Sidhardh S — AI/ML Engineer & Researcher
I design intelligent systems that learn from structure.
Final-year Integrated M.Tech student building research-grade models across graph neural networks, state space models, and generative AI.
Built on rigor, driven by curiosity.
I am a final-year Integrated M.Tech (Software Engineering) student at VIT, focused on machine learning and deep learning research. I am a co-author on IEEE-targeted papers, and I care about the full arc of a project — from dataset construction through model design, evaluation, and ablation.
My work spans graph neural networks, state space models, and generative AI. I like problems where the structure of the data is the hard part, and I enjoy turning a vague research idea into a reproducible experiment with results I can defend.
Focus areas
Graph Neural Networks
01Message passing, graph transformers, and representation learning on relational and molecular data.
State Space Models
02Long-sequence modeling with structured state spaces (S4 / Mamba-style) as efficient alternatives to attention.
Generative AI
03Diffusion and latent generative models for synthesis, augmentation, and controllable generation.
Evaluation & Ablation
04Reproducible pipelines, honest baselines, and ablations that isolate what actually drives performance.
Selected work.
A few projects that show how I think — from data to model to results. Code is on GitHub where it can be shared.
Graph neural network for molecular property prediction with a reproducible benchmark suite and full ablation harness.
- PyTorch Geometric
- Cheminformatics
- GNN
Conditional latent diffusion pipeline for dataset augmentation, with classifier-free guidance and quality/diversity metrics.
- Diffusion
- Generative AI
- PyTorch
Lightweight experiment framework for sweeping, logging, and ablating deep-learning runs without the boilerplate.
- Python
- MLOps
- Weights & Biases
Research & publications.
Peer-review-targeted work. Co-authored unless noted otherwise.
- 01
Structured State Spaces for Long-Range Reasoning on Graphs
S. S. et al.
IEEE (target venue)2025Under review - 02
Ablation-First Evaluation of Message-Passing Architectures
S. S. et al.
IEEE (target venue)2025In preparation
Skills & stack.
The tools I reach for, grouped by what they do.
01ML Frameworks
- PyTorch
- PyTorch Geometric
- TensorFlow
- JAX
- Hugging Face
- scikit-learn
02Infra & MLOps
- CUDA
- Docker
- Weights & Biases
- MLflow
- DVC
- Linux
- Git
03Languages
- Python
- C++
- TypeScript
- SQL
04Data & Tooling
- NumPy
- Pandas
- Matplotlib
- Jupyter
- NetworkX
- RDKit
Let's build something intelligent.
Open to research collaborations, ML engineering roles, and interesting problems. The fastest way to reach me is email.
Available for 2026 roles & research collaborations