Mrs. Naima Rahiel | Change Management | Young Researcher Award

Professional Profile 

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• Research gate

Early Academic Pursuits:

Aizan Zafar embarked on his academic journey with a Bachelor's degree in Information Technology from Guru Ghasidas Central University, Chhattisgarh, graduating with distinction and a commendable CGPA of 8.71 in 2016. This early academic success laid the foundation for his subsequent pursuits in the field of computer science.

He continued his educational journey by pursuing a Master's degree in Information Technology at the University of Hyderabad, Telangana. During this period (2017-2019), Aizan engaged in research on machine learning, focusing on an efficient approach for effective clustering using validity index. His dedication and scholarly pursuits earned him a CGPA of 8.1 in the first division with distinction.

Professional Endeavors:

Following his Master's degree, Aizan Zafar transitioned into a Ph.D. program at the prestigious Indian Institute of Technology Patna, commencing in July 2019. As a Ph.D. scholar, he delved into the realm of Natural Language Processing, particularly concentrating on Medical Question Answering and Dialogue Systems.

In the course of his doctoral journey, Aizan showcased exceptional research prowess by contributing to various projects and initiatives. His experience as a Teaching Assistant for courses such as Artificial Intelligence, Natural Language Processing, and Computer Network reflects his commitment to academic responsibilities.

Contributions and Research Focus:

Aizan's research endeavors have been prolific, spanning diverse areas within Natural Language Processing and Conversational AI. His notable contributions include the development of "Sevak," an intelligent Indian language chatbot, and "PERCURO," a holistic solution for clinical text employing conversational data for Question Answering systems.

Aizan's research has culminated in several published and accepted papers, demonstrating his expertise in knowledge-infused abstractive question answering systems, medical dialogue generation, and knowledge graph-assisted medical dialog generation. He has also showcased his commitment to advancing the field through organizing workshops and courses on Deep Learning for NLP.

Accolades and Recognition:

Aizan Zafar's academic prowess is underscored by his qualification in AIEEE (2012), GATE in 2017 (for M.Tech.), and GATE in 2019 (for Ph.D.). These achievements underscore his consistent dedication to academic excellence.

Impact and Influence:

Aizan's influence extends beyond his individual achievements. His organizational roles in workshops and courses, such as the GIAN Workshop on Deep Learning Techniques for Conversational AI and the CEP course on Deep Learning for NLP, highlight his commitment to knowledge dissemination and community building.

Legacy and Future Contributions:

As an emerging scholar in the field of Natural Language Processing and Conversational AI, Aizan Zafar's legacy is shaped by his significant contributions to medical question answering systems. With ongoing works focusing on enhancing abstractive question answering through first-order logic, Aizan is poised to leave a lasting impact on the intersection of AI and healthcare.

Looking forward, Aizan's dedication to learning, collaborative spirit, and leadership qualities position him as a future leader in the field, with the potential to shape the trajectory of Conversational AI and Natural Language Processing. His multifaceted approach, from research to teaching and organizational roles, signifies a holistic commitment to the advancement of knowledge in the realm of computer science and AI.

Notable Publications

• Knowledge grounded medical dialogue generation using augmented graphs
• Knowledge graph assisted end-to-end medical dialog generation
• Novel Initialization Strategy for K-modes Clustering Algorithm
• Cdialog: A multi-turn covid-19 conversation dataset for entity-aware dialog generation
• KI-MAG: A knowledge-infused abstractive question answering system in medical domain
• End to End Sign Language Translation via Multitask Learning

 

Citation

A total of 14 citations for his publications, demonstrating the impact and recognition of his research within the academic community.

• Citations     14           14
• h-index       3              3
• i10-index    0             0