Machine Learning Scientist | BTech in Data Science|CGI Generalist (Blender Cult Follower)
Salt Lake City, Utah, United States
Mobile: 5053095360 [cite: 1]
Email: charlessstrauss@gmail.com [cite: 1]
LinkedIn: www.linkedin.com/in/charles-strauss [cite: 1]
I'm a Data Scientist and creative technologist passionate about-me merging deep learning with the visual arts-specifically CGI and film. [cite: 3]
With a strong foundation in neural networks, diffusion models, and computer vision, I specialize in applying cutting-edge AI techniques to cinematic storytelling, generative visuals, and emotional scene analysis. [cite: 3]
From modeling antibody structures using VAEs and diffusion pipelines at Genentech, to analyzing emotional tropes in film using facial recognition and GPU-optimized inference, my work bridges the technical with the artistic. [cite: 4]
I've built AI toolkits, led cross-functional teams, and published research in experimental deep learning, always with a focus on pushing boundaries. [cite: 5]
Now, I'm seeking opportunities that live at the edge of innovation- where film meets AI, and creativity meets computation. [cite: 6]
If your team is exploring the future of visual effects, generative cinema, or AI-enhanced storytelling, I'd love to connect. [cite: 7]
Intern Machine Learning Scientist May 2024 - September 2024 (5 months)
South San Francisco, California, United States
Implemented diffusion-based antibody conformation sampling deep-learning pipeline. [cite: 9]
Consulting for Catalunya Red Cross June 2023 - July 2023 (2 months)
Barcelona, Catalonia, Spain
Researched innovative changes to the Catalunya Red Cross for better engaging their community. [cite: 10]
Research Experience: Using DNNs to Explainably Detect Tumors January 2021 - September 2021 (9 months)
Los Alamos, New Mexico, United States
Advancing the frontiers of medical imaging as an intern, I took on a research endeavor to replicate and innovate on pixel-level tumor detection using deep neural networks. [cite: 11] Leveraging the CAMELYON 17 dataset, I replicated and expanded upon Google's research to conduct robustness analysis, successfully training and evaluating various neural network architectures (Inception, U-Net, VGG, CycleGAN) on tumor detection tasks. [cite: 12] To assess model robustness, I employed statistical analysis techniques (FROC, ROC, PR) and tested multiple adversarial attack methods, providing valuable insights into model reliability and vulnerability. [cite: 13]
Surveying the Robustness & Performance of Biologically Inspired Deep Learning Methods May 2019 - January 2021 (1 year 9 months)
Los Alamos, New Mexico, United States
Discovered and implemented method for adversarially attacking sparse coding based neural networks layers. [cite: 14] Paper accepted to Medical NeuralPS. [cite: 15]
Explainable AI Research Intern May 2018 - August 2018 (4 months)
Los Alamos, New Mexico, United States
Published "Classifiers Based on Deep Sparse Coding Architectures are Robust to Deep Learning Transferable Examples", https://arxiv.org/abs/1811.07211 [cite: 15]
Ad Creation 2015-2015 (less than a year)
Made ads for local companies. [cite: 16] Shown at local movie theater. [cite: 16]
Bachelor of Technology - BTech, Data Science August 2022 - April 2025 [cite: 17]
Minor, Entrepreneurship/Entrepreneurial Studies May 2023 - July 2023 [cite: 17]
Computer Science September 2021 - June 2022 [cite: 17]
2016-2020 [cite: 17]