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
- Texture Synthesis with Recurrent Variational Auto-EncoderRohan Chandra, Sachin Grover, Kyungjun Lee, Moustafa Meshry, Ahmed Taha, 2017arXiv preprint
- Phasepack User GuideRohan Chandra, Ziyuan Zhong, Justin Hontz, Val McCulloch, Christoph Studer, Tom Goldstein, 2017arXiv preprint
- Phasepack: A Phase Retrieval LibraryRohan Chandra, Ziyuan Zhong, Justin Hontz, Val McCulloch, Christoph Studer, Tom GoldsteinSubmitted to IEEE Proceedings of the 51st Asilomar Conference on Signals, Systems and Computers.
- Multiply by 9Arthur Benjamin, Rohan ChandraThe College Mathematics Journal.
- Voltage Mode Second Order Notch/All-Pass Filter Realization Using OTRARashika Anurag, Neeta Pandey, Rohan Chandra, Rajeshwari Pandeyi-Manager's Journal on Electronics Engineering.
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.
PhasePack - A MATLAB based library for solving Phase Retrieval.
Structure from Motion - Implemented the full pipeline to reconstruct the 3D scene from multiple images.  Code
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)
Reviewer for MS applications to the CS department at UMD for Fall 2017 and Fall 2018.