Joseph F. JaJa

Department of Electrical and Computer Engineering

Institute for Advanced Computer Studies

University of Maryland, College Park

josephj@umd.edu
Phone: 301-405-1925

Research Interests: High Performance Computing, Machine Learning, Data Science, Computational Neuroscience, Visualization.

Biographical Sketch:

• Professor: Electrical and Computer Engineering, and Institute for Advanced Computer Studies, University of Maryland.
• 2018 – 2022: Interim Chair, Department of Electrical and Computer Engineering, University of Maryland, College Park.
• 2010-2011: Interim VP and CIO, University of Maryland, College Park.
• 1994-2004: Director of the University of Maryland Institute for Advanced Computer Studies (UMIACS)
• 2002-2004: Interim Director of the Center for Bioinformatics and Computational Biology
• Ph.D. 1977, M.S. 1976, Applied Mathematics, Division of Engineering and Applied Physics, Harvard University.
• Over 250 refereed publications in parallel and distributed computing, data intensive computing, combinatorial algorithms, algebraic complexity, and VLSI architectures.
• IEEE and ACM Fellow
• more

Current Projects:

• Machine Learning and Data Science. This research involves a number of efforts related to adversarial machine learning, disentangled representations, federated learning, lifelong learning, and techniques related to computational neuroscience. The latter project addresses the structural and functional organization of the brain and the characteristics of the corresponding brain networks using DTI and fMRI data. The project involves collaborations with a number of faculty at UMD and UMB, and was partially funded under the Center for Health-Related Informatics and Bioimaging (CHIB) and the Brain and Behavior Initiative (BBI).
• Parallel Computing. Development of parallel algorithms and their implementations on current and emerging heterogeneous multicore/GPU platforms. Applications of interest range from scientific computing to large scale graph –theoretic problems to machine learning algorithms for big data.
• Partnership with the Laboratory for Telecommunication Sciences. This partnership involves collaborative efforts involving a broad set of research areas and outreach activities. Current research projects cover topics in machine learning, quantum networks, wireless, network security, and visualization.

Teaching:

• Spring 2024: ENEE 436: Foundations of Machine Learning
• Fall 2023: ENEE 290: Introduction to Ordinary Differential Equations and Linear Algebra
• Fall 2022: ENEE759G: Unsupervised Learning
• Fall 2020 and 2021: ENEE 436: Foundations of Machine Learning
• Fall 2019: ENEE759G: Unsupervised Learning
• Fall 2018, Spring 2019: ENEE101: Introduction to Electrical and Computer Engineering

Selected Recent Publications:

• HoloCamera: Advanced Volumetric Capture for Cinematic-Quality VR Applications, J. Heagerty, S. Li, et. al., accepted to IEEE Transactions on Visualization and Computer Graphics, 2024.
• ProtoVAE: Prototypical Networks for Unsupervised Disentanglement, V. Patil, M. Evanusa, and J. JaJa, arXiv.2305.09092  https://arxiv.org/abs/2305.09092, 2023.
• Improving Graph Neural Network with Learnable Permutation Pooling, Y. Jin and J. JaJa, 2022 IEEE Conference on Data Mining Workshops, Nov. 28-Dec 1, 2022, Orlando, FL.
• TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation, J. Wang, M. Gao, Y. Hu, F. Selvaraju, C. Ramaiah, R. Xu, J. JaJa, and L. Davis , to appear in the Proceedings of BMVC 2022, Nov. 21-24, London, UK.
• DOT-VAE: Disentangling One Factor at a Time using Variational Autoencoders, V. Patil, M. Evanusa, and J. JaJa, to appear in the Proceedings of the 2022 ICANN conference, Bristol, UK 6-9 September, 2022.
• FedNet2Net: Saving Communication and Computations in Federated Learning with Model Growing, A. Kundu and J. JaJa, Proceedings of the 2022 ICANN, Bristol, UK, 6-9 September, 2022.
• Class-Similarity Based Label Smoothing for Confidence Calibration, C. Liu and J. JaJa, Proceedings of the 30the International Conference on Artificial Neural Networks (ICANN’2021), 2021.
• Learning Brain Dynamics for Decoding and Predicting Individual Differences, J. Misra, S. Surampudi, M. Venkatesh, C. Limbachia, J. JaJa, and L. Pessoa, accepted to PLOS Computational Biology, 2021.
• Graph Coarsening with Preserved Spectral Properties, Y. Jin, A. Loukas, and J. JaJa, accepted to The 23^rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020).
• Comparing Functional Connectivity Matrices: A Geometry-Aware Approach applied to Participant Identification, M. Venkatesh, J. JaJa, and L. Pessoa, Neuroimage, vol 207, Feb 2020.
• Towards a Physiological Scale of Vocal Fold Agent-based Models of Surgical Inquiry and Repair: Sensitivity Analysis, Calibration and Validation, A. Garg, S. Yuen, N. Seekhao, G. Yu, J. Karwowski, M. Powell, J. Sakata, L. Mongeau, J. JaJa, and N. Li-Jessen, Applied Sciences, 9(15), 2019.
• Geodesic Distances between Functional Connectivity Matrices: a Geometry-aware Approach, M. Venkatesh, J. JaJa, and L. Pessoa, Poster Presentation, Neuroscience 2019, October 19-23, Chicago, IL.
• Analysis and Forecasting for Traffic Flow Data, Y. Wang and J. JaJa, Sensors and Materials, 31(6), 2143-2154, 2019.
• Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness, C. Liu and J. JaJa, to appear in Proceedings for the International Joint Conference on Artificial Intelligence (IJCAI), August 2019.
• Brain Dynamics and Temporal Trajectories during Task and Naturalistic Processing, M. Venkatesh, J. JaJa, and L. Pessoa, Neuroimage, 2019.
• High-Performance Agent-based Modeling Applied to Vocal Fold Inflammation and Repair, N. Seekhao, C. Shung, J. JaJa, L. Mongeau, and N. Li-Jessen, Frontiers in Physiology, April 2018, Vol. 9.
• LEICA: Laplacian Eigenmaps for Group ICA Decomposition, C. Liu, J. JaJa, and L. Pessoa, Neuroimage, 2017.
• In Situ Visualization for 3D Agent-Based Vocal Fold Inflammation and Repair Simulation, N. Skeekhao, J. JaJa, L. Mongeau, and N. Li-Jessen, accepted to Supercomputing Frontiers and Innovations, 2017.
• Real-time Agent-Based Modeling and Simualtion with in-situ Visualization of Complex Biological Systems: A Case Study on Vocal Fold Inflammation and Healing, N. Seekhao, C. Shung, J. JaJa, L. Mongeau, and N. Li, Proceedings of the HiCOMB 2016 Workshop, May 2016, Chicago.
• A High Performance Implementation of Spectral Clustering on CPU-GPU Platforms, Y. Jin and J. JaJa, Proceedings of the Parallel Computing and Optimization (PCO 2016) Workshop, May 2016, Chicago.
• Linking “Toxic Outliers” to Environmental Justice Communities, M. Collins, I. Munoz, and J. JaJa, Environmental Research Letters, December 2016. Won the ERL Best Article for 2016.
• Connectivity-Based Brain Parecellations: A Connectivity-Based Atlas for Schizophrenia Research, Q. Wang, R. Chen, J. JaJa, Y. Jin, E. Hong, and E. Herskovits, Neuroinformatics, October 2015.
• A Data-Driven Approach to Extract Connectivity Structures from Diffusion Tensor Imaging Data, Y. Jin, J. JaJa, R. Chen, and E. Herskovits, Proceedings of the 2015 IEEE International Conference on Big Data (IEEE BigData 2015), Oct 29 – Nov 1, 2015, Santa Clara, CA.
• Achieving Native GPU Performance for Out-of-Card Large Matrix Multiplication, J. Wu and J. JaJa, Parallel Processing Letters, 2016.
• Resting State Dynamic Functional Network Analysis in Mild Traumatic Brain Injury, W. Hou, C. Chandler, J. JaJa, and R. Gullapalli, International Society for Magnetic Resonance in Medicine ISMRN 2015, Toronto, Ontario, May 30—31, 2015.
• Optimized FFT Computations on Heterogeneous Platforms with Application to the Poisson Equation. J. Wu and J. JaJa, Journal of Parallel and Distributed Computing, 2014.
• From Maxout to Channel-Out: Encoding Information on Sparse Pathways, Q. Wang and J. JaJa, Proceedings of the International Conference on Artificial Neural Networks, 15-19 September, 2014, Hamburg, Germany.
• High Performance FFT Based Poisson Solver on a CPU-GPU Heterogeneous Platform, J. Wu and J. JaJa, International Parallel and Distributed Processing Symposium (IPDPS), May 2013, Boston, Massachusetts.
• Hierarchical Exploration of Volumes Using Multilevel Segmentation of the Intensity-Gradient Histograms, C. Yiu Ip, A. Varshney, and J. JaJa, VIS 2012, October 2012 (won Best Paper Award for SciVis 2012)
• An Effective Approach to Temporally Anchored Information Retrieval, Z. Wei and J. JaJa, UMIACS-TR-2012-10, University of Maryland, College Park, August, 2012.
• Optimized Strategies for Mapping Three-dimensional FFTs onto CUDA GPUs, J. Wu and J. JaJa, Proceedings of Innovative Parallel Computing (INPAR) Workshop, San Jose, CA, May 13-14, 2012.
• Constructing Inverted Files: To MapReduce or Not Revisited, Z. Wei and J. JaJa, Technical Report, University of Maryland, College Park, 2011. Also, to appear in IEEE Transactions on Parallel and Distributed Computing.
• A Fast Algorithm for Constructing Inverted Files on Heterogeneous Platforms, Z. Wei and J. JaJa, International Parallel and Distributed Processing Symposium (IPDPS), May 2011, Anchorage, Alaska.
• Optimization of Linked List Prefix Computations on Multithreaded GPUs Using CUDA, Z Wei and J. JaJa, International Parallel and Distributed Processing Symposium (IPDPS), April 2010, Atlanta, GA.
• Techniques to Audit and Certify the Long Term Integrity of Digital Archives, S. Song and J. JaJa, International Journal of Digital Libraries, 2010.

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Last Updated, June, 2022.