UC Research Profile - University of Canterbury

Dr James Atlas

Contact

Department: Computer Science and Software Engineering

Direct Dial: +64 3 3690307

Office: Jack Erskine 301

Language: English

I love computer science and explore research, teaching, and applications in data science, artificial intelligence, scientific computing, and education

About

Research / Creative works

Fields of Research

Artificial Intelligence: constraint optimization, multi-agent systems
CS Education: teacher professional development, service learning
Data Science: learning and optimization algorithms for deep networks
Scientific Computing: heterogeneous computing, monte carlo tree search
Medical Applications: machine learning for imaging and clinical data, smart apps
Space Exploration: simulation of trajectories, autonomous agents

Researcher Summary

My research interests are artificial intelligence, machine learning, constraint optimization, distributed and high performance computing, scientific computing, and computer science education. I explore applications in medical data, earth and space exploration, and multi-agent and mobile systems.

My current projects include machine learning models for prediction of health conditions and treatment efficacy, improved optimization for designing and training deep learning networks, adaptive sampling for scientific simulation, uncertainty reasoning for dynamic agent systems, improved curriculum and policies for primary and secondary professional development, service learning partnerships between students and community educators, and gamification of threshold concepts in computer science.

Subject Area: Disciplines

Computer Science, Information Technology, Information Sciences: Computer Science ; High Performance Computing (HPC); Information Sciences
Education: Technology Education
Statistics: Applied Statistics

Research/Scholarly/Creative Works

Conference Contributions - Published

Dapamede T., Chapagain KM., Moghiseh M., Atlas J., Butler PH. and Butler APH. (2023) Probability Mapping of Spectral CT Material Decomposition to Aid in Determining Material Identification and Quantification Likelihood. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13836 LNCS: 202-213. http://dx.doi.org/10.1007/978-3-031-25825-1_15.

Dobbs H., Batchelor O., Green R. and Atlas J. (2023) Smart-Tree: Neural Medial Axis Approximation of Point Clouds for 3D Tree Skeletonization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 14062 LNCS: 351-362. http://dx.doi.org/10.1007/978-3-031-36616-1_28.

Smith A., Atlas J. and Atharifard A. (2023) Correcting Charge Sharing Distortions in Photon Counting Detectors Utilising a Spatial-Temporal CNN. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13836 LNCS: 75-90. http://dx.doi.org/10.1007/978-3-031-25825-1_6.

Butler PH., Adebileje SA., Alexander SD., Amma MR., Amjomrouz M., Asghariomabad F., Atharifard A., Atlas J., Bamford B. and Bell ST. (2019) MARS pre-clinical imaging: The benefits of small pixels and good energy data. In Proceedings of SPIE - The International Society for Optical Engineering 11113 http://dx.doi.org/10.1117/12.2532367.

Roberts D., Enszer JA., Jayne AA., Rooney SI., Trauth A., Novocin A. and Atlas J. (2019) FLC E²T: A faculty learning community on effective (and efficient) teaching. In ASEE Annual Conference and Exposition, Conference Proceedings.

Pollock L., Atlas J., Bell T. and Henderson T. (2018) A Computer Science Study Abroad with Service Learning: Design and Reflections. In Proceedings SIGCSE '18: 485-490. ACM Digital Library. http://dx.doi.org/10.1145/3159450.3159589.

Pollock L., Harvey T., Atlas J. and Mouza C. (2018) Customizing a Field Experience for CS Undergrads in Teaching Computer Science for Your School Context. In Proceedings SIGCSE 2018: 1057-1057. ACM Digital Library.

Duncan C., Bell T. and Atlas J. (2017) What do the teachers think? Introducing computational thinking in the primary school curriculum. In ACM International Conference Proceeding Series: 65-74. http://dx.doi.org/10.1145/3013499.3013506.

McGill M., Johnson C., Atlas J., Bouchard D., Merkle LD., Messom C., Pollock I. and Scott MJ. (2017) Working group: Game development for computer science education. In Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE Part F128680: 385. http://dx.doi.org/10.1145/3059009.3081325.

McGill MM., Johnson C., Atlas J., Bouchard D., Messom C., Pollock I. and Scott MJ. (2017) If Memory Serves: Towards Designing and Evaluating a Game for Teaching Pointers to Undergraduate Students. In Proceedings ITiCSE-WGR'17: 25-46. ACM Digital Library. http://dx.doi.org/10.1145/3174781.3174783.

Bell T., Duncan C. and Atlas J. (2016) Teacher feedback on delivering computational thinking in primary school. In Proceedings of WiPSCE '16: 100-101. http://dx.doi.org/10.1145/2978249.2978266.

Mouza C., Pollock L., Pusecker K., Guidry K., Yeh C-Y., Atlas J. and Harvey T. (2016) Implementation and outcomes of a three-pronged approach to professional development for CS principles. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education: 66-71.

Pollock L., Mouza C., Atlas J. and Harvey T. (2015) Field Experiences in Teaching Computer Science: Course Organization and Reflections. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education: 374-379.

Mouza C., Pan Y., Pollock L., Atlas J. and Harvey T. (2014) Partner4CS: Bringing computational thinking to middle school through game design. In Proceedings of the Stanford University FabLearn Conference on Creativity and Fabrication, Palo Alto, CA, USA: 25-26.

Atlas J. and Decker K. (2010) Coordination for uncertain outcomes using distributed neighbor exchange. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1-Volume 1: 1047-1054.

Atlas J. (2009) A distributed constraint optimization approach for coordination under uncertainty. In Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems-Volume 2: 1263-1264.

Atlas J. (2009) A distributed constraint optimization approach for coordination under uncertainty. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2: 1280-1281.

Atlas J., Estrada T., Decker K. and Taufer M. (2009) Balancing scientist needs and volunteer preferences in volunteer computing using constraint optimization. In International Conference on Computational Science: 143-152.

Atlas J., Warner M. and Decker K. (2008) A memory bounded hybrid approach to distributed constraint optimization. In Proc. 10th International Workshop on Distributed Constraint Reasoning: 37-51.

Atlas J. and Decker K. (2007) A complete distributed constraint optimization method for non-traditional pseudotree arrangements. In Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems: 111-111.

Atlas J. and Decker K. (2007) A complete distributed constraint optimization method for non-traditional pseudotree arrangements. In Proceedings of the International Conference on Autonomous Agents: 741-748. http://dx.doi.org/10.1145/1329125.1329262.

Atlas J., Swany M. and Decker KS. (2005) Flexible grid workflows using taems. In Workshop on Exploring Planning and Scheduling for Web Services, Grid and Autonomic Comp.

Journal Articles

Healy J., Searle E., Panta RK., Chernoglazov A., Roake J., Butler P., Butler A., Gieseg SP., Adebileje SA. and Alexander SD. (2023) Ex-vivo atherosclerotic plaque characterization using spectral photon-counting CT: Comparing material quantification to histology. Atherosclerosis 378 http://dx.doi.org/10.1016/j.atherosclerosis.2023.06.007.

Kanithi P., De Ruiter NJA., Amma MR., Lindeman RW., Butler APH., Butler PH., Chernoglazov AI., Mandalika VBH., Adebileje SA. and Alexander SD. (2021) Interactive Image Segmentation of MARS Datasets Using Bag of Features. IEEE Transactions on Radiation and Plasma Medical Sciences 5(4): 559-567. http://dx.doi.org/10.1109/TRPMS.2020.3030045.

Lowe C., Ortega-Gil A., Moghiseh M., Anderson NG., Munoz-Barrutia A., Vaquero JJ., Raja AY., Matanaghi A., Chernoglazov AI. and Dapamede T. (2021) Molecular Imaging of Pulmonary Tuberculosis in an Ex-Vivo Mouse Model Using Spectral Photon-Counting Computed Tomography and Micro-CT. IEEE Access 9: 67201-67208. http://dx.doi.org/10.1109/ACCESS.2021.3076432.

Wu W., Hu D., Niu C., Broeke LV., Butler APH., Cao P., Atlas J., Chernoglazov A., Vardhanabhuti V. and Wang G. (2021) Deep learning based spectral CT imaging. Neural Networks 144: 342-358. http://dx.doi.org/10.1016/j.neunet.2021.08.026.

Becce F., Viry A., Stamp LK., Pascart T., Budzik J-F., MARS Collaboration. and Raja A. (2019) Winds of change in imaging of calcium crystal deposition diseases.. Joint Bone Spine 86(6): 665-668. http://dx.doi.org/10.1016/j.jbspin.2019.04.005.

Hobbs M. and Atlas J. (2019) Environmental influences on behaviour and health: a call for creativity and radical shifts in thinking within contemporary research. The New Zealand medical journal 132(1505): 97-99.

Mouza C., Pan E., Pollock L., Atlas J. and Harvey T. (2015) Bringing Computational Thinking to Middle School: A School-University Partnership. .

Atlas J. and Decker K. (2007) Task scheduling using constraint optimization with uncertainty. Proceedings of CAPS 7: 25-27.

Theses / Dissertations

Atlas J. (2009) Efficient coordination techniques for non-deterministic multi-agent systems using distributed constraint optimization. PhD Thesis.