I'm passionate about mathematics, data science, and programming. My other interests include
research and teaching. Moreover, I like traveling, meeting new people, and exploring new places to have
novel experiences.
I'm also a great chess player.
I participated in two machine learning research projects. The first project, focused on statistical
downscaling for rainfall prediction in Hawaii, aimed to predict rainfall in the Hawaiian islands with
high precision using coarse-resolution inputs. The CHANGE-HI National Science Foundation (NSF) grant
funded this research. In this project, my contributions included implementing site-specific linear
regression and neural network models for rainfall forecasting. I assisted in comparing results with a
novel approach incorporating Digital Elevation Maps (DEM) alongside neural networks. I also implemented
a Gaussian Process approach (Kriging) as the baseline comparison model.
The second project involved estimating net radiation over the Hawaiian islands in collaboration with
MITRE (Virginia). Our goal was to generate detailed and up-to-date estimates of net radiation across the
Hawaiian Islands, with a resolution of 250 meters and hourly updates. In this project, I contributed by
predicting incoming long-wave radiation during the night using various machine-learning models. This
analysis involved examining satellite images and applying simple linear models like linear regression
and more complex non-linear models like neural network models.
I worked as a full-time Graduate Teaching Assistant (TA) at the Department of Information and Computer Sciences, University of Hawaii at Manoa, HI, USA. I was the TA for ICS 332: Operating Systems and ICS 451: Data Networks for Fall 2022. Then, I worked on ICS 355: Security and Trust I and ICS 332: Operating Systems for Spring 2023. I assist in grading assignments and teaching in these courses.
I worked as a lecturer on contract at the Department of Computer Engineering, Faculty of Engineering, University of Peradeniya. The courses I was the Instructor In Charge (IIC) were Computer Communication Networks, Embedded Systems, and Network & Web Application Design. In these, I helped with tutorial classes, student discussions, and lab work. Further, I taught sections in Image Processing and Networking for Electrical Engineering courses.
I worked as a Temporary Instructor (TA) at the Department of Computer Engineering, Faculty of Engineering, University of Peradeniya. Some courses I assisted with include computer architecture, advanced computer communication networks, and a computing course for first-year students. Further, I was the Instructor in Charge (IIC) of the abovementioned courses. I assisted in quiz and lab preparation, grading of labs, and lab review sessions. Each course averaged roughly 60 students per semester.
I worked as a Trainee Associate Software Engineer at Zone24x7 (Pvt) Ltd, Sri
Jayawardenepura. I was a member of the Big Data and Data Science team. My projects included a Research &
Development (R&D) project named log machine learning and a production project named video machine
learning.
In the machine learning R&D project, I collaborated as the lead developer alongside my supervisor (Hansa Perera) to analyze log
file data from a prominent retail chain company in the United States. Our objective was to predict
errors or critical events before they occurred. I conducted a thorough analysis of the log data. Next, I
implemented a topic modeling technique to categorize log events into groups. Subsequently, I identified
patterns among these groups. I utilized them to feed sequences of patterns into a neural network for
predicting upcoming log events.
In another project, we collected and analyzed video feeds from the same large retail chain company to
detect anomaly patterns in the data. My contribution to this project involved the development of data
science components, including creating machine learning algorithms tailored to identify anomalies in
customer visit counts of stores over a given period.
Indika, Amila, Nethmal Warusamana, Erantha Welikala, and Sampath Deegalla. "Ensemble Stock Market Prediction using SVM, LSTM, and Linear Regression." (2021). DOI:
10.36227/techrxiv.16626019(Best Paper Award) S. Jayasundara, A. Indika and D. Herath, "Interpretable Student Performance Prediction Using Explainable Boosting Machine for Multi-Class Classification," 2022 2nd International Conference on Advanced Research in Computing (ICARC), 2022, pp. 391-396, DOI:
10.1109/ICARC54489.2022.9753867A Indika, PY Washington, A Peruma, “Performance Comparison of Binary Machine Learning Classifiers in Identifying Code Comment Types: An Exploratory Study,” 2023 IEEE/ACM 2nd International Workshop on Natural Language-Based Software Engineering (NLBSE), pp. 20-23, DOI:
10.48550/arXiv.2303.01035N. Warusamana, A. Indika, E. Welikala, S. Deegalla, “Stock Market Prediction using SVM, LSTM, and Linear Regression”, ESCaPe 2020 Project Symposium, pp. 21
https://ESCaPE-2020-Proceedings/Currently, I'm engaged in following research project/s.
I'm pursuing an M.Sc. in Computer Science from Department of Information and Computer Sciences, University of Hawaii at Manoa, Hawaii (CGPA: 3.96/4.00).
I graduated as a Bachelor of the Science of Engineering specialized in Computer Engineering with First Class Honours (GPA: 3.85/4.00).
I attended Maliyadeva College from Grade 12 to Grade 13 for my Advanced Level Education.
I attended Kegalu Vidyalaya from Grade 1 to Grade 11 during my Ordinary Level Education.
