CV
Summary
Head of Research at Deep Render, leading a team of 10+ research scientists and engineers in developing the world’s first production-ready AI-based video compression codec. Our AI-based codec outperforms competitors by over 40%, setting new industry benchmarks. With a PhD in Applied Mathematics, I have contributed to top machine learning conferences and hold over a dozen patents.
Skills & Expertise
AI Research Leadership – Directing a team of research scientists and engineers, driving cutting-edge AI advancements in video compression.
Strategic Project Execution – Leading research initiatives from ideation to deployment, aligning with business and technological goals.
Innovation & Problem-Solving – Developing novel AI solutions, overcoming complex research challenges, and optimising model performance.
Team Development & Process Optimisation – Mentoring researchers, refining workflows, and fostering a high-impact, productive research culture.
Work Experience
- 2022—
- Head of Research, Deep Render, London, UK
Lead a team of 10+ scientists and engineers developing real-time AI-based video compression algorithms. - 2021—2022
- Senior Research Scientist, Deep Render, London, UK
Led a small team working on probabilistic modeling for AI-driven compression algorithms. - 2020—2021
- Research Scientist, Deep Render, London, UK
Researched, implemented, and tested probabilistic models for an AI-based image compression pipeline. - 2019—2020
- Postdoctoral Researcher, McGill University, Montréal, Canada
Conducted machine learning research at the intersection of computer vision, applied mathematics, and probabilistic modeling. Published in top-tier conferences and was a Visiting Research Fellow at UCLA’s Institute for Pure and Applied Mathematics. - 2012—2014
- Data Analyst, Canadian Forest Service, Natural Resources Canada, Edmonton, Canada
Developed statistical models for projects including: predicting wood quality from historical climate data; modeling pine beetle spread; and creating ensemble models for climate change projections.
Education
- 2014—2019
Ph.D in Applied Mathematics, McGill University, Montréal, Canada
Thesis Title: “On some applied problems using nonlinear elliptic PDEs”
Supervisor: Adam Oberman
- 2007—2011
- BSc (Hons), Mathematics, University of Alberta, Edmonton, Canada
- 2005—2007
- Dip Music, Jazz Guitar, MacEwan University, Edmonton, Canada
Publications
Journals
- 2024
- Voleti, Vikram et al. “Multi-Resolution Continuous Normalizing Flows.” Ann. Math. Artif. Intell. 92 (5): 1295–1317.
- 2021
- Finlay, Chris, and Adam M Oberman. “Scaleable Input Gradient Regularization for Adversarial Robustness.” Machine Learning with Applications 3. Elsevier: 100017.
- 2019
- Finlay, Chris, and Adam M. Oberman. “Improved Accuracy of Monotone Finite Difference Schemes on Point Clouds and Regular Grids.” SIAM J. Sci. Comput. 41 (5): A3097–117.
- 2018
- Finlay, Chris, and Adam M. Oberman. “Approximate Homogenization of Fully Nonlinear Elliptic PDEs: Estimates and Numerical Results for Pucci Type Equations.” J. Sci. Comput. 77 (2): 936–49.
- Finlay, Chris, and Adam M Oberman. “Approximate Homogenization of Convex Nonlinear Elliptic PDEs.” Communications in Mathematical Sciences 16 (7). International Press of Boston: 1985–06.
- 2015
- Sattler, Derek F, Chris Finlay, and James D Stewart. “Annual Ring Density for Lodgepole Pine as Derived from Models for Earlywood Density, Latewood Density and Latewood Proportion.” Forestry: An International Journal of Forest Research. Oxford University Press.
- 2011
- Gong, Jiafen et al. “Are More Complicated Tumour Control Probability Models Better?” Mathematical Medicine and Biology. Oxford University Press.
- 2010
- Hillen, Thomas et al. “From Cell Population Models to Tumor Control Probability: Including Cell Cycle Effects.” Acta Oncologica. Taylor & Francis.
Conferences
- 2021
- Pan, Shi et al. “Three Gaps for Quantisation in Learned Image Compression.” In IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2021, Virtual, June 19-25, 2021, 720–26. Computer Vision Foundation / IEEE.
- 2020
- Pooladian, Aram-Alexandre et al. “A Principled Approach for Generating Adversarial Images Under Non-Smooth Dissimilarity Metrics.” In International Conference on Artificial Intelligence and Statistics, 1442–52. PMLR.
- Finlay, Chris et al. “How to Train Your Neural ODE: The World of Jacobian and Kinetic Regularization.” In Proceedings of the 37th International Conference on Machine Learning, edited by Hal Daumé III and Aarti Singh, 119:3154–64. Proceedings of Machine Learning Research. PMLR.
- 2019
- Finlay, Chris, Aram-Alexandre Pooladian, and Adam M. Oberman. “The LogBarrier Adversarial Attack: Making Effective Use of Decision Boundary Information.” In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, 4861–69. IEEE.
Preprints
- 2020
- Finlay, Chris et al. “Learning Normalizing Flows from Entropy-Kantorovich Potentials.” CoRR abs/2006.06033.
- 2018
- Finlay, Chris et al. “Lipschitz Regularized Deep Neural Networks Generalize and Are Adversarially Robust.” CoRR abs/1808.09540.
Patents
- 2024
- Abbasi, Bilal et al. Method and data processing system for lossy image or video encoding, transmission and decoding. US Patent 12113985, issued October 8, 2024.
- Cherganski, Aleksandar et al. Method and data processing system for lossy image or video encoding, transmission and decoding. US Patent 12026924, issued July 2, 2024.
- Besenbruch, Chri et al. Image compression and decoding, video compression and decoding: Methods and systems. US Patent 12028525, issued July 2, 2024.
- Besenbruch, Chri et al. Image compression and decoding, video compression and decoding: Methods and systems. US Patent 12022077, issued June 25, 2024.
- Besenbruch, Chri et al. Image compression and decoding, video compression and decoding: Methods and systems. US Patent 12015776, issued June 18, 2024.
- Besenbruch, Chri et al. Image compression and decoding, video compression and decoding: Methods and systems. US Patent 11985319, issued May 5, 2024.
- Finlay, Chris et al. Method and data processing system for lossy image or video encoding, transmission and decoding. US Patent 11936866, issued March 19, 2024.
- Besenbruch, Chri et al. Image compression and decoding, video compression and decoding: Training methods and training systems. US Patent 11881003, issued January 23, 2024.
- 2023
- Besenbruch, Chri et al. Image encoding and decoding, video encoding and decoding: Methods, systems and training methods. US Patent 11843777, issued December 12, 2023.
- Besenbruch, Chri et al. Image compression and decoding, video compression and decoding: Methods and systems. US Patent 11677948, issued June 13, 2023.
- Besenbruch, Chri et al. Image encoding and decoding, video encoding and decoding: Methods, systems and training methods. US Patent 11606560, issued March 14, 2023.
- Besenbruch, Chri et al. Image encoding and decoding, video encoding and decoding: Methods, systems and training methods. US Patent 11558620, issued January 17, 2023.
- Finlay, Chris et al. Method and data processing system for lossy image or video encoding, transmission and decoding. US Patent 11544881, issued January 3, 2023.