Amit Ashok
 Associate Professor, Optical Sciences
 Assistant Professor, Electrical and Computer Engineering
 (520) 6264815
 Meinel Optical Sciences, Rm. 421
 Tucson, AZ 85721
 ashoka@optics.arizona.edu
Biography
Dr. Amit Ashok is an Associate Professor in the College of Optical Sciences and the Department of Electrical and Computer Engineering at the University of Arizona. He received his Ph.D. and M.S. degrees in Electrical and Computer Engineering from the University of Arizona and the University of Cape. Before joining the University of Arizona as a faculty, he was a senior research scientist in the research and development division of Omnivision CDM Optics, and worked on novel computational imaging system designs for commercial applications ranging from security to mobile phone cameras. Since joining the academia in 2009, he has served as a Program Chair and a General Chair of OSA’s Computational Optical Sensing and Imaging (COSI) conference in 2013 and 2014 respectively. In 2016 he helped launch two SPIE conferences on Computational Imaging (CI) and Anomaly Detection and Imaging with Xrays (ADIX) as a General Chair. He served as the lead editor for a JOSA A feature issue on single molecule imaging in 2016 and currently, serving as a topical editor for the JOSA A journal. Dr. Ashok’s research interests include computational/compressive imaging and sensing, Bayesian inference, statistical optics, and information theory. He has made several key contributions in taskbased jointdesign framework for computational imaging and informationtheoretic system performance measures across several imaging modalities spanning RF to visible/IR and Xray domains. He has over 50 peerreviewed publications, holds several patents, and has been invited to speak at OSA, IEEE, SIAM, SPIE and Gordon research conferences.
Awards
 Senior Member
 OSA, Fall 2019
 SPIE, Fall 2019
Interests
Research
Computational/compressive imaging and sensing, Bayesian inference, statistical optics, and information theory.
Courses
202021 Courses

Dissertation
OPTI 920 (Fall 2020) 
Independent Study
OPTI 599 (Fall 2020) 
Statistical Optics
OPTI 509 (Fall 2020)
201920 Courses

Dissertation
OPTI 920 (Spring 2020) 
Physical Optics II
OPTI 330 (Spring 2020) 
Dissertation
OPTI 920 (Fall 2019) 
Thesis
OPTI 910 (Fall 2019)
201819 Courses

Directed Graduate Research
OPTI 792 (Spring 2019) 
Dissertation
ECE 920 (Spring 2019) 
Dissertation
OPTI 920 (Spring 2019) 
Physical Optics II
OPTI 330 (Spring 2019) 
Directed Graduate Research
OPTI 792 (Fall 2018) 
Dissertation
ECE 920 (Fall 2018) 
Dissertation
OPTI 920 (Fall 2018) 
Line Sys,Fourier Transfm
OPTI 512R (Fall 2018)
201718 Courses

Directed Research
OPTI 492 (Summer I 2018) 
Directed Graduate Research
OPTI 792 (Spring 2018) 
Dissertation
ECE 920 (Spring 2018) 
Dissertation
OPTI 920 (Spring 2018) 
Physical Optics II
OPTI 330 (Spring 2018) 
Directed Graduate Research
OPTI 792 (Fall 2017) 
Dissertation
ECE 920 (Fall 2017) 
Dissertation
OPTI 920 (Fall 2017) 
Line Sys,Fourier Transfm
OPTI 512R (Fall 2017) 
Statistical Optics
OPTI 509 (Fall 2017)
201617 Courses

Directed Research
OPTI 492 (Summer I 2017) 
Dissertation
ECE 920 (Spring 2017) 
Dissertation
OPTI 920 (Spring 2017) 
Physical Optics II
OPTI 330 (Spring 2017) 
Research
ECE 900 (Spring 2017) 
Dissertation
ECE 920 (Fall 2016) 
Dissertation
OPTI 920 (Fall 2016) 
Line Sys,Fourier Transfm
OPTI 512R (Fall 2016) 
Research
ECE 900 (Fall 2016)
201516 Courses

Current Subj in Opti Sci
OPTI 595A (Spring 2016) 
Dissertation
ECE 920 (Spring 2016) 
Dissertation
OPTI 920 (Spring 2016) 
Master's Report
OPTI 909 (Spring 2016) 
Physical Optics II
OPTI 330 (Spring 2016)
Scholarly Contributions
Journals/Publications
 Ding, Y., Kerviche, R., Ashok, A., & Pau, S. (2018). Eavesdropping of display devices by measurement of polarized reflected light. Applied Optics, 57(19), 54835491.
 Lohit, S., Kulkarni, K., Kerviche, R., Turaga, P., & Ashok, A. (2018). Convolutional Neural Networks for Noniterative Reconstruction of Compressively Sensed Images. IEEE Transactions on Computational Imaging, 4(3), 326340.
 Niazi, M., Lin, Y., Liu, F., Ashok, A., Marcellin, M. W., Tozbikian, G. H., Gurcan, M. N., & Bilgin, A. (2018). Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer.. Artificial intelligence in medicine.
 Ding, Y., Ashok, A., & Pau, S. (2017). Realtime robust direct and indirect photon separation with polarization imaging. Opt. Express, 25(23), 2943229453.
 Huang, J., Neifeld, M. A., & Ashok, A. (2016). Face recognition with nongreedy informationoptimal adaptive compressive imaging. Applied Optics, 55, 97449755. doi:https://doi.org/10.1364/AO.55.009744
 Lee, M., Neifeld, M. A., & Ashok, A. (2016). Capacity of electromagnetic communication modes in a noiselimited optical system. Applied Optics, 55, 13331342.
 Gu, Y., Goodman, N. A., & Ashok, A. (2014). Radar Target Profiling and Recognition Based on TSIOptimized Compressive Sensing Kernel. Signal Processing, IEEE Transactions on, 62(12), 31943207.
 Zhao, M., Zhang, H., Li, Y., Ashok, A., Liang, R., Zhou, W., & Peng, L. (2014). Cellular imaging of deep organ using twophoton Bessel lightsheet nonlinear structured illumination microscopy. Biomed. Opt. Express, 5(5), 12961308.
 Kaylor, B. M., Ashok, A., Seger, E. M., Keith, C. J., & Reibel, R. R. (2012). Dynamically programmable, dualband computational imaging system. Computational Optical Sensing and Imaging, COSI 2012, CM4B.3.More infoAbstract: A dynamically programmable computational imaging system has been demonstrated. The system operates in the visible and near infrared bands. Principal components and random binary measurements were used with the imaging hardware to demonstrate compressive imaging. © 2012 Optical Society of America.
 Ashok, A., & Wilkinson, A. J. (2001). Topographic mapping with multiple antenna SAR interferometry: A Bayesian modelbased approach. International Geoscience and Remote Sensing Symposium (IGARSS), 5, 20582060.More infoAbstract: Multipleantenna SAR interferometry involves the use of three or more antennas to reduce the overall phase ambiguities and phase noise in interferometric data. This paper presents a Bayesian approach to topographic mapping with multipleantenna SAR interferometry. Topographic reconstruction is formulated as a parameter estimation problem in the modelbased Bayesian inference framework. An InSAR simulator based on a forward model is developed for simulating SAR data from multipleantenna InSAR for evaluating the Bayesian topographic reconstruction algorithms. A Bayesian point position algorithm is developed to estimate the height of a point in the image. A measure of the uncertainty in estimated position and height is also defined in terms of the spread of the dominant mode of the posterior distribution. An example demonstrating the performance of the algorithm for a threeantenna InSAR system is reported, and conclusions are drawn regarding the Performance and improvements are proposed.
Proceedings Publications
 Carpenter, J., Ding, Y., Hurlock, A., Coccarelli, D., Gregory, C., Diallo, S. O., Ashok, A., Gehm, M. E., & Greenberg, J. A. (2019). Motivations and methods for the analysis of multimodality xray systems for explosives detection. In Anomaly Detection and Imaging with XRays (ADIX) IV, 10999.
 Ding, Y., & Ashok, A. (2019). Xray measurement model and informationtheoretic metric incorporating material variability with energy correlations. In Anomaly Detection and Imaging with XRays (ADIX) IV, 10999.
 Lee, K. K., & Ashok, A. (2019). Surpassing Rayleigh limit: Fisher information analysis of partially coherent source(s). In Optics and Photonics for Information Processing XIII, 11136.
 Lin, Y., Liu, F., HernandezCabronero, M., Ahanonu, E., Marcellin, M. W., Bilgin, A., & Ashok, A. (2019, 5). PerceptionOptimized Encoding for Visually Lossy Image Compression. In Proceedings  DCC 2019.
 Masoudi, A., & Ashok, A. (2019). Multiplexed measurement design for fixed Gantry xray computed tomography system (Conference Presentation). In Anomaly Detection and Imaging with XRays (ADIX) IV, 10999.
 Voris, J., Ding, Y., Thamvichai, R., Greenberg, J. A., Coccarelli, D., Gehm, M. E., Johnson, E., Bosch, C., & Ashok, A. (2019). Informationtheoretic analysis of fixed Gantry xray computed tomography transmission system for threat detection (Conference Presentation). In Anomaly Detection and Imaging with XRays (ADIX) IV, 10999.
 Yang, S., Lee, K. K., & Ashok, A. (2019). Passive indirect diffuse imaging. In Wavelets and Sparsity XVIII, 11138.
 Bilgin, A., Ashok, A., & Mandava, S. (2018, April). Deep learning based Sparse View Xray CT Reconstruction for Checked Baggage Screening. In SPIE Defense + Security, Anomaly Detection and Imaging with XRays (ADIX) II.
 Bilgin, A., Ashok, A., Marcellin, M. W., Ahanonu, E., HernandezCabronero, M., Lin, Y., & Liu, F. (2018, March). A visual discrimination model for JPEG2000 compression. In Data Compression Conference.
 Coccarelli, D., Greenberg, J. A., Thamvichai, R., Voris, J., Masoudi, A., Ashok, A., & Gehm, M. E. (2018, April). An information theoretic approach to system optimization accounting for material variability. In Proc. SPIE 10632, Anomaly Detection and Imaging with XRays (ADIX) III, 106320F, 10632, 10632  10632  8.
 Lin, Y., Marcellin, M. W., Bilgin, A., & Ashok, A. (2018, March). Taskbased JPEG2000 image compression: An informationtheoretic approach. In Data Compression Conference.
 Masoudi, A., Voris, J., Coccarelli, D., Greenberg, J., Gehm, M., & Ashok, A. (2018, April). Xray measurement model and informationtheoretic system metric incorporating material variability. In SPIE Anomaly Detection and Imaging with XRays (ADIX) III, 10632.
 , D., Greenberg, J. A., , S., , Q., Huang, L., , A., & Gehm, M. E. (2017, 2017). Creating an experimental testbed for informationtheoretic analysis of architectures for xray anomaly detection. In Creating an experimental testbed for informationtheoretic analysis of architectures for xray anomaly detection, 10187, 1018709101879.
 Bilgin, A., Bilgin, A., Ashok, A., Ashok, A., Lin, Y., Lin, Y., Marcellin, M. W., Marcellin, M. W., Ahanonu, E. L., Ahanonu, E. L., Liu, F., & Liu, F. (2017, April). Visibility thresholds in reversible JPEG2000 compression. In Data Compression Conference.
 Greenberg, J. A., Huang, L., & Gehm, M. E. (2017, 2017). Creating an experimental testbed for informationtheoretic analysis of architectures for xray anomaly detection. In SPIE Anomaly Detection and Imaging with Xrays II, 10187, 1018709101879.
 Kerviche, R., Guha, S., & Ashok, A. (2017, 2017). Achieving the Ultimate Limit of Two Point Resolution by Computational Imaging. In OSA Technical Digest (online), CW4B.5.
 Kerviche, R., Guha, S., & Ashok, A. (2017, 2017). Fundamental limit of resolving two point sources limited by an arbitrary point spread function. In 2017 IEEE International Symposium on Information Theory (ISIT), 441445.
 Mandava, S., Greenberg, J. A., Gehm, M. E., Ashok, A., & Bilgin, A. (2017, 2017). Image reconstruction for viewlimited xray CT in baggage scanning. In SPIE Anomaly Detection and Imaging with Xrays II, 10187, 101870F101879.
 Ashok, A. (2016, June). Fundamental limit of resolving two point sources limited by an arbitrary point spread function. In International Symposium on Information Theory.More infoEstimating the angular separation between two incoherently radiatingmonochromatic point sources is a canonical toy problem to quantify spatialresolution in imaging. In recent work, Tsang {\em et al.} showed, using aFisher Information analysis, that Rayleigh's resolution limit is just anartifact of the conventional wisdom of intensity measurement in the imageplane. They showed that the optimal sensitivity of estimating the angle is onlya function of the total photons collected during the camera's integration timebut entirely independent of the angular separation itself no matter how smallit is, and found the informationoptimal mode basis, intensity detection inwhich achieves the aforesaid performance. We extend the above analysis, whichwas done for a Gaussian point spread function (PSF) to a hardaperture pupilproving the information optimality of imageplane sincBessel modes, andgeneralize the result further to an arbitrary PSF. We obtain newcounterintuitive insights on energy vs. information content in spatial modes,and extend the Fisher Information analysis to exact calculations of minimummean squared error, both for Gaussian and hard aperture pupils.[Journal_ref: ]
 Coccarelli, D., Gong, Q., Stoian, R., Greenberg, J. A., Gehm, M. E., Lin, Y., Huang, J., & Ashok, A. (2016, April). Informationtheoretic analysis of xray scatter and phase architectures for anomaly detection. In SPIE DSS  Anomaly Detection and Imaging with Xrays (ADIX), 98470B.More infodoi:10.1117/12.2223175Conventional performance analysis of detection systems confounds the effects of the system architecture (sources, detectors, system geometry, etc.) with the effects of the detection algorithm. Previously, we introduced an informationtheoretic approach to this problem by formulating a performance metric, based on CauchySchwarz mutual information, that is analogous to the channel capacity concept from communications engineering. In this work, we discuss the application of this metric to study novel screening systems based on xray scatter or phase. Our results show how effective use of this metric can impact design decisions for xray scatter and phase systems.
 Huang, J., Huang, J., Ashok, A., & Ashok, A. (2016, April). Information optimal compressive xray threat detection. In SPIE DSS  Anomaly Detection and Imaging with Xrays, 98470T.More infodoi:10.1117/12.2223784We present an informationtheoretic approach to Xray measurement design for threat detection in passenger bags. Unlike existing Xray systems that rely of a large number of sequential tomographic projections for threat detection based on 3D reconstruction, our approach exploits the statistical priors on shape/material of items comprising the bag to optimize multiplexed measurements that can be used directly for threat detection without an intermediate 3D reconstruction. Simulation results show that the optimal multiplexed design achieves higher probability of detection for a given false alarm rate and lower probability of error for a range of exposure (photon) budgets, relative to the nonmultiplexed measurements. For example, a 99% detection probability is achieved by optimal multiplexed design requiring 4x fewer measurements than nonmultiplexed design.
 Kerviche, R., & Ashok, A. (2016, April). Scalable informationoptimal compressive target recognition. In SPIE DSS  Computational Imaging, 987008.More infodoi:10.1117/12.2228570We present a scalable informationoptimal compressive imager optimized for the target classification task, discriminating between two target classes. Compressive projections are optimized using the CauchySchwarz Mutual Information (CSMI) metric, which provides an upperbound to the probability of error of target classification. The optimized measurements provide significant performance improvement relative to random and PCA secant projections. We validate the simulation performance of informationoptimal compressive measurements with experimental data.
 Lin, Y., Allouche, G. G., Huang, J., Ashok, A., Gong, Q., Coccarelli, D., Stoian, R., & Gehm, M. E. (2016, April). Informationtheoretic analysis of xray photoabsorption based threat detection system for checkpoint. In SPIE DSS  Anomal Detection and Imaging with Xrays (ADIX), 98470F.More infodoi:10.1117/12.2223803In this work we present an informationtheoretic framework for a systematic study of checkpoint xray systems using photoabsorption measurements. Conventional system performance analysis of threat detection systems confounds the effect of the system architecture choice with the performance of a threat detection algorithm. However, our system analysis approach enables a direct comparison of the fundamental performance limits of disparate hardware architectures, independent of the choice of a specific detection algorithm. We compare photoabsorptive measurements from different system architectures to understand the affect of system geometry (angular views) and spectral resolution on the fundamental limits of the system performance.
 Liu, F., Bilgin, A., Krupinski, E., Lin, Y., Ahanonu, E., Ashok, A., Marcellin, M. W., Marcellin, M. W., Ahanonu, E., Ashok, A., Lin, Y., Krupinski, E., Liu, F., & Bilgin, A. (2016, August). Visibility Thresholds for Visually Lossy JPEG2000. In SPIE Optics and Photonics  Application of Digital Image Processing XXXIX 99711.More infodoi:10.1117/12.2238411
 Ashok, A. (2015). Compressive Imaging at Extreme Limits of Coherence. In OSA Technical Digest (online), FTh3G.1.
 Huang, J., & Ashok, A. (2015). Information Optimal Compressive Xray Threat Detection. In OSA Technical Digest (online), CTh2E.4.
 Kulkarni, K., Lohit, S., Turaga, P. K., Kerviche, R., & Ashok, A. (2016, June, 2016). ReconNet: NonIterative Reconstruction of Images from Compressively Sensed Random Measurements.. In Computer Vision and Pattern Recognition (CVPR16), abs/1601.06892.More infoHas the highest hindex of any conference in any area. In this community such a paper is considered equivalent to a journal paper.
 Lin, Y., & Ashok, A. (2015). Measurement Quantization in Compressive Imaging and Image Compression. In OSA Technical Digest (online), JT5A.37.
 Pu, L., Marcellin, M. W., Bilgin, A., & Ashok, A. (2015, April). Compression Based on a Joint TaskSpecific Information Metric. In 2015 Data Compression Conference.
 Pu, L., Marcellin, M. W., Bilgin, A., & Ashok, A. (2015, April). Compression based on a joint taskspecific information metric. In Data Compression Conference (DCC), 2015.
 Ashok, A., Huang, J., Lin, Y., & Kerviche, R. (2014, August). Information optimal compressive imaging: design and implementation. In Proc. SPIE 9186, Fifty Years of Optical Sciences at The University of Arizona, 9186, 91860K91860K11.
 Ashok, A., Huang, J., Lin, Y., Kerviche, R., Ashok, A., Huang, J., Lin, Y., & Kerviche, R. (2014).
 Kaylor, B. M., Seger, E. M., Crouch, S., Reibel, R. R., & Ashok, A. (2014). 2 Megapixel Computational Imaging System with 5 Hz Frame Rate. In Classical Optics 2014, CTh1C.6.
 Kerviche, R., Zhu, N., & Ashok, A. (2014). Informationoptimal Scalable Compressive Imaging System. In Classical Optics 2014, CM2D.2.
 Kerviche, R., Zhu, N., & Ashok, A. (2014, Oct). Information optimal scalable compressive imager demonstrator. In Image Processing (ICIP), 2014 IEEE International Conference on, 21772179.
 Pu, L., Marcellin, M. W., Bilgin, A., & Ashok, A. (2014, Oct). Image compression based on taskspecific information. In Image Processing (ICIP), 2014 IEEE International Conference on, 48174821.
Presentations
 Ashok, A. (2018, April). Fundamental limits of threedimensional imaging and sensing from scattering surfaces. SPIE Defense and Commercial Sensing. Orlando, FL: SPIE.More infoInvited Talk
 Ashok, A. (2018, August). Impact of Material Variability on Threat Detection Performance of Xray Systems. Concealed Explosive Detection Workshop. Santa Fe, NM: DHS.More infoInvited Talk
 Ashok, A. (2018, December). Fundamental Limits of Passive Imaging: Quantum Information Theoretic Approach. IEEE ICEE Conference. Bangalore, India: IEEE.More infoInvited Talk
 Ashok, A. (2018, June). Imaging and Sensing Around the Corner: An Informationtheoretic Approach,. SIAM Imaging Science Conference. Bologna, Italy: SIAM.More infoInvited Talk
 Ashok, A. (2018, September). Fundamental Limits of Imaging: A Computational Imaging Approach. OSA FiO Meeting. Washington D.C.: OSA.More infoInvited Talk
 Ashok, A. (2017, August). Fundamental Limits of Threat Detection with Xrays: Transmission and Diffraction Studies. TSA Deep Learning Workshop. Washington D.C.: TSA.
 Ashok, A. (2017, June 2017). Computational Imaging at Information Theoretic Limits. Invited talk at Beijing Institute of Technology and Tsinghua University, China. Beijing, China: Beijing Institute of Technology.
 Ashok, A. (2017, June). Scalable Informationtheoretic Computation Imaging: From Theory to Practice. NATO Panel Meeting (Set 232). Orlando, FL: NATO  LMCO.
 Ashok, A. (2017, March, 2017). Seeing beyond the visible: Thermal Imaging. Science City, Tucson Book Festival. Tucson.
 Ashok, A., Mandava, S., Thamvichai, R., Bilgin, A., Coccarelli, D., Greenburg, J., & Gehm, M. (2017, November). Sparse View Bag Reconstruction visavis Threat Detection. Concealed Explosive Detection Workshop. Charlottesville, VA: DHS.
 Huang, J., Clarkson, E. W., Pattanaik, S., & Ashok, A. (2017, August). Direct and Indirect Photon Pathway Imaging: An Information Theoretic Analysis. SPIE Optics and Photonics. San Diego, CA: SPIE.
 Ashok, A. (2016, May). Scalable Information Optimal Imaging: Recent Progress  EO/IR to Xray. SIAM Conference on Imaging Science. Albuquerque, NM: SIAM.
 Ashok, A. (2016, September). Scalable Informationtheoretic Analysis Framework for Xray Threat Detection Architectures: Metric and Bag Model. Concealed Explosive Detection Workshop. Cambridge, UK: DHS.
 Ashok, A. (2015, April). Scalable Compressive Imaging. GE Photonics Symposium. New York: GE Research.
 Ashok, A. (2015, November, 2015). Scalable InformationTheoretic Compressive Imaging: From Theory to Practice. Invited Talk  Hong Kong University  Department of Electrical and Electronic Engineering. Hong Kong: Hong Kong University.
 Ashok, A. (2015, October, 2015). InformationTheoretic Compressive Imaging. Raytheon BBN Research Seminar. Boston, MA: Raytheon BBN.
 Ashok, A. (2015, September). Information in Images/Videos: Acquisition, Storage and Extraction. IBM Tech Day. UA Tech Park (IBM): IBM/UA.
 Ashok, A., Neifeld, M. A., Clarkson, E. W., & Gehm, M. E. (2015, October, 2015). Information‐Theoretic System Analysis and Design Framework: Advanced X‐ray Explosive Threat Detection. ALERT  ADSA Workshop 13  Northeastern University. Boston, MA: Northeastern University.
 Ashok, A. (2014, August, 2014). An informationtheoretic approach to compressive imaging. SPIE Optics and Photonics: Special Symposium on OSC's 50th Anniverssary. San Diego, CA: SPIE.
 Ashok, A. (2014, June, 2014). An informationtheoretic approach to computational imaging. GRC Image Science. Easton, MA: GRC, NIH and ONR.
 Ashok, A. (2014, March, 2014). Computational Optical Sensing and Imaging: A new approach to measurement design. OSA's ICOL 2014. Dehradun, India: OSI, OSA and SPIE.
 Ashok, A. (2014, November, 2014). Compression and Adaptation in Imaging. Fitzpatrick Optics and Photonics Seminar at Duke University. Duke University, NC: Duke University.