| --- |
| tags: |
| - machine-learning |
| - ml-systems |
| - robust-ml |
| - llm-inference |
| - computer-vision |
| --- |
| |
| <div align="center"> |
|
|
| # Aman Singh |
|
|
| ### ML Systems Engineer · Robust ML · GPU Inference · LLM Systems |
|
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| I build reliable machine-learning systems across training, evaluation, inference, and secure deployment. |
|
|
| [GitHub](https://github.com/amanyagami) · [Portfolio](https://aman-ai-portfolio.netlify.app) · [Résumé](https://asinghresume.netlify.app) · [LinkedIn](https://www.linkedin.com/in/amanyagami) |
|
|
| </div> |
|
|
| ## Hub highlights |
|
|
| | Type | Resource | What it shows | |
| | --- | --- | --- | |
| | **Space** | [Viyog OOD-vs-ADV Leaderboard](https://huggingface.co/spaces/amanyagami/viyog) | Interactive evaluation for separating adversarial and out-of-distribution inputs | |
| | **Dataset** | [viyog-adversarial](https://huggingface.co/datasets/amanyagami/viyog-adversarial) | Adversarial/OOD evaluation data for reproducible robustness experiments | |
| | **Model** | [Cifar100_Finetuned](https://huggingface.co/amanyagami/Cifar100_Finetuned) | Fine-tuned vision model checkpoint and evaluation artifacts | |
| | **Model** | [viyog-weights](https://huggingface.co/amanyagami/viyog-weights) | Viyog research weights for robust input-separation experiments | |
| | **Model** | [ViT-Base checkpoint](https://huggingface.co/amanyagami/Vit_base_patch16_224.pth) | Vision Transformer checkpoint for downstream experiments | |
|
|
| ## Research |
|
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| ### Viyog: Separating Adversarial and Out-of-Distribution |
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| **Accepted at ESWEEK CODES 2026.** |
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| Viyog studies how intermediate-representation geometry can separate adversarial inputs from out-of-distribution inputs. This Hub profile contains the accompanying models, dataset, and live evaluation Space. |
|
|
| ## Engineering focus |
|
|
| - **Robust ML:** adversarial evaluation, out-of-distribution detection, representation geometry, and reproducible PyTorch experiments. |
| - **GPU systems:** H100 training with DDP and FP16/BF16, FSDP checkpointing, and sub-10 ms A6000 inference. |
| - **LLM infrastructure:** model integration, streaming tool calls, bounded concurrency, vLLM rollout networking, and evaluation. |
| - **Secure execution:** Rust sandboxing, Windows AppContainer/LPAC confinement, and Linux filesystem isolation. |
|
|
| ## Open-source systems work |
|
|
| Contributions across [SGLang](https://github.com/sgl-project/sglang), [VERL](https://github.com/verl-project/verl), [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), [PyTorch AO](https://github.com/pytorch/ao), [LLVM/MLIR](https://github.com/llvm/llvm-project), and [arapuca](https://github.com/sergio-correia/arapuca), including KV/radix-cache correctness, FSDP checkpoint integrity, pipeline-parallel execution, FP8/quantization, compiler correctness, and sandbox isolation. |
|
|
| ## Background |
|
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| - **Arizona State University, MPS Lab** — Graduate Researcher · Tempe, Arizona |
| - **Amazon** — Software Development Engineer · Bangalore, India |
|
|
| ## Education |
|
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| - **M.S. in Computer Engineering** — Arizona State University, 2024–2026 |
| - **B.Tech in Electrical and Electronics Engineering** — National Institute of Technology Karnataka, 2018–2022 |
|
|
| ## Toolkit |
|
|
| **PyTorch · CUDA · DDP/FSDP · SGLang · vLLM · Transformers · Python · C++ · Rust · Go · Linux · Docker · Kubernetes · AWS · Google Cloud** |
|
|
| ## Connect |
|
|
| For robust ML, GPU systems, LLM infrastructure, or research engineering: [email me](mailto:2amansingh2@gmail.com) or connect on [LinkedIn](https://www.linkedin.com/in/amanyagami). |
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|
