Eugenio Culurciello
Head Of AI And Machine Learning at Purdue University
Greater Chicago Area United States
Summary
👤
Senior🎓
Top SchoolEugenio Culurciello is a serial entrepreneur and academic leader with 14+ years of experience building and commercializing deep learning hardware and algorithms, currently serving as Head of AI and Machine Learning. He has pioneered five generations of neural network accelerators, led startups (FWDNXT, Teradeep) to product and exit, and advanced efficient network architectures like ENet and LinkNet used in industry. As a professor and fellow, he blends foundational research—over 150 papers and two books—with hands-on engineering across hardware, compilers, and system-level AI for cameras, robotics, and data centers. He also contributes to open-source ML tooling and reinforcement learning environments (e.g., ViZDoom/Torch demos), reflecting practical expertise in RL and vision. Known for leading small teams to outsized impact, he pairs fundraising, licensing and M&A experience with a long-term goal of building multi-modal, life-long learning “artificial scientists.” Based in Indiana, he balances high-impact technical ambition with a collaborative, human-centered view of technology’s role in improving lives.
14 years of coding experience
22 years of employment as a software developer
Johns Hopkins University
English, Italian, Korean
Stackoverflow
109reputation
13kreached
0answers
5questions
Github Skills (25)
pytorch10
python10
image-processing10
gymnasium10
reinforcement-learning10
enviroment10
mask-rcnn10
deeplearning-ai10
deep-learning10
om10
computer-vision10
faster-rcnn10
environ10
front-end-development9
optical-flow9
Programming languages (12)
Github contributions (5)
torch/demos
Feb 2012 - Jul 2014
Demos and tutorials around Torch7.
Role in this project:
ML Engineer
ML EngineerContributions:54 commits, 1 comment in 2 years 5 months
Contributions summary:Eugenio contributed to the `torch/demos` repository by implementing and testing Gabor filter layers within a Torch7 environment, demonstrating an understanding of image processing techniques. They also added demo applications related to optical flow, temporal difference, and saliency maps, showcasing expertise in computer vision and related algorithms. Furthermore, the user worked on integrating camera input and GUI elements to create interactive demos.
Farama-Foundation/ViZDoom
Dec 2016 - Aug 2017
Reinforcement Learning environments based on the 1993 game Doom :godmode:
Role in this project:ML Engineer
ML EngineerContributions:13 commits, 13 PRs, 15 comments in 8 months
Contributions summary:Eugenio contributed to training scripts for a CNN using the Torch7 framework within the ViZDoom environment. They focused on developing and improving the deep Q-learning model. The commits demonstrate an understanding of reinforcement learning principles, specifically within the context of a game environment, along with model testing and evaluation. The user also modified the codebase to include options to clamp reward.
godmodepythondeep-learningreinforcement-learninggymnasium
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