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Shigehiko SchamoniView profile

Lecturer

• Natural Language Processing
• Medical Data Analysis
• Speech Translation
• +3 more

Shigehiko Schamoni is a Lecturer and Compute Lab Manager at Heidelberg University's Institute of Computer Engineering (ZITI), where he oversees scientific computing infrastructure and teaches computer science courses. He is completing his PhD under Prof. Stefan Riezler in the Statistical NLP group. His dual roles bridge technical management and academic instruction, with teaching responsibilities spanning undergraduate and graduate courses since 2011. Research Focus: Schamoni's work intersects clinical AI and natural language processing, with emphasis on: Machine learning for medical applications (sepsis prediction, clinical validity) Speech translation and automatic speech recognition Cross-lingual information retrieval Data augmentation techniques Multimodal machine learning His 15 most recent publications (2016-2024) demonstrate strong thematic clustering: 47% focus on medical AI (primarily sepsis prediction and clinical data validation), while 53% address NLP challenges (speech translation, ASR, and multimodal systems). This bifurcation reflects consistent collaboration with medical researchers alongside core NLP innovation. Teaching Experience includes instruction across 10+ courses since 2011, such as: Graduate courses: "Tools – Werkzeuge für effizientes wissenschaftliches Arbeiten" (2023-2024) Undergraduate courses: "Einführung in die Nutzung computerlinguistischer Ressourcen" (2021-2022) Programming courses: "Advanced Programming" and "Parallel Programming Paradigms" (2012-2015) He maintains affiliations with both the ZITI infrastructure team and Statistical NLP research group.

• Music Information Retrieval
• Machine Learning
• Evaluation of Computer Experiments
• +4 more

Arthur Flexer is a Post-Doctoral Researcher at the Institute of Computational Perception, Johannes Kepler University Linz (JKU). His work bridges music informatics, machine learning, and computational humanities. He leads projects like 'Pop Music Culture' and 'Spirits in Complexity' exploring AI in cultural contexts. Flexer holds a PhD and has advised multiple students, including Katharina Hoedt (PhD) and Selene Ximena Andrade Lopez (Master's). Research focuses on music similarity, adversarial attacks in recommendation systems, and ethical AI applications in art curation. He has organized major conferences like ISMIR 2019 and serves on editorial boards for Transactions of the International Society for Music Information Retrieval. His collaborations span museums like the Belvedere and institutions in Stockholm, Vienna, and Barcelona. Key projects include 'Dust and Data' (AI-driven museum curation), protein sequence analysis with the University of Vienna, and EU-funded initiatives on music perception. Flexer's work emphasizes validity in experiments, hubness reduction in high-dimensional data, and interdisciplinary approaches to music technology.

Maryam TavakolView profile

Assistant Professor

• Uncertainty in Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +3 more

Maryam Tavakol is an Assistant Professor at Eindhoven University of Technology, affiliated with the Uncertainty in AI group within the Mathematics and Computer Science department. Her expertise bridges artificial intelligence, machine learning, and computational modeling for sequential systems. PhD in Machine Learning, TU Darmstadt B.Sc. & M.Sc. in Computer Science, University of Tehran Her research focuses on uncertainty estimation , reinforcement learning , and transformer architectures applied to domains like drug-target interaction prediction, soccer analytics, and music generation. Recent work explores activation sparsity in LLMs and robust VAE frameworks. Key trends in her publications (2012–2024) span reinforcement learning , neural network architectures , and applications in healthcare and creative domains . Notable themes include contextual bandits, variational inference, and energy-efficient computing. Contributed to UN Sustainable Development Goals (Education/Academic Qualification) She has supervised multiple research projects and served as corresponding author in peer-reviewed conferences. Collaborations include TU Dortmund, Leuphana University, and industry internships at Criteo.

Andrew FanoView profile

Clinical Professor

• Artificial Intelligence
• Machine Learning
• Applied Artificial Intelligence
• +7 more

Andrew Fano is a Clinical Professor of Computer Science at Northwestern University and serves as the McCormick Director of the Kellogg-McCormick MBAi Program. He previously spent 25 years at Accenture Labs as Global Managing Director for AI Research, leading global teams in applied AI projects across industries like healthcare, retail, and pharma. His research focuses on combining emerging AI technologies with human capabilities to solve industry-specific challenges, with notable contributions in causality detection, quantum computing, and responsible AI deployment. Fano is a top patent holder at Accenture, emphasizing practical AI applications and ethics. He also led the Accenture Labs University program, fostering academic-industry collaborations. Education: PhD in Computer Science, Northwestern University AB in Cognitive Science, Vassar College His research interests span causal reasoning in NLP, AI ethics, and cross-industry AI integration. Recent work includes analyzing diabetes-related social media for causal insights and developing quantum optimization systems. He prioritizes real-world applicability, ensuring technologies address practical business and societal needs responsibly. Awards: None explicitly listed, though recognized as a top patent holder at Accenture. Advising & Grants: Advised on multiple AI projects across industries, coordinated research sponsorships with 20+ universities through Accenture Labs. At Northwestern, leads the MBAi Program to bridge academic and corporate AI expertise. Labs/Teams: Oversees the MBAi Program’s interdisciplinary initiatives and previously managed global AI research teams at Accenture Labs, collaborating with partners in the US, Ireland, India, China, and France.

• Machine Learning
• Natural Language Processing
• Speech Enhancement
• +3 more

Dr. Peter Ochieng is a Research Fellow at the Department of Computer Science and Technology, University of Cambridge. His work focuses on machine learning, natural language processing, and speech enhancement, with notable contributions to deep neural networks and ontology matching. He holds a position as a Fellow within the University's academic community. His research interests include advanced techniques for speech separation, multilingual language models, and semantic web technologies. Key areas of exploration involve contrastive learning, modular deep networks, and large-scale data integration. Dr. Ochieng’s articles highlight trends in AI-driven solutions for speech processing, ontology alignment, and conversational AI systems. He has contributed to open-access models like Bloom and developed tools for natural language to SPARQL translation. Contact: po304@cam.ac.uk .

Tong XiaView profile

Research Fellow

• Machine Learning
• Artificial Intelligence
• Public Health
• +7 more

Tong Xia is a postdoctoral research associate at the Department of Computer Science and Technology, University of Cambridge , where he completed his PhD under Prof. Cecilia Mascolo . He will join the Vanke School of Public Health at Tsinghua University as an Assistant Professor in Fall 2025. His research focuses on human-centric data from mobile and wearable devices, with applications in public health , behavioral understanding, and urban management. Technological specializations include: Data-efficient machine learning (self-supervised learning, foundation models) Trustworthy deep learning (uncertainty quantification, federated learning) Statistical methods (clustering analysis, causal inference) His work explores ubiquitous sensing technologies and AI tools for affordable health screening, including projects like OPERA system for respiratory acoustic foundation models and RespLLM for multimodal health prediction. Research has been supported by ERC EAR and EPSRC RELOAD grants. Scientific awards include: Rising Star in Women in Engineering, Asian Dean’s Forum (2024) Chinese Government Award for Outstanding Self-financed Students Abroad (2023) Best Postgraduate Poster, Oxbridge Women in Computer Science Conference (2022) Better Future Award, Cambridge Hall of Fame (2022) As a mentor, he supervised: Yuwei Evelyn Zhang (MPhil, Uncertainty quantification in federated learning , 2022) Alex Wang (Part II, Bayesian model ensemble uncertainty , 2022) He co-organized the Mobile and Wearable Health Seminar Series (2023-2024) and serves as an associate editor for Frontiers in Digital Health (IF 3.2). Non-academic roles include Vice Secretary-General of UK Tsinghua Alumni Association (2023-present).

• Natural Language Processing
• Cyberbullying Detection
• Semantic Web
• +3 more

Dr. Maral Dadvar is a Research Fellow at Stuttgart Media University, specializing in Natural Language Processing (NLP) and its applications to social challenges. She holds a PhD in Computer Science from the University of Twente, Netherlands, where her doctoral work focused on cyberbullying detection in social networks. Her current research integrates NLP with identity resolution techniques for the FID Judaica project, aiming to enhance data integration in Jewish cultural heritage domains. Key areas of expertise include cyberbullying detection algorithms, domain-specific knowledge bases (e.g., JudaicaLink), and ethical AI applications. Her work bridges technical innovations with societal needs, addressing issues like online safety and cultural heritage digitization. Dadvar collaborates internationally with institutions like LIBER and the European Research Libraries network. Publications span interdisciplinary topics such as conservation conflict analysis using NLP, linked open data standards, and multilingual entity disambiguation. She actively mentors students pursuing Bachelor’s and Master’s theses in these domains. Her research emphasizes practical solutions for real-world challenges, combining computational methods with human expertise.

Juan Diego Gutiérrez GallardoView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +3 more

Juan Diego Gutiérrez Gallardo is an Assistant Professor at the Universidade de Santiago de Compostela, specializing in applying Artificial Intelligence to diverse domains. His research emphasizes optimizing low-cost adaptations of base models for specialized tasks, particularly in medical imaging and educational technology. He holds a Doctorate in Modeling and Experimentation from the Universidad de Extremadura (2022), following degrees in Computer Engineering and a Master’s in Secondary Education. Education: BSc in Computer Engineering (2010), MSc in Computer Engineering (2013), Master’s in Education (2016), PhD in Modeling and Experimentation (2022). Research Interests: AI-driven medical imaging, machine learning optimization, and leveraging AI in educational evaluation. His work focuses on reducing computational costs while enhancing model adaptability. His recent articles explore SAM-based medical image segmentation, audio classification via EnCodec latent spaces, and the impact of ChatGPT on engineering education. He has authored over 20 computer science books and translated 10 technical works from English to Spanish. Awards include the JENUI 2023 Best Work Award for impactful research. Teaching responsibilities include 'Technological Tools for Business' and 'Digital Technologies for Cultural Management.' He actively contributes to reproducibility frameworks in data science and hybrid recommendation systems for media.

Constantine LignosView profile

Assistant Professor

• Computational Linguistics
• Natural Language Processing
• Low-resource Language Technology
• +3 more

Constantine Lignos is an Assistant Professor of Computational Linguistics at Brandeis University, affiliated with the Michtom School of Computer Science and the Computational Linguistics Program. His research focuses on broadening human language technology for understudied and low-resource languages. He directs the Broadening Linguistic Technologies Lab and has held postdoctoral and industry roles at The Children's Hospital of Philadelphia, BBN Technologies, and USC Information Sciences Institute. Education : Ph.D. in Computer Science, University of Pennsylvania (2013) M.S. in Computer Science, University of Pennsylvania B.A. in Computer Science, Yale University Research Interests : Low-resource language technology Machine translation and multilingual NLP Named entity recognition and entity linking Corpus creation (e.g., LR-Sum, ParaNames) Ethical considerations in NLP resource development Key Contributions : Developed SeqScore for reproducible NER evaluation Coded the QueryNER e-commerce query segmentation dataset Created the multilingual MOT corpus Awards : 2021: Honorable mention for Best Paper Award (Eval4NLP Workshop) Labs & Teams : Broadening Linguistic Technologies Lab at Brandeis

Xi TongView profile

Research Fellow

• Relativity and Gravitation
• Machine Learning Applications
• Theoretical Physics
• +3 more

Dr. Xi Tong is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) within the University of Cambridge's Faculty of Mathematics. His research focuses on two primary areas: Relativity and Gravitation, with contributions to black hole dynamics and cosmological observables, and Machine Learning Applications, particularly in healthcare diagnostics and urban infrastructure analysis. He is affiliated with the Relativity and Gravitation research group at DAMTP. Dr. Tong's work bridges theoretical physics and applied machine learning. In theoretical physics, he explores phenomena like black hole superradiance termination and cosmological parameter mapping. In machine learning, he develops uncertainty-aware models for healthcare diagnostics, federated learning frameworks for imbalanced data, and geospatial analysis tools for urban infrastructure. HIGHLIGHTED RESEARCH THEMES: Cosmological simulations and gravitational wave dynamics Audio-based disease detection (e.g., respiratory and cardiac health) Federated learning for privacy-preserving healthcare analytics Urban mobility modeling through spatiotemporal data PUBLICATIONS HIGHLIGHTS: Pioneering work on black hole superradiance modulation (2024-2025) Development of RespLLM for respiratory health prediction (2024) FLea framework for federated learning optimization (2024) GUI dataset for global urban infrastructure analysis (2024) LAB AFFILIATION: Active contributor to DAMTP's Relativity and Gravitation group, collaborating on gravitational physics and astrophysical simulations.

• Artificial Intelligence
• Machine Learning
• Information Retrieval
• +3 more

Zalán Bodó is an Associate Professor at the Faculty of Mathematics and Computer Science , Babeș-Bolyai University, Cluj-Napoca, Romania. His research focuses on Artificial Intelligence, Machine Learning, Information Retrieval, Natural Language Processing, Formal Languages, and Computer Vision . He holds an Erdős number of 4 and maintains an active presence in academic networks like Google Scholar and DBLP. His work bridges theoretical and applied computer science, with contributions to malware analysis, explainable AI, and dataset compilation. He has published extensively on topics such as graph-based malware detection, automated code generation, and medical imaging applications. His office is located at Mathematicum Building (room 207), and he can be reached via zalan.bodo@ubbcluj.ro or zalan.bodo@gmail.com .

Bob LawlorView profile

Lecturer

• Digital Signal Processing
• Speech Processing
• Audio Signal Processing
• +5 more

Bob Lawlor is a Lecturer in the Department of Electronic Engineering at Maynooth University (Faculty of Science & Engineering). His career spans academic research and industry experience at Sony Corp in the UK and Japan. Research interests include digital signal processing , speech and audio processing , and problem-based learning (PBL) in education. Key Research Areas Digital Signal Processing Speech and Audio Signal Analysis Audio Time-Scale Modification Computer-Assisted Language Learning Engineering Pedagogy Notable Awards 2012 NUI Maynooth Teaching Fellowship 1994 International Design Competition 1993 IEEE Chester Sall Award Leadership Co-chair, national Enquiry and Problem-Based Learning Network (Facilitate)

• Artificial Intelligence
• Natural Language Processing
• Deep Learning
• +3 more

Reza Haffari is a Professor in the Department of Data Science & AI at Monash University, within the Faculty of Information Technology. His research focuses on Artificial Intelligence, particularly in low-level perception and high-level reasoning under uncertainty from text and other modalities like vision and speech. Key areas include natural language understanding, semi-supervised learning, and model explainability for critical applications such as healthcare. Education: Doctor of Philosophy in Computer Science, Simon Fraser University (2010) Master of Science in Artificial Intelligence, Sharif University of Technology (2002) Bachelor of Science in Software Engineering, Sharif University of Technology (2000) Teaching Commitments: Chief Examiner for courses: FIT1045, FIT1053, FIT2004, FIT5201 Lecturer for units: FIT1045, FIT2004, FIT3080, FIT4005/FIT5185, FIT4009, FIT5201 Research Projects (2023–2027): Principal Investigator for Aligning Large Language Models with Human Intention Chief Investigator in Trustworthy Generative AI , Hierarchical Abstractions and Reasoning for Neuro-Symbolic Systems , and CSIRO Mental Health AI Research Trends in Articles: Recent work emphasizes continual learning , multimodal models , and ethical AI alignment . Key themes include adapting models to dynamic environments, ensuring safety in large language systems, and integrating causal reasoning with machine learning. Labs & Teams: Active in the Data Science & AI Group at Monash, collaborating on projects involving generative AI, neuro-symbolic systems, and healthcare applications.

• Information Retrieval
• Web Search
• Machine Learning
• +4 more

Prof. Jian-Yun Nie is a Full Professor at the Université de Montréal's Faculté des arts et des sciences , within the Département d'informatique et de recherche opérationnelle . He holds a Canada Research Chair in Natural Language Information Processing and Applications. His research focuses on advancing information retrieval and web search technologies, leveraging machine learning, natural language processing, and multilingual systems. He has led numerous projects funded by grants from organizations like the CRSNG and MITACS. Key research interests include improving search algorithms through contextual understanding, diversifying query expansion using user logs and knowledge bases, and applying retrieval techniques to healthcare, e-commerce, and social media analysis. His work emphasizes practical applications, such as health goal-oriented recommendations and cross-lingual information retrieval. Grants & Projects: Building next-generation information access systems (2025–2031) Canada Research Chair (2023–2030) Computing servers for NLP applications (2022–2026) Labs & Teams: Member of RALI (Recherche appliquée en linguistique informatique) and the Laboratory for Artificial Intelligence in Cybersecurity (LIA). Collaborates on initiatives like Axel (social companion for seniors) and Myelin (AI for autism). Awards: Canada Research Chair in Natural Language Information Processing and Applications.

• Data Science
• Fact Checking
• Generative AI
• +4 more

Dr. Lihu Chen is a Research Fellow in the Department of Computing at Imperial College London's Faculty of Engineering, specializing in fact checking, data science, and large language models. His work bridges generative AI, knowledge graphs, and biomedical informatics, focusing on model interpretability and robustness. He contributes to projects like Pub-Guard-LLM for detecting fraudulent biomedical articles and YAGO 4.5, a knowledge base with advanced taxonomy. His research also explores query-relevant neuron identification in LLMs and efficient entity disambiguation systems. Key research interests include uncertainty estimation in AI systems, lightweight neural architectures, and applications in healthcare. He co-organized the GenAIK workshop on generative AI and knowledge graphs, reflecting his interdisciplinary approach to advancing AI's societal impact.