Amit Ashok

Interests

Research

Computational/compressive imaging and sensing, Bayesian inference, statistical optics, and information theory.

Courses

Scholarly Contributions

Journals/Publications

• Ding, Y., & Ashok, A. (2022). Bounds on mutual information of mixture data for classification tasks. J. Opt. Soc. Am. A, 39(7), 1160--1171.
• Lee, K. K., Gagatsos, C. N., Guha, S., & Ashok, A. (2022). Quantum-inspired Multi-Parameter Adaptive Bayesian Estimation for Sensing and Imaging. IEEE Journal of Selected Topics in Signal Processing, 1-11.
• Bilgin, A., Ashok, A., Lin, Y., Marcellin, M. W., Ahanonu, E. L., & Liu, F. (2020). Visibility of Quantization Errors in Reversible JPEG2000. Signal Processing: Image Communication, 84, 115812.
• Ding, Y., Clarkson, E. W., & Ashok, A. (2021). Invertibility of multi-energy X-ray transform. Medical Physics, n/a(n/a).
• Grace, M. R., Dutton, Z., Ashok, A., & Guha, S. (2020). Approaching quantum-limited imaging resolution without prior knowledge of the object location. J. Opt. Soc. Am. A, 37(8), 1288--1299.
• Bilgin, A., Gurcan, M. N., Tozbikian, G., Marcellin, M. W., Ashok, A., Liu, F., Lin, Y., & Niazi, M. K. (2019). Pathological image compression for big data image analysis: Application to hotspot detection in breast cancer. Artificial Intelligence in Medicine, 95, 82–87.
• Ding, Y., Kerviche, R., Ashok, A., & Pau, S. (2018). Eavesdropping of display devices by measurement of polarized reflected light. Applied Optics, 57(19), 5483--5491.
• 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), 326-340.
• 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). Real-time robust direct and indirect photon separation with polarization imaging. Opt. Express, 25(23), 29432-29453.
• Huang, J., Neifeld, M. A., & Ashok, A. (2016). Face recognition with non-greedy information-optimal adaptive compressive imaging. Applied Optics, 55, 9744-9755. doi:https://doi.org/10.1364/AO.55.009744
• Lee, M., Neifeld, M. A., & Ashok, A. (2016). Capacity of electromagnetic communication modes in a noise-limited optical system. Applied Optics, 55, 1333-1342.
• Gu, Y., Goodman, N. A., & Ashok, A. (2014). Radar Target Profiling and Recognition Based on TSI-Optimized Compressive Sensing Kernel. Signal Processing, IEEE Transactions on, 62(12), 3194-3207.
• Zhao, M., Zhang, H., Li, Y., Ashok, A., Liang, R., Zhou, W., & Peng, L. (2014). Cellular imaging of deep organ using two-photon Bessel light-sheet nonlinear structured illumination microscopy. Biomed. Opt. Express, 5(5), 1296--1308.
• Kaylor, B. M., Ashok, A., Seger, E. M., Keith, C. J., & Reibel, R. R. (2012). Dynamically programmable, dual-band computational imaging system. Computational Optical Sensing and Imaging, COSI 2012, CM4B.3.
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  Abstract: 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.
• Neifeld, M. A., Ashok, A., & Ke, J. (2010).

  Adaptive Compressive Imaging for Object Reconstruction

  . Proceedings of SPIE, 7818. doi:10.1117/12.861738
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  Static Feature-specific imaging (SFSI) employing a fixed/static measurement basis has been shown to achieve superior reconstruction performance to conventional imaging under certain conditions.1-5 In this paper, we describe an adaptive FSI system in which past measurements inform the choice of measurement basis for future measurements so as to maximize the reconstruction fidelity while employing the fewest measurements. An algorithm to implement an adaptive FSI system for principle component (PC) measurement basis is described. The resulting system is referred to as a PC-based adaptive FSI (AFSI) system. A simulation study employing the root mean squared error (RMSE) metric to quantify the reconstruction fidelity is used to analyze the performance of the PC-based AFSI system. We observe that the AFSI system achieves as much as 30% lower RMSE compared to a SFSI system.
• Ashok, A., & Wilkinson, A. J. (2001). Topographic mapping with multiple antenna SAR interferometry: A Bayesian model-based approach. International Geoscience and Remote Sensing Symposium (IGARSS), 5, 2058-2060.
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  Abstract: Multiple-antenna 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 multiple-antenna SAR interferometry. Topographic reconstruction is formulated as a parameter estimation problem in the model-based Bayesian inference framework. An InSAR simulator based on a forward model is developed for simulating SAR data from multiple-antenna 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 three-antenna InSAR system is reported, and conclusions are drawn regarding the Performance and improvements are proposed.

Proceedings Publications

• Lee, K. K., Gagatsos, C., Guha, S., & Ashok, A. (2022). Quantum-limited Optical Super-resolution Imaging of Multiple Point-sources. In Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP).
• Ratchaneekorn, T., & Ashok, A. (2022). Machine learning based threat detection for dual modality X-ray transmission and coherent diffraction security screening systems. In Anomaly Detection and Imaging with X-Rays (ADIX) VII, PC12104.
• Cox, J., Ashok, A., & Morley, N. (2020). An analysis framework for event-based sensor performance. In Unconventional Imaging and Adaptive Optics 2020, 11508.
• Cox, J., Morley, N., & Ashok, A. (2020). Motion analysis of event-based sensors. In Computational Imaging V, 11396.
• Ding, Y., & Ashok, A. (2020). X-ray measurement model and information-theoretic metric incorporating material variability with spatial and energy correlations. In Anomaly Detection and Imaging with X-Rays (ADIX) V, 11404.
• Ding, Y., Coccarelli, D., Hurlock, A., Greenberg, J. A., Gehm, M., & Ashok, A. (2020). Task-specific information in x-ray diffraction and transmission modalities: a comparative analysis. In Anomaly Detection and Imaging with X-Rays (ADIX) V, 11404.
• Grace, M. R., Dutton, Z., Ashok, A., & Guha, S. (2020). Approaching Quantum-Optimal Imaging without Prior Knowledge of the Object Location. In Imaging and Applied Optics Congress.
• Lee, K. K., & Ashok, A. (2020). Partially Coherent Object Length Estimation: Information-theoretic Analysis. In Imaging and Applied Optics Congress.
• Ashok, A., Marcellin, M. W., Bilgin, A., Liu, F., & Lin, Y. (2019, March). Perception-Optimized Encoding for Visually Lossy Image Compression. In 2019 IEEE Data Compression Conference, Snowbird, UT.
• 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 multi-modality x-ray systems for explosives detection. In Anomaly Detection and Imaging with X-Rays (ADIX) IV, 10999.
• Ding, Y., & Ashok, A. (2019). X-ray measurement model and information-theoretic metric incorporating material variability with energy correlations. In Anomaly Detection and Imaging with X-Rays (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., Bilgin, A., Hernandez-Cabronero, M., Liu, F., Marcellin, M. W., Ahanonu, E., & Ashok, A. (2019, March). Perception-optimized encoding for visually lossy image compression. In 2019 Data Compression Conference, Snowbird, Utah.
• Lin, Y., Liu, F., Hernandez-Cabronero, M., Ahanonu, E., Marcellin, M. W., Bilgin, A., & Ashok, A. (2019, 5). Perception-Optimized Encoding for Visually Lossy Image Compression. In Proceedings - DCC 2019.
• Masoudi, A., & Ashok, A. (2019). Multiplexed measurement design for fixed Gantry x-ray computed tomography system (Conference Presentation). In Anomaly Detection and Imaging with X-Rays (ADIX) IV, 10999.
• Voris, J., Ding, Y., Thamvichai, R., Greenberg, J. A., Coccarelli, D., Gehm, M. E., Johnson, E., Bosch, C., & Ashok, A. (2019). Information-theoretic analysis of fixed Gantry x-ray computed tomography transmission system for threat detection (Conference Presentation). In Anomaly Detection and Imaging with X-Rays (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 X-ray CT Reconstruction for Checked Baggage Screening. In SPIE Defense + Security, Anomaly Detection and Imaging with X-Rays (ADIX) |II.
• Bilgin, A., Ashok, A., Marcellin, M. W., Ahanonu, E., Hernandez-Cabronero, 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 X-Rays (ADIX) III, 106320F, 10632, 10632 - 10632 - 8.
• Lin, Y., Marcellin, M. W., Bilgin, A., & Ashok, A. (2018, March). Task-based JPEG2000 image compression: An information-theoretic approach. In Data Compression Conference.
• Masoudi, A., Voris, J., Coccarelli, D., Greenberg, J., Gehm, M., & Ashok, A. (2018, April). X-ray measurement model and information-theoretic system metric incorporating material variability. In SPIE Anomaly Detection and Imaging with X-Rays (ADIX) III, 10632.
• , D., Greenberg, J. A., , S., , Q., Huang, L., , A., & Gehm, M. E. (2017, 2017). Creating an experimental testbed for information-theoretic analysis of architectures for x-ray anomaly detection. In Creating an experimental testbed for information-theoretic analysis of architectures for x-ray anomaly detection, 10187, 1018709-10187-9.
• 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 information-theoretic analysis of architectures for x-ray anomaly detection. In SPIE Anomaly Detection and Imaging with X-rays II, 10187, 1018709-10187-9.
• 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), 441-445.
• Mandava, S., Greenberg, J. A., Gehm, M. E., Ashok, A., & Bilgin, A. (2017, 2017). Image reconstruction for view-limited x-ray CT in baggage scanning. In SPIE Anomaly Detection and Imaging with X-rays II, 10187, 101870F-10187-9.
• Ashok, A. (2016, June). Fundamental limit of resolving two point sources limited by an arbitrary point spread function. In International Symposium on Information Theory.
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  Estimating 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 information-optimal 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 hard-aperture pupilproving the information optimality of image-plane sinc-Bessel 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). Information-theoretic analysis of x-ray scatter and phase architectures for anomaly detection. In SPIE DSS - Anomaly Detection and Imaging with X-rays (ADIX), 98470B.
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  doi: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 information-theoretic approach to this problem by formulating a performance metric, based on Cauchy-Schwarz 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 x-ray scatter or phase. Our results show how effective use of this metric can impact design decisions for x-ray scatter and phase systems.
• Huang, J., Huang, J., Ashok, A., & Ashok, A. (2016, April). Information optimal compressive x-ray threat detection. In SPIE DSS - Anomaly Detection and Imaging with X-rays, 98470T.
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  doi:10.1117/12.2223784We present an information-theoretic approach to X-ray measurement design for threat detection in passenger bags. Unlike existing X-ray 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 non-multiplexed measurements. For example, a 99% detection probability is achieved by optimal multiplexed design requiring 4x fewer measurements than non-multiplexed design.
• Kerviche, R., & Ashok, A. (2016, April). Scalable information-optimal compressive target recognition. In SPIE DSS - Computational Imaging, 987008.
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  doi:10.1117/12.2228570We present a scalable information-optimal compressive imager optimized for the target classification task, discriminating between two target classes. Compressive projections are optimized using the Cauchy-Schwarz Mutual Information (CSMI) metric, which provides an upper-bound 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 information-optimal 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). Information-theoretic analysis of x-ray photoabsorption based threat detection system for check-point. In SPIE DSS - Anomal Detection and Imaging with X-rays (ADIX), 98470F.
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  doi:10.1117/12.2223803In this work we present an information-theoretic framework for a systematic study of checkpoint x-ray 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.
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  doi: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 X-ray Threat Detection. In OSA Technical Digest (online), CTh2E.4.
• Kulkarni, K., Lohit, S., Turaga, P. K., Kerviche, R., & Ashok, A. (2016, June, 2016). ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements.. In Computer Vision and Pattern Recognition (CVPR16), abs/1601.06892.
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  Has the highest h-index 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 Task-Specific Information Metric. In 2015 Data Compression Conference.
• Pu, L., Marcellin, M. W., Bilgin, A., & Ashok, A. (2015, April). Compression based on a joint task-specific 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, 91860K-91860K-11.
• 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). Information-optimal 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, 2177-2179.
• Pu, L., Marcellin, M. W., Bilgin, A., & Ashok, A. (2014, Oct). Image compression based on task-specific information. In Image Processing (ICIP), 2014 IEEE International Conference on, 4817-4821.

Presentations

• Ashok, A. (2018, April). Fundamental limits of three-dimensional imaging and sensing from scattering surfaces. SPIE Defense and Commercial Sensing. Orlando, FL: SPIE.
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  Invited Talk
• Ashok, A. (2018, August). Impact of Material Variability on Threat Detection Performance of X-ray Systems. Concealed Explosive Detection Workshop. Santa Fe, NM: DHS.
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  Invited Talk
• Ashok, A. (2018, December). Fundamental Limits of Passive Imaging: Quantum Information Theoretic Approach. IEEE ICEE Conference. Bangalore, India: IEEE.
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  Invited Talk
• Ashok, A. (2018, June). Imaging and Sensing Around the Corner: An Information-theoretic Approach,. SIAM Imaging Science Conference. Bologna, Italy: SIAM.
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  Invited Talk
• Ashok, A. (2018, September). Fundamental Limits of Imaging: A Computational Imaging Approach. OSA FiO Meeting. Washington D.C.: OSA.
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  Invited Talk
• Ashok, A. (2017, August). Fundamental Limits of Threat Detection with X-rays: 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 Information-theoretic 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 vis-a-vis 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 X-ray. SIAM Conference on Imaging Science. Albuquerque, NM: SIAM.
• Ashok, A. (2016, September). Scalable Information-theoretic Analysis Framework for X-ray 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 Information-Theoretic 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). Information-Theoretic 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 information-theoretic 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 information-theoretic 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.