Mr Chirath Hettiarachchi

PhD Candidate

B.Sc. Eng (Hons) (Electronics & Telecommunication Engineering), M.Sc (Research)

Hanna Neumann Blg (145), 2.33 J

chirath.hettiarachchi@anu.edu.au

Bio
Research interests
Publications

Chirath Hettiarachchi is a PhD Candidate at the Australian National University, in the Big Data program of the “Our Health In Our Hands (OHIOH)” project, a strategic initiative of the university. His research focuses on using ML to develop a control system for the artificial pancreas. He received his BSc (Hons) in Electronics & Telecommunication Engineering & MSc (Research) degrees from the University of Moratuwa, Sri Lanka in 2018 and 2020. He completed his professional qualification in Management Accounting at the Chartered Institute of Management Accountants in 2014 (CIMA-UK). He worked as a Machine Learning Engineer in the FinTech industry from 2018 - 2019, developing algorithms for identifying outliers in corporate financial transactions, forecasting, name screening and risk prediction applications. During the final year of his undergraduate studies, he pursued his entrepreneurial aspirations by co-founding a healthcare startup which achieved multiple awards and publications. His research interests include ML, reinforcement learning, control systems, biomedical signal processing, health informatics and financial data analytics.

Reinforcement Learning
Machine Learning
Control Systems
Signal Processing
Biomedical Engineering

C.Hettiarachchi,N.Malagutti,C.Nolan,E.Daskalaki,H.Suominen,“CAPSML:BridgingtheGapBetween Clinicians, Lived Experience Experts, and Artificial Intelligence Systems for Glucose Regulation in Type 1 Diabetes”, 34th Medical Informatics Europe, May 2023 (In-print). [Online Tool].
C. Hettiarachchi, N. Malagutti, C. Nolan, H. Suominen, E. Daskalaki, “G2P2C: A Modular Reinforcement Learning Algorithm for Glucose Control by Glucose Prediction and Planning in Type 1 Diabetes”, Journal of Biomedical Signal Processing and Control, Elsevier, June 2023. (In-review). [Code].

C. Hettiarachchi, N. Malagutti, C. Nolan, H. Suominen, E. Daskalaki, “Non-linear Continuous Action Spaces for Reinforcement Learning in Type 1 Diabetes”, Australasian Joint Conference on Artificial Intel- ligence (AJCAI), Springer, December 2022. [Paper][Code].
C. Hettiarachchi, N. Malagutti, C. Nolan, E. Daskalaki, H. Suominen, “A Reinforcement Learning Based System for Blood Glucose Control without Carbohydrate Estimation in Type 1 Diabetes”, 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, July 2022. [Paper][Code].
C. Hettiarachchi, N. Malagutti, C. Nolan, H. Suominen, E. Daskalaki, “Deep Reinforcement Learning for Eliminating Carbohydrate Estimation in Glucose Regulation in Type 1 Diabetes”, Diabetes Technology & Therapeutics, Vol. 24, Mary Ann Liebert Inc, April 2022. [Paper][Code].

C. Hettiarachchi, E. Daskalaki, J. Desborough, C. Nolan, D. O’Neal, H. Suominen, “Integrating Multiple Inputs Into an Artificial Pancreas System: Narrative Literature Review”, Journal of Medical Internet Research (JMIR) Diabetes, February 2022. [Paper].
H. Weng, C. Hettiarachchi, C. Nolan, H. Suominen, A. Lenskiy, “Ensuring Security of Artificial Pancreas Device System Using Homomorphic Encryption”, Biomedical Signal Processing and Control, Elsevier, Jan- uary 2022. [Paper].
C.Hettiarachchi,N.Malagutti,C.Nolan,E.Daskalaki,H.Suominen,“Model-freeInferenceofInformation Flow Among Physiological Signals in Type 1 Diabetes Subjects Using Multivariate Transfer Entropy”, Diabetes Technology & Therapeutics, Vol. 23, Mary Ann Liebert Inc, June 2021. [Paper].

C. Hettiarachchi, C. Chitraranjan, “A Machine Learning Approach to Predict Diabetes Using Short Recorded Photoplethysmography and Physiological Characteristics”, Artificial Intelligence in Medicine in Europe (AIME), Springer, 2019. [Paper][Code].

R. Cui, C. Hettiarachchi, C. Nolan, E. Daskalaki, H. Suominen, “Personalised Short-Term Glucose Pre- diction via Recurrent Self-Attention Network”, IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), IEEE, June 2021. [Paper][Code].

C. Hettiarachchi, J. Kodithuwakku, B. Manamperi, A. Ifham, P. Silva, “A Wearable System to Analyze the Human Arm for Predicting Injuries Due to Throwing”, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2019. [Paper].
C. Hettiarachchi, B. Manamperi, D. Uthpala, “Identifying the Optimum Region of the Human Sole to Extract the PPG Signal for Pulse Rate Estimation”, In Proceedings of the 9th International Conference on Signal Processing Systems, ACM, 2017. [Paper].