“One must imagine Sisyphus happy.” ― Albert Camus
Open Source AI 🤖
- sktime (ex-Senior AI Engineer & Maintainer): Redesigned the Deep Learning model architecture, built a PyTorch-based RNN classifier/regressor for time-series, and led the TensorFlow-to-PyTorch backend migration. Also redesigned the Forecasting Horizon module to decouple it from pandas, and contributed to dependency resolution, testing infrastructure, and reviewing community PRs across the framework.
- LightlySSL: Implementing LeJEPA (self-supervised learning for vision foundation models by Balestriero & LeCun et al.) - core modules, loss functions, multi-view transforms, and distributed training. Migrated the project's linting and formatting infrastructure to ruff.
AI Safety 🦾
- Replicating Anthropic's Synthetic Document Finetuning study to deepen my understanding of how post-training interventions affect model behaviour, on Llama-3.1-8B using gpt-4o-mini generation and QLoRA finetuning on free Kaggle GPUs). Check here.
- Contributing to TransformerLens and Inspect AI, both crucial pieces of AI safety tooling and infrastructure (LLM evaluation and mechanistic interpretability).
- Completed Blue Dot Impact's Technical AI Safety course (alignment, RLHF, mechanistic interpretability, evaluations, red-teaming, scalable oversight)
- ML Tech Lead at Honeywell Aerospace: architected an org-wide ML framework, shipped models across time-series forecasting, anomaly detection and NLP search
✈️ - Built Bayesian probabilistic forecasting at PriceLabs, serving 500K+ users daily 📈
- Co-founded Zencat Studios, a product and UX design agency for AI products 📲
- Built a wooden cabin in the lower Himalayas 🪵
- going for long walks 🚶
- reading and all things literary 📚
- looking at trees outside my window 🌲
- How interpretability could help bridge the gap between what a model knows and what we can verify it knows.
- Why classical statistical models worked well with less data, while modern ML models built on the same foundations are so data-hungry.
- Machine learning design patterns and MLOps!!
LinkedIn: https://www.linkedin.com/in/nirbhai/
👨🏫 Work-related 📒
- Designing Machine Learning Systems - Chip Huyen
- Practical Statistics for Data Scientists - Andrew Bruce, Peter Bruce, and Peter Gedeck
- Designing Data Intensive Applications - Martin Kleppmann
🤩 Just for fun 📚
- To Hell With Poets - Baqytgul Sarmekova
- Network Effect (The Murderbot Diaries, #5) - Martha Wells
- my wife's thoughts (trying to) 💭
⚡ Fun fact: import antigravity