Aswin C Sankaranarayanan

Assistant Professor, ECE
Carnegie Mellon University

mail. 5000 Forbes Ave, Porter B17, Pittsburgh, PA 15213

office. Porter B17
email. saswin -at- andrew .dot. cmu .dot. edu
phone. (412) 268 1087

image science lab @cmu

Our new lab website is at imagesci.ece.cmu.edu.


My research deals with understanding the interaction of light with materials, devising theories and imaging architectures to capture these interactions, and finally developing a deeper understanding of the world around us based on these interactions. While these interactions involve very high-dimensional signals, there are underlying structures that enable them to be modeled parsimoniously using low-dimensional models. My research identifies low-dimensional models for high-dimensional visual signals using both physics-based and learning-based formulates, and develop imaging architectures and algorithms that exploit these low-dimensional models for efficient sensing and inference.

compressive sensing and computational photography

FlatCam: Thin, bare-sensor cameras using coded aperture
ICCVW 2015, TCI*

LiSens: Scalable imaging architectures for video CS
ICCP 2015

FPACS: Video CS in short-wave infrared
CVPR 2015

Compressive epsilon photography

Video compressive sensing using spatial multiplexing cameras
ICCP 2012, SIIMS 2015

BlurBurst: Removing blur using multiple images

Compressive acquisition of linear dynamical systems
ECCV 2010, SIIMS 2013

SpaRCS: Compressive sensing of low rank and sparse matrices
NIPS 2011

Flutter shutter video camera for compressive sensing of high-speed videos
ICCP 2012

Compressive sensing for background subtraction
ECCV 2008

shape and reflectance estimation

Small baseline photometric stereo
ICCV 2015

What a single light ray reveals about a transparent object?
ICIP 2015

Shape and spatially-varying reflectance from photometric stereo
ICCP 2015

Image invariants for smooth reflective surfaces
ECCV 2012

Specular surface reconstruction from sparse reflection correspondences
CVPR 2012

non-linear signal models

Learning manifolds in the wild

A convex approach for learning near-isometric linear embeddings
TSP 2015

Greedy feature selection for subspace clustering
JMLR 2013

Optical flow-based transport on image manifolds
ACHA 2014

multi-camera systems

Pose-invariant face recognition from multi-view videos
TIP 2014

Joint albedo estimation and pose tracking from video
PAMI 2013

Real time head pose tracking from multiple cameras with a generic model
AMFG 2010

Distributed detection, tracking and recognition using a network of video cameras
PIEEE 2008


post-doctoral researchers

David shaw (co-advised with Soummya Kar)

phd students

Zhuo Hui
Jian wang
Chia-Yin Tsai
Vishwanath Saragadam (co-advised with Xin Li)
Jen-Hao Chang (co-advised with Kumar Bhagavatula)

undergraduate students


John Lee, (UG student, fall 14 - spring 15)
Research topic: Computational deblurring for microscopy

Nikhil Bikhchandani, (undergraduate student, fall 13 - spring 14)
Research topic: Compressive photometric stereo


Fall 2015. Compressive sensing and sparse optimization (18-799J)

Spring 2014. Computational Sensors (18-799N)

Fall 2013. Signals and Systems (18-290)

Spring 2013. Compressive Sensing and Sparse Optimization (18-799J)


  • NuMax.
    A linear dimensionality reduction technique that is near-isometric (aka preserves pairwise distances) on a dataset. (code) (paper)

  • CS-MUVI.
    Video compressive sensing using the CS-MUVI algorithm.
    (project page) (code) (paper)

  • SpaRCS.
    A greedy algorithm for recovering a matrix that is a sum of low rank and sparse matrices from its compressive measurements.
    (code) (paper)

  • CS-LDS.
    Video compressive sensing under a linear dynamical system model for the data. This encompasses data lying on an unknown low-dimensional subspace.
    (project page) (code) (paper)