Honors & Awards


  • Graduate Scholar in Residence, El Centro Chicano y Latino, Stanford University (2020)
  • Tau Beta Pi Membership, Tau Beta Pi Honor Society (2017)
  • ADVANCE Fellow, Stanford University (2018)
  • Boeing Scholarship, The Boeing Company (2017)
  • Harold P. Brown Engineering Fellowship, Washington University in St. Louis McKelvey School of Engineering (2016)
  • Goldwater Scholarship Honorable Mention, Barry Goldwater Scholarship Foundation (2016)

Education & Certifications


  • Master of Science, Stanford University, BIOPH-MS (2021)
  • M.S., Stanford University, Biophysics (2021)
  • B.S., Washington University in St. Louis, Systems Engineering (2018)
  • B.S., Washington University in St. Louis, Computer Science (2018)
  • B.S., WUStL/Whitworth dual-degree program, Biophysics (2018)

Current Research and Scholarly Interests


I am an MD candidate with a background in multiple areas relevant to the quantitative study of complex biological systems.

My research interests are in the development of novel quantitative approaches for tackling medical problems, including algorithms, machine learning techniques, methods of interpreting complex data, physics-based medical interventions, and mathematical frameworks for improving our understanding of biological processes relevant to disease. Disconnected as these might seem, they are integrally linked by a common collection of mathematical methods. Indeed, I aim to develop both foundational and applied solutions to quantitative problems in healthcare, and am especially interested in problems relevant to neurosurgery. Few things make my ears perk up as much as variants of the phrase, "we have all this data, but aren't quite sure what to do with it."

Previously I've worked in such areas as machine learning for neuro-imaging (with Olivier Gevaert at Stanford), mathematical biophysics (with Shamit Kachru at Stanford), algorithms for computational neuroscience (with Ralf Wessel and Benjamin Moseley at WUStL), inverse optimization for quantum computing (with Alejandro Rodriguez at Princeton), and computational molecular dynamics for drug design (with Matt Jacobson at UCSF).

I am additionally interested in medical ethics and mathematics education within the medical community.

Current Clinical Interests


  • Neurosurgery
  • Pediatric Neurosurgery
  • Medical Oncology
  • Functional Neurosurgery

All Publications


  • Pre-Synaptic Pool Modification (PSPM): A supervised learning procedure for recurrent spiking neural networks. PloS one Bagley, B. A., Bordelon, B., Moseley, B., Wessel, R. 2020; 15 (2): e0229083

    Abstract

    Learning synaptic weights of spiking neural network (SNN) models that can reproduce target spike trains from provided neural firing data is a central problem in computational neuroscience and spike-based computing. The discovery of the optimal weight values can be posed as a supervised learning task wherein the weights of the model network are chosen to maximize the similarity between the target spike trains and the model outputs. It is still largely unknown whether optimizing spike train similarity of highly recurrent SNNs produces weight matrices similar to those of the ground truth model. To this end, we propose flexible heuristic supervised learning rules, termed Pre-Synaptic Pool Modification (PSPM), that rely on stochastic weight updates in order to produce spikes within a short window of the desired times and eliminate spikes outside of this window. PSPM improves spike train similarity for all-to-all SNNs and makes no assumption about the post-synaptic potential of the neurons or the structure of the network since no gradients are required. We test whether optimizing for spike train similarity entails the discovery of accurate weights and explore the relative contributions of local and homeostatic weight updates. Although PSPM improves similarity between spike trains, the learned weights often differ from the weights of the ground truth model, implying that connectome inference from spike data may require additional constraints on connectivity statistics. We also find that spike train similarity is sensitive to local updates, but other measures of network activity such as avalanche distributions, can be learned through synaptic homeostasis.

    View details for DOI 10.1371/journal.pone.0229083

    View details for PubMedID 32092107