CV

Download this CV as a PDF

Education

• Ph.D., Chemical Engineering, University of California San Diego, 2023
• M.S., Chemical Engineering, University of California San Diego, 2019
• B.A./B.S., Mechanical Engineering, University of San Diego, 2016
  • Mathematics and Chemistry minors; Honors Program
  • Dean’s List, First Honors

Research experience

• 01/2026–Present: Postdoctoral Researcher
  • University of California Santa Cruz, Santa Cruz, CA
  • Advisor: Xavier Prochaska
  • Characterizing density-based ocean fronts and associated submesoscale dynamics in high-resolution ocean model output
  • Developing scalable analysis and visualization workflows for large ocean model datasets
• 02/2024–12/2025: Postdoctoral Researcher
  • Université catholique de Louvain, Louvain-la-Neuve, Belgium
  • Advisor: François Massonnet
  • Developed machine learning and explainable AI approaches to predict and understand rapid Arctic sea ice loss events on seasonal to interannual timescales
• 10/2023–01/2024: Postdoctoral Researcher
  • Scripps Institution of Oceanography, UC San Diego, San Diego, CA
  • Advisor: Matt Mazloff
  • Implemented components of an operational forecast system for sea-ice motion prediction using machine learning
• 06/2019–09/2023: Graduate Researcher
  • Scripps Institution of Oceanography, UC San Diego, San Diego, CA
  • Advisor: Matt Mazloff
  • Investigated upper-ocean salinity response to atmospheric river events in the California Current System using observations and ocean model output
  • Developed machine learning and explainable AI approaches to predict and understand Arctic sea-ice dynamics from remote sensing data
• 09/2018–03/2019: Graduate Researcher
  • University of California San Diego, San Diego, CA
  • Advisor: Andrea Tao
• 03/2018–08/2018: Research Assistant
  • University of San Diego, San Diego, CA
  • Advisor: Truc Ngo
• 03/2015–06/2017: Undergraduate Research Assistant and Honors Thesis
  • University of San Diego, San Diego, CA
  • Advisor: Truc Ngo

Teaching and Mentoring

• 2020 & 2021: Instructor; Climate Change and the Ocean; UC San Diego Academic Connections; 4-week online pre-college course; students accessed, plotted, analyzed and presented real oceanographic data
• 09/2018–12/2020: Teaching Assistant; Chemical Engineering, UC San Diego; fluid mechanics (8 offerings), material and energy balances (1); 5 hrs/week of lecture and discussion sections
• 2025: Guest Lecturer; Introduction to Machine Learning for Earth and Climate Science; Forecast, Prediction, and Projection in Climate Science; UCLouvain
• 2023: Guest Lecturer; Introduction to Machine Learning for Earth and Climate Science; Analysis of Physical Oceanographic Data; Scripps Institution of Oceanography
• 2024–2025: Co-Mentor; Antonio Martinez Soares, Master’s Student; Université catholique de Louvain
• 2021: Co-Mentor; Kayli Matsuyoshi, Undergraduate Student; Scripps Institution of Oceanography
• 2020–2021: Co-Mentor; Aniruddh Varadarajan, Undergraduate Student; UC San Diego

Leadership and Professional Service

• 2026–Present: Leadership Team Member; Southern Ocean Observing System (SOOS) Observing System Design (OSD) Capability Working Group (CWG)
• 2026: Invited Participant; Roundtable on climate modelling with the UN Secretary-General’s Scientific Advisory Board
• 2025–Present: Co-Lead; OAISIS Working Group; international Antarctica InSync working group on AI-enabled approaches to Antarctic sea ice observing-system design and recommendations
• 2025–Present: Working Group Member; ORCAS (Observational Requirements in the Context of AI prediction Systems)
• 2025: Student Poster Judge; AMS Conference on Polar Meteorology and Oceanography
• 2022–2025: Mentor Group Member; MPOWIR (Mentoring Physical Oceanography Women+ to Increase Retention)
• 2021: Pod Member; URGE (Unlearning Racism in the Geosciences)
• 2013–2015: Fundraising Director; Engineers Without Borders
• Peer Review: Journal of Climate; Geophysical Research Letters; Journal of Geophysical Research: Atmospheres; npj; Environmental Modelling & Software; Open Science Europe.

Honors and awards

• 2021: Interdisciplinary Research Award, UC San Diego.
• 2020: Teaching Assistant Commendation, “For Extraordinary Impact as a Teaching Assistant.”
• 2015–2016: Achievement Rewards for College Scientists (ARCS), $5,000.
• 2011–2016: Alcala Award, Merit Based Scholarship, $20,000 annually.
• 2015–Present: Tau Beta Pi, Engineering Honor Society.
• 2016–Present: Pi Tau Sigma, Engineering Honor Society.

Technical expertise

• Programming: Python; MATLAB; UNIX/Linux; LaTeX; BibTeX
• Software/Tools: TensorFlow/Keras; Git; Claude
• Methods: Machine learning; neural networks; explainable artificial intelligence; predictive modeling; data analysis; remote sensing; numerical modeling; scientific computing
• Models: MITgcm; LLC4320; CESM; CMIP6
• Languages: French, intermediate; Spanish, intermediate

Publications

First-author publications

• In progress: Hoffman, L., and F. Massonnet. eXplainable AI reveals sea surface temperature patterns associated with slowdowns in the decline of Arctic September sea ice extent in CESM2-LE.
• 2025: Hoffman, L., F. Massonnet, and A. Sticker. Probabilistic forecasts of September Arctic sea ice extent at the interannual timescale with data-driven statistical models. Journal of Geophysical Research: Machine Learning and Computation, 2, e2025JH000669. DOI: 10.1029/2025JH000669
• 2025: Hoffman, L., M. Mazloff, S.T. Gille, D. Giglio, and P. Heimbach. Evaluating the trustworthiness of explainable artificial intelligence (XAI) methods applied to regression predictions of Arctic sea-ice motion. Artificial Intelligence for the Earth Systems, 4, e240027. DOI: 10.1175/AIES-D-24-0027.1
• 2023: Hoffman, L., M. Mazloff, S.T. Gille, D. Giglio, P. Heimbach, C. Bitz, and K. Matsuyoshi. Machine learning for daily forecasts of Arctic sea-ice motion: an attribution assessment of model predictive skill. Artificial Intelligence for the Earth Systems, 2, 230004. DOI: 10.1175/AIES-D-23-0004.1
• 2022: Hoffman, L., M. Mazloff, S.T. Gille, D. Giglio, and A. Varadarajan. Ocean salinity response to atmospheric river precipitation events in the California Current System. Journal of Physical Oceanography, 52, 1867–1885. DOI: 10.1175/JPO-D-21-0272.1
• 2018: Hoffman, L., and T.T. Ngo. Affordable solar thermal water heating solution for rural Dominican Republic. Renewable Energy, 115, 1220–1230. DOI: 10.1016/j.renene.2017.09.046

Co-author publications

• 2026: Dong, X., F. Massonnet, Y. Nie, B. Richaud, Y. Gao, L. Hoffman, Y. Wang, and Q. Yang. Incorporating subsurface oceanic variables improves seasonal Antarctic sea ice prediction with neural networks. Journal of Geophysical Research: Atmospheres, 131, e2025JD045470. DOI: 10.1029/2025JD045470
• 2025: Giglio, D., J. Sala, J. Gilson, L. Hoffman, and B. Kawzenuk. Adaptive sampling of the upper ocean by autonomous floats during atmospheric river precipitation. Geophysical Research Letters, 52, e2025GL117069. DOI: 10.1029/2025GL117069
• 2020: Ngo, T.T., L. Hoffman, G. Hoople, W. Trevena, U. Shakya, and G. Barr. Surface morphology and drug loading characterization of 3D-printed methacrylate-based polymer facilitated by supercritical carbon dioxide. The Journal of Supercritical Fluids, 160, 104786. DOI: 10.1016/j.supflu.2020.104786

Invited talks

• 2025: “Introduction to Machine Learning for Earth and Climate Scientists.” International Global Atmospheric Chemistry (IGAC) Early Career Researchers Online Conference. Virtual. Oral presentation.
• 2025: “Machine learning is a useful tool to predict and understand sea ice motion in the Arctic.” Earth Science Information Partners (ESIP) Machine Learning Cluster Meeting. Virtual. Oral presentation.
• 2024: “Machine learning is a useful tool to predict and understand sea-ice motion in the Arctic.” Interagency Arctic Research Policy Committee (IARPC) Modelers’ Community of Practice March 2024 Meeting – Combining Modeling and Machine Learning Approaches to Understanding the Arctic Earth System. Virtual. Oral presentation.
• 2023: “Machine learning is a useful tool to predict and understand sea-ice motion in the Arctic.” AI for the Study of Environmental Risk (AI4ER) Seminar Series, University of Cambridge. Cambridge, UK.
• 2022: “Machine Learning for Evaluating the Drivers of Variability in Arctic Sea-Ice Dynamics to Improve Forecasting.” American Meteorological Society Collective Madison Meeting, 17th Conference on Polar Meteorology and Oceanography. Madison, WI. Oral presentation.
• 2022: “Ocean surface salinity response to atmospheric river (AR) events in the California Current System.” NOAA Science Seminar Series. Virtual. Oral presentation.

Presentations

• 2026: “Introduction to Machine Learning for Ocean Sciences.” UC Santa Cruz Communicating Research Effectively (CORE) Workshop. Santa Cruz, CA.
• 2025: “AI4InSync - Optimizing Southern Ocean Observing Systems using AI.” Kavli Institute for Theoretical Physics (KITP), The Physics of Changing Polar Climate. Santa Barbara, CA. Oral presentation.
• 2025: “Probabilistic Forecasts of Interannual Arctic Sea Ice Extent with Data-Driven Statistical Models.” American Meteorological Society Denver Summit, 18th Conference on Polar Meteorology and Oceanography. Denver, CO. Oral presentation.
• 2025: Earth and Climate (ELIC) Seminar Series, Université catholique de Louvain. Louvain-la-Neuve, Belgium. Seminar.
• 2024: “Freshwater Processes in the Upper Ocean.” Physical Oceanography Dissertation Symposium (PODS). Lihue, HI. Oral presentation.
• 2024: Workshop on the Role of Sea Ice and its Variability in the Climate System, International Centre for Theoretical Physics (ICTP). Trieste, Italy. Poster.
• 2024: Earth and Climate (ELIC) Seminar Series, Université catholique de Louvain. Louvain-la-Neuve, Belgium. Seminar.
• 2023: 54th International Liège Colloquium on Ocean Dynamics: Machine learning and data analysis in oceanography. Liège, Belgium. Poster.
• 2023: “Machine learning is a useful tool to predict and understand sea-ice motion in the Arctic.” NCAR Sea Ice Modeling Group Meeting. Boulder, CO. Seminar.
• 2023: “Machine learning is a useful tool to predict and understand sea-ice motion in the Arctic.” Atmospheric and Ocean Sciences Forum, University of Colorado Boulder. Boulder, CO. Seminar.
• 2023: “Machine learning is a useful tool to predict and understand sea-ice motion in the Arctic.” Scientific Machine Learning Symposium. San Diego, CA. Poster.
• 2023: “Machine learning is a useful surrogate model to parameterize and understand sea-ice motion in the Arctic.” ECCO Annual Meeting. Pasadena, CA. Oral presentation.
• 2022: 9th Annual FIRO Workshop. San Diego, CA. Poster.
• 2022: Observing, Modeling, and Understanding the Circulation of the Arctic Ocean and Sub-Arctic Seas Workshop. Seattle, WA. Poster.
• 2022: Center for Western Weather and Water Extremes (CW3E) Annual Meeting. San Diego, CA. Poster.
• 2022: “Ocean surface salinity response to atmospheric river (AR) events in the California Current System.” Ocean Salinity Conference. New York, NY. Oral presentation.
• 2022: “Machine learning is a useful tool to predict and understand sea-ice dynamics in the Arctic.” UCSD Jacobs School of Engineering Research Expo. San Diego, CA. Poster.
• 2022: “Machine Learning for Evaluating the Drivers of Variability in Arctic Sea-Ice Dynamics to Improve Forecasting.” Ocean Sciences Meeting. Virtual. Oral presentation.
• 2021: “Ocean surface salinity response to atmospheric river events in the California Current System.” American Meteorological Society 101st Annual Meeting. Virtual. Oral presentation.
• 2016: Institute of Electrical and Electronics Engineers Global Humanitarian Technology Conference. Seattle, WA. Poster.

Meetings and Workshops

• 2025: Antarctica InSync International Science Planning Workshop. Frascati, Italy.
• 2023: Mentoring Physical Oceanography Women+ to Increase Retention (MPOWIR) Pattullo Conference. Warrenton, VA.
• 2023: SeaSAR 2023. Svalbard, Norway.
• 2020: International Atmospheric Rivers Conference Sponsored Symposium. Virtual.
• 2020: Ocean Sciences Meeting. San Diego, CA.

Industry experience

• 06/2016–06/2018: Lead Engineer
  • Primo Wind, Inc., San Diego, CA
  • Implemented design solutions for a small wind turbine system that improved power output and structural stability and optimized sustainability of materials and manufacturing
  • Patent: McMahon, E., and L. Hoffman (2017). High torque wind turbine blade, turbine, and associated systems and methods. U.S. Patent No. 9,797,370, U.S. Patent and Trademark Office

Personal interests

• Bouldering, handstands, backpacking, surfing, guitar, violin, yoga, aimless wandering, and cats. ☪︎