Ph.D. Candidate (2019)
I'm a Ph.D. candidate in Computer Science at Columbia University (expected graduation date: May 2019). My research
advisor is Liam
Paninski. I work on machine learning and statistical methods for computational neuroscience.
We've been developing spike sorting method and software (YASS) for large multi-electrode array recordings (NIPS paper).
I'm currently working on auto-encoding variational inference for
latent non-linear dynamical systems applied to modelling neural population activity.
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 inference, deep generative models, and reinforcement learning.
My resume (as of August 2018).
- 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
Neural Data Analysis
- Processing dense multi-electrode array recordings of neural activity. We developed a method and a software (YASS)
to efficiently and accurately carry out spike sorting (open source code).
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.
- 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.
- 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.
- 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|Code]
- JinHyung Lee, David Carlson, Hooshmand Shokri, Weichi Yao, Georges Goetz,
Espen Hagen, Eleanor Batty, EJ Chichilnisky, Gaute Einevoll, Liam Paninski, NIPS 2017.
- 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