Find Academic Supervisors

Anys BachaView profile

Associate Professor

• Cybersecurity
• Computer Systems
• Mobile and Cloud Computing
• +5 more

Anys Bacha is an Associate Professor at the University of Michigan-Dearborn and leads the Security and Systems Lab . He has over 13 years of R&D experience at Hewlett-Packard and holds a Ph.D. from The Ohio State University. His work focuses on secure and energy-efficient computer systems, bridging mobile/cloud computing with security research. Education : Ph.D. in Computer Science from The Ohio State University (2016) Expertise : Systems security, hardware-software co-design, machine learning for security, side-channel attacks, non-volatile memory His research explores: Novel malware defenses (ransomware, cryptojacking) Side-channel attack mitigation in speculative execution Machine learning acceleration using emerging memory technologies Security implications of non-volatile caches Peer-reviewed publications appear in top venues like IEEE TDSC, DSN, RAID, and MICRO. Notable trends include: Integration of deep learning and hardware security Systematic approaches to firmware trust Energy-conscious security mechanisms Scientific recognition includes: NSF grants totaling $1.4M+ IEEE Micro Top Picks Honorable Mentions Best Paper Award at CAL 2019 12 issued/pending patents Professional service includes committee roles at ISCA, IPDPS, and MICRO symposia, alongside memberships in IEEE and ACM.

Daniel SchlörView profile

Researcher

• Machine Learning
• Anomaly Detection
• Natural Language Processing
• +5 more

Dr. Daniel Schlör is a researcher at the Chair of Data Science (Informatics X) at the University of Würzburg, with additional affiliations to the CLiGS (Computational Literary Genre Stylistics) research group in Digital Humanities. His work focuses on machine learning for cybersecurity, fraud detection, and explainable AI, with recent projects exploring synthetic data generation, knowledge graph integration, and deep learning for imbalanced datasets. Research interests include Explainable AI (XAI) for anomaly detection Deep learning architectures for domain-specific relationships Multi-agent simulations for fraud scenario modeling Computational stylistics in digital humanities Article trends show expertise in Developing novel neural units (e.g., ModeConv) for structural anomaly differentiation Advancing XAI methods with generative inpainting techniques Creating open ERP datasets for occupational fraud research Applying graph neural networks to water distribution leakage detection Labs & collaborations include the Data Science Chair’s AI Institute at Hubland Nord campus and CLiGS research group for computational literary analysis.

Haim DubossarskyView profile

Lecturer

• Natural Language Processing
• Artificial Intelligence
• Computational Linguistics
• +3 more

Haim Dubossarsky is a Lecturer at the School of Electronic Engineering and Computer Science, Queen Mary University of London, with a secondary affiliation as an Affiliated Lecturer in the Language Technology Lab at the University of Cambridge. His research bridges artificial intelligence, natural language processing, and cognitive science, with specialized focus areas including: Computational modeling of lexical semantic change across languages Intersections of linguistics, cognition, and neuroscience Robustness and interpretability in NLP systems Cross-lingual transfer learning for low-resource scenarios His publication trends (2022-2025) reveal concentrated work in semantic change detection methodologies, with innovations in evaluation frameworks (LSC-Eval), multilingual applications (SenWiCh), and model comparisons (GPT vs. BERT). Recent expansions include cognitive neuroscience integrations for narrative processing and sentiment-aware diachronic analysis. A consistent theme is improving NLP robustness through topological analysis, adversarial training, and logical reasoning frameworks. Dubossarsky contributes to the Language Technology Lab at Cambridge, focusing on interdisciplinary approaches to language modeling. No information is available regarding awards, students, or grants in the provided materials.

• Audiology
• Auditory Electrophysiology
• Audiologic Rehabilitation
• +5 more

Samuel Atcherson, Ph.D., is a Professor at the University of Arkansas for Medical Sciences (UAMS) in both the College of Health Professions Department of Audiology and Speech Pathology and the College of Medicine Department of Otolaryngology—Head and Neck Surgery. His research spans audiologic rehabilitation, auditory electrophysiology, and health literacy, with a focus on addressing challenges faced by individuals with hearing loss, particularly in healthcare communication during mask use and accessibility for people with disabilities. Research Interests: Atcherson's work integrates audiologic rehabilitation through innovations like transparent face masks and amplified stethoscopes, auditory electrophysiology using evoked potential systems, and health literacy research examining readability of medical materials, especially for those with disabilities. Publications: With over 60 peer-reviewed articles, his recent publications explore AI integration in audiology, masked speech challenges, and health disparities. A recurring theme is the intersection of technology, accessibility, and multidisciplinary collaboration. Grants & Awards: He has secured over $477,000 in grants, including NIH funding, and holds the Distinguished Scholar Fellow title from the National Academies of Practice. Leadership: Atcherson serves on the Editorial Board of the Journal of the American Academy of Audiology and has contributed to textbooks like Auditory Electrophysiology: A Clinical Guide .

Keyvan VakiliView profile

Associate Professor

• Innovation Strategy
• Collaboration and Innovation
• Economics of Science
• +6 more

Keyvan Vakili is an Associate Professor of Strategy and Entrepreneurship at London Business School. His research examines how technological and institutional contexts influence collaboration decisions and innovation outcomes, spanning fields like innovation strategy, economics of science, and knowledge recombination. He has published extensively on topics including collaboration trade-offs, patent policy impacts, and the role of social liberalization in creativity. Education: BA and MBA from Sharif University of Technology; PhD from University of Toronto Research Streams: Technological context effects on collaboration, environmental influences on collaborative arrangements, and scientists' topic selection strategies Advising: Provides strategic guidance to startups in mobile apps, cloud computing, and blockchain. His entrepreneurial experience includes founding a video game company acquired by Samsung. Key Trends: Recent work explores innovation biases, job redesign in platform markets, and the economics of scientific effort allocation. Earlier studies focus on university-industry collaboration, patent pools, and creativity in regulated environments. Labs & Teams: Collaborates with London Business School's Data Science and AI initiative, integrating interdisciplinary perspectives to study AI's business implications.

Julian Robledo MejiaView profile

Researcher

• 5G Networks
• Baseband Systems
• Real-time Systems
• +3 more

Julian Robledo Mejia serves as a Researcher at Dresden University of Technology's Chair for Compiler Construction, Germany, since January 2020. His work focuses on optimizing 5G baseband systems for performance and energy efficiency within heterogeneous multi-core environments under real-time constraints. His academic foundation includes: Bachelor of Electronic Engineering from University of Antioquia (UdeA), Medellín, Colombia Master of Embedded Systems from Polytechnic University of Turin, Italy (2017), thesis on fault injection for real-time operating systems Mejia's research tackles 5G network challenges through adaptive scheduling and dataflow models to enhance baseband system flexibility. His methodologies address critical demands for high data rates, ultra-low latency, and workload heterogeneity while optimizing energy consumption—key for next-generation mobile infrastructure. This work bridges theoretical modeling with practical implementation in resource-constrained environments. Publication analysis (2021-2025) reveals consistent innovation in reactive programming for heterogeneous systems, with Lingua Franca as a recurring framework. His contributions emphasize timing-energy trade-offs in base station optimization, advancing embedded real-time processing for wireless networks through model-based approaches and adaptive resource management. As part of TU Dresden's Compiler Construction research group, Mejia collaborates on cutting-edge projects in compiler design and embedded systems, contributing to tools like Mocasin for rapid prototyping of heterogeneous multi-core mappings. His industry background in automotive embedded software informs practical implementations of theoretical frameworks.

Michael JippingView profile

Professor

• Assistive Technology
• Mobile Software Development
• Information Security
• +7 more

Dr. Michael Jipping is a Professor of Computer Science and Department Chair at Hope College, where he has served since 1987. His expertise spans assistive technology, mobile software development, and computer security, with a focus on leveraging smartphones for accessibility. Education : Ph.D. (1986), M.S. (1984) in Computer Science from the University of Iowa; B.S. (1981) in Computer Science from Calvin College. His research emphasizes assistive technologies for low-vision users and cognitive disabilities, alongside mobile operating systems and network security. Recent publications include work on augmented reality applications and mobile education frameworks. Grants include funding for undergraduate research in biomedical applications (2009) and classroom technology innovations (1996–2003). Outside academia, he is active in community and church life, with interests in camping, photography, and science fiction.

• Acoustic Signal Processing
• Hearing Assessment
• Speech Communication
• +7 more

Tron Vedul Tronstad is an Associate Professor at the Department of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU), and a Researcher at Sustainable Communication Technologies, SINTEF AS. His interdisciplinary work bridges engineering and clinical applications in hearing and communication technologies. His research interests focus on acoustic signal processing , speech communication , hearing assessment tools , and classroom acoustics , particularly in low-resource settings. He has led projects adapting standardized hearing tests like the Digit Triplet Test for Swahili speakers and evaluating real-world acoustic conditions in Tanzanian classrooms. His work emphasizes global hearing health and accessible auditory diagnostics. The recent publications reflect a strong trend in speech intelligibility under noise and reverberation , cross-linguistic validation of hearing tests , and measurement of listening effort in assistive technologies. These efforts span disciplines including audiology, acoustical engineering, and public health. Scientific Contributions: Development of culturally and linguistically adapted hearing assessments Field studies on educational acoustics in sub-Saharan Africa Innovative evaluation methods for speech enhancement systems Tronstad actively collaborates with institutions such as SINTEF, NTNU, University of Oslo (UIO), and the University of Girona (UdG). He contributes to advancing sustainable communication technologies through research, development, and international dissemination. While no formal students or grants are listed in the provided text, his project-based work suggests involvement in funded research initiatives and supervision within SINTEF-NTNU collaborative frameworks. He is affiliated with research environments focused on auditory technology innovation and sustainable communication solutions , particularly through the SINTEF-NTNU partnership in electronic systems and health-related signal processing.

• Stochastic Optimization
• Machine Learning
• Distributed Optimization
• +7 more

Eduard Gorbunov is a Tenure-Track Assistant Professor at the Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) , where he focuses on Stochastic Optimization, Distributed Optimization, Derivative-Free Optimization, and Variational Inequalities. His work bridges theoretical advancements with practical applications in Machine Learning and Federated Learning. Education: PhD in Computer Science (2021), Moscow Institute of Physics and Technology (MIPT), advised by Alexander Gasnikov and Peter Richtárik MSc in Applied Mathematics (2020), MIPT BSc in Applied Mathematics (2018), MIPT Research Trends: Key contributions to Heavy-Tailed Noise Analysis in Stochastic Optimization Communication-Efficient Methods for Distributed/Federated Learning Byzantine-Robust Algorithms with Partial Participation Gradient Compression and Adaptive Optimization Techniques Awards & Grants: Ilya Segalovich Award (2019) A. M. Raigorodskii Scholarship (2020, 2021) Outstanding Reviewer at NeurIPS 2022 and ICML 2022 Huawei Scholarship for Academic Achievements (2019) Leadership: Action Editor at Transactions on Machine Learning Research (TMLR) (2024) Area Chair at NeurIPS (2025)

Frank KammerView profile

Researcher

• Computer Science
• Algorithms and Data Structures
• Graph Theory
• +4 more

Frank Kammer is a researcher at the Technical University of Central Hesse , actively engaged in teaching and research projects. He co-leads the RegioTainment platform with Biebertal municipality and contributes to the SEA Project , an open C++ library for space-efficient algorithms. Teaching : Database Systems (CS1020), Software Engineering (CS1023), Efficient Algorithms (CS2353), Artificial Intelligence (CS2364/WK1613), etc. Research : Focuses on space-efficient graph algorithms, temporal graph exploration, and algorithm engineering. His work addresses computational challenges in huge datasets and memory-constrained devices , with applications in planar graph encoding, vertex separators, and student feedback systems. Advising : Supervised theses on topics including data-driven analytics platforms, low-code integration, cloud orchestration, and AI-optimized marketing campaigns. Collaboration : Works with students like Max Stephan, Timon Pellekoorne, and Johannes Meintrup on algorithm implementation and educational tools. Projects like RegioTainment emphasize digital community networking to preserve rural culture while embracing modernity.

Thierry PoibeauView profile

Researcher

• Natural Language Processing
• Artificial Intelligence
• Computational Linguistics
• +4 more

Thierry Poibeau is a CNRS Research Director at Lattice, a research unit affiliated with the Ecole normale supérieure in Paris. He is also an associate member of Cambridge Digital Humanities (University of Cambridge) and contributes to the Paris Research Artificial Intelligence Institute (PRAIRIE). His work centers on advancing Natural Language Processing (NLP) for low-resource languages and applying AI to interdisciplinary studies in literature and social sciences. Research Interests: Developing NLP techniques for marginalized languages Interdisciplinary applications of AI in humanities Digital Humanities and computational cultural analysis Contact: Email: thierry.poibeau@ens.psl.eu

• Information Systems
• e-Government
• ERP Systems
• +3 more

Агрон Чауши is an Associate Professor at the Faculty of Business and Economics of South East European University in Tetovo, North Macedonia. He holds a PhD in Economic Sciences with a specialization in Information Systems, a Master of Science in Information Systems from Indiana University, and a Bachelor of Arts in Business Administration from the same university. PhD: Economic Sciences (Information Systems), SEEU (2012-2017) MS: Information Systems, Indiana University (2006-2007) BA: Business Administration, SEEU (2001-2005) His research focuses on the intersection of information systems and economics, particularly e-Government maturity models, ERP system implementations, and machine learning applications in education and healthcare. Recent work explores AI ethics, digital organizational transformation, and renewable energy automation. Publications between 2016-2025 demonstrate consistent engagement with information systems, e-Government, and business analytics. Key article keywords include artificial intelligence, ERP systems, digitalization, and renewable energy technologies. Sub-fields span machine learning applications, data mining, and educational analytics. He teaches business and technology courses at SEEU, having held progressively senior roles from Junior Assistant (2005-2009) to current Associate Professor (2023-present). His professional experience includes systems analysis at Indiana University's Kelley School of Business and consulting at SEEU's Business Development Center.

Tatiana KalganovaView profile

Professor

• Artificial Intelligence
• Evolutionary Design and Optimisation
• Evolvable Hardware
• +9 more

Professor Tatiana Kalganova is a faculty member at Brunel University London, affiliated with the Department of Electronic and Electrical Engineering and the College of Engineering, Design and Physical Sciences. With a career spanning over two decades at Brunel, she has established expertise in Artificial Intelligence, Evolvable Hardware, and Operational Research. Education: PhD in Evolutionary Computing, Napier University MSc (distinction) in Informatics, Belarusian State University of Informatics and Radio-Electronics Research-Engineer Degree, Belarusian State University of Informatics and Radio-Electronics Her research focuses on Evolutionary Design , Swarm Optimization , and Robotics , with applications in supply chain modeling, neuromorphic computing, and intelligent systems. Recent publications emphasize Large Language Models and data-efficient machine learning techniques. Scientific Awards: 2nd place in Caterpillar's Research and Innovation in Demand Strategy Competition (2012) AFWERX Challenge Award (2019-2020) Professor Kalganova has supervised numerous PhD students on topics like 3D Autorouting Systems and Ambidextrous Robot Hands . Her funded projects include the Horizon Europe Guarantee's ReCharged initiative for climate-resilient infrastructure and collaborations with Intel, Caterpillar, and the Nuffield Foundation.

Matthew R. GormleyView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Dialogue Systems
• +7 more

Matthew R. Gormley is an Associate Professor at Carnegie Mellon University , affiliated with the School of Computer Science and the Machine Learning Department . He also serves as an affiliate of the Language Technologies Institute and directs the ML Minor/Concentration program. Research interests include Natural language processing for dialogue systems Summarization (multi-document and long-document) NLP applied to medical text analysis Low-resource language and domain adaptation Syntactic and semantic parsing Autoregressive and approximation-aware machine learning Computationally efficient models His work spans both theoretical and applied domains, focusing on unsupervised learning and multilinguality. Recent publications highlight advancements in long-context modeling, data contamination taxonomies, and medical summarization. His collaborations with prominent researchers like Graham Neubig, Jason Eisner, and Thomas Schaaf demonstrate his interdisciplinary approach. Scientific contributions include Developing novel training methods for conditional random fields Advancing neural finite-state transducers Building concrete NLP pipelines for Chinese Creating annotated datasets like Annotated Gigaword Teaching roles at CMU include co-instructing 10-301/10-601 Introduction to Machine Learning and 10-423/10-623 Generative AI . He has mentored numerous PhD and Master's students in areas such as multilingual NLP, medical text analysis, and structured data processing.

Pascal FelberView profile

Professor

• Distributed Systems
• Concurrent Systems
• Trusted Execution Environments
• +7 more

Pascal Felber is a Full Professor at the Institute of Computer Science within the Faculty of Science at the University of Neuchâtel. His career spans significant industry experience at Oracle Corporation and Bell Labs before transitioning to academia, where he has established himself as a leading researcher in complex computing systems. His research interests focus on the theoretical foundations and practical applications of complex computing systems, particularly reliable, distributed, concurrent, and secure systems. Felber's work addresses critical challenges in large-scale systems including cloud computing, Internet of Things, and big data technologies. His research bridges theoretical computer science with practical system implementations. Analysis of his recent publications reveals a strong emphasis on Trusted Execution Environments (TEEs), secure computing, blockchain security, and privacy-preserving technologies. His work spans multiple domains from secure DNA alignment to phishing detection in smart contracts, demonstrating both theoretical depth and practical impact in addressing security challenges in modern distributed systems. Professor Felber has participated as a (co-)applicant in approximately twenty research projects funded by the EU (including VELOX, SRT-15, LEADS, ParaDIME, SafeCloud, SecureCloud, EBSIS, LEGaTO, VEDLIoT) and the Swiss National Science Foundation (SNSF). His teaching portfolio includes Bachelor's degree courses in French (Programming I & II, Languages and compilation, Web and network technologies, Concurrent and distributed systems) and Master's courses in English (Concurrency: Multi-core programming and data processing, Hot topics in operating systems seminar).