The team is growing! If you’re interested in joining, see below.
PhD students
Ankush Roy; jointly with Justin
Carlo Saccardi; jointly with Justin
MSc students
Haozhang Jia
Dániel Fekete
Bachelor students
Rainier van Trigt, Oliver van den Ende, Ben Siwpersad, Robin Janssen, Atakan Mertoğlu, and Ömer Özkul (2026)
Guus Dohmen, Thymen Vrijenhoek, Jesse van der Kooij, and Evert-Jan Beiboer (2025)
Postdoc
Chen Quan; joint with Nitin Myers (2024-26)
PhD Students
Yanbin He (2026)
MSc Students
Jasper Brinkgreve (2026)
Aybars Manav; Lobster Robotics (2026)
Kaishen Lin; jointly with Jac Romme, IMEC (2025)
Nikos Fotopoulos; jointly with Ashish Pandharipande, NXP (2025)
Anthony Dai (2025)
Frank Harraway (2025)
Kunlei Yu; jointly with Jac Romme, IMEC (2024)
Yi Weijia; jointly with Nitin Myers (2023)
Çağan Önen; jointly with Ashish Pandharipande, NXP (2023)
Visitors
Areena Nisar, University of Modena and Reggio Emilia, Italy (April-May 2026)
Irawati Rahul Thete, BITS Pilani, Goa Campus (Mar-May 2024)
I'm looking for passionate PhD candidates keen on exploring machine learning and signal processing. Candidates interested in signal processing, communications, and machine learning are encouraged to apply. A solid foundation in applied mathematics is crucial for my projects. If this resonates with you, kindly write to me with your CV. If your qualifications align strongly with an available opportunity, I'll reach out to discuss the next steps. Please understand that due to time constraints, I may be unable to respond individually to every email. Your understanding is appreciated.
If you are currently an MSc student at TU Delft and wish to sign up to do your project with me, please feel free to write to me or drop by my office for a chat. My office is on the 17th floor of Building 36, Makelweg 4. If you are interested in joining the team, you should have some experience or coursework in signal processing/applied mathematics, and ideally, some experience with MATLAB or Python.
Some example areas in which you could work with me include:
Sensor fusion for automotive applications
Sparse recovery algorithms
Wireless channel estimation and communication system performance
Here are some past joint publications with my past MSc/BSc students (in bold):
C. Quan, W. Yi, N. J. Myers, and G. Joseph, Sparse Millimeter Wave Channel Estimation Under Partially Coherent Phase Noise, accepted in IEEE Transactions on Wireless Communications.
F. Harraway, P. Zhai, G. Joseph, and A. Pandharipande, Accelerated Pattern-Coupled Sparse Bayesian Learning for Automotive Occupancy Mapping, in IEEE Sensors Journal, vol. 25, pp. 41801-41810, 2025.
C. Quan, W. Yi, N. J. Myers, and G. Joseph, Sparse Millimeter Wave Channel Estimation Under Partially Coherent Phase Noise, accepted in IEEE Transactions on Wireless Communications.
L. Ballotta, G. Joseph, I. R. Thete, "Pointwise-Sparse Actuator Scheduling for Linear Systems with Controllability Guarantee," accepted in IEEE Control Systems Letters, Oct. 2024
Ç. Önen, A. Pandharipande, G. Joseph, and N. J. Myers, Occupancy Grid Mapping for Automotive Driving Exploiting Clustered Sparsity, in IEEE Sensors, Apr. 2023
F. Harraway, P. Zhai, G. Joseph, and A. Pandharipande, Computationally-Efficient Sparsity-Aware Occupancy Grid Mapping for Automotive Driving, in Proc. IEEE Sensors, Vancouver, Canada, Oct. 2025.
W. Yi, N. J. Myers, and G. Joseph, "Sparse Millimeter Wave Channel Estimation From Partially Coherent Measurements," in Proc. ICC, Denver, CO, USA, Jun. 2024
Ç. Önen, A. Pandharipande, G. Joseph, and N. J. Myers, LiDAR-Based Occupancy Grid Map Estimation Exploiting Spatial Sparsity, in Proc. IEEE Sensors, Vienna, Austria, Oct.-Nov. 2023