kourosh
Eshghi, Hooshmand Shokri Razaghi, American Journal of
Operations Research (AJOR), Vol.2 No.3, September