Shirley Ho

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Shirley Ho
Shirley Ho 2018.png
Ho in 2018
Alma mater University of California, Berkeley, Princeton University
Known for dark matter, dark energy, Machine Learning in Astrophysics
Scientific career
FieldsAstrophysics, Deep Learning, Cosmology
Institutions Flatiron Institute, New York University
Thesis Baryons, Universe and Everything Else in Between
Doctoral advisor David 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]

Contents

Biography

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

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

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

Related Research Articles

<span class="mw-page-title-main">Big Bang</span> Physical theory

The Big Bang is a physical theory that describes how the universe expanded from an initial state of high density and temperature. The notion of an expanding universe was first scientifically originated by physicist Alexander Friedmann in 1922 with the mathematical derivation of the Friedmann equations. The earliest empirical observation of the notion of an expanding universe is known as Hubble's law, published in work by physicist Edwin Hubble in 1929, which discerned that galaxies are moving away from Earth at a rate that accelerates proportionally with distance. Independent of Friedmann's work, and independent of Hubble's observations, physicist Georges Lemaître proposed that the universe emerged from a "primeval atom" in 1931, introducing the modern notion of the Big Bang.

<span class="mw-page-title-main">Cosmic microwave background</span> Trace radiation from the early universe

The cosmic microwave background, or relic radiation, is microwave radiation that fills all space in the observable universe. With a standard optical telescope, the background space between stars and galaxies is almost completely dark. However, a sufficiently sensitive radio telescope detects a faint background glow that is almost uniform and is not associated with any star, galaxy, or other object. This glow is strongest in the microwave region of the electromagnetic spectrum. The accidental discovery of the CMB in 1965 by American radio astronomers Arno Penzias and Robert Wilson was the culmination of work initiated in the 1940s.

<span class="mw-page-title-main">Galaxy formation and evolution</span> Subfield of cosmology

In cosmology, the study of galaxy formation and evolution is concerned with the processes that formed a heterogeneous universe from a homogeneous beginning, the formation of the first galaxies, the way galaxies change over time, and the processes that have generated the variety of structures observed in nearby galaxies. Galaxy formation is hypothesized to occur from structure formation theories, as a result of tiny quantum fluctuations in the aftermath of the Big Bang. The simplest model in general agreement with observed phenomena is the Lambda-CDM model—that is, clustering and merging allows galaxies to accumulate mass, determining both their shape and structure. Hydrodynamics simulation, which simulates both baryons and dark matter, is widely used to study galaxy formation and evolution.

<span class="mw-page-title-main">Hubble's law</span> Observation in physical cosmology

Hubble's law, also known as the Hubble–Lemaître law, is the observation in physical cosmology that galaxies are moving away from Earth at speeds proportional to their distance, i.e. the farther they are, the faster they are moving away. For this purpose, the recessional velocity of a galaxy is typically determined by measuring redshift, a shift in the light it emits toward the red end of the visible light spectrum.

<span class="mw-page-title-main">Galaxy rotation curve</span> Observed discrepancy in galactic angular momenta

The rotation curve of a disc galaxy is a plot of the orbital speeds of visible stars or gas in that galaxy versus their radial distance from that galaxy's centre. It is typically rendered graphically as a plot, and the data observed from each side of a spiral galaxy are generally asymmetric, so that data from each side are averaged to create the curve. A significant discrepancy exists between the experimental curves observed, and a curve derived by applying gravity theory to the matter observed in a galaxy. Theories involving dark matter are the main postulated solutions to account for the variance.

<span class="mw-page-title-main">Wilkinson Microwave Anisotropy Probe</span> NASA satellite of the Explorer program

The Wilkinson Microwave Anisotropy Probe (WMAP), originally known as the Microwave Anisotropy Probe, was a NASA spacecraft operating from 2001 to 2010 which measured temperature differences across the sky in the cosmic microwave background (CMB) – the radiant heat remaining from the Big Bang. Headed by Professor Charles L. Bennett of Johns Hopkins University, the mission was developed in a joint partnership between the NASA Goddard Space Flight Center and Princeton University. The WMAP spacecraft was launched on 30 June 2001 from Florida. The WMAP mission succeeded the COBE space mission and was the second medium-class (MIDEX) spacecraft in the NASA Explorer program. In 2003, MAP was renamed WMAP in honor of cosmologist David Todd Wilkinson (1935–2002), who had been a member of the mission's science team. After nine years of operations, WMAP was switched off in 2010, following the launch of the more advanced Planck spacecraft by European Space Agency (ESA) in 2009.

<span class="mw-page-title-main">Plasma cosmology</span> Non-standard model of the universe; emphasizes the role of ionized gases

Plasma cosmology is a non-standard cosmology whose central postulate is that the dynamics of ionized gases and plasmas play important, if not dominant, roles in the physics of the universe at interstellar and intergalactic scales. In contrast, the current observations and models of cosmologists and astrophysicists explain the formation, development, and evolution of large-scale structures as dominated by gravity.

<span class="mw-page-title-main">Lambda-CDM model</span> An anomaly in astronomical observations of the Cosmic Microwave Background

The Lambda-CDM, Lambda cold dark matter, or ΛCDM model is a mathematical model of the Big Bang theory with three major components:

  1. a cosmological constant, denoted by lambda (Λ), associated with dark energy
  2. the postulated cold dark matter, denoted by CDM
  3. ordinary matter
<span class="mw-page-title-main">Atacama Cosmology Telescope</span> Telescope in the Atacama Desert, northern Chile

The Atacama Cosmology Telescope (ACT) was a cosmological millimeter-wave telescope located on Cerro Toco in the Atacama Desert in the north of Chile. ACT made high-sensitivity, arcminute resolution, microwave-wavelength surveys of the sky in order to study the cosmic microwave background radiation (CMB), the relic radiation left by the Big Bang process. Located 40 km from San Pedro de Atacama, at an altitude of 5,190 metres (17,030 ft), it was one of the highest ground-based telescopes in the world.

<span class="mw-page-title-main">GADGET</span> Computer software for cosmological simulations

GADGET is free software for cosmological N-body/SPH simulations written by Volker Springel at the Max Planck Institute for Astrophysics. The name is an acronym of "GAlaxies with Dark matter and Gas intEracT". It is released under the GNU GPL. It can be used to study for example galaxy formation and dark matter.

<span class="mw-page-title-main">Diffusion damping</span> Physical process in cosmology

In modern cosmological theory, diffusion damping, also called photon diffusion damping, is a physical process which reduced density inequalities (anisotropies) in the early universe, making the universe itself and the cosmic microwave background radiation (CMB) more uniform. Around 300,000 years after the Big Bang, during the epoch of recombination, diffusing photons travelled from hot regions of space to cold ones, equalising the temperatures of these regions. This effect is responsible, along with baryon acoustic oscillations, the Doppler effect, and the effects of gravity on electromagnetic radiation, for the eventual formation of galaxies and galaxy clusters, these being the dominant large scale structures which are observed in the universe. It is a damping by diffusion, not of diffusion.

<span class="mw-page-title-main">Dark energy</span> Energy driving the accelerated expansion of the universe

In physical cosmology and astronomy, dark energy is a proposed form of energy that affects the universe on the largest scales. Its primary effect is to drive the accelerating expansion of the universe. Assuming that the lambda-CDM model of cosmology is correct, dark energy dominates the universe, contributing 68% of the total energy in the present-day observable universe while dark matter and ordinary (baryonic) matter contribute 26% and 5%, respectively, and other components such as neutrinos and photons are nearly negligible. Dark energy's density is very low: 7×10−30 g/cm3, much less than the density of ordinary matter or dark matter within galaxies. However, it dominates the universe's mass–energy content because it is uniform across space.

<span class="mw-page-title-main">AMiBA</span> Radio telescope on Mauna Loa, Hawaii

The Yuan-Tseh Lee Array for Microwave Background Anisotropy, also known as the Array for Microwave Background Anisotropy (AMiBA), is a radio telescope designed to observe the cosmic microwave background and the Sunyaev-Zel'dovich effect in clusters of galaxies. It is located on Mauna Loa in Hawaii, at 3,396 metres (11,142 ft) above sea level.

<span class="mw-page-title-main">Baryon acoustic oscillations</span> Fluctuations in the density of the normal matter of the universe

In cosmology, baryon acoustic oscillations (BAO) are fluctuations in the density of the visible baryonic matter of the universe, caused by acoustic density waves in the primordial plasma of the early universe. In the same way that supernovae provide a "standard candle" for astronomical observations, BAO matter clustering provides a "standard ruler" for length scale in cosmology. The length of this standard ruler is given by the maximum distance the acoustic waves could travel in the primordial plasma before the plasma cooled to the point where it became neutral atoms, which stopped the expansion of the plasma density waves, "freezing" them into place. The length of this standard ruler can be measured by looking at the large scale structure of matter using astronomical surveys. BAO measurements help cosmologists understand more about the nature of dark energy by constraining cosmological parameters.

Idit Zehavi is an Israeli astrophysicist and researcher who discovered an anomaly in the mapping of the cosmos, which offered insight into how the universe is expanding. She is part of the team completing the Sloan Digital Sky Survey and is one of the world's most highly cited scientists according to the list published annually by Thomson Reuters.

Claudia Maraston is a Professor of Astrophysics at the University of Portsmouth. She designs models for the calculation of spectro-photometric evolution of stellar populations. She is the winner of the 2018 Royal Astronomical Society Eddington Medal.

Barbara Sue Ryden is an American astrophysicist who is a Professor of Astronomy at Ohio State University. Her research considers the formation, shape and structure of galaxies. She was elected a fellow of the American Association for the Advancement of Science in 2016.

Viviana Acquaviva is an Italian astrophysicist who is a professor in the Department of Physics at the New York City College of Technology. Her research interests consider data science and machine learning for physics and astronomy. She was named one of Italy's most inspirational technologists in 2019.

<span class="mw-page-title-main">Sultan Hassan (astrophysicist)</span> Sudanese computational astrophysicist

Sultan Hassan is a Sudanese computational astrophysicist and NASA Hubble Fellow.

Raphael Flauger is a German theoretical physicist and cosmologist.

References

  1. 1 2 3 4 "Shirley Ho". Simons Foundation. 6 October 2017. Retrieved 13 September 2020.
  2. ↑ "Homepage of Shirley Ho". users.flatironinstitute.org. Retrieved 13 September 2020.
  3. 1 2 "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. 1 2 "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. 1 2 Vu, Linda (14 May 2018). "Planck Collaboration Wins 2018 Gruber Cosmology Prize". Lawrence Berkeley National Laboratory . Retrieved 6 September 2024.
  38. 1 2 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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