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Shirley Ho

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Shirley Ho
Ho in 2018
Alma materUniversity of California, Berkeley, Princeton University
Known fordark matter, dark energy, Machine Learning in Astrophysics
Scientific career
FieldsAstrophysics, Deep Learning, Cosmology
InstitutionsFlatiron Institute, New York University
Thesis Baryons, Universe and Everything Else in Between
Doctoral advisorDavid Spergel

Shirley Ho is an American astrophysicist and machine learning researcher, currently at the Center for Computational Astrophysics at the Flatiron Institute, and an affiliated faculty at the Center for Data Science at New York University.[1][2]

Biography

[edit]

Ho graduated with a B.A. in physics and a B.A. in computer science from the University of California at Berkeley.[3] She pursued her Ph.D. at the Department of Astrophysical Sciences of Princeton University.[1][4] In 2008 she obtained her doctorate in Astrophysical Sciences.[1] Subsequently, she worked in the Lawrence Berkeley National Laboratory between 2008 and 2012 in a postdoctoral position as a Chamberlain and a Seaborg Fellow.[1]

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.[5] In 2016, she moved back to the Lawrence Berkeley National Laboratory as a Senior Scientist while being on leave from Carnegie Mellon University.

In 2018, Ho joined the Simons Foundation as leader of the Cosmology X Data Science group[6] at the Center for Computational Astrophysics (CCA) at the Flatiron Institute.[7]

Research

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Ho researches cosmology, deep learning and its applications in astrophysics and data science.[8] In particular, she is interested in developing and deploying deep learning to better understand the Universe, and other astrophysical phenomena.[9]

She has contributed to several areas of astrophysics: cosmic microwave background,[10] cosmological models, dark energy, dark matter,[11][12] spatial distribution of galaxies and quasars,[13] Baryon Acoustic Oscillations,[14][15] and cosmological simulations.[16]

Regarding deep learning and its and applications to cosmology and astrophysics.,[17][18][19] Ho has been involved in the development of accelerated astrophysical simulations.[20] She took part in the development and deployment of deep-learning-accelerated simulation-based inference framework for large spectroscopic surveys,[21] and further accelerated physical simulations ranging from fluid dynamics to planetary dynamics simulations.[22][23][24] Her current team at the Flatiron Institute and Princeton University combines symbolic regression and neural networks to recover physical laws directly from observations, demonstrating symbolic regression as an example of good inductive bias for interpretable machine learning for science.[25][26][27]

While she almost always failed to balance her research interests in machine learning and the universe,[28] her passion for science management has allowed her to attribute much of her scientific success to the students and collaborators she has been fortunate enough to work with.[28] Indeed, her ability to intercept scientific funding through connections with private foundations such as the Simons Foundation and the Schmidt Futures Foundation[29] culminates into the Polymathic AI[30][31] funding endeavor.[32] Her team benefits from large enough resources to train large-scale machine learning models not only for astrophysics but also for Earth climate simulation,[33] which is generally hard to achieve in a non-commercial setting.[34] Her affiliations with multiple institutions, each with its own press department, ensure that she receives substantial media coverage, often in the form of first-person interviews that give her a direct platform to share her perspectives.[35][36][37][38]

Prizes

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Ho has won several prizes for her contributions to cosmology and astrophysics, including:

References

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  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. ^ a b "Shirley Ho Named a Finalist for the 2023 Blavatnik National Awards for Young Scientists". Simons Foundation. 26 July 2023. Retrieved 23 August 2023.
  4. ^ University, Carnegie Mellon. "Shirley Ho - Department of Physics - Carnegie Mellon University". www.cmu.edu. Retrieved 13 September 2020.
  5. ^ University, Carnegie Mellon. "Physicist Shirley Ho Receives Cooper-Siegel Professorship - Mellon College of Science - Carnegie Mellon University". www.cmu.edu. Archived from the original on 3 August 2020. Retrieved 30 October 2020.
  6. ^ "Cosmology X Data Science".
  7. ^ Chang, Kenneth (22 November 2016). "James Simons's Foundation Starts New Institute for Computing, Big Data". The New York Times.
  8. ^ "Home". users.flatironinstitute.org. Retrieved 16 February 2021.
  9. ^ "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.
  10. ^ 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.
  11. ^ 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.
  12. ^ Ho, Shirley; Dedeo, Simon; Spergel, David (1 March 2009). "Finding the Missing Baryons Using CMB as a Backlight". arXiv:0903.2845 [astro-ph.CO].
  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 (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.
  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 (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.
  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 (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.
  16. ^ "The first AI universe sim is fast and accurate and its creators don't know how it works". ScienceDaily. Retrieved 13 September 2020.
  17. ^ 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.
  18. ^ 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.
  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. arXiv:2007.10340. Bibcode:2021ApJ...916...42W. doi:10.3847/1538-4357/ac033a. S2CID 220665447.
  20. ^ 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.
  21. ^ Hahn, Chang-Hoon (2022). "SIMBIG : A Forward Modeling Approach To Analyzing Galaxy Clustering". arXiv:2211.00723 [astro-ph.CO].
  22. ^ 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.
  23. ^ 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].
  24. ^ 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].
  25. ^ Cranmer, Miles (2020). "Discovering Symbolic Models from Deep Learning with Inductive Biases" (PDF). NeurIPS 2020. arXiv:2006.11287.
  26. ^ 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.
  27. ^ 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].
  28. ^ a b "Artificial Intelligence". Shirley Ho. Retrieved 5 September 2024.
  29. ^ "Polymathic". polymathic-ai.org. Retrieved 7 September 2024.
  30. ^ "From Field to Fork: A Polymathic AI Journey to a More Sustainable Salsa". www.linkedin.com. Retrieved 7 September 2024.
  31. ^ "Polymathic AI". www.envisioning.io. Retrieved 7 September 2024.
  32. ^ "Breaking Boundaries with Polymathic AI: A Game-Changer for Researchers".
  33. ^ "Scientists working on Polymathic AI, a new tool that will help make scientific discoveries". The Indian Express. 16 October 2023. Retrieved 7 September 2024.
  34. ^ Stutz, David (27 March 2024). "Thoughts on Academia and Industry in Machine Learning Research • David Stutz". David Stutz. Retrieved 7 September 2024.
  35. ^ Science, NYU Center for Data (11 September 2024). "Meet the Research Scientist: Shirley Ho". Medium. Retrieved 6 November 2024.
  36. ^ Thomas, Sumner (Simons Foundation). "The first AI universe sim is fast and accurate—and its creators don't know how it works". Phys.org.
  37. ^ a b Vu, Linda (14 May 2018). "Planck Collaboration Wins 2018 Gruber Cosmology Prize". Lawrence Berkeley National Laboratory. Retrieved 6 September 2024.
  38. ^ a b University, Carnegie Mellon (January 2015). "Shirley Ho Wins Carnegie Science Award - Department of Physics - Carnegie Mellon University". www.cmu.edu. Retrieved 13 September 2020.
  39. ^ "OYRA Award (MACRONIX PRIZE) | OCPA". Archived from the original on 4 July 2019. Retrieved 13 September 2020.
  40. ^ "SDSS Researcher Awarded for Outstanding Research". Sloan Digital Sky Survey. 5 November 2014. Retrieved 6 September 2024.
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