Eden Research Laboratory


  Eden research laboratory aims to make impacts by solving real-world problems through the use of machine learning. Our current focus includes image recognition, bioinformatics, and artificial neural network optimization.


Nawanol Theera-Ampornpunt

Lab Director

Email: nawanol.t@phuket.psu.ac.th

Tel: 0-7627-6000 Ext. 6499

Research Interests: Image Recognition, Machine Learning, Artificial Intelligence [Curriculum Vitae]

I am willing to serve as a reviewer for Thai and international conferences and journals, as well as research grant reviewer and similar roles.

Panisa Treepong


Email: panisa.t@phuket.psu.ac.th

Tel: 0-7627-6000 Ext. 6108

Research Interests: Bioinformatics, Machine Learning

Kevalin Wannaprom


Email: kevalin.w@phuket.psu.ac.th

Tel: 0-7627-6000 Ext. 6198


Select Publications

  • P. Treepong, N. Theera-Ampornpunt, “Early bread mold detection through microscopic images using convolutional neural network,” Current Research in Food Science, vol. 7, Aug. 2023, Art. no. 100574. [PDF] [Dataset]
  • N. Theera-Ampornpunt, P. Treepong, “Optimizing hyperparameters for Thai cuisine recognition via convolutional neural networks,” Traitement du Signal, vol. 40, no. 3, pp. 1187–1193, 2023. [PDF]
  • N. Theera-Ampornpunt, S. Suryavansh, S. Manchanda, R. Panta, K. Joshi, M. Ammar, M. Chiang, S. Bagchi, “AppStreamer: reducing storage requirements of mobile games through predictive streaming,” in Proceedings of the International Conference on Embedded Wireless Systems Networks, Lyon, France, Feb. 2020, pp. 37–48.
  • C. Fang, N. Theera-Ampornpunt, M. A. Roth, A. Grama, and S. Chaterji, “AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU,” BMC Bioinformatics, vol. 20, pp. 1–17, Oct. 2019.
  • P. Treepong, C. Guyeux, A. Meunier, C. Couchoud, D. Hocquet, and B. Valot, “panISa: ab initio detection of insertion sequences in bacterial genomes from short read sequence data,” Bioinformatics, vol. 34. pp. 3795–3800, 2018.
  • K. Dittakan, N. Theera-Ampornpunt, and P. Boodliam, “Non-destructive grading of Pattavia pineapple using texture analysis,” in Proceedings of the 21st International Symposium on Wireless Personal Multimedia Communications, Chiang Rai, Thailand, Nov. 2018, pp. 144-149.
  • K. Dittakan and N. Theera-Ampornpunt, “Pum-Riang Thai silk pattern classification using texture analysis,” in Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence, Nanjing, China, Aug. 2018, pp. 82–90.
  • P. Treepong, V.N. Kos, C. Guyeux, D.S. Blanc, X. Bertrand, B. Valot, D. Hocquet, “Global emergence of the widespread Pseudomonas aeruginosa ST235 clone,” Clinical Microbiology and Infection, vol. 24, no. 3. pp. 258–266, 2018.
  • N. Theera-Ampornpunt and S. Chaterji, “Prediction of enhancer RNA activity levels from ChIP-seq-derived histone modification combinatorial codes,” in IEEE Workshop Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics, Kansas City, MO, USA, Nov. 2017, pp. 1206–1214.
  • H. Zhang, N. Theera-Ampornpunt, H. Wang, S. Bagchi, and R. Panta, “Sense-Aid: a framework for enabling network as a service for participatory sensing,” in Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference, Las Vegas, Nevada, USA, Dec. 2017, pp. 68–80.
  • K. Dittakan, N. Theera-Ampornpunt, W. Witthayarat, S. Hinnoy, S. Klaiwan, and T. Pratheep, “Banana cultivar classification using scale invariant shape analysis,” in Proceedings of the 2nd International Conference on Information Technology, Nakhon Pathom, Thailand, Nov. 2017, pp. 171–176.
  • N. Theera-Ampornpunt, T. Mangla, S. Bagchi, R. Panta, K. Joshi, M. Ammar, and E. Zegura, “TANGO: toward a more reliable mobile streaming through cooperation between cellular network and mobile devices,” in Proceedings of the 35th Symposium on Reliable Distributed Systems, Budapest, Hungary, Sep. 2016, pp. 297–306.
  • S. G. Kim, N. Theera-Ampornpunt, C. Fang, M. Harwani, A. Grama and S. Chaterji, “Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions,” BMC Systems Biology, vol. 10, no. 2, pp. 243–258, Aug. 2016.
  • T. Mangla, N. Theera-Ampornpunt, M. Ammar, E. Zegura, and S. Bagchi, “Video through a crystal ball: effect of bandwidth prediction quality on adaptive streaming in mobile environments,” in Proceedings of the 8th ACM Workshop on Mobile Video, Klagenfurt am Wörthersee, Austria, May 2016, pp. 1–6.
  • N. Theera-Ampornpunt, S. G. Kim, A. Ghoshal, S. Bagchi, A. Grama, and S. Chaterji, “Fast training on large genomics data using distributed support vector machines,” in Proceedings of the 8th International Conference on Communication Systems and Networks, Bangalore, India, Jan. 2016, pp. 1–8.
  • S. G. Kim, N. Theera-Ampornpunt, A. Grama, and S. Chaterji, “Interpretable deep neural networks for enhancer prediction,” in Proceedings of the 2015 IEEE International Conference on BioInformation and BioMedicine, Washington, DC, USA, Nov. 2015, pp. 242–249.



Eden Research Laboratory,
College of Computing, Prince of Songkla University Phuket Campus
80 M.1 Vichitsongkram Rd., Kathu, Phuket, Thailand 83120

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Tel: (+66)7627-6000 Ext. 6499