Machine Learning Researcher
(Deep Learning, LLMs, Reasoning Systems)
Peer Reviewer @ ICML · NeurIPS | Prospective PhD Student
About :
- A Machine Learning Researcher specializing in Deep Learning, Large Language Models,
and Reasoning Systems, with a focus on building principled, efficient, and reliable
ML systems. My current research interests center on:
- LLM Reasoning and Inference-Time Compute: Studying how language models reason under real-world constraints — including resource-bounded and anytime reasoning settings — to make inference more efficient and predictable.
- Reliable and Robust ML Systems: Designing systems that maintain correctness and performance under deployment constraints, with an emphasis on measurable guarantees and rigorous evaluation.
- Empirical Deep Learning: Applying careful experimental methodology to understand model behavior, generalization, and the limits of modern architectures.
- Prospective PhD student (Spring/Fall 2027) — actively seeking research opportunities in Deep Learning, LLMs, and related areas.
- Active peer reviewer at top-tier ML venues: ICML 2026 and NeurIPS 2026.
Recipient of the 🥇 Gold Reviewer Award at ICML 2026. - Committed to rigorous research practices, reproducibility, and the integrity of scientific evaluation — values I carry both as a researcher and as a reviewer.
- Passionate about continuous learning, precise thinking, and contributing meaningfully to the ML research community.
- Languages: Python (Advanced), C, C++, SQL
- DS & ML Tools (Python): NumPy, Pandas, Matplotlib, Scikit-learn, PyTorch
- Machine Learning Techniques: Deep Learning, NLP, Large Language Models, Reasoning in LLMs, Inference-Time Compute, Reinforcement Learning, Generative Models, Empirical Evaluation and Benchmarking
- Research Tools: LaTeX (Advanced), Git/GitHub, Colab, HPC/Distributed Training
- Others: OpenReview, Academic Writing, Structured Peer Review