Find Academic Supervisors

• Complex Systems
• Computational Epidemiology
• Artificial Intelligence
• +5 more

Professor Mikhail Prokopenko is a leading academic in complex systems research at the University of Sydney's School of Computer Science. He holds a PhD in Computer Science (Macquarie University, 2002), MA in Economics (USA), and MSc in Applied Mathematics (USSR). As Director of the Centre for Complex Systems and Theme Co-Leader for Pathogen Emergence and Spread at the Institute for Infectious Diseases, his work focuses on modeling self-organizing systems, computational epidemiology, and AI applications in pandemic control. Roles: Director of Centre for Complex Systems, Theme Co-Leader (Pathogen Emergence), Theme Leader (Complex Systems) Education: PhD Computer Science (2002), MA Economics (USA), MSc Applied Mathematics (USSR) Affiliations: Fellow of Royal Society of NSW, Specialty Editor for Frontiers in Robotics and AI His research addresses challenges in complex systems like power grids, communication networks, and epidemiological models. Notable contributions include: RoboCup World Champion teams (2016, 2019) in Simulation League COVID-19 modeling featured in Nature's Top 50 SARS-CoV-2 articles (2020) Development of AMTraC-19 agent-based pandemic model Key research areas include: Guided self-organization principles Information dynamics in collective systems Thermodynamic efficiency of complex processes He has supervised over 200 publications and patents, with recent focus on pandemic response optimization and urban system resilience.

Ana DyresonView profile

Assistant Professor

• Renewable Energy Systems
• Climate Change Mitigation in Energy Systems
• Electric Power Systems
• +5 more

Ana Dyreson is an Assistant Professor in Mechanical and Aerospace Engineering at Michigan Technological University and Associate Research Director at the Center for Innovation in Sustainability & Resilience (CISR). She leads the Great Lakes Energy Group, focusing on climate change impacts on electric power systems , energy transitions in cold climates , and thermal power plant modeling . Her work bridges solar photovoltaic design , electricity grid operational modeling , and the energy-water nexus . Education : PhD in Mechanical Engineering, University of Wisconsin–Madison (2018) MS in Mechanical Engineering, Northern Arizona University (2014) BS in Engineering Mechanics, University of Wisconsin–Madison (2011) Research emphasizes climate-resilient energy systems , particularly solar energy in cold climates and grid-scale modeling . Her 2025-2022 publications investigate snow mitigation on PV panels , heat pump adoption , floating solar-hydropower hybrids , and climate stressor impacts on thermoelectric plants . She develops inclusive teaching methods and advises on renewable energy deployment through initiatives like the Tech Forward Initiative on Sustainability and Resilience . Her team collaborates on multisector dynamics and energy-water-climate research in the Great Lakes region.

Rao V. MulpuriView profile

Professor

• Semiconductor Materials
• Device Processing
• Ion Implantation
• +7 more

Rao V. Mulpuri is a Professor in the Department of Electrical and Computer Engineering at George Mason University's School of Information Technology and Engineering. He joined GMU in September 1984 and attained the rank of Professor in September 1993. His research has focused on semiconductor materials and device processing, with particular expertise in ion implantation technology for compound semiconductors. Dr. Mulpuri earned his BTech in Electronics and Communications Engineering from Jawaharlal Nehru Technological University (1977), MTech in Materials Science from the Indian Institute of Technology (1979), and MS (1983) and PhD (1985) in Electrical Engineering from Oregon State University. His research spans semiconductor device physics, with emphasis on large bandgap materials like silicon carbide (SiC) and gallium nitride (GaN). Key areas include ion implantation doping, ohmic contact development, microwave annealing techniques, and fabrication of high-power/high-temperature devices. More recently, his work has expanded into microfluidic systems for biological applications and infrared focal plane arrays. Analysis of his recent publications reveals a strong focus on microwave processing of wide-bandgap semiconductors, particularly examining strain effects in GaN heterostructures, optimizing SiC device performance, and developing novel annealing techniques. His work bridges fundamental materials science with practical device applications. Outstanding Research Award, School of Information Technology and Engineering, George Mason University (2002) Engineering Excellence Award, Telugu Association of North America (TANA) (2005) Dr. Mulpuri has secured over $3 million in research funding through 22 consecutive years of support (1987-2009), including nine consecutive NSF awards plus funding from ONR, ARO, NASA, NRL, and NIST. He has advised 12 PhD students to completion (with 2 more in progress) and 13 MS students, with graduates employed at major technology companies including Intel, GE, and IBM. He has served as a Program Director at NSF and reviewed numerous proposals and journal articles. His laboratory has established strong collaborations with government research facilities including Naval Research Laboratory, Army Research Laboratory, National Institute of Standards and Technology, Oak Ridge National Laboratory, and GE Corporate Research, focusing on semiconductor device development for defense and commercial applications.

• Mechanical Engineering
• Machine Design
• Tribology
• +4 more

Miloš Matejić serves as an Associate Professor at the University of Kragujevac's Faculty of Engineering Sciences, within the Department of Mechanical Constructions and Mechanization. His academic position involves active research and teaching in mechanical systems design. Research interests center on advanced mechanical engineering topics including: Cycloid drive efficiency optimization and stress analysis Tribological testing methodologies and material interactions Computational modeling of gear systems using FEM Parametric design automation for mechanical components Composite materials performance in dynamic applications Recent publications demonstrate strong focus on experimental validation of cycloid reducers, novel tribometers, and material optimization. No awards, student advisories, or grant activities are documented in available sources. Institutional collaboration occurs through the Faculty of Engineering Sciences' laboratories and research centers.

• Wireless Sensor Networks
• Next-Generation Computer Networks
• Wireless Mesh Networks
• +5 more

Professor Cormac J. Sreenan is a full professor in Computer Science at University College Cork (UCC), leading the Mobile & Internet Systems Lab (MISL) since 1999. He previously served as Head of School (2019-2021) and Head of Department (2015-2018 and 2000-2004). His research focuses on wireless sensor networks, multimedia networking, IoT, and adaptive video streaming. He has published over 200 peer-reviewed papers and holds 9 patents. He is a Science Foundation Ireland Principal Investigator and a Fellow of both the British Computer Society (2005) and the Irish Academy of Engineering (2022). Education: PhD from the University of Cambridge Computer Laboratory Member of Christ's College, Cambridge Research Interests: Wireless sensor networks and fault-tolerant designs Next-generation computer networks and IoT infrastructure Adaptive video streaming and QoE optimization Network security and technology transfer Grants & Collaborations: Principal Investigator on SFI grants including the €1M ENABLE project Collaborations with Irish companies and international agencies Experience in technical due diligence and expert witness roles Labs & Teams: Directs the Mobile & Internet Systems Lab (MISL), a multidisciplinary research group focused on mobile and multimedia network systems.

Mohamed ElamienView profile

Assistant Professor

• Circuit Theory
• CMOS Integrated Circuits
• Analog and Mixed-Signal Circuit Design
• +7 more

Dr. Mohamed Elamien is an Assistant Professor in the Department of Electrical and Computer Engineering at McMaster University . His research focuses on automating high-performance integrated circuit (IC) design, addressing Power-Performance-Area (PPA) challenges in semiconductors, and developing self-powered platforms for emerging technologies like biomedical and IoT applications. He holds a BSc (Highest Honors) and MSc from the University of Sharjah (2015–2017), and a PhD in Electrical and Computer Engineering from the University of Calgary (2021). Prior to academia, he worked as a senior analog/mixed-signal circuit design engineer at Synopsys (2022). Education: BSc (Highest Honors) in Electrical & Electronics Engineering – University of Sharjah (2015) MSc in Electrical & Electronics Engineering – University of Sharjah (2017) PhD in Electrical & Computer Engineering – University of Calgary (2021) His research interests span analog and mixed-signal circuit design , CMOS integrated circuits , biomedical technologies , and energy-efficient systems . He has developed innovative designs for oscillators, filters, and low-power circuits with applications in IoT and healthcare. His work emphasizes automation and symbolic math tools to streamline circuit synthesis. Dr. Elamien has published extensively in top-tier journals like IEEE Transactions on VLSI Systems and IEEE Transactions on Microwave Theory and Techniques , focusing on topics such as all-pass filters , low-power oscillators , and AI-driven circuit design . His articles often address challenges in high-frequency communication systems and noise reduction . Awards: Alberta Innovates Graduate Student Award (2020 – Technology Category) Alberta Graduate Excellence Scholarship (2019–2020) 2022 Schulich School of Engineering Graduate Student Research Excellence Award He currently teaches ELECENG 2CJ4: Circuits and Systems and ELECENG 3TP3: Signals and Systems , emphasizing advanced circuit analysis and systems theory. His lab explores next-generation circuit architectures for energy-efficient and biomedical applications.

Lamine M. MiliView profile

Professor

• Power Systems
• Signal Processing
• Robust Estimation Theory
• +7 more

Lamine M. Mili is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His expertise spans power systems, signal processing, and robust estimation theory. He holds an IEEE Fellowship (2016) for contributions to robust state estimation in power systems. Mili's research focuses on advancing methodologies for power system reliability, control, and integration of renewable energy sources. His work includes studies on dynamic state estimation, nonlinear dynamics, bifurcation theory, and quantum computing applications. He has contributed extensively to resilience engineering and computational social science in power systems. Mili’s recent articles address challenges in smart grids, quantum circuit error prediction, and multifractal signal analysis in EEG. His research often combines advanced statistical techniques with real-world grid data, emphasizing robustness and adaptability in dynamic environments. Education: Ph.D., University of Liège, 1987 M.S., University of Tunis, 1983 B.S., Swiss Federal Institute of Technology, Lausanne, 1976 Research Interests: Power system stability and control State estimation and robust filtering Quantum computing for power systems Resilience and cyber-physical-social systems Nonlinear dynamics and bifurcation analysis His recent publications reflect a focus on hybrid power systems, probabilistic methods, and data-driven approaches for grid optimization. The 2025 articles highlight advancements in photovoltaic state estimation, quantum error prediction, and robust modulation techniques. Mili’s work often bridges theoretical models with practical grid applications, emphasizing uncertainty quantification and real-time monitoring.

Vishal SaxenaView profile

Professor

• Silicon Photonic Integrated Circuits
• Neuromorphic Computing
• CMOS Electronics
• +3 more

Professor Vishal Saxena is a faculty member in the Department of Electrical and Computer Engineering at the University of Delaware since 2019. Previously, he held positions at Boise State University (2010–2016) as Assistant and Associate Professor, and served as the Micron Endowed Professor of Microelectronics at the University of Idaho (2016–2019). His research focuses on analog electronic and photonic integrated circuits (ICs), particularly in sustaining IC design advancements post-Moore scaling through hybrid CMOS-photonic integration, neuromorphic computing, and energy-efficient embedded intelligence. Dr. Saxena earned his B.Tech. in Electrical Engineering from IIT Madras (2002), followed by M.S. and Ph.D. in Electrical and Computer Engineering from Boise State University (2007–2010). He has industry experience in semiconductor and telecommunication engineering. His work is supported by NSF, AFOSR, DARPA, NASA, and industry collaborators. Notable awards include the NSF CAREER (2015), AFOSR YIP (2016), and DARPA YFA (2019). His research interests span silicon photonic ICs for optical interconnects, RF photonic systems, neuromorphic circuits using emerging NVM devices, and high-speed analog-to-digital converters. He has pioneered compact modeling techniques for photonic components and developed energy-efficient architectures for spiking neural networks. Dr. Saxena’s publications reflect advancements in photonic integration, neuromorphic hardware, and mixed-signal IC design. He actively contributes to the IEEE community through editorial roles and conference steering committees, including MWSCAS and ISCAS.

Arnout DevosView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Generative AI
• +5 more

Dr. Arnout Devos is a Lecturer at the Department of Computer Science at ETH Zurich and a staff member at the ETH AI Center and ELLIS. His work focuses on Artificial Intelligence research and education, with specializations in generative AI, few-shot learning, and accelerating deep tech innovation. He co-founded the Sciencepreneurship Community to foster entrepreneurial scientists and leads initiatives like the ETH AI Center Academic Talk Series (AICATS) to bridge academic collaborations. Education: PhD in Machine Learning (EPFL, 2024) as a Marie-Curie fellow; Master’s in Computer Science (USC, 2018); degrees in Management, Electrical Engineering, and EECS (KU Leuven). Awards include the EPFL Teaching Assistant Award (2024). Research interests span AI ethics, healthcare applications, and interdisciplinary collaborations. His PhD thesis advanced few-shot learning techniques for efficient model adaptation. Projects include the Sciencepreneurship Summer School and investments via S2S Ventures, including exits like Digit Soil. Key Activities: Academic leadership, startup mentorship, venture capital advising Labs/Teams: ETH AI Center, ELLIS, S2S Ventures Grants: Marie-Curie Fellowship (PhD funding)

• Power Electronics
• Semiconductor Device Modeling
• Electromagnetic Modeling
• +2 more

Dr. Ivana Kovacevic is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich. Her research focuses on power electronics, semiconductor device modeling, and electromagnetic analysis of wide bandgap devices. ETH Zürich, Department of Information Technology and Electrical Engineering Contact: kovacevic@aps.ee.ethz.ch Her research explores SiC power MOSFETs, emphasizing their dynamic performance, reliability, and optimization through advanced modeling techniques like the Partial Element Equivalent Circuit (PEEC) method. She investigates parasitic extraction, thermal behavior, and stability issues in power modules, contributing to design improvements for high-efficiency systems. Her publications highlight trends in electromagnetic modeling, device-circuit interactions, and reliability analysis under extreme conditions. Key subfields include gate resistance dynamics, frequency-dependent capacitances, and multi-chip module design. Current projects involve virtual prototyping for power electronics and mission profile-based optimization of wearable power systems.

S.V. SreenivasanView profile

Professor

• Nanotechnology
• Nanofabrication
• Advanced Manufacturing
• +3 more

S.V. Sreenivasan is a Professor and holds the Cockrell Family Regents Endowed Chair #7 in Engineering at The University of Texas at Austin. He is a leading nanotechnologist specializing in high-throughput nanofabrication techniques for electronics, displays, and healthcare applications. As the Director of the NSF-funded NASCENT Center, he leads interdisciplinary research in nanomanufacturing systems. He co-founded Molecular Imprints Inc. and currently serves as Chief Technologist at Canon Nanotechnologies, Inc. His research focuses on scalable nanotechnologies, including molecular imprint lithography, metal-assisted chemical etching, and advanced 3D integrated circuits. He has authored over 130 papers and holds 100+ patents. His work emphasizes bridging academic innovation with industrial applications, particularly in semiconductor manufacturing and nanoelectronics. Dr. Sreenivasan has received prestigious awards such as the ASME Leonardo da Vinci Award (2009), TAMEST O'Donnell Award (2010), and election to the National Academy of Engineering (2021). He is a Fellow of the National Academy of Inventors (2016) and ASME (2020). His research groups develop novel fabrication methods like nanoshape imprint lithography and precision inkjet printing systems. Collaborations include Magic Leap and Canon, focusing on next-gen displays and semiconductor tools. He advocates for accessible nanotechnology through portable fabrication platforms.

Linghao SongView profile

Assistant Professor

• Accelerator Architecture
• FPGAs
• Hardware Acceleration Systems
• +4 more

Linghao Song is an Assistant Professor in the Department of Electrical & Computer Engineering at Yale University. His research focuses on accelerator architecture design, FPGA-based acceleration systems, and ReRAM-based computing. He is affiliated with the School of Engineering & Applied Science and holds expertise in sparse matrix processing, high-level synthesis, and task-parallel programming frameworks. Education: Ph.D. and M.S. from Duke University and University of Pittsburgh, respectively, with a B.S.E. from Shanghai Jiao Tong University. Research Interests: Song's work spans FPGA-accelerated computing, resistive memory (ReRAM) architectures for deep learning and graph processing, and high-performance sparse matrix operations. His recent projects include the TAPA framework for FPGA programming, ReFloat for iterative linear solvers, and the Sextans/Serpens accelerators for sparse matrix computations. Key Contributions: Over 20 peer-reviewed publications on topics like ReRAM-based accelerators (e.g., GraphR, PipeLayer), FPGA optimization frameworks (TAPA, RapidStream), and novel hardware designs for neural networks and graph analytics. His work emphasizes energy efficiency, scalability, and cross-domain applicability of hardware accelerators. Awards: Recipient of the Duke ECE Outstanding Dissertation Award (2021), EDAA Outstanding Dissertation Award (2020), and National Scholarship of China (2012). Labs/Teams: Leads research in FPGA acceleration and memory-based computing, collaborating on projects involving HBM integration, resistive memory architectures, and task-parallel dataflow frameworks.

Anders N. AndersenView profile

Part-Time Lecturer

• Sustainable Energy Planning
• District Heating Systems
• Energy Systems Analysis
• +3 more

Anders N. Andersen is a Part-time Lecturer in the Department of Sustainability and Planning at Aalborg University's The Technical Faculty of IT and Design . His research focuses on sustainable energy systems, district heating optimization, and renewable energy integration. He holds a Cand. Scient. in Mathematics and Physics from Aarhus University (year unspecified). Key projects include 'Etablering af nordjysk netværk for elregulering' (2008-2012) and 'Mosaik - Model af samspillet mellem integrerede kraftproducenter' (2001-2004). His recent work emphasizes energy storage in district systems, heat pump flexibility, and market zone impacts on power-to-heat technologies. Publications highlight technical innovations like energyPRO modelling tools and policy frameworks for heat pump adoption. He has presented at international conferences, including a 2024 lecture on German electricity market impacts. Media engagements include commentary on local aviation incidents.

• Low Energy Nuclear Reaction (LENR)
• Metal Hydride Thermal Energy Conversion
• Direct Borohydride Fuel Cell Systems
• +3 more

Kyu-Jung Kim is an Adjunct Lecturer and Visiting Research Assistant Professor at the University of Illinois at Urbana-Champaign's Department of Nuclear, Plasma, and Radiological Engineering. He holds a PhD in Technical Science from the Belarusian National Academy of Sciences and has held roles at INHA University (South Korea), LG Electronics, and multiple research institutes. His research focuses on Low Energy Nuclear Reactions (LENR), metal hydride thermal energy conversion, and direct borohydride fuel cell systems, with notable contributions to fuel cell efficiency, nanoparticle catalysis, and space power applications. Education: BS (1989) and MS (1991) in Mechanical Engineering from INHA University, Korea; PhD (2000) in Technical Science from Belarusian National Academy of Sciences. His career spans academic and industrial roles, including Chief Research Engineer at LG Electronics and advisory positions at Proton Scientific and Controlled Thermal Energy Engineering. Research interests emphasize advanced energy systems, including LENR-based power units, thermal wave phenomena in porous media, and fuel cell innovation. Recent work explores nanoscale catalysts for LENR, regenerative energy storage, and hydrogen-based technologies. His over 50 peer-reviewed articles and patents cover fuel cell design, thermal management, and energy conversion systems. Honors include LG Electronics' Innovative Idea Award (2006) and recognition as 'Great Man of The Month' (2003). He actively consults for energy companies and serves on scientific advisory boards. His interdisciplinary approach bridges nuclear engineering, materials science, and sustainable energy solutions.

Qilun ZhuView profile

Research Professor

• Optimal Control
• Vehicle Dynamics
• Powertrain Systems
• +4 more

Dr. Qilun Zhu is an Associate Research Professor at Clemson University's Department of Automotive Engineering. His research focuses on optimal control and estimation of automotive powertrain systems, vehicle dynamics, and autonomous driving technologies. He integrates cross-domain solutions for future mobility systems using rapid prototyping techniques. Dr. Zhu teaches courses in Advanced and Electrified Powertrains and Automotive Control Systems. He earned his B.S. from Jilin University (2010), M.S.E. from Cornell University (2011), and Ph.D. from Clemson University (2015). His publications demonstrate a consistent focus on predictive control strategies for thermal management and energy optimization in electrified and autonomous vehicles. Recent work emphasizes reinforcement learning applications and model-based control of power electronics. Honors include: 2019 SAE Young Professional Technical Paper Competition Best Paper award at the 2014 American Control Conference Dr. Zhu serves on technical committees including the Automotive and Transportation Systems Technical Committee and Automotive Controls Technical Committee, and as associate editor for IEEE Conference on Control Technology and Applications.