Applied machine-learning scientist and research software engineer. I build and evaluate neural models for noisy, high-dimensional scientific time series and imaging data — with a focus on distribution shift, uncertainty quantification, and simulation-based inference — and ship them as reproducible pipelines, not just notebooks.
Background: PhD in astrophysics (supernova cosmology, LSST/DESC pipelines). Currently applying that research experience to general applied-ML problems: time-series classification, computer vision, and probabilistic ML.
- 📄 Publications: arXiv listing · ADS library
- ✉️ ayan@illinois.edu
- 🔭 Currently: LSST Time-Domain Pipeline (NCSA / DESC), open to applied-ML / ML-engineering roles
SNANA is the supernova simulation, light-curve fitting, and time-domain analysis package used across the Rubin Observatory LSST Dark Energy Science Collaboration and prior DES/SDSS-era SN cosmology surveys — facility software, not a personal project, used and validated by a large external collaboration. I'm its 2nd-largest contributor by code volume,
| Lines changed (all-time) | Merged PRs | Rank | |
|---|---|---|---|
| am610 | 181,032 | 74 | #2 of 28 contributors |
| Lead maintainer (RickKessler) | 2,693,737 | — | #1 |
Representative contributions:
- Generalized spline-interpolation library, wired into photo-z quantile and log-mass-vs-redshift estimation — #1664, #1669
- Data-driven host-galaxy weighting (
WGTMAP) modes for simulation realism — #1632 - DiffSky host-galaxy catalog integration pipeline (magnitude joins, dedup, HDF5→pandas conversion) — #1640, #1704, #1728
- Core-collapse-supernova contamination prior implementation — #1524, #1534
Full contribution history → · Contributor graph →
| Project | What it shows | Stack |
|---|---|---|
| GWCCSN_EOS_Ye 🟢 (live demo) | 1D-CNN classifying nuclear equation-of-state from gravitational-wave time series — real dataset included, python train.py && python app.py gets you a working local demo (86.5% held-out accuracy, honestly reported) (arXiv:2310.15649) |
TensorFlow, scikit-learn, Gradio |
| Firecrown_wrapper_TD | Python pipeline orchestrating Firecrown + CosmoSIS for supernova time-domain cosmology inference — CLI, tests, SACC I/O, MIT-licensed | Python, pytest, SACC |
| scone_tools | Data-product and heatmap generation utilities supporting neural supernova classification (SCONE) | Python |
| DeepFake | End-to-end deep-learning video-classification pipeline (face extraction → Inception-ResNet-v2 classifier) — being audited for split leakage and video-level metrics | PyTorch/Keras, OpenCV, dlib |
| nnogada (fork, with I. Gómez-Vargas) | Genetic-algorithm hyperparameter search used to train the uncertainty-aware neural regressor in my first-author dark-energy paper (arXiv:2402.18124) | Python, TensorFlow/PyTorch, DEAP |
- arXiv:2310.15649 (first author) — 1D CNN classification of nuclear equation-of-state from core-collapse-supernova gravitational-wave time series; robustness across sampling rate, signal window, and physical nuisance parameters.
- arXiv:2402.18124 (first author) — Neural regression with genetic-algorithm hyperparameter search (nnogada) and Monte Carlo dropout for uncertainty-aware reconstruction of cosmological observables from simulated Rubin/LSST data.
- arXiv:2409.14508 — Benchmarked CNNs, RNNs, and six classical ML methods (random forest, SVM, XGBoost, etc.) on gravitational-wave time-series classification; quantified performance drop under simulation-domain mismatch.
- arXiv:2603.11165 — Simulation-based inference with conditional normalising flows + hierarchical Bayesian modelling to correct survey-selection effects in supernova cosmology (JAX / NumPyro).
(Full list: arXiv search)
ML / DL: PyTorch, TensorFlow, scikit-learn, uncertainty quantification (MC dropout, ensembles), genetic-algorithm hyperparameter optimization, simulation-based inference, normalising flows, CNNs, time-series classification Scientific computing: JAX, NumPyro, HPC / batch pipelines, SNANA, CosmoSIS, Firecrown Engineering: Python, Git, Docker, pytest, LaTeX, R, C#