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Doctoral Student Prizes

Awarded to Arthur Onno, Russell Anam and Jure Sokolic

The department's 2015 doctoral student prizes were awarded to research students at the departmental Barlow 2015 Research Poster Day and Lecture on 18th May, including for a second year, the Cisco Prize for the Best Internet of Things related poster, which was presented by Cisco SVP and UCL EEE alumnus John Manville.

Each year the UCL Department of Electronic and Electrical Engineering, gives two prizes to research students for their work. The first, The Cullen Prize, is awarded to the best poster by a Research Student. The second, The Lombardi prize, is given to the best doctoral thesis of the year. Finally, since 2014 Cisco Systems have awarded a prize for the best poster about the Internet of Things (IoT).

This year, the Cullen prize was won by Arthur Onno, supervised by Professor Huiyun Liu, with a poster entitled "Impact of the threading dislocation density in the GaAsP layers on the performances of GaAsP/Si tandem solar cells". There were honourable mentions for Clemens Tummeltshammer, supervised by Dr Ioannis Papakonstantinou for a poster titled "Losses in Luminescent Solar Concentrators Unveiled" and also for Alaric Taylor, also supervised by Dr Papakonstantinou for his poster titled "Self-assemblied bio-inspired windows".

The 2015 Lombardi prize was won by Mohammad Ashraful (Russell) Anam, supervised by Dr Yiannis Andreopoulos, with a thesis entitled "Precision-energy-throughput scaling of error tolerant signal processing applications"). An honourable mention was given to Dr Vassiliki Giagka, supervised by Professor Andreas Demosthenous for her thesis 'The design of flexible, active electrode arrays for epidural spinal cord stimulation' and also to Allann Al-Armaghany, supervised by Dr Kenneth Tong for his thesis 'Development of a Hybrid Microwave-Optical System to Monitor Human Thermoregulation'.

Finally the Cisco Prize was presented to Jure Sokolic, supervised by Dr Miguel Rodrigues, for his poster titled 'Smart and Energy Efficient Sensing for the Internet of Things'

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