Abhiram Dharme
Research

What I’m working on.

Selected work around deep learning for wearable biosignals: models that respect sensor geometry, experiments that probe data scarcity, and systems that make those ideas measurable.

2025 —
First author

CycloFormer

A rotation-invariant transformer for wrist-sEMG hand pose estimation, used to study when biosignals complement vision under occlusion and how the modality scales with data.

NeurIPS 2026 (under review)

  • Built exact ℤ₁₆ invariance to wristband donning rotation through channel-shared TDS-CNNs, circular RoPE, and permutation-invariant attention pooling.
  • Achieved new state of the art on emg2pose: a 4M-parameter model beats the previous SoTA 6M model by meta on every generalization split with 33% fewer parameters; a 48M model widens the Stage split margin by 2.5° / 3.3 mm.
  • Constructed the first controlled sEMG-vs-vision comparison under fingertip self-occlusion; vision wins when the hand is visible, while sEMG becomes more reliable once two or more fingertips are occluded.
  • Fit a data-scarcity-aware scaling law across 8 model sizes and 5 data fractions, explaining 98.8% of PA-MPJPE variance and projecting that closing the vision gap would require roughly 34× more session-hours than emg2pose.