Research Themes

Here at the Imaging Lyceum, we explore several research topics in the areas of computational cameras and visual media. Below are a selection of our current themes of research, but we are always open to new and exciting ventures! Please see our Publications page for a comprehensive list. 

Never heard of computational imaging before? Check out these quick introduction videos recorded by Dr. J (accessible for a general public audience):

Computational Optics 

Computational Image Sensors 

Computational Illumination 



Light Transport


Light travels along many different paths from illumination sources to camera detectors, including multiple bounces, scattering, and absorption events. We have been exploring ways to probe and selectively capture these light transport paths, to be more judicious in the photons we receive, for various applications in computer graphics and vision. These include seeing through skin for blood vessel visualization, optically masking objects from an image, and rendering light interactions for dynamic, moving scenes, and even using indirect light to image things in the non-line-of-sight of the camera/projector. 


Selected Publications:

Acoustic Imaging, Remote Sensing, and Tomography



We are interested in computational imaging and its application to both remote sensing and tomography problems. This includes areas such as acoustic imaging (especially synthetic aperture sonar), hyperspectral imaging, long range imaging through turbulence, and computed tomography for medical imaging. We are currently researching ways to blend the physics of image formation in these modalities with recent advances in both fully, self-, and unsupervised machine learning. 

Selected Publications:

1. Albert Reed, Juhyeon Kim, Thomas Blanford, Adithya Pediredla, Daniel C. Brown, Suren Jayasuriya, Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar, ACM Transactions on Graphics (Proceedings of SIGGRAPH) 2023 [website] [pdf] [code+data] [presentation video]

2. Albert Reed, Thomas Blanford, Daniel Brown, Suren Jayasuriya, SINR: Deconvolving Circular SAS Images Using Implicit Neural Representations, IEEE Journal of Selected Topics in Signal Processing (special issue) 2023 [pdf] [Code]

3. Albert Reed, Hyojin Kim, Rushil Anirudh, K. Aditya Mohan, Kyle Champley, Jingu Kang, Suren Jayasuriya, Dynamic CT Reconstruction from Limited Views with Implicit Neural Representations and Parametric Motion Fields, IEEE International Conference on Computer Vision (ICCV) 2021 [pdf] [supplemental pdf]

4. John Janiczek, Parth Thaker, Gautam Dasarathy, Christopher Edwards, Philip Christensen, Suren Jayasuriya, Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model, European Conference on Computer Vision (ECCV) 2020 [pdf + supplement] [Github code] 

5. Albert Reed, Isaac Gerg, John D. McKay, Daniel C. Brown, David P. Williams, Suren Jayasuriya, Coupling Rendering and Generative Adversarial Networks for Artificial SAS Image Generation, MTS/IEEE Oceans 2019 [pdf]

Software-Defined Imaging




Computer vision and image sensing for embedded devices requires a lot of energy and data bandwidth, which can limit visual computing applications. We have been exploring how image sensors can be made programmable, and designed for energy-efficient domain-specific tasks rather than solely taking aesthetic pictures. This includes new image signal processing pipelines and accelerators for vision algorithms. To validate these ideas, we have focused on hardware acceleration via FPGA platforms as well as exploring CMOS sensor design.

Selected Publications: 

1. Suren Jayasuriya, Odrika Iqbal, Venkatesh Kodukula, Victor Torres, Robert LiKamWa, Andreas Spanias, Software-Defined Imaging: A Survey, Proceedings of the IEEE 2023 [pdf]

2. Sameeksha Katoch*, Odrika Iqbal*, Andreas Spanias, Suren Jayasuriya, Energy-Efficient Object Tracking using Adaptive ROI Subsampling and Deep Reinforcement Learning, IEEE Access 2023 [pdf] (* = joint first authors)


3. Odrika Iqbal, Victor Torres, Sameeksha Katoch, Andreas Spanias, Suren Jayasuriya, Adaptive Subsampling for ROI-based Visual Tracking: Algorithms and FPGA Implementation, IEEE Access 2022 [pdf]


4. Mark Buckler, Philip Bedoukian, Suren Jayasuriya, Adrian Sampson, EVA2: Exploiting Temporal Redundancy for Live Computer Vision, International Symposium on Computer Architecture (ISCA'18) [pdf]


5. Mark Buckler, Suren Jayasuriya, and Adrian Sampson, Reconfiguring the Imaging Pipeline for Computer Vision, International Conference on Computer Vision (ICCV) 2017

STEAM Education

Integrated engineering and media arts programs offer interdisciplinary experiences for students and teachers, helping to improve student engagement in these topics. We are researching effective new ways to innovate pedagogically in this domain, blending skills from philosophy and qualitative research to help improve teaching and learning. In particular, we are focused on issues including artificial intelligence and society, visual media, and diversity/equity/inclusion of LGBTQIA+ and other underrepresented students into these fields. 

(photo courtesy of the Digital Culture Summer Institute)

We highly recommend you check out our ImageSTEAM curriculum page which contains lessons for middle school teachers and students surrounding visual computing. 


Selected Publications:

1. Suren Jayasuriya, Kimberlee Swisher, Joshua Rego, Sreenithy Chandran, John Mativo, Terri Kurz, Cerenity Collins, Dawn Robinson, Ramana Pidaparti, ImageSTEAM: Teacher Professional Development for Integrating Visual Computing into Middle School Lessons, The 14th Symposium on Educational Advances in Artificial Intelligence (EAAI-24) 2024 [pdf] [presentation slides]

2. Joshua Cruz, Noa Bruhis, Nadia Kellam, Suren Jayasuriya, Students’ Implicit Epistemologies when Working at the Intersection of Engineering and the Arts, International Journal of STEM Education 2021 [pdf]

3. Dominique Dredd, Nadia Kellam, Suren Jayasuriya, Zen and the Art of STEAM: Student Knowledge and Experiences in Interdisciplinary and Traditional Engineering Capstone Experiences, IEEE Frontiers in Education (FIE) 2021 [pdf]

4. Madeleine Jennings, Rod Roscoe, Nadia Kellam, Suren Jayasuriya, A Review of the State of LGBTQIA+ Student Research in STEM and Engineering Education, American Society of Engineering Education (ASEE) Conference 2020 [pdf] [video presentation] (Finalist for the Best Diversity, Equity & Inclusion Paper Award)

Angle Sensitive Pixels

Angle Sensitive Pixels (ASPs) are a new class of CMOS image sensor which features integrated diffraction gratings above the pixel. We have shown how ASPs sample the plenoptic dimension of light, including angle and polarization, and can even be used to optically compute the first layer of convolutional neural networks. 

Selected Publications:

1. Huaijin Chen*, Suren Jayasuriya*, Jiyue Yang, Judy Stephen, Sriram Sivaramakrishnan, Ashok Veeraraghavan, Alyosha Molnar, ASP Vision: Optically Computing the First Layer of CNNs using Angle Sensitive Pixels, IEEE Computer Vision and Pattern Recognition (CVPR) 2016 [code] (oral presentation, < 4% acceptance) (* = shared first authorship)

2. Matthew Hirsch*, Sriram Sivaramakrishnan*, Suren Jayasuriya*, Albert Wang, Alyosha Molnar, Ramesh Raskar, Gordon Wetzstein, A Switchable Light Field Camera Architecture using Angle Sensitive Pixels and Dictionary-based Sparse Coding, IEEE International Conference on Computational Photography (ICCP), 2014 (received the Best Paper Award at ICCP 2014)  (*=shared first authorship)