Find Academic Supervisors

Oh Joon-YeoulView profile

Associate Professor

• Network Optimization
• Wireless Telecommunication Network Expansion and Optimization
• Non-Linear Programming
• +6 more

Oh Joon-Yeoul serves as Associate Professor in the Department of Mechanical and Industrial Engineering at Texas A&M University-Kingsville's College of Engineering, specializing in optimization methodologies for telecommunications and public infrastructure systems. His academic credentials include: Ph.D. in Industrial Engineering (Operations Research), New Mexico State University (2003) M.S. in Industrial Engineering (Engineering Management), New Mexico State University (2000) M.S. in Industrial Engineering (Operations Research), Chong-Ju University (1998) B.S. in Industrial Engineering, Chong-Ju University (1995) Dr. Oh's research integrates mathematical programming and algorithmic development to solve complex industrial problems, with core expertise in Network Optimization , Wireless Telecommunication Systems , and Queueing Theory . His work bridges theoretical operations research with practical applications in municipal services, robotics, and emergency management, emphasizing non-linear and heuristic integer programming approaches for large-scale systems. Analysis of his 15 publications (2015-2020) reveals consistent focus on vehicle routing optimization (including multi-vehicle scenarios), facility location for public safety infrastructure, and multi-criteria decision frameworks . His research demonstrates strong methodological continuity in applying combinatorial optimization to real-world challenges like garbage collection routing, fire station allocation, and telecommunication network expansion, primarily through conference presentations at INFORMS and industrial engineering forums. Dr. Oh actively mentors graduate researchers as evidenced by student co-authorship on projects involving blockchain applications, humanoid robotics, and municipal service optimization. While specific grant details aren't documented, his collaborative work with colleagues like Amir Hessami indicates engagement in funded research initiatives addressing regional infrastructure challenges in South Texas. His research team focuses on practical implementation of optimization models for Kingsville municipal operations, particularly in waste management and emergency response systems, though dedicated laboratory facilities aren't specified in available materials.

Gabriela NicolescuView profile

Professor

• Cybersecurity for Avionics
• Security by Design for Cyber-Physical Systems
• Advanced Programming Models for Avionic Multi and Many-Core Systems
• +11 more

Gabriela Nicolescu is a Professor at Polytechnique Montréal , affiliated with the Department of Computer Engineering and Software Engineering and the LAMA-WeST (Web, Semantics and Text) research group. Her work focuses on design methodologies , programming , and security for advanced systems integrating 3D SoCs , optical networks , and heterogeneous technologies . Education: B.Sc. and M.Sc., Politehnica Bucharest Ph.D., Institut National Polytechnique de Grenoble (2002), awarded Best Thesis in Microelectronics Research Interests include cybersecurity for avionics , security by design for cyber-physical systems , advanced programming models for multi-core systems , and modeling/simulation of heterogeneous systems . She has contributed to fields like optical networks on chip , real-time embedded systems , and edge computing security . Recent Publications highlight her leadership in all-optical neural networks , formal verification , and security for avionic systems . Her work spans machine learning-driven intrusion detection , photonic integrated circuits , and ontology-based cybersecurity frameworks . Scientific Awards: Best Thesis in Microelectronics, Institut National Polytechnique de Grenoble (2002) Student Supervision encompasses doctoral and master’s theses in areas such as multi-core RTOS , security in autonomous systems , AI-based penetration testing , and performance optimization in avionics . Current advisees include Felipe Magalhaes , Julien Carayol , and Kacem Khaled , while completed supervision includes A. S. Shahnejat Bushehri and Rami Zrelli . She is actively involved in collaborative research projects addressing national cybersecurity priorities and safer aircraft design , as highlighted in media outlets like The Globe and Mail and Science-Presse .

Nir ShavitView profile

Professor

• Algorithms
• Computer Science
• Artificial Intelligence
• +5 more

Nir Shavit is a Professor of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Multiprocessor Algorithmics Group and Computational Connectomics Group, focusing on algorithms for multi-core systems and neural tissue computation modeling. Education: B.Sc. and M.Sc. in Computer Science from the Technion - Israel Institute of Technology (1984, 1986); Ph.D. in Computer Science from the Hebrew University of Jerusalem (1990). Research interests include multiprocessor algorithms, software transactional memory, connectomics, and neural network analysis. Notable contributions include applying algebraic topology to shared memory computability and co-authoring The Art of Multiprocessor Programming . Awards: 2004 Gödel Prize and 2012 Dijkstra Prize. Active in MIT’s Theory of Computation Community of Research and affiliated with the Algorithms Group.

• Computer Architecture
• Parallel Computing
• High-Performance Computing
• +1 more

Laure Erwin is a Researcher at the Department of Computer Architecture and Parallel Systems, Faculty of Informatics, Technical University of Munich. She is affiliated with the Lehrstuhl für Rechnerarchitektur & Parallele Systeme (Prof. Schulz). Her research interests focus on computer architecture and parallel systems, including high-performance computing and hardware design optimization. Education: Holds a Doctorate (Dr. rer. soc. oec.) in a related field. Research Interests: Laure's work addresses challenges in scalable parallel architectures, energy-efficient computing systems, and advanced system-on-chip (SoC) designs. Her expertise includes optimizing memory hierarchies and interconnect technologies for modern multi-core processors. Labs/Teams: Active within the Rechnerarchitektur & Parallele Systeme research group led by Prof. Schulz, contributing to collaborative projects in high-performance computing systems.

Shervin HajiaminiView profile

Assistant Professor

• Green Computing
• Energy Efficiency
• Multi-Core Systems
• +3 more

Shervin Hajiamini is an Assistant Professor of the Practice of Computer Science at Vanderbilt University's School of Engineering. He holds a Ph.D. from Washington State University, an M.Sc. from Delft University of Technology, and a B.Sc. from Azad University. His research focuses on green computing, particularly energy efficiency in multi-core systems, including dynamic voltage/frequency scaling (DVFS), voltage-frequency islands (VFI), and task scheduling optimizations. Recent work explores energy efficiency through heuristic algorithms, stochastic models, and dynamic programming frameworks. His articles (2015–2023) emphasize energy-time tradeoffs, cache optimization, and power-aware scheduling in multi-core architectures. No scientific awards are explicitly listed. His advising and grants sections are currently empty. He is affiliated with the School of Engineering's Computer Science department at Vanderbilt University.

Steven Xiaotian DaiView profile

Lecturer

• Real-Time Systems
• Cyber-Physical Systems (CPS)
• Robotics
• +6 more

Dr. Steven Xiaotian Dai is a Lecturer in Cyber-Physical Systems at the Department of Computer Science, University of York, UK. He leads the ReFLEX Lab and is affiliated with the Real-Time and Distributed Systems Group. His research focuses on real-time scheduling, digital twins, and safety assurance in autonomous systems, with applications in aerospace, robotics, and avionics. He holds a PhD from the University of York (2019, Best Thesis Award) and has contributed to prestigious projects like SCHEME (Innovate UK) and MOCHA (Huawei). Dr. Dai’s academic qualifications include a PhD in Real-Time Systems (York), MSc in Control Systems (Sheffield), and BSc in Automatic Control (Nanjing). His research grants include £2.2M from Innovate UK for SCHEME and £985K from Huawei for MOCHA. He actively serves on program committees for leading conferences like RTSS and RTAS. His research interests encompass real-time scheduling on multi-cores, timing assurance for CPS, and hardware-software co-design. Collaborations span industries and academia globally, addressing challenges in safety-critical systems. Dr. Dai supervises multiple PhD and MSc students, emphasizing adaptive scheduling, digital twins, and robotics. He is a member of IEEE CS, IEEE RAS, and ACM SIGBED, and his work has been published in over 30 peer-reviewed articles. The ReFLEX Lab explores cutting-edge topics like routerless network-on-chip optimization and ROS 2 executor timing analysis.

E. Ricky ChanView profile

Academic

• Bioinformatics
• Genomics
• Immunology
• +4 more

E. Ricky Chan, PhD is an Instructor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine. He serves as Director of the Bioinformatics Core at the Institute for Computational Biology. His work integrates genomics, computational biology, and systems biology to study immune mechanisms, metabolic pathways, and microbiome interactions. Dr. Chan holds a PhD from Case Western Reserve University. His research focuses on translational applications of big data analytics in precision medicine, with emphasis on inflammatory bowel disease, cancer biology, and infectious diseases. Recent work includes groundbreaking studies on GSDMB's role in IBD epithelial repair, microbiome metabolic interactions, and synthetic lethality in cancer therapy. Key research trends include multi-omic spatial analysis of neuroinflammation, transcriptomic profiling of drug resistance mechanisms, and leveraging genomic data to understand disease progression. His studies have identified critical biomarkers like the NRF2 gene set and MBOAT7-driven lipid pathways. Current projects involve developing computational tools for viral genomic surveillance and improving microelectrode implant biocompatibility. Dr. Chan leads the Bioinformatics Core supporting over 50 research groups, providing expertise in next-gen sequencing analysis and machine learning applications. His team bridges basic research and clinical translation through collaborative initiatives in immunology, oncology, and metabolic disorders.

Benjamin BerkelsView profile

Associate Professor

• Image Processing
• Computer Vision
• Variational Methods
• +3 more

Benjamin Berkels is an apl. Professor (equivalent to Associate Professor) at the Institute for Geometry and Practical Mathematics (IGPM) within the Faculty of Mathematics, Computer Science and Natural Sciences at RWTH Aachen University, Germany. His office is located at Rogowski, Raum 124, Schinkelstraße 2, 52062 Aachen. He has held his current position since May 2025 and also serves as Akademischer Rat at IGPM since October 2024. Previously, he was a Juniorprofessor for Mathematical Image and Signal Processing and Junior Research Group Leader at AICES, RWTH Aachen from 2013 to 2024, with several interim professorships at RWTH Aachen and the University of Lübeck. Dr. Berkels received his educational foundation with a Dipl.-Math. from the University of Duisburg-Essen in 2005, followed by a Dr. rer. nat in Mathematics from the University of Bonn in 2010, and completed his Habilitation-equivalent with a positive intermediate evaluation as Juniorprofessor from RWTH Aachen in 2016. His professional journey includes postdoctoral positions at the University of Bonn and the University of South Carolina, establishing his expertise in mathematical image analysis before returning to Germany for his faculty positions. His research focuses on the intersection of mathematical theory and practical image analysis applications, with core interests in Image Processing, Computer Vision, Variational Methods, Joint Methods, Registration, and Segmentation. Berkels' work demonstrates exceptional interdisciplinary reach, applying advanced mathematical techniques to solve complex problems in materials science, microscopy, medical imaging, and environmental monitoring. His recent publications reveal a strategic expansion into machine learning applications while maintaining strong foundations in variational methods and mathematical image analysis. Analyzing his 15 most recent publications reveals a clear research trajectory emphasizing atomic-scale image analysis for materials characterization. Approximately 70% of his recent work focuses on applying sophisticated image processing techniques to electron microscopy data for materials science applications, particularly in analyzing grain boundaries, phase transformations, and defect structures. The remaining publications show increasing integration of machine learning approaches, especially deep learning and GANs, for industrial and scientific image analysis problems. This demonstrates his ability to bridge fundamental mathematical research with practical applications across multiple scientific domains. Dr. Berkels maintains an exceptionally active research profile with consistent publication output across high-impact journals in both mathematics and materials science. His extensive collaboration network spans multiple continents and disciplines, with frequent co-authorship with materials scientists, microscopists, and computer vision researchers. While specific grant information isn't provided in the text, his sustained research output and leadership of a junior research group suggest successful grant acquisition throughout his career. His work at IGPM positions him at the forefront of mathematical approaches to image analysis with significant impact on materials characterization techniques.

• Condensed Matter Physics
• Superconductivity
• Superfluidity
• +4 more

Egor Babaev is a Professor in the Department of Theoretical Physics at KTH Royal Institute of Technology in Stockholm, Sweden. His research focuses on condensed matter theory with an emphasis on superconductivity, superfluidity, topological defects, and quantum phase transitions. He has held faculty positions at KTH since 2006, including Assistant Professor (2006–2008), Associate Professor (2013–2015), and his current role since 2015. He also held a concurrent position at the University of Massachusetts Amherst (2007–2013) and served as a Fellow of the Royal Swedish Academy of Sciences (2008–2014). Education: PhD in Theoretical Physics from Uppsala University (2001). Postdoctoral research at Uppsala University (2001–2003) and Cornell University (2003–2006). Research interests span multicomponent superconductors/superfluids, fractional vortices, skyrmion dynamics, and topological states. Key contributions include the theoretical prediction of type-1.5 superconductivity and the discovery of Borromean supercounterfluids. Selected awards include the 2018 American Physical Society Fellowship, 2015 Göran Gustafsson Prize, and 2010 NSF CAREER Award. His work has been supported by prestigious grants like the Swedish Research Council's Outstanding Young Researcher Award (2013). Advising: Supervised over 20 PhD and Master's students, many of whom have continued to academic/research roles. Active in curriculum development, teaching courses like Condensed Matter Theory (SI2600) and Statistical Mechanics (SI2510). Labs/Teams: Leads a research group exploring quantum matter phenomena, collaborating internationally on projects involving superconductivity, topological materials, and vortex physics.

Arumugam NallanathanView profile

Professor

• Wireless Communications
• Networking
• 6G Technology
• +6 more

Arumugam Nallanathan is a Professor of Wireless Communications at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science. He leads the Communication Systems Research (CSR) Group and is a core member of the Centre for Networks, Communications and Systems. His research focuses on 6G technologies, AI-driven wireless systems, reconfigurable intelligent surfaces (RIS), UAV-enabled communications, ultra-reliable low-latency communications (URLLC), and IoT applications. He teaches the advanced 5G Mobile and Beyond module, integrating theoretical frameworks with practical implementations. Nallanathan's work bridges cutting-edge research with real-world challenges, emphasizing secure and energy-efficient communication systems. His recent publications explore topics such as IRS-based DOA estimation, covert communication techniques, and federated learning in edge networks. He actively contributes to the design of integrated sensing and communication systems, with applications in smart cities and beyond. His research group collaborates on grants addressing future network architectures and AI-enabled solutions. Research interests span: 6G systems, AI for communications, RIS optimization, UAV networks, URLLC protocols, and IoT security. His teaching emphasizes 5G fundamentals and emerging technologies. Notable contributions include advancements in STAR-RIS aided communication, multi-modal learning for satellite-ground networks, and secure resource allocation in NOMA systems. He leads projects on edge intelligence, digital twins for URLLC, and generative AI for channel estimation. The CSR Group actively engages in industry partnerships to translate research into practical solutions.

Tobias KlausView profile

Researcher

• Real-Time Systems
• Distributed Systems
• Operating Systems
• +3 more

Tobias Klaus is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg. His work focuses on real-time systems, distributed systems, and operating systems, with a particular emphasis on embedded real-time control and multicore processing. He has contributed to projects such as qron OS, AORTA, and the RTSC compiler framework. His research addresses challenges in real-time scheduling, job-level dependencies, and quality-aware system design. Education and Background: No explicit education details provided in the text. Research Interests: Klaus’s research spans real-time systems, distributed architectures, and embedded control systems. He explores topics such as deadline-driven scheduling, static analysis for job migration optimization, and tool-supported design of time-triggered systems. His work often bridges theoretical foundations with practical implementation in embedded and robotics domains. Grants and Funding: No specific grants or funding opportunities are mentioned in the text. Awards: No scientific awards explicitly listed. Teaching and Advising: Klaus has been involved in teaching courses such as Real-Time Systems, System Programming, and Distributed Systems. He has advised multiple theses, including projects on distributed real-time systems, WCET analysis, and multicore resource protocols. Notable advisees include Hausmann Jannis, Florian Güthlein, Thomas Reichinger, and Felix Bräunling. Labs and Teams: He contributes to interdisciplinary projects like the I4 Copter quadrocopter system and collaborates on frameworks such as RTSC and AORTA. His work often involves cross-disciplinary efforts in real-time control and embedded systems.

Benedict HerzogView profile

Researcher

• Energy-Efficient Systems
• Operating Systems
• Embedded Systems
• +3 more

Benedict Herzog is a Researcher at the Bochum Operating Systems and System Software (BOSS) group at Ruhr-Universität Bochum (RUB). Previously, he was part of the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). His work focuses on energy-efficient systems, operating systems optimization, and embedded computing. Herzog holds a Master's degree in Computer Science from FAU, where his thesis explored energy demand estimation using artificial neural networks, and a Bachelor's degree in Energy-aware error correction in wireless sensor networks. His research interests span energy-aware computing, system software design, and machine learning applications for optimizing edge and embedded systems. He actively participates in academic activities, serving as a reviewer for conferences like TECS, SBESC, and EMSOFT. Herzog has taught courses such as 'Systemnahe Programmierung in C' and 'Energy-Aware Computing Systems' at FAU, and advised multiple students on topics ranging from interrupt handling overhead to Meltdown/Spectre mitigation impacts. Key technical contributions include frameworks for energy measurement integration (EnergyBudgets), system-call aggregation (AnyCall), and automated OS configuration optimization. His work bridges hardware-software co-design challenges in energy efficiency, with applications in edge computing and real-time systems.

Lee-Anne KhorView profile

Senior Lecturer

• Urban Regeneration
• Sustainable Housing
• Established Suburban Transformation
• +4 more

Dr Lee-Anne Khor is a Senior Lecturer in the Department of Architecture at Monash University and a key member of the Monash Urban Lab. She specialises in urban regeneration and affordable, sustainable housing solutions across building, precinct, and regional scales. Her PhD (2018, Monash University) explored small-scale housing infill in Melbourne’s middle suburbs, advancing policies for 'greyfield redevelopment'. She teaches design and communication at Monash and RMIT, and her professional background includes registered architectural practice on education, civic, and residential projects. Research interests include speculative design methodologies for navigating urban regeneration complexities, multi-scalar design approaches, and interdisciplinary collaboration (e.g., with economics and urban history departments). Notable projects include studies on mixed tenure neighbourhoods, suburban industrial renewal, and transit-oriented urban design. She leads research on spatial morphology for precinct-scaled renewal and collaborates on federal initiatives like the A$4 million Homes NSW project. Education: PhD in Architecture (Monash University, 2018) Bachelor of Architecture (RMIT University, 2004) Key Projects: Industrial Revolution: Post-industrial land-use frameworks CoDev Townhouse Model: Small-lot housing viability From mixed tenure development to mixed tenure neighbourhoods (AHURI-funded) Dr Khor supervises PhD candidates addressing urban challenges through design-led research. She is a frequent contributor to government housing policy discussions and academic conferences, advocating for integrative spatial policies that balance social, environmental, and economic imperatives.

Timothy WileyView profile

Lecturer

• Autonomous Robotics
• Artificial Intelligence
• Online Learning
• +6 more

Dr. Timothy Wiley is a Lecturer in Computer Science at RMIT University's School of Computing Technologies within the STEM College. He specializes in Artificial Intelligence and Robotics, focusing on autonomous systems, online learning, and human-robot interaction. His roles include leading the RedbackBots RoboCup team and designing core courses in AI and robotics. He holds a PhD from UNSW Sydney and has extensive industry and academic collaborations, including with Rheinmetall Defence Australia and Nova Systems. Affiliations: RMIT AI Innovation Lab, CIAIRI, RUASRT Education: PhD (Computer Science, UNSW), BSc (Computer Science, UNSW) Research: Autonomous robotics, Explainable AI, UAV systems, fatigue crack modeling Research interests center on data-efficient machine learning for robotics, explainable AI, and applications in agriculture, aviation, and defense. Recent projects include UAV delivery systems and AR visualization for RoboCup. He teaches courses like Programming Autonomous Robots and leads the first-year Programming Studios. Notable achievements include the 2025 Best Paper Award at HRI and finalist status in RMIT's 2023 Research Leadership Awards. His work bridges academic research with industry applications, such as food waste reduction in macadamia harvesting and urban wind environment modeling for AAM.

Yuke WangView profile

Assistant Professor

• Deep Learning Systems
• High-Performance Computing
• Parallel Programming
• +3 more

Yuke Wang is an incoming Assistant Professor at Rice University's Department of Computer Science starting Fall 2025. He earned his Ph.D. in Computer Science from the University of California, Santa Barbara (2024) and B.E. in Software Engineering from the University of Electronic Science and Technology of China (2018). His research focuses on optimizing deep learning systems through compiler and hardware co-design, with expertise in GPU acceleration, parallel computing, and distributed training. Education: Ph.D., UC Santa Barbara (2024); B.E., UESTC (2018). Professional experience includes postdoctoral research at Amazon AWS AI and internships at NVIDIA, Microsoft Research, and Alibaba DAMO Academy. Research interests include accelerating graph neural networks (GNNs), recommendation systems, and large language models (LLMs). He has developed frameworks like GNNAdvisor, MGG, and ZEN to enhance efficiency and scalability in deep learning workloads. His work has been recognized with awards such as the NVIDIA Graduate Fellowship and ACM PACT Student Research Competition. Awards include NVIDIA Graduate Fellowship (2022-2023), UCSB Dissertation Fellowship (2023), and multiple best paper nominations. He actively serves on program committees for top conferences like OSDI, ASPLOS, and ICS. Current hiring: Seeking graduate/undergraduate students for projects in deep learning systems, compiler optimization, and GPU acceleration. Collaborates closely with industry partners including NVIDIA and Amazon.