Terrence Tricco

I am an Associate Professor in the Department of Computer Science at the Memorial University of Newfoundland, and I am cross-appointed with the Department of Physics and Physical Oceanography. I also Chair the Master's of Data Science program.

I am a data scientist, astrophysicist, and computational scientist. I am broadly interested in synthetic data generation for a variety of domains and using a variety of techniques. I am interested in the numerical details of smoothed particle hydrodynamics in astrophysical settings, building high-performance simulation and analysis software, and using deep-learning techniques for generation of synthetic data.

If you are a student interested in one of my research areas, please check out my currently available student opportunities.

I am involved with the following:

  • Phantom -- I am a development lead for the Phantom international collaboration to create a high-performance smoothed particle hydrodynamics (SPH) code specialised in modeling galaxies, stars, accretion discs, and the interstellar medium.
  • Sarracen -- Our open-source Python analysis and visualization package for astrophysical SPH data.
  • CAIR -- I am a PI for the Centre for Analytics, Informatics and Research. CAIR offers a high-performance computing cluster serving the research community at the Memorial University of Newfoundland.
  • VBHC-NL -- Our goal is to use a data-driven approach to create value-based health care for the pathway of care for patients with stroke.
  • Verafin -- I work closely with researchers at Verafin on synthetic data generation of financial data.

Contact

Terrence Tricco
tstricco@mun.ca
orcid.org/0000-0002-6238-9096
ca.linkedin.com/in/terrencetricco
user=CaQ9mSsAAAAJ

Department of Computer Science
Memorial University of Newfoundland
St. John's, NL, A1B 3X5
Canada

Reviews of Tricco

Learn what people are saying about Tricco.

  • "TriCCo’s thirst for memory thus can become immense, and while it might be satisfied on high-performance computers, it poses a problem for the general applicability of TriCCo."
  • "However, TriCCo in its current form is too slow for larger grids."
  • "We welcome contributions from data and computational scientists to study if and how TriCCo can be improved."