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Summary

Overview PhD-level Machine Learning Scientist with 5+ years of experience developing end-to-end deep learning frameworks for complex time-series and physiological signal analysis. Proven ability to architect novel CNN and Transformer models that deliver real-time, interpretable, and scalable solutions from raw sensor data. Passionate about translating multimodal biosignals into discernable insights for MedTech and human performance applications.

Technical Skills

Languages & Frameworks Python (TensorFlow, PyTorch, JAX, Scikit-learn, MNE), MATLAB, SQL, R, Git
ML Architectures Transformers, CNNs (1D-CNN, WaveNet), RNNs/LSTMs, Autoencoders, Foundation Models
ML Concepts End-to-End Learning, Self-Supervised Learning, Time-Series Analysis, Explainable AI (XAI), Attention Mechanisms, Few-Shot Learning, Sensor Fusion
Domain Knowledge Biosignal Processing (fNIRS, EEG, ECG), Brain-Computer Interfaces (BCI), Clinical Study Design, Human Motor Skill Assessment

Research Experience

  • Sep 2025 – Present
    Postdoctoral Researcher
    CeMSIM, RPI
    • Pioneering multimodal learning by integrating auxiliary physiological signals (HRV, pupillometry) with neuroimaging data to build a comprehensive model of human performance.
    • Expanding the model's core function from binary classification to regression, enabling the prediction of precise, quantitative scores for surgical certification (FLS).
    • Developing advanced modeling techniques for high-bitrate time-series data, focusing on adapting Transformer architectures for complex EEG signal analysis.
  • Jan 2020 – Aug 2025
    Graduate Researcher, AI/ML (Health)
    CeMSIM, RPI
    • **Architected and validated a novel 1D-CNN framework** that achieved **98.6% accuracy** in classifying motor skills from raw fNIRS data, enabling real-time analysis.
    • **Designed and implemented a Transformer-based foundation model** with novel attention mechanisms, providing spatiotemporal explanations that transformed a "black box" model into an interpretable diagnostic tool.
    • **Demonstrated state-of-the-art generalization**, adapting the foundation model to a novel medical task with **>87% accuracy using fewer than 30 labeled samples**, proving the model's scalability.
    • **Owned the end-to-end creation of a foundational 2,100+ trial neuroimaging dataset**, managing curation and processing of 100GB+ of data to enable training a first-of-its-kind foundation model.

Professional Experience

  • May 2018 – Sep 2023
    R&D Co-Director & Co-Founder
    GRIT Engineering Pvt. Ltd.
    • Co-founded an engineering firm, leading a cross-functional team of 5+ engineers and technicians from concept to delivery of custom electromechanical systems.
    • **Directed the full project lifecycle** for a high-visibility automated camera system for "The Voice Nepal," managing client requirements, system integration, and successful on-time deployment.
    • Owned the R&D roadmap, driving iterative product improvements through rigorous design testing and root cause analysis, directly impacting system reliability and client satisfaction.

Education

  • Aug 2025
    Ph.D. in Mechanical Engineering
    Rensselaer Polytechnic Institute
    • Dissertation: "*End-to-end bimanual motor skill assessment from raw neuroimaging data*"
  • May 2024
    M.Eng. in Mechanical Engineering
    Rensselaer Polytechnic Institute

Select Publications

  • **A. Subedi** et al. “An Interpretable Transformer-Based Foundation Model for Cross-Procedural Skill Assessment...”, *arXiv:2506.22476*, 2025.
  • **A. Subedi** et al. “End-to-End Deep Learning for Real-Time Neuroimaging-Based Assessment of Bimanual Motor Skills”, *npj Digital Medicine (Under Review)*, *arXiv:2504.03681*, 2025.
  • C. Eastmond, **A. Subedi**, S. De, X. Intes, “Deep Learning in fNIRS: A Review”, *Neurophotonics 9(4).*

Professional Activities & Awards

  • **Peer Reviewer** for scientific journals including *Neurophotonics* and *Heliyon*.
  • **Teaching Assistant** for Numerical Methods & Machine Dynamics (RPI, 2020).
  • ABU Robocon (Asia-Pacific Robot Contest): Team Nepal (**Best Engineering Awards: 2015, 2016**).
  • IOE Entrance Scholarship: Full-tuition, merit-based award.