Summary
Askery Canabarro is a senior researcher and experienced ML practitioner applying machine learning and quantum machine learning to problems in quantum information, condensed matter, and finance, with a decade of research and teaching at institutions including TII, Harvard, and Universidade Federal de Alagoas. He specializes in classification, unsupervised learning, novelty detection, feature engineering, dimensionality reduction, and AutoML-driven benchmarking, and routinely implements models with TensorFlow, PyTorch and Qiskit. His work includes high-impact publications (PRL, PRB, Quantum Information Processing) on tasks such as entanglement detection, bi-locality, and phase transition identification using automated ML. Beyond academia he contributes to Kaggle competitions and blends applied NLP/CV experiments (CNNs, RNN/LSTM) for scientific and financial forecasting. Unusually, he pairs this technical portfolio with a law degree from Brazil and a passion for surfing and language learning, reflecting a multidisciplinary outlook and practical deployment experience.
10 years of coding experience
1 year of employment as a software developer
Postdoctoral, Machine Learrning and Quantum Information, Postdoctoral, Machine Learrning and Quantum Information at International Institute of Physics
Bachelor's degree, Law, Bachelor's degree, Law at Universidade Federal de Alagoas
Postdoctoral Studies, Statistical Mechanics, Postdoctoral Studies, Statistical Mechanics at Boston University
English, Portuguese, Spanish, German