Find Academic Supervisors

Hannah K. BakoView profile

Assistant Professor

• Data Visualization
• Human-Computer Interaction
• Design
• +5 more

Hannah K. Bako is an Assistant Professor at the School of Data Science , University of Virginia, leading the ViDAR Lab . Her work bridges data visualization, human-computer interaction (HCI), and design, focusing on enhancing creativity through computational tools and example-aided workflows. Recruiting students for Fall 2026 Program Committee member for IUI'26, VISCOMM, and VIS'25 Research explores: How examples inspire visualization design processes Techniques to improve diversity in automated design generation Code augmentation strategies for D3.js authoring Semantic decomposition of visualization design workflows Teaching : Spring 2026 course DS 2003: Communicating with Data . Recent News : August 2025: Became Assistant Professor at UVA SDS July 2025: Two papers accepted at IEEE VIS'25 May 2025: Completed PhD dissertation defense

• Artificial Intelligence
• Machine Learning
• Software Engineering
• +6 more

Talal Shaikh is an Associate Professor at Heriot-Watt University's School of Mathematical and Computer Sciences in Dubai. He serves as Director of Undergraduate Studies and Programme Director for BSc Computer Science, BSc CS (AI), and MSc Software Engineering. With a decade of industry experience as a Chief Information Officer and Software Engineer, he bridges practical insights with academic research. Research Interests: Pervasive Computing, IoT/M2M, AI/ML, WiFi Sensing for Healthcare, Financial Machine Learning, Educational Technology Awards: Teaching Excellence Awards (2017/18), Fellow of the Higher Education Academy (FHEA), multiple Learning and Teaching Oscars (2016, 2017, 2018) His work spans Ubiquitous Computing and IoT , focusing on sensor networks and WiFi-based sensing for healthcare. In Artificial Intelligence , he applies ML to robotics, financial analytics, and educational innovation. Recent articles analyze Reinforcement Learning , Emotion Recognition , and WiFi Sensing applications. His teaching emphasizes student-centric learning, with over 100 supervised dissertations achieving distinctions. Collaborations include international conferences and interdisciplinary research in smart environments and adaptive systems.

• 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.

• Data Management
• Digital Preservation
• Machine Learning
• +4 more

Andreas Rauber is an Associate Professor in the Department of Data Science at Technical University of Vienna. He serves as Curriculum Coordinator for Bachelor and Master programs in Business Informatics and Data Science, and chairs the Curriculum Commission for Business Informatics. His research focuses on Information Systems Engineering, Logic and Computation, and Visual Computing, addressing challenges in data management, digital preservation, and reproducibility in e-science. He leads projects like OS Trails and FAIR-AI, emphasizing FAIR principles and trustworthy research infrastructures. Rauber has contributed to over 150 publications, including works on data citation frameworks, adversarial ML defenses, and reproducibility in IR. His work bridges technical innovation with policy, exemplified through roles in the EOSC Support Office Austria and RDA Austria initiatives. Key projects include establishing FAIR data practices across universities and advancing digital preservation through repositories like DBRepo. He coordinates international collaborations, such as the EU-funded EOSC-Life and EGI Advanced Computing projects. His teaching spans courses in machine learning, information retrieval, and research methods, fostering next-generation data scientists.

Mauro PezzèView profile

Professor

• Software Engineering
• Software Testing and Analysis
• Self-Adaptive Systems
• +5 more

Mauro Pezzè is a Full Professor of Software Engineering at the Università della Svizzera italiana (USI) and Università di Milano Bicocca, leading the STAR research group since 2006. He holds a laurea from the University of Pisa and a PhD from Politecnico di Milano. His research focuses on software testing, analysis, self-adaptive systems, and cloud systems. He has held editorial roles, including Editor-in-Chief of ACM Transactions on Software Engineering and Methodologies (TOSEM), and served on numerous program committees. Education: Laurea (Pisa), PhD (Politecnico di Milano). Professional roles include Dean of the Faculty of Informatics at USI (2009-2013), visiting scientist at UC Irvine and Edinburgh, and technical lead for international projects. He co-authored a seminal book on software testing (Wiley, 2007), with over 670 citations. Research Interests: Software Testing, Self-Adaptive Systems, Cloud Computing, AI in SE, Sustainable Software. Projects include work on field-based testing, failure prediction in distributed systems, and neuro-symbolic approaches for test oracles. Grants and Advising: Led STAR Lab projects in self-healing systems, GUI testing, and semantic matching. Advised numerous PhD/postdoc students (e.g., Ciniselli, Di Grazia, Qiu). Collaborations with European tech firms on R&D initiatives. Labs/Teams: STAR Group at USI/Constructor Institute, Bicocca, and Politecnico di Milano. Current members include postdocs and PhD students working on AI-driven testing and cloud reliability.

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.

Giuliano CasaleView profile

Professor

• Performance Engineering
• Cloud Computing
• Resource Management
• +6 more

Giuliano Casale is a Professor in the Department of Computing at Imperial College London, leading the Quality of Service Research Lab (QORE). His research focuses on performance assurance, resource management, and fault-tolerance in distributed systems. He teaches courses on Probability and Statistics and Scheduling and Resource Allocation at undergraduate and Master’s levels. Casale’s work spans cloud computing, edge AI, and machine learning applications in system modeling. Key contributions include methodologies for performance engineering, anomaly detection, and automated resource management in large-scale systems. He actively participates in international conferences, delivering keynote speeches on topics such as performance evaluation and AI-driven systems. His research integrates queueing theory, machine learning, and generative models to address challenges in distributed software systems. Casale also engages in service activities like PhD admissions tutoring and collaborates on projects involving resilience planning and cloud service optimization. His lab, QORE, emphasizes practical solutions for real-world distributed systems, including edge federations and serverless architectures. Casale’s work bridges theoretical performance analysis with industrial applications, contributing to advancements in both academia and industry.

• Machine Learning
• IoT Security
• Autonomous Systems
• +3 more

Professor Rashid Rashidzadeh is a faculty member in the Faculty of Engineering at the University of Windsor. He specializes in Machine Learning, IoT Security, and Autonomous Systems, with a focus on integrating these technologies into engineering education. He has advised numerous students in first-year design courses and advanced research projects, including work on autonomous emergency vehicles, IoT security for 5G devices, and hyperloop pod development. His teaching responsibilities include the Cornerstone Design course, where students develop autonomous systems and navigate engineering challenges. He has organized workshops on Python and Machine Learning, engaging both university and high school students. His research projects span industrial automation (e.g., Hiram Walker distillery software integration) and high-stakes competitions like the SpaceX Hyperloop Pod Challenge. Professor Rashidzadeh has mentored over 30 students in projects such as: Programming model railcars to navigate obstacle courses Designing cybersecurity safeguards for 5G IoT devices Building hyperloop pods for high-speed transport competitions His work emphasizes hands-on learning and industry collaboration, with projects showcased in media and academic platforms.

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.

Chen BinbinView profile

Associate Professor

• Wireless Networking
• Distributed Systems
• Cyber Security for Critical Infrastructures
• +3 more

Chen Binbin is an Associate Professor and Associate Head of Pillar (Innovation and Enterprise) in the Information Systems Technology and Design (ISTD) pillar at Singapore University of Technology and Design (SUTD). He serves as Deputy Director for the Future Communications Research and Development Programme (FCP), Singapore. Previously, he was a Principal Research Scientist at the Advanced Digital Sciences Center (now Illinois ARCS), affiliated with the University of Illinois. Education: PhD in Computer Science from National University of Singapore, and Bachelor's from Peking University. Research focuses on wireless networking, distributed systems, and cyber security for critical infrastructures like smart grids and industrial control systems. His work addresses secure communications, intrusion detection, and resilience against cyber-physical threats. Notable contributions include error-estimating coding, provenance verification in ICS, and AI-driven network security solutions. Key awards include the 2010 ACM SIGCOMM Best Paper Award for error-estimating coding research. His grants span agencies like Singapore's National Research Foundation (NRF), Cyber Security Agency (CSA), and Energy Market Authority (EMA). He leads projects on secure smart grid communication, industrial control system defense, and AI-enhanced cybersecurity tools. Technical leadership involves developing frameworks like CyberSAGE for security assessment and CMD for IoT malware detection. Active in collaborations with industry and government, his work bridges theory and practice in securing critical infrastructure systems.

• Software Evolution
• Software Libraries
• APIs
• +5 more

Thomas Degueule is a CNRS researcher in the Software Engineering group at LaBRI (Bordeaux, France). His research focuses on software evolution , modularity , and comprehension , with a particular emphasis on software libraries , APIs , software ecosystems , and domain-specific languages (DSLs) . Prior to his current role, he worked as a postdoctoral researcher at CWI (Amsterdam) and earned his PhD at IRISA (Rennes, France). Research Themes : Software evolution, API analysis, dependency management, empirical software engineering, DSL engineering Projects : ANR JCJC ALIEN (PI), H2020 CROSSMINER (WP leader), ALE (associate member), LEOC Clarity (member), ANR INS GEMOC (member), ITEA2 MERgE (member) Software Tools : Roseau, Maracas, BreakBot, Melange, Eclipse GEMOC Studio, K3, Alex, Scava His recent publications highlight trends in API breaking changes , dependency evolution , DSL interoperability , and automated software analysis (see articles list for details). He also contributes to software education through tools like MOON and Immediate Feedback for Jupyter Notebooks. Thomas has supervised PhD students including Gustave Monce (University of Bordeaux), Lina Ochoa (now assistant professor at Eindhoven University), Corentin Latappy (post-doc at University of Bordeaux), and Christophe Casseau (lecturer at University of Bordeaux). He actively participates in academic service, serving on program committees for conferences such as ICSE , ASE , ICSME , ISSTA , and SLE , and organizing workshops like SLEBoK and MLE 2021 .

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.

• Internet of Things
• Edge Computing
• Artificial Intelligence
• +6 more

Professor Xiaodong Liu is a faculty member at Edinburgh Napier University, affiliated with the School of Computing, Engineering and the Built Environment . His research spans Internet of Things , Edge Computing , Artificial Intelligence , and Cybersecurity , with a focus on decentralized systems and data-driven decision-making. Research Themes : IoT orchestration, federated learning, smart city infrastructure, building maintenance optimization, and automotive cybersecurity. Current Projects : Leading Swarmchestrate (EU-funded), Long-range Perceptive Autonomous Vehicles (Royal Society), and Met-Bot for Disaster Surveillance (Royal Society). His recent publications emphasize privacy-preserving edge learning , semantic IoT data validation , and deep learning for weather prediction . As a supervisor, he has guided PhD students in areas like federated learning, smart building systems, and IoT security. Collaborations include partnerships with institutions in Scotland, China, and Italy, alongside funding from European Commission , Royal Society , and Scottish Funding Council . He contributes to international conferences and journals, with notable work in IEEE Transactions , ACM TAAS , and MDPI publications.

Ruben MartinsView profile

Assistant Professor

• Constraint Programming
• Program Synthesis
• Formal Verification
• +3 more

Ruben Martins is an Assistant Professor at Carnegie Mellon University's School of Computer Science and serves as the program director of the Master of Science in Computer Science (MSCS) . His research focuses on the intersection of constraint programming, program synthesis, analysis, and verification, with recent work aiming to make formal methods tools more accessible through automated reasoning. Ruben earned his Ph.D. with honors from the Technical University of Lisbon, Portugal (2013) , followed by postdoctoral research at the University of Oxford (2014-2015) and UT Austin (2015-2017) . Research Interests : Ruben's work bridges constraint programming and program synthesis , with applications in software verification , optimization , and automated reasoning . He has developed award-winning tools like Open-WBO , a modular MaxSAT solver that has won gold medals in international competitions. His publications span top-tier venues such as POPL , PLDI , FSE , SAT , and CP , often addressing real-world challenges from program analysis to network security. Scientific Awards include: Distinguished Paper Award at PLDI 2018 Distinguished Paper Award at FSE 2021 Distinguished Paper Award at SAT 2022 Gold medals for Open-WBO in MaxSAT competitions Teaching & Advising : Ruben mentors Ph.D., Master’s, and undergraduate students in research projects related to program synthesis, formal methods, and constraint solving. He teaches courses such as Bug Catching: Automated Program Verification and Advanced Topics in Logic: Automated Reasoning and Satisfiability , emphasizing hands-on experience with tools like Why3. His advising spans topics from AI-driven program repair to network protocol verification , fostering collaboration across disciplines.

• Software Engineering
• Software Security
• Foundations of Program Analysis
• +9 more

Marcel Böhme is a faculty member at the Max Planck Institute for Security and Privacy (MPI-SP) , leading the Software Security research group. His work focuses on foundational advancements in fuzzing , statistical program analysis, and scalable vulnerability discovery. Education: PhD from National University of Singapore (NUS) Research interests span: Statistical and causal frameworks for software testing Efficiency/Scalability of automated testing Fundamental limits of vulnerability detection Practical fuzzing technology (e.g., Entropic in LibFuzzer) Recent publications highlight trends in: Machine learning for security analysis Privacy-preserving statistical methods Future-proof security frameworks Protocol fuzzing with large language models Scientific accolades include: ERC Consolidator Grant (2024) NUS Outstanding Young Alumni Award (2022) ARC DECRA (2019) Multiple ACM Distinguished Paper Awards He serves as: Spokesperson for Research Group Leaders at Max Planck Society Guest Editor-in-Chief for ACM TOSEM PC Chair for ASE'25 and ISSTA'26