Edge AI for Animal Welfare: Wild & Aquatic Life
- commitment
- 4 hrs/week
- format
- Creative project, product design, and entrepreneurship · Research and writing · Field-building, outreach, and coordination · Project scoping and preparing grant applications
- topic
- Neglected and emerging animal groups (e.g. fish, insects) · Wild animal welfare · AI tools to empower advocates
- open to mentee proposals
- Yes, with the right mentee
The project
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I built this project some time ago to bring edge ai to camera traps, to detect poachers. It proved very interesting because it also allowed us to provide insight into what's being detected on the camera, not just wild life related, but also including weather patterns! AI tech has much advanced since then, what can we update in terms of tech, structure (how poachers are detected, how wild life is detected, how we use the AI/tech), or reporting
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For the Sentient Futures fellowhip program, we built this. It shows a simulation of how potential datacenters would affect aquatic life and species, including potential extinction or mass migration events for the species.
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How can we enhance monitoring? -> Can we build edgeAI (underwater camera streaming, with analytics on aquatic life and species)
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How can we enhance structure/analysis? -> how do we go about utilizing the tech for various purposes
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How can we establish a pipeline for (accurate) reporting, as well as actionable reporting (reporting that leads to policy changes
The mentee's role
- Research/literature review of the ai tools that we can leverage for this project.
- Research on how to design, build, and integrate this project in a responsible approach.
- Leverage existing vLLM, multi-modal modals, to run on edge devices, whether in the wild or below water.
- How can we leverage this solution to make law enforcers' job easier?
- How can we leverage this solution to create better policy to ensure the better welfare of animals.
Who I'm looking for
Must-haves:
- Strong curiosity about AI for real-world impact, especially in environmental or animal welfare contexts
- Basic experience with machine learning, computer vision, or data analysis
- Ability to work independently and communicate progress clearly
- Willingness to engage with ethical considerations (e.g., surveillance, environmental impact, policy implications) Nice-to-haves:
- Experience with edge AI, embedded systems, or low-power deployments
- Familiarity with multimodal models (vision + audio + sensor data)
- Background or interest in ecology, marine biology, or conservation
- Experience with simulation, geospatial data, or policy research
Questions for applicants
Short answers or point form answers are fine.
- Which part of this project interests you most (edge AI hardware, model development, data analysis, or policy/reporting), and why?
- Describe a project where you worked with real-world, messy data or constraints (e.g., limited compute, noisy data, field conditions). What did you learn?
- If you had to prioritize, would you focus more on technical performance or real-world deployability? How would you balance the two?
- Do you have any experience or interest in working with environmental, wildlife, or marine datasets? If yes, please share examples.
- What specific contribution would you aim to make within 8–10 weeks?
Support offered
- Technical guidance on AI system design (edge AI, multimodal models, deployment constraints)
- Project scoping and helping refine ideas into actionable prototypes
- Feedback on architecture, experimentation, and iteration cycles
- Guidance on responsible AI and real-world deployment considerations
- Support in translating technical outputs into actionable insights or policy-relevant reporting
- Accountability through regular check-ins and milestone setting
- Sharing relevant tools, prior work, and research directions
Ibrahim El-chami
ai-rd.ca , vancouveraisafety.org
Ibrahim El-Chami is a researcher and technology entrepreneur with a PhD in sensors microfabrication for IoT and edge AI for sustainable smart city applications. He completed postdoctoral research at UBC as a part of the Rogers-UBC smart 5G campus. Ibrahim advises governments on advancing responsible AI governance with a focus on agentic and embodied AI. He has authored AI governance frameworks for Canada, the EU, and other parts of the world, and developed responsible AI and AI safety curricula internationally. At UBC, Ibrahim collaborates with the DASH cluster on AI education initiatives for the Department of Medicine, contributing to curriculum design for DASH educational activities and events. He also supervises ECE students developing edge AI sensors for climate change monitoring and adaptation, supported by various NSERC Alliance grants. Ibrahim is a founding engineer at Agrobotic, Mostar Labs, and IoT-World with global project scopes, and has served as an AI consultant to the UN Food and Agriculture Organization. His work has been recognized with over 30 global awards in IoT and AI.
Ibrahim is the founder of the Institute for AI Safety and Strategy (ai-rd.ca) brianchami.com