Assistant Professor, Amity University, India
Rupali Goyal is an Assistant Professor at Amity University, Mohali, Punjab, specializing in Natural Language Processing (NLP) and Artificial Intelligence. She holds a Ph.D. in Computer Science and Engineering from Thapar Institute of Engineering and Technology, Patiala, where her research focused on NLP. She has a track record of publishing in high-impact journals and conferences, with six published papers in the fields of language modeling, text summarization, and question-answering systems. Rupali is passionate about advancing AI technologies and mentoring students while contributing to interdisciplinary research projects. Her work has real-world applications, particularly in the domains of education, information retrieval, and conversational AI. Rupali’s academic excellence and dedication to innovation make her a promising figure in the NLP and AI research community.
Profile
Education
Rupali Goyal holds a Doctor of Philosophy (Ph.D.) in Computer Science and Engineering from Thapar Institute of Engineering and Technology, Patiala, Punjab, India. Her doctoral research was focused on Natural Language Processing (NLP), where she specialized in developing context-aware models for automated question answering and text summarization. Before her Ph.D., she completed a Master’s degree in Computer Science, where she developed a strong foundation in algorithms, programming, and AI techniques. Rupali qualified for the Graduate Aptitude Test in Engineering (GATE) and the University Grants Commission National Eligibility Test (UGC-NET), further enhancing her academic credentials. Her academic journey has been characterized by a commitment to both theoretical and applied research in AI and NLP. Throughout her career, she has continuously pursued knowledge and skill enhancement to stay at the forefront of technological advancements in the field of AI.
Experience
Rupali Goyal currently serves as an Assistant Professor at Amity University, Mohali, Punjab, India, where she teaches courses on Artificial Intelligence, Natural Language Processing, and Computer Science. In addition to her teaching responsibilities, Rupali is deeply involved in research activities, particularly in the development of advanced models for question-answering systems and text summarization. She has published six papers in prestigious journals and conferences, contributing to the academic discourse on NLP and AI. Rupali has also played an active role in mentoring students, guiding them in their academic and research projects. She has collaborated on various interdisciplinary research initiatives, aiming to address practical challenges in fields such as education, information retrieval, and conversational AI. Her ongoing research focuses on creating more efficient and context-aware NLP models. She has a proven ability to bridge the gap between academia and real-world applications through her innovative research.
Research Focus
Rupali Goyal’s research is centered around Natural Language Processing (NLP), with a specific focus on developing models for question-answering systems, text summarization, and language modeling. Her work aims to improve the accuracy and efficiency of NLP applications in real-world scenarios, such as education, conversational AI, and information retrieval. Rupali has contributed to advancements in extractive and abstractive summarization methods and automated question generation, enhancing semantic understanding and context-aware responses. One of her key research interests is developing generative AI models that can produce more human-like responses while considering context and domain-specific requirements. Her goal is to make NLP models more reliable, scalable, and adaptable to various applications. Rupali has a strong commitment to innovation and research that contributes to the advancement of AI technologies. Her work is highly interdisciplinary, collaborating across fields to push the boundaries of what is possible in NLP and AI.
Publication Top Notes
- Deep learning based question generation using T5 transformer 🧠📚
- Automated question and answer generation from texts using text-to-text transformers 🤖💬
- A Systematic survey on automated text generation tools and techniques 📑🔍
- Data Mining: Techniques, Applications and Issues 🔎💾
- Apriori based algorithms and their comparisons 🔢📊
- QFAS-KE: Query focused answer summarization using keyword extraction 📝🔑