Alexander Rockhill
Data Scientist
Los Angeles, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAlexander Rockhill is a data scientist specializing in clinical neurotechnology, blending a decade of experience in electrophysiology, neuroimaging, and computational neuroscience to improve treatments for neurological and psychiatric disorders. He has driven translational projects from intracranial electrophysiology and deep brain stimulation studies at OHSU and MGH to functional ultrasound stimulation work at Forest Neurotech. His technical contributions span open-source scientific software—improving numpy's digitize behavior, extending hmmlearn examples for Poisson HMMs, and adding EEG/MEG features in MNE—demonstrating both numerical rigor and domain-specific tooling. Trained in neurobiology and applied math with a computational neuroscience focus, he translates complex neural signals into robust analyses and reproducible pipelines. A Pacific Northwesterner who enjoys running and sustainable living, he brings curiosity and collaborative energy to interdisciplinary teams and clinical research studies.
10 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS, Neuroscience, Bachelor of Science - BS, Neuroscience at University of Washington
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (16)
scikit10
scikit-learn10
machine-learning10
python10
time-frequency10
numpy10
testing10
neuroimaging9
algorithms8
data-structures8
algorithm8
data-analysis8
data-structure8
matplotlib7
documentation6
Programming languages (11)
Github contributions (5)
mne-tools/mne-python
Nov 2019 - Jan 2023
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:367 reviews, 127 commits, 208 PRs in 3 years 2 months
Contributions summary:Alexander primarily contributed to bug fixes and the implementation of new features related to the analysis and visualization of EEG/MEG data. Their work involved reading, writing, and processing data, as well as working with time-frequency representations and source localization techniques, particularly Dynamic Imaging of Coherent Sources (DICS) beamformers. The user's commits also demonstrate an understanding of neuroimaging data formats and processing pipelines.
visualizationneuroimagingneurosciencepythonstatistics
hmmlearn/hmmlearn
Mar 2022 - May 2022
Hidden Markov Models in Python, with scikit-learn like API
Role in this project:Data Scientist
Data ScientistContributions:13 reviews, 8 commits, 10 PRs in 1 month
Contributions summary:Alexander contributed significantly to example code within the hmmlearn library. Their work involved creating and refining examples that demonstrate Hidden Markov Model (HMM) usage, including sampling, decoding, and the application of HMMs to real-world scenarios like a dishonest casino example. They added new functionalities, such as examples for the Poisson HMM, and incorporated advanced techniques with random priors, enhancing the library's practical applications and illustrative power.
apipythonlogistic-regressiondata-sciencestatistical-inference
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