Pandarasamy Arjunan
Postdoctoral Scholar at Berkeley Education Alliance for Research in Singapore (BEARS) Limited
Bengaluru, Karnataka, Singapore
Summary
👤
Senior🎓
Top SchoolPandarasamy Arjunan is an energy-focused data scientist and postdoctoral scholar with 14 years of experience applying data-driven methods to optimize smart buildings and solar plant operations. With a PhD in Computer Engineering from IIIT Delhi and industry experience at DataGlen, IBM, and HCL, he builds forecasting, fault-detection, and maintenance-scheduling systems that boost renewable energy yields. Based in Bengaluru and affiliated with BEARS in Singapore, he bridges academic research and production analytics to translate models into operational improvements. Notably, his work combines systems thinking with field-savvy insights—such as estimating optimal cleaning schedules for panels—showing a rare focus on practical constraints as well as algorithmic accuracy.
14 years of coding experience
4 years of employment as a software developer
Bachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at Mano College, Manonmaniam Sundaranar UniversityDoctor of Philosophy (Ph.D.), Computer Engineering, Doctor of Philosophy (Ph.D.), Computer Engineering at Indraprastha Institute of Information Technology, DelhiMaster’s Degree, Computer Science - MCA, Master’s Degree, Computer Science - MCA at Madurai Kamaraj University
Github Skills (27)
prediction-model10
kaggle10
energy-data10
energy10
predictor10
feature-extraction9
energy-efficiency9
temporal-data9
arduino-library9
jupyter-notebook8
arduino8
python8
open-data8
feature-engineering8
benchmarking7
Programming languages (2)
Github contributions (5)
samy101/awesome-energy-forecasting
Oct 2019 - Dec 2019
A curated list of awesome energy forecasting resources such as, code libraries, datasets, courses, tutorials, research papers, competitions and communities.
Contributions:27 commits, 18 pushes in 1 month
energy-forecasting
nesl/OpenBAN
Jan 2014 - Sep 2014
Contributions:42 commits in 8 months
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