Dr. Ramdas Khomane
Symbiosis International University, National Chemical Laboratory
0000-0002-8806-7351
About
Dr. Ramdas Khomane is a distinguished Nanotechnologist and Advanced Materials Research Leader with over 24 years of experience in research, innovation, and higher education. His professional journey spans premier institutions, including CSIR–National Chemical Laboratory, CSIR–Central Electrochemical Research Institute, and the National University of Singapore.
His expertise encompasses sustainable nanotechnology, advanced energy materials, environmental remediation, and AI-driven materials innovation. He has authored 50+ international peer-reviewed publications, secured multiple patents including international filings, and received prestigious recognitions such as the DST Fast-Track Young Scientist Award and the CSIR Quick-Hire Scientist Award.
His research vision focuses on developing high-impact, interdisciplinary ecosystems that integrate materials science with sustainability, clean energy technologies, and industry-oriented innovation. His work emphasizes translational research, technology development, and strengthening academia–industry collaboration to deliver measurable societal and environmental impact.
In addition to research contributions, Dr. Khomane actively contributes to academic leadership through curriculum innovation, mentoring of students and early-career researchers, institutional development initiatives, and fostering a culture of innovation and collaboration.
He is open to:
Professor and Research Leadership positions
Establishing and leading centers or programs in sustainable materials and energy systems
Strategic national and international research collaborations
Industry consulting and advisory roles in advanced materials, sustainability, and technology innovation
Employment
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Symbiosis International University
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Symbiosis International University
Education
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National Chemical Laboratory Ph.D.
Projects & Funding
Projects & funding information is unavailable.
Publications (44)
- Comparative Analysis of MobileNetV2 and EfficientNetB0 for Automated Rose Leaf Disease Detection Save
- Impact of payload, speed, and rolling resistance on battery performance in electric vehicles Save
- ResNet101-Based Guava Disease Classifier: Boosting Detection Accuracy with Deep Learning Save
- InceptionV3-Based Classification of Night Jasmine Leaf Diseases: Advancing Early Detection in Precision Agriculture Save
- EfficientNetB0-Based Deep Learning Framework for Robust Classification of Hop Plant Diseases and Pest Infestations Save
- Deep Learning-Based Nail Disease Classification: Application of MobileNetV3-Large for Accurate Detection Save
- Deep Learning-Based Classification of Bean Leaf Diseases Using DenseNet121 Save
- DenseNet201-Driven Deep Learning Framework for Multiclass Histopathological Classification of Bladder Tissue Save
- Anemia detection and classification from blood samples using data analysis and deep learning* Save
- Comparative Analysis of Polarity of Text-based Sentiment Analysis Save