Find Academic Supervisors

Jos H.V. den OudenView profile

Researcher

• Autonomous Driving
• Internet of Things
• Mobile Networks
• +4 more

Jos H.V. den Ouden serves as a Researcher and Project Manager within the Electrical Engineering department at Eindhoven University of Technology (TU/e), affiliated with the Mobile Perception Systems Lab and Video Coding & Architectures group. His work bridges mobile networking, IoT infrastructure, and autonomous vehicle systems to advance connected mobility solutions. Education: Master of Science (MSc) in Engineering (Dutch "ir." designation) from TU/e, 2007 Thesis: "Study on the misalignment properties of a pushbelt variator" Research Interests: Den Ouden specializes in applying Internet of Things frameworks to autonomous driving challenges, with emphasis on remote operation systems, pedestrian behavior prediction, and 5G network integration. His work addresses critical safety requirements in automated mobility through local breakout architectures and user interface innovations, particularly focusing on road engineering applications for cooperative vehicle systems. Publication Trends: His 2018-2026 publications demonstrate evolving expertise from experimental cooperative driving validation toward AI-driven mobility solutions. Recent work explores trustworthy AI frameworks for CCAM development, while earlier research established foundations in IoT-enabled pedestrian detection and 5G remote driving architectures, consistently addressing mobile network service optimization. Project Leadership: Den Ouden has secured funding for six major initiatives: TOWR CST (2021-2027): Project communication officer Buurauto-Noom VCA (2021-2022): Project manager 5G-MOBIX (2018-2022): Project member for cross-border CAM corridors AUTOmated driving via IoT (2017-2020): Invoice contact Vision-based Driver Assistance (2016-2019): Project member Research Environment: As core personnel in TU/e's Mobile Perception Systems Lab, he contributes to Video Coding & Architectures research while teaching Automotive Sensing and Smart Vehicles courses. His work connects with industry through press features on autonomous vehicle testing and 5G remote driving demonstrations, positioning him at the intersection of academic research and real-world mobility innovation.

Rajeev GangulaView profile

Academic

• Signal Processing
• Wireless Communications
• Software-Defined Radio
• +3 more

Rajeev Gangula is a Research Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University's College of Engineering. His research focuses on signal processing, physical layer protocol design, and prototyping with software-defined radios and connected robotics systems. Education background includes a PhD in Mobile Communications from Télécom ParisTech (EURECOM, 2015), MSc in Mobile Communications from Télécom ParisTech (EURECOM, 2012), and Master of Technology in Signal Processing from Indian Institute of Technology Guwahati (2010). Research interests span wireless communication systems, UAV-aided networking, 5G/6G technologies, and real-time prototyping. Current work explores AI-enabled open RAN control, bistatic sensing in 5G NR, and UAV-assisted localization using hybrid radio models. Recent publications demonstrate strong focus on O-RAN implementation, spectrum sharing techniques, and advanced positioning methodologies using OpenAirInterface platforms.

Mark C. JeffreyView profile

Assistant Professor

• Computer Architecture
• Parallel Computing
• Computer Systems
• +3 more

Mark C. Jeffrey is an Assistant Professor in the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto, cross-appointed to the Department of Computer Science. His research focuses on computer architecture and systems, aiming to simplify parallel programming for software engineers through novel compilers and large-scale multicore architectures. Previously, he was a Research Scientist at Facebook AI Research and earned his PhD from MIT under Daniel Sanchez. He holds a MASc and BASc from the University of Toronto. Education: PhD in Computer Science, MIT (2019) MASc in Computer Engineering, University of Toronto (2011) BASc in Engineering Science, University of Toronto (2009) Research interests include parallel computing, compilers, multicore architectures, and energy-efficient systems. His work bridges hardware-software co-design to enable scalable parallelism in modern systems. Notable awards include the George M. Sprowls PhD Thesis Award (2nd place, 2019), Best Paper at NoCArc 2023, and a Best Paper Award at ARC 2010. He teaches advanced courses in computer architecture and parallel programming (ECE1755, ECE552). His GitHub repository bf-intersect explores Bloom filter configurations for address-set disambiguation, tied to his SPAA 2011 and MASc thesis work.

D. Keith WilliamsView profile

Professor

• Biostatistics
• Linear Models
• Statistical Graphics
• +5 more

D. Keith Williams serves as Professor and Vice-Chair of Education in the Department of Biostatistics at the University of Arkansas for Medical Sciences (UAMS), with dual appointments in the College of Medicine and College of Public Health. He additionally directs Biostatistics and Data Innovation at ACNC, demonstrating leadership in academic administration and research infrastructure. His academic foundation includes: B.S. in Mathematics Education from University of Central Oklahoma M.S. in Statistics from Oklahoma State University M.P.H. from University of Oklahoma Ph.D. in Biostatistics from University of Oklahoma Dr. Williams' research centers on advanced statistical methodologies including linear models, graphical techniques, and simulation approaches. His current focus targets efficient analysis of complex multivariate datasets from microbiome, metabolic, genetic, and whole-organism studies. This work addresses critical challenges in handling high-dimensional biological data where traditional methods fail, particularly in modeling multiple interdependent responses common in modern biomedical research. Analysis of his 2024-2025 publications reveals a strong interdisciplinary pattern spanning pediatric obesity, pregnancy complications, toxicology, and nutritional science. His biostatistical expertise consistently enables novel insights across diverse domains—from cannabis extract pharmacology to rheumatoid arthritis immunology—by developing specialized analytical frameworks for complex datasets. A notable trend involves translating methodological innovations into clinical applications, especially for maternal-child health and metabolic disorders. As Vice-Chair of Education, Dr. Williams oversees biostatistics curriculum development and training programs. His directorship at ACNC involves leading data science initiatives that support UAMS research enterprise. While specific grant details aren't provided, his extensive publication record indicates sustained extramural funding. He directs the Biostatistics and Data Innovation unit at ACNC, which integrates statistical expertise with cutting-edge computational resources to advance research across UAMS. This role positions him at the nexus of methodological development and translational science, fostering collaborations between statisticians and domain experts in medicine and public health.

Craig ZillesView profile

Professor

• Computer Science Education
• Learning Analytics
• Computer Architecture
• +7 more

Craig Zilles is a Professor and Severns Faculty Scholar in the Department of Computer Science at the University of Illinois at Urbana-Champaign. His primary affiliation is within the College of Engineering. He holds a Ph.D. from the University of Wisconsin-Madison (2002), where his research focused on compiler-architecture interactions and speculation techniques. Research Interests: Zilles' work spans computing education (e.g., computer-based testing, learning analytics, concept inventories) and computer architecture (e.g., compilers, dynamic optimization, managed languages). He pioneered the Computer-based Testing Facility (CBTF), a platform revolutionizing assessment practices in STEM education. Awards & Recognition: Recipient of the NSF CAREER Award, IEEE Micro Top Picks, ASPLOS Best Paper Awards (2010, 2013), and numerous teaching accolades including the Excellence in Undergraduate Teaching Award (2018) and the IEEE Education Society's Mac Van Valkenburg Early Career Teaching Award (2010). Teaching: Instructs courses such as CS 105 (Intro Computing for Non-Tech), CS 233 (Computer Architecture), and advanced topics in computer science education. Known for innovative pedagogical approaches, including mastery-based grading and automated assessment systems. Labs & Projects: Leads the CBTF initiative and contributes to PrairieLearn, an open-source platform enabling adaptive, randomized problem generation. His research also explores AI-driven autograding and anti-plagiarism strategies in the age of generative AI.

Matthias KatzfussView profile

Professor

• Computational Spatial Statistics
• Spatio-Temporal Statistics
• Gaussian Processes
• +5 more

Matthias Katzfuss is a Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on computational spatial and spatio-temporal statistics, Gaussian processes, uncertainty quantification, and data assimilation, with applications in environmental science and satellite remote sensing. He has received funding from NSF, NASA, NOAA, USDA, Sandia National Laboratory, and Jet Propulsion Laboratory. His scientific achievements include awards such as the NSF Career Award, a Fulbright Scholarship, and the Early Investigator Award from the American Statistical Association’s Section on Statistics and the Environment. His work bridges theoretical advancements and real-world applications, particularly in environmental monitoring and large-scale data fusion. Recent research trends include scalable methods for high-dimensional spatial data, nonstationary covariance modeling, and Bayesian transport maps. His publications address challenges in remote sensing, climate science, and machine learning, emphasizing computational efficiency and methodological innovation. Matthias collaborates extensively with institutions like JPL and NASA, contributing to global-scale environmental studies. His lab focuses on developing open-source software tools, such as the GPgp package, to enable reproducible research in spatial statistics.

• Robotic Imaging
• Computational Imaging
• Light Field Processing
• +1 more

Donald Dansereau is a Senior Lecturer in the School of Aerospace, Mechanical and Mechatronic Engineering at the University of Sydney and Perception Theme Lead for the Sydney Institute for Robotics and Intelligent Systems. His research develops novel imaging systems for robotic perception. His work pioneers light field and computational imaging techniques to enable robust robotic vision in challenging environments. Current projects address underwater imaging, low-light conditions, space applications, and privacy-preserving vision systems. Recent advances include event-based satellite docking simulations, adaptive neural radiance fields for 3D reconstruction, and task-specific camera optimization frameworks. Key research directions include: Real-time light field processing for robotic navigation Neural rendering techniques for scene understanding Privacy-preserving computer vision architectures Hardware-software co-design for specialized imaging He received the Best Paper Award at ACRA (2014) and Distinguished Poster Awards from Stanford SCIEN (2016, 2017). Teaching includes Mechatronic Design and System Design courses.

Elżbieta LewańskaView profile

Assistant Professor

• Artificial Intelligence
• Blockchain Technology
• Data Analysis
• +5 more

Dr. Elżbieta Lewańska is an Assistant Professor at the Department of Information Systems , Poznań University of Economics and Business. Her work focuses on artificial intelligence, blockchain technology, and semantic systems for data analysis and business transformation. Research Interests : AI-driven data visualization (LumiViz, 2025) Blockchain alignment in public registers (2025) Business-IT integration frameworks Publications Trends : 2025: Generative AI for business analytics 2024-2025: Blockchain-enabled business models and adversarial text generation 2020-2022: Supply chain optimization and open data efficiency Awards : Won first place in an international competition for information credibility (2024) Professional Engagement : Active in conferences like BIS 2025, CLEF 2024, ICEIS 2011 Contributed to DIS website development (since 1998)

Aurel Cornel STANCAView profile

Lecturer

• Control Systems
• Embedded Systems
• Data Acquisition
• +3 more

Aurel Cornel STANCA is a Lecturer at the Department of Electronics and Computers within the Faculty of Electrical Engineering and Computer Science at the Technical University of Brasov. His research focuses on control systems, embedded systems, data acquisition, IoT integration, automotive electronics, and power systems. He has contributed to advancements in smart grid technologies, automotive safety systems, and reliability testing of power protection devices. Dr. STANCA's work spans interdisciplinary applications, including IoT-enabled power system protection, distributed temperature control systems, and thermoelectric heat recovery from engines. His publications emphasize practical implementations, such as embedded system designs for supercapacitor-aided vehicle starters and SCADA operator training platforms. He holds a strong industrial collaboration background, particularly in automotive electronics education and curriculum development. His research also touches on educational technologies, including adaptive learning pathways and EMC training methodologies. Affiliated with the Technical University of Brasov, his professional network includes involvement with international conferences like ICATE and OPTIM, where he has presented on topics ranging from supercapacitor models to distributed control systems.

Hyunsook DoView profile

Associate Professor

• Software Engineering
• Software Quality
• Machine Learning Applications
• +3 more

Hyunsook Do is an Associate Professor in Computer Science and Engineering at the University of North Texas, specializing in software quality, testing methodologies, and AI applications in software engineering. Her research develops innovative approaches for quality assurance in modern software systems. Research spans: Automated testing and debugging techniques AI/ML applications in software engineering Safety analysis for autonomous systems Quality metrics and bug detection Empirical software engineering studies Recent publications demonstrate strong focus on leveraging natural language processing, machine learning, and semantic analysis to improve software quality detection, bug triaging, and safety verification across diverse application domains including mobile apps and autonomous vehicles.

Stephen WattView profile

Professor

• Symbolic Computation
• Computer Algebra Systems
• Programming Languages
• +5 more

Stephen Watt is a Professor at the University of Waterloo, affiliated with the Department of Computer Science within the Faculty of Mathematics. He holds a PhD in Computer Science (1986), MMath in Applied Mathematics (1981), and BSc in Honors Mathematics and Physics (1979). His research focuses on enabling computers to handle mathematics intelligently, with key areas including programming languages for mathematical software, symbolic computation algorithms, computer algebra system interoperability, mathematical knowledge management, and STEM education technology. His work spans theoretical and applied domains, such as developing algorithms for block matrices and handwriting recognition systems like the Legendre-Sobolev approach. He has contributed to initiatives like the International Mathematical Knowledge Trust (IMKT) and collaborated on projects like InkML for mathematical collaboration. Recent publications explore generative AI in STEM assessments, software portability, and GPU-optimized arithmetic operations. Dr. Watt’s research also addresses historical perspectives on symbolic computation and its evolution, alongside modern challenges in mathematical collaboration tools and digital ink compression. His contributions span conferences such as ISSAC, CICM, and SYNASC, reflecting interdisciplinary engagement with both theoretical and applied computer science.

Chase MurrayView profile

Associate Professor

• Operations Research
• Autonomous Vehicles
• Vehicle Routing & Logistics
• +5 more

Chase Murray is an Associate Professor in the Department of Industrial and Systems Engineering at the University at Buffalo (SUNY). He directs the SOAR Drone Facility and Optimator Lab , focusing on operations research applications for autonomous systems. His work includes drone logistics, routing optimization, and AI integration in delivery networks. Education: PhD, Industrial & Systems Engineering, University at Buffalo, 2010 ME, Industrial Engineering, Texas A&M University, 2000 BS, Industrial Engineering, Texas A&M University, 1998 Research Interests: Dr. Murray’s research emphasizes leveraging autonomous vehicles for logistics and surveillance. Key areas include: - Routing algorithms for drone-truck systems - Weather and traffic impact modeling - Integer programming for problem classification - FAA-certified drone pilot with hands-on SOAR facility testing . Publications: Over 30 peer-reviewed articles focus on drone coordination, vehicle routing, and optimization frameworks. Recent work includes graph-based ILP classification and weather-aware delivery systems. Labs & Software: SOAR : Drone test facility with 3D digital twin simulations Optimator Lab : Develops tools like VeRoViz (routing visualization) and tex2solver (LaTeX-to-code translator) Open-source projects: Ground Control Station software, ROS-enabled web interfaces Grants & Advising: Advised PhD students Ritwik Raj (traffic congestion modeling) and Lan Peng (weather-impact scheduling). Active in collaborative projects on AI-driven logistics systems.

• AI Education
• Human Factors in Software Engineering
• ML-Enabled Systems
• +3 more

Aidin Azamnouri is affiliated with the Chair of Software Engineering at the Technical University of Munich (TUM), located at Bildungscampus 2 in Heilbronn. His research focuses on ML-Enabled Systems, collaboration challenges in ML engineering teams, human factors in software engineering and AI, and AI education. Current research includes automated documentation generation for ML systems using large language models. A thesis in progress explores this topic under his advisement. Research Interests: He investigates how interdisciplinary teams collaborate on machine learning projects, student perceptions of self-study with open-source AI resources, and testing methodologies for software systems. His work bridges software engineering practices with modern AI development challenges. Publications highlight trends in collaborative engineering challenges, educational practices in AI/ML contexts, and testing innovations like parameterization for automated test suites. His 2025 works emphasize team dynamics and student learning in AI environments, while earlier research addressed test suite reliability and mobile game robustness. Advising: Currently mentoring a thesis on automated documentation generation for ML systems. No grants are explicitly listed, though his involvement in projects like InnoVET PLUS MEKI (from the project menu) suggests collaborative team engagements. He is part of the Software Engineering research group led by Prof. Stefan Wagner.

Jungpil HahnView profile

Professor

• Digital Transformation
• Platforms & Innovation
• Computational Social Science
• +6 more

Jungpil Hahn is a Provost's Chair Professor at the National University of Singapore (NUS) School of Computing, where he holds multiple leadership positions including Vice-Dean of Communications, Director of the NUS Fintech Lab, Deputy Director of AI Singapore (AI Governance), and Deputy Director of the Centre for Technology, Robotics, Artificial Intelligence & the Law. Previously, he served as Head of the Department of Information Systems and Analytics from July 2015 to June 2021. Before joining NUS, he was an Assistant Professor at Purdue University's Krannert School of Management and a Visiting Assistant Professor at Carnegie Mellon University's Tepper School of Business. Ph.D. in Information & Decision Sciences, University of Minnesota (2003) M.B.A. in Business Administration, Yonsei University, Seoul (1998) B.B.A. in Business Administration, Yonsei University, Seoul (1998) Professor Hahn's research spans multiple cutting-edge domains, with a particular focus on organizational learning in digital contexts, open innovation, and the impact of emerging technologies on business processes. His work examines how organizations adapt to technological change, with special attention to decentralized autonomous organizations (DAOs), blockchain governance, and the effects of privacy-enhancing technologies on business analytics. He investigates the intersection of human behavior and technology, particularly in crowdsourcing platforms and software development teams, exploring how team composition, knowledge diversity, and organizational structures impact innovation outcomes. His research also addresses practical challenges in data science, including missing data problems and the impact of privacy technologies on firms' analytics capabilities. His recent publications reveal a strong trend toward studying decentralized organizational forms enabled by blockchain technology, with multiple papers examining DAOs and consensus mechanisms. There's also a clear focus on the practical challenges of implementing AI and data analytics in business settings, particularly around data quality issues and the impact of privacy technologies. His work bridges theoretical organizational science with practical business applications, often using simulation-based approaches to develop and test theories. Recipient of multiple Best Paper Awards at ICIS (2020-2023) AIS Distinguished Member (2022) Faculty Teaching Excellence Award at NUS School of Computing (2014) Best 2013 Published Paper Award from Academy of Management's OCIS Division Best Reviewer Award from INFORMS Information Systems Society (2009) Professor Hahn has successfully mentored numerous PhD students who have secured prestigious academic positions at institutions worldwide, including the University of Colorado, Georgia State University, and Central University of Finance and Economics. His research is supported by significant grants focused on digital transformation, blockchain applications, and AI governance. He serves as Senior Editor of MIS Quarterly and has previously served as Associate Editor of Information Systems Research, demonstrating his leadership in the academic community. His research projects often involve interdisciplinary collaboration with computer scientists, economists, and legal scholars. He leads the Garbage Can Lab (https://garbcan.com/), which conducts research on complex socio-technical systems using an 'organized anarchy' approach inspired by the Garbage Can Model of Organizational Choice. The lab brings together researchers from diverse backgrounds to tackle problems related to digital transformation, platform innovation, computational social science, and data science. Current projects include studying organizational learning in DAOs, AI-enabled organizational decision-making, interventions for crowdsourcing platforms, and the impact of privacy technologies on business analytics.

Ali MohammadiView profile

Senior Lecturer

• Microelectromechanical Systems (MEMS)
• Nanoelectromechanical Systems (NEMS)
• Electronic Interface Circuits
• +6 more

Dr. Ali Mohammadi is a Senior Lecturer in the Department of Electronic & Electrical Engineering within the Faculty of Engineering & Design at the University of Bath. He leads innovative research in Micro-electromechanical Systems (MEMS) and serves as an Associate Editor for IEEE Sensors. His work is supported by multiple EPSRC-funded research projects with strong industry collaboration, totaling over £1.5 million across five projects. Dr. Mohammadi is embedded within several key research units: Electronics Materials, Circuits & Systems Research Unit (EMaCS), The Foundry: Centre for Digital, Manufacturing & Design, Centre for Bioengineering & Biomedical Technologies (CBio), and the Bath Institute for the Augmented Human. Dr. Mohammadi's academic background includes postdoctoral research at the Department of Engineering Science, University of Oxford (2016-2017) and the Department of Electrical and Computer Systems Engineering, Monash University, Australia (2014-2016). This foundation has enabled his interdisciplinary approach to micro/nano-electromechanical systems and electronic circuit design. His research program addresses fundamental challenges in micro/nano-electromechanical transducers and electronic interface circuits, with specific innovations in on-chip atomic force microscopy, implantable energy harvesters, and high precision coupled resonator sensors. These contributions span multiple UN Sustainable Development Goals, particularly advancing clean energy technologies and healthcare solutions. Dr. Mohammadi's work uniquely bridges electrical engineering, mechanical systems, and materials science to develop next-generation sensing and energy harvesting technologies with real-world applications. Analysis of his 48 research outputs reveals a clear trajectory from fundamental MEMS device development toward integrated sensor systems with practical applications. His most recent publications (2023-2025) demonstrate increasing integration of machine learning with precision sensing technologies, particularly for manufacturing condition monitoring and biomedical applications. The research shows progression from individual components to complete systems, with growing emphasis on real-time data processing at the sensor edge and human-machine interfaces. Dr. Mohammadi's professional standing includes: Member of the Institute of Electrical and Electronics Engineers (IEEE) Associate Editor of IEEE Sensors Journal As a doctoral supervisor, Dr. Mohammadi actively mentors students in Microelectromechanical Systems and Electronic Integrated Circuits. His research portfolio includes two active EPSRC projects: 'Transforming the use of Ansys simulation software within engineering curricula' and 'SENSYCUT- Sensor Enabled Systems for Precision Cutting,' demonstrating strong industry-academic collaboration. These projects focus on practical engineering solutions for manufacturing optimization, condition monitoring, and human-computer interaction, with direct applications in industrial settings. Dr. Mohammadi's research ecosystem spans multiple interdisciplinary centers at Bath. Within EMaCS, he advances fundamental electronic materials and circuit design. Through The Foundry, he contributes to digital manufacturing innovation. His CBio affiliation enables medical applications of his sensor technologies, while the Bath Institute for the Augmented Human provides context for human-centered applications of his tactile display research. This multi-faceted institutional integration allows his work to progress from laboratory prototypes to real-world implementations across healthcare, manufacturing, and human augmentation domains.