I am a lighting researcher and educator.
My research focuses on the interaction between light and the built environment, both indoors and outdoors.
I aspire to bridge the gap between lighting research and its practical applications. For this, I work on advanced models for human vision and non-vision, and make them accessible to designers via algorithmic modeling in Rhino Grasshopper.
Extended IntroductionI am affiliated with the Architecture et Climat group within the LAB Research Institute, where I work as an FNRS Scientific Collaborator.
My research projects are promoted by Prof Sergio Altomonte, with whom I currently co-supervise 5 PhD candidates.
I am a member of 3 international task committees of the IEA and the CIE, where I represent Belgium via the Belgian Institute for Lighting.
My research explores how lighting exposure impacts human factors of comfort, productivity, health and well-being, etc. This includes quantifying individual exposures to lighting and daylighting within the built environment (through measurement and simulation), while also understanding the effect of these exposures on human factors through visual and non-visual pathways (via user testing).
My research combines data collection (subjective and objective), analysis, mathematical modeling, and computational simulation. I am also passionate about advancing the simulation capacities, and develop innovative tools for advanced lighting simulation.
I have formerly served as Assistant Professor and Chair for the MTech program in Building Energy Performance at CEPT University, Ahmedabad, India (Feb 2019-Jun 2020), where I was the principal supervisor for 4 Masters theses (+1 as co-supervisor). I have also worked as consultant/advisor for Energy Lab at IIHS Bangalore, India, as part of Solar Decathlon India.
I hold an integrated MSc-PhD degree in Energy Science and Engineering from IIT Bombay, India. My PhD thesis was titled "Assessment of Sky Luminance for Indoor Daylight Modeling." (2018)
I developed OWL as a design support tool, to evaluate Non Image Forming metrics of Light at any user's position.
Evaluated for any point in time, these metrics indicate how spectral light exposure affects an Occupant's circadian wellbeing -- through regulation of their body clock.
OWL works with Rhino + Grasshopper + Radiance + Python and is peer reviewed in Energy and Buildings journal.
More details on OWL are available at its dedicated website.
AnnuOWL is an evolution in OWL simulation tools. This tool simultaneously evaluates the visual and non-visual metrics for (day)light across the annual hours, for multiple occupied positions and viewing angles. These metrics can be used for early phase interventions in facade and lighting design.
AnnuOWL was presented at the International Radiance Workshop, and this presentation can be found at the Radiance website.
SpectrOWL is yet another step in OWL's evolution, where multichannel spectral simulations are used for evaluating grid-based and image-based metrics for vision and non-vision. These extended metrics can also be evaluated for any point in time, across the entire year. SpectrOWL uses pre-simulated spectral sky data (evaluated via spectral sky models) for incorporating the impact of dynamic sky spectra on the simulated metrics.
I developed sky scanners for continuously measuring the distribution of daylight-luminance across the skydome. Low-cost prototypes were developed and deployed for measurement, prior to data analysis. These were granted a patent in 2024. The development process of these devices is published in the Building and Environment journal.
I used the sky-scanners for a data collection campaign spanning 8 months in Mumbai, India, to measure the luminance distribution of daylight across the skydome from pre-dawn to post-dusk hours. The measured data was compared to the expected distribution for various sky-types of the CIE sky model, and the Frequency of occurence of each CIE skytype was identified. This study is published in the Building and Environment journal.
As a teaching aid for the MTech BEP course at CEPT-U, a tool was developed to identify potential sources of discomfort glare in a scene. This tool used OpenCV-Python for post-processing HDR images, and was inspired by HDRScope. This was complementary to supervised research on low-cost luminance imaging via Phone cameras and Raspberry Pi camera. This tool is freely available here.
This teaching-support tool for emulating and visualising CIE standard skies is available here.
RaspberryPi + fisheye camera + custom Python code was used to generate HDR images, as teaching exercise at CEPT-U.
An ARSandbox was built for summer exhibition 2019 at CEPT-U.
Alternatively, you can find me on ORCID / Google Scholar / Researchgate; or via social media.