Publications

2023

  • K. Echenim, L. Elluri, and K. P. Joshi, “Ensuring Privacy Policy Compliance of Wearables with IoT Regulations“, IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (IEEE TPS 2023), November 2023.
  • S. Menon, J. Mangalagiri, J. Galita, M. Morris, B. Saboury, Ya. Yesha, Ye. Yesha, P. Nguyen, A. Gangopadhyay, D. Chapman,  “CCS-GAN: COVID-19 CT-scan generation and classification with very few positive training images,” accepted for publication in Springer Journal of Digital Imaging.
  • J. Bolton, L. Elluri, and K. P. Joshi, “An Overview of Cybersecurity Knowledge Graphs Mapped to the MITRE ATT&CK Framework Domains“, IEEE International conference on Intelligence and Security Informatics (ISI 2023), October 2023.
  • D. Dahiphale, A. Wadkar, and K. P. Joshi, “CDFMR: A Distributed Statistical Analysis of Stock Market Data using MapReduce with Cumulative Distribution Function“, IEEE CLOUD Summit Conference 2023, July 2023.
  • Maksim Eren, Manish Bhattarai, Robert Joyce, Edward Raff, Charles Nicholas, Boian Alexandrov. “Semi-supervised Classification of Malware Families Under Extreme Class Imbalance via Hierarchical Non-Negative Matrix Factorization with Automatic Model Determination”, ACM Transactions on Privacy and Security, accepted with minor revisions
  • Eren, M. E., Rasmussen, K.O., Nicholas, C., and Alexandrov B.S. . “MalwareDNA”, Presented at the Conference on Data Analysis (CoDA), Santa Fe, March  2023.
  • Hamer, S., S.Saha, M. Halem, “A Data Driven Machine Learning Approach to OSSEs”, 103rd  AMS conference, Jan. 10, 2023.
  • Halem et. al., “Towards a Dynamic Data Driven Wildfire Digital Twin (WDT)”  2nd Edition of Volume 1 Dynamic Data Driven Application Systems, HandBook by Springer, Jan. 27, 2023 Accepted, to appear.
  • Ayanzadeh, R., M. Halem “ Convergence: Artificial Intelligence and Quantum Computing. Social, Economic and Policy Impacts. By G. Viggiano and D. Brin 1st Edition Chapter 5. Wiley Press Feb. 2023

2022

  • R. Razavisousan and K. P. Joshi, “Building Textual Fuzzy Interpretive Structural Modeling to Analyze Factors of Student Mobility Based on User Generated Content“, Article, International Journal of Information Management Data Insights, November 2022.
  • D. L. Kim, N. Alodadi, Z. Chen, K. P. Joshi, A. Crainiceanu, and D. Needham, “MATS: A Multi-aspect and Adaptive Trust-based Situation-aware Access Control Framework for Federated Data-as-a-Service Systems“, InProceedings, IEEE International Services Computing Conference (SCC) 2022 in IEEE World Congress on Services 2022, July 2022.
  • S. Dixit, K. P. Joshi, S. Choi, and L. Elluri, “Semantically Rich Access Control in Cloud EHR Systems Based on MA-ABE“, InProceedings, 8th IEEE International Conference on Big Data Security on Cloud (BigDataSecurity 2022), May 2022.
  • D. N. Ganapathy and K. P. Joshi, “A Semantically Rich Framework to Automate Cloud Service Level Agreements“, Article, IEEE Transactions on Services Computing, January 2022.
  • M. E. Eren and M. Bhattarai and N. Solovyev and L. E. Richards and R. Yus and C. Nicholas and B. S. Alexandrov, “One-Shot Federated Group Collaborative Filtering,” 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), Nassau, Bahamas, 2022, pp. 647-652, doi: 10.1109/ICMLA55696.2022.00107. Awarded Best M.S. Research at 2023 UMBC CSEE Research Day.
  • Maksim E. Eren, Nick Solovyev, Manish Bhattarai, Kim Rasmussen, Charles Nicholas, and Boian S. Alexandrov. 2022. SeNMFk-SPLIT: Large Corpora Topic Modeling by Semantic Non-negative Matrix Factorization with Automatic Model Selection. In ACM Symposium on Document Engineering 2022 (DocEng ’22), September 20-23, 2022, San Jose, CA, USA. ACM, New York, NY, USA, 4 pages.
  • Liu, R., Eren, M. E., and Nicholas, C. (2022). “Can Feature Engineering Help Quantum Machine Learning for Malware Detection?”. Presented at the 13th Annual Malware Technical Exchange Meeting, Online, 2022.
  • Bhandary, P, Vieson, C., Kiendrebeogo, A., Adetunji, I., Joyce, R., Eren, M. E., and Nicholas, C. (2022). “Malware Antivirus Scan Pattern Mining via Tensor Decomposition”. Presented at the 13th Annual Malware Technical Exchange Meeting, Online, 2022.
  • Halem, M. A. Kochanski, J. Mandel, P. Nguyen, R. Atlas, Z. Yang, A. Bargteil, J. Sleeman, V.  Caicedo, R. Delgado, B. Demoz, D. Chapman, K. Ptel, S. Patil, S. Shivadekar, J. Mackinnon, S. Chiao, Ya. Yesha, J.Sorkin, J. Dorband, E. Kalnay, “A Machine Learning Plume Resolving Model  Implementation  over N. America, AGU Fall conference 2022
  • Ayanzadeh, R., J. Dorband, M. Halem, T. Finin, “Quantum-Assisted Greedy Algorithms”, International Geosciences Remote Sensing   Sysytems, Kuala Lumpur, Maylasia  Sept. 2022
  • Halem, M. et. al. , “Towards A Wildfire Digital Twin:  Deforestation, Smoke Impact on Air Quality, Cardiopulmonary Health” Dynamic Data Driven Application Systems Oct. 9 2022  conference MIT Cambridge Mass.
  • Shivadekar, S., Ya. Yesha, Z. Yang, “Tensor Decomposition Analytics for Spatial-Temporal Observations of PBLH” , AGU Fall conference Dec. 2022
  • Halem, M. A. Kochanski, J. Mandel, P. Nguyen, R. Atlas, Z. Yang, A. Bargteil, J. Sleeman, V. Caicedo, R. Delgado, B. Demoz, D. Chapman, K. Ptel, S. Patil, S. Shivadekar, J. Mackinnon, S. Chiao, Ya. Yesha, J. Sorkin, J. Dorband, E. Kalnay, “A Machine Learning Plume Resolving Model Implementation over N. America for Mega Wildland Fire Smoke Transport Impacts on the Planetary Boundary Layer” American Meteor. Soc.  102nd Conf. Jan. 2022
  • Ayanzadeh, R., J. Dorband, M. Halem, T. Finin, “Quantum-Assisted Greedy Algorithms”, International Geosciences Remote Sensing   Sysytems, Kuala Lumpur, Maylasia 2022

2021

  • Ankur Nagar, Lavanya Elluri and Karuna P. Joshi, “Automated Compliance of Mobile Wallet Payments for Cloud Services” In Proceedings, 7th IEEE International Conference on Big Data Security on Cloud (BigDataSecurity 2021), May 2021.
  • Dae-young Kim and Karuna P. Joshi, ” A Semantically Rich Knowledge Graph to Automate HIPAA Regulations for Cloud Health IT Services” In Proceedings, 7th IEEE International Conference on Big Data Security on Cloud (BigDataSecurity 2021), May 2021.
  • Yusen Wu, Hao Chen, Xin Wang, Chao Liu, Phuong Nguyen, and Yelena Yesha. “Tolerating Adversarial Attacks and Byzantine Faults in Distributed Machine Learning”. IEEE BigData’21 BigCyber-2021.
  • Jayalakshmi Mangalagiri, Jones Sam Sugumar, Sumeet Menon, David Chapman, Yaacov Yesha, Aryya Gangopadhyay, Yelena Yesha, and Phuong Nguyen. “Classification of COVID-19 using Deep Learning and Radiomic Texture Features extracted from CT scans of Patients Lungs”. IEEE BigData’21 BDA COVID-2021.
  • Sourajit Saha, Sumeet Menon, Jayalakshmi Mangalagiri, Yelena Yesha, Aryya Gangopadhyay, Phuong Nguyen, David Chapman. “Pairwise meta learning pipeline: classifying COVID-19 abnormalities on chest radio-graphs”. Computer-Aided Diagnosis, SPIE Medical Imaging conference, February 2022.
  • Samit Shivadekar, Jayalakshmi Mangalagiri, Rahul Gite, Phuong Nguyen, David Chapman, Milton Halem, “An Intelligent Parallel Distributed Streaming Framework for Near Real-time Science Sensors and High Resolution Medical Images”, ACM ICPP workshop 2021.
  • Phuong Nguyen, Rahul Gite, Zhifeng Yang, Milton Halem “Deep Neural Network Architecture Search for Emulating Physical Parameterization of Planetary Boundary Layer Height”, IEEE IGARSS conference 2021.
  • Mehta, K., Jain, A., Mangalagiri, J., Mangalagiri, J., Menon, S., Nguyen, Phuong, Chapman David. “Lung Nodule Classification Using Biomarkers, Volumetric Radiomics, and 3D CNNs”. Journal Digit Imaging (2021). https://doi.org/10.1007/s10278-020-00417-y
  • Eren, M. E., Solovyev, N., Hamer, C., McDonald, R., Alexandrov, B. S., & Nicholas, C. (2021, August). COVID-19 multidimensional kaggle literature organization. In Proceedings of the 21st ACM Symposium on Document Engineering (pp. 1-4).
  • Boutsikas, J., Eren, M. E., Varga, C., Raff, E., Matuszek, C., & Nicholas, C. (2021). Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery. arXiv preprint arXiv:2106.07860.
  • Maksim E. Eren, Charles Nicholas, Renee McDonald, and Chris, Hamer, Random Forest of Tensors (RFoT), MTEM 2021
  • Eren, M. E., Solovyev, N., Raff, E., Nicholas, C., & Johnson, B. (2020, September). COVID-19 kaggle literature organization. In Proceedings of the ACM Symposium on Document Engineering 2020 (pp. 1-4). (Video)
  • Halem, M., Z. Yang, C. Cruz, J. Sleeman “ Coupling AI to Aerosol Model Parameterizations for Inferring Boundary Layer Heights” AMS 11R2O Jan. 2021
  • Sleeman, J., D. Ziaei, V. Caiceido, C. Calderella, M. Halem, R. Delgado, B. Demoz, “ A Deep Multi-Stacked Neural Network Approach for Improved Planetary Boundary Layer Height Estimation” AMS Jan. 2021
  • Sleeman J., T. Finin, M. Halem, “Understanding Cybersecurity Threat trends Using Dynamoc Topic Modeling” Frontiers in Big Data June 2021
  • Kinjal Patel, Jennifer Sleeman, and Milton Halem “Physics-aware deep edge detection network”, Proc. SPIE 11859, Remote Sensing of Clouds and the Atmosphere XXVI, 1185908 (12 September 2021); https://doi.org/10.1117/12.2600327
  • Sleeman J., Finin T., and Halem M., “Understanding Cybersecurity Threat Trends Through Dynamic Topic Modeling” Frontiers in Big Data: Accepted May 2021 4:601529. doi: 10.3389/fdata.2021.601529
  • Ayanzadeh, R., J. Dorband, m. Halem, T. Finin, “Multi-Qubit Correction for Quantum Annealers” Nature: Scientific Reports 2021 (Accepted)

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), https://doi.org/10.1145/3425192
  • 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), https://doi.org/10.1145/3427097
  • 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 of 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.
  • Prathapan S., N. Golpayegani, B. Wyatt, M. Halem, J. Dorband, J. Trantham, C. Markey, “Astor – A compute framework for Scalable Distributed Big Data Processing” , Society of Photo-Optical Instrumentation Engineers (SPIE) Defense + Commercial Sensing, 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
  • J. Mangalagiri, D. Chapman, A. Gangopadhyay, Y. Yesha, J. Galita, S. Menon, Y. Yesha, B. Saboury, M. Morris, P. Nguyen “Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network”, CSCI, Symposium on Health Informatics and Medical Systems (CSCI-ISHI), Dec 2020
  • S. Menon and J. Galita and D. Chapman and A. Gangopadhyay and J. Mangalagiri and P. Nguyen and Y. Yesha and Y. Yesha and B. Saboury and M. Morris. “Generating Realistic COVID19 X-rays with a Mean Teacher+ Transfer Learning GAN” IEEE Bigdata conference 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 conference 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 conference, 114230Y (22 April 2020); https://doi.org/10.1117/12.2558319
  • Ramin Ayanzadeh, Milton Halem, Tim Finin, ”An Ensemble Approach for Compressive Sensing with Quantum Annealers” IGARSS 7/11/20 to appear 9/20/2020
  • Halem, M., J. Sleeman, Z. Yang, M. Chin, D. Watson-Parris, B. Demoz; “Harnessing HPC for Cloud Resolving NU-WRF Subseasonal Forecasts with AI Emulations” AGU Fall 2020
  • Gite, R., M. Halem, P. Nguyen, “Compressive Sensing and Deep Learning framework for Multiple Satellite Sensor Data Fusion, AGU Fall 2020

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, 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)
  • Ziaei, D., D. Chapman, M.Halem, “Adapted Ensemble of Deep Convolutional Networks for Empirical Study of Bias-Variance Tradeoff ” Data Science and Intelligent Systems 2019, Las Vegas, Nevada Aug. 1- 3,2019 (Accepted)

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.,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., Oct 2018
  • Sleeman J.       “Variational Autoencoders using D-Wave Quantum Annealing.” AGU Fall Meeting Abstracts Dec. 2018.
  • Dashtestani, H., Zaragoza, R., Kermanian, R., Knutson, K., Halem, M., Anderson, A.,  Gandjbakhche, A. “Importance of left dorsolateral prefrontal cortex in moral judgment using functional near-infrared spectroscopy. In Microscopy Histopathology and Analytics”  (pp. JW3A-52). Optical Society of America.
  • Ziaei, D., M. Halem, T. Blattner, Ya. Yesha, “Training A Deep Convolutional Neural Network for Large High ResolutionBiomedical Stem Cell Image Segmentation”,  ????
  • Bhattacharyya D., M.Halem, A. Borle, D. Ziaei “Scalable Noisy Image Restoration Using Quantum Markov Random Field” AGU 2018 Fall Meeting, Washington DC.

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
  • Blattner, T., Keyrouz, W., Bhattacharyya, S.S., and Halem, M., A Hybrid Task Graph Scheduler for High Performance Image Processing Workflows.  Journal of Signal and Information Processing, (Accepted June 2017)
  • Sleeman, J.,  M. Halem, T. Finin, M. Cane, “Modeling the Evolution of Climate Change Assessment Research Using Dynamic Topic Models and Cross-Domain Divergence Maps” AAAI 2017 Spring Symposium on AI for Social Good (AISOC), Stanford Univ. March 27-29, 2017.
  • Nguyen, P., M. Halem, J. Dorband, A. Radov, D. Frankel, “Evaluation of Deep Learning Models for Predicting CO2 Fluxes”, AGU Fall Meeting, Dec. 2017, New Orleans
  • Pelissier, C., Jacqueline Lemoigne, Gyorgy Fekete, Milton Halem, “Quantum Assisted Learning for Registration of MODIS Images ” AGU Fall Meeting Dec. 2017, New Orleans

Books

  • Jennifer Sleeman, 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, 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