Wazib Ansar
Tata Consultancy Services, University of Calcutta, Techno India Salt Lake
About
Wazib Ansar is currently pursuing his Ph.D. in Information Technology from Data Science Lab, A. K. Choudhury School of IT, University of Calcutta, Kolkata, India and has been awarded with Senior Research Fellowship from UGC. He is also an active member of IEEE and ACM. He received his M.Tech. degree in Information Technology from A. K. Choudhury School of IT, University of Calcutta, Kolkata, India in which he secured the First Class First Rank. He received his B.Tech. degree in Information Technology from Techno India Salt Lake, Kolkata, India. He also has a brief experience in the industry as an Assistant Systems Engineer in Tata Consultancy Services Ltd. He has a list of publications in notable journals and conferences. His areas of research include Natural Language Processing, Data Science, Transformers NLP, and Artificial Intelligence.
Employment
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Tata Consultancy Services Assistant Systems Engineer2016 - 2017
Education
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University of Calcutta Research Scholar2020 - Present
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University of Calcutta M.Tech (IT)2018 - 2020
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Techno India Salt Lake B.Tech (IT)2012 - 2016
Projects & Funding
Projects & funding information is unavailable.
Publications (12)
- LeSum: Cost-effective LLM-driven hybrid summarization of Indian legal judgments Save
- BEExformer: A Fast Inferencing Binarized Transformer With Early Exits Save
- TexIm FAST: Text-to-Image Encoding for Semantic Similarity Evaluation of Disproportionate Sequences Save
- Advancing Multimodal Classification: An In-depth Analysis of Amazon Reviews Using Early Fusion with Gating Mechanism Save
- An EfficientNet-Based Ensemble for Bird-Call Recognition with Enhanced Noise Reduction Save
- TexIm: A Novel Text-to-Image Encoding Technique Using BERT Save
- A novel selective learning based transformer encoder architecture with enhanced word representation Save
- Combating the menace: A survey on characterization and detection of fake news from a data science perspective Save
- An efficient methodology for aspect-based sentiment analysis using BERT through refined aspect extraction Save
- A Data Science Approach to Analysis of Tweets Based on Cyclone Fani Save