Alfredo Canziani
Assistant Professor Of Computer Science at Courant Institute of Mathematical Sciences
New York, New York, United States
Summary
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Rockstar🎓
Top SchoolAlfredo Canziani is an assistant professor of computer science at NYU with 13 years of experience bridging deep learning research, teaching, and hands-on ML engineering. His work spans academic research on autonomous driving, uncertainty estimation and latent forward models, co-teaching flagship deep learning courses, and contributing production-focused code to influential open-source Torch and PyTorch repositories. He has a strong track record of practical model engineering—adding recurrent and normalization features, batch support, and autoencoder/VAE implementations—alongside clear educational communication through multimedia course materials. Alfredo pairs a formal PhD background in deep learning with prior industry consulting to optimize demos and systems for real hardware. Based in New York, he blends technical rigor with creative pursuits—musician, dancer and cook—which inform a collaborative, interdisciplinary approach to research and teaching. Notably, he contributed to popular NYU deep learning course materials and Torch demos that help bridge tutorial content and real-model improvements.
13 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (PhD), Deep Learning, Doctor of Philosophy (PhD), Deep Learning at Purdue UniversityMaster of Engineering (MEng), Electrical and Electronics Engineering, 110 / 110 cum laude, Master of Engineering (MEng), Electrical and Electronics Engineering, 110 / 110 cum laude at Università degli Studi di TriesteMaster of Science, Microsystems & Nanotechnology, First (of five grades), Master of Science, Microsystems & Nanotechnology, First (of five grades) at Cranfield University
Italian, English, Spanish, Chinese, American Sign Language, Slovenian
Stackoverflow
693reputation
63kreached
3answers
17questions
Github Skills (27)
pytorch10
python10
image-processing10
machine-learning10
recurrent-neural-networks10
autoencoder10
lua10
keras10
deep-learning10
neural-network10
computer-vision10
jupyter-notebook10
face-detection9
automatic-differentiation9
normalizing8
Programming languages (27)
Github contributions (5)
Atcold/NYU-DLSP20
Jul 2018 - Apr 2022
NYU Deep Learning Spring 2020
Role in this project:
Data Scientist
Data ScientistContributions:1 release, 405 reviews, 399 commits in 3 years 9 months
Contributions summary:Alfredo added a Keras-based notebook for regularisation in neural networks, exploring different regularisation techniques such as L2 regularization, L1 regularization, and dropout. They also included data loading and preprocessing steps utilizing the IMDB dataset and tokenization. Furthermore, the user implemented a model to study regularisation and visualised train/test loss and accuracy, suggesting a focus on understanding and demonstrating the impact of regularisation on model performance.
deep-learningjupyter-notebookpytorchneural-nets
Atcold/NYU-DLSP21
Apr 2021 - Aug 2022
NYU Deep Learning Spring 2021
Role in this project:ML Engineer
ML EngineerContributions:133 reviews, 37 commits, 37 PRs in 1 year 4 months
Contributions summary:Alfredo primarily contributed to the development and experimentation of deep learning models within the repository. Their work included the addition of an autoencoder and VAE implementations, leveraging PyTorch. The user also made bug fixes and updated existing notebook files.
deep-learningyann-le-cunnnyuebm
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