Chris is a multidisciplinary artist, creative technologist, and machine learning engineer and their work explores the interconnections between humans, technology, and the environment. Chris is inspired by ecological systems, queer experiences of belonging, and contemporary dance, and their previous work explored the ethical, emotional, and relational dimensions of AI and technology.
In their current practice, they are interested in challenging human-centric perspectives, embracing ecological interconnections, and celebrating the multiplicity of minds, perspectives, and ecosystems through playful and exploratory methods.
In addition to their artistic practice, Chris has developed advanced machine learning solutions for organizations like Forensic Architecture, with work exhibited at the De Young Museum. They have collaborated with artists like Zach Blas on immersive installations such as Cultus (Arebyte and Secession) and Profundior (Berlin Biennale, Hamburger Bahnhof), works which examine AI religiosity and interrogate the extractive data practices underlying AI's rapidly advancing emotional intelligence.
In their practice, they use sound, movement, material, generative models, 3D modeling, and computer vision.
Chris completed a Master's in Machine Learning at the University of Cambridge in August 2017.
ml research interests video language models — few shot learning — generative models — Bayesian modeling — computer vision: video understanding, video generation, object and movement detection
other interests contemporary dance — internal family systems
residencies
Spreepark International Art Residency (2025), Berlin, Germany
Infrastructures of AI — ongoing research and visual essay exploring Big Tech’s AI dominance through improvised Global South infrastructures, accepted for presentation at the Connective (t)Issues Workshop, with Data & Society
Ecological Dialogues — ongoing research, workshop and work on movement, play and technology
Scattered Minds — short film / coded animation exploring multiplicity of the mind
The Doors with Zach Blas (2020) — commissioned by Edith-Russ-Haus für Medienkunst, Oldenburg, de Young Museum, San Fransisco, and Van Abbemuseum, Eidhoven
D’Cruz, A.∗, Tegho, C.∗, Greaves, S.∗, & Kermode L. (2022). Detecting Tear Gas Canisters With Limited Training Data. IEEE/CVF
Winter Conference on Applications of Computer Vision (WACV). ∗equal contribution
Tegho, C., Budzianowski, P., & Gašić, M. (2018). Benchmarking Uncertainty Estimates With Deep Reinforcement Learning for Dialogue
Policy Optimisation. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP).
Tegho, C., Budzianowski, P., & Gašić, M. (2017). Uncertainty Estimates for Efficient Neural Network-based Dialogue Policy Optimisation. Accepted at the Bayesian Deep Learning Workshop, 31st Conference on Neural Information Processing Systems (NeurIPS).
machine learning work
2022 - 2024 Unitary, London, UK — Develop and deploy multimodal machine learning models and pipelines for detecting harmful content in videos, images and text
2017 - 2022 Calipsa, London, UK — Design, implement and evaluate models and software prototypes for object detection and motion detection in videos
Developped a few shot detector for detecting tear-gas canisters in videos, for assisting human rights investigations Human rights investigations often require triaging large volumes of open source data in order to find moments within image, audio, or video that are relevant to a given investigation and warrant further inspection. Searching for images of tear gas online manually is laborious and time-consuming. This allows to automate discovery, identification, and archiving, which is hugely beneficial to human rights monitors. This project highlights how ML can be used for real-world applications, and how recent advances in ML can be leveraged for the greater good.
