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Ruparagunath G — AI Researcher, Neuro-Symbolic & Scientific Machine Learning

Portfolio LinkedIn Email


I build systems that are measurable rather than merely impressive — equation discovery for physical transport, explanations you can falsify, and research infrastructure that keeps its own record.

The through-line across everything below is one question: can this system show its work, and does the account it gives actually hold?

Overview. Research: Research Assistant on an Autonomous Research Supervision Agent at Anna University since Aug 2026; Undergraduate Researcher in Symbolic Regression for Packed-Bed Transport at IIT Madras since Feb 2026; Research Engineer in AI Research Infrastructure at University of Toronto, remote; Undergraduate Researcher in Sign-Language Translation and Explainable Medical Imaging at Anna University MIT Campus since Apr 2025. Focus: symbolic regression, equation discovery, neuro-symbolic AI, explainable AI, computer vision, multi-agent systems. Publications 2026: IEEE Xplore ICIETSD, and Springer ICCCSP, accepted.
Research & publications — text version
Since Role Where
Aug 2026 Research Assistant — Autonomous Research Supervision Agent Anna University · supervised by an IBM India Enterprise Architect
Feb 2026 Undergraduate Researcher — Symbolic Regression for Packed-Bed Transport IIT Madras · Advisor: Dr. Himanshu Goyal
Research Engineer — AI Research Infrastructure University of Toronto (remote) · Collaborator: Tong Li
Apr 2025 Undergraduate Researcher — Sign-Language Translation & Explainable Medical Imaging Anna University, MIT Campus

Neural Translation of Tamil to Indian Sign Language via Pivot-Transformer with 2D and 3D Avatar Motion Through English as a Medium Language
Radha Senthilkumar (Supervisor) · Ruparagunath G · Jayanathi P (Mentor) — ICIETSD 2026 · IEEE Xplore

From ASL to ISL: Translating Gestures across Sign Languages
Co-author — ICCCSP 2026 · Springer (IFIP-endorsed) · Accepted, to appear


Selected work

SynapseDoes a retrieval system's explanation actually cause its ranking?
A counterfactual edge-ablation protocol: delete exactly the edges an explanation cited, re-rank, and test the displacement against a count-matched random-edge control. Personalized PageRank written from scratch so per-arc flow is recorded, not reconstructed. 215 tests · Python

NeuroLensFaithful XAI for multiple sclerosis screening
Explanations gated on whether the model actually decided inside the interpretable basis, rather than assumed to be faithful.

Universal Pressure-Drop CorrelationScientific ML · equation discovery
Symbolic regression recovering closed-form transport correlations for packed beds.

MS vs. Cerebral Small Vessel DiseaseBayesian ML under scarce clinical data
Distinguishing MS from CSVD on MRI with severely limited data — 20 of 21 patient cases correctly classified.

Computer Vision Lab — UG semester-4 coursework, kept public as a working record.

Several projects above are research in progress or live in private repositories; the portfolio carries the full write-ups.


Education

B.Tech, Artificial Intelligence & Data Science — Anna University, MIT Campus · Chennai
Diploma — Central Polytechnic College · Chennai



An explanation can be true and still be a non-explanation.