I am a fourth-year Ph.D. candidate at Tufts University, where I am a part of the High-dimensional learning and data analysis (HILDA) research group. My Ph.D. advisor is James Murphy.

My thesis research analyzes multiscale structure in data. Typically, any single dataset contains multiple latent separations that could be considered correct. In one problem setting, a coarse separation of one’s dataset may be desired, while another problem setting may call for a finer separation within the data. In my thesis research, I leverage tools from harmonic analysis, graph theory, and linear algebra to build fast machine learning algorithms for finding and comparing these different latent separations within data.

Current projects:

  1. Applications of diffusion geometry to multiscale clustering problems.
  2. Applications of diffusion geometry to biclustering, or finding groups of data that similarly express groups of features.
  3. The inverse problem to the Affine Wealth Model: given that we want some distribution of wealth, what redistribution policy should we implement? Whom should we tax if we wish to decrease wealth inequality?

Articles in Review:

  1. Sam L. Polk and Bruce M. Boghosian. (2019) “The Non-Universality of Wealth Distribution Tails Near Wealth Condensation Criticality.” (Link)
  2. Sam L. Polk*, Kimia Kashkooli*, …, and Oluwaseun Akeju (2020) “ECG-Derived Autonomic Nervous System Dynamics Predict Anaesthetic states.”

Published Papers:

  1. Kimia K. Kashkooli*, Sam L. Polk*, …, Oluwaseun Akeju, and Shubham Chamadia (2020) “Improved Tracking of Sevoflurane Anesthetic States with Drug-Specific Machine Learning Models.” Journal of Neural Engineering. (Link)
  2. Sam L. Polk*, Kimia Kashkooli*, …, and Oluwaseun Akeju. (2019). “Automatic Detection of General Anesthetic-States using ECG-Derived Autonomic Nervous System Features.” 2019 IEEE Engineering in Medicine and Biology Conference (EMBC). IEEE. (Link)
  3. Kimia Kashkoooli*, Sam L. Polk*, …, and Oluwaseun Akeju. (2019). “Drug-Specific Models Improve the Performance of an EEG-based Automated Brain-State Prediction System. 2019 IEEE Engineering in Medicine and Biology Conference (EMBC). IEEE. (Link)

* Indicates Co-First Author