John F. Wu, Phd Email and Phone Number
John F. Wu, Phd work email
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John F. Wu, Phd personal email
I am an astronomer who likes to tackle messy data with scientific machine learning algorithms and cutting-edge AI tools. I have led teams that built and evaluated Retrieval-Augmented Generation apps to improve the usage of LLMs in research astronomy. I also led development of the Roman Data Monitoring Tool for autonomously monitoring data for the upcoming NASA Roman Space Telescope mission.My research group actively publishes on cutting-edge astronomical machine learning topics. We've demonstrated that deep convolutional neural networks and graph neural networks can transform our understanding of how galaxies grow and evolve. I have five years of experience supervising ML projects, and I have over ten years of experience mentoring and supervising PhD and undergraduate students. On the technical side, I primarily code in Python (~15 years). My expertise covers the Numpy/Scipy/Pandas/Scikit-learn stack, the Pytorch deep learning framework, modern AI tools such as Sthe HuggingFace Transformers and OpenAI API, and 7+ years experience deploying local and cloud platforms for serverless, HPC, and GPU processing (GCP and AWS).
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Associate AstronomerSpace Telescope Science InstituteBaltimore, Md, Us -
Assistant AstronomerSpace Telescope Science Institute Jan 2022 - PresentBaltimore, Maryland, United StatesI am currently a tenure-track Assistant Astronomer and the AI Scientist in the Data Science Mission Office. between Jan 2022 and Aug 2024, I was an Instrument Scientist in the Roman Telescope Branch of the Instruments Division.
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Postdoctoral ResearcherSpace Telescope Science Institute Aug 2020 - Jan 2022Baltimore, Maryland, United StatesI am a postdoctoral member of the ISM*@ST research group, and I focus on understanding how galaxies grow and evolve by using massive imaging data sets and sophisticated machine learning methods. I am collaborating with various other research groups in order to study various galaxy populations, including dwarf galaxy satellites, rare local analogs of very distant galaxies, and other gas-rich systems. Using novel deep learning methods, I have identified the largest ever sample of faint nearby galaxies, with which we can investigate cosmic substructure in unprecedented detail.
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Associate Research ScientistThe Johns Hopkins University Aug 2022 - PresentBaltimore, Maryland, United States -
Postdoctoral FellowThe Johns Hopkins University Sep 2019 - Jul 2020I created a novel way to learn about a galaxy's cold gas content based on just an image and achieved state-of-the-art results by combining convolutional neural networks for regression and pattern recognition. I also worked on three ways to investigate and interpret the neural networks:- use Gradient-weighted Class Activation Maps to identify salient galaxy features in the images- analyze the encoded representations using dimensionality reduction and data visualization tools- probe correlations between variables (e.g., galaxy properties) by studying out-of-distribution neural network performanceThis work will appear in the Astrophysical Journal; see https://arxiv.org/abs/2001.00018 for a preprint, or the blog post for a light introduction to visualizing the trained models.
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Graduate Research AssistantRutgers University–New Brunswick Sep 2013 - Aug 2019Greater New York City AreaMy thesis research included small independent projects to large international collaborations. I worked on multi-wavelength observations from all kinds of telescopes, and analyzed data using models ranging from simple Bayesian regression to deep convolutional neural networks. - Trained and tested machine learning models to recognize galaxy properties from astronomical imaging- Cleaned and analyzed data from heterogeneous sources, including the Hubble, Herschel, and ALMA telescopes- Performed statistical analyses in order to constrain physical models of galaxy growth and evolution- Developed and tested software pipelines for the calibration and imaging of the new MeerKAT telescope array (LADUMA survey)See my blog post on using deep learning to better understand elemental abundances in other galaxies below. -
Undergraduate Research AssistantCarnegie Mellon University Jun 2012 - May 2013Greater Pittsburgh AreaAnalyzed the stellar properties of a million cluster galaxies by writing a custom Python-based pipeline. -
InternCmu Cylab May 2011 - Aug 2011Implemented and tested new facial recognition software based on PCA.
John F. Wu, Phd Education Details
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Astrophysics
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Physics/Astrophysics
Frequently Asked Questions about John F. Wu, Phd
What company does John F. Wu, Phd work for?
John F. Wu, Phd works for Space Telescope Science Institute
What is John F. Wu, Phd's role at the current company?
John F. Wu, Phd's current role is Associate Astronomer.
What is John F. Wu, Phd's email address?
John F. Wu, Phd's email address is jw****@****sci.edu
What schools did John F. Wu, Phd attend?
John F. Wu, Phd attended Rutgers University–new Brunswick, Carnegie Mellon University.
Who are John F. Wu, Phd's colleagues?
John F. Wu, Phd's colleagues are Harish Khandrika, Sabrina Volfman, Ray Villard, Tyler Desjardins, Deena Mickelson, Brigette Hesman, Quyen Hart.
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