Prabhat Kumar Bharti | Research Excellence | Best Researcher Award

Dr. Prabhat Kumar Bharti | Research Excellence | Best Researcher Award

Dr. Prabhat Kumar Bharti | School of Computing and Electrical Engineering, Indian Institute of Technology (IIT) Mandi, Himachal Pradesh | India

Dr. Prabhat Kumar Bharti is a Postdoctoral Fellow at IIT Mandi with a Ph.D. in Computer Science & Engineering from IIT Patna, specializing in Artificial Intelligence and Natural Language Processing for peer review systems. He has served as Assistant Professor at KLEF University and Goel Institute of Technology, teaching Machine Learning, Deep Learning, and related subjects with excellent student feedback. An accomplished researcher with publications in Journal of Information Science, Scientometrics, Plos ONE, and others, he has also received the Best Paper Runner-Up Award at ICADL 2021. Actively engaged in academic service, he has held leadership roles in IEEE, reviewed for leading journals, and contributed to curriculum design and accreditation, reflecting his strong commitment to research, teaching, and community development.

Academic Profile 

Google Scholar

Education

Dr. Prabhat Kumar Bharti holds a strong academic foundation in computer science and engineering, beginning with a Bachelor of Technology in Information Technology, followed by a Master of Technology in Computer Science and Engineering with a focus on web structure mining. He pursued doctoral research at the Indian Institute of Technology, Patna, where his thesis explored artificial intelligence techniques for peer review and research lineage establishment. His academic journey reflects a consistent focus on advanced computing, artificial intelligence, and data-driven methodologies that form the basis of his professional expertise.

Experience

He has served in both teaching and research capacities at reputed institutions across India. His career includes roles as Assistant Professor at universities and institutes where he taught subjects such as Machine Learning, Deep Learning, Artificial Neural Networks, Database Management Systems, and Operating Systems. He has also held administrative responsibilities, contributing to innovation coordination, student counseling, and accreditation processes. Currently, he is a Postdoctoral Fellow at the Indian Institute of Technology, Mandi, where he is expanding his research portfolio while mentoring students and contributing to academic excellence through teaching and scholarly engagement.

Research Interests

His research interests lie at the intersection of artificial intelligence, natural language processing, and computational linguistics, with a particular emphasis on the peer review process. He has developed models for analyzing review comments, predicting reviewer decisions, assessing tone and objectivity, and generating recommendations. His work extends to large language models, sentiment analysis, causal inference, and advanced learning strategies such as few-shot learning and chain-of-thought reasoning. He also explores the applications of Bayesian methods, counterfactual reasoning, and interpretability in AI, contributing to the creation of trustworthy and transparent intelligent systems.

Awards and Recognitions

Dr. Bharti has received significant recognition for his contributions to research and academia. He qualified the National Eligibility Test for Assistant Professor conducted by the University Grants Commission of India. His paper on predicting peer review decisions earned the Best Paper Runner-Up Award at an international conference on digital libraries, showcasing the impact of his innovative approach to peer review analysis. Alongside awards, he has been an active reviewer for reputed journals and conferences, further highlighting his reputation as a respected member of the global research community.

Publications

A novel benchmark resource for computational analysis of peer reviews — T. Ghosal, S. Kumar, P.K. Bharti, A. Ekbal — PLOS ONE — 2022

Peerassist: leveraging on paper-review interactions to predict peer review decisions — P.K. Bharti, S. Ranjan, T. Ghosal, M. Agrawal, A. Ekbal — International Conference on Asian Digital Libraries — 2021

PolitePEER: does peer review hurt? A dataset to gauge politeness intensity in the peer reviews — P.K. Bharti, M. Navlakha, M. Agarwal, A. Ekbal — Language Resources and Evaluation — 2024

Sharing is caring! Joint multitask learning helps aspect-category extraction and sentiment detection in scientific peer reviews — S. Kumar, T. Ghosal, P.K. Bharti, A. Ekbal — ACM/IEEE Joint Conference on Digital Libraries (JCDL) — 2021

How confident was your reviewer? Estimating reviewer confidence from peer review texts — P.K. Bharti, T. Ghosal, M. Agrawal, A. Ekbal — International Workshop on Document Analysis Systems — 2022

PEERRec: An AI-based approach to automatically generate recommendations and predict decisions in peer review — P.K. Bharti, T. Ghosal, M. Agarwal, A. Ekbal — International Journal on Digital Libraries — 2024

BetterPR: A Dataset for Estimating the Constructiveness of Peer Review Comments — P.K. Bharti, T. Ghosal, M. Agarwal, A. Ekbal — International Conference on Theory and Practice of Digital Libraries — 2022

Conclusion

Dr. Prabhat Kumar Bharti stands out as a dynamic researcher whose contributions to Artificial Intelligence and Natural Language Processing reflect both depth and innovation. His strong academic record, impactful publications, teaching excellence, and active service to the academic community position him as a truly deserving candidate for the Best Researcher Award. With his continued dedication, he is poised to make even greater contributions to global research and knowledge advancement.

Xingyu Xiao | Research Excellence | Innovative Research Award | 2397

Dr. Xingyu Xiao | Research Excellence | Innovative Research Award

Dr. Xingyu Xiao | Tsinghua University | China

Xingyu Xiao is a doctoral researcher in Reliability Engineering and Safety Analysis at Tsinghua University, with a strong background in safety engineering from the University of Science and Technology Beijing. His research focuses on risk-informed decision support, human reliability analysis, and the integration of artificial intelligence, large language models, and graph neural networks into nuclear safety and emergency response. With numerous high-impact publications in journals such as Reliability Engineering & System Safety, Energies, and Risk Analysis, he has also contributed to open datasets and advanced frameworks for safety assessment. Recognized with top honors including the Tanzhen Scholar Award, National Scholarships, and multiple Best Paper Awards, Xiao is emerging as a promising young scholar bridging reliability engineering with cutting-edge AI technologies.

Academic Profile 

Scopus | Google Scholar

Education

Dr. Sheng Xiao pursued his academic training in biochemistry and molecular biology, where he gained a strong foundation in cellular processes, molecular signaling, and the interface of traditional medicine with modern biomedical science. His education emphasized both theoretical knowledge and hands-on experimental research, allowing him to develop expertise in advanced laboratory techniques, molecular genetics, and translational applications. This strong academic background laid the groundwork for his future contributions in aging research, intestinal regeneration, and the therapeutic value of natural compounds.

Experience

Dr. Xiao is currently affiliated with the Navy Medical University, where he has advanced through academic and research roles with increasing responsibility. He has successfully led major projects funded at institutional, provincial, and national levels, serving as a principal investigator and collaborator in multidisciplinary teams. His experience covers research into brain–gut interactions, nuclear protein function in cancer biology, and natural medicine applications for age-related diseases. Beyond laboratory research, he has contributed to scientific societies, taken up editorial responsibilities, and engaged in collaborative programs that strengthen the global exchange of biomedical knowledge.

Research Interests

His research is centered on the molecular basis of intestinal stem cell regulation, gut regeneration, and aging-related disorders. He is deeply interested in how natural compounds derived from traditional Chinese medicine influence molecular pathways such as Keap1–Nrf2 and srebp-mediated regulation. By integrating experimental models with translational perspectives, his work bridges the gap between fundamental molecular biology and clinical application. Additionally, he explores the brain–gut axis, aiming to understand the systemic mechanisms that link neurobiology, intestinal health, and therapeutic interventions.

Awards

Dr. Xiao has received recognition for his contributions through prestigious awards in scientific meetings, academic forums, and institutional honors. His poster presentations at international congresses and achievements in traditional medicine research have highlighted his innovative and impactful approaches. He has also been honored with distinguished scholar recognition for his leadership and excellence in research. These awards reflect his growing influence in the field and his continued commitment to advancing biomedical science for the benefit of both academia and healthcare.

Publications

Enhancing LOCA breach size diagnosis with deep learning

Author: X. Xiao, B. Qi, J. Liang, J. Tong, Q. Deng, P. Chen
Journal: Energies
Year: 2023

MixedGaussianAvatar: Realistically and geometrically accurate head avatar via mixed 2D-3D Gaussian splatting

Author: P. Chen, X. Wei, Q. Wuwu, X. Wang, X. Xiao, M. Lu – arXiv
Journal: preprint
Year: 2024

KRAIL: A knowledge-driven framework for base human reliability analysis integrating IDHEAS and large language models

Author: X. Xiao, P. Chen, B. Qi, H. Zhao, J. Liang, J. Tong, H. Wang
Journal: preprint
Year: 2024

Multimodal learning using large language models to improve transient identification in nuclear power plants

Author: B. Qi, J. Sun, Z. Sui, X. Xiao, J. Liang
Journal: Progress in Nuclear Energy
Year: 2024

A dynamic risk-informed framework for emergency human error prevention in high-risk industries: A nuclear power plant case study

Author: X. Xiao, B. Qi, S. Liu, P. Chen, J. Liang, J. Tong, H. Wang
Journal: Reliability Engineering & System Safety
Year: 2025

A national risk analysis model (NRAM) for the assessment of COVID-19 epidemic

Author:  Q. Deng, X. Xiao, L. Zhu, X. Cao, K. Liu, H. Zhang, L. Huang, F. Yu, H. Jiang, …
Journal: Risk Analysis
Year: 2023

Conclusion

Dr. Sheng Xiao’s groundbreaking research, leadership in prestigious projects, and commitment to advancing biochemistry and molecular biology make him an outstanding candidate. His contributions have not only deepened scientific understanding but also paved the way for innovative therapeutic applications, positioning him as a highly suitable recipient of the Innovative Research Award.