Recent & Upcoming Events
Presenting at the Yale Data Science x Astronomy-Astrophysics Seminar at Yale University, New Haven, CT.
Presented at the Center for Astrophysics Seminar at Harvard University, Cambridge, MA.
International Conference on Machine Learning (ICML) in Honolulu, Hawaii.
European Astronomical Society Meeting in Krakow, Poland.
Presented a talk at NASA Jet Propulsion Laboratory’s Machine Learning and Instrument Autonomy Team in Pasadena, WA.
Presented a public talk for Astronomy on Tap at Aeronaut Brewery in Somerville 6:30pm – 8:00pm.
Jan 30 – Feb 1:
Co-organized a workshop on Accelerating Physics with ML at MIT.
Jan 6 – 13:
Presented talk at the 241st American Astronomical Society (AAS) meeting in Seattle, WA.
Presented talk at the University of Queensland, Brisbane, Australia.
Presented talk at the University of Southern Queensland, Australia.
Presented talk at Centre for Astrophysics at Swinburne University in Melbourne, Australia.
August 21 – 24:
NASA Time Domain and Multimessenger (TDAMM) workshop in Annapolis, MD.
July 11 – 15:
Presented talk at “The National Astronomy Meeting (NAM) 2022” at The University of Warwick, UK.
May 16 – 20:
Presented talk at the “SciOps 2022: Artificial Intelligence for Science and Operations in Astronomy” conference in Garching near Munich, Germany.
Guest Speaker at the MIT Kavli Institute for Astrophysics and Space Research Journal Club.
Invited speaker at the Korea Astronomy and Space Science Institute Early Career Researcher Cosmology Seminar Series.
December 13, 2021:
Presented paper to the 2021 NeurIPS Workshop on Machine Learning and the Physical Sciences. Paper title “Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data”.
November 2, 2021:
Submitted paper titled “Real-time detection of anomalies in large-scale transient surveys” to the Monthly Notices of the Royal Astronomical Society (MNRAS), https://arxiv.org/abs/2111.00036.
September 28, 2021:
Presented talk to the Transiting Exoplanet Survey Satellite team.
September 1, 2021:
Started work as a postdoctoral researcher at the Massachusetts Institute of Technology.
June 15, 2021:
Invited speaker at the Dark Energy Science Collaboration Supernova Machine Learning Topical Team
June 7, 2021:
Passed PhD Viva Examination.
April 23rd, 2021:
Submitted PhD Thesis to the University of Cambridge titled “Data-driven Discovery of Transients in the New Era of Time-Domain Astronomy”.
March 11, 2021:
Invited to speak at the Telstra (the major Australian Telescommunications company) AI ML forum on the applications of machine learning to time-series data.
January 8, 2021:
Jointly awarded the Paul Murdin Prize for the Best Published Journal Paper by a PhD student at the Institute of Astronomy, University of Cambridge, UK.
December 2, 2020:
Invited speaker at the Astronomy Seminar at the University of Sheffield, UK.
June 15, 2020:
April 8, 2020:
February 27, 2020:
Guest Speaker at the AI Seminar Series at SLAC National Accelerator Laboratory at Stanford University, California, USA.
February 26, 2020:
Guest Speaker at the Massachusetts Institute of Technology’s TESS (Transiting Exoplanet Survey Satellite) Science Seminar at MIT, Cambridge, USA.
February 24, 2020:
Guest Speaker at the MIT Kavli Institute’s Monday Brown Bag Lunch Talk Series at MIT, Cambridge, USA.
February 20, 2020:
Guest Speaker at the weekly SETI (Search for Extraterrestrial Intelligence) Meeting in San Francisco, California, USA.
February 17-18, 2020:
Invited speaker at the Astronomical Data Science Workshop at Texas A&M University, College Station, Texas, USA.
November 22, 2019:
Invited speaker at the Astroparticle Seminar series in DESY (Deutsches Elektronen-Synchrotron) in Zeuthen near Berlin, Germany.
October 23 – 25, 2019:
TiDES (Time-Domain Extragalactic Survey) collaboration meeting in Dublin, Ireland.
September 23 – 26, 2019:
Presented a talk on classifying transients and detecting anomalies in upcoming large-scale surveys at the ESO conference on The extragalactic explosive Universe: the new era of transient surveys and data-driven discovery in Garching near Munich, Germany.
August 19 – 23, 2019:
Invited speaker at the Hotwiring the transient universe workshop at Northwestern University, Evanston, IL, USA.
July 27 – August 1, 2019:
Presented a paper at the Joint Statistical Meeting 2019 conference in Denver, Colorado. Invited to the conference for being nominated for the Best Student Paper in Astrostatistics Award.
July 7 – August 17, 2019:
Research Fellow at the Kavli Summer Program in Astrophysics at the University of California, Santa Cruz, USA.
June 25 – 28, 2019:
Presented a talk on “Early classification of transients” at the Time-domain symposium at the European Week of Astronomy and Space Science (EWASS) in Lyon, France.
June 17 – 21, 2019:
Lectured an intensive summer computing course at Faculty of Maths, University of Cambridge. I helped construct a course that begins with the basics of Bash and Python, and continues on to complex useful problems for computational research such as: dealing with big data, solving ODEs, and using machine learning for classification. I will be lecturing the short course to undergraduate and summer students. See my GitHub for course details.
June 5, 2019:
Presented an IoA Wednesday seminar on “Real-time classification of explosive transients using Deep Learning”, Cambridge, UK
April 25 – 26, 2019:
Presented talk on RAPID at the “Enabling Multi-Messenger Astrophysics in the Big Data Era Workshop” at Space Telescope Science Institute in Baltimore, USA.
April 22 – 24, 2019:
Attended the “Deaths & Afterlives of Stars” Spring Symposium at Space Telescope Science Institute in Baltimore, USA.
March 8, 2019:
Presented work on Recurrent Neural Networks applied to time-domain astronomy at the Royal Astronomical Society Meeting on Machine Learning and Artificial Intelligence applied to astronomy in London, UK.
March 3 – 6, 2019:
Visited University of California, Santa Cruz. Presented talk on “RAPID: Real-time classification of astronomical transients” to the UCSC Transients group
February 25 – March 1, 2019:
Attended the Dark Energy Survey Collaboration LSST Meeting at the University of California, Berkeley.
February 21-22, 2019:
Attended the LSST Broker Meeting at the University of California, Berkeley. Presented work on my RAPID software (https://astrorapid.readthedocs.io) for real-time photometric identification of astronomical transient objects.
February 19, 2019:
Presented talk at the Harvard University CHASC Topics in Astrostatistics on the Real-time classification of explosive transients using deep recurrent neural networks.
January 28, 2019:
January 10, 2019:
November 14, 2018:
Presented a poster at the Cantab Capital Institute for the Mathematics of Information – connecting with industry workshop in Cambridge, UK. Poster title: “Deep Learning for Real-Time Classification of Transient Time Series from Massive Astronomical Data Streams”
October 11, 2018:
Public talk for Clareity at the MCR, Clare College, University of Cambridge. Talk title: “What is “Dark Energy, and how can exploding stars and machine learning help us find out if Einstein was right?”
October 9, 2018:
LASAIR ZTF Broker meeting at the Royal Observatory Edinburgh, Scotland.
September 19 – 30, 2018:
Research visit to the Space Telescope Science Institute (STScI), Baltimore, USA.
September 16 – 19, 2018:
Presented cadence metric based on early transient classification at the LSST (Large Synoptic Survey Telescope) Cadence Hackathon at the Flatiron Institute in New York City, USA.
September 13 – 14, 2018:
Attended 4MOST-UK (4-metre Multi-Object Spectrograph Telescope) meeting at the University of Sussex, UK. Presented transient classification software to the TiDES collaboration (Time Domain Extragalactic Survey).
August 6 – 10, 2018:
Attended “Astro Hack Week: Data Science for Next-Generation Astronomy” at the Lorentz Centre, Leiden, Netherlands.
July 9 – 13, 2018:
Attended a workshop on “Transients in New Surveys: the Undiscovered Country ” at the Lorentz Centre, Leiden, Netherlands.
June 19 – 21, 2018:
Presented a talk at a workshop titled: “Planning for Surprises – Data Driven Discovery in the age of Large Data” at Oxford University, UK.
May 23 – 24, 2018:
Helped write National Science Foundation (NSF) whitepaper proposal at the Cyberinfrastructure for Multi-Messenger Astrophysics Workshop at the University of Maryland, USA.
May 9 – 26, 2018:
Research visit to the Space Telescope Science Institute (STScI), Baltimore, USA.
May 18, 2018:
Presented lunch talk at STScI Transient meeting, Baltimore, USA.
May 7-8, 2018:
Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) Workshop at the Simons Foundation Center for Computational Astrophysics, New York City, USA.
October 1, 2017:
I began my PhD at the University of Cambridge, UK.
August 9, 2017:
I attended and helped present the CAASTRO poster at the Australian Parliament House as part of the celebration of the ARC Centre of Excellence for All-Sky Astrophysics.
July 31, 2017:
I presented an update on OzDES and my development of a new spectral classification tool using Deep Learning at the ARC Centre for All-sky Astrophysics (CAASTRO) local area meeting.
July 9 – 14, 2017:
I presented my results and upcoming paper on a spectral classification tool for the Australian Dark Energy Survey at the Astronomical Society of Australia’s Annual Scientific Meeting in Canberra, Australia.
July 6 – 8, 2017:
I attended the Harley Wood Winter School at the Kioloa Coastal Campus, ANU.
June 11 – 16, 2017:
I attended the Dark Energy Survey Collaboration Meeting at the Kavli Institute of Cosmological Physics at the University of Chicago, USA. I presented my results on a new spectral classification software and my re-analysis of the SNIa cosmological sample.
April 13, 2017:
I presented work on the use of deep neural networks for supernova classification to the online “Machine Learning in Astronomy” meetings hosted by CSIRO.
March 27 – 31, 2017:
I worked with the Skymapper and Zooniverse team at the Siding Spring Observatory to search for Planet 9 in the worldwide citizen science project. I helped develop the pipeline to process the several million citizen classifications during the BBC’s Stargazing Live television series.
March 1, 2017:
I began work at the Research School of Astronomy and Astrophysics, Mt Stromlo Observatory, Australian National University.
February 14, 2017:
I presented my work on modelling emission line spectra with multiple Gaussian components at the Gemini South Observatory in La Serena, Chile.
February 8, 2017:
I presented a tutorial on DASH for classifying supernova spectra to the OzDES collaboration.
January 22 – 26, 2017:
I attended the SOCHIAS annual conference in Marbella, Santiago, Chile. I co-presented work on “Violent star formation in galaxy interactions” with Dr. Veronica Firpo.
January 27, 2017:
I gave a presentation on the use of Deep Learning for supernova and spectral classification to the Astronomy and Astroinformatics departments at the University of Chile, Santiago.
Title: DASH: Deep Learning for the Automated Spectral Classification of Supernovae”
Abstract: We have reached a new era of ‘big data’ in astronomy with surveys now recording an unprecedented number of spectra. In particular, new telescopes such as LSST will soon increase the transient catalogue by a few orders of magnitude. Moreover, the Australian sector of the Dark Energy Survey (DES) is currently in the process of spectroscopically measuring several thousands of supernovae. To meet this new demand, novel approaches that are able to automate and speed up the classification process of these spectra is essential. To this end, I have developed a software package, “DASH” that uses deep learning to classify supernova spectra. The difficulties in this classification lie in the contamination from the host galaxies, and the degeneracies with type, age, and redshift of each supernova. DASH minimises the human-time involved in supernova classification, while also limiting human-bias and error so that any spectrum can be objectively, quickly, and accurately classified. It is over 100 times faster than other classification alternatives, being able to classify hundreds of spectra within seconds to minutes. DASH has achieved this by employing a deep neural network built with Tensorflow to train a matching algorithm. It is available as an easy to use graphical interface, and as an importable python library on GitHub and PyPI with ‘pip install astrodash’.
December 15, 2016:
I graduated with Honours Class I in a Bachelor of Engineering (Electrical & Aerospace) and a Bachelor of Science (Physics) from the University of Queensland, Brisbane, Australia.
December 5, 2016:
I began work at the Gemini South Observatory in La Serena, Chile as part of the AAO’s (Australian Astronomical Observatory) Australian Government Undergraduate Summer Studentship (AGUSS).