I am a PhD Student in Earth and Space Sciences at York University. I completed my Bachelors in Space Engineering, graduating first class with distinctions. Space Engineering, is a branch of systems engineering focused on the Space Sector.
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Lassonde School of Engineering / York UniversityDepartment of Earth and Space Sciences
Ph.D. StudentSep. 2025 - present -
Lassonde School of Engineering / York UniversityB.Eng Space EngineeringSep. 2021 - Jun. 2025
Honors & Awards
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Enbridge Graduate Student Award2026
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Queen Elizabeth II Graduate Scholarship in Science and Technology2026
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Mitacs Globalink Research Award Abroad2026
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York Graduate Scholarship2025
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Lassonde Undergraduate Research Award (LURA), Fall / Winter2024
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Lassonde Undergraduate Research Award (LURA), Summer2024
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Lassonde Undergraduate Research Award (LURA), Fall / Winter2023
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Lassonde Undergraduate Research Award (LURA), Summer2023
Selected Publications (view all )
Hardware-in-the-loop validation of physics-informed neural network for angles-only initial orbit determination in very short arcs
Ian Porto, Marissa Myhre, Sofia Meson-Perez, Angel Porras-Hermoso, Gunho Sohn, Regina S.K. Lee
Accepted to 46th Committee on Space Research (COSPAR) Scientific Assembly 2026 Spotlight
The exponential growth of the Resident Space Object (RSO) population in Low Earth Orbit (LEO) necessitates robust Space Situational Awareness (SSA) capabilities. However, the high relative velocities in LEO frequently result in very short arc observations, which cause traditional Initial Orbit Determination (IOD) methods to diverge or yield physically impossible solutions. Developing algorithms capable of handling these regimes requires data that accurately reflects the complex, non-Gaussian noise characteristics of real optical sensors. This work presents a robust IOD pipeline developed and validated using a hardware-in-the-Loop optical starfield simulator incorporating the engineering model of the UPMSAT-4 multi-use star tracker. The payload comprises a 4.2-megapixel sCMOS sensor paired with a 29.7$^\circ$ $\times$ 29.7$^\circ$ field-of-view lens. By physically stimulating this sensor with high-fidelity generated starfields and RSOs, we generate a training dataset that preserves flight-representative radiometric properties. Crucially, this hardware-in-the-loop setup captures degradation modes that cannot be reliably simulated. This hardware-validated imagery is used to train and evaluate a Physics-Informed Neural Network (PINN). The architecture employs a two-stage hybrid approach to navigate the difficult very-short arc solution space. First, a metaheuristic global optimizer identifies the approximate orbital basin by searching the admissible region. Second, the PINN performs local refinement by embedding the governing equations of orbital motion as a regularization constraint. This physics-guided loss function allows the network to distinguish between true orbital dynamics and the specific sensor artifacts present in the Hardware-in-the-Loop data. Preliminary analysis highlights the algorithm's resilience against these realistic sensor degradations compared to industry-standard iterative solvers. Results indicate that the physics-informed regularization significantly mitigates the singularity issues common in angles-only inversion, maintaining solution stability even when subjected to the non-linear noise profiles. These findings provide critical validation for the use of physics-guided machine learning in processing real-world optical data, demonstrating a pathway toward more robust, noise-resilient SSA architectures for future distributed sensor networks.
Hardware-in-the-loop validation of physics-informed neural network for angles-only initial orbit determination in very short arcs
Ian Porto, Marissa Myhre, Sofia Meson-Perez, Angel Porras-Hermoso, Gunho Sohn, Regina S.K. Lee
Accepted to 46th Committee on Space Research (COSPAR) Scientific Assembly 2026 Spotlight
The exponential growth of the Resident Space Object (RSO) population in Low Earth Orbit (LEO) necessitates robust Space Situational Awareness (SSA) capabilities. However, the high relative velocities in LEO frequently result in very short arc observations, which cause traditional Initial Orbit Determination (IOD) methods to diverge or yield physically impossible solutions. Developing algorithms capable of handling these regimes requires data that accurately reflects the complex, non-Gaussian noise characteristics of real optical sensors. This work presents a robust IOD pipeline developed and validated using a hardware-in-the-Loop optical starfield simulator incorporating the engineering model of the UPMSAT-4 multi-use star tracker. The payload comprises a 4.2-megapixel sCMOS sensor paired with a 29.7$^\circ$ $\times$ 29.7$^\circ$ field-of-view lens. By physically stimulating this sensor with high-fidelity generated starfields and RSOs, we generate a training dataset that preserves flight-representative radiometric properties. Crucially, this hardware-in-the-loop setup captures degradation modes that cannot be reliably simulated. This hardware-validated imagery is used to train and evaluate a Physics-Informed Neural Network (PINN). The architecture employs a two-stage hybrid approach to navigate the difficult very-short arc solution space. First, a metaheuristic global optimizer identifies the approximate orbital basin by searching the admissible region. Second, the PINN performs local refinement by embedding the governing equations of orbital motion as a regularization constraint. This physics-guided loss function allows the network to distinguish between true orbital dynamics and the specific sensor artifacts present in the Hardware-in-the-Loop data. Preliminary analysis highlights the algorithm's resilience against these realistic sensor degradations compared to industry-standard iterative solvers. Results indicate that the physics-informed regularization significantly mitigates the singularity issues common in angles-only inversion, maintaining solution stability even when subjected to the non-linear noise profiles. These findings provide critical validation for the use of physics-guided machine learning in processing real-world optical data, demonstrating a pathway toward more robust, noise-resilient SSA architectures for future distributed sensor networks.
Shallow Neural Network Centroid Algorithm Comparison for a Dual-Use Star Tracker Payload for Space Situational Awareness
Marissa Myhre, Ian Porto, Vithurshan Suthakar, Regina S.K. Lee
Accepted to 46th Committee on Space Research (COSPAR) Scientific Assembly 2026
In this study, a shallow neural network centroid algorithm consisting of an input layer of size 20, a single hidden layer with 8 neurons, and an output layer of size 2 representing the centroid location is developed. The shallow neural network is compared with the NASA grey-weighted benchmark method and a moments centroid method. The centroid algorithm was trained and tested using 1,200 real images and 6000 augmented from stratospheric balloon flights. The performance of the three methods was compared to a plate-solved reprojected star centroid reference solution from astrometry.net.
Shallow Neural Network Centroid Algorithm Comparison for a Dual-Use Star Tracker Payload for Space Situational Awareness
Marissa Myhre, Ian Porto, Vithurshan Suthakar, Regina S.K. Lee
Accepted to 46th Committee on Space Research (COSPAR) Scientific Assembly 2026
In this study, a shallow neural network centroid algorithm consisting of an input layer of size 20, a single hidden layer with 8 neurons, and an output layer of size 2 representing the centroid location is developed. The shallow neural network is compared with the NASA grey-weighted benchmark method and a moments centroid method. The centroid algorithm was trained and tested using 1,200 real images and 6000 augmented from stratospheric balloon flights. The performance of the three methods was compared to a plate-solved reprojected star centroid reference solution from astrometry.net.
Thermo-Optical Characterization and Qualification of a Space Situational Awareness Payload
Ian Porto, Marissa Myhre, Vithurshan Suthakar, Regina S.K. Lee
5th IAA Conference on Space Situational Awareness 2026 Spotlight
In this study, we present a Technology Readiness Level (TRL) 7 payload comprising a 4.2-megapixel sCMOS sensor and a lens with a 29.7 by 29.7 degree field of view, which has flown three times on stratospheric balloon missions with the Canadian Space Agency and the Centre National d'Études Spatiales.
Thermo-Optical Characterization and Qualification of a Space Situational Awareness Payload
Ian Porto, Marissa Myhre, Vithurshan Suthakar, Regina S.K. Lee
5th IAA Conference on Space Situational Awareness 2026 Spotlight
In this study, we present a Technology Readiness Level (TRL) 7 payload comprising a 4.2-megapixel sCMOS sensor and a lens with a 29.7 by 29.7 degree field of view, which has flown three times on stratospheric balloon missions with the Canadian Space Agency and the Centre National d'Études Spatiales.
Centroid Algorithm Comparison in a Dual-Use Star Tracker for Space Situational Awareness
Marissa Myhre, Ian Porto, Vithurshan Suthakar, Regina S.K. Lee
5th IAA Conference on Space Situational Awareness 2026
In this study, four centroid algorithms were tested with the NASA open-source commercial off-the-shelf star tracker software: grey-weighted, moments-based, intensity-squared weighted, and 1D Gaussian centroiding methods. The four centroid algorithms were evaluated using images from the dual-use star tracker payload taken on a stratospheric balloon qualification flight, with the centroid performance compared to the reprojected plate-solved reference solution from astrometry.net.
Centroid Algorithm Comparison in a Dual-Use Star Tracker for Space Situational Awareness
Marissa Myhre, Ian Porto, Vithurshan Suthakar, Regina S.K. Lee
5th IAA Conference on Space Situational Awareness 2026
In this study, four centroid algorithms were tested with the NASA open-source commercial off-the-shelf star tracker software: grey-weighted, moments-based, intensity-squared weighted, and 1D Gaussian centroiding methods. The four centroid algorithms were evaluated using images from the dual-use star tracker payload taken on a stratospheric balloon qualification flight, with the centroid performance compared to the reprojected plate-solved reference solution from astrometry.net.
Stratospheric Observation of Resident Space Objects Using Photometric and Infrared Sensors
Vithurshan Suthakar, Ian Porto, S. K. M. Dasanayaka, Regina S.K. Lee, Miron Ionut Robert, Radui Olteanu, A. Vucur, Melina Kubler, Joost I. Hubbard, Marta Scherillo, Franz Newland, Vitali Braun
9th European Conference on Space Debris 2025
This paper introduces the Space Object Brightness Evaluation and Reference (SOBER) mission, an SSA initiative leveraging optical and infrared observations from a stratospheric balloon platform.
Stratospheric Observation of Resident Space Objects Using Photometric and Infrared Sensors
Vithurshan Suthakar, Ian Porto, S. K. M. Dasanayaka, Regina S.K. Lee, Miron Ionut Robert, Radui Olteanu, A. Vucur, Melina Kubler, Joost I. Hubbard, Marta Scherillo, Franz Newland, Vitali Braun
9th European Conference on Space Debris 2025
This paper introduces the Space Object Brightness Evaluation and Reference (SOBER) mission, an SSA initiative leveraging optical and infrared observations from a stratospheric balloon platform.
RSONAR: Data-Driven Evaluation of Dual-Use Star Tracker for Stratospheric Space Situational Awareness (SSA)
Vithurshan Suthakar, Ian Porto, Marissa Myhre, Aiden Alexander Sanvido, Ryan Clark, Regina S.K. Lee
MDPI Sensors 2024
This study demonstrates a dual-purpose star tracker (ST) for SSA using data from the Resident Space Object Near-space Astrometric Reconnaissance (RSONAR) stratospheric balloon campaign under the 2022 Canadian Space Agency–Centre National d’Études Spatiales (CSA–CNES) STRATOS program.cover: /assets/images/covers/cover3.jpg
RSONAR: Data-Driven Evaluation of Dual-Use Star Tracker for Stratospheric Space Situational Awareness (SSA)
Vithurshan Suthakar, Ian Porto, Marissa Myhre, Aiden Alexander Sanvido, Ryan Clark, Regina S.K. Lee
MDPI Sensors 2024
This study demonstrates a dual-purpose star tracker (ST) for SSA using data from the Resident Space Object Near-space Astrometric Reconnaissance (RSONAR) stratospheric balloon campaign under the 2022 Canadian Space Agency–Centre National d’Études Spatiales (CSA–CNES) STRATOS program.cover: /assets/images/covers/cover3.jpg