I am currently accepting new undergraduate / graduate research assistant. If you are interested in doing research with me, please contact me for more information!
[I] Active Projects
1) Human Activity Recognition
In this project, we aim to develop computational tools that assist artists in creating human movements with aesthetic value and meaning. By generating these movements, we can gain insights into the mechanics of human figure movement and uncover the significance behind them. The project encompasses various facets, including:
- idanceForms2 – A creative choreography sketchbook on the iPad. Download idanceForms here.
- Computational Conductor – A machine learning initiative focused on understanding and recognizing conducting gestures.
Publications (selected)
- S. Pierce and H. H. Tsang, "Enhancing Conducting Gesture Analysis: Integrating Laban Movement Analysis with Tree Ensembles and Neural Networks", in Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Dec 6-8, 2023, Mexico City, Mexico, pp. 421-426, doi: 10.1109/SSCI52147.2023.10371944.
- G. Woo, F. Tan, and H. H. Tsang, "Enhancing Gesture Recognition for Musical Conducting: A Study on Diverse Data Classification and Stacked Neural Network Architectures", in Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Dec 6-8, 2023, Mexico City, Mexico, pp. 677-682, doi: 10.1109/SSCI52147.2023.10371840.
- J. Pettigrew, G. Woo and H. H. Tsang, "Computational Intelligence in Human Feature Analysis and Pose Selection", in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Dec 1-4, 2020, Canberra Australia.
- F. Tan, G. Woo, and H. H. Tsang, "CGLER: Laban Effort Framework Analysis with Conducting Gestures Using Neural Networks", in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Dec 1-4, 2020, Canberra Australia.
- K. Carlson, P. Pasquier, H.H. Tsang, J. Phillips, T. Schiphorst and T. Calvert. “CoChoreo: A Generative Feature in iDanceForms for Creating Novel Keyframe Animation for Choreography”, in Proceedings of the Seventh International Conference on Computational Creativity, Paris, France, June 27 – July 1, 2016.
- K. Carlson, H.H. Tsang, J. Phillips, T. Schiphorst, and T. Calvert. “Sketching Movement: Designing Creativity Tools for In-Situ, Whole-Body Authorship,” in Proceedings of the 2nd International Workshop on Movement and Computing, Vancouver, August 14-15, 2015, pp. 68-75. [Acceptance rate: 26/56, 46%]
- K. Carlson, T. Schiphorst, K. Cochrane, J. Phillips, H.H. Tsang, and T. Calvert. “Moment by Moment: Creating Movement Sketches with Camera Stillframes,” in Proceedings of the ACM SIGCHI Conference on Creativity and Cognition, Glasgow, United Kingdom, June 22-25, 2015, pp. 131-140. [Acceptance rate: 23/88, 26%]
2) RNA Prediction and Design
The RNA design algorithm takes a description of an RNA secondary structure as input and seeks to identify an RNA strand that will fold into this function-specific target structure. With recent advances in biotechnology and synthetic biology, a reliable RNA design algorithm is essential for creating new biochemical components. Our lab is focused on employing various computational intelligence techniques to establish a new paradigm for addressing the RNA design challenge. Recently, we developed an algorithm called SIMARD, which is based on the simulated annealing approach.
Publications (selected)
- R. McBride and H. H. Tsang, "Length-Based Substructure Mutation Policies for Improved RNA Design via Simulated Annealing" in Proeedings of the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Ottawa, ON, Canada, 2022, pp. 1-9, doi: 10.1109/CIBCB55180.2022.9863040.
- R. McBride and H. H. Tsang, "SIMARD-LinearFold: Long Sequence RNA Design with Simulated Annealing", in 2021 Proceedings of the IEEE Congress on Evolutionary Computation, Jun 28, 2021 - Jul 1, 2021, Krakow, Poland.
- R. McBride and H. H. Tsang, "Examination of Annealing Schedules for RNA Design", in 2020 Proceedings of the IEEE Congress on Evolutionary Computation, 19-24 July, 2020, Glasgow, UK. https://ieeexplore.ieee.org/document/9185702
- D. J. D. Hampson, T. Wiebe, and H. H Tsang, “Comparison of Two Folding Functions for RNA Secondary Structure Design”, in 2019 Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 2019, pp. 2946-2953. https://doi.org/10.1109/SSCI44817.2019.9002762
- D.J.D. Hampson and H.H. Tsang. “Using Matching Substructures as an Optimization Objective for RNA Design", the IEEE Symposium Series on Computational Intelligence, Honolulu, Hawaii, USA, Nov. 27 - Dec 1, 2017, pp. 668-674.
- S. Sav, D.J.D. Hampson and H.H. Tsang. “SIMARD: A Simulated Annealing Based RNA Design Algorithm with Quality Pre-Selection Strategies", the IEEE Symposium Series on Computational Intelligence, Athens, Greece, December 6 - 9, 2016.
- D.J.D. Hampson, S. Sav, and H.H. Tsang. “Investigation of Multi-Objective Optimization Criteria for RNA Design", the IEEE Symposium Series on Computational Intelligence, Athens, Greece, December 6 - 9, 2016.
- H.E. Erhan, S. Sav, S. Kalashnikov and H.H. Tsang. “Examining the Annealing Schedules for RNA Design Algorithm”, in Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, July 24-29, 2016, pp. 1295-1302.
3) Computational Crime Analysis
With the advance of big data and machine learning techniques, law enforcement agencies are keen to use computational techniques to help them to prevent criminal activities or even to solve crime. Our lab's work in this area has two main paradigms
- Using a virtual environment to help with emergency preparedness
- Employing computational techniques and crime data, to help law enforcement agencies visualize and resource management
- More information about our Crime Analytics for Public Safety projects
Publications (selected)
- A.J. Park, R.N. Quadari, and H.H. Tsang. “Phishing Website Detection Framework Through Web Scraping and Data Mining”, the 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, Vancouver, Canada, Oct 3-5, 2017. Received the best paper award
- A.J. Park, B. Beck, D. Fletche, P. Lam, and H.H. Tsang. "Temporal Analysis of Radical Dark Web Forum Users", in Proceedings of the International Symposium on Foundations of Open Source Intelligence and Security Informatics, San Francisco, CA, USA, August 18-21, 2016, pp. 880-883.
- B. Chersinoff, H.H. Tsang, and V. Spicer. "iPatrol+: Patterns of Public Disorder from a Community Volunteer Perspective," in Proceedings of the 43rd Annual Conference of the Western Society of Criminology, Vancouver, BC, Canada, February 4 -6, 2016, pp. 6.
- A.J. Park and H.H. Tsang, “A Systematic Approach to Develop a Computational Framework for Counter-terrorism and Public Safety” Martin Bouchard, editor, Social Networks, Terrorism and Counter-terrorism: Radical and Connected, Routledge, 2015, 196-217.
- A.J. Park, H.H. Tsang, M.Sun, and U. Glässer, “An Agent-Based Model and Computational Framework for Counter-Terrorism and Public Safety Based on Swarm Intelligence.” Journal of Security Informatics, 2012, 1:23.
4) Mobile Applications Development
In my classroom and lab, I have overseas the development of over 150 mobile applications. See here for some examples.
[II] Past Projects
Computational approach to predict species-specific type III secretion system (T3SS) effectors
We are using machine learning techniques to seek out the Type III secretion system (T3SS) effectors which are harmful to human.
Publications
- C.K. Hobbs, V.L. Porter, M.L.S. Stow, B.A. Siame, H.H. Tsang, K.Y. Leung, "Computational approach to predict species-specific type III secretion system (T3SS) effectors using single and multiple genomes", BMC Genomics, 17:1048, 2016.
- G. Yang, V.L. Porter, C.K. Hobbs, H.H. Tsang, B.A. Siame, Y. Zhang, Q. Wang, K.Y. Leung, "Genome-wide search for type III secretion system effectors of Edwardsiella tarda using a meta-analytical approach", the 114th General Meeting of the American Society for Microbiology, Boston, Massachusetts, May 17-20, 2014.
Copyright © Dr. Herbert H. Tsang. All rights reserved.
www.HerbertTsang.org | www.applied-research.org