Mushir Akhtar

About

Mushir Akhtar is a Ph.D. scholar in the Department of Mathematics at the Indian Institute of Technology (IIT) Indore, India, under the supervision of Prof. M. Tanveer and Dr. Mohd. Arshad. He received his B.Sc. and M.Sc. degrees in Mathematics from Chaudhary Charan Singh University, Meerut, India, in 2018 and 2020, respectively, and was awarded the Gold Medal for securing the highest rank in M.Sc. Mathematics. His research focuses on developing robust, interpretable, and uncertainty-aware machine learning frameworks, with particular emphasis on support vector machines, randomized neural networks, probabilistic neural networks, and advanced loss-function design. His work also integrates statistical dependency modeling and copula theory to improve stability and generalization of learning systems, especially for biomedical and healthcare applications. He has authored several high-impact journal publications in venues such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition, Neural Networks, and Applied Soft Computing. He is the recipient of the Institute Best Research Paper Award (IIT Indore, 2025) and has multiple papers accepted at leading international conferences including AISTATS, IJCNN, and ICPR. Beyond research, he actively contributes to the academic community through reviewing for top journals and conferences and currently serves as Vice-Chair of the IEEE Computational Intelligence Society Student Branch Chapter at IIT Indore and Treasurer of the IEEE Student Branch Chapter at IIT Indore.

Employment

• Indian Institute of Technology Indore Senior Research Fellow (SRF)
  2021 - 2026

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (12)

• RoBoTS: A Robust Bounded Twin SVM Based on RoBoSS Loss Function
  Pattern Recognition 2026 DOI: 10.1016/j.patcog.2026.113653
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• Dual-center RAPID-LSSVM: Radius-adaptive, probability and imbalance driven weighting for Alzheimer’s diagnosis
  Neural Networks 2026 DOI: 10.1016/j.neunet.2026.108956
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• Intuitionistic Fuzzy and Robust Loss Fused Framework for Stable and Efficient RVFL Learning
  IEEE Transactions on Fuzzy Systems 2026 DOI: 10.1109/TFUZZ.2026.3700647
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• Towards robust and inversion-free randomized neural networks: The XG-RVFL framework
  Pattern Recognition 2026 DOI: 10.1016/j.patcog.2025.112711
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• Enhancing multiview synergy: Robust learning by exploiting the wave loss function with consensus and complementarity principles
  Neural Networks 2025 DOI: 10.1016/j.neunet.2025.107433
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• HawkEye: A robust loss function for regression with bounded, smooth, and insensitive zone characteristics
  Applied Soft Computing 2025 DOI: 10.1016/j.asoc.2025.113118
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• RoBoSS: A Robust, Bounded, Sparse, and Smooth Loss Function for Supervised Learning
  IEEE Transactions on Pattern Analysis and Machine Intelligence 2025 DOI: 10.1109/TPAMI.2024.3465535
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• Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach
  IEEE Transactions on Emerging Topics in Computational Intelligence 2025 DOI: 10.1109/TETCI.2024.3524718
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• Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approach
  Pattern Recognition 2024 DOI: 10.1016/j.patcog.2024.110637
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• Fuzzy Deep Learning for the Diagnosis of Alzheimer's Disease: Approaches and Challenges
  IEEE Transactions on Fuzzy Systems 2024 DOI: 10.1109/TFUZZ.2024.3409412
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