Sathishkumar V E
| Sathishkumar V E | |
|---|---|
| Born | Sathishkumar Easwaramoorthy 21 October 1991 Veerappampalayam, Arachalur, Erode district, Tamil Nadu, India |
| 🏡 Residence | Petaling Jaya, Selangor, Malaysia |
| 🏳️ Nationality | Indian |
| 💼 Occupation | |
| Known for |
|
| Title | Senior Lecturer |
| 👩 Spouse(s) | Jayanthi Elango |
| 👶 Children | 1 |
| 🏅 Awards |
|
| 🌐 Website | sites |
Sathishkumar V E (born Sathishkumar Easwaramoorthy; 21 October 1991) is an Indian academic and data scientist working in Malaysia. He is Senior Lecturer in the Department of Data Science and Artificial Intelligence at Sunway University, Selangor. He is best known in the machine learning community as the creator of the Seoul Bike Sharing Demand dataset, a widely downloaded benchmark released through the UCI Machine Learning Repository and cited in published research across transportation, energy, and urban planning.[1][2] He has appeared in the Stanford University/Elsevier Top 2% of Scientists ranking, compiled from the Scopus database, in both 2024 and 2025.[3]
Background
Sathishkumar was born on 21 October 1991 in Veerappampalayam, a village in Arachalur, Erode district, Tamil Nadu, India. His father, Easwaramoorthy Palanisamy, works as a farmer, and his mother, Devi Easwaramoorthy, is Secretary of the Veerappampalayam Co-Operative Bank. He is married to Jayanthi Elango; the couple have one daughter, Hanvika Sathishkumar. His sister, Pavithra E, holds a doctoral degree in English Literature from Vellore Institute of Technology.
Growing up in an agricultural household in rural Tamil Nadu shaped his early interest in the practical problems of crop management and resource prediction, themes that informed his later doctoral work on smart farming datasets in South Korea.
He founded the Sathishkumar Veerappampalayam Foundation, a non-profit initiative in Tamil Nadu that supports students from rural communities in pursuing higher education.
Education
Sathishkumar completed a Bachelor of Technology in Information Technology at the Madras Institute of Technology, affiliated with Anna University, Chennai, in 2013. His undergraduate dissertation, supervised by Prof. Gunasekaran Raja, addressed the small-files problem in the Apache Hadoop Distributed File System, examining how distributed storage architectures handle large numbers of small files without performance degradation.
He then studied at PSG College of Technology, Coimbatore, completing a Master of Engineering in Biometrics and Cyber Security in 2015. His thesis, supervised by Prof. Umamaheswari Kandasamy, investigated biometric authentication through the morphological characteristics of fingernails — a modality that had received limited prior study and does not carry the same spoofing vulnerabilities as conventional fingerprint methods.
In 2017, Sathishkumar was awarded the Global Korean Scholarship (GKS) by the National Institute for International Education (NIIED), South Korea, a fully-funded government programme for international students.[4] Under this scholarship he completed a one-year Korean Language Programme at Inha University, Incheon — attaining TOPIK Level 4 proficiency — before proceeding to doctoral study.
He earned his Ph.D. in Computer and Communication Engineering at Sunchon National University (SCNU), Suncheon, South Korea, in August 2021, under the supervision of Prof. Yongyun Cho. His dissertation, A Study on Regression Accuracy Improvement Using Hybrid Feature Selection Method, proposed the MRMR-EHO algorithm, which combined the minimum-redundancy maximum-relevance (MRMR) information criterion with elephant herding optimisation (EHO), a nature-inspired metaheuristic, to select predictive features for regression models while reducing inter-feature redundancy.[5]
Academic career
Early appointments
Following his master's degree, Sathishkumar worked as a Research Associate at the School of Information Technology and Engineering, Vellore Institute of Technology, from 2015 to 2017. During his doctoral years at SCNU he concurrently held a position as Assistant Professor at Kongu Engineering College, Perundurai, Erode, Tamil Nadu, from December 2020 to November 2021, where he supervised master's-level research.
Postdoctoral research (2021–2023)
Between 2021 and 2023 Sathishkumar held two consecutive postdoctoral fellowships in South Korea. At Hanyang University, Seoul (December 2021 – November 2022), he worked in the Department of Industrial Engineering on a project examining decentralised cooperation in educational systems, and taught a graduate course on academic research writing. He then moved to Jeonbuk National University (JBNU), Jeonju (December 2022 – September 2023), where he was attached to the AI Convergence Research – Data Analytics Wing under Prof. Jaehyuk Cho. The JBNU period was productive in publication terms, producing a substantial body of jointly authored work in IEEE Access and other indexed journals on healthcare AI, deep learning, and wireless network optimisation.
Sunway University (2023–present)
Sathishkumar joined Sunway University, Malaysia, in October 2023 as Senior Lecturer in the Department of Data Science and Artificial Intelligence, School of Computing and Artificial Intelligence.[6] His teaching covers data science programming, data mining, database systems, and research methodology. He serves on the programme committees for Sunway's undergraduate data analytics degree and its master's programme in data science, and acts as a doctoral thesis examiner at several Indian universities.
His active funded research at Sunway includes a project on pandemic management through blockchain and AI (funded by the Sunway University Research Accelerator Grant Scheme, 2024–2025), and a groundwater monitoring collaboration with India's Ministry of Electronics and Information Technology.
Research
Sathishkumar's research has developed across three interconnected areas: feature selection and regression modelling, smart-city prediction systems, and healthcare AI.
Feature selection and regression
The central methodological contribution of his doctoral dissertation, the MRMR-EHO algorithm, addressed a well-known problem in applied machine learning: high-dimensional datasets frequently contain correlated or redundant features that inflate model complexity and weaken generalisation performance. By combining MRMR's information-theoretic criterion with EHO's population-based global search, the framework was shown to outperform standalone feature selection baselines on energy and agricultural prediction tasks.[5] The work was published in the peer-reviewed journal Tehnički vjesnik in 2023.
Smart-city transportation and energy
During his doctoral research at SCNU, Sathishkumar collected and published data from Seoul's municipal bike-sharing system and from the DAEWOO Steel Company's South Korean manufacturing facility. The resulting publications applied ensemble regression methods — random forests, gradient boosting, and rule-based approaches — to predict hourly bike rental demand and industrial energy consumption.
A 2020 study in Computer Communications used 365 days of Seoul rental data alongside weather variables and achieved an R² of 0.96 on the training set, drawing on seven predictive algorithms including Random Forest and Extra Trees.[2] A companion paper in the European Journal of Remote Sensing showed that interpretable rule-ensemble models could compete with black-box approaches in hourly demand forecasting while providing actionable explanations of the prediction.[7] The energy consumption work, published in Building Research & Information, used reactive power, load type, and CO₂ emission features from the DAEWOO plant to predict hourly electricity use in what the authors described as a "smart city" industrial context.[8]
Healthcare AI and deep learning
From his postdoctoral period onward, Sathishkumar has contributed to a growing body of work applying deep learning to medical imaging. A 2023 survey in Ageing Research Reviews — a journal with one of the highest impact factors in geriatric medicine — systematically reviewed convolutional, recurrent, and transformer-based architectures applied to MRI and PET scans for Alzheimer's disease detection, providing a reference framework that has been cited in subsequent clinical AI studies.[9] A 2023 paper in Fire Ecology applied the learning-without-forgetting (LwF) framework to forest fire and smoke detection in video surveillance, enabling models trained in one environmental setting to transfer to new scenes without retraining from scratch.[10]
An invited article in Alexandria Engineering Journal extended his methods to chemistry, using ensemble machine learning to predict the catalytic reduction rates of nitrophenols and azo dye pollutants — industrial contaminants that are toxic to aquatic ecosystems — contributing a publicly replicable predictive framework to environmental chemistry.[11]
Public datasets
Sathishkumar has released four research datasets that have found independent use in the broader machine learning community:
| Dataset | Host repository | Persistent identifier | Field of use |
|---|---|---|---|
| Seoul Bike Sharing Demand | UCI Machine Learning Repository | DOI: 10.24432/C5F62R | Transportation demand forecasting; regression benchmarking |
| Seoul Bike Trip Duration Prediction | Mendeley Data | — | Trip-time prediction; intelligent transport systems |
| Steel Industry Energy Consumption | IEEE DataPort | DOI: 10.21227/112a-dk82 | Industrial energy forecasting; smart manufacturing |
| Nutrient Water Supply for Strawberry Plants in Greenhouse | Mendeley Data | — | Agricultural IoT; smart farming optimisation |
The Seoul Bike Sharing Demand dataset, released in 2020 under a CC BY 4.0 licence, contains hourly rental counts from Seoul's public bike-sharing network across a full calendar year, paired with weather and public-holiday records. It has been adopted as a standard benchmark in courses and research projects internationally, with independent citations recorded in peer-reviewed journals, PubMed Central preprints, and student repositories on GitHub.[1][12] The Steel Industry Energy Consumption dataset (IEEE DataPort, DOI: 10.21227/112a-dk82) provides load-type, reactive-power, and CO₂ records from the DAEWOO Steel plant; it has been used independently in graduate theses and comparative machine learning studies on industrial energy prediction.[13][14]
Recognition
Stanford/Elsevier Top 2% Scientists
Sathishkumar appeared in the Stanford University/Elsevier Top 2% Scientists ranking for 2024 and again for 2025.[3] This database is maintained by a team led by Prof. John P. A. Ioannidis at Stanford University and published annually in partnership with Elsevier. Selection requires that a scientist rank either among the top 100,000 globally by composite citation score (c-score), or at or above the 98th percentile within their subfield, based on Scopus data. The ranking uses multiple bibliometric indicators — total citations, h-index, co-author-adjusted hm-index, and citation position data — computed both over a career and for the most recent single citation year.[15]
Awards
In 2025 Sathishkumar received the AI and Data Science Impact Award at the ElevateX Global Summit Digital Vision, Dubai, and a Best Paper Award at the International Conference on Artificial Intelligence in Networks (ICAIN), BITS Pilani Dubai Campus. An earlier Best Paper Award at the KIPS Fall Conference, Jeju National University, South Korea, in 2019 recognised his work on steel industry energy consumption prediction. As a student, he received a Government of India Scholarship from the Tamil Nadu State Board (2009) and a Gold Medal in the Tamil Nadu State Government Mathematics Examination (2009). He was earlier honoured with the Rajya Puraskar Award by The Bharat Scouts and Guides in 2007. The Global Korean Scholarship (2017) enabled his doctoral training in South Korea.
IEEE Senior Membership
Sathishkumar was elevated to IEEE Senior Member in 2024. Senior Membership is the highest grade of IEEE membership for which engineers and scientists may apply directly; it requires at least ten years of significant performance in an IEEE-designated field and is held by fewer than ten percent of IEEE members.
Editorial and peer-review activity
Sathishkumar serves as Editor-in-Chief of Information Research Communications (since 2024) and as an Editor of BMC Research Notes (Springer Nature, SCIE-indexed, since 2023). He was an Academic Editor at PLOS ONE from 2021 to 2024 and joined the editorial boards of Current Medical Imaging (Bentham Science, SCIE) and Journal of Computer Science (Scopus) in 2023–2024. His Web of Science account records more than 2,500 verified peer-review reports across more than 200 journals, reflecting sustained engagement with the scholarly publication process over five years.[6]
Patents
Two Indian patent applications have been published in his name: one covering an IoT-based remote solar monitoring system (Application No. 202141010711 A) and a second describing a malicious-node detection method for peer-to-peer networks using a coactive neuro-fuzzy inference system (Application No. 202141008276 A).
Selected publications
The following works represent the most widely cited outputs from Sathishkumar's research. A complete publication list is available through his Scopus profile (Author ID: 59310074600)[16] and Google Scholar.[17]
- Sathishkumar V E; Park, Jangwoo; Cho, Yongyun (2020). "Using data mining techniques for bike sharing demand prediction in metropolitan city". Computer Communications. 153: 353–366. doi:10.1016/j.comcom.2020.02.007.
- Sathishkumar V E; Shin, Changsun; Cho, Yongyun (2021). "Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city". Building Research & Information. 49 (1): 127–143. doi:10.1080/09613218.2020.1809983.
- Sathishkumar V E; Cho, Yongyun (2020). "A rule-based model for Seoul Bike sharing demand prediction using weather data". European Journal of Remote Sensing. 52 (sup1): 166–183. doi:10.1080/22797254.2020.1725789.
- Sathishkumar V E; Park, Jangwoo; Cho, Yongyun (2020). "Seoul Bike Trip duration prediction using data mining techniques". IET Intelligent Transport Systems. 14 (11): 1465–1474. doi:10.1049/iet-its.2019.0796.
- Kogilavani Shanmugavadivel; Sathishkumar V E; et al. (2023). "Advancements in computer-assisted diagnosis of Alzheimer's disease: A comprehensive survey of neuroimaging methods and AI techniques for early detection". Ageing Research Reviews. 91: 102072. doi:10.1016/j.arr.2023.102072.
- Sathishkumar V E; Cho, Yongyun (2023). "MRMR-EHO-Based Feature Selection Algorithm for Regression Modelling". Tehnički vjesnik. 30: 574–583. doi:10.17559/TV-20220424213641.
- Sathishkumar V E; Ramu, A. G.; Cho, Jaehyuk (2023). "Machine learning algorithms to predict the catalytic reduction performance of eco-toxic nitrophenols and azo dyes contaminants". Alexandria Engineering Journal. 72: 673–693. doi:10.1016/j.aej.2023.03.091. (Invited article)
- Zhang, Ronggang; Sathishkumar V E; Samuel, R. Dinesh Jackson (2020). "Fuzzy Efficient Energy Smart Home Management System for Renewable Energy Resources". Sustainability. 12 (8): 3415. doi:10.3390/su12083415.
- Sathishkumar V E; Cho, Jaehyuk; Subramanian, Malliga; Naren, Obuli Sai (2023). "Forest fire and smoke detection using deep learning-based learning without forgetting". Fire Ecology. 10. doi:10.1186/s42408-022-00165-0.
- Lua error in Module:Citation/CS1/Utilities at line 54: bad argument #1 to 'message.newRawMessage' (string expected, got nil).
See also
- UCI Machine Learning Repository
- IEEE DataPort
- Sunway University
- Sunchon National University
- Global Korean Scholarship
- Feature selection
- Bike-sharing system
References
- ↑ 1.0 1.1 "Seoul Bike Sharing Demand". UCI Machine Learning Repository. 2020. doi:10.24432/C5F62R. Retrieved 17 May 2026.
- ↑ 2.0 2.1 Sathishkumar V E; Park, Jangwoo; Cho, Yongyun (2020). "Using data mining techniques for bike sharing demand prediction in metropolitan city". Computer Communications. 153: 353–366. doi:10.1016/j.comcom.2020.02.007.
- ↑ 3.0 3.1 "Stanford/Elsevier Top 2% Scientists List 2024". Elsevier/Scopus. Retrieved 17 May 2026.
- ↑ "Global Korea Scholarship". NIIED, Ministry of Education, Republic of Korea. Retrieved 17 May 2026.
- ↑ 5.0 5.1 Sathishkumar V E; Cho, Yongyun (2023). "MRMR-EHO-Based Feature Selection Algorithm for Regression Modelling". Tehnički vjesnik. 30: 574–583. doi:10.17559/TV-20220424213641.
- ↑ 6.0 6.1 "Dr Sathishkumar Veerappampalayam – Staff Profile". Sunway University. Retrieved 17 May 2026.
- ↑ Sathishkumar V E; Cho, Yongyun (2020). "A rule-based model for Seoul Bike sharing demand prediction using weather data". European Journal of Remote Sensing. 52 (sup1): 166–183. doi:10.1080/22797254.2020.1725789.
- ↑ Sathishkumar V E; Shin, Changsun; Cho, Yongyun (2021). "Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city". Building Research & Information. 49 (1): 127–143. doi:10.1080/09613218.2020.1809983.
- ↑ Kogilavani Shanmugavadivel; Sathishkumar V E; et al. (2023). "Advancements in computer-assisted diagnosis of Alzheimer's disease: A comprehensive survey of neuroimaging methods and AI techniques for early detection". Ageing Research Reviews. 91: 102072. doi:10.1016/j.arr.2023.102072.
- ↑ Sathishkumar V E; Cho, Jaehyuk; Subramanian, Malliga; Naren, Obuli Sai (2023). "Forest fire and smoke detection using deep learning-based learning without forgetting". Fire Ecology. 10. doi:10.1186/s42408-022-00165-0.
- ↑ Sathishkumar V E; Ramu, A. G.; Cho, Jaehyuk (2023). "Machine learning algorithms to predict the catalytic reduction performance of eco-toxic nitrophenols and azo dyes contaminants". Alexandria Engineering Journal. 72: 673–693. doi:10.1016/j.aej.2023.03.091.
- ↑ "A bike-sharing demand prediction model based on Spatio-Temporal Graph Convolutional Networks". PMC. Retrieved 17 May 2026.
- ↑ Sathishkumar V E (2022). "Steel Industry Energy Consumption". IEEE DataPort. doi:10.21227/112a-dk82. Retrieved 17 May 2026.
- ↑ Power consumption prediction for steel industry (Report). arXiv. 2023.
- ↑ "Stanford University's Top 2% Scientists List: Understanding the Selection Criteria". ScholarsColab. Retrieved 17 May 2026.
- ↑ "Sathishkumar V E – Scopus Author Profile". Elsevier Scopus. Retrieved 17 May 2026.
- ↑ "Sathishkumar V E – Google Scholar". Retrieved 17 May 2026.
External links
- Official website
- Staff profile, Sunway University
- Google Scholar profile
- Scopus author profile
- ORCID: 0000-0002-8271-2022
- IEEE Xplore author page
- ResearchGate profile
- Steel Industry Energy Consumption – IEEE DataPort
- Seoul Bike Sharing Demand – UCI ML Repository
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