Find Academic Supervisors

Amr MahmoudView profile

Assistant Professor

• Neuromorphic Computing
• Machine Learning
• Deep Learning
• +3 more

Dr. Amr Mahmoud is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Pittsburgh. His work focuses on neuromorphic computing systems, machine learning, and FPGA-based accelerators for image processing and video prediction. PhD in Computer Engineering, University of Pittsburgh (2019) MSc in Engineering Physics, Cairo University (2015) BSc in Electronics and Electrical Communications Engineering, Cairo University (2011) Dr. Mahmoud’s research bridges neuromorphic computing and machine learning, particularly for biomedical and sensor applications. He designs systems leveraging memristor crossbar arrays to emulate biological neural networks, enabling energy-efficient hardware solutions for tasks like cardiac arrhythmia classification and optical sensor prediction. His recent publications highlight advancements in memristor modeling, hybrid spiking neural networks, and hardware implementations using neuromorphic architectures. Key trends include real-time biomedical diagnostics, LSTM-based sensor prediction, and crossbar array optimization for machine learning. Contact: AMM418@pitt.edu

François-Raymond BoyerView profile

Associate Professor

• Computer Architecture
• VLSI Systems
• Microelectronics
• +4 more

François-Raymond Boyer is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He holds a B.Sc. and Ph.D. from Université de Montréal and teaches courses in programming and digital audio. His research spans: Computer architecture and VLSI systems Network processing and traffic management Digital audio signal processing Hardware acceleration and optimization He is affiliated with ReSMiQ (Strategic Cluster for Microsystems) and OICRM (Music Research Observatory). Recent publications focus on 5G network processing, modular programming frameworks, and high-speed traffic management architectures, primarily in IEEE Access and IEEE Transactions on VLSI Systems. Professor Boyer has supervised 3 PhD and 6 Master's students, with research topics including network architecture, audio processing, and hardware design. No scientific awards are mentioned in the available records.

Syed Asad AlamView profile

Research Fellow

• Machine Learning
• Embedded Systems
• Quantization
• +5 more

Syed Asad Alam is a Post-Doctoral Research Fellow at the School of Computer Science and Statistics, Trinity College Dublin, The University of Dublin, Ireland, specializing in optimization and efficient implementation of digital systems for signal processing and communication algorithms. Education: Bachelors in Computer and Information Systems Engineering, N.E.D. University of Engineering and Technology, Karachi, Pakistan M.Sc. in Electrical Engineering (System-on-Chip Design), Linköping University, Sweden PhD in Electrical Engineering (Computer Engineering/Electronics Systems), Linköping University, Sweden His research integrates Machine Learning with Embedded Systems through novel approaches to Quantization and Logarithmic Number Systems , targeting efficient Neural Networks implementation on FPGAs and ASICs . This work builds on extensive industry experience with multimedia processors, communication devices, and SOC bus design, including specialized projects on filters, wireless systems, frequency multipliers, and DSP processors. His 2020 LCTES publication on non-base-2 logarithmic systems exemplifies his focus on numerical representation efficiency for resource-constrained embedded applications, reflecting broader trends in hardware-aware machine learning optimization. No scientific awards or grant information was documented in the source material. He has collaborated with multiple industry teams on FPGA-based design and Integrated Circuit Development across startup environments, though specific advising roles or student supervision were not indicated.

Ali Al BatainehView profile

Assistant Professor

• Artificial Intelligence
• Hardware Security
• Cybersecurity
• +5 more

Ali Al Bataineh serves as Director of the Center for Artificial Intelligence and Assistant Professor in the Department of Engineering at Norwich University's David Crawford School of Engineering. He holds a Ph.D. in Electrical Engineering from the University of Toledo (2021), with doctoral research focused on computational models inspired by the adaptive immune system. His research spans artificial intelligence applications in hardware security (including FPGA/ASIC Trojan detection), cybersecurity, natural language processing, and humanitarian disaster response. Key projects involve AI-driven optimization techniques and military/health sector implementations, supported by international collaborations with École Spéciale Militaire de Saint-Cyr (France) and Helmut Schmidt University (Germany). Dr. Al Bataineh co-leads Norwich University's $5.3 million NSF-funded cybersecurity initiative and is establishing a Military AI Virtual Data Center with German partners. He advises students to embrace persistence and curiosity in research, emphasizing failure as part of the learning process. No scientific awards are documented in the provided information.

Runchun Mark WangView profile

Researcher

• Neuromorphic Engineering
• Mixed-signal/analog VLSI design
• ASIC/SoC/FPGA design
• +5 more

Dr Runchun Mark Wang is a Researcher at the International Centre for Neuromorphic Systems (ICNS) at Western Sydney University, specializing in hardware acceleration for neural systems. He develops FPGA and mixed-signal analog VLSI platforms capable of simulating human-brain-scale neural networks and leads hardware design for the Neuromorphic Imager for Space Situational Awareness, providing open-source software tools for broader research access. His educational background includes: PhD from Western Sydney University (2013) MSEE from Shanghai Jiaotong University (2008) BSEE from East China University of Science and Technology (2003) Wang's research spans neuromorphic engineering, computational neuroscience, and hardware implementation of deep learning systems. His work bridges theoretical neural models with practical electronic systems, emphasizing scalable architectures for cognition simulation and space applications through integrated circuit design. Key recognitions include: Australian Research Council Postdoctoral Research Fellowship (2014–2017) Australian Postgraduate Research Award (2009–2013) Prior to academia, Wang worked as an industry SoC/ASIC design engineer, bringing practical hardware expertise to his current research leadership at ICNS. His projects focus on open-source neuromorphic tools and space-imaging technology with real-world deployment potential.