[2026 Ambassador Application] Samaresh Kumar Singh

[ssam18]

Application Type

Self-nomination

Your Name

Samaresh Kumar Singh

Your Email

ssam3003@gmail.com

Please confirm the following:

• The nominee is 18 years of age or older.
• The nominee agrees to follow the PyTorch Foundation Code of Conduct.
• The nominee agrees to comply with the Linux Foundation Antitrust Policy.
• The nominee has active involvement in PyTorch Foundation projects or communities.
• I confirm that this nominee has not already been submitted for the 2026 Ambassador cycle.

Nominee Full Name

Samaresh Kumar Singh

Nominee Email

ssam3003@gmail.com

Current Location

Austin, Texas, United States

Years of Community Involvement

5+ years

Which platforms or profiles would you like to share?

• GitHub
• GitLab
• LinkedIn
• X / Twitter
• Personal Website / Portfolio
• Other

GitHub Profile

https://github.com/ssam18

GitLab Profile

No response

LinkedIn Profile

https://www.linkedin.com/in/samaresh-singh-9772ba23/

X / Twitter Profile

No response

Personal Website / Portfolio

https://scholar.google.com/citations?hl=en&user=3z9qzooAAAAJ

If 'Other' was selected, please specify

Google Scholar: https://scholar.google.com/citations?hl=en&user=3z9qzooAAAAJ arXiv: https://arxiv.org/pdf/2512.23809 GitHub secondary profile: https://github.com/SamareshSingh

Which PyTorch Foundation-hosted projects or ecosystem projects is the nominee familiar with?

• PyTorch
• vLLM
• DeepSpeed
• Ray
• Helion
• Safetensors
• Other

If 'Other' was selected, please specify the project name

Keras, TensorFlow, OpenVINO, ONNX Runtime, TensorRT-LLM, llama.cpp, Ollama, OpenCV, MLCommons Inference, Cilium, Grafana

How has the nominee contributed to the community?

• Open source contributions
• Technical content creation
• Community leadership
• Events or workshops
• Research or academic work
• Mentorship
• University or student outreach
• Translation or localization efforts
• Other

If 'Other' was selected, please specify

Conference speaking, peer review, technical book reviewing, hackathon judging, developer advocacy, and AI infrastructure research.

Please describe the nominee’s contributions to PyTorch Foundation projects and communities

I am a Principal Engineer at HP Inc. with more than 21 years of experience in AI/ML infrastructure, distributed systems, cybersecurity, Edge AI, cloud-edge platforms, and production software architecture. My current work focuses on enterprise AI, remote GPU workflows, Edge AI systems, entitlement infrastructure, Cache-as-a-Service, and reliable cloud-edge platforms.

Selected recognitions and memberships:

• IEEE Senior Member
• Google Developer Expert in AI
• NVIDIA Developer Champion
• Docker Captain
• Snort.org Major Contributor
• Active open-source contributor across AI, ML, cybersecurity, cloud-native, observability, and infrastructure projects

Selected open-source areas:

• AI and ML infrastructure: PyTorch, vLLM, Ray, Keras, TensorFlow, OpenVINO, ONNX Runtime, ONNX Script, NVIDIA TensorRT-LLM, MLCommons Inference, llama.cpp, Ollama, OpenCV, MLflow, SciPy, pandas
• Cybersecurity and networking: Snort3, Suricata, OpenSSL, Cilium, Falco, KubeEdge
• Cloud-native and developer infrastructure: Docker Compose, Grafana, gRPC, Cobra, nlohmann/json, spdlog, Beman Project

Selected IEEE research publications:

• https://ieeexplore.ieee.org/document/11542411
• https://ieeexplore.ieee.org/document/11476268
• https://ieeexplore.ieee.org/document/11506571
• https://ieeexplore.ieee.org/document/11395783
• https://ieeexplore.ieee.org/document/11476181
• https://ieeexplore.ieee.org/document/11465695
• https://ieeexplore.ieee.org/document/11491850

Selected community contributions:

• Speaker and keynote speaker at AI, Edge AI, cloud-native, cybersecurity, C++, and distributed systems events
• Peer reviewer for international conferences and journals through EDAS and Microsoft CMT
• Judge and mentor for multiple innovation, AI, and developer hackathons, including NVIDIA Hackathon, Perforated AI Hackathon, Google Developers Group Hackathon, TAMUHack 2026, TidalHack:26, Hack4Health, Aethera Hackathons, DevForge Hackathon, and Founder Forge Hackathon. These roles included evaluating technical depth, innovation, real-world impact, AI/ML implementation quality, and mentoring participants on solution design, architecture, and presentation.
• Technical book reviewer for AI, GPU programming, and software engineering topics
• Mentor for experienced engineers and industry professionals through ADPList and Linux Foundation mentorship platforms
• Published technical author on Edge AI, Agentic AI, intelligent intrusion prevention, industrial AI, cloud-edge systems, and feature management workflows

Selected speaking engagements:

• The Twelve Clouds of Christmas, Year 15, December 2025
• AI Loves Data Austin at Oracle HQ, Agentic AI and Governance, February 2026
• AI, ML and Computer Vision Meetup at Voxel51, March 2026
• C++Online 2026, “Building High Performance Distributed Systems in Modern C++,” March 2026
• AI Governance Leadership Forum: Digital Summit 2026, March 2026
• CNCF Kubernetes Austin, NVIDIA RAG on Kubernetes and policy platform modernization, March 2026
• PyTorch Conference Europe 2026, April 2026
• IEEE CTSoc Technical Talks, invited keynote speaker, April 2026
• ML Week Hybrid AI 2026, keynote speaker, May 2026

Selected open-source contribution links:

GitHub: https://github.com/ssam18
Secondary GitHub: https://github.com/SamareshSingh
PyTorch: pytorch/pytorch#178524
PyTorch: pytorch/pytorch#178957
PyTorch: pytorch/pytorch#184614
vLLM: vllm-project/vllm#39342
vLLM: vllm-project/vllm#41486
Ray: ray-project/ray#62588
MLCommons Inference: mlcommons/inference#2466
MLCommons Inference: mlcommons/inference#2439
NVIDIA TensorRT-LLM: NVIDIA/TensorRT-LLM#10320
NVIDIA TensorRT-LLM: NVIDIA/TensorRT-LLM#13166
NVIDIA TensorRT-LLM: NVIDIA/TensorRT-LLM#14538
OpenVINO: openvinotoolkit/openvino#35428
OpenVINO: openvinotoolkit/openvino#36016
ONNX Runtime: microsoft/onnxruntime#28057
ONNX Script: microsoft/onnxscript#2916
OpenCV: opencv/opencv#27979
OpenCV: opencv/opencv#27980
OpenCV: opencv/opencv#28724
OpenCV: opencv/opencv#28780
OpenCV: opencv/opencv#28826
OpenCV: opencv/opencv#28939
OpenCV: opencv/opencv#28954
OpenCV: opencv/opencv#29081
OpenCV: opencv/opencv#29253
Keras: keras-team/keras#22054
Keras: keras-team/keras#21860
Keras: keras-team/keras#21834
Keras: keras-team/keras#22598
Keras: keras-team/keras#22617
llama.cpp: ggml-org/llama.cpp#19581
llama.cpp: ggml-org/llama.cpp#17331
llama.cpp: ggml-org/llama.cpp#19532
llama.cpp: ggml-org/llama.cpp#22063
llama.cpp: ggml-org/llama.cpp#22102
llama.cpp: ggml-org/llama.cpp#21653
Ollama: ollama/ollama#14210
Ollama: ollama/ollama#13124
Cilium: cilium/cilium#45370
Cilium: cilium/cilium#45721
Cilium: cilium/cilium#45888
Cilium: cilium/cilium#45868
Cilium: cilium/cilium#45966
Grafana: grafana/grafana#121603
Grafana: grafana/grafana#122288
Grafana: grafana/grafana#124516
Grafana: grafana/grafana#125029
Snort3: snort3/snort3#433
Snort3: snort3/snort3#438
Suricata: OISF/suricata#15570
Suricata Verify: OISF/suricata-verify#3149
OpenSSL: openssl/openssl#29166
OpenSSL: openssl/openssl#30888
OpenSSL: openssl/openssl#30587
MLflow: mlflow/mlflow#22649
pandas: pandas-dev/pandas#65052
SciPy: scipy/scipy#23979
Docker Compose: docker/compose#13669
Falco: falcosecurity/falco#3858
KubeEdge: kubeedge/kubeedge#6739
Beman Project span: bemanproject/span#12

Selected links:

• LinkedIn: https://www.linkedin.com/in/samaresh-singh-9772ba23/
• GitHub: https://github.com/ssam18
• Secondary GitHub: https://github.com/SamareshSingh
• Google Scholar: https://scholar.google.com/citations?hl=en&user=3z9qzooAAAAJ
• Public implementation: https://github.com/ssam18/zta-federated-learning
• Hugging Face dataset: https://huggingface.co/datasets/ssam17/Edge-Industrial-Anomaly-Phi3
• Hugging Face model: https://huggingface.co/ssam17/phi3-industrial-anomaly

My long-term goal as a PyTorch Foundation Ambassador is to help developers, students, researchers, and enterprise engineers build reliable, secure, scalable, and production-ready AI systems using PyTorch and the broader PyTorch Foundation ecosystem. I would like to contribute through open-source work, practical education, mentorship, technical workshops, content creation, and production-focused guidance across AI infrastructure, Edge AI, LLMOps, MLOps, secure AI, and industrial AI adoption.

Why does the nominee want to become a PyTorch Foundation Ambassador?

I want to become a PyTorch Foundation Ambassador because PyTorch has become one of the most important platforms for modern AI development, research, and production deployment. It is widely used by researchers, students, startups, enterprises, and open-source communities, and I would like to help expand its impact through education, mentorship, technical advocacy, and practical production-focused guidance.

My background is strongly aligned with the needs of the PyTorch community. I have spent more than two decades building distributed systems, AI/ML infrastructure, cloud-edge platforms, cybersecurity systems, and production-grade software. At HP Inc., my work focuses on enterprise AI, Edge AI, hybrid AI, GPU-enabled workflows, AI infrastructure, and reliable deployment patterns. I understand the challenges teams face when moving from a notebook or prototype to a production system that must meet latency, reliability, security, monitoring, and cost requirements.

I am especially motivated to support developers who want to use PyTorch beyond experimentation. Many teams understand model training, but they struggle with deployment, inference performance, observability, model drift, security, scaling, MLOps, and real-world infrastructure tradeoffs. I would like to help make these topics more accessible through talks, tutorials, workshops, articles, code examples, and mentorship.

I also want to help connect the PyTorch ecosystem with important growth areas such as Edge AI, federated learning, secure AI, industrial IoT, model serving, LLMOps, vLLM, Ray, OpenVINO, ONNX Runtime, and heterogeneous compute. These areas are becoming increasingly important as AI systems move closer to production users, enterprise workflows, industrial environments, and edge devices.

Becoming a PyTorch Foundation Ambassador would allow me to serve the community more formally, share practical knowledge, support new contributors, and help grow PyTorch adoption across industry, research, and developer communities.

How would this nominee contribute as a PyTorch Foundation Ambassador?

As a PyTorch Foundation Ambassador, I would contribute through a combination of technical education, open-source contribution, community building, mentorship, and production-focused developer advocacy.

First, I would create practical educational content focused on production PyTorch. Topics would include model training to deployment workflows, PyTorch inference optimization, model serving, observability, model drift detection, Edge AI deployment, distributed inference, GPU and heterogeneous compute usage, and reliability patterns for production AI systems. I would aim to create content that helps developers move from prototype to production with confidence.

Second, I would organize and support community sessions, workshops, webinars, and meetups. I am especially interested in sessions such as:

• Building production-grade PyTorch inference pipelines
• Deploying PyTorch models at the edge
• PyTorch with Ray, vLLM, OpenVINO, and ONNX Runtime
• Observability and drift monitoring for PyTorch models
• Secure and trustworthy AI systems using PyTorch
• Federated learning and Edge AI with PyTorch
• MLOps and LLMOps patterns for enterprise AI teams

Third, I would mentor new contributors and developers. Many engineers want to contribute to AI open source but do not know how to start. I can help them identify approachable issues, understand contribution workflows, write better bug reports, improve documentation, and build confidence submitting pull requests.

Fourth, I would continue contributing to PyTorch Foundation and ecosystem projects through GitHub. My contribution focus would include documentation, examples, reliability improvements, tests, production usability, inference workflows, and integration guidance across related projects such as PyTorch, vLLM, Ray, Safetensors, and other serving or deployment tools.

Fifth, I would help grow PyTorch adoption in enterprise, edge, and industrial AI communities. My background in distributed systems, cybersecurity, IoT/IIoT, and cloud-edge infrastructure allows me to explain PyTorch in contexts where reliability, compliance, latency, and security matter. This can help bring more production engineering voices into the PyTorch community.

Finally, I would use my speaking, research, reviewer, and open-source experience to represent the PyTorch Foundation professionally and responsibly. I would actively promote the PyTorch Code of Conduct, support inclusive community growth, and help make the ecosystem welcoming to students, researchers, engineers, and first-time contributors.

Ambassador Focus Areas

• Community Building
• Technical Education
• Open Source Contributions
• Research & Academia
• Developer Advocacy
• Mentorship
• Events & Workshops
• Content Creation
• Regional Community Growth
• Other

If 'Other' was selected, please specify

Production AI, Edge AI, MLOps, LLMOps, AI infrastructure, secure AI, and industrial AI adoption.

Primary Community Region

North America

If 'Other' was selected, please specify

No response

Primary Country During Ambassador Term

United States

Additional Information (Optional)

Additional supporting context:

I am a Principal Engineer at HP Inc. with more than 21 years of experience in AI/ML infrastructure, distributed systems, cybersecurity, Edge AI, cloud-edge platforms, and production software architecture.

Selected highlights:

• IEEE Senior Member
• Google Developer Expert in AI
• Nvidia Developer Champion
• Docker Captain
• Active open-source contributor across AI, ML, cloud-native, observability, and infrastructure projects
• Speaker at AI, Edge AI, cloud, cybersecurity, and distributed systems events
• Reviewer and session chair for multiple technical conferences
• Technical book reviewer for AI and software engineering topics
• 1st Place Best Paper Award at IEEE SaTC 2026 for “Zero-Trust Agentic Federated Learning for Secure IIoT Defense Systems”

Selected links:

• LinkedIn: https://www.linkedin.com/in/samaresh-singh-9772ba23/
• GitHub: https://github.com/ssam18
• Google Scholar: https://scholar.google.com/citations?hl=en&user=3z9qzooAAAAJ
• Award-winning paper: https://arxiv.org/pdf/2512.23809
• Public implementation: https://github.com/ssam18/zta-federated-learning
• PyTorch PR: Fix allow_in_graph crashing when called inside a compiled function pytorch/pytorch#178524
• PyTorch PR: [inductor] Remove fp8 special handling in triton_utils.py pytorch/pytorch#178957
• vLLM PR: [CPU] Fix AttributeError when loading GeluAndMul and similar activations vllm-project/vllm#39342
• MLCommons PR: Fix PyTorch version issue for Llama2-70b and Mixtral-8x7b mlcommons/inference#2466
• MLCommons PR: Fix DLRMv3 fbgemm_gpu loading with hardcoded Bazel paths mlcommons/inference#2439

My long-term goal as a PyTorch Foundation Ambassador is to help developers, students, researchers, and enterprise engineers build reliable, secure, and scalable AI systems using PyTorch and the broader PyTorch Foundation ecosystem.

[github-actions]

Applications and nominations for the 2026 PyTorch Foundation Ambassador Program are now closed.

Application Issue: #324

This issue has been locked to allow for application review.

For questions regarding your application or the program, please contact ambassadors@pytorch.org.

Timeline and program information:
https://pytorch.org/programs/ambassadors/application-process/