Find Academic Supervisors

Iwona M JasiukView profile

Professor

• Composite Materials
• Materials Science
• Biomedical Engineering
• +4 more

Professor Iwona M. Jasiuk is a multi-disciplinary academic affiliated with the University of Illinois, holding professorships in Mechanical Science and Engineering, Biomedical and Translational Sciences, Bioengineering, Aerospace Engineering, and other departments. She is also affiliated with the National Center for Supercomputing Applications (NCSA), Beckman Institute for Advanced Science and Technology, and the Carl R. Woese Institute for Genomic Biology. Her research focuses on composite materials, bio-inspired structures, additive manufacturing, and computational mechanics, with a strong emphasis on integrating artificial intelligence into materials science. Her work spans topics such as material characterization, metamaterials design, and radiation effects on materials. Notable research areas include thin-ply composites, lattice structures derived from geometric principles, and the mechanical properties of bio-inspired systems like equine hoof walls. She has pioneered the use of deep learning networks for predicting material behavior in complex systems. Professor Jasiuk has received prestigious awards, including the ASME Fellow, SES Fellow, and Vebleo Scientist Award. Her research is supported by collaborations across engineering, biology, and computational fields, leveraging advanced facilities like NCSA for high-performance computing.

Xiaoming LiView profile

Associate Professor

• Compiler Optimization
• Machine Learning for Code Generation
• Hardware-Software Interaction
• +5 more

Xiaoming Li is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Delaware , focusing on compiler optimization, GPU computing, and hardware-software interaction. His work bridges machine learning with code generation to enhance program efficiency. B.S. and M.E. from Nanjing University (1998, 2001) Ph.D. in Computer Science from University of Illinois at Urbana-Champaign (2006) Research interests include: Compiler optimizations for static and dynamic code transformation Machine learning-driven code generation techniques FFT algorithms for sparse and hybrid systems Non-traditional compilers for SAT solvers and virtual machines GPU acceleration for large-scale computational problems His publications span 15 years , emphasizing: FFT optimization across GPU/CPU architectures Compiler techniques for heterogeneous systems Adaptive scheduling and error resilience Integration of empirical and model-driven approaches Notable awards: NSF CAREER Award (2008) Best Paper Award at ADAPT Workshop (2013) Advising highlights: Current students: Ryan Taylor, Sha Li, Shuo Chen, Yuanfang Chen, Chao Yang, Chaoyu Chen Graduates: Liang Gu (FFT Libraries), Jakob Siegel (GPGPU Frameworks), Murat Bolat (Context-Aware Compilation)

Jonas EliassonView profile

Visiting Professor

• Transport Policy
• Cost-Benefit Analysis
• Transport Pricing
• +5 more

Jonas Eliasson is a Visiting Professor at Linköping University's Department of Science and Technology (ITN), focusing on Communications and Transport Systems. He concurrently serves as Director of Transport Access at the Swedish National Transport Administration and chairs the Planning & Construction committee of the Royal Academy of Engineering Sciences. Previously, he was Director of the Stockholm City Transportation Administration (2016–2019) and a professor at the Royal Institute of Technology (KTH) from 2007 to 2016. His research emphasizes transport policy design and evaluation, including cost-benefit analysis, transport pricing, railway capacity allocation, and public acceptability of policies. He advises governments on sustainable transport planning, congestion pricing, and socio-economic appraisals. His work bridges theoretical frameworks and practical implementation, with a focus on accessibility, equity, and climate targets. Key research themes include infrastructure cost overruns, traffic demand reduction strategies, and the integration of passenger and freight transport systems. Recent publications analyze policy impacts, forecasting methods, and carbon-neutral transportation systems. His interdisciplinary approach addresses both technical and socio-political dimensions of transport challenges. Eliasson collaborates with research groups like Railway and Public Transport and contributes to initiatives like the 'Next Generation Smart Transportation Systems' project. Despite extensive professional roles, no formal student advisees are listed. His work is disseminated through journals like Transportation Research Part A and policy reports for national and regional governments.

Dusan MilosevicView profile

Assistant Professor

• RF Electronics
• Wireless Communication
• Power Amplifiers
• +5 more

Dusan Milosevic is an Assistant Professor at Eindhoven University of Technology (TU/e) in the Electrical Engineering department. He is affiliated with the Mixed-Signal Microelectronics research group and the RF Sensing & Communication Lab. His research focuses on analog and RF electronics, particularly in power amplifiers, ultra-low-power RF systems, and energy harvesting. He holds an MSc from the University of Niš and a PhD from TU/e. Education: MSc in Electronics and Telecommunications Engineering (University of Niš, 2001) PhD in Electrical Engineering (TU/e, 2009) Research Interests: Design of RF power amplifiers and mm-wave circuits Ultra-low-power communication systems RF energy harvesting technologies High-efficiency analog circuit design His work emphasizes circuit techniques for wireless communication and integrates analog methods with digital control for improved performance. Teaching: Electronic Circuits 1 RF Transceivers 1: Fundamentals Electronics: Selected Topics Collaborations: Active in mm-wave communication, satellite links, and sensor networks. His recent work includes inter-satellite link front-ends and optoelectronic modulation systems. Labs/Teams: Leads the RF Sensing & Communication Lab, focusing on mm-wave systems and energy-efficient RF design.

• Cyber-Physical Systems
• Machine Learning
• Self-Adaptive Systems
• +4 more

Dr. Ilias Gerostathopoulos is an Assistant Professor at the Faculty of Science, Vrije Universiteit Amsterdam, affiliated with the Network Institute and the Department of Information Management & Software Engineering. He specializes in self-adaptive systems, cyber-physical systems, and machine learning operations (MLOps). His work focuses on software architectures for autonomous systems, decision-making under uncertainty, and experiment-driven adaptation frameworks. He teaches courses such as 'Fundamentals of Adaptive Software' and 'Information Management', emphasizing practical applications of adaptive systems and data-driven decision-making. Gerostathopoulos has been awarded the Best Presentation Award (2021) for contributions to evaluating self-adaptive systems. His research addresses challenges in industrial self-adaptation, MLOps architectures, and robotics. Key research themes include: Architecture-based self-adaptation in robotics and CPS MLOps frameworks and systematic analysis of AI systems Uncertainty management in autonomous systems Experiment-driven learning and tool development Notable contributions include the ExpEngine tool for workflow optimization and the ReBeT framework for robotic systems. His work bridges theoretical software engineering with practical industrial implementations.

Felix Xiaozhu LinView profile

Associate Professor

• Systems Software
• Operating Systems
• Computer Architecture
• +4 more

Felix Xiaozhu Lin serves as Associate Professor and William Wulf Faculty Fellow in the Department of Computer Science at the University of Virginia's School of Engineering and Applied Science, where he directs the Computer Science Ph.D. Program and MCS/MS Program. Previously a tenured Associate Professor at Purdue University's School of Electrical and Computer Engineering, Lin joined UVA Engineering in August 2020 after completing his doctoral research at Rice University. His educational credentials include: Ph.D. in Computer Science, Rice University (2014) M.S. in Computer Science, Tsinghua University (2008) B.S. in Automation, Tsinghua University (2006) Lin's research centers on systems software at the intersection of operating systems, compilers, and computer architecture, with emphasis on accelerating and safeguarding software systems. His current projects target on-device large language models and speech processing for low-cost hardware ( Analysis of his recent publications reveals a strong trajectory in edge computing and efficient AI systems. His research demonstrates increasing focus on hardware-software co-design for autonomous devices, with significant contributions in video analytics for energy-constrained cameras, kernel virtualization for heterogeneous architectures, and stream processing frameworks leveraging emerging memory technologies. The work consistently addresses real-world constraints like power limitations and network intermittency while maintaining rigorous academic standards. His scientific recognition includes: National Science Foundation CAREER Award (2019) Google Faculty Research Award (2016) NSF CISE Research Initiation Initiative Award (2015) ACM ASPLOS Best Paper Award (2014) Lin leads the XSEL research group mentoring graduate and undergraduate students in systems software development. His educational initiatives include CS4414/CS6456, a modern operating systems course featuring Arm64 baremetal kernel development, multicore systems, trusted execution environments, and filesystem forensics. The course's experiential approach has received strong student feedback for its modern content and practical relevance. His group actively recruits for projects spanning on-device AI, hardware-accelerated speech processing, and next-generation OS development. Based in Charlottesville, Virginia, Lin's research benefits from UVA's proximity to Shenandoah National Park and collaborative opportunities within the university's vibrant computing ecosystem, including the 2024 LLM Workshop he co-organized with Professor Yangfeng Ji.

• Multi-Agent Systems
• Distributed Optimization
• Network Control
• +1 more

Guodong Shi is Associate Professor at the University of Sydney's Australian Centre for Robotics, heading the Centre for Robotics and Intelligent Systems. His research develops theoretical frameworks for multi-agent coordination, distributed optimization, and networked control systems. Current projects investigate collective decision-making under information constraints, privacy-preserving optimization, and game-theoretic formulations for social and robotic networks. His group develops algorithms for distributed solution of linear equations, Boolean networks, and equilibrium seeking. Doctoral supervision includes projects on acrobatic legged robots, reinforcement learning for robotic stability, and safe control under dynamic environments. Laboratory capabilities support theoretical and experimental validation. Research has applications in autonomous swarm robotics, smart grid optimization, and social network analysis. Teaching includes graduate courses on networked systems and optimization.

David WentzlaffView profile

Professor

• Computer Architecture
• Green Computing
• Cloud Computing
• +6 more

David Wentzlaff is a Professor of Electrical and Computer Engineering at Princeton University, with associated faculty roles in Computer Science and the High Meadows Environmental Institute (HMEI). He leads research in computing architecture, green computing, and sustainable system design. As Director of Undergraduate Studies, he shapes educational programs in his field. Education: Ph.D., Electrical Engineering, MIT (2012) M.S., Electrical Engineering and Computer Science, MIT (2002) B.S., Electrical Engineering, University of Illinois at Urbana-Champaign (2000) Research Focus: Future Computing Systems: Designing manycore architectures, cloud computing infrastructure, and chiplet-based systems for exascale computing. Sustainability: Developing energy-efficient hardware, recyclable computing systems, and eco-friendly decommissioning strategies. Hardware-Software Co-Design: Exploring FPGA integration, in-memory computing, and parallel processing frameworks. Advising & Grants: Advises 8 current graduate students, focusing on topics like chiplet design, neural acceleration, and sustainable computing. Recipient of NSF grants for projects like OpenPiton (open-source manycore research platform) and CAREER awards for energy-efficient architectures. Labs & Collaborations: Leads the Wentzlaff Research Group at Princeton. Develops open-source frameworks like PRGA (FPGA prototyping) and OpenPiton (manycore processor).

Yizhou SunView profile

Professor

• Machine Learning
• Artificial Intelligence
• Graph Neural Networks
• +3 more

Professor Yizhou Sun is affiliated with the University of California Los Angeles (UCLA) and the Henry Samueli School of Engineering and Applied Science . Her academic work focuses on Machine Learning , Artificial Intelligence , and Graph Neural Networks within the Computer Science department. Her research spans High-Level Synthesis , Causal Inference , and Computational Biology , with recent publications addressing neural network compression, language model safety, and dynamical system modeling. The trends in her recent 2025 and 2024 publications emphasize Deep Learning , Graph Theory , and Language Model Optimization , reflecting interdisciplinary applications in Biomedical Data , Hardware Design , and Physical Simulation .

Arpan Man SainjuView profile

Assistant Professor

• Spatial Big Data Analytics
• Spatial and Spatiotemporal Data Mining
• Geographic Information Systems (GIS)
• +3 more

Dr. Arpan Man Sainju is an Assistant Professor and Internship Coordinator in the Department of Computer Science at Middle Tennessee State University (MTSU). He holds a PhD (2021) and MS (2020) from the University of Alabama, and a B.E. (2011) from Tribhuvan University. His research focuses on spatial big data analytics, spatiotemporal data mining, and GIS applications in environmental modeling, disaster management, and geospatial science. He develops innovative algorithms for Earth imagery segmentation, flood inundation mapping, and physics-aware machine learning models. Education: PhD in Computer Science, University of Alabama (2021) MS in Computer Science, University of Alabama (2020) B.E. in Computer Science, Tribhuvan University (2011) Key research interests include deep learning for geospatial tasks, semi-supervised learning with limited labels, and parallel computing for big spatial data. His work bridges computer science and environmental science, addressing challenges in hydrology, urban safety, and disaster response. He has published extensively in top journals like ACM TIST, IEEE TKDE, and Environmental Modelling & Software, focusing on applications like flood modeling, road safety analysis, and 3D shape analysis. Dr. Sainju collaborates on interdisciplinary projects involving physics-guided models, hidden Markov structures, and GPU-accelerated algorithms. His research has been applied to real-world scenarios such as hurricane flood analysis and malware detection through Windows log analysis.

• Information Systems
• Operations Management
• Business Strategy
• +5 more

Henrique O'Neill is an Associate Professor (with Habilitation) in the Department of Marketing, Operations and General Management at ISCTE - University Institute of Lisbon, Portugal. He is an Integrated Researcher at ISTAR-Iscte - Research Center in Information Sciences, Technologies and Architecture. His research focuses on information systems adoption, organizational strategy, and process optimization in healthcare, finance, and public administration. Education: PhD in Business Organization and Management - University of Cranfield (1995) Master's in Electrical and Computer Engineering - Higher Technical Institute (1987) Bachelor's in Electrical Engineering - Higher Technical Institute (1983) Research Interests: His work spans business/IT strategy, systems modeling, process analysis, and technology implementation. Key domains include healthcare informatics, banking systems, and Industry 4.0 applications. He emphasizes practical solutions for organizational performance through technology integration. Publication Trends: Recent articles explore intelligent business systems, telemedicine, design science methodologies, and supply chain innovation. His work consistently bridges theoretical frameworks with sector-specific applications in healthcare, education, and logistics. Professional Engagement: Member: Portuguese Telemedicine Association, Order of Engineers Commissioner: INEM (National Institute of Medical Emergency) reform study Director: Center for IT Development (2010-2014) Advising & Projects: Supervises 7 graduate students (1 PhD, 6 Master's). Leads EU-funded projects including: Atlantic Crossing (2024-2025): US-Portugal academic collaboration in AI/cybersecurity AAL4ALL (2011-2015): Ambient Assisted Living ecosystem UNITE (2000-2002): Ubiquitous teamwork platforms

Ravi AronView profile

Professor

• Healthcare IT
• Emergent Technologies
• Healthcare Startups Valuation
• +6 more

Ravi Aron is a Professor of Healthcare Strategy & Technology at the C. T. Bauer College of Business, University of Houston, and Research Director of the Healthcare Business Institute. He holds a joint appointment in the Department of Health Systems & Population Health Sciences at the Tilman J. Fertitta Family College of Medicine. He earned his Ph.D. in Management Information Systems from New York University's Stern School of Business. His research focuses on healthcare IT, emergent technologies in healthcare operations, valuation of healthcare startups, and AI applications in healthcare. He has published widely in top journals like Management Science and Information Systems Research, and his work bridges information systems, operations management, and technology strategy. Dr. Aron has extensive teaching experience at The Wharton School, Johns Hopkins Carey Business School, and NYU Stern, winning multiple teaching awards. He advises Fortune 500 firms, startups, and policymakers on technology strategy, digital transformation, and risk assessment. His executive education programs address AI, machine learning, and digital business models for global executives. Key awards include the Dean's Faculty Excellence Award (2016), multiple teaching accolades from Wharton and Johns Hopkins, and the Herman E. Kross Best Dissertation Award (1999). His current projects explore healthcare supply chains, predictive models using machine learning, and valuing technology-enabled startups. He regularly participates in global forums like the World Economic Forum, advising on healthcare innovation and technology policy.

• Artificial Intelligence
• Machine Learning
• Pattern Recognition
• +11 more

Jianhua Zhang is a Professor of Computer Science and founding deputy head of the AI Lab at the Department of Computer Science, OsloMet - Oslo Metropolitan University, Norway. He holds affiliations with the Faculty of Technology, Art and Design. His career includes roles as Scientific Director at Vekia (France), Head of Machine Learning Lab, and Professorships at East China University of Science and Technology and Beijing University of Technology. He has held visiting positions at TU Berlin, TU Dresden, and the University of Catania. Educations: PhD in Electrical Engineering and Information Sciences (Ruhr University Bochum, 2005), Postdoctoral Research at the University of Sheffield (2005-2006). Research focuses on artificial intelligence, computational intelligence, cognitive human-machine systems, neuroergonomics, affective computing, and AI-driven neuroergonomics. Applications span engineering, biomedicine, finance, and business. He has led over 20 large-scale projects and published extensively (4 books, 13 chapters, ~200 papers). Leadership roles include Chair of IFAC Technical Committee on Human-Machine Systems (2017-2023), Vice Chair of IEEE Norway Section, and editorial roles at journals like Frontiers in Neuroscience and Cognitive Neurodynamics . He organized major conferences like IFAC HMS2025 (Beijing) and ICMLT 2024 (Oslo). Awards: Stanford/Elsevier Top 2% Scientists (2023/2024), Senior Research Fellowship (CSC, 2012), Max Planck Fellowship (2011), Shanghai Pujiang Talent (2007), DAAD Scholarship (2002-2004). Grants and advising: PI for 20+ projects, advising PhD students in AI, machine learning, and control systems. Teaching includes courses on computational intelligence, IoT, and fuzzy systems at both undergraduate and graduate levels. Labs/Teams: AI Lab at OsloMet, Machine Learning Lab (Vekia), and collaborations with institutions globally. Current work emphasizes AI ethics, neuroergonomics in smart cities, and adaptive human-machine systems.

Jane GuView profile

Professor

• High Efficiency Interconnect
• Low Power Interconnect
• Millimeter-Wave Systems
• +6 more

Q. Jane Gu is a Professor at the School of Electrical and Computer Engineering at Georgia Tech since September 2024, holding the Ed and Pat Joy Professorship. Previously, she was at the University of California, Davis (2012–2024) and the University of Florida (2010–2012). She earned her Ph.D. in Electrical Engineering from UCLA in 2007. Her research focuses on high-efficiency, low-power interconnects, millimeter-wave, sub-mm-wave, and terahertz integrated circuits and systems for applications in communication, radar, and imaging. Notable contributions include sub-THz resonator-based sensors, dielectric waveguide interconnect channels, and energy-efficient transmitters. Recipient of NSF CAREER Award (2013) 2015 UC Davis Outstanding Junior Faculty Award 2017 and 2018 Qualcomm Faculty Awards 2019 UC Davis Chancellor’s Fellow 2022–2023 IEEE SSCS Distinguished Lecturer Her group has garnered nine best paper awards, including top honors at the 2016, 2017, and 2020 IEEE MTT-S International Microwave Symposia. Research interests span terahertz interconnects, millimeter-wave radar systems, and integrated circuits for high-speed communication. Publications reflect advancements in sensor design, high-frequency transceivers, and phased-array systems, emphasizing energy efficiency and signal integrity.

Zelalem NegeriView profile

Assistant Professor

• Biostatistics
• Meta-Analysis
• Diagnostic Test Accuracy
• +3 more

Zelalem Negeri is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, affiliated with the Faculty of Mathematics. His work focuses on statistical methods for diagnostic test accuracy, depression screening tools, and meta-analytic techniques. He collaborates with groups like DEPRESSD (Depression Screening Data) to improve mental health assessment methodologies. Research interests include robust meta-analysis models for accommodating outliers, optimal cutoff determination for screening tools (e.g., PHQ-9, EPDS), and evaluating diagnostic accuracy across diverse populations. His studies often use individual participant data (IPD) to enhance precision in estimating depression prevalence and screening tool performance. Recent work emphasizes validating shortened screening tools (e.g., EPDS-Dep-5) and comparing depression assessment methods across modes (e.g., self-administered vs. clinician-administered). He also investigates pandemic-related mental health impacts in populations with systemic sclerosis and explores statistical methods to mitigate biases in data-driven cutoff selection. No scientific awards are explicitly mentioned. His contributions span collaborations with interdisciplinary teams, including the Scleroderma Patient-centered Intervention Network (SPIN), and involve developing robust statistical frameworks to address variability in diagnostic studies.