gadamc / couchdbkit

CouchDB python framework

G. Adam Cox

I am an experimental physicist (Ph.D. UW 2008), with research interest in quantum information sciences (algorithms and hardware), neutrinos (Sudbury Neutrino Observatory), and direct dark matter detection (EDELWEISS). I also have extensive experience in industry building ML and AI tools for both scientific and business applications.

Below is a somewhat organized, though not exhaustive (due to proprietary work or forgetfulness), list of projects, courses, code, notes, etc.

Main GH Page

Quantum Information Science

• Research Scientist at University of Washington Quantum Defects Lab (Kai-Mei Fu Lab)

  • Quantum Diamond Processor / Quantum Light Microscope at QT3 Lab
  • Nitrogen-Vacancy centers in diamond modeling
  • Data Acquisition Systems Development
  • Basic Experiments for community use
  • QT3 Lab hardware control (pulser, piezostage, microwave source)
  • Implementation of the projected Variational Quantum Dynamics algorithm with Pennylane

• QC coursework

  • MIT course 8.370x QIS 1, parts 1 & 2
  • QHack 2022 notes / exercises (certificate)
  • Xanadu Codebook notes / exercises
  • IBM Quantum Challenge Fall 2021 challenge solutions (certificate)
  • Coursera / University of St. Petersburg Intro to Quantum notes / exercises (certificate)

Neutrinos & Dark Matter

• Organizer: First Topical Workshop of the HAP Dark Universe
• Principle Investigator KSETA
• Developer of Radiopurity database
• KData - Data Structure, Analysis Framework and Signal Processing Tools - for EDELWEISS III dark matter detection experiment, developed at Karlsruhe Inst. of Technology.
  • Optimal / Wiener Filter
• Sudbury Neutrino Observatory custom-built electronics modeling, calibration and detector commissioning. Digital signal processing for characterizations anomalous detectors. (Ph.D. Dissertation)

Maching Learning & Deep Learning

• Signal Classification at Gridware (proprietary: distinguishing event types that cause vibrational signals on utility poles for the purpose of prevening wildfires caused by power lines)
• Lead Data Scientist at Porch Group (proprietary: mostly classification models to predict customer events for the purpose of measuring expected revenues of leads and customer lifetime values)
• IBM + SETI Institute Deep Learning Algorithms for Anomalous Radio Signal Detection
• Founder of Seattle Deep Learning Meetup. Group Projects
• Stanford CS231n: https://github.com/gadamc/cs231n
• https://github.com/gadamc/soundeffectsapp
• Coursera(1, 2, 3)

Select Publications

todo: add links

• Edelweiss III Results
• Main SNO results (phases 1, 2, 3)
• SNO Phase 3 Technical Papers
• Persephone Radioactivity Database (https://github.com/gadamc/persephone)

Courses Taught

• Introductory Physics (mechanics, waves, thermodynamic, labs, etc. -- calculus based). Digipen Inst. of Tech (2008-2010)
• Numerical Methods & Lagrangian Mechanics. Digipen
• Modern Physics Lab (muon magnetic moment, ). Karlsruhe (with Prof. Joachim Wolf)
• Contemporary Particle Detector Systems. Karlsruhe (with Prof. Johannes Bluemer)

IBM & SETI Institute Collaboration

• Simulation of time-series signals from the Allen Telescope Array: https://github.com/gadamc/seti_signal_sim
• Data Analysis Software: https://github.com/ibm-watson-data-lab/ibmseti
• SETI Signal Classification
• SETI Public Data Server

Other Experience & Tools

• Programming Languages: python, C/C++, objective-c, scala, java
• AWS, IBM Cloud / Watson Data Platform, Google Cloud experience
• PyTorch, Apache Spark
• Standard DS Python Stack: numpy, scipy, pandas, xgboost, scikit-learn, etc
• CouchDB (couchapp to monitor Muon Veto Detector at EDW III: https://github.com/gadamc/muonvetohv)
• RL course

Education

• Ph.D., Physics, University of Washington, 2008
• B.S., Physics, Arizona State University, 2000