Achyuthan |

Introduces the Frequency Synchronization Degree (FSD) — a permutation-tested metric for Fourier circuit synchronisation that requires no prior circuit knowledge. FSD predicts grokking 500–3,000 steps in advance across nine modular-addition configs (mean lead +1,722 steps). Weight-decay intervention at the FSD ceiling causally confirms Phase 2 is regularisation, not computation: ∆t ∝ 1/λ across three primes.

A statistics-driven routing layer that selects among 6 function bases (Fourier, Wavelet, Chebyshev, RBF, Rational, Sigmoid) using distributional input statistics — mean, variance, skewness, kurtosis, entropy, sparsity — with zero routing supervision. Emergent specialization measured via per-layer Shannon entropy dynamics across training.

Pip-installable toolkit for routing entropy, expert utilisation, and collapse across OLMoE, JetMoE, and Qwen1.5-MoE. Includes domain-matched prompts (math/code/general), bootstrap confidence intervals, L_div LoRA training, MMLU evaluation, and a Gradio demo. Key finding: collapse is architecture-dependent, not universal.

Research assistant in Prof. Anna Choromanska's lab (NYU) working with postdoc Haoran Zhu on AD-LiST-JEPA — a Joint Embedding Predictive Architecture for automotive LiDAR object detection and occupancy completion/forecasting on the Waymo Open Dataset. Completed SSL pretraining pipeline; running downstream OCF finetuning experiments.

Evolutionary REINFORCE meta-learner over discrete MoE topology space (1–3 tiers × 2–5 experts/tier), eliminating hand-designed architectures. Discovered topology (3,3,2) achieving 100% task success at 11.5M params vs. 23.3M for fixed Hierarchical MoE — 2× parameter efficiency at equal performance.