Dr. Kianoosh Tahani

B.Sc(University of Tehran), M.Sc, Ph.D (University of Calgary)

Surrey Office: Fir 345

Richmond Office: Main 2900

Kianoosh Tahani, PhD, is a faculty member in the Department of Physics & Astronomy at Kwantlen Polytechnic University. His expertise spans astrophysics, astronomy, physics, data science, machine learning, artificial intelligence, and scientific computing. He has developed and taught courses that connect physics with emerging technologies, including data science and AI, and has supervised student projects in physics, astronomy, and machine learning. He is also involved in STEM outreach and curriculum development related to AI, intelligent systems, and applied science education.

Areas of Interest

Star formation; star formation efficiency; molecular clouds; physical properties of star-forming regions; observational astronomy; data analysis; stochastic modelling; scientific computing; machine learning applications in astronomy; extraction of physical properties from astronomical datasets; applications of artificial intelligence in physics and astronomy.

Dr. Tahani’s research interests focus on understanding how stars form within molecular clouds and how the physical conditions of star-forming regions influence star formation efficiency. His work combines observational astronomy with computational, statistical, and machine learning methods to extract physical properties from complex astronomical datasets and to better characterize the structure and evolution of star-forming environments.

Courses Taught

PHYS 3800
PHYS 2420
PHYS 1120
PHYS 1101
PHYS 1100
ASTR 3111
ASTR 3110
ASTR 1120
ASTR 1100

Publications

Plume, R., et al. (2025). "MAJORS II: HCO+ & HCN Abundances in W40."
King, S. M., et al. (2024). "CHIMPS2: 13CO J=3-2 Emission in the Central Molecular Zone."
Eden, D. J., et al. (2020). "CHIMPS2: Survey Description and 12CO Emission in the Galactic Centre."
Eden, D. J., et al. (2020). "Characteristic Scale of Star Formation. I. Clump Formation Efficiency on Local Scales."
Eden, D. J., et al. (2017). "The JCMT Plane Survey: First Complete Data Release — Emission Maps and Compact Source Catalogue."
Tahani, K., Plume, R., Bergin, E. A., et al. (2016). "Analysis of the Herschel/HEXOS Spectral Survey Toward Orion South."

Student Projects

2025 — Star Formation Efficiency of Star-Forming Regions
Eva Ruban
Undergraduate research project focused on investigating star formation efficiency in star-forming regions.
2023 — Physical Properties of Star-Forming Regions via a Machine Learning Algorithm
Yehya Mohamad
A third-year project applying the Random Forest technique to extract physical properties, such as mass, luminosity, and temperature, from approximately 95,000 star-forming clumps.
2022–2023 — Artificial Neural Network to Classify Star-Forming Regions
Kouji Pan
A fourth-year project using artificial neural networks to determine the stellar evolutionary classes of approximately 25,000 star-forming regions.
2020–2021 — Statistical Study of Star-Forming Regions via SED Fitting
Morgan Langford
A fourth-year project focused on extracting physical properties of approximately 250 star-forming clumps through spectral energy distribution fitting.
2020 — Developing an SED Fitting Algorithm to Extract Physical Properties of Star-Forming Regions
Erin Psajd
A third-year project focused on extracting physical properties of Hi-GAL star-forming regions.
2020 — LTE Modeling of NH3 within Orion-S
Benjamin McClennon
A third-year project focused on extracting physical properties of a star-forming region in Orion-S using LTE modelling of NH3.