publications

2026

• CoRL-W
  How Should a Home Robot Update Its Memories When Human Routines Change?

  O. Wang, M. Srivastava

  CoRL 2026 Workshop on Memory for Robot Foundation Models (RoboMem) accepted; companion versions at the CoRL 2026 workshops on Modeling Uncertainty in Robotic World Models and on Continually Self-Improving Robots

  LLM-written memory summaries often forget what they learned when an old routine comes back. Keeping hypotheses about routines, backed by raw observations, makes predictions hold up better under these shifts.

• AAAI-W
  Spectral Predictability as a Fast Reliability Indicator for Time Series Forecasting Model Selection

  O. Wang, P. Quan, K. Yang, M. Srivastava

  AAAI 2026 Workshop on AI for Time Series (AI4TS) oral

  A score computed in seconds tells you whether a forecasting foundation model will beat a small, cheap model on your data.

  arXivcode

2025

• AROB
  Robots Reading Recipes: Large Language Models as Translators Between Humans and Machines

  O. Wang, G. Cheng, L. Caspar, A. Yokota, M. Khosravy, O. Witkowski

  Artificial Life and Robotics, 30(3), 2025

  Turning written cocktail recipes into robot commands with an off-the-shelf LLM and no fine-tuning.

  paper

2024

• ACS Nano
  An Insertable Glucose Sensor Using a Compact and Cost-Effective Phosphorescence Lifetime Imager and Machine Learning

  A. Goncharov, ..., O. Wang, ..., A. Ozcan

  ACS Nano, 18(34), 2024

  arXiv

2022

• JSR
  LSOP: Layer-Scaled One-shot Pruning

  O. Wang, C. Dovrolis, J. Lee

  Journal of Student Research, 11(1), 2022

  paper