Dan Crawford Email & Phone Number

Machine Learning Specialties:- Leadership: > 3 years leading client-facing applied machine learning projects in an agile work environment.- Applied machine learning: time series estimation / forecasting, reinforcement learning, supervised learning in healthcare, retail, finance, and agriculture.- Classical machine learning and deep learning: FFNN, LSTM/GRU/RNN, 1,2-dimensional CNN, kernel machines, ensemble methods, hyperparameter search and optimization, applications of quantum computing in ML, etc.- Optimal Stochastic Control: sequential decision making under uncertainty, Markov decision processes, dynamic programming- External client management: Client attraction, pitching, project scoping, contract+fee negotiationSoftware:Python (Keras, scikit-learn, pandas, numpy, scipy, matplotlib, Tensorflow), cloud services (AWS, GCP)Currently learning: Vue.js, flask, SQLOther interests:- Creating virtual reality environments for meditation and mood adjustments (Unreal Engine).- Automated Greenhouses: optimal control of greenhouse to quickly grow great tasting fruits and vegetables.- Laboratory experiment design (LabVIEW control and data acquisition, MATLAB, Simulink)Research Work:
“Adiabatic Quantum Kitchen Sinks for Learning Kernels Using Randomized Features,” NeurIPS Workshop Paper(2019)“Multi-Community Detection in Signed Graphs Using Quantum Hardware,'' arXiv preprint, arXiv:1901.04873 (2019)“Reinforcement Learning Using Quantum Boltzmann Machines," Quantum Information and Computation (2018)“Free energy-based reinforcement learning using a quantum processor," The Conference on the Theory of Quantum Computation, Communication and Cryptography (2017)“Monotone Optimal Policies in Portfolio Liquidation Problems,” IEEE International Conferences on Acoustics, Speech and Signal Processing (2015).Patents:
- Method and system for identifying at least one community in a dataset comprising a plurality of elements (WO2019IB55226) (2019)- Method and system for improving a policy for a stochastic control problem (US15/590614) (2017)- Method for estimating the thermodynamic properties of a quantum Ising model with transverse field (US15/831751) (2017)- Quantum processor and its use for implementing a neural network (US15/097823) (2017)More details: https://scholar.google.ca/citations?user=dlr9LNsAAAAJ&hl=en

Listed skills include Artificial Neural Networks, Python, Reinforcement Learning, Consulting, and 25 others.