Benjamin K. Cooper

Burlingame, CA benjamin.k.cooper@gmail.com

Skills

  • Scientific Python stack, especially numpy, scipy, matplotlib, scikit-image; some experience with pandas, scikit-learn, numba
  • statistical inference: maximum likelihood, hidden Markov models, Bayesian inference
  • numerical optimization
  • microscopy: brightfield, DIC, fluorescence, confocal, Micro-Manager
  • electronics and measurement
  • other programming: previous experience with C, C++, Java, MATLAB; familiarity with HTML, CSS, SQL
  • strong written and verbal communicator: won prizes both in undergrad and graduate work for best student talk

Experience

Scientist, Calico Life Sciences
2015 - 2020

  • Performed research on computational microscopy in Andrew York’s lab, with a particular emphasis on incorporating photophysics (e.g. photobleaching, FRET) into maximum likelihood reconstruction algorithms. Developed and implemented new reconstruction algorithms in Python. Developed new theory of photophysics induced noise in fluorescence imaging.
  • Assisted in support of microscopy core for ~50 biologists. Responsible for training, support and troubleshooting of Micro-Manager (Java/C++ open source microscopy control software) on core instruments, including modifications to hardware device adapters.
  • Collaborated on time lapse movie image analysis project with Calico biologists and Google machine learning engineers, using deep learning and classical image analysis techniques for object detection, segmentation, and downstream analysis.

Scientific computing contractor, National Institutes of Health
2015

  • Developed Python code to implement 3d structured illumination microscopy (SIM) reconstruction using maximum likelihood estimation for George Patterson at the NIH.

Postdoctoral research associate, University of Maryland
2014

  • Supervised by Ian Appelbaum. Implemented proof-of-principle experiment for measuring Majorana fermions in semiconductor nanowires using novel capacitively coupled approach. Fabricated tunnel junctions and measured using custom-built low-capacitance cryogenic probe. Developed simulations in Python to assess experimental results.

Education

Ph.D., physics, University of Maryland
Dec. 2013

  • Thesis title: Multi-junction effects in dc SQUID phase qubits, advised by Fred Wellstood.
  • Designed, fabricated (e-beam and photolithography) and measured superconducting devices for quantum computing. Developed quantum theory of these devices, including effective Jaynes-Cumming Hamiltonian. Prior to Wellstood lab, did theoretical work in condensed matter.

B.A., math/physics, Williams College
Jun. 2001

  • Graduated cum laude, with honors
  • Thesis title: Color tuning through mechanical stretching in polyacetylene, advised by Daniel Aalberts. Theoretical examination of features of a model of conjugated polyenes, motivated by fast photoisomerization of rhodopsin.