PhD candidate combining climate science and policy expertise with advanced data science and Python skills to build data-driven solutions to our most pressing energy and environmental challenges. Specializing in climate technology assessment, geospatial deep learning, and building AI-enabled tools to inform decision-making at the intersection of climate policy and technology innovation.
Ph.D. Candidate, Applied Ocean Science Scripps Institution of Oceanography, UC San Diego August 2023 – Present
Relevant Coursework: Applied Mathematics, Satellite Remote Sensing, Marine GIS, Policy Making Processes, Energy Systems & Innovation, Environmental Economics, The Policy and Politics of Climate Change (TA)
MAS, Climate Science & Policy Scripps Institution of Oceanography, UC San Diego August 2021 – June 2022
Bachelor of Science in Business, Minor in Data Science Haas School of Business, UC Berkeley August 2017 – June 2021 Division 1 Varsity Athlete
Graduate Student Researcher | Applied Ocean Science PhD Scripps Institution of Oceanography
Data Analyst Center for Coastal Studies, Scripps Institution of Oceanography
Languages & Tools Python (PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy, Jupyter, Xarray), Large Language Models (LLMs), Self-supervised Learning (SSL), RAG Pipelines, APIs, Git/GitHub, Claude Code, Gemini CLI, AWS, Computer Vision, SQL, MATLAB
Skills Writing, Remote Sensing & EO, Policy Analysis, GPU Acceleration, Science Communication, Techno-Economic Analysis (TEA), Life-Cycle Assessment (LCA), Climate & Ocean Modeling, Integrated Assessment Modeling (IAMs)
Selected Projects:
| Foundation models for Earth observation | Decision-making under deep uncertainty | Remote sensing & satellite imagery | LLM-enabled research | Climate modeling & AI emulation | Physics-informed ML | Vision Transformers |
Mack, C.J., Maclay, M., Krier-Mariani, R., & Young, A.P. (2026). "Integrated machine learning segmentation and 3D change detection for a scalable coastal cliff monitoring workflow." Computers & Geosciences, 106165.
Mack, C.J., Hanna, R., Faggiani-Dias, D., & Victor, D.G. "The Scalability and Carbon Removal Potential of Ocean Alkalinity Enhancement." Nature Communications (Under Review). Pre-print: doi.org/10.21203/rs.3.rs-7956805/v1
Mack, C. (2024). "Smart regulations needed for promising marine carbon removal." San Diego Union-Tribune. Opinion/Commentary.
Poster at AGU Fall Meeting 2025, TBD
Poster at AGU Fall Meeting 2025, TBD
Workshop at MIT Climate and Energy Decision Making Workshop, Cambridge, MA
Talk at AGU Fall Meeting 2024, Washington, D.C.
Poster at AGU Fall Meeting 2024, Washington, D.C.
Poster at AGU Fall Meeting 2024, Washington, D.C.
Poster at AGU Fall Meeting 2024, Washington, D.C.
Conference presentation at MIT Technology Data and Policy Conference, Cambridge, MA
Conference presentation at Graduate Climate Conference, TBD
Poster at AGU Fall Meeting 2023, San Francisco, CA
Poster at AGU Fall Meeting 2023, San Francisco, CA