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amanyagami/README.md

👋 Aman Singh — ML Research Engineer / Software Engineer

Portfolio · Resume · LinkedIn · Email


🧭 About

M.S. (Thesis), Arizona State University — ML robustness, adversarial/OOD detection, and reliable inference.
Former Software Engineer at Amazon building production-grade, high-throughput systems and CI/CD pipelines.
Research submissions under review (DAC 2026).


🔧 Technical Focus

  • ML: PyTorch, TensorFlow, LoRA/QLoRA, HuggingFace, CUDA
  • Systems: AWS (EC2, S3, Lambda, DynamoDB, SageMaker), Docker, Spark
  • Engineering: Distributed training (DDP), mixed precision (FP16/BF16), low-latency GPU inference, RAG pipelines, evaluation & benchmarking
  • Languages: Python, C++, Java, Go, C#, Rust

📌 Featured Repositories

📊 GitHub Stats

 


Open to ML research collaborations, internships, and ML/SDE roles.

Pinned Loading

  1. Fine_Tuning_Vision_Transformers_on_Cifar100 Fine_Tuning_Vision_Transformers_on_Cifar100 Public

    🧠 Fine-tuning Vision Transformers and modern CNNs (ViT, Swin, MobileVit, EfficientNetV2-L ) on CIFAR-100 using PyTorch, timm, and uv.

    Jupyter Notebook

  2. Probablistic-modelling-of-features Probablistic-modelling-of-features Public

    This project explores the geometry and probabilistic structure of deep neural network feature spaces, with a focus on class separability, representation collapse, and robustness under adversarial p…

    Jupyter Notebook

  3. SLM-based-QA SLM-based-QA Public

    A Flask-based PDF question answering chatbot that compares direct prompting vs retrieval-augmented generation using Supermemory across multiple small and large language models.

    HTML

  4. Detecting-Silent-Data-Corruptions-in-Deep-Neural-Networks Detecting-Silent-Data-Corruptions-in-Deep-Neural-Networks Public

    A PyTorch-based implementation of DrDNA, a post-hoc framework for detecting and mitigating soft errors (SDCs) in deep neural networks. The project profiles layer-wise activation statistics and comp…

    Jupyter Notebook

  5. Reasoning-of-LLM Reasoning-of-LLM Public

  6. MPSLab-ASU/Seperating_OOD_and_ADV MPSLab-ASU/Seperating_OOD_and_ADV Public

    A lightweight PyTorch framework for distinguishing out-of-distribution (OOD) inputs from adversarial (ADV) samples using intermediate feature representations.

    Python