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.
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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
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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)
- 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.
- Sudbury Neutrino Observatory custom-built electronics modeling, calibration and detector commissioning. Digital signal processing for characterizations anomalous detectors. (Ph.D. Dissertation)
- 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)
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)
- 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)
- 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
- 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
- Ph.D., Physics, University of Washington, 2008
- B.S., Physics, Arizona State University, 2000