I work on non-convex optimization problems.

I also answer questions on mathematical optimization on Quora where I am the top writer in this category.

Here is my research statement. Here is my one page cover letter on how I differ from other candidates. And here are my most recent TA evaluations.

NEW!  Our paper on texture synthesis using stacked variational auto-encoders is up on arXiv. Paper

NEW!  Received an A in both Jordan Boyd-Graber's graduate Machine Learning class and Furong Huang's Spectral Methods & Reinforcement Learning class in Fall 2017!

NEW!  We created PhasePack- a MATLAB based library to solve phase retrieval using classical and contemporary routines. Get the Pack here!

NEW!  Our project on texture synthesis using deep learning is complete!  Report  Slides


  • Phasepack: A Phase Retrieval Library
    Rohan Chandra, Ziyuan Zhong, Justin Hontz, Val McCulloch, Christoph Studer, Tom Goldstein
    Submitted to IEEE Proceedings of the 51st Asilomar Conference on Signals, Systems and Computers.
  • Multiply by 9
    Arthur Benjamin, Rohan Chandra
    The College Mathematics Journal.

Current Projects

Low Rank Matrix Estimation Without Lifting - As part of my MS thesis, I am trying to solve the problem of low rank matrix recovery without lifting i.e. in the natural parameter space. Initial sketches of the proof look promising and I am currently in the process of formalizing this.
Texture Synthesis - Generating textures using deep learning. Results,  Report
PhasePack - A MATLAB based library for solving Phase Retrieval.

Past Projects

Autonomous Cars - Developed a real-time lane detection module. Video
Structure from Motion - Implemented the full pipeline to reconstruct the 3D scene from multiple images.  Code
Semantic Mapping of 3D pointcloud - Given images of single and muliple cluttered table-top objects, generate 3-D pointclouds and do the following: Video 1 Video 2 Code
  • Construct 3-D model of the scene
  • 3D pointcloud Segmentation
  • Generating a semantic map

Relevant Courses

MATH 461 and MATH 405: Linear Algebra
STAT 400: Probability and Statistics
CMSC 828G: Graduate Computer Vision I by Rama Chellapa
CMSC 733: Graduate Computer Vision II by Yiannis Aloimonos
CMSC 764: Graduate Optimization by Tom Goldstein
CMSC 726: Graduate Machine Learning by Jordan Boyd-Graber (current)
CMSC 828R: Spectral Methods and Reinforcement Learning by Furong Huang (current)
CMSC 818G: Information-Centric Design of Systems by Ashok Agrawala


Fall 2016: CMSC 417  - Computer Networks (as TA)
Spring 2017: CMSC 131  - Object Oriented Programming (Java) (as TA)
Fall 2017: CMSC 250 - Discrete Structures (as TA)

Department Service

Reviewer for MS applications to the CS department at UMD for Fall 2017 and Fall 2018.