Shirley Ho Explained

Shirley Ho
Fields:Astrophysics, Deep Learning, Artificial Intelligence, Cosmology
Alma Mater:University of California, Berkeley, Princeton University
Thesis Title:Baryons, Universe and Everything Else in Between
Doctoral Advisor:David Spergel
Known For:CMB, dark matter, dark energy, BAO, Machine Learning in Astrophysics
Workplaces:Flatiron Institute, Carnegie Mellon University, New York University

Shirley Ho is an American astrophysicist and machine learning expert, currently at the Center for Computational Astrophysics at the Flatiron Institute, New York University, and Carnegie Mellon University.[1] [2]

Education

Ho graduated summa cum laude with a B.A. in physics and a B.A. in computer science from the University of California at Berkeley after completing multiple senior thesis projects in both physics and theoretical computer science in 2004. As an undergraduate, she has researched under the guidance of Kam-Biu Luk in particle physics for three years, before working on weak lensing of Cosmic Microwave Background under the supervision of Uros Seljak at Princeton. She wrote two papers in cosmology under the guidance of Martin White as a senior.

Ho moved to Princeton University to pursue her Ph.D. at the Department of Astrophysical Sciences of Princeton University[3] under the supervision of astrophysicist and cosmologist David Spergel. In 2008 she obtained her doctorate in Astrophysical Sciences, with a thesis entitled "Baryons, Universe and Everything Else in Between". After earning her Ph.D., she worked in the Lawrence Berkeley National Laboratory between 2008 and 2012 in a postdoctoral position as a Chamberlain and a Seaborg Fellow.

Career

Ho worked at Carnegie Mellon University, first as an assistant professor and then as an associate (with indefinite tenure) professor in physics. Ho was named Cooper-Siegel Development Chair Professor in 2015 at Carnegie Mellon University.[4]

In 2016, Ho joined Lawrence Berkeley National Laboratory as a Senior Scientist while being on leave from Carnegie Mellon University. In 2018, she joined the Simons Foundation as leader of the Cosmology X Data Science group[5] at the Center for Computational Astrophysics (CCA) at the Flatiron Institute.[6] She also currently holds faculty positions at New York University and Carnegie Mellon University. In 2021, Ho was named the Interim Director of CCA at the Flatiron Institute in 2021.[7]

Research

A cited expert in cosmology, deep learning and its applications in astrophysics and data science,[8] her interests include developing and deploying deep learning techniques to better understand our Universe, and other astrophysical phenomena.[9]

She significantly contributed to the development of several fields, including: cosmic microwave background,[10] cosmological models, dark energy, dark matter,[11] [12] spatial distribution of galaxies and quasars,[13] Baryon Acoustic Oscillations,[14] [15] cosmological simulations[16] and applications of machine learning to cosmology and astrophysics.[17] [18] [19]

Ho is noted for her work in leading the early adoption of Artificial Intelligence in astrophysics. In particular, her team at Carnegie Mellon University was the first to apply 3D convolutional neural network in astrophysics.[20] Her team is also credited with accelerating astrophysical simulations with deep learning for the first time.[21] Her current team at the Center for Computational Astrophysics and Princeton University is the first to combine symbolic regression and neural network to recover physical laws from observations directly.[22] Her team also led the first development and deployment of deep learning accelerated simulation based inference framework for large spectroscopic surveys.[23]

Her team further accelerated physical simulations ranging from fluid dynamics simulations to planetary dynamics simulations using modern deep learning techniques,[24] [25] [26] and developed techniques in interpretable machine learning for science.[27] [28]

Prizes

Ho won several prizes for her significant contributions to the fields of cosmology and astrophysics, including:

Notes and References

  1. Web site: 2017-10-06. Shirley Ho. 2020-09-13. Simons Foundation. en-US.
  2. Web site: Homepage of Shirley Ho. 2020-09-13. users.flatironinstitute.org.
  3. Web site: University. Carnegie Mellon. Shirley Ho - Department of Physics - Carnegie Mellon University. 2020-09-13. www.cmu.edu. en.
  4. Web site: University. Carnegie Mellon. Physicist Shirley Ho Receives Cooper-Siegel Professorship - Mellon College of Science - Carnegie Mellon University. 2020-10-30. www.cmu.edu. en.
  5. Web site: Cosmology X Data Science.
  6. News: James Simons's Foundation Starts New Institute for Computing, Big Data. The New York Times. 22 November 2016. Chang. Kenneth.
  7. Web site: 2017-10-06. Shirley Ho. 2021-07-19. Simons Foundation. en-US.
  8. Web site: Home . 2021-02-16 . users.flatironinstitute.org.
  9. Web site: 2019-06-26 . First AI Simulation of the Universe Is Fast and Accurate — and Its Creators Don't Know How It Works . 2021-02-16 . Simons Foundation . en-US.
  10. Ho . Shirley . Hirata . Christopher . Padmanabhan . Nikhil . Seljak . Uros . Bahcall . Neta . 2008-08-01 . Correlation of CMB with large-scale structure. I. Integrated Sachs-Wolfe tomography and cosmological implications . Physical Review D . 78 . 4 . 043519 . 0801.0642 . 2008PhRvD..78d3519H . 10.1103/PhysRevD.78.043519 . 1550-7998 . 38383124.
  11. Vagnozzi . Sunny . Giusarma . Elena . Mena . Olga . Freese . Katherine . Gerbino . Martina . Ho . Shirley . Lattanzi . Massimiliano . 2017-12-01 . Unveiling $\ensuremath$ secrets with cosmological data: Neutrino masses and mass hierarchy . Physical Review D . 96 . 12 . 123503 . 1701.08172 . 10.1103/PhysRevD.96.123503 . 119521570 . free.
  12. 0903.2845 . astro-ph.CO . Shirley . Ho . Simon . Dedeo . Finding the Missing Baryons Using CMB as a Backlight . 2009-03-01 . Spergel . David.
  13. Ho . Shirley . Cuesta . Antonio . Seo . Hee-Jong . de Putter . Roland . Ross . Ashley J. . White . Martin . Padmanabhan . Nikhil . Saito . Shun . Schlegel . David J. . Schlafly . Eddie . Seljak . Uros . 2012-12-01 . Clustering of Sloan Digital Sky Survey III Photometric Luminous Galaxies: The Measurement, Systematics, and Cosmological Implications . The Astrophysical Journal . 761 . 1 . 14 . 1201.2137 . 2012ApJ...761...14H . 10.1088/0004-637X/761/1/14 . 15716313.
  14. Anderson . Lauren . Aubourg . Éric . Bailey . Stephen . Beutler . Florian . Bhardwaj . Vaishali . Blanton . Michael . Bolton . Adam S. . Brinkmann . J. . Brownstein . Joel R. . Burden . Angela . Chuang . Chia-Hsun . 2014-06-11 . The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: baryon acoustic oscillations in the Data Releases 10 and 11 Galaxy samples . Monthly Notices of the Royal Astronomical Society . en . 441 . 1 . 24–62 . 1312.4877 . 2014MNRAS.441...24A . 10.1093/mnras/stu523 . 0035-8711 . 5011077 . free.
  15. Vargas-Magaña . Mariana . Ho . Shirley . Cuesta . Antonio J. . O'Connell . Ross . Ross . Ashley J. . Eisenstein . Daniel J. . Percival . Will J. . Grieb . Jan Niklas . Sánchez . Ariel G. . Tinker . Jeremy L. . Tojeiro . Rita . 2018-06-11 . The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: theoretical systematics and Baryon Acoustic Oscillations in the galaxy correlation function . Monthly Notices of the Royal Astronomical Society . 477 . 1 . 1153–1188 . 1610.03506 . 2018MNRAS.477.1153V . 10.1093/mnras/sty571 . 0035-8711 . 54838269 . free.
  16. Web site: The first AI universe sim is fast and accurate and its creators don't know how it works . 2020-09-13 . ScienceDaily . en.
  17. Ravanbakhsh . Siamak . 2016 . Estimating Cosmological Parameters from the Dark Matter Distribution . Proceedings of the 33rd International Conference on Machine Learning . 48 . 2407–2416 . 1711.02033.
  18. He . Siyu . Li . Yin . Feng . Yu . Ho . Shirley . Ravanbakhsh . Siamak . Chen . Wei . Póczos . Barnabás . 2019-07-09 . Learning to predict the cosmological structure formation . Proceedings of the National Academy of Sciences . en . 116 . 28 . 13825–13832 . 1811.06533 . 2019PNAS..11613825H . 10.1073/pnas.1821458116 . 0027-8424 . 6628645 . 31235606 . free.
  19. Wadekar . Digvijay . Villaescusa-Navarro . Francisco . Ho . Shirley . Perreault-Levasseur . Laurence . 2021 . HInet: Generating Neutral Hydrogen from Dark Matter with Neural Networks . The Astrophysical Journal . 916 . 1 . 42 . 2007.10340 . 2021ApJ...916...42W . 10.3847/1538-4357/ac033a . 220665447 . free.
  20. 1711.02033 . astro-ph.CO . Siamak . Ravanbakhsh . Estimating Cosmological Parameters from the Dark Matter Distribution . 2017.
  21. He . Siyu . 2019 . Learning to predict the cosmological structure formation . Proceedings of the National Academy of Sciences . 116 . 28 . 13825–13832 . 1811.06533 . 2019PNAS..11613825H . 10.1073/pnas.1821458116 . 6628645 . 31235606 . free.
  22. Cranmer . Miles . 2020 . Discovering Symbolic Models from Deep Learning with Inductive Biases . NeurIPS 2020 . 2006.11287.
  23. 2211.00723 . astro-ph.CO . Chang-Hoon . Hahn . SIMBIG : A Forward Modeling Approach To Analyzing Galaxy Clustering . 2022.
  24. Tamayo . Daniel . Cranmer . Miles . Hadden . Samuel . Rein . Hanno . Battaglia . Peter . Obertas . Alysa . Armitage . Philip J. . Ho . Shirley . Spergel . David N. . Gilbertson . Christian . Hussain . Naireen . 2020-08-04 . Predicting the long-term stability of compact multiplanet systems . Proceedings of the National Academy of Sciences . en . 117 . 31 . 18194–18205 . 2007.06521 . 2020PNAS..11718194T . 10.1073/pnas.2001258117 . 0027-8424 . 7414196 . 32675234 . free.
  25. 2006.11287 . cs.LG . Miles . Cranmer . Alvaro . Sanchez-Gonzalez . Discovering Symbolic Models from Deep Learning with Inductive Biases . 2020-06-19 . Battaglia . Peter . Xu . Rui . Cranmer . Kyle . Spergel . David . Ho . Shirley.
  26. 1910.07813 . astro-ph.CO . Jacky H. T. . Yip . Xinyue . Zhang . From Dark Matter to Galaxies with Convolutional Neural Networks . 2019-10-17 . Wang . Yanfang . Zhang . Wei . Sun . Yueqiu . Contardo . Gabriella . Villaescusa-Navarro . Francisco . He . Siyu . Genel . Shy . Ho . Shirley.
  27. Lemos . Pablo . Jeffrey . Niall . Cranmer . Miles . Ho . Shirley . Battaglia . Peter . 2022-02-04 . Rediscovering orbital mechanics with machine learning . Machine Learning: Science and Technology . 4 . 4 . 045002 . 2202.02306 . 2023MLS&T...4d5002L . 10.1088/2632-2153/acfa63 . 246607780.
  28. 2006.11287 . cs.LG . Miles . Cranmer . Alvaro . Sanchez-Gonzalez . Discovering Symbolic Models from Deep Learning with Inductive Biases . 2020-11-17 . Battaglia . Peter . Xu . Rui . Cranmer . Kyle . Spergel . David . Ho . Shirley.
  29. Web site: https://www.facebook.com/tsumner . 2023-07-26 . Shirley Ho Named a Finalist for the 2023 Blavatnik National Awards for Young Scientists . 2023-08-23 . Simons Foundation . en-US.
  30. Web site: High Energy Particle Physics Board . . 23 June 2023 . https://web.archive.org/web/20230507052647/http://eps-hepp.web.cern.ch/eps-hepp/prizes.php . May 7, 2023 . en . 2023 . live.
  31. Web site: OYRA Award (MACRONIX PRIZE) OCPA. 2020-09-13. en-US.
  32. Web site: University. Carnegie Mellon. January 2015. Shirley Ho Wins Carnegie Science Award - Department of Physics - Carnegie Mellon University. 2020-09-13. www.cmu.edu. en.
  33. 1347554190139256833. AlanHeavens. Congratulations to the International Astrostatistics Association 2020 Award winners, Jeffrey Scargle, Giuseppe Long… . 8 January 2021.

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