Alex Andonian is a machine learning engineer and MIT-trained PhD candidate with a decade of experience building computer vision and AI systems across academia and industry. He has interned as a research scientist at Google and DeepMind and applied vision research during roles at Amazon, Adobe, and MIT CSAIL, blending theoretical rigor with production-minded implementation. His work spans generative modeling, unsupervised representation learning, and practical video classification tooling—contributing improvements to a notable Temporal Relation Networks PyTorch repo for robust single-video evaluation and frame handling. Based in San Francisco, he combines deep technical grounding in neuroscience, physics, and math with an aptitude for shipping research into usable code and tooling.
10 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD AI & ML, Doctor of Philosophy - PhD AI & ML at Massachusetts Institute of Technology
Bachelor of Science - BS Neuroscience Physics Mathematics, Bachelor of Science - BS Neuroscience Physics Mathematics at Bates College
Contributions:12 commits, 8 pushes, 14 comments in 1 year 1 month
Contributions summary:Alex primarily focused on developing and updating the single video testing script, which involved loading pre-trained models and predicting video classifications. They implemented features to handle video frame extraction using FFmpeg and incorporated frame loading from a text file. The user also updated the script to work with different datasets and model architectures and added functionality for rendering output frames with prediction text.
Pretrained Image & Video ConvNets for PyTorch: NASNet, ResNeXt (2D + 3D), ResNet (2D + 3D), InceptionV4, InceptionResnetV2, Xception, DPN, NonLocalNets, R(2+1)D nets, MultiView CNNs, Temporal Relation Networks, etc.
Contributions:251 commits, 1 PR, 140 pushes in 3 years 11 months
pytorchresnetmultiviewefficientnetcnns
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Alex Andonian - Machine Learning Engineer at MIT CSAIL