Find Academic Supervisors

Azza AbouziedView profile

Associate Professor

• Human-Data Interaction
• Database Systems
• Human-Computer Interaction
• +7 more

Azza Abouzied is Associate Professor of Computer Science at New York University Abu Dhabi and Global Network Associate Professor at the Tandon School of Engineering. She serves as Vice Provost for Faculty Advancement and Engagement at NYUAD starting September 2024. Her research bridges database systems and human-computer interaction, focusing on intuitive tools for data querying and decision-making in uncertain, collaborative environments. PhD, Yale University (2013) MPhil, Yale University MSc, Dalhousie University BSc, Dalhousie University Her research centers on human-data interaction, designing systems that make data accessible to non-experts. She combines techniques from UI design, machine learning, and databases to build tools that simplify complex data tasks. Her earlier work focused on example-driven querying and synthetic data generation, while her recent work explores in-database prescriptive analytics and decision support in domains like disinformation mitigation and epidemic planning. Her publications span database and HCI venues, with a recurring theme of enhancing usability without sacrificing scalability. She co-founded Hadapt, a Big Data analytics platform, and has led interdisciplinary research through the Human-Data Interaction Lab and the Center for Interacting Urban Networks. Her teaching includes foundational courses such as Database Systems, Operating Systems, and Data, as well as the critical thinking course Techruption. VLDB Test of Time Award (2019) Best Paper Award in Database Systems Honorable Mention in HCI Publications Azza mentors undergraduate capstone students and advises prospective PhDs, research assistants, and postdocs. She is actively involved in academic leadership, having chaired NYUAD’s faculty council in 2024 and co-chaired the SIGMOD 2025 program. Her work emphasizes empowering users to critically engage with data and AI, both in research and education.

Qiang ZhuView profile

Professor

• Data Science
• Data Management
• Machine Learning
• +3 more

Qiang Zhu is a Professor in the Department of Computer and Information Science at the University of Michigan-Dearborn, holding the William E. Stirton Professorship (2017–2024). He founded the Data Science/Management Research Laboratory and is affiliated with the Michigan Institute for Data Science (MIDAS). His research spans data science, data management, and machine learning. Ph.D., University of Waterloo M.S., McMaster University M.Eng., Southeast University B.S., Southeast University Research focuses on advanced data indexing, query optimization, and AI-driven data management, with applications in genomics, network systems, and education. His work integrates machine learning with database systems for scalable solutions. Recent publications include topics in federated learning fairness, digital twin middleware, project-based CS education, and genome data indexing. Scientific contributions recognized through awards like the Wilkes Award (2008), ACM Distinguished Scientist (2013), and Springer Nature Editor of Distinction (2025). 2013–2018: Department Chair NSF, IBM, and Ford grants Over 250 conference committee roles He directs the Data Science/Management Research Lab, focusing on collaborative projects in genome analytics and smart computing infrastructures.

Foto N. AfratiView profile

Professor

• Parallel and distributed computation
• Processing of very large data
• MapReduce
• +6 more

Professor Foto N. Afrati is a Distinguished Faculty Member at the National Technical University of Athens, specifically within the School of Electrical and Computing Engineering and the Division of Communication, Electronic and Information Engineering. She has held this position since 1993, following previous academic ranks at the same university as Associate Professor (1989-1993), Assistant Professor (1985-1989), Lecturer (1982-1985), and Research Fellow (1980-1982). She completed her PhD in Electrical Engineering at Imperial College of the University of London in March 1980, with a dissertation focused on Error Correcting Codes by Algorithms. Her academic journey also included a Diploma from Imperial College (March 1980) and an earlier Diploma in Electrical and Mechanical Engineering from the National Technical University of Athens (June 1976). Professor Afrati's research interests span several critical areas in computer science: Parallel and distributed computation Processing of very large data (including MapReduce) Data and web mining Database Systems Information integration Query optimization Computation and complexity of algorithms Approximation algorithms Her most recent publications demonstrate expertise in MapReduce environments, query optimization with views, and data exchange frameworks. These works are published in prestigious venues like EDBT, VLDB, PODS, and ICDT, with specific focus areas including adaptive sampling techniques, data source integrity, and algorithm complexity in database environments. Professor Afrati has received significant recognition in her field, including Fellow of the Association for Computing Machinery (ACM) Best Paper Award at the International Conference on Database Theory (ICDT) 2009 She has advised numerous PhD students throughout her career, including Theodoros Mitakos, Ezz Hattab, Nikos Kiourtis, and Angelos Vasilakopoulos. Her current PhD students include Victor Kyritsis and Nikos Stassinopoulos. Professor Afrati maintains strong professional networks through her various visiting positions at institutions such as Google, Stanford University, IBM Research Center, University of Helsinki, University of Paris, DIMACS, and others. She has served as associate editor and reviewer for major academic journals and conferences including IEEE TKDE, ACM Transactions of Database Systems (TODS), Journal of ACM (JACM), and Theoretical Computer Science (TCS). Her extensive work in research projects spans both national and international initiatives, with funding from sources including the European Union's Thalis project, ESPRIT working groups, HCM networks, and Greek General Secretariat of Research and Technology grants.

Aws AlbarghouthiView profile

Researcher

• Program Synthesis
• Formal Verification
• Quantum Computing
• +5 more

Aws Albarghouthi is affiliated with the University of Wisconsin-Madison, USA. He is an active researcher with significant contributions to program synthesis, formal verification, and machine learning. Key roles: Author, Session Chair, Committee Member in conferences like PLDI, POPL, VMCAI, SPLASH, and ICFP. Research spans quantum computing, differential privacy, and static analysis. Research Trends include: Quantum Circuit Compilation and Optimization Probabilistic Verification of Fairness and Privacy Synthesis of Datalog and MapReduce Programs Neural-Augmented Static Analysis Bias Detection in Data Security Robustness in Machine Learning

Tova MiloView profile

Professor

• Databases
• Big Data Management
• Machine Learning for Data Management
• +7 more

Professor Tova Milo is the Chair for Information Management at the School of Computer Science, Tel Aviv University, leading the prominent Databases Lab (DB Group). Her research spans databases, big data management, crowd-based data sourcing, and business process querying, with significant contributions to data integration and semi-structured data systems. Her research interests focus on innovative approaches to data management challenges through machine learning integration, crowd computing, and business process analysis. Key projects include Business Process Querying (BPQ) for analyzing BPEL specifications, MoDaS for crowd-based data management, and PROX for data provenance summarization. Her work bridges theoretical foundations with practical applications in fraud detection, recommendation systems, and data cleaning. Analysis of her recent publications reveals strong trends in human-in-the-loop data management systems, with growing emphasis on crowd integration for data cleaning and knowledge acquisition. Her research increasingly combines traditional database theory with machine learning techniques for scalable big data processing, while maintaining focus on business process modeling and provenance tracking. ACM PODS Alberto O. Mendelzon Test-of-Time Award (2010) ERC Advanced Investigators grant MoDaS (2011) The Weizmann Prize for Exact Sciences (2017) VLDB Women in Database Research award (2017) IEEE TCDE Impact award (2022) ISF Breakthrough Research Grant (2022) Doctorate Honoris Causa, University of Zurich (2023) ACM Fellow Member of Academia Europaea Professor Milo has advised over 30 graduate students including PhD candidates like Yael Amsterdamer and Ohad Greenshpan, and numerous MSc students working on projects including MoDaS, BPQ, and EDOS. Her research has been supported by major grants including the ERC Advanced Investigators grant and ISF Breakthrough Research Grant. She actively collaborates with industry partners including IBM and Microsoft, particularly in business process management standards. She directs Tel Aviv University's Databases Lab, which maintains the DB Group with multiple research streams including business process querying (BPQ), crowd-based data management (MoDaS), and self-adaptive data dissemination (EDOS/COLT). The lab operates from the Schreiber Building (M-20) and maintains strong international collaborations, particularly with European institutions through the ERC-funded MoDaS project.

• Machine Learning
• Deep Learning
• Video Classification
• +3 more

Reza Bosagh Zadeh is an Adjunct Professor at the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning, deep learning, and their applications in video classification, healthcare analytics, and distributed algorithms. He specializes in developing scalable computational methods for real-time data processing and has contributed to advancements in neural networks and optimization techniques. Reza's work spans theoretical and applied domains, with notable contributions to TensorFlow frameworks, video summarization systems, and medical imaging analysis. His research often integrates interdisciplinary approaches, leveraging both academic and industrial collaborations. Notable projects include developing machine learning models for glaucoma detection and creating efficient algorithms for large-scale data processing in environments like Apache Spark. His publications emphasize real-time video stream analysis, distributed computing architectures, and practical implementations of deep learning. Reza holds a strong presence in both academic and tech sectors, with contributions to platforms like Twitter's Who-to-Follow system and innovations in edge computing for video surveillance.

Cevdet AykanatView profile

Professor

• Parallel Computing
• High-Performance Computing
• Graph and Hypergraph Partitioning
• +4 more

Prof. Cevdet Aykanat is a Professor of Computer Engineering at Bilkent University, Ankara, Turkey. He earned his BS/MS in Electrical Engineering from METU and PhD from Ohio State University as a Fulbright scholar. His research focuses on parallel computing, sparse matrix algorithms, graph partitioning, and high-performance computing for big data. He has been affiliated with Bilkent since 1989 and has held roles like Associate Provost. Education: BSc/MSc (METU Electrical Engineering), PhD (Ohio State University Electrical & Computer Engineering). Research interests include parallel scientific computing, combinatorial optimization, distributed systems, and large-scale data analysis. His work spans over 100 publications in top journals like IEEE Transactions and SIAM, with 5,500 citations and an H-index of 40. Awards: 1996 TUBITAK Investigator Award, 2007 METU Parlar Science Award. He led 6 TUBITAK projects and participated in EU-funded projects like PRACE-1IP to PRACE-6IP. Publications emphasize efficient parallel algorithms for sparse computations, graph partitioning, and distributed systems. His work addresses latency reduction, load balancing, and scalable data processing in HPC environments. Grants: Funded by TUBITAK, Intel SSD, and EU programs. Academic service includes editorial roles at IEEE Transactions on Parallel and Distributed Systems.

Steven DingView profile

Adjunct Assistant Professor

• Artificial Intelligence
• Data Analytics
• Security
• +4 more

Steven Ding is an Adjunct Assistant Professor and Lab Director at the School of Computing, Queen's University , leading the L1NNA Artificial Intelligence and Security Lab. His research focuses on the intersection of machine learning, data mining, and cybersecurity with applications in malware analysis, reverse engineering, and authorship attribution. Education: PhD in Information Studies (McGill University, 2019), M.A.Sc. in Information Systems Security (Concordia University), B.S. in Information Systems (University of the Fraser Valley), B.S. in Computer Science (University of Shanghai for Science & Technology) Research Interests: His work integrates artificial intelligence and security systems to develop solutions for malware phenotype decomposition, neural malware analysis, binary provenance, and explainable AI in cybersecurity. He explores data analytics for assembly code evolution and authorship analysis using machine learning and differential privacy . Publication Trends: His peer-reviewed work (IEEE Transactions, ACM conferences) emphasizes binary code analysis , neural networks , and cybersecurity , with a focus on static malware characterization , authorship attribution , and cross-architecture code similarity . Scientific Awards: FRQNT Doctoral Research Scholarship (2017-2018) Dean’s Graduate Award at McGill University (4 years, 2014-2018) NSERC Alliance Grant (2021-2024) Best Poster Award, SERENE-RISC Research Showcase (2016) Second Prize, Hex-Rays Software Plug-in Contest (2015) ACM SIGKDD Student Travel Award (2016) Advising & Grants: Dr. Ding has supervised numerous Ph.D. and Master’s students (e.g., Li Tao Li, Mark M. Adams) and secured significant funding from BlackBerry Cylance , DRDC , and NSERC , including a 1.24M CAD NSERC Alliance Grant (50%) and 1.5M CAD DRDC Contract (50%). Labs & Teams: The L1NNA lab collaborates with DRDC Canada, NVIDIA, and IEEE Security & Privacy, focusing on AI-driven security tools (e.g., Kam1n0 assembly clone search engine) and neural malware analysis . The lab is located in Goodwin Hall, Queen's University, and emphasizes practical software development and industrial partnerships .

László LengyelView profile

Professor

• Model-Driven Engineering
• Internet of Things (IoT)
• Software Obfuscation
• +7 more

László Lengyel is a Professor at the Budapest University of Technology and Economics (BME), affiliated with the Department of Automation and Applied Informatics . His work bridges theoretical and applied computer science, focusing on industrial automation, IoT systems, and model-driven engineering. Research interests include Model transformations and domain-specific languages IoT device management and multi-domain integration Software obfuscation and cybersecurity Graph algorithms and distributed computing (MapReduce) Real-time data analysis in manufacturing Automotive sensor networks His recent publications reflect expertise in model-driven IoT architectures , granule manufacturing automation , and MapReduce-based graph analysis , with a focus on industrial and automotive applications. He contributes to open-source frameworks like SensorHUB and explores gamification in driver behavior systems.

• Distributed Systems
• Networking
• Operating Systems
• +7 more

Dwight Makaroff is a Professor in the Department of Computer Science at the University of Saskatchewan . He leads the DISCUS research group , focusing on distributed systems, networking, and performance analysis. Makaroff holds a Ph.D. from the University of British Columbia (1998), an M.Sc. (1988), and a B.Comm. (1985) from the University of Saskatchewan. Research Interests: Distributed Data Processing & Hadoop Network Support for Multiplayer Games Information-Centric Networking Energy Efficiency in Mobile Devices Multicore Architectures Wireless Network Security Sensor Networks & Data Aggregation Teaching: Courses include Operating Systems Principles , Topics in Parallel & Distributed Systems , and advanced systems courses. He coordinated the ACM ICPC programming contest teams for over a decade. Committees: Graduate Committee Chair (2013-2015) University Council Member (2006-2014) Program Committee roles at IEEE/ACM conferences (IPCCC, CASCON, etc.) Recent Research Highlights: IoT security via blockchain Wearable device communication challenges Caching strategies for information-centric networks

• Computer Architecture
• High-Performance Computing
• Distributed Systems
• +12 more

Yolanda Becerra Fontal is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the Barcelona School of Informatics (FIB). She is actively involved in research projects and collaborations, notably with the Barcelona Supercomputing Center, and is a member of prominent research groups such as the High Performance Computing Group (CAP) and CROMAI (Computing Resources Orchestration and Management for AI). Research Interests: Her research spans a broad spectrum of computer systems, with a consistent focus on performance, efficiency, and scalability. Key areas include Computer Architecture , High-Performance Computing (HPC) , Distributed and Cloud Systems , Resource and Energy Management in virtualized environments, and Data-Intensive Computing . More recently, her work has centered on innovative time-series database systems and data management for edge and cloud analytics. Publication Trends: Her recent scholarly output (2020-2022) shows a strong emphasis on time-series data management, proposing novel database architectures like NagareDB and strategies for polyglot persistence. Earlier work (2009-2013) was pivotal in MapReduce workload management, energy accounting for virtualized systems, and optical data center networks, demonstrating a long-standing contribution to foundational distributed computing challenges. Scientific Contributions: Her work has been published in top-tier journals and conferences such as Future Generation Computer Systems , IEEE Transactions , and Nucleic Acids Research . She has also contributed to significant competitive R&D projects and holds patents related to data flow management and distributed indexing. Advising and Grants: Dr. Becerra Fontal has served as a thesis advisor for doctoral students. Her research has been funded through competitive grants from national and regional programs, including Spanish State Research Plans (Plan Estatal de Investigación) and Catalonia's RIS3CAT strategy, supporting projects on high-performance computing and data management. Research Groups and Labs: She is a core member of the CAP - High Performance Computing Group and the CROMAI - Computing Resources Orchestration and Management for AI group at UPC. Her work is closely associated with the Barcelona Supercomputing Center (BSC) , one of Europe's leading supercomputing facilities, indicating access to advanced computational infrastructure.

• Parallel Computing
• High Performance Computing
• Data-Intensive Computing
• +3 more

Geoffrey C. Fox serves as Professor and Director of the Digital Science Center at Indiana University Bloomington's Luddy School of Informatics, Computing, and Engineering, with over 1,200 publications spanning physics and computer science. His career encompasses foundational contributions to parallel computing methodology, algorithms, and data-intensive systems. Educational background includes a PhD in Theoretical Physics from Cambridge University. Fox's research focuses on synergies between high-performance computing and data analysis, pioneering decomposition principles for MIMD hypercubes and message-passing efficiency. He developed innovative systems like Twister (enhanced MapReduce for iterative computations) and SPIDAL (Scalable Parallel and Interoperable Data-intensive Application Library), bridging HPC with data-intensive applications through architectures comparable to Apache Spark and Flink. Major recognitions include: ACM-IEEE CS Ken Kennedy Award (2019) for foundational contributions to parallel computing methodology, algorithms, software, and data analysis interfaces with broad applications ACM Fellow (2011) for contributions to high-performance computing software applications and diversity outreach Fox demonstrates exceptional commitment to community engagement through teaching Java and parallel computing courses to Historically Black Colleges and Universities since 1997, creating a Computerworld-recognized 'Big Data Applications' MOOC, and leadership roles in Open Grid Forum and Java Grande Forum standards development. As principal investigator of the FutureGrid cyberinfrastructure testbed, he enabled novel scientific computing approaches while serving as General Chair for multiple conferences. He directs Indiana University's Digital Science Center and leads the SPIDAL project, which supports diverse data-intensive applications across high-performance computing platforms through interoperable library frameworks.

Andy HopperView profile

Professor

• Ubiquitous Computing
• Sentient Computing
• Distributed Systems
• +3 more

Sir Andy Hopper is a Professor and Head of the Digital Technology Group within the Computer Laboratory at the University of Cambridge. He is a Fellow of Corpus Christi College, Cambridge. His research focuses on creating a world where everything is always connected in a personalized way and developing sentient computing systems that observe the real world to ensure planetary sustainability. His research interests span ubiquitous computing, sentient computing, distributed systems, computer networking, green computing, and provenance systems. He has pioneered work in location-aware systems, energy-efficient computing, and provenance in distributed environments. His vision extends computing to automatically adapt to real-world conditions for sustainable solutions. Recent publications indicate a strong focus on provenance systems, data reliability in cloud environments, and the integration of renewable energy in datacentre computing. His work often involves collaboration with researchers at the University of Cambridge and beyond, addressing challenges in virtualization, MapReduce, and resource accounting. Professor Hopper leads the Digital Technology Group, which is part of the Computer Laboratory. The group conducts cutting-edge research in networking, systems, and sustainable computing, contributing to both theoretical advances and practical applications in the field.

• Mathematical Physics
• Differential Geometry
• Topology
• +4 more

Prof. Alberto S. Cattaneo is a faculty member at the University of Zurich, affiliated with the Department of Mathematics. He holds the rank of Professor and has been actively involved in teaching and research since at least 1998. His research interests span mathematical physics, differential geometry, topology, and interdisciplinary areas like computational biology, genomics, and digital forensics. He has developed courses on topics such as field theory, quantum mechanics, and differential manifolds, reflecting his expertise in theoretical and applied mathematics. Prof. Cattaneo has contributed to numerous publications, including works on distributed genomic analysis, sensor pattern noise (PNU) in forensics, and algorithm optimization for big data frameworks like Hadoop and Spark. His work bridges pure mathematics with applications in bioinformatics and cybersecurity. Though no awards are explicitly mentioned, his extensive publication record and teaching roles highlight his academic standing. He maintains an active presence through courses and research collaborations, with no indication of part-time roles or retirement.

Ana MilanovaView profile

Professor

• Programming Languages
• Compilers
• Software Engineering
• +6 more

Dr. Ana Milanova is a Professor in the Department of Computer Science at Rensselaer Polytechnic Institute, where she has been since 2003. Her research focuses on programming languages, compilers, and software engineering, with emphasis on static program analysis, security, and applications in Android app taint analysis, secure cryptographic protocols, and machine learning library verification. Research Interests: Her work addresses challenges in secure software development, privacy-preserving techniques, and static analysis methodologies. Recent projects include federated learning frameworks, Python-based static analysis tools, and secure computation protocols. She has contributed to tools like Submitty for automated programming assignment grading and frameworks for secure MapReduce applications. Scientific Awards: NSF CAREER Award, Google Faculty Research Award Grants: SaTC: CORE grants for secure computation and multi-party optimization Labs/Teams: Leads research in secure computation, federated learning, and static analysis tool development. Collaborates on open-source platforms for educational grading systems (Submitty).