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Shirley Ho

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Shirley Ho
Alma materUniversity of California, Berkeley, Princeton University
Known forCMB, dark matter, dark energy, BAO, Machine Learning in Astrophysics
Scientific career
FieldsAstrophysics, Deep Learning, Artificial Intelligence, Cosmology
InstitutionsFlatiron Institute, Carnegie Mellon University, New York University
Thesis Baryons, Universe and Everything Else in Between
Doctoral advisorDavid Spergel
Websitehttps://users.flatironinstitute.org/~sho/index.html

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

A cited expert in cosmology, deep learning and its applications in astrophysics and data science,[3] her interests include developing and deploying deep learning techniques to better understand our Universe, and other astrophysical phenomena.[4]

She significantly contributed to the development of several fields, including: cosmic microwave background,[5] cosmological models, dark energy, dark matter,[6][7] spatial distribution of galaxies and quasars,[8] Baryon Acoustic Oscillations,[9][10] cosmological simulations[11] and applications of machine learning to cosmology and astrophysics.[12][13][14]

More recently, Shirley 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,[15] the same team then accelerated astrophysical simulations with deep learning for the first time.[16] Her current team at Center for Computational Astrophysics and Princeton University is the first to combine symbolic regression and neural network to recover physical laws from observations directly.[17] Her team also led the first development and deployment of deep learning accelerated simulation based inference framework for large spectroscopic surveys.[18]

Her team further accelerated physical simulations ranging from fluid dynamics simulations to planetary dynamics simulations using modern deep learning techniques,[19][20][21] and developed techniques in interpretable machine learning for science.[22][23]

Education

[edit]

Shirley Ho graduated summa cum laude with a B.A. in Physics and a B.A. in Computer Science at 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 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 then wrote two papers in cosmology under the guidance of Martin White as a senior. Shirley Ho moved to Princeton University to pursue her Ph.D. at the Department of Astrophysical Sciences of Princeton University[1][24] 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".[1]

Career

[edit]

After her Ph.D., she moved to the Lawrence Berkeley National Laboratory between 2008 and 2012, in a postdoctoral position as a Chamberlain and a Seaborg Fellow.[1] Later on, she moved to the Carnegie Mellon University, first as an assistant professor and then as an associate (with indefinite tenure) professor in Physics. Shirley Ho was named Cooper-Siegel Development Chair Professor in 2015 at Carnegie Mellon University.[25]

In 2016, Shirley Ho joined Lawrence Berkeley National Laboratory as a Senior Scientist while being on leave from Carnegie Mellon University. In 2018, Shirley Ho joined the Simons Foundation as leader of the Cosmology X Data Science group[26] at Center for Computational Astrophysics (CCA) at the Flatiron Institute.[27] She also currently holds faculty positions at New York University and Carnegie Mellon University. In 2021, Shirley Ho was named the Interim Director of CCA at the Flatiron Institute in 2021.[28]

Prizes

[edit]

Shirley Ho won several prizes for her significant contributions to the fields of cosmology and astrophysics. The list includes:

  • National Blavatnik Award Finalist, 2023 [29]
  • European Physical Society Giuseppe and Vanna Cocconi Prize in cosmology 2023 (as part of SDSS/BOSS/eBOSS work)[30]
  • NASA Group Achievement Award for contribution to Planck mission (2011) and Roman Space Telescope (2022).
  • Macronix Prize (2014): The Outstanding Young Researcher Award by International Organization of Chinese Physicists and Astronomers.[31]
  • Carnegie Science Award (2015)[32]
  • Elected as International Astrostatistics Association Fellow, 2020.[33]

References

[edit]
  1. ^ a b c d "Shirley Ho". Simons Foundation. 6 October 2017. Retrieved 13 September 2020.
  2. ^ "Homepage of Shirley Ho". users.flatironinstitute.org. Retrieved 13 September 2020.
  3. ^ "Home". users.flatironinstitute.org. Retrieved 16 February 2021.
  4. ^ "First AI Simulation of the Universe Is Fast and Accurate — and Its Creators Don't Know How It Works". Simons Foundation. 26 June 2019. Retrieved 16 February 2021.
  5. ^ Ho, Shirley; Hirata, Christopher; Padmanabhan, Nikhil; Seljak, Uros; Bahcall, Neta (1 August 2008). "Correlation of CMB with large-scale structure. I. Integrated Sachs-Wolfe tomography and cosmological implications". Physical Review D. 78 (4): 043519. arXiv:0801.0642. Bibcode:2008PhRvD..78d3519H. doi:10.1103/PhysRevD.78.043519. ISSN 1550-7998. S2CID 38383124.
  6. ^ Vagnozzi, Sunny; Giusarma, Elena; Mena, Olga; Freese, Katherine; Gerbino, Martina; Ho, Shirley; Lattanzi, Massimiliano (1 December 2017). "Unveiling $\ensuremath{\nu}$ secrets with cosmological data: Neutrino masses and mass hierarchy". Physical Review D. 96 (12): 123503. arXiv:1701.08172. doi:10.1103/PhysRevD.96.123503. S2CID 119521570.
  7. ^ Ho, Shirley; Dedeo, Simon; Spergel, David (1 March 2009). "Finding the Missing Baryons Using CMB as a Backlight". arXiv:0903.2845 [astro-ph.CO].
  8. ^ 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 (1 December 2012). "Clustering of Sloan Digital Sky Survey III Photometric Luminous Galaxies: The Measurement, Systematics, and Cosmological Implications". The Astrophysical Journal. 761 (1): 14. arXiv:1201.2137. Bibcode:2012ApJ...761...14H. doi:10.1088/0004-637X/761/1/14. S2CID 15716313.
  9. ^ 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 (11 June 2014). "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. 441 (1): 24–62. arXiv:1312.4877. Bibcode:2014MNRAS.441...24A. doi:10.1093/mnras/stu523. ISSN 0035-8711. S2CID 5011077.
  10. ^ 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 (11 June 2018). "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. arXiv:1610.03506. Bibcode:2018MNRAS.477.1153V. doi:10.1093/mnras/sty571. ISSN 0035-8711. S2CID 54838269.
  11. ^ "The first AI universe sim is fast and accurate and its creators don't know how it works". ScienceDaily. Retrieved 13 September 2020.
  12. ^ Ravanbakhsh, Siamak (2016). "Estimating Cosmological Parameters from the Dark Matter Distribution". Proceedings of the 33rd International Conference on Machine Learning. 48: 2407–2416. arXiv:1711.02033.
  13. ^ He, Siyu; Li, Yin; Feng, Yu; Ho, Shirley; Ravanbakhsh, Siamak; Chen, Wei; Póczos, Barnabás (9 July 2019). "Learning to predict the cosmological structure formation". Proceedings of the National Academy of Sciences. 116 (28): 13825–13832. arXiv:1811.06533. Bibcode:2019PNAS..11613825H. doi:10.1073/pnas.1821458116. ISSN 0027-8424. PMC 6628645. PMID 31235606.
  14. ^ 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. arXiv:2007.10340. Bibcode:2021ApJ...916...42W. doi:10.3847/1538-4357/ac033a. S2CID 220665447.
  15. ^ Ravanbakhsh, Siamak (2017). "Estimating Cosmological Parameters from the Dark Matter Distribution". arXiv:1711.02033 [astro-ph.CO].
  16. ^ He, Siyu (2019). "Learning to predict the cosmological structure formation". Proceedings of the National Academy of Sciences. 116 (28): 13825–13832. arXiv:1811.06533. Bibcode:2019PNAS..11613825H. doi:10.1073/pnas.1821458116. PMC 6628645. PMID 31235606.
  17. ^ Cranmer, Miles (2020). "Discovering Symbolic Models from Deep Learning with Inductive Biases" (PDF). NeurIPS 2020. arXiv:2006.11287.
  18. ^ Hahn, Chang-Hoon (2022). "SIMBIG : A Forward Modeling Approach To Analyzing Galaxy Clustering". arXiv:2211.00723 [astro-ph.CO].
  19. ^ Tamayo, Daniel; Cranmer, Miles; Hadden, Samuel; Rein, Hanno; Battaglia, Peter; Obertas, Alysa; Armitage, Philip J.; Ho, Shirley; Spergel, David N.; Gilbertson, Christian; Hussain, Naireen (4 August 2020). "Predicting the long-term stability of compact multiplanet systems". Proceedings of the National Academy of Sciences. 117 (31): 18194–18205. arXiv:2007.06521. Bibcode:2020PNAS..11718194T. doi:10.1073/pnas.2001258117. ISSN 0027-8424. PMC 7414196. PMID 32675234.
  20. ^ Cranmer, Miles; Sanchez-Gonzalez, Alvaro; Battaglia, Peter; Xu, Rui; Cranmer, Kyle; Spergel, David; Ho, Shirley (19 June 2020). "Discovering Symbolic Models from Deep Learning with Inductive Biases". arXiv:2006.11287 [cs.LG].
  21. ^ Yip, Jacky H. T.; Zhang, Xinyue; Wang, Yanfang; Zhang, Wei; Sun, Yueqiu; Contardo, Gabriella; Villaescusa-Navarro, Francisco; He, Siyu; Genel, Shy; Ho, Shirley (17 October 2019). "From Dark Matter to Galaxies with Convolutional Neural Networks". arXiv:1910.07813 [astro-ph.CO].
  22. ^ Lemos, Pablo; Jeffrey, Niall; Cranmer, Miles; Ho, Shirley; Battaglia, Peter (4 February 2022). "Rediscovering orbital mechanics with machine learning". Machine Learning: Science and Technology. 4 (4): 045002. arXiv:2202.02306. Bibcode:2023MLS&T...4d5002L. doi:10.1088/2632-2153/acfa63. S2CID 246607780.
  23. ^ Cranmer, Miles; Sanchez-Gonzalez, Alvaro; Battaglia, Peter; Xu, Rui; Cranmer, Kyle; Spergel, David; Ho, Shirley (17 November 2020). "Discovering Symbolic Models from Deep Learning with Inductive Biases". arXiv:2006.11287 [cs.LG].
  24. ^ University, Carnegie Mellon. "Shirley Ho - Department of Physics - Carnegie Mellon University". www.cmu.edu. Retrieved 13 September 2020.
  25. ^ University, Carnegie Mellon. "Physicist Shirley Ho Receives Cooper-Siegel Professorship - Mellon College of Science - Carnegie Mellon University". www.cmu.edu. Retrieved 30 October 2020.
  26. ^ "Cosmology X Data Science".
  27. ^ Chang, Kenneth (22 November 2016). "James Simons's Foundation Starts New Institute for Computing, Big Data". The New York Times.
  28. ^ "Shirley Ho". Simons Foundation. 6 October 2017. Retrieved 19 July 2021.
  29. ^ https://www.facebook.com/tsumner (26 July 2023). "Shirley Ho Named a Finalist for the 2023 Blavatnik National Awards for Young Scientists". Simons Foundation. Retrieved 23 August 2023. {{cite web}}: |last= has generic name (help); External link in |last= (help)
  30. ^ "High Energy Particle Physics Board". European Physical Society. 2023. Archived from the original on 7 May 2023. Retrieved 23 June 2023.
  31. ^ "OYRA Award (MACRONIX PRIZE) | OCPA". Retrieved 13 September 2020.
  32. ^ University, Carnegie Mellon (January 2015). "Shirley Ho Wins Carnegie Science Award - Department of Physics - Carnegie Mellon University". www.cmu.edu. Retrieved 13 September 2020.
  33. ^ @AlanHeavens (8 January 2021). "Congratulations to the International Astrostatistics Association 2020 Award winners, Jeffrey Scargle, Giuseppe Long…" (Tweet) – via Twitter.
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