Find Academic Supervisors

• 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).

Francesco BellottiView profile

Associate Professor

• Artificial Intelligence
• Embedded Systems
• Automated Driving
• +3 more

Francesco Bellotti is an Associate Professor at the University of Genoa's Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN). He teaches advanced courses including Computational Intelligence, Cyber Physical Systems, and Machine Learning for Automated Driving across master's programs in Electronic Engineering and Engineering Technology for Strategy and Security. His institutional role includes membership in the steering committee of the Italian Excellence Center for Logistics, Transport, and Infrastructure (CIELI). Bellotti's research focuses on artificial intelligence and embedded systems , with applications in automotive technology, edge computing, and TinyML. Key areas include real-time driver distraction detection, deep reinforcement learning for autonomous parking, neural architecture optimization for microcontrollers, and cross-domain activity recognition. His work bridges theoretical machine learning with resource-constrained hardware implementations. Recent publications (2024-2025) demonstrate a strong emphasis on optimizing deep learning models for edge deployment, particularly in automotive and IoT contexts. Dominant themes include real-time neural network inference on microcontrollers, adversarial training for robust autonomous agents, video preprocessing for driving scenarios, and benchmarking lightweight vision models. The research consistently targets efficiency-critical applications, reflecting Bellotti's focus on scalable embedded AI solutions.

Zihao FuView profile

Lecturer

• Natural Language Processing
• Large Language Models
• Text Generation
• +5 more

Zihao Fu is an Affiliated Lecturer at the University of Cambridge within the Faculty of Modern and Medieval Languages and Linguistics, specifically at the Language Technology Lab. He also serves as a Research Assistant Professor at The Chinese University of Hong Kong (CUHK). His research spans Natural Language Processing , Large Language Models , and Biomedical Applications with a focus on Parameter-Efficient Fine-Tuning and Algorithmic Fairness . Previously, he was a Postdoctoral Researcher at both University of Cambridge (2021-2024) and University of Oxford (2024-present). Dr. Fu's academic background includes a Ph.D. in Systems Engineering from The Chinese University of Hong Kong (2017-2021), followed by postdoctoral training at Cambridge and Oxford. His technical expertise integrates Computational Linguistics , Machine Learning , and Algorithmic Fairness to address challenges in Biomedical Named Entity Recognition and Knowledge Base-to-Text Generation . His recent publications (2020-2025) demonstrate a trajectory from foundational work in Text Generation and KB-to-Text Systems to cutting-edge research in Biomedical LLMs and Algorithmic Fairness Toolkits . Notable contributions include the BAND Biomedical Alert Dataset (AAAI 2024) and theoretical studies on LLM Watermarking and Parameter Stability . While no scientific awards are explicitly listed, his service as a Reviewer for top conferences (AAAI, ACL, NeurIPS) indicates field recognition. As an educator, he has taught courses like Computational Linguistics at Cambridge (2022-2023) and Advanced Financial Infrastructure at CUHK. His technical projects include open-source tools like StreamTask (parallel processing framework) and CSTL (C++ STL wrapper for Python), reflecting practical implementation skills alongside theoretical contributions.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +6 more

Vivek Gupta is an incoming Assistant Professor at the School of Computing and Augmented Intelligence , Arizona State University (ASU), starting Fall 2024. Previously, he served as a Postdoctoral Researcher at the University of Pennsylvania's Cognitive Computation Group , and as a Research Fellow at Microsoft Research India . His academic journey includes a Ph.D. in Computer Science from the Kahlert School of Computing, University of Utah (advisor: Prof. Vivek Srikumar), supported by the Bloomberg Data Science Fellowship, and an MTech/BTech Dual Degree from IIT Kanpur . Dr. Gupta's research focuses on Natural Language Processing for semi-structured data , particularly tables, flowcharts, and maps. His work addresses trustworthy AI systems , multilingual tabular inference , and knowledge integration in low-resource environments. Current projects explore temporal reasoning , multimodal table analysis , and neurosymbolic agents for complex data. Recent publications examine adaptive prompting for temporal table reasoning , flowchart attribution , and multimodal benchmarking . His MMTBENCH and TabXEval frameworks evaluate robustness and quality standards in table processing. Recruitments highlight his CORAL Lab seeking candidates for Trustworthy AI projects. Bloomberg Data Science Fellowship (2021-2023) Ericsson Innovation Award (2016) Best Paper, DeeLIO-2022 Outstanding Paper, NLP4ConvAI-2022 As a mentor, Dr. Gupta has guided 30+ students across institutions including ASU, University of Utah, IIT Kanpur, and UPenn. His professional service includes organizing NAACL Student Research Workshops , serving as iKDD Student Ninja , and coordinating the University of Utah Data Science Club . Contact: vgupt140@asu.edu , keviv9@gmail.com

Nedjma Djouhra OusidhoumView profile

Lecturer

• Natural Language Processing (NLP)
• Computational Social Science
• Automated Fact-Checking
• +5 more

Nedjma Djouhra Ousidhoum is a Lecturer at the School of Computer Science and Informatics, Cardiff University, and a Visiting Academic at University of Cambridge. She leads the Cardiff NLP Group and focuses on Natural Language Processing and Computational Social Science. Cardiff University (2021–present) University of Cambridge (2021–2025) Hong Kong University of Science and Technology (PhD, 2014–2021) Her research explores NLP for low-resource languages , automated fact-checking , and toxic content detection , with recent work on emotion recognition datasets across 28 languages (ACL 2025 Best Resource Paper) and creating benchmarks for culturally grounded NLP (NAACL 2025 Best Theme Paper). Selected publications demonstrate her impact in multilingual dataset creation and cross-cultural NLP : BRIGHTER (ACL 2025): Best Resource Paper for emotion datasets across 28 languages WorldCuisines (NAACL 2025): Best Theme Paper for multicultural visual question answering AfriHate (NAACL 2025): Multilingual hate speech datasets for African languages Awarded Best Task Description Paper at SemEval 2025 and Outstanding Senior Area Chair at EMNLP 2023, she actively contributes to NLP community through workshop organization and program committee roles.

Abdelkader OudaView profile

Associate Professor

• Information Security
• Cryptography
• Network Security
• +9 more

Dr. Abdelkader Ouda is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Western Ontario, with over three decades of experience in academia and the software industry. He holds a Ph.D. in Computer Science from Western University (2005), an M.Sc. in Computer Science and Pure Mathematics from Ain Shams University (1993), and a B.Sc. (Hons) in the same disciplines from Ain Shams University (1986), graduating with the highest honor (96.80%). His research spans information security, cryptography, network security, big data security and privacy, and data analytics . He leads the Information and Computer Networks Security (iCNS) Research Laboratory, focusing on innovative solutions for IoT, fog–cloud computing, and behavioral biometrics. His recent work includes developing the ADG Engine for synthetic data generation and HSM4SSL for intra-domain security. Dr. Ouda’s publications highlight advancements in authentication protocols, lightweight IoT security frameworks, database benchmarking, and machine translation . His industry experience includes 14 years in software development, project management, and system analysis, which he integrates into his teaching of courses like Software Design, Algorithms & Data Structures, and Information Security. He also contributes to engineering accreditation through the CEAMS application. Key trends in his articles include secure communication in hybrid architectures, energy-efficient IoT protocols, and reinforcement learning for continuous authentication . His work bridges academic theory with practical applications, emphasizing efficiency and scalability in modern computing environments.

• Distributed Systems
• Blockchain Technology
• Middleware Integration
• +4 more

Julian Kalinowski is a researcher at the University of Hamburg's Department of Computer Science within the Faculty of Mathematics, Informatics, and Natural Sciences (MIN). His work focuses on distributed systems, blockchain technology, and middleware integration for mobile environments. Key projects include Jadex BDI Agent System and the cadeia initiative for distributed code execution in information markets. PhD candidate in Computer Science Email: kalinowski@informatik.uni-hamburg.de Room F510, Office hours by appointment Research interests span decentralized information markets, blockchain compliance (e.g., GDPR), and mobile middleware optimization. His 2015 best paper award at MATES conference highlights contributions to agent platform customization. Publications from 2012-2020 reveal trends in blockchain applications, smart grid optimization, and IoT security. Supervised 9 student theses covering decentralized billing, smart home systems, and localization technologies. Key awards: Best Paper Award (MATES-2015).

Joshua OehmsView profile

Researcher

• Information Retrieval
• Conversational Systems
• NLP for Healthcare Applications
• +1 more

Joshua Oehms is a Researcher at the Technical University of Munich (TUM) within the School of Computation, Information and Technology, specifically in the Department of Computer Science (I19 Software Engineering for Business Information Systems). He joined sebis in July 2024 and focuses on Natural Language Processing (NLP) applications in healthcare, conversational systems, and low-resource NLP tasks. B.Sc. and M.Sc. in Games Engineering from TUM Research expertise in text simplification and NLP for healthcare Teaching role as lecture organizer for "Software Engineering for Business Applications" (WS 24/25, Bachelor level) His recent work addresses challenges in German text simplification through style-specific pre-training of language models, targeting scenarios with limited parallel data. He collaborates with external partners like MRI (Medical Research Institute) on automating medical literature screening processes. Key technical skills include PyTorch/Keras, transformer models, and traditional information retrieval systems. Joshua actively supervises student thesis projects focusing on AI applications in healthcare contexts.