Find Academic Supervisors

Samaneh RashidibajganView profile

Lecturer

• Cybersecurity
• Ad Hoc Networks
• Opportunistic Networks (OppNets)
• +5 more

Dr. Samaneh Rashidibajgan is a Lecturer in Computer Science at the School of Computer Science & Engineering , Constructor University Bremen, Germany. She holds a Doctoral Degree in Information Systems and Cybersecurity from Münster University (2023). Her academic career spans roles as a Research Assistant at Rostock University (2015–2019) and Research & Teaching Assistant at Münster University (2019–2022). Research Interests : Security in Opportunistic Networks (OppNets), Privacy Preservation in Ad Hoc Networks, Solutions for Vehicular OppNets, and Security in Cyber-Physical Systems. She explores blockchain integration for network integrity, machine learning for performance optimization, and trust models to counter attacks like Sybil and selective dropping. Publications : Her work focuses on cybersecurity trends in decentralized systems, including blockchain for OppNets, privacy-preserving routing algorithms, and attack detection mechanisms. Key contributions include cluster-based routing for privacy, trust frameworks for Sybil attacks, and key-sharing methods in OppNets. Work Experience : 2024–Present : Lecturer and Researcher, Constructor University. 2019–2022 : Research & Teaching Assistant, Münster University. 2015–2019 : Research Assistant, Rostock University.

• Intelligent Transportation Systems
• Machine Learning
• Computer Networks
• +3 more

Zongtao Duan is an active researcher with a focus on intelligent transportation systems, cybersecurity, and machine learning applications. His work spans areas including traffic flow prediction, silent data corruption detection, vehicular networks security, and deep learning architectures for autonomous systems. He collaborates frequently with colleagues like Lei Tang and Junchi Ma, publishing in top-tier journals such as IEEE Transactions on Intelligent Transportation Systems and Future Generation Computer Systems. His research emphasizes practical solutions for urban mobility challenges, including ridesharing optimization and railway congestion control, while also addressing foundational issues in hardware reliability and error propagation modeling. Recent contributions include advancements in multimodal retrieval systems and knowledge graph applications in scientific data management. Zongtao has contributed to over 40 peer-reviewed publications since 2004, with notable work in 2025 exploring SDC evaluation frameworks and scenario-based testing for autonomous vehicle safety. His interdisciplinary approach bridges computer science, transportation engineering, and cybersecurity to address real-world technical challenges.

• French Language Practice
• Romance Studies
• Computer Vision
• +3 more

Viridiane Fay is a researcher in the Institute of Modern Languages and Romance Studies at the University of Würzburg , specializing in French language practice and applied computational methods. During lecture periods, she holds office hours every Tuesday from 1 p.m. to 2 p.m., with appointments available during semester breaks. Role: French Editor, Language Practice Location: Am Hubland, 97074 Würzburg Contact: viridiane.fay@uni-wuerzburg.de Her research spans two distinct domains: French language pedagogy and computer vision/AI . While her institutional affiliation focuses on Romance Studies, her publication record reveals expertise in: Medical and underwater image processing Transformer-based architectures Federated learning and network optimization Watermarking and security algorithms Multi-modal AI applications Neuroscience signal analysis Recent work includes lightweight segmentation models (e.g., ContextFormer), drone/aerial imagery detection frameworks (CH-YOLO-Lite), and privacy-preserving AI systems (Find). Despite limited biographical details in the scrape, her 30+ publications since 2017 suggest significant technical contributions.

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +7 more

Anh Nguyen-Duc is a researcher affiliated with the University of South-Eastern Norway (School of Business) and Norwegian University of Science and Technology , focusing on Software Engineering , Artificial Intelligence , and Startup Research . His work bridges technical and entrepreneurial aspects of software development. University of South-Eastern Norway, School of Business Norwegian University of Science and Technology Research Themes : Software startups, AI integration in development, agile practices, technical debt, and requirements engineering. Recent studies include fairness in educational machine learning models and generative AI adoption in software organizations. Collaborative work with Pekka Abrahamsson, Xiaofeng Wang, and Kai-Kristian Kemell. Key Trends : 2025 studies on AI fairness and generative AI in software processes; 2024 work on hybrid work impacts; 2023 research on startup AI tools and education. Advising & Grants : Collaborates with academic institutions and industry partners on empirical studies about software development practices.

• Network Virtualization
• Software-Defined Networking (SDN)
• Context-Aware Systems
• +4 more

Ahmed Karmouch is a Professor at the University of Ottawa, Canada, affiliated with the College of Engineering and Computer Science. His research focuses on Network Virtualization , Software-Defined Networking (SDN) , and Context-Aware Systems , with significant contributions to Cloud Computing Resource Allocation , Programmable Data-Planes , and Overlay Networks . He has co-authored over 161 publications since 1980, emphasizing scalable infrastructure management, QoS optimization, and autonomic network design. His recent work includes machine learning integration in P4-programmable networks (2024) and survivable IaaS management with SDN (2021). Publications span IEEE Transactions and ACM journals, with frequent collaborations with Nancy Samaan, Abdallah Jarray, and Hamid Harroud. Key research themes: Network Programmability Service Specific Overlay Networks Autonomic Network Management ML for Traffic Optimization Geographically Distributed Cloud

Lukas LaskowskiView profile

Researcher

• Data Matching
• Data Integration
• Ontology Learning
• +3 more

Lukas Laskowski is a researcher at the Hasso Plattner Institute (HPI) in Potsdam, Germany, affiliated with the Information Systems Group under Prof. Felix Naumann. His work bridges database systems, machine learning, and real-world applications, with publications in premier venues including SIGMOD, NeurIPS, and BTW. He maintains an active research profile with multiple forthcoming publications through 2026. His research concentrates on data matching and integration , where he pioneers explainable methods and active learning frameworks. He significantly contributes to ontology learning from relational databases through benchmark development and explores event sequence modeling with mixed data types. His interdisciplinary work extends to computer vision for ecological conservation, specifically developing open-set re-identification systems for wildlife monitoring in natural habitats. Analysis of his publication trajectory (2022-2026) reveals a cohesive evolution from foundational platform development (Frost) to sophisticated benchmarking (Burr) and explainability in data matching. His research increasingly integrates machine learning with database systems to address data quality challenges for AI applications, while maintaining strong connections to ecological and biomedical domains through projects like GorillaVision. Scientific Awards: No awards listed in available information Advising and Grants: No specific information regarding advised students or secured grants appears in the provided materials, though he participates in master's thesis supervision through group activities. Labs and Teams: Laskowski operates within the Information Systems Group at HPI, contributing to major initiatives including Metanome (large-scale data profiling), KITQAR (data quality for AI applications), and Metis (data quality assessment). The group emphasizes open-source tool development, reproducibility, and cross-institutional collaboration in data management research.

Basavesh Ammanaghatta ShivakumarView profile

Researcher

• Program Analysis
• Reliable Systems
• Systems Security
• +3 more

Basavesh Ammanaghatta Shivakumar is a Researcher and Postdoctoral Associate in the Systems Software Research Group at the Bradley Department of Electrical and Computer Engineering, Virginia Tech, working under Professor Binoy Ravindran. His research focuses on program analysis, reliable systems, systems security, and fuzzing, with a strong emphasis on cryptographic implementations and hardware-software co-security. He holds a Ph.D. from Radboud University (Netherlands) and conducted doctoral research at the Max Planck Institute for Security and Privacy (Germany), supervised by Gilles Barthe and Peter Schwabe. He also earned a Master’s degree from Purdue University (USA) and a Bachelor’s in Computer Engineering from NITK Surathkal (India). His work addresses critical challenges in securing systems software against side-channel attacks, microarchitectural vulnerabilities, and concurrency errors. Recent contributions include HMTRace for dynamic data race detection and robust constant-time cryptographic implementations. Publications span topics like Spectre mitigation, kernel race bug detection, and IoT security. His research bridges theoretical cryptographic principles with practical system-level protections, emphasizing both software and hardware domains. No specific grants or awards are mentioned, but his extensive academic and industrial collaborations reflect his impactful contributions to computer security and systems research.

Anthony Widjaja LinView profile

Professor

• Automated Reasoning
• String Constraint Solving
• Formal Language Theory
• +5 more

Anthony Widjaja Lin is a Full Professor (W3) in Theoretical Computer Science (Automated Reasoning) and Max-Planck Fellow at University of Kaiserslautern-Landau, Germany. Previously, he was an Associate Professor in Programming Languages at Oxford University Department of Computer Science and Governing Body Fellow at Kellogg College (2016-2019), and an Assistant Professor at Yale-NUS, Singapore (2014-2016). He completed his PhD in Informatics at University of Edinburgh in 2010 under Leonid Libkin (supervisor) and Richard Mayr (co-advisor). Dr. Lin's educational background includes: PhD in Informatics, University of Edinburgh (2010) MSc, University of Toronto BSc (Honours), Melbourne University Dr. Lin's research focuses on automated reasoning, particularly over strings, formal language theory, learning/synthesis, and foundations of machine learning. His work has significant applications in software verification, program synthesis, querying graph databases, and computer security. He leads the development of the OSTRICH string solver, which won the QF_S (Single Query Track) in SMT-COMP 2023. His research has evolved from foundational work on string constraint solving to applications in verification of string-manipulating programs and more recently to connections with machine learning models like transformers. Dr. Lin has received numerous prestigious awards including an ERC Consolidator Grant (2023), Amazon Research Award (2021), ERC Starting Grant (2017), Google Faculty Award (2017), and the LICS Kleene Award (2010). Dr. Lin has advised several PhD students to completion, including Pascal Bergsträßer, Chih-Duo Hong, and Xuan-Bach Le, who have gone on to become Assistant Professors at institutions like National Chingchi University and Nanyang Technical University. He currently supervises multiple PhD students and postdocs working on string solving, automated reasoning, and verification. Dr. Lin leads the AV-SMP project (Algorithmic Verification of String-Manipulating Programs), which was supported by an ERC Starting Grant (2017-2022) and an Amazon Research Award (2021). His research group develops tools like OSTRICH, SLOTH, and CertiStr for string constraint solving and verification.

• Human-Computer Interaction
• Gesture Recognition
• Child-Computer Interaction
• +5 more

Lisa Anthony is a Professor affiliated with the University of Florida, specializing in Human-Computer Interaction (HCI) with a strong focus on gesture recognition, touchscreen interfaces, and child-computer interaction. Her research emphasizes the design of user-centered technologies for diverse populations, including children, older adults, and individuals with disabilities. Key contributions include studies on children's touchscreen interaction patterns, multimodal authentication systems, and mHealth applications for behavior change. Her work spans over two decades, with publications in top-tier venues such as CHI, ICMI, and IEEE Transactions. Notable projects include the MTAGIC initiative exploring child motion characteristics, the design of MyTrack+ for weight management, and studies on spherical interfaces. She collaborates extensively with researchers in computer science, education, and healthcare to address real-world challenges in technology accessibility and usability. Lisa's research has implications for improving educational tools, healthcare apps, and collaborative learning environments. While no grants or awards are explicitly listed, her prolific publication record underscores her sustained contributions to HCI and related fields.

• Blockchain Technology
• Cybersecurity
• Cloud Computing
• +3 more

Wei-Tek Tsai is a Professor affiliated with Arizona State University's School of Computing, Informatics, and Decision Systems Engineering, and previously with Beihang University and the University of Minnesota. His research focuses on blockchain technology, cybersecurity, cloud computing, and software engineering. He has contributed significantly to areas such as blockchain-based systems, smart contract development, distributed testing methodologies, and crowdsourcing in software development. His work emphasizes practical applications like fraud detection, supply chain supervision, and decentralized financial systems. Research interests include permissioned blockchains, anomaly detection in cryptocurrency transactions, and optimizing consensus mechanisms. He has published extensively in top-tier journals and conferences, including IEEE Transactions and international workshops on software engineering and security. His work bridges theoretical advancements with real-world challenges in distributed systems and cybersecurity.

• Machine Learning
• Computer Vision
• Safety-Critical Systems
• +7 more

Ran Wei is a researcher at the University of Science and Technology of China, Department of Chemistry, with extensive contributions across interdisciplinary domains. His work bridges Computer Science , Biomedical Imaging , and Transportation Systems , focusing on active inference models, digital twins, and multimodal learning. Academic Affiliation : University of Science and Technology of China, Department of Chemistry Research Themes : Ran Wei's research explores active inference for adaptive systems, multimodal fusion in computer vision, and domain-specific knowledge graphs for engineering applications. He also investigates federated learning on heterogeneous networks and automated safety analysis for cyber-physical systems. Recent Article Trends : His publications from 2025–2021 span AI-driven engineering (e.g., construction project management), biomedical imaging (lesion detection, radiomics), and transportation systems (driver behavior modeling). Methodologically, he integrates transformers , Bayesian inference , and active learning frameworks. Scientific Collaborations : Ran Wei collaborates with experts in autonomous vehicles (Alfredo García, Anthony D. McDonald), biomedical engineering (Qiaolin Ye, Yifan Cai), and safety-critical systems (Tim Kelly, Simon Foster).

• Bioinformatics
• Artificial Intelligence
• Computer Science
• +3 more

Jie Hao is a researcher at the Information Security Center of Beijing University of Posts and Telecommunications , with a focus on interdisciplinary applications spanning Bioinformatics , Artificial Intelligence , and Medical Informatics . His work bridges computational methods with real-world challenges in healthcare, ecology, and network optimization. Recent publications highlight his contributions to Single-cell RNA sequencing deconvolution (2025) AI-driven intergenerational communication in VR (2025) Digital health applications for COPD management (2025) Deep reinforcement learning for vehicle routing (2025) His methodological innovations include adaptive attention mechanisms for object detection (2025), memory-efficient DNN accelerators (2025), and bilevel optimization algorithms with unbounded smoothness (2024). Collaborations span institutions like University of Melbourne and Chinese Academy of Sciences , reflecting his cross-disciplinary impact.

• Machine Learning
• Deep Learning
• Mathematical Optimization
• +3 more

Juncai He is affiliated with King Abdullah University of Science and Technology (KAUST) in the Computer, Electrical and Mathematical Sciences and Engineering Division, with additional connections to Hebei Agricultural University in China. His research spans theoretical machine learning, mathematical optimization, and practical applications in earth system science and natural language processing. Dr. He's research focuses on the mathematical foundations of deep learning, particularly examining the expressive power of ReLU networks and geometric properties of manifold-structured data. His work demonstrates strong theoretical rigor while maintaining practical relevance, as evidenced by applications in earth system forecasting and LLM-generated text detection. Recent publications show increasing interest in multiscale analysis techniques and optimization methods for complex data structures. His collaborative network includes significant partnerships with Liangchen Liu and Richard Tsai (Yen-Hsi Richard Tsai), with whom he has published multiple papers on manifold learning. Additional collaborations with Guangsheng Bao, Yanbin Zhao, and Yue Zhang indicate expanding research interests in large language model security and detection techniques.

Sebastian PeitzView profile

Professor

• Multiobjective Optimization
• Optimal Control
• Machine Learning
• +3 more

Sebastian Peitz is Professor (previously Assistant Professor) at Paderborn University's Department of Computer Science, leading the Data Science for Engineering group. He obtained his PhD in Multiobjective Optimization from Paderborn University and MSc in Mechanical Engineering from RWTH Aachen. His research develops computational methods for multiobjective optimization, optimal control, and machine learning with applications in fluid dynamics, autonomous systems, and industrial processes. He leads the BMBF-funded Multicriteria Machine Learning group. Research trends show consistent focus on Koopman operator theory, reinforcement learning applications in control systems, and physics-informed machine learning across publications. Achievements include the 2019 PRECEDE Best Paper Award and leadership in international optimization conferences.

Frank R. KschischangView profile

Professor

• Digital Communications
• Coding Theory
• Information Theory
• +4 more

Frank R. Kschischang is a Distinguished Professor of Digital Communication in the Department of Electrical and Computer Engineering at the University of Toronto. He holds the Canada Research Chair in Communications Algorithms and has been a faculty member since 1991. His research focuses on coding theory, digital communications, and information theory, with applications in optical, wireless, and fiber-optic systems. He is a Hans Fischer Senior Fellow at the Institute for Advanced Study (TUM-IAS), Technische Universität München, collaborating on advancing fiber-optic communication capabilities. Education: B.A.Sc. (University of British Columbia, 1985), M.A.Sc. and Ph.D. (University of Toronto, 1988 and 1991). He has held visiting roles at MIT (1997–1998) and ETH Zurich (2005). Academic roles include IEEE Information Theory Society President (2010) and editorial roles for the IEEE Transactions on Information Theory. Research emphasizes error-correcting codes, fiber-optic channel modeling, and nonlinear Fourier transform-based communication. Notable contributions include staircase codes for 100 Gb/s optical networks and rank-metric codes for network coding. His work has led to practical advancements in high-speed optical systems and improved channel capacity understanding. Awards include IEEE Fellow (2006), Killam Research Fellowship (2010), and multiple teaching and service awards. He advises numerous graduate students and collaborates internationally on projects like spectral modulation in fiber optics and machine learning for signal processing. Labs and teams: Leads the Coding and Information Theory group at UofT, focusing on cutting-edge research in communications algorithms and hardware implementation. Collaborates with industry partners like the Communications Research Centre Canada and Inphi Corp.