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Educational Details
- Doctor of Philosophy in Mathematics (2021 – Present)
Malaviya National Institute of Technology (MNIT), Jaipur, India
CGPA: 8.67/10.00
- Master of Science in Mathematics and Scientific Computing (2018 – 2020)
Motilal Nehru National Institute of Technology (MNNIT), Allahabad, India
CGPA: 7.05/10.00
- Bachelor of Science (Hons.) in Mathematics (2015 – 2018)
University of Delhi (DU), India
CGPA: 6.7/10.00
Professional Experience
Mr. Sushant Yadav is a dedicated researcher and academic in the field of Applied Mathematics and Artificial Intelligence. He is currently pursuing his Ph.D. in Mathematics at MNIT Jaipur, where his research focuses on the intersection of Spiking Neural Networks (SNN) and biologically inspired computational models. Throughout his doctoral studies, he has actively contributed to cutting-edge research involving neuron models, plasticity mechanisms, and machine learning applications in healthcare and biological systems. His work involves developing new mathematical models and computational techniques to enhance AI systems’ performance and adaptability. With strong programming skills in Python, C/C++, and expertise in frameworks such as PyTorch, TensorFlow, and SnnTorch, he aims to bridge the gap between theoretical mathematics and AI applications.
Research Interest
- Spiking Neural Networks (SNN)
- Neuromorphic Computing
- Artificial Intelligence and Machine Learning
- Biologically Inspired Computation
- Mathematical Modeling in Computational Neuroscience
- Application of Machine Learning in Healthcare
Top Noted Publication
1. Comparative Analysis of Biological Spiking Neuron Models for Classification Task
- Authors: S. Yadav, S. Chaudhary, R. Kumar
- Conference: 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)
- Publisher: IEEE
- Pages: 1-6
- Date: July 2023
- DOI: https://doi.org/10.1109/ICCCNT57981.2023.10245626
- Summary:
This paper presents a comparative evaluation of various biological spiking neuron models with respect to their effectiveness in solving classification tasks. It focuses on models such as Leaky Integrate-and-Fire (LIF), Izhikevich, and Hodgkin-Huxley neurons, assessing their performance on benchmark datasets. The study provides insight into the suitability of each model for machine learning applications based on accuracy and computational efficiency.
2. Consciousness Driven Spike Timing Dependent Plasticity
- Authors: S. Yadav, S. Chaudhary, R. Kumar
- Journal: Expert Systems with Applications (Elsevier)
- DOI: https://doi.org/10.1016/j.eswa.2025.126490
- Preprint: arXiv preprint arXiv:2405.04546
- Publication Year: 2024
- Summary:
This paper introduces a novel approach integrating consciousness-like behavior into the Spike Timing Dependent Plasticity (STDP) learning rule. The proposed mechanism enhances synaptic adaptability by incorporating contextual and attention-based weight adjustments, leading to improved learning outcomes in spiking neural networks (SNNs). The study demonstrates the effectiveness of this approach in enhancing performance in classification and pattern recognition tasks.
3. Machine Learning-Based Recognition of White Blood Cells in Juvenile Visayan Warty Pigs
- Authors: S. Saxena, S. Yadav, B. Singh, R. Kumar, S. Chaudhary
- Conference: 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI)
- Publisher: IEEE
- Date: December 2023
- Summary:
This research proposes a machine learning framework for the automated recognition and classification of White Blood Cells (WBCs) in juvenile Visayan Warty Pigs, a rare and endangered species. The system employs image processing and supervised learning algorithms to enhance diagnostic accuracy and aid veterinarians in wildlife health monitoring.
4. Deep Learning Solutions for WBC Classification in Juvenile Visayan Warty Pigs
- Authors: S. Saxena, S. Yadav, B. Singh, R. Kumar, S. Chaudhary
- Conference: 2023 IEEE Engineering Informatics
- Publisher: IEEE
- Pages: 1-6
- Date: November 2023
- Summary:
This paper presents a deep learning-based approach leveraging Convolutional Neural Networks (CNNs) to classify White Blood Cells in juvenile Visayan Warty Pigs. The study demonstrates improved classification accuracy compared to traditional image processing techniques, showcasing the potential of deep learning in veterinary diagnostics and wildlife conservation.
Conclusion:
Mr. Sushant Yadav is a highly promising researcher with a robust foundation in mathematical modeling, spiking neural networks, and biologically inspired AI. His research contributions, particularly his innovative work on STDP and white blood cell classification using machine learning, position him as a deserving candidate for the Best Researcher Award. While his profile is strong, further enhancing his publication impact, international collaborations, and real-world implementations would elevate his standing in the global research community.