Google Scholar : http://scholar.google.com/citations?user=QnRaxS0AAAAJ
I received my B.S. degree from Istanbul Technical University in Electrical and Communications Engineering in 1988, M.S. degree from Johns Hopkins University Electrical and Computer Engineering (ECE) in 1992, and Ph.D. Degree from University of Rochester (UR) ECE in 2000. I formed my own business that is engaged in IT outsourcing and copier sales/service after earning my PhD and sold the copier division of my business in 2008 and joined UR ECE the same year as a Visiting Assistant professor. Later, I became a Research Scientist in 2011 and Assistant Professor - Research in 2012. In March 2015, I sold the remaining computer/IT division of my business to focus entirely on academia.
While at UR ECE, I taught six different courses: ECE114 Introduction to C Programming, ECE405 Advanced FPGA-Based System Design, ECE262/462 Advanced CMOS VLSI Design, ECE206/406 GPU Parallel Programming, ECE207/407 Advanced GPU Project Development, and DSC450 Data Science Practicum. I managed the Xilinx University Program (XUP) and MOSIS Educational program (MEP for ASIC Design) for the UR ECE Department and the NVidia GPU Education Center and GPU Research Center programs for the University of Rochester.
In May 2016, I have accepted an Associate Professor position at SUNY Albany, Department of ECE and have been a faculty member at SUNY Albany ECE since then. My research is focused on three main areas: 1) Cyber Physical Systems, 2) Digital Health (D-Health), and 3) High-performance medical data processing and visualization.
Cyber Physical Systems research investigates the design of near-maintenance-free, supercapacitor-based energy supply design for high-performance embedded field devices (e.g., Nvidia TEGRA3-based). A recent collaboration focuses on Cyber Physical Systems for volcano seismic activity detection and real-time earthquake and tsunami warning. The sensors that we use are either active (including a battery), or passive RFID-based.
Digital Health (D-Health) research investigates a) RFID-based passive or battery-based active medical data acqusition circuit/system design, b) secure transportion of medical data using non-traditional encryption algorithms such as homomorphic encryption, and c) decision support for doctors using machine learning techniques such as SVMs and random forests, applied to medical data.
High-Performance Medical Data Processing research investigates a) GPU implementation of computationally-intensive medical algorithms, b) novel visualization methodologies for medical data, especially within the cardiology field.