Technical Lead-ML-GNSS Engineer, GMV NSL, United Kingdom
Dr. Shishir Priyadarshi is an experienced engineer specializing in Machine Learning (ML) and Global Navigation Satellite Systems (GNSS) at GMV, UK. With over a decade of experience, he has significantly contributed to various ESA projects aimed at enhancing the accuracy and resilience of GNSS through innovative ML algorithms. His work spans multiple international research projects and academic appointments, reflecting a deep commitment to advancing space science and technology.
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Education š
Dr. Shishir Priyadarshi earned his Ph.D. in Space Physics (Geophysics) from the Space Research Centre in Warsaw, Poland, in 2014. His thesis, titled “B-spline model of ionospheric scintillation,” was supervised by Professor Andrzej Wernik. Prior to this, he completed his Master of Science (M.Sc.) in Physics, specializing in Electronics and Radio Physics, at Banaras Hindu University in Varanasi, India, in 2009. His master’s thesis was titled “GPS-Based Measurement of TEC and Its Variability Over Ground Station Varanasi, India.”
Experience š¼
Dr. Shishir Priyadarshi currently serves as an ML-GNSS Engineer and Technical Lead at GMV in Nottingham, UK, since February 2022. In this role, he spearheads the development and validation of machine learning techniques for resilient time provisioning in ESA NAVISP projects, with a particular focus on ionospheric GNSS modeling and the detection of GNSS signal interference. Prior to this, he was a Research Associate at the University of Bath, UK, from February 2021 to January 2022, where he contributed to research on ionospheric modeling and ML applications in GNSS. From November 2019 to January 2021, Dr. Priyadarshi worked as a Postdoctoral Researcher at SUSTech in Shenzhen, China, focusing on ionospheric data assimilation and GNSS signal processing. He also served as an Adjunct at the University of Wroclaw, Poland, from July 2018 to June 2019, concentrating on atmospheric research and ionospheric scintillation. From June 2015 to May 2018, he was a Postdoctoral Research Scientist at the Institute of Space Sciences, Shandong University in Weihai, China, where he investigated the coupling of atmospheric layers and GNSS signal propagation. His earlier role as a Space Physicist (Postdoc) at the Space Research Centre in Warsaw, Poland, from August 2014 to May 2015, involved researching the effects of ionospheric disturbances on GNSS signals. As a Marie-Curie Fellow (Early-Stage Researcher) from August 2011 to July 2014 at the Space Research Centre in Warsaw, he developed models for ionospheric scintillation and its impact on satellite communications. Dr. Priyadarshi began his career as a Junior Research Fellow at the Atmospheric Research Laboratory of Banaras Hindu University in Varanasi, India, from August 2009 to 2011, where he studied ionospheric perturbations using GPS data.
Research Interests š¬
Dr. Shishir Priyadarshi focuses on Magnetosphere-Ionosphere-Thermosphere (MIT) Coupling, exploring how the interactions between various atmospheric layers influence Global Navigation Satellite System (GNSS) signals. His work in Ionospheric Scintillation involves modeling and predicting the responses of ionospheric scintillation to various space weather phenomena. To enhance the accuracy and integrity of GNSS signals, Dr. Priyadarshi employs advanced machine learning (ML) and artificial intelligence (AI) algorithms in GNSS Signal Processing. Additionally, he leverages ML, recurrent neural networks (RNN), and AI to provide real-time Space Weather Nowcasting/Forecasting. His expertise extends to the development of ML-based algorithms for Jamming and Spoofing Detection, which aim to identify and mitigate interference in GNSS signals.
Awards and Achievements š
URSI Young Scientist Award (2017), Montreal, Canada
Young Scientist Award (2014), Space Research Centre, Poland
Marie-Curie Fellow (2011-2014), European Commission
IAPT Award (2006), Indian Association of Physics Teachers
Sadbhavana Club U.P. Award (2003), Indian Government
Publications Top Notes š
Priyadarshi, S. (2023). Machine Learning-based ionospheric modelling performance during high ionospheric activity, Acta-Geophysica, cited by 15 articles. link
Priyadarshi, S. (2023). Fast and reliable forecasting for satellite clock bias correction with transformer deep learning, Radio-Science, Space Weather, cited by 30 articles. link
Priyadarshi, S. (2020). Near-Earth plasma-sheet cumulative magnetic, American Geoscience Union (AGU), cited by 10 articles.
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