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Matias Gerardo Carrasco Kind

Director Data Science Research Services

University of Illinois Urbana Champaign

Champaign, Estados Unidos

Líneas de Investigación


I specialize in multidisciplinary data science, focusing on machine/deep learning, AI, visualization, image processing, software/cloud development, and data mining. My work spans astronomy, finance, Earth sciences, bio-imaging, agriculture, and more.

Educación

  •  Astrophysics, PONTIFICIA UNIVERSIDAD CATOLICA DE CHILE. Chile, 2005
  •  Astrophysics, UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN. Estados Unidos, 2011
  •  Astrophysics + Computer Science and Engineering, UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN. Estados Unidos, 2014

Experiencia Académica

  •   Research Professor Full Time

    UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN

    Business

    Champaign, Estados Unidos

    2021 - A la fecha

  •   Director Data Science Research Services Full Time

    UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN

    Business

    Estados Unidos

    2021 - A la fecha

  •   Research Professor Other

    UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN

    Astronomy

    Estados Unidos

    2018 - A la fecha

  •   Research Professor Other

    UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN

    National Center for Supercomputing Applications

    Estados Unidos

    2021 - A la fecha

  •   Senior Research Scientist Full Time

    UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN

    National Center for Supercomputing Applications

    Estados Unidos

    2014 - 2021

  •   Adjunct Professor Part Time

    UNIVERSIDAD ANDRES BELLO

    Physics

    Chile

    2008 - 2010

  •   IMPRS PhD Student Full Time

    MAX-PLANCK INSTITUT FUR ASTROPHYSIK

    Astrophysics

    Alemania

    2006 - 2008


 

Article (25)

AGNet: weighing black holes with deep learning

Lin, Joshua Yao-Yu; Pandya, Sneh; Pratap, Devanshi; Kind, Matias Carrasco; Kindratenko, Volodymyr

Article ISI
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY  (2023)

Detection, instance segmentation, and classification for astronomical surveys with deep learning (<sc>deepdisc</sc>): <sc>detectron2</sc> implementation and demonstration with Hyper Suprime-Cam data

Merz, Grant; Burke, Colin J.; Aleo, Patrick D.; Liu, Yufeng

Article ISI
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY  (2023)

DeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification

Park, J. W.; Kim, A. G.

Article ISI
ASTROPHYSICAL JOURNAL  (2022)

A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest

Mucesh, S.; Bluck, A. F. L.; DES Collaboration

Article ISI
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY  (2021)

Dark Energy Survey Year 3 Results: Photometric Data Set for Cosmology

Becker, M. R.; Harrison, I; Stringer, K. M.; Troxel, M. A.; Bridle, S. L.; Eckert, K.; Huff, E. M.

Article ISI
ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES  (2021)

Extended Isolation Forest

Hariri, Sahand; Brunner, Robert J.

Article ISI
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING  (2021)

GHOST: Using Only Host Galaxy Information to Accurately Associate and Distinguish Supernovae

Gagliano, Alex; Engel, Andrew; LSST Dark Energy Sci Collabor

Article ISI
ASTROPHYSICAL JOURNAL  (2021)

Survey2Survey: a deep learning generative model approach for cross-survey image mapping

Buncher, Brandon; Sharma, Awshesh Nath

Article ISI
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY  (2021)

Probabilistic cosmic web classification using fast-generated training data



Article ISI
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY  (2020)

An Extended Catalog of Galaxy-Galaxy Strong Gravitational Lenses Discovered in DES Using Convolutional Neural Networks

Diehl, H. T.; McCarthy C.; Dial P.; Yung V.J.; Burke, D. L.; Costa L.N.D.; Goldstein, D. A.; Gruendl, R. A.; James, D. J.; Maia, M. A. G.; et al.

Article ISI SCOPUS
ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES  (2019)

easyaccess: Enhanced SQL command line interpreter for astronomical surveys

Koziol, A.

Article
Journal of Open Source Software  (2019)

Enabling real-time multi-messenger astrophysics discoveries with deep learning

Andreoni I.; Antelis J.M.; Biswas R.; Carrasco Kind M.; Chard K.; Cho M.; Gibbs T.; Graham, M.; Gropp W.; Haas R.; et al.

Article ISI SCOPUS
Nature Reviews Physics  (2019)

Finding high-redshift strong lenses in DES using convolutional neural networks

Jacobs, C; McCarthy C.; Qin, AK; Abbott, TMC; Burke, DL; Rosell, AC; Kind, MC; Diehl, HT; Eifler, TF; Gerdes, DW; et al.

Article ISI SCOPUS
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY  (2019)

Transfer learning for galaxy morphology from one survey to another

Kaviraj, S.; Abbott, TMC; Rosell, AC; Kind, MC; Cunha, CE; D'Andrea, CB; Evrard, AE; Gerdes, DW; Gruendl, RA; Hartley, WG; et al.

Article ISI SCOPUS
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY  (2019)

Batch and online anomaly detection for scientific applications in a Kubernetes environment

ACM

Article ISI
PROCEEDINGS OF THE ACM WORKSHOP ON SCIENTIFIC CLOUD COMPUTING (SCIENCECLOUD'18)  (2018)

The Dark Energy Survey Image Processing Pipeline

Chen, Y. -C.; Friedel, D. N.; Johnson, M. W. G.; Goldstein, D. A.; Paech, K.; DES Collaboration

Article ISI
PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC  (2018)

The Dark Energy Survey: Data Release 1

Abbott, T. M. C.; Abdalla F.B.; Baruah L.; Becker, M. R.; Bernstein, G. M.; Burke, D. L.; Busti V.; Castander, F. J.; Conselice C.; Cunha, C. E.; et al.

Article ISI SCOPUS
Astrophysical Journal, Supplement Series  (2018)

Vizic: A Jupyter-based interactive visualization tool for astronomical catalogs



Article ISI
ASTRONOMY AND COMPUTING  (2017)

Galaxy clustering with photometric surveys using PDF redshift information

Brunner, R. J.; Thaler, J.

Article ISI
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY  (2016)

A hybrid ensemble learning approach to star-galaxy classification

Kim, Edward J.

Article ISI
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY  (2015)

Exhausting the information: novel Bayesian combination of photometric redshift PDFs



Article ISI
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY  (2014)

Photometric redshift analysis in the Dark Energy Survey Science Verification data

Sánchez C; Kind, MC; Lin, H; Miquel, R; Amara, A; Banerji, M; Bonnett, C; Brunner, R; Capozzi, D; Carnero, A; et al.

Article ISI SCOPUS
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY  (2014)

SOM<i>z</i>: photometric redshift PDFs with self-organizing maps and random atlas



Article ISI
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY  (2014)

Sparse representation of photometric redshift probability density functions: preparing for petascale astronomy



Article ISI
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY  (2014)

TPZ: photometric redshift PDFs and ancillary information by using prediction trees and random forests



Article ISI
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY  (2013)

ConferencePaper (1)

Scalable Research Infrastructure in a Business School Context: A Case Study of the Data Science Research Services



ConferencePaper
 (2023)

1
Alfredo Zenteno

Assistant Scientist

Cerro Tololo Inter-American Observatory

La Serena, Chile

26
Matias Carrasco

Director Data Science Research Services

University of Illinois Urbana Champaign

Champaign, Estados Unidos