Find Academic Supervisors

Caroline BassettView profile

Professor

• Digital Media
• Computational humanities
• AI and the transformation of knowledge cultures
• +15 more

Caroline Bassett is Professor of Digital Humanities at the University of Cambridge and Director of Cambridge Digital Humanities. She previously held positions at the University of Sussex as Lecturer (2000-2008), Reader (2008-2016), and Professor of Digital Media and Communications (2016-2019), where she co-founded the Sussex Humanities Lab. Her career began in technology journalism before transitioning to academia. Her research critically examines computational technologies' societal and cultural impacts. Key interests include digital media theory, AI's transformation of knowledge cultures, feminist analyses of technology, media archaeologies, automation studies, and the intersection of science fiction with technological utopianism. Recent work explores sound studies, mobile media, gender politics in tech, and everyday digital practices. Bassett's publications consistently engage with digital culture through critical, feminist, and historical lenses. Major thematic clusters include: technological feminism and gender politics (35%), automation histories and anxieties (25%), media archaeology and theory (20%), digital humanities methodologies (15%), and sound/silence studies (5%). Her work increasingly addresses AI ethics and explainability in recent years. Honors and Awards: Helsingin Sanomat Foundation Fellow, Helsinki Collegium for Advanced Studies (2015-2017) IGSF Visiting Fellow, McGill University (Jan-Sept 2010) Leverhulme-funded Associate Visiting Faculty, George Mason University (2002-2003) She directs Cambridge Digital Humanities, a major interdisciplinary research initiative. Previously, she established the Sussex Humanities Lab, fostering digital research collaborations across arts, humanities, and social sciences.

Sanat K. SarkarView profile

Professor

• Multiple Testing
• Statistical Methodologies
• High-Dimensional Statistical Inference
• +1 more

Sanat K. Sarkar serves as a Professor in the Department of Statistics, Operations, and Data Science at Temple University's Fox School of Business and Management. An internationally renowned expert, he has pioneered foundational work in multiple testing theory with applications spanning genomics, neuroimaging, and high-dimensional data analysis. His methodological innovations address critical challenges in false discovery rate control under complex dependency structures. Research Interests: Dr. Sarkar specializes in Multiple Testing, Statistical Methodologies, High-Dimensional Statistical Inference, and Multivariate Statistics. His work develops rigorous frameworks for hypothesis testing in modern scientific contexts where thousands of simultaneous tests are performed, ensuring reliable discoveries in fields like genetic association studies and brain connectivity mapping. Key contributions include adaptive FDR procedures and methods for structured hypothesis groups. Publication Trends: Over 2020-2025, his 11 publications demonstrate sustained leadership in refining false discovery rate methodologies. Recent work tackles correlated data (2025), knockoff variable selection (2022), and hierarchical hypothesis structures (2021-2024), reflecting his focus on real-world applicability in biomedical big data. His research bridges theoretical statistics with practical computational solutions. Honors and Awards: Fellow, Institute of Mathematical Statistics Fellow, American Statistical Association Elected Member, International Statistical Institute Musser Award for Research Excellence (Fox School) Multiple Dean's Research Honor Roll Inductions Research Support and Service: Funded continuously by NSF and NSA grants, Dr. Sarkar co-organized the NSF-CBMS conference on Multiple Comparisons and serves on editorial boards of Annals of Statistics , American Statistician , and Sankhya . He regularly delivers invited talks at international venues and mentors junior researchers in statistical methodology development.

• 5G/6G Networking
• Network Slicing
• Intent-Based Networking
• +5 more

Adlen Ksentini is a Professor at EURECOM, a leading graduate school and research center in Sophia Antipolis, France, specializing in digital science and communication systems. His extensive research focuses on next-generation mobile networks (5G/6G), network management, and the integration of artificial intelligence with telecommunications infrastructure. Dr. Ksentini actively contributes to major EU research initiatives including 6G-BRICKS and AC3, serving as a key researcher and project leader in the development of future network architectures. Dr. Ksentini's research interests center around network slicing, intent-based networking, edge computing, and the application of machine learning to network management problems. His work bridges theoretical advancements with practical implementations in 5G/6G systems, with particular emphasis on zero-touch network management, energy efficiency optimization, quality of service assurance, and the integration of large language models with network operations. His research has significantly contributed to the development of O-RAN (Open Radio Access Network) frameworks and the evolution of network automation. His recent publication trends reveal a strategic shift toward AI-native network architectures, with increasing focus on integrating large language models (LLMs) with network management systems. His work demonstrates a clear progression from traditional network management approaches to more autonomous, AI-powered systems capable of intent-based configuration, self-optimization, and predictive maintenance. The publications show strong emphasis on practical implementations within the 6G research ecosystem, addressing critical challenges in network slicing, resource allocation, and energy efficiency. As a research supervisor, Dr. Ksentini mentors several PhD students including Abdelkader Mekrache, Karim Boutiba, Bouziane Brik, and Houda Hafi, who frequently appear as co-authors on his publications. His research is primarily funded through major EU research projects such as 6G-BRICKS (Building Reusable Testbed Infrastructures for Cloud-to-Device Breakthrough Technologies) and AC3 (which focuses on Cloud Edge Continuum). Dr. Ksentini is actively involved with the 6G-BRICKS project consortium and the AC3 project team, where he contributes to developing next-generation network architectures that integrate communication, computing, and sensing capabilities. His work within these projects focuses on creating reusable testbed infrastructures and addressing security and trust management challenges in the cloud-edge continuum.

• Machine Learning
• Bioinformatics
• Medical Imaging
• +5 more

Paul J. Kennedy is a Professor at the University of Technology Sydney's Centre for Artificial Intelligence. He holds a PhD from the same institution (1999). His research focuses on machine learning applications in healthcare, bioinformatics, medical imaging, and data mining. Key areas include developing algorithms for genomic data analysis, healthcare pathway modeling, and edge-cloud frameworks for omics data. Education: PhD in Artificial Intelligence (1999, UTS). Research interests span machine learning, health informatics, and data compression. Notable work includes studies on administrative health records, lung nodule detection, and virtual reality-based cancer cohort analysis. He has co-authored over 100 publications across journals like BMC Bioinformatics, IEEE Transactions, and Artificial Intelligence in Medicine. Advising: Collaborates extensively with students/researchers but no explicit student list provided. Grants and labs: Active in interdisciplinary projects involving medical and computational teams, though specific grants are not detailed here.

Alva L. CouchView profile

Associate Professor

• Network and System Management
• Geo-informatics
• Data Management
• +9 more

Alva L. Couch is an Associate Professor at Tufts University's School of Engineering, Department of Computer Science, with a career spanning over 30 years. His work bridges network/system administration, autonomic computing, and hydrologic data science, focusing on scalable solutions for data management and automated system administration. Education: Ph.D. in Mathematics (1988), B.S. in Architecture (1978), and B.A. in Bassoon/Contrabassoon Performance (1978). Research Interests His research centers on: Network and System Administration: Tools like SLINK, Maelstrom, and Babble for dependency analysis, cloud migration, and policy enforcement. Geo-informatics: MEDFORD metadata language and HydroShare platform for hydrologic data curation and discovery. Autonomic Computing: Promise theory, convergent operators, and closure models for self-managing systems. Recent Work Trends His 2024-2018 publications emphasize: Cloud-based hydrologic data management (AnVILMEDFORD, HydroShare) Metadata standards for interdisciplinary research Machine learning for system administration Agent-based resource sharing models Scientific Awards Liebner Teaching Award (1996) Seymour Simches Advising Award (2017) Best Paper Awards: LISA 1996, AIMS 2008, LISA 2001 LISA 2000 Best Student Paper (with Michael Gilfix) Contributions He developed key software like Peep (network auralization) and Slink (configuration management), supported by NSF grants and industry partnerships. His work with CUAHSI's Water Data Center shapes national hydrologic data infrastructure. He also advocates for science education and privacy in computing.

Minglun GongView profile

Professor

• Visual Computing
• Computer Graphics
• Computer Vision
• +6 more

Dr. Minglun Gong is a Professor and Director of the School of Computer Science at the University of Guelph (since 2019). Previously, he served as Professor and Head of the Department of Computer Science at Memorial University of Newfoundland. He holds a Ph.D. from the University of Alberta (2003), M.Sc. from Tsinghua University (1997), and B.Engr. from Harbin Engineering University (1994). His research focuses on visual computing, including computer graphics, computer vision, visualization, image processing, and pattern recognition. He has authored over 150 referred papers and holds patents in the field. He is an Associate Editor for Pattern Recognition and IEEE Signal Processing Letters , and has received awards such as the Izaak Walton Killam Memorial Award and multiple best paper awards. Dr. Gong has advised numerous students, including Ph.D./M.Sc. candidates and visiting scholars. His lab's recent work includes UAV path planning for urban reconstruction, image stylization techniques, and 3D human pose estimation. He actively participates in academic service, including editorial roles, conference program committees, and administrative roles at multiple institutions. His teaching spans courses in image processing, computational photography, and technical communication. He is also involved in administrative committees, such as Graduate Studies and Promotion at Memorial University. Key research contributions include advancements in transparent object modeling, underwater 3D reconstruction, and image-to-image translation. His work emphasizes practical applications in fields like medical imaging, autonomous systems, and environmental modeling.

David E. BreenView profile

Professor

• Biomedical Image Informatics
• Geometric Modeling
• Textile Modeling and Design
• +12 more

David E. Breen is a Professor in the Department of Computer Science within the College of Computing & Informatics (CCI) at Drexel University. He leads the Geometric Biomedical Computing Group and is affiliated with the Metadata Research Center and the Center for Biological Discovery from Big Data. His research spans interdisciplinary domains including biomedical image informatics, geometric modeling, textile modeling, and bio-inspired self-organization algorithms. Education: PhD, Computer and Systems Engineering, Rensselaer Polytechnic Institute MS, Computer and Systems Engineering, Rensselaer Polytechnic Institute BA, Physics, Colgate University His research interests focus on computational methods for biomedical applications, including shape and image analysis for cancer diagnosis, 3D reconstruction of biological tissues, and video analysis of animal behavior. He also investigates geometric modeling techniques for textiles and self-organizing systems. His work integrates computer science with biology, medicine, and engineering to solve complex problems in biomedical computing. The recent publications highlight a strong trend in computational modeling of textiles, biomedical image informatics, and AI-driven data analysis. Key themes include geometric modeling of knitted fabrics, deep learning for medical image classification, agent-based modeling of cancer metastasis, and metadata generation for biological image collections. His work bridges fundamental geometric algorithms with practical applications in healthcare and digital archives. Scientific Awards: No specific awards mentioned in the provided text. Breen has advised numerous students and collaborators across multiple domains, particularly in biomedical computing and textile modeling. His research has been supported through affiliations with major centers and collaborations with institutions such as Johns Hopkins University and the Max Planck Institute. He has been involved in projects related to NSF Center for Visual & Decision Informatics and has contributed to over 100 technical publications. He leads the Geometric Biomedical Computing Group , which conducts research at the intersection of biology, medicine, engineering, and computer science. The group develops algorithms and software for geometry-related computing problems in biomedical applications. Collaborations include the Drexel Integrated Laboratory for Cellular Tissue Engineering, Dr. Dan Marenda's Lab, and Dr. Aleister Saunder's Lab in Drexel's Biology Department.

• Machine Automation
• Autonomous Mobile Working Machines
• Heavy-Duty Robotics
• +4 more

Professor Jouni Mattila is a leading academic in Machine Automation at Tampere University's Faculty of Engineering and Natural Sciences, affiliated with the Automation Technology and Mechanical Engineering department. He is part of the IHA-Innovative Hydraulics and Automation research group. His expertise spans autonomous mobile working machines, nonlinear control engineering, and safety-critical systems like those in the ITER project. He holds a Technical Editor role in ASME/IEEE Transaction on Mechatronics (2015-2020). Research interests include real-world autonomous systems, whole-body motion control for rough-terrain robots, energy-efficient actuators, and teleoperation systems. His work integrates advanced control theory, AI, and robotics for heavy-industry applications. Recent publications focus on robust control frameworks, LiDAR-inertial SLAM navigation, and fault-tolerant systems for mobile robots. Publications highlight advancements in hydraulic/electromechanical actuator systems, visual-inertial feedback control, and energy-efficient robotics. Awards/recognitions are not explicitly listed, but his contributions are evident through collaborations with Finnish industry and big science projects. Advising focuses on MSc and Dr (Tech) students in robotics and automation, with a mission to bridge academia and industry for high-tech innovation. Labs/teams include the Intelligent Hydraulics and Automation (IHA) group, emphasizing practical R&D in cleantech and heavy-duty robotics. Ongoing projects address challenges in autonomous rock-breaking systems, exoskeleton control, and energy-efficient robotic actuators.

Shirin NilizadehView profile

Associate Professor

• Cybersecurity
• Privacy
• Social Media Analysis
• +7 more

Dr. Shirin Nilizadeh is an Associate Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington's College of Engineering. She leads the Security and Privacy Research Lab, conducting interdisciplinary research at the intersection of cybersecurity, privacy, machine learning, and social media analysis. Her work addresses critical societal issues related to online security, privacy, and safety through data-driven approaches. Dr. Nilizadeh received her PhD in Computer Science from Indiana University in 2014, followed by MS in Computer Science from Amirkabir University (2007) and BS in Computer Engineering from Islamic Azad University (2004). Her research focuses on security and privacy in systems and social networks, employing techniques from machine learning and big data analytics. She takes a highly interdisciplinary approach, integrating AI, NLP, social sciences, and public health to address societal issues in cybersecurity and privacy. Her research objectives include: (1) detecting and characterizing emerging threats in online social networks like social engineering attacks, misinformation, and online hate speech; (2) advancing the adversarial robustness and fairness of ML and NLG systems; and (3) studying humans' online behaviors through data-driven interdisciplinary research. Analysis of her recent publications reveals a strong focus on AI-generated security threats, particularly phishing scams using LLMs, NFT fraud detection, social media toxicity analysis, and content moderation systems. Her work bridges theoretical security research with practical applications, often addressing real-world security challenges through innovative technical solutions. Among her notable scientific achievements are the prestigious NSF CAREER award (2023), Comcast Innovation Awards (2022 and 2024), College of Engineering Outstanding Early Career Research award (2024), and IEEE SP 2024 Distinguished Paper Award. Her work has also received best paper and technical poster awards at eCrime 2021 and NDSS 2022. Dr. Nilizadeh has successfully mentored numerous doctoral and master's students while securing significant research funding, including multiple NSF grants and Comcast Innovation Fund awards. She leads a vibrant research group that has produced impactful work cited in official reports submitted to The Supreme Court and the EU Committee on Civil Liberties, Justice, and Home Affairs. Her lab has also received coverage from WIRED, MIT Technology Review, Orange's Hello Future, and Communications of the ACM. She serves on numerous program committees for top international conferences including ACM CCS, USENIX Security, and POPETS, and has organized outreach programs like OurCS@DFW to broaden participation of underrepresented students in computing.

Zaman NoorView profile

Senior Lecturer

• Distributed Systems
• Large Scale Computation
• Big Data Analytics
• +7 more

Zaman Noor is a Senior Lecturer in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA), where he has been serving since September 2022. He previously held an Adjunct Professor role at the same institution from January to August 2022 and was a Lecturer at Port City International University from 2015 to 2016. PhD in Computer Science, The University of Texas at Arlington, 2021 BS in Computer Science, Chittagong University of Engineering, 2019 His research focuses on Distributed Systems , Large Scale Computation , and Big Data Analytics , with an emphasis on optimizing array-based programming models for distributed environments such as Spark SQL. He explores compiler techniques for translating high-level array operations into efficient distributed queries, enabling scalable data analytics. Zaman Noor's publications reveal a strong trend in bridging programming languages with database systems, particularly through the translation of array-based loops and graph programs into optimized SQL-based distributed execution plans. His work intersects computer science, database systems, and high-performance computing, targeting applications in cloud computing, big data management, and machine learning infrastructure. He has received recognition for his research, including the Best Paper Award at IEEE BigData Congress in 2018 . Best Paper Award, IEEE BigData Congress, July 6, 2018 Zaman Noor advises master's students, including Priyank Gupta, and serves on thesis committees. He is actively involved in teaching and curriculum development, offering courses in distributed systems, cloud computing, and algorithms. He also contributes to academic service as a faculty advisor for the Google Developer Student Club and The Cornerstone, and as a member of the Publicity Committee. He leads and participates in educational initiatives and student mentorship, particularly in cloud computing and big data technologies, and supports student research through thesis supervision and committee roles. Zaman Noor is affiliated with research and teaching teams focused on data-intensive systems and distributed computing. His GitHub profile indicates engagement with open-source machine learning frameworks, including contributions to TensorFlow-related repositories, reflecting his interest in practical implementations of large-scale computation.

Markus ListView profile

Professor

• Systems Biology
• Data Science
• Bioinformatics
• +3 more

Prof. Dr. Markus List is a Professor of Data Science in Systems Biology at the Technical University of Munich (TUM), affiliated with the TUM School of Life Sciences. His research focuses on integrating data science with systems biology to understand gene regulatory mechanisms across genomic levels. He leads the Big Data in Biomedicine group and has held roles including Group Leader at the Chair of Experimental Bioinformatics (2018–2023) and PostDoc at the Max Planck Institute for Informatics (2015–2018). Educational Background: BSc and MSc in Bioinformatics from Eberhard Karls University of Tübingen (2005–2011) PhD in Molecular Oncology from University of Southern Denmark (2011–2015) Research Interests: Prof. List’s work bridges biology, medicine, and informatics using machine learning and data integration. Key areas include: Systems Biology of gene regulation in cancer Epigenetic data analysis Computational methods for biomedical big data Awards & Recognition: Research Prize of the Hamburg Cancer Society (2023) TUM Teaching Award (2023) TUM School of Life Sciences Supervisory Award (2022) Labs & Teams: Leads the DaisyBio group, focusing on experimental and computational bioinformatics. Collaborates with interdisciplinary teams in bioinformatics and systems medicine.

Vassilis ChristophidesView profile

Professor

• Databases
• Web Information Systems
• Big Data Processing
• +9 more

Vassilis Christophides is a Professor of Computer Science at the University of Crete and holds an advanced research position at Inria Paris, where he leads work in the MiMove team. His research spans databases, web information systems, big data processing, and IoT analytics, with a strong emphasis on entity resolution, data integration, and scalable data mining. He has supervised numerous research projects funded by the European Union and the Greek State, and has published over 130 articles in top-tier conferences and journals. Research Interests: His primary research areas include Databases, Web Information Systems, Big Data Processing and Analytics, and Information Systems for the Internet of Things. He also focuses on entity resolution, knowledge graphs, streaming data, and explainable AI, particularly in the context of anomaly detection and fairness-aware data systems. His recent work explores hybrid attention models for entity alignment and causal analysis in time series classification. Recent Research Trends: Analysis of his recent publications (2021–2025) reveals a strong focus on entity resolution with fairness constraints, explainable anomaly detection, and adaptive scheduling in IoT edge analytics. He also investigates deepfake detection, crop type mapping using satellite data, and structural bias in knowledge graphs, demonstrating a broad and impactful research portfolio at the intersection of data management and machine learning. Scientific Awards: 2004 SIGMOD Test of Time Award Best Paper Award, ISWC 2003 Best Paper Award, ISWC 2007 Advising and Grants: While specific student names are not listed in the provided texts, Christophides has co-authored numerous papers with researchers such as Vasilis Efthymiou, Ioannis Tsamardinos, and Nikolaos Myrtakis, suggesting active mentorship. He has been the scientific coordinator of multiple EU and national research projects, indicating substantial grant leadership and project management experience. Labs and Teams: He is affiliated with the MiMove team at Inria Paris, a research group focused on mobility and data-intensive systems. His work bridges academic and applied research, leveraging Inria’s infrastructure for large-scale data experimentation and innovation in IoT and edge computing environments.

Dennis FehrenbacherView profile

Associate Professor

• Information Systems
• Management Accounting
• Digital Technologies
• +12 more

Dennis Fehrenbacher is an Associate Professor in the Department of Accounting at Monash Business School, Monash University. He is actively engaged in research and teaching at the intersection of information systems and management accounting, with a strong focus on digital technologies and their organizational impact. Monash Business School, Monash University Department of Accounting Chair, Monash Business School Behavioral Laboratory Organizer, Experimental Research Cluster Associate Editor, Decision Support Systems Editorial Board Member, Behavioral Research in Accounting His research interests center on information system success, performance measurement, analytics, and the behavioral aspects of accounting. He employs empirical methods such as experiments, surveys, and archival analysis, often integrating emerging technologies like eye tracking and big data analytics to study cognitive biases in performance evaluation and decision-making. Dennis has developed innovative teaching methods, including workshops on Social Media Analytics using Python and MongoDB, and machine learning for business decisions. His recent research outputs span topics such as professional scepticism, multitasking, accountability in capital budgeting, and causal inference in judgment using balanced scorecards. These works reflect a consistent trend toward understanding human behavior in accounting systems through technological and experimental lenses. He has received recognition for his work, including: Best Paper Award - Management Accounting Stream (2020) Dennis has served as a reviewer for top journals like Journal of Management Information Systems and Accounting, Organizations and Society , and has been invited to speak internationally. He has led two major research projects funded by CIMA and AFAANZ, focusing on performance evaluation and information processing. His work contributes to the UN Sustainable Development Goals, particularly those related to quality education and decent work. He leads the Monash Business School Behavioral Laboratory, supporting experimental research in accounting and management. The lab facilitates advanced research using tools like eye tracking and data analytics, fostering a collaborative environment for empirical inquiry.

Mateo Valero CortésView profile

Professor

• Computer Architecture
• High-Performance Computing
• Parallel Computing
• +3 more

Mateo Valero Cortés is a renowned Professor of Computer Architecture at the Polytechnic University of Catalonia and Director of the Barcelona Supercomputing Center (BSC). He has held academic and leadership roles since 1974, advancing high-performance computing (HPC) and computer architecture research. His work includes pioneering contributions to vector architectures, multithreading, and instruction-level parallelism. Education includes a Telecommunications Engineering degree from the Polytechnic University of Madrid (1974) and a PhD in Telecommunications Engineering from the Polytechnic University of Catalonia (1980). His research spans over 700 publications, focusing on HPC systems, parallel computing, and supercomputing infrastructure. Key research interests include vector processing, super-scalar processors, and task-based programming models. Recent work emphasizes scalable architectures for exascale computing and energy-efficient hardware-software co-design. Notable achievements include the Eckert-Mauchly Prize (highest in computer architecture), Seymour Cray Award, and Charles Babbage Prize. He has led initiatives like the Spanish Supercomputing Network (RES) and PRACE (European HPC partnership). Academic affiliations include the Royal Academy of Engineering of Spain, ACM Fellow, and IEEE Fellow. He has received 13 honorary doctorates and awards such as Mexico’s Order of the Aztec Eagle. Current projects include the Mont-Blanc HPC prototype and ERC-funded research on multi-core chip design. His BSC oversees over 300 researchers and manages MareNostrum supercomputers.

• Artificial Intelligence
• Medical Informatics
• Data Mining
• +3 more

José Manuel Ferreira Machado is a Full Professor at the Department of Informatics, School of Engineering, University of Minho, where he has been affiliated since 1988. His research integrates Artificial Intelligence, Medical Informatics, and Data Mining, with applications in healthcare, industrial systems, and public services. He founded ALGORITMI's 'Knowledge Engineering' group and served as Director of the ALGORITMI R&D Center (2018-2024). Education: Agregado in Informatics (AI), Universidade de Trás-os-Montes e Alto Douro (2011-2012) PhD in Informatics (AI), Universidade do Minho (1995-2002) Licenciatura in Systems and Informatics Engineering, Universidade do Minho (1982-1988) Research Interests: Machado's work focuses on intelligent systems for healthcare (e.g., AIDA medical platform), industrial automation, and smart cities. He emphasizes real-world applications, including clinical decision support, predictive maintenance, and sustainable urban infrastructure. Publications: His recent articles (2022-2025) demonstrate a strong focus on healthcare AI (e.g., disease prediction, COVID-19 analytics) and smart systems (e.g., traffic optimization, energy sustainability), utilizing data mining and machine learning. Awards: Hospital of the Future (2007, 2008, 2009) Good Practices in Health (2014) Portugal Digital Awards (2016) IHF Awards (2021) Advising & Grants: Supervised 16 PhD, 101 MSc, and 4 post-doc students. Leads sub-projects in PRR agendas GReenAuto and Be.Neutral. Secured funding from FCT, EU, and industry partners (e.g., Bosch) for 50+ projects. Labs & Teams: Coordinates the 'Knowledge Engineering' research group at ALGORITMI, focusing on intelligent decision support and interoperability in healthcare and industry.