About
Dr Sandeep Kumar (Senior Member IEEE, Senior Member ACM) is currently working as a Professor in the Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Roorkee, India. He has supervised six PhD thesis and about fourty five master dissertations. He has published about sixty research papers in international/national journals and conferences, has written three books and some book-chapters with Springer, andhas filed two patents and two copyrights. He is the member of board of studies of various universities and institutions. He is currently handling multiple national and international research/consultancy projects. He has received Young Faculty Research Fellowship award from MeitY, Govt. of India, NSF/TCPP early adopter award-2014, 2015, ITS Travel Awards and others. His name has also been enlisted in major directories such as Marquis Whos Who, IBC and others. His areas of interest include Semantic Web, Web Services, Machine Learning, and Software Engineering.
Employment
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Indian Institute of Technology Roorkee Professor
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (171)
- FairLabeler: Achieving Fairness Via Biased Label Detection and Correction Save
- Secure Code Generation With Open Source Generative AI Models Save
- Trust But Verify: Analyzing the Tradeoffs of AI-Generated Code Save
- A Multi-Scale Hypergraph-Based Approach for Third-Party Library Recommendation in Mobile App Development Save
- WSSR: an approach for web service selection based on replaceability Save
- Adaptive Spectral Clustering and Structural Alignment for Cross-Project Defect Prediction Save
- An Approach for Cross-Project Defect Prediction Using Composite Distribution Alignment and Domain-Adversarial Neural Networks Save
- ChatGPT Choreography: Discovering Developer Dialogues and Potential Software Development Lifecycle Applications Save
- An approach to software defect prediction for small-sized datasets Save
- FairGenerate: Enhancing Fairness Through Synthetic Data Generation and Two-Fold Biased Labels Removal Save