Shirley Ho

Last updated

Shirley Ho
Alma mater University of California, Berkeley, Princeton University
Known forCMB, dark matter, dark energy, BAO, Machine Learning in Astrophysics
Scientific career
FieldsAstrophysics, Deep Learning, Artificial Intelligence, Cosmology
Institutions Flatiron Institute, Carnegie Mellon University, New York University
Thesis Baryons, Universe and Everything Else in Between
Doctoral advisor David Spergel
Website https://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.

Contents

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

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

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

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

Related Research Articles

<span class="mw-page-title-main">Big Bang</span> Physical theory describing the expansion of the universe

The Big Bang is a physical theory that describes how the universe expanded from an initial state of high density and temperature. The Big Bang theory was inspired by the discovery of the expanding Universe by Edwin Hubble. It was first proposed in 1927 by Roman Catholic priest and physicist Georges Lemaître. Lemaître reasoned that if we go back in time, there must be fewer and fewer matter, until all the energy of the universe is packed in a unique quantum. Various cosmological models of the Big Bang explain the evolution of the observable universe from the earliest known periods through its subsequent large-scale form. These models offer a comprehensive explanation for a broad range of observed phenomena, including the abundance of light elements, the cosmic microwave background (CMB) radiation, and large-scale structure. The overall uniformity of the universe, known as the flatness problem, is explained through cosmic inflation: a sudden and very rapid expansion of space during the earliest moments. However, physics currently lacks a widely accepted theory of quantum gravity that can successfully model the earliest conditions of the Big Bang.

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

The cosmic microwave background is microwave radiation that fills all space in the observable universe. It is a remnant that provides an important source of data on the primordial 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 radio 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.

In astronomy, dark matter is a hypothetical form of matter that appears not to interact with light or the electromagnetic field. Dark matter is implied by gravitational effects which cannot be explained by general relativity unless more matter is present than can be seen. Such effects occur in the context of formation and evolution of galaxies, gravitational lensing, the observable universe's current structure, mass position in galactic collisions, the motion of galaxies within galaxy clusters, and cosmic microwave background anisotropies.

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, that 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. In other words, the farther they are, the faster they are moving away from Earth. The velocity of the galaxies has been determined by their redshift, a shift of the light they emit toward the red end of the visible spectrum. The discovery of Hubble's law is attributed to Edwin Hubble's work published in 1929.

<span class="mw-page-title-main">Extragalactic astronomy</span> Study of astronomical objects outside the Milky Way Galaxy

Extragalactic astronomy is the branch of astronomy concerned with objects outside the Milky Way galaxy. In other words, it is the study of all astronomical objects which are not covered by galactic astronomy.

<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">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.

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
  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.

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">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.

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.

<span class="mw-page-title-main">Uroš Seljak</span> Slovenian cosmologist

Uroš Seljak is a Slovenian cosmologist and a professor of astronomy and physics at University of California, Berkeley. He is particularly well-known for his research in cosmology and approximate Bayesian statistical methods.

Edmund Bertschinger is an American theoretical astrophysicist and cosmologist and professor of physics at MIT.

Daniel Pomarède is a staff scientist at the Institute of Research into the Fundamental Laws of the Universe, CEA Paris-Saclay University. He co-discovered Laniakea, our home supercluster of galaxies, and Ho'oleilana, a spherical shell-like structure 1 billion light-years in diameter found in the distribution of galaxies, possibly the remnant of a Baryon Acoustic Oscillation. Specialized in data visualization and cosmography, a branch of cosmology dedicated to mapping the Universe, he also co-authored the discoveries of the Dipole Repeller and of the Cold Spot Repeller, two large influential cosmic voids, and the discovery of the South Pole Wall, a large-scale structure located in the direction of the south celestial pole beyond the southern frontiers of Laniakea.

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.

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. ↑ "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.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.