Talks and Lectures
Recorded lectures and talks, followed by a record of invited seminars and conference presentations since 2016. I speak regularly to astrophysics, machine learning and industry audiences; please get in touch about invitations.
Recorded lectures and talks
Recurrent Neural Networks: From Theory to Astronomical Light Curve Classification
Invited lecture for NASA’s AI/ML Science and Technology Interest Group (Module 3: Neural Network Basics). Builds vanilla RNNs, LSTMs and GRUs from first principles in PyTorch and trains a real-time classifier on supernova light curves. The lecture is now Chapter 6 of the open textbook Deep Learning for Astrophysics.
Modeling and Removal of Scattered Light in TESS Full-Frame Images Using Generative AI
TESS Science Conference III, Data Analysis session, MIT · 30 July 2024
From Squiggles to Signals: AI’s Hunt for Astronomical Anomalies
Invited talk, Breakthrough Discuss 2024: New Searches for Intelligence, University of Oxford · July 2024
Prioritising Follow-up for Transient Surveys in the New Era of Time-Domain Astronomy
Workshop on Debating the Potential of Machine Learning in Astronomical Surveys, Center for Computational Astrophysics, Flatiron Institute, New York · November 2023
Data-driven Discovery of Transients in the New Era of Time-Domain Astronomy
Invited seminar, Early Career Researcher Cosmology Seminar Series, Korea Astronomy and Space Science Institute · March 2022
Real-time Anomaly Detection of Transients
237th Meeting of the American Astronomical Society · January 2021
Real-time Classification of Explosive Transients Using Deep Recurrent Neural Networks
Royal Astronomical Society Specialist Discussion Meeting on Machine Learning and AI Applied to Astronomy, London · 8 March 2019
RAPID: Real-time Classification of Astronomical Transients
Presentation to the LSST Informatics and Statistics Science Collaboration · 2019
Invited talks and seminars
2026
- CERN, Geneva · Gravitational Waves in the Era of AIInvited
- NASA · Cosmic Origins AI/ML Science and Technology Interest Group lecture series: Recurrent Neural Networks (online)Invited
- NASA Goddard Space Flight Center, Greenbelt, MD · TESS Partners’ MeetingInvited
2025
- University of Alabama in Huntsville · Sys2025: Systematic Errors across the SciencesInvited
- Johns Hopkins University, Baltimore · Institute for Data-Intensive Engineering and ScienceInvited
- Harvard University, Cambridge, MA · AstroAI workshopInvited
- ICML 2025, Vancouver · Workshop on Foundation Models for Structured Data and Machine Learning for Astrophysics Workshop: two papers presented
2024
- Tufts University, Somerville, MA · Astronomy seminarInvited
- University of Oxford · Breakthrough Discuss 2024: New Searches for Intelligence, “From Squiggles to Signals: AI’s Hunt for Astronomical Anomalies”Invited
- TESS Science Conference III, MIT · “Modeling and removal of scattered light in TESS full-frame images using generative AI” · Contributed talk
- ICML 2024, Vienna · AI for Science Workshop
- Center for Astrophysics | Harvard & Smithsonian · AstroAI workshop, “Modeling Image Systematics Using Conditional Diffusion Models”
- University of Washington, Seattle · DiRAC Institute seminar, “Finding the Needle in the Haystack: Discovering Transients in Large Astronomical Surveys”Invited
2023
- NeurIPS 2023, New Orleans · Machine Learning and the Physical Sciences Workshop
- Flatiron Institute, New York · Center for Computational Astrophysics workshop on Debating the Potential of Machine Learning in Astronomical Surveys
- Yale University, New Haven · Data Science x Astronomy and Astrophysics seminarInvited
- Center for Astrophysics | Harvard & Smithsonian · CfA seminarInvited
- NASA Jet Propulsion Laboratory, Pasadena · Machine Learning and Instrument Autonomy group
- Astronomy on Tap, Somerville, MA · Public talk on AI in astronomy
- 241st Meeting of the American Astronomical Society, Seattle · Contributed talk
2022
- University of Queensland, University of Southern Queensland and Swinburne University, Australia · Departmental seminars
- National Astronomy Meeting 2022, University of Warwick · Contributed talk
- ESO Garching · SciOps 2022: Artificial Intelligence for Science and Operations in Astronomy · Contributed talk
- Korea Astronomy and Space Science Institute · Early Career Researcher Cosmology Seminar Series (online)Invited
2021
- LSST Dark Energy Science Collaboration · Supernova Machine Learning Topical Team seminarInvited
- Telstra · AI/ML forum, industry seminar on machine learning for time-series data (online)Invited
- 237th Meeting of the American Astronomical Society · “Real-time Anomaly Detection of Transients”
2020
- University of Sheffield · Astronomy seminar (online)Invited
- SLAC National Accelerator Laboratory, Stanford University · AI Seminar SeriesInvited
- MIT · TESS Science Seminar and the Kavli Institute Brown Bag series
- University of California, Berkeley · SETI group weekly meeting
- Texas A&M University · Astronomical Data Science WorkshopInvited
2019
- DESY, Zeuthen · Astroparticle seminarInvited
- ESO Garching · The Extragalactic Explosive Universe, “Classifying transients and detecting anomalies in upcoming large-scale surveys” · Contributed talk
- Northwestern University, Evanston · Hotwiring the Transient Universe, “RAPID: Early Classification of Explosive Transients using Deep Learning”Invited
- Joint Statistical Meeting, Denver · Astrostatistics Best Student Paper session, “Time-series classification with recurrent neural networks”
- European Week of Astronomy and Space Science, Lyon · Time-domain symposium, “Early classification of transients”
- Institute of Astronomy, University of Cambridge · Wednesday seminar
- Space Telescope Science Institute, Baltimore · Enabling Multi-messenger Astrophysics in the Big Data Era · Contributed talk
- Royal Astronomical Society, London · Machine Learning and AI Applied to Astronomy · Contributed talk
- University of California, Santa Cruz · Transients groupInvited
- Harvard University · CHASC Topics in Astrostatistics (online)
- LSST Broker Meeting, University of California, Berkeley · RAPID presentation
- 4MOST Time-Domain Extragalactic Survey (TiDES) · RAPID presentation
Earlier talks (2016–2018)
- Clare College, University of Cambridge · Clareity public talk, “What is dark energy, and how can exploding stars and machine learning help us find out if Einstein was right?”Invited
- 4MOST-UK meeting, University of Sussex · Transient classification software for TiDES
- Flatiron Institute, New York · LSST Cadence Hackathon, cadence metric based on early transient classification
- University of Oxford · Wetton Workshop: Planning for Surprises in the Era of Data-Driven Astronomy, “Transient classification with machine learning” · Contributed talk
- Space Telescope Science Institute, Baltimore · Transients lunch talk
- CAASTRO Local Area Meeting, Sydney · “DASH: Deep learning for supernova classification in OzDES”
- Astronomical Society of Australia Annual Meeting, Canberra · “DASH: Deep learning for supernova classification” · Contributed talk
- Dark Energy Survey Collaboration Meeting, University of Chicago · “Spectral classification of the Type Ia supernova sample”
- CSIRO Machine Learning in Astronomy meetings (online) · “DASH: Deep Learning for the Spectral Classification of Supernovae”Invited
- Gemini South Observatory, La Serena, Chile · “Modelling emission line profiles in giant HII regions”
- OzDES Science Workshop, Canberra · DASH tutorial for OzDES classifiers
- University of Chile, Santiago · Astronomy and Astroinformatics departments, “DASH: Deep Learning for the Spectral Classification of Supernovae”Invited
- Australian National University, Canberra · Feast of Facts, “Model-independent cosmology”
Courses and lectures
Data-driven Astronomy: Machine Learning and Statistics for Modern Astronomy
Thirteen-week undergraduate-level course I designed and lecture for the Cambridge Centre for International Research (2023–present): fundamentals of astrophysics and observation, data analysis with SDSS, Gaia, ZTF and LSST simulations, Bayesian inference, maximum likelihood and machine learning, and independent research projects. Students have won science-fair competitions, presented at undergraduate conferences and published in MNRAS.
NASA AI/ML lecture: Recurrent Neural Networks
Invited lecture in NASA’s Cosmic Origins AI/ML Science and Technology lecture series (February 2026), published as a chapter of the open textbook Deep Learning for Astrophysics with the recording, slides and a hands-on PyTorch notebook.
Scientific computing for mathematicians, University of Cambridge
Organised and lectured the intensive summer computing course for Part II and Part III students at the Centre for Mathematical Sciences (2018, 2019): Python and shell environments, big-data analysis, Markov chain Monte Carlo, ODEs, visualisation and machine learning with scikit-learn and TensorFlow. Earlier, I tutored physics, mathematics and electrical engineering courses at the University of Queensland (2013–2016).