Publications

2021

2020

  • Karuna Pande Joshi and Srishty Saha. 2020. A Semantically Rich Framework for Knowledge Representation of Code of Federal Regulations, Digit. Gov.: Res. Pract. 1, 3, Article 21 (October 2020).
  • James Clavin, Sisi Duan, Haibin Zhang, Vandana Janeja, Karuna P. Joshi, Yelena Yesha, Lucy C. Erickson, and Justin Li. 2020, Blockchains for Government: Use Cases and Challenges. Digit. Gov.: Res. Pract. 1, 3, Article 22 (October 2020).
  • Karuna P. Joshi, Lavanya Elluri, and Ankur Nagar, An Integrated Knowledge Graph to Automate Cloud Data Compliance. IEEE Access, vol. 8, pp. 148541-148555, 2020, doi: 10.1109/ACCESS.2020.3008964.
  • Anantaa Kotal, Karuna P. Joshi, and Anupam Joshi, ViCLOUD: Measuring Vagueness in Cloud Service Privacy Policies and Terms of Services, In Proceedings, IEEE International Conference on Cloud Computing (IEEE CLOUD), October 2020.
  • Abhishek Mahindrakar and Karuna P. Joshi, Automating GDPR Compliance using Policy Integrated Blockchain, In Proceedings, 6th IEEE International Conference on Big Data Security on Cloud (BigDataSecurity 2020), May 2020.
  • Smriti Prathapan, Navid Golpayegani, Bryan Wyatt, Milton Halem, John Dorband, Jon Trantham, and Chris Markey, Astor: A compute framework for Scalable Distributed Big Data Processing, Proc.  Society of Photo-Optical Instrumentation Engineers (SPIE) Defense + Commercial Sensing, SPIE 11395, Big Data II: Learning, Analytics, and Applications, 113950O (18 May 2020);  April 2020 (Accepted)
  • A Kaplunovich, Y Yesha, Refactoring of Neural Network Models for Hyperparameter Optimization in Serverless Cloud, 4th International Workshop on Refactoring Co-located with 42nd International Conference on Software Engineering,  ICSE 2020, May 2020
  • Sisi Duan, Chao Liu, Xin Wang, Yusen Wu, Shuai Xu, Yelena Yesha, Haibin Zhang, “Intrusion-Tolerant and Confidentiality-Preserving Publish/Subscribe Messaging”, in proceedings of 39th International Symposium on Reliable Distributed Systems (SRDS 2020)
  • Gajera, B, Dorsa Ziaei, Mangalagiri, J., and Chapman, D., “CT-Scan Denoising using a Charbonnier Loss Generative Adversarial Network”, IEEE Access Journal 2020, under review
    Dorsa Ziaei, Goudarzi. N., “A Take on Wake Modeling of Turbines Based on Machine Learning”, 28th International Conference on Nuclear Engineering 2020, Power Conference
  • Dorsa Ziaei, David Chapman, Yaacov Yesha. and Milton Halem, “Segmentation of Stem cell Colonies in Fluorescence Microscopy Images with Transfer Learning”, Proceedings of SPIE Medical Imaging 2020, Image Processing Conference (Winner of Best Poster Award at SPIE Medical Imaging 2020 Conference, for “Segmentation of stem cell colonies in fluorescence microscopy images with transfer learning”)
  • Dorsa Ziaei, Li, W., Cheng, W., Lam, S., Chen, W., “Characterization of color normalization methods in digital pathology whole slide images”, Proceedings of SPIE Medical Imaging, Digital Pathology Conference 2020 (Oak Ridge Institute for Science and Education (ORISE) Fellowship at Food and Drug Administration)
  • Dorsa Ziaei, Hekmati. P., Goudarzi, N., “Assessment of a CFD-Based Machine Learning Algorithm on Turbulent Flow Approximation”, Proceedings of ASME 2019, 13th International Conference on Energy Sustainability
  • Hekmati., P., Dorsa Ziaei, Goudarzi, N., “Artificial Intelligence for Optimal Sitting of Individual and Networks of Wind Farms”, Proceedings of ASME 2019, Power Conference
  • J. Sleeman, Z. Yang, V.Caicedo, M. Halem, B. Demoz, R. Delgado, “A Deep Machine Learning Approach for LIDAR Based Boundary Layer Height Detection”, International Geoscience and Remote Sensing Symposium (IGARSS), To be published September 2020.
  • J. Sleeman, T. Finin, and M. Halem, “Temporal Understanding of Cybersecurity Threats”, InProceedings, IEEE International Conference on Big Data Security on Cloud, May 2020
  • J. Sleeman, J. E. Dorband, and M. Halem, “A hybrid quantum enabled RBM advantage: convolutional autoencoders for quantum image compression and generative learning”, InProceedings, Quantum Information Science, Sensing, and Computation XII, May 2020
  • J. Sleeman, J. E. Dorband, and M. Halem, “A Hybrid Quantum Enabled RBM Advantage: Convolutional Autoencoders for Quantum Image Compression and Generative Learning”, arXiv preprint arXiv:2001.11946, January 2020
  • Phuong Nguyen, Samit Shivadekar, Sai Sree Chukkapalli, Milton Halem. “Satellite Data Fusion of Multiple Observed XCO2 using Compressive Sensing and Deep Learning” IEEE IGARSS 2020. Accepted to appear Sept 2020.
  • Arshita Jain David R. Chapman, Phuong Nguyen, Sumeet Menon, Jayalakshmi Mangalagiri, Kushal Mehta. LUNG NODULE MALIGNANCY ESTIMATION OF CT SCANS COMBINING IMAGE BIOMARKERS WITH 3D CNNS. Society for Imaging Informatics SIIM 2020
  • Phuong Nguyen, Samit Shivadekar, Sai Sree Chukkapalli, Milton Halem, “Satellite data fusion of multiple observed XCO2 using compressive sensing,” Proc. SPIE 11423, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX, 114230Y (22 April 2020); https://doi.org/10.1117/12.2558319
  • Phuong Nguyen, David Chapman, Sumeet Menon, Michael Morris, Yelena Yesha, “Active semi-supervised expectation maximization learning for lung cancer detection from Computerized Tomography (CT) images with minimally label training data,” Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142E (16 March 2020); https://doi.org/10.1117/12.2549655 

2019

  • Alex Kaplunovich, Karuna P. Joshi, and Yelena Yesha, “Scalability Analysis of Blockchain on a Serverless Cloud”, in proceedings of IEEE Big Data 2019, December 2019
  • Phuong Nguyen and Milton Halem “Deep Learning Models for Predicting CO2 Flux Employing Multivariate Time Series” SIGKDD MileTS, Alaska 2019. https://milets19.github.io/papers/milets19_poster_2.pdf 
  • Sumeet Menon, David Chapman, Phuong Nguyen, Yelena Yesha, Michael Morris, Babak Saboury, “Deep Expectation-Maximization for Semi-Supervised Lung Cancer Screening”, SIGKDD DCCL, Alaska 2019. https://sites.google.com/view/kdd-workshop-2019/accepted-papers
  • Nguyen, Phuong, and Milton Halem. 2019. “Machine Learning for Inferring CO2 Fluxes: The New Metaphysics of Neural Nets.” EarthArXiv. October 24. doi:10.31223/osf.io/284f5.
  • Nguyen, P., Bhaskar, A.V., Shivadekar, S., Yesha, Y. and Halem, M., 2019, December. A Super Resolution Convolutional Neural Network approach for simulating NASA’s SMAP Radar observations from Radiometer Data. In AGU Fall Meeting 2019. AGU.
  • Halem, M., Chukkapalli, S.S.L., Shivadekar, S., and Nguyen, P., 2019. Satellite Data Fusion of Multiple Observed XCO2 using Compressive Sensing and Deep Learning. AGU FM, 2019, pp.B11F-2400
  • J. Sleeman, V. Caicedo, M. Halem, and B. Demoz, “Using Lidar and Machine Learning to Identify Planetary Boundary Layer Heights”, InProceedings, American Geophysical Union Fall Meeting Abstracts, December 2019.
  • J. Sleeman, M. Halem, and J. E. Dorband, “RBM Image Generation Using the D-Wave 2000Q”, Poster, Presentation Presented at the 2019 Rising Stars in EECS Workshop, October 2019.
  • Ketki Joshi, Karuna P. Joshi, and Sudip Mittal, “A Semantic Approach for Automating Knowledge in Policies of Cyber Insurance Services“, in Proceedings of IEEE International Conference on Web Services (IEEE ICWS 2019), July 2019.
  • Karuna P. Joshi and Agniva Banerjee, “Automating Privacy Compliance Using Policy Integrated Blockchain” Special Issue on Advances of Blockchain Technology and Its Applications, Cryptography 2019, 3(1), 7; MDPI . [Journal]
  • Velusamy Kaushik, Prathapan S, Halem M. Exploring the Behavior of Coherent Accelerator Processor Interface (CAPI) on IBM Power8+ Architecture and FlashSystem 900, International Workshop on OpenPOWER for HPC (IWOPH’19)
  • Ayanzadeh, Ramin, Milton Halem, and Tim Finin. “SAT-based Compressive Sensing.” (Submitted to the NeurIPS 2019) arXiv preprint arXiv:1903.03650 (2019).
  • Ayanzadeh, Ramin, Seyedahmad Mousavi, Milton Halem, and Tim Finin. “Quantum Annealing Based Binary Compressive Sensing with Matrix Uncertainty.” arXiv preprint arXiv:1901.00088 (2019).
  • Velusamy Kaushik, Thomas B Rolinger, Janice McMahon and Tyler Simon Exploring Parallel Bitonic Sort on Migratory Thread Architecture.
  • Irena Bojanova, Yaacov Yesha, Paul E. Black, Yan Wu, Information Exposure (IEX) A New Class in the Bugs Framework (BF), accepted to the Proceedings of the IEEE 43rd Annual Computer Software and Applications Conference, 2019
  • Ayanzadeh R.,  M. Halem, T. Finin, “SAT-based Compressive Sensing”, Neural Information Processing Systems Dec. 2019, Vancouver CA. (Submitted)
  • Ayanzadeh R., S. Mousavi, M. Halem and T. Finin, “Quantum Annealing Based Binary Compressive  Sensing  with Matrix Uncertainty” arXiv:1901.00088  Jan. 2019
  • Dashtestani H., R.Zaragoza, R. Kermanian, K.M. Knutson, M. Halem, A. Casey, N.S. Karamzadeh, A.A. Anderson, A.C. Boccara, A. Gandibakhche, “ The Role of Prefrontal cortex in a Moral
  • missing cite
  • Dashtestani, H., R. Zaragoza, H. Pirsiavash, K. M. Knutson, R. Kermanian, J. Cui, J D. Harrison Jr, M. Halem, , A. Casey, N.S. Karamzadeh, A.A. Anderson, A.C. Boccara,  A. Gandjbakhche, “Canonical correlation analysis of brain prefrontal activity measured by functional near infrared spectroscopy during a moral judgement task” Jnl Behavioral Brain Research  Feb. 2019 Vol. 359 pg. 73-80
    Dorband J. E.    “Applying Multi-qubit Correction to Frustrated Cluster Loops on an Adiabatic Quantum Computer.” arXiv preprint arXiv:1902.05827 (2019).
    Halem M.,  H. Vashsista, “ A New Machine Learning Approach for NWP Data Assimilation” NOAA AOML Research Symposium,  University of Miami Rosenstiel School, Miami Fl, Feb. 2019
    Nguyen P., M. Halem, “Deep Learning Models for Predicting CO2 Flux Employing Multivariate Time Series”  IEEE International Conference on Big Data, Dec. 2019,  Anchorage, AL. (Submitted)
    Prathapan S., N. Golpayegani, B. Wyatt, M. Halem, J. Dorband, J. Trantham, C. Markey, “Active Storage Cluster for Big Data Processing” IEEE Cluster 2019 Sept. 2019, Albuquerque, NM  (Submitted)
    Ziaei D., T. Blattner, Ya. Yesha, M. Halem, “Training A Deep Convolutional Neural Network for Large High Resolution Biomedical Stem Cell Image Segmentation” IEEE International Conference on Big Data, Dec. 2019,  Anchorage, AL. (Submitted).
    Ziaei D., D. Chapman, M. Halem, “Adapted Ensemble of Deep Learning for Empirical Study of Bias-Variance Trade off ” IEEE Conference on Machine Learning and Applications, Dec. 2019 Boca Raton, FL.  (Submitted)
    Ziaei D., T. Blattner, D. Chapman, M.Halem,” Deep Learning Models for the Segmentation of Fluorescent Microscopy Images of Stem Cell Colonies” IEEE International Conference on Data Science Advanced Analytics, Oct. 2019 Washington DC (Submitted)
    2018

    Lavanya Elluri, Ankur Nagar, and Karuna P. Joshi, “An Integrated Knowledge Graph to Automate GDPR and PCI DSS Compliance“, In Proceedings of IEEE International Conference on Big Data 2018, December 2018
    Lavanya Elluri and Karuna P. Joshi, “A Knowledge Representation of Cloud Data controls for EU GDPR Compliance“, InProceedings, 11th IEEE International Conference on Cloud Computing (CLOUD), July 2018.
    Ankur Nagar and Karuna P. Joshi, “A Semantically Rich Knowledge Representation of PCI DSS for Cloud Services“, In Proceedings of 6th International IBM Cloud Academy Conference ICACON 2018, Japan
    Ayanzadeh, Ramin, Milton Halem, and Tim Finin. “Solving Hard SAT Instances with Adiabatic Quantum Computers.” In AGU Fall Meeting Abstracts. 2018.
    Ayanzadeh, Ramin. “Quantum Artificial Intelligence for Natural Language Processing Applications.” In Proceedings of the 49th ACM Technical Symposium on Computer Science Education, pp. 273-273. ACM, 2018
    Prathapan, S., N. Golpayegani, B. Wyatt, M. Halem, J. E. Dorband, J. D. Trantham, and C. A. Markey. “In-Storage Processing of MODIS data using Active Disk Devices.” In AGU Fall Meeting Abstracts. 2018.
    Irena Bojanova, Yaacov Yesha, Paul E. Black, Randomness Classes in Bugs Framework (BF): True-Random Number Bugs (TRN) and Pseudo-Random Number Bugs (PRN), Proceedings of IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), 2018.
    Dashtestani H., R.Zaragoza, R. Kermanian, K.M. Knutson, M. Halem, A. Casey, N.S. Karamzadeh, A.A. Anderson, A.C. Boccara, A. Gandibakhche, “ The Role of Prefrontal cortex in a Moral Judgement Task Using Functional Near-Infrared Spectroscopy.” Journal of Brain and Behavior, Wiley, Sept. 2018 Vol.8 pg. 1116
    Dorband J. E.    “Extending the d-wave with support for higher precision coefficients.”arXiv preprint arXiv:1807.05244(2018).
    Dorband J. E.    “A method of finding a lower energy solution to a qubo/ising objective function.” arXiv preprint arXiv:1801.04849 (2018).
    Prathapan, S., N. Golpayageni, B. Watt, M. Halem, J. Dorband, J. Trantham, C. Markey, “ In-Storage Processing of MODIS data on Active Disks”, AGU 2018 Fall Meeting, Washington DC.
    Sleeman, J.,T. Finin, M. Halem, “Ontology-Grounded Topic Modeling for Climate Science Research”, Proc. of Semantic Web for Social Good Workshop of the Int. Semantic Web Conf., 2018
    Sleeman J., Tim Finin, Milton Halem, “Ontology-Grounded Topic Modeling for Climate Science Research” Chapter to appear I “Emerging Topics in Semantic Technologies.” ISWC 2018 Satellite Events, E. Demidova, A.J. Zaveri, E. Simperl (Eds.), ISBN: 978-3-89838-736-1, 2018, AKA Verlag Berlin, (edited authors) ACM-class: I.2.4; I.2.6; I.2.7
    Sleeman J.       “Variational Autoencoders using D-Wave Quantum Annealing.” AGU Fall Meeting Abstracts Dec. 2018.
    2017

    Golpayegani N., Prathapan S., Warmka R., Wyatt B., Halem M., Trantham J., Markey C., “Bringing MapReduce Closer To Data  With Active  Drives”, American Geophysical Union, Dec 2017

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