Moumita Ghosh | Computer Science | Best Researcher Award

Dr. Moumita Ghosh | Computer Science | Best Researcher Award

Assistant Professor, Heritage Institute of Technology, India

Dr. Moumita Ghosh (PhD, Engg.) is a passionate researcher from Kolkata, India 🇮🇳, currently working as an Assistant Professor in the Department of Computer Science and Engineering at the Heritage Institute of Technology. Her core research interests lie at the intersection of Data Science and Computational Biodiversity. With a deep commitment to innovation and academia, she integrates machine learning and data mining techniques to address biodiversity conservation and complex ecological data analysis. 👩‍🏫🌿📊

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Education 🎓

Dr. Ghosh holds a Ph.D. in Engineering (2019–2024) from Jadavpur University, Kolkata, with her thesis focusing on “Algorithms for Data Mining: Applications in Biodiversity” 🧠🌱. She earned her M.E. in Multimedia Development from the same university (2011–2013) and completed her B.Tech. in Info Technology from WBUT in 2011. She also achieved outstanding academic performance in both her higher secondary and secondary education at Ichapur Girls’ High School. 🎓

Experience 💼

With over a decade of academic experience, Dr. Ghosh has served in key teaching roles at several premier institutions. She currently teaches Data Structures at Heritage Institute of Technology (2024–Present). Previously, she worked at Narula Institute of Technology (2022–2024), Jadavpur University (as a guest faculty and later PI for a DST-funded project), and held Assistant Professor roles at Institute of Engineering and Management (2015–2017) and Bengal College of Engineering and Technology (2013–2015). 💻📚

Research Interest 🔍

Dr. Ghosh’s research bridges Data Science and Ecology through Computational Biodiversity 🌍🧬. Her work includes pattern mining, remote sensing data, complex networks, and biodiversity modeling using advanced machine learning algorithms. She explores how AI and statistical methods can help mitigate biodiversity loss, emphasizing ecological data interpretation and predictive modeling. Her interests extend to deep learning, natural language processing, and ecological network analysis. 📈🌐

Awards 🏆

Dr. Ghosh is a UGC NET qualifier (2017 & 2018) and was awarded the prestigious DST Women Scientists Fellowship (2019–2022), where she led a ₹22 lakh project on biodiversity data mining. She collaborates internationally with Universitas Islam Indonesia and has served as a reviewer and TPC member for various global conferences. She is a proud member of the Computer Society of India (CSI) since 2021. 🏅🌟

Publications 📄

📖 Ghosh et al. (2023). “An Irregular CLA-based Novel Frequent Pattern Mining Approach.” International Journal of Data Mining, Modelling and Management. DOI

📖 Ghosh et al. (2022). “Recognition of Coexistence Pattern of Salt Marshes and Mangroves.” Ecological Informatics. DOI

📖 Ghosh et al. (2022). “Frequent itemset mining using FP-tree.” Innovations in Systems and Software Engineering.

📖 Ghosh et al. (2021). “Knowledge Discovery of Sundarban Mangrove Species.” SN Computer Science. DOI

📖 Ghosh et al. (2021). “Prediction of Interaction between SARS-CoV-2 and Human Protein.” Journal of The Institution of Engineers (India): Series B. DOI

📖 Mondal, Ghosh et al. (2022). “Suffix forest for mining tri-clusters from time-series data.” Innovations in Systems and Software Engineering.

📖 Ghosh & Parekh (2013). “Fish shape recognition using multiple shape descriptors.” International Journal of Computer Applications.

Conclusion

Dr. Moumita Ghosh is a highly suitable candidate for the Best Researcher Award. Her innovative integration of machine learning and biodiversity studies, coupled with a solid record of publications, a granted patent, and a DST fellowship, reflects both depth and societal relevance in her research. With continued international exposure and independent research leadership, she is poised to make significant contributions to science and sustainability.

Sarah Di Grande | Analytics | Best Researcher Award

Ms. Sarah Di Grande | Analytics | Best Researcher Award

PhD student, University of Catania, Italy

Sarah Di Grande is a driven researcher and data scientist currently pursuing a PhD in Systems, Energy, Computer, and Telecommunications Engineering at the University of Catania, Italy. With expertise in machine learning and a focus on sustainable water-energy optimization, she has contributed extensively to data science applications in renewable energy and smart city initiatives.

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Education 🎓

Sarah completed a Master’s in Data Science for Management at the University of Catania in 2022, graduating summa cum laude with a thesis on unsupervised machine learning for photovoltaic systems. She also holds a Bachelor’s degree in Business Economics from the same institution and graduated from Liceo Megara with top honors in 2017. Her studies have centered on advanced machine learning, big data, and data security.

Experience 💼

Currently, Sarah is a PhD student and researcher at the University of Catania, working in collaboration with Darwin Technologies on machine learning-based water-energy optimization. She previously interned as a data scientist at BaxEnergy, where she applied predictive maintenance techniques for photovoltaic panels, gaining hands-on experience in industrial data science applications.

Research Interests 🔬

Her research is dedicated to leveraging artificial intelligence for sustainable energy systems, focusing on machine learning applications in hydropower forecasting, urban traffic prediction, and water distribution network optimization. Sarah’s work aims to enhance resource management and promote sustainability in smart cities.

Awards 🏆

Sarah has received recognition for her innovative contributions, winning the Start-Cup Sicilia 2023 for her work on the “Smart Knee Project,” a device aimed at diagnosing knee osteoarthritis. She also secured second place in the University of Catania’s Start-Cup competition for the same project.

Publications Top Notes📚

Sarah has contributed numerous papers to international conferences and journals, exploring AI in hydropower, water distribution, and urban traffic management. Some key publications include:

“A Proactive Approach for the Sustainable Management of Water Distribution Systems” (2023) in 12th International Conference on Data Science, Technology and Applications – DATA [cited by 10 articles].

“Detection and Prediction of Leakages in Water Distribution Networks” (2023) in DATA 2023 [cited by 7 articles]

“A Machine Learning Approach for Hydroelectric Power Forecasting” (2023) in 14th International Renewable Energy Congress – IREC [cited by 5 articles].

“Data Science for the Promotion of Sustainability in Smart Water Distribution Systems” (2024) in Communications in Computer and Information Science, Springer [cited by 12 articles].