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David GreggView profile

Professor

• Software performance optimization
• Embedded systems
• Accelerating deep neural networks on embedded systems
• +12 more

David Gregg is a Professor in the Department of Computer Science at Trinity College Dublin's School of Computer Science and Statistics, where he serves as Global Director for Computer Science (since 2020) and previously as Head of the Discipline of Software and Systems (2018-2022). His research focuses on software performance optimization and embedded systems, with particular expertise in accelerating deep neural networks on resource-constrained platforms. He teaches Systems Programming (CS2014/5) and Concurrent Systems I (CS3014). Gregg's research spans multiple areas of computer systems including: Compiler optimization and program analysis Processor microarchitecture and parallelism (multi-core, vector, instruction-level) Computer arithmetic and domain-specific languages Low-energy embedded systems and FPGA implementations Deep neural network acceleration He has served on numerous program committees including PACT, PLDI, CC, and other major computer systems conferences, and has been on the Board of Distinguished Reviewers for ACM TACO multiple times. Professor Gregg has advised numerous PhD and MSc students, many of whom have gone on to prominent positions at companies like Intel-Movidius, Google, Amazon, and Synopsys. He leads the triNNity project which includes optimized libraries and compilers for implementing convolutional neural networks on CPUs.

• Natural Language Processing
• Machine Learning
• Dialogue Systems
• +3 more

Ana Valeria Gonzalez is a researcher in the Department of Computer Science at the University of Copenhagen's Faculty of Science, specializing in Machine Learning with a focus on human-centered natural language processing. Her work bridges computational linguistics and cognitive science within the department's Machine Learning research section. Her research investigates bias mitigation in multilingual systems, interpretable AI evaluation frameworks, and affect-aware dialogue generation. Key contributions include developing testbeds for gender bias analysis in coreference resolution, novel methodologies for evaluating model interpretability through reverse Turing tests, and retrieval-based approaches for goal-oriented conversational agents. She employs techniques spanning attention mechanisms, BiLSTMs, and domain adaptation to address challenges in low-resource NLP settings. Analysis of her 2019-2021 publications reveals a cohesive research trajectory toward building transparent, equitable NLP systems that account for human cognitive factors. Her work consistently addresses real-world deployment challenges, particularly in dialogue systems requiring emotional intelligence and cross-lingual fairness. Collaborations with Anders Søgaard, Isabelle Augenstein, and other Copenhagen researchers demonstrate strong integration within the university's AI ecosystem. Gonzalez completed her Ph.D. in 2021 with the dissertation "Towards Human-Centered Natural Language Processing". She maintains active research in the Machine Learning group, though specific advising roles and grant details aren't documented in the available materials. Her publication record shows significant impact with multiple Scopus-cited works and substantial reader engagement across platforms.

• labor economics
• education economics
• economics of religion
• +5 more

Benjamin W. Arold is an Assistant Professor (promotion-track to Associate Professor) at the Faculty of Economics at the University of Cambridge. Previously, he was a postdoctoral researcher at the Center for Law & Economics at ETH Zurich and earned his Ph.D. in Economics from LMU Munich. He has held visiting positions at Harvard University and Princeton University. Dr. Arold is an applied microeconomist with research interests in labor and education economics, the economics of religion, and AI & economics. His work investigates how school curricula influence student achievement, attitudes, beliefs, choices, and long-term labor market outcomes. Another significant strand of his research employs tools from AI, natural language processing (NLP), and computational linguistics to examine the effects of economic policies and educational interventions. His publications span prestigious journals including the Quarterly Journal of Economics, Journal of Political Economy Microeconomics, and Journal of Human Resources. His work has received widespread attention from media outlets including The Guardian, Frankfurter Allgemeine Zeitung, and VoxEU. Scientific Awards: Prize for Best Dissertation 2021/2022 by the German Economic Association's Section on Economics of Education Fürther Ludwig Erhard Prize 2023 Dr. Arold has taught courses on Economic Analysis of Non-Standard Data at Cambridge, Text Data in Business and Economics at Basel University, and NLP and Computational Linguistics in Economics at Princeton University. His teaching materials are publicly available on GitHub, demonstrating his commitment to open educational resources.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +4 more

Caio Corro is a Maître de Conférences (Associate Professor) at INSA Rennes, conducting research in the LinkMedia team of the IRISA laboratory. His work bridges theoretical computer science with practical applications in natural language understanding, with particular expertise in structured prediction problems for NLP systems. Dr. Corro's research program spans multiple interconnected domains: Natural Language Processing, with special focus on named entity recognition (including nested and discontinuous entities) Machine Learning algorithms for linguistic structure modeling Combinatorial optimization approaches to NLP challenges Application of AI techniques to linguistic research (AI for science) Development of efficient algorithms balancing theoretical soundness with practical performance His recent publication trajectory shows increasing emphasis on domain adaptation and multilingual capabilities while maintaining rigorous theoretical foundations. Corro has pioneered approaches that reduce computational complexity in structured prediction tasks without sacrificing accuracy, with significant contributions to named entity recognition methodologies. His work on EuroBERT demonstrates commitment to European language technologies, while his insurance domain research shows practical application focus. Dr. Corro maintains an active supervision portfolio: Current PhD students: Ayoub Hammal (co-supervised with Pierre Zweigenbaum and Miguel Couceiro), Benno Uthayasooriyar (co-supervised with Franck Vermet and Antoine Ly) Former students include Alban Petit (PhD graduate), Nicolas Devatine (now PhD student at IRIT), and several master's students who have contributed to his research program As a core member of the LinkMedia team at IRISA, Dr. Corro collaborates across institutional boundaries on cutting-edge NLP challenges, with particular emphasis on adapting techniques to specialized domains where data constraints present unique challenges.

Sabur ButtView profile

Research Fellow

• Natural Language Processing
• Personality and Emotion Detection
• Low Resource Languages
• +2 more

Sabur Butt serves as a Postdoctoral Researcher at Tecnológico de Monterrey's Institute for the Future of Education (IFE), leading interdisciplinary projects at the intersection of Natural Language Processing and Education to develop AI-driven insights into human behavior. His educational background includes: PhD in Computer Science from Centro de Investigación en Computación, Instituto Politécnico Nacional (IPN) Master of Science in Computing (with honors) from Centro de Investigación en Computación, Instituto Politécnico Nacional (IPN) Bachelor of Science in Computer Science from Forman Christian College Dr. Butt's research centers on Natural Language Processing applications, with core interests in Personality and Emotion Detection, Low Resource Languages (e.g., Urdu), and Question Answering. He develops machine/deep learning models to analyze behavioral patterns in text, particularly focusing on educational contexts and social dynamics. His work bridges computational linguistics with practical solutions for understanding human interactions. Recent publications demonstrate strong thematic consistency in applying NLP to social and educational challenges. Key trends include personality profiling through ambivert scales, hope speech detection in low-resource languages, and AI systems for faculty development. These efforts emphasize real-world impact in education technology, mental health informatics, and inclusive language processing across diverse linguistic contexts. No scientific awards were documented in the source material. Information regarding student advising or research grants was not provided in the available text. Dr. Butt contributes to the Engaging and Motivating Learning Models research group at IFE, collaborating on interdisciplinary projects that advance AI applications in educational settings under the United Nations Sustainable Development Goal for Quality Education (SDG 4).

Maliheh IzadiView profile

Assistant Professor

• Software Engineering
• Machine Learning
• Large Language Models for Code
• +7 more

Maliheh Izadi is a tenure-track assistant professor in the Faculty of Electrical Engineering, Mathematics, and Computer Science at Delft University of Technology (TU Delft), Netherlands. She leads the AISE (AI-enabled Software Engineering) research lab and serves as the scientific manager for the TU Delft/JetBrains Collaboration (AI4SE). She is also a member of the Software Engineering Research Group (SERG) at TU Delft and actively supervises PhD, MSc, and BSc students. Dr. Izadi's research focuses on enhancing software development tools through building smarter software and tailoring machine learning and NLP techniques to source code. Her primary interests include building and tailoring large language models (LLMs) and autonomous agents to source code, with specific focus areas including evaluation, benchmarking, model memorization, IDE integration, in-IDE Human-AI interaction, and extending models' capabilities to low-resource programming languages. Her work bridges the gap between deep learning and source code analysis, with applications in code understanding, generation, documentation, and developer productivity enhancement. Her recent publications reveal a strong trend toward evaluating and improving LLMs for code, with emphasis on safety, usability, and efficiency. She has made significant contributions to benchmark development, harmfulness assessment of LLMs in programming contexts, and improving the integration of AI tools within development environments. Her research consistently addresses real-world industrial challenges while advancing theoretical understanding of model behavior. Google Research Scholar Award (2025) for proposal on Tackling LLM Hallucinations Amazon Research Award (2024) for proposal on Addressing Memorization in Code LLMs ACM SIGSOFT Distinguished Paper Award (2025) for How Much Do Code Language Models Remember? ACM SIGSOFT Distinguished Paper Award (2024) for A Transformer-Based Approach for Smart Invocation of Automatic Code Completion Best Tool Award at SaTML'22 competition for STACC Best Tool Award at NLBSE'22 competition for Catiss Dr. Izadi actively collaborates with industry partners, particularly JetBrains Research, where she leads the AI4SE ICAI lab. She has supervised multiple PhD and master's students and has served on program committees for major software engineering conferences including ASE, ICSE, FSE, and MSR. Her research has been published in top venues such as IEEE/ACM ICSE, FSE, ASE, TOSEM, EMSE, MSR, ICSME, SANER, and JSS. She is also organizing the First International workshop on Autonomous Agents in Software Engineering (AgenticSE) co-located with ASE'25.

Fatemeh Hendijani FardView profile

Assistant Professor

• Code Intelligence
• Transfer Learning
• AI4SE
• +5 more

Fatemeh Hendijani Fard is an Assistant Professor in the Department of Computer Science at the University of British Columbia's Okanagan campus. She serves as a graduate student supervisor and teaches courses in Computer Science and Data Science. Dr. Fard is a member of the CITECH program and MMRI, part of the Killam family of scholars, and an active member of both IEEE and ACM. Her research focuses on the intersection of Natural Language Processing and Software Engineering, with particular emphasis on developing code intelligence models for low-resource programming languages like R. She conducts empirical studies and develops techniques to improve the computational efficiency of code-language models while making them accessible to communities with restricted GPU access. Her work strongly advocates for Diversity and Inclusion in STEM, particularly for underrepresented females. Analysis of Dr. Fard's recent publications reveals a strong research trajectory in adapting Large Language Models for code intelligence with a focus on efficiency and accessibility. Her work spans multiple dimensions including code summarization, method name prediction, code search, code clone detection, and program repair, with special attention to low-resource programming languages. A notable trend is her exploration of adapter-based approaches for knowledge transfer that reduce computational requirements while maintaining performance. Izaak Walton Killam Memorial Scholarship Alberta Innovates Technology Futures (AITF) NSERC Discovery NSERC CREATE Mitacs Accelerate UBC Start-up Fund Dr. Fard has secured significant research funding including NSERC Discovery, NSERC CREATE, Mitacs Accelerate, and UBC Start-up funds to support her work on code intelligence for low-resource programming languages. She actively serves as a graduate student supervisor, guiding research in areas related to code representation learning and mining software repositories. Her service to the academic community is extensive, having served on program committees for major conferences including FSE, MSR, ASE, SANER, and ICSME across multiple years. Dr. Fard leads research initiatives focused on making code intelligence accessible to communities working with understudied programming languages. Her team conducts empirical studies and develops new techniques specifically designed for low-resource languages, with particular attention to the R programming language. This work addresses diversity and inclusion in AI tools by ensuring developers with limited computational resources can benefit from advances in neural networks and automated tools.

• Computational Linguistics
• Dependency Parsing
• Universal Dependencies
• +5 more

Joakim Nivre is a Professor of Computational Linguistics at Uppsala University in the Department of Linguistics and Philology. His research primarily focuses on grammatical analysis, studying how to analyze typologically different languages in a uniform way within the Universal Dependencies project, and developing better computational models for automatic analysis within this framework. His teaching is associated with the international Master's Program in Language Technology. His research interests include: Computational Linguistics and Natural Language Processing Dependency Parsing and Universal Dependencies Cross-linguistic Analysis and Multilingual NLP Syntax Analysis and Grammatical Frameworks Language Resource Development Nivre's publication record demonstrates significant contributions to dependency parsing, treebank development, and cross-linguistic analysis. His recent work (2023-2024) shows expansion into evaluating generative language models, resource-efficient NLP techniques, climate change impact analysis, causality detection in governmental documents, and historical text processing. His research bridges theoretical linguistics with practical computational applications, with particular emphasis on creating frameworks that work across diverse languages. His work on the Universal Dependencies framework has been particularly influential, establishing a standard resource that enables consistent syntactic annotation across numerous languages, significantly impacting the field of multilingual natural language processing.

• Natural Language Processing
• Computational Linguistics
• Syntax Analysis
• +4 more

Vojtěch Kovář is a researcher at Masaryk University with dual appointments at the Institute of the Czech Language (Faculty of Arts) and the Center for Natural Language Processing (Faculty of Informatics). He has been affiliated with the university since 2006, holding positions as researcher and lecturer while also working as a software analyst at Lexical Computing CZ since 2012. His research focuses on automatic syntactic analysis of natural languages , particularly Czech and related languages, evaluation of syntactic analysis quality , integration of syntactic tools into downstream applications , and corpus linguistics . His work bridges theoretical linguistics with practical language technology applications. Recent publications reveal a strong trend toward dictionary creation methodologies , lexicographic infrastructure development , and corpus-based language resource compilation . His research increasingly addresses rapid dictionary development for both European and Asian languages, as well as bilingual resource creation in response to emerging language needs. PACLIC 2010 best student paper award Member of eLex conference program committee (2017-present) Kovář supervises bachelor's and master's theses at both the Faculty of Arts and Faculty of Informatics, with recent topics including semi-automatic dictionary creation, web-based proofreading systems, and automatic punctuation correction. He teaches multiple courses including Fundamentals of Mathematics and Statistics for Humanities, Algorithmic Description of Syntax, and Programming Basics for Humanities. His research is supported by projects including LINDAT/CLARIAH-CZ (Digital Research Infrastructure for Language Technologies) and the Web-based spelling, grammar and typographical corrector for Czech language initiative.

• Natural Language Processing
• Computational Linguistics
• Dependency Parsing
• +3 more

Şaziye Betül Özateş is an Assistant Professor at Boğaziçi University's Institute for Data Science and Artificial Intelligence, specializing in NLP and machine learning. She develops resources and models for Turkish and low-resource languages, including the BOUN Treebank and dependency parsers. Research focuses on dependency parsing for agglutinative languages, computational linguistics, and deep learning applications, with emphasis on Turkish morphology, code-switching parsing, and historical language processing. Recent projects address Ottoman Turkish NLP and calligraphy analysis. Publications demonstrate strong focus on Turkish language resources (40%), parsing algorithms (30%), historical text processing (20%), and code-switching NLP (10%). Recent works show increasing attention to Ottoman Turkish and Arabic script processing, with methods leveraging multilingual BERT and semi-supervised learning. Developed key resources: BOUN Treebank (Turkish syntactic corpus), PUD Treebank (multilingual parallel corpus), BOUN-Pars (LSTM-based Turkish parser), and semi-supervised parsers for code-switched languages. Supervises projects on neural network applications in NLP.

Sami AzamView profile

Senior Lecturer

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +5 more

Dr. Sami Azam is a Senior Lecturer and Undergraduate Course Coordinator at Charles Darwin University's Faculty of Science and Technology. He holds a PhD in Biomedical Engineering, focusing on binaural processing in the human brain. His research spans machine learning, artificial intelligence, deep learning, advanced signal processing, and medical image analysis, with applications in biosignal classification (e.g., EEG, ECG) and cybersecurity for IoT devices. Dr. Azam is a key member of the Biomedical Engineering and Health Informatics research group and actively engages in grant writing, conference presentations, and professional organizations such as IEEE and ACS. His work emphasizes interdisciplinary projects, including automated disease diagnosis (e.g., skin cancer, retinal diseases), smart grid security, and privacy-preserving data analysis. He has secured numerous grants and developed frameworks like PBDINEHR for secure electronic health records. Dr. Azam’s contributions bridge biomedical engineering and computational methods, addressing global health challenges through innovative AI applications.

Günter NeumannView profile

Professor

• Computational Linguistics
• Artificial Intelligence
• Deep Learning
• +5 more

Günter Neumann is a Professor of Computational Linguistics at Saarland University and a Research Fellow at the German Research Center for Artificial Intelligence (DFKI). His work spans computational linguistics, artificial intelligence, and biomedical informatics, with a focus on information extraction, question answering systems, and knowledge graph reasoning. Education: PhD in Computer Science (1994) and Computational Linguistics (2004), both from Saarland University. Neumann's research interests include multilingual natural language processing, low-resource language modeling, and neural architectures for information retrieval and biomedical relation extraction. He pioneered techniques in dense passage retrieval for Urdu, feature textualization for BERT interpretability, and low-rank temporal knowledge graph embeddings. Recent publications focus on cross-lingual transfer learning, code-mixed clinical text de-identification, and robust biomedical benchmarks. He has led EU/national projects in language technology and served on program committees for ACL, EMNLP, and LREC. His work with the EXCITEMENT Open Platform and DOMLIN system demonstrates expertise in textual entailment and fact verification, achieving top rankings in competitions like CLEF and TAC.

Georg RehmView profile

Adjunct Professor

• Language Technology
• Computational Linguistics
• Artificial Intelligence
• +4 more

Georg Rehm holds an Adjunct Professorship in Computational Linguistics and Language Technology at Humboldt-Universität zu Berlin's Faculty of Philosophy. He is a Principal Researcher and Research Fellow at DFKI Berlin's Speech and Language Technology Lab, and Head of the W3C Germany/Austria Chapter. His roles include coordinating major EU initiatives like the European Language Grid (ELG) and QURATOR. Rehm's work spans Language Technology, AI, and multilingual systems, with a focus on digital language equality, curation technologies, and open science infrastructure. He leads projects such as HIVEMIND (2025-2027) and Language Data Space (2023-2025), and has contributed over 275 publications in computational linguistics and related fields. His research addresses challenges in multilingual NLP, fake news detection, and ethical AI deployment. Rehm oversees strategic initiatives like the META-NET network and chairs conferences such as ACL Industry Track and Sci-K workshops. He advises on EU policy frameworks for language technology and collaborates with organizations like the DIN Presidential Committee and FOCUS.ICT. His academic network spans global institutions, and he actively participates in program committees for top venues including ACL, NAACL, and COLING. Rehm's lab develops tools like the Knowledge Storage Ecosystem and contributes to open-source LLM projects like Occiglot and OpenGPT-X. He holds a PhD and has over 25 years of industry-research experience. His research interests include semantic storytelling, interoperable metadata, and AI ethics. He is affiliated with multiple institutes, including the DFKI Lab Berlin and the W3C, and serves on editorial boards for journals like Language Resources and Evaluation.

• Natural Language Processing (NLP)
• Deep Learning
• Data Science
• +3 more

Bang LIU is an Associate Professor at the University of Montreal's Faculty of Arts and Science, specifically within the Department of Computer Science and Operations Research. His research focuses on Natural Language Processing (NLP), Deep Learning, and Data Science with applications in healthcare, materials science, and embodied AI systems. He leads multiple research projects funded by organizations like NSERC and MITACS, exploring causal inference, multimodal reasoning, and AI-driven material discovery. Education: PhD in Computer Science (University of Montreal). Research Highlights : Developing causal discovery frameworks (e.g., CausalNET, OCDB) Pioneering materials science applications via AI agents (MatExpert, Honeycomb) Advancing embodied instruction following systems (OPEx) Grants & Projects : NSERC Discovery Grants (2021-2027) MITACS Accelerate Projects (2022-2025) focusing on healthcare LLM trust, CAD generation, and procedural knowledge extraction He supervises Master's students in low-resource NLP tasks and serves as a lead researcher in over 15 active projects. Affiliated with the CRM and RALI labs, his work bridges theoretical AI advancements with practical industrial applications.

Sailaja RajanalaView profile

Lecturer

• Machine Learning
• Computer Vision
• Cryptography
• +4 more

Sailaja Rajanala is a Lecturer at the Malaysian School of Information Technology, Monash University. She specializes in interdisciplinary research at the intersection of machine learning, computer vision, and cryptography. Her work focuses on areas such as adversarial machine learning, generative models, and ethical AI applications. Rajanala is a Chief Investigator in the Æinstein project (2024–2026), exploring adversarial AI in materials discovery domains. Her research interests include facial expression analysis, bias detection in AI systems, and cryptographic applications of neural networks. Notable contributions include techniques for distinguishing synthesized music from human performances and causal analysis of micro-expression recognition systems. She actively publishes in top-tier conferences like IEEE ICASSP, GLOBECOM, and APSIPA ASC. Rajanala collaborates internationally, with projects addressing topics like unbiased generative editing (GENIE), medical image denoising, and legal text classification using BERT models. Her publications reflect a strong emphasis on practical AI solutions with ethical and security considerations.