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Dr. Sinem Solak
Dr. Sinem Solak is an academic and researcher with a strong foundation in Electronics and Communication Engineering, specializing in signal processing, machine learning, and intelligent systems. Her doctoral research at the University of Sussex focused on nanoscale sensor networks and statistical signal processing for abnormality detection, resulting in multiple peer-reviewed publications. With experience across higher education institutions in the UK, Dr. Solak has designed and delivered innovative, student-centred curricula in computing and engineering. In addition to her academic work, she has held leadership and consultancy roles in deep tech startups, contributing to the development of AI-powered healthcare technologies and securing over £800,000 in competitive research and innovation funding. She brings a transdisciplinary and applied perspective to her teaching, blending academic rigor with practical impact.
Previously worked for:
Regent College London, Hirehoot, Stealth Mode Biotechnology Company, Study Group, University of Sussex, Conception X, Bogazici University, AIR Telekmünikasyon Çözümleri, Yıldız Teknik Üniversitesi and Turkcell
Courses taught on:
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Skills you can learn
Foundation Computing
Foundation Mathematics
Human Computer Interaction Web Technologies
Machine Fundamentals
Electronic Circuits and Components
Creative Engineering
Programming Lab
Electrical Circuits and Devices
Core Physics
Physics Lab
Electromagnetism and Introduction to Electrical Machines
Papers
S. Solak and M. Öner, "Sequential Decision Fusion for Abnormality Detection via Diffusive Molecular Communications," in IEEE Communications Letters, vol. 25, no. 3, pp. 825-829, March 2021, doi: 10.1109/LCOMM.2020.3040146.
keywords: {Sensors;Delays;Nanoscale devices;Task analysis;Sensor fusion;Receivers;Sensor phenomena and characterization;Molecular communications;distributed detection;nanoscale sensor networks},
S. N. Solak and M. Oner, "Neural Network Based Decision Fusion for Abnormality Detection via Molecular Communications," 2020 IEEE Workshop on Signal Processing Systems (SiPS), Coimbra, Portugal, 2020, pp. 1-5, doi: 10.1109/SiPS50750.2020.9195212. keywords: {Channel models;Task analysis;Sensor phenomena and characterization;Training;Artificial neural networks;Nanoscale devices;deep learning;molecular communication;distributed detection;sensor networks},
RNN based abnormality detection with nanoscale sensor networks using molecular communications
Split-ring resonator-based sensors on flexible substrates for glaucoma monitoring
SAR imaging techniques for ground penetrating impulse radar
Professional Community Services
Membership
Reviewing
Program committee memberships and chairing
Judging & mentoring in competitions
Other
Prizes
Talks
Consulting / Entrepreneurship projects
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