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Diego Molla-AliodView profile

Senior Lecturer

• Natural Language Processing
• Question Answering Systems
• Medical Text Processing
• +4 more

Diego Molla-Aliod is a Senior Lecturer in the School of Computing at Macquarie University, affiliated with the Data Horizons Research Centre and Frontier AI Research Centre. He holds a PhD in Linguistics from the University of Edinburgh and has held academic positions at Macquarie since 2005. His research focuses on Natural Language Processing (NLP), particularly in medical text processing, question answering systems, and applications of Large Language Models (LLMs). He leads projects like AnswerFinder, a question-answering system participating in TREC evaluations, and develops tools for clinical evidence summarization. Notable contributions include pioneering work in logical-form-based answer extraction and applying Bayesian methods to medical text analysis. Education: PhD in Linguistics (University of Edinburgh), MSc in Speech and Language Processing (Edinburgh), 5-year Computer Science degree (Universitat Politècnica de València). Research spans medical NLP, conversational agents, and social media analysis for mental health. Recent work explores causal inference in text and LLM-driven clinical evaluation. He has supervised over a dozen PhD/Master’s students and secured grants totaling AUD $5M+. Recipient of the 2023 Faculty of Science and Engineering Award for Inter-School Collaboration. Collaborations include projects at IIT Patna (India) and institutions in Zurich, Spain, and the U.S. His lab work involves agentic conversational agent design and evidence-based summarization systems.

Thomas RoellekeView profile

Senior Lecturer

• Information Retrieval
• Probability Theory
• Database Systems
• +5 more

Dr. Thomas Roelleke is a Senior Lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science. His primary research focuses on information retrieval (IR), probability theory, and the integration of database systems with IR and machine learning. He specializes in probabilistic models, structured document retrieval, and knowledge-oriented systems. Research Interests Information Retrieval (IR) and probability theory Structured, semantic, and knowledge-oriented retrieval Integration of databases, IR, and AI Probabilistic modeling of uncertainty in data Generalizations of ranking functions and probabilistic reasoning Teaching Teaches Database Systems at the undergraduate level, covering database management systems, object-relational implementations, and future trends like data mining. Publications Active in publishing foundational work in IR, including Information Retrieval Models: Foundations and Relationships (2013) and recent contributions on investigative IR and structured document retrieval (2023–2024). His work often bridges theoretical advancements with practical database and AI applications. Affiliations Member of the Centre for Multimodal AI at Queen Mary Past contributions to projects like the HySpirit retrieval platform

Xu ChenView profile

Researcher

• Computer Vision
• Deep Learning
• Medical Image Computing
• +3 more

Dr Xu Chen is a Postdoctoral Research Associate in Cardiac AI/Digital Twins at the Digital Environment Research Institute (DERI) of Queen Mary University of London. He holds a visiting scholar role at the Department of Medicine, University of Cambridge. His research focuses on computer vision, deep learning, and medical image computing, particularly in multi-modality cardiac imaging. With over 450 citations, his work bridges AI advancements with clinical applications such as coronary artery disease diagnostics via OCT scans and myocardial infarction detection in MRI. Dr Chen’s academic journey includes a PhD in AI for Medical Image Analysis from the University of Liverpool (2022), an MSc in AI from the University of Manchester (2017), and a BSc in Computer Science from the University of Electronic Science and Technology of China (2015). His industry experience includes contributions to AIMES, collaborating with UCL cardiologists on AI-driven medical imaging solutions. His 2024-2025 research outputs emphasize video-based AI innovations, such as benchmarking video language models (V-STaR) and developing novel segmentation techniques (INT). These advancements reflect his dual focus on foundational AI research and clinical translation.

Wenwu WangView profile

Professor

• Signal Processing
• Machine Learning
• Artificial Intelligence
• +4 more

Wenwu Wang is a Professor of Signal Processing and Machine Learning at the University of Surrey's School of Computer Science and Electronic Engineering, and serves as Associate Head (External Engagement). He is Co-Director of the Machine Audition Lab within CVSSP and an AI Fellow at Surrey's Institute for People Centred Artificial Intelligence. Education: Holds a BSc, MSc, and PhD (specific disciplines not specified). Research focuses on unsupervised learning techniques , computational auditory scene analysis , and multimodal machine learning . Notable projects include audio captioning systems, text-to-audio generation (e.g., AudioLDM), and anomalous sound detection frameworks. Grants: £30M+ total portfolio (as PI: £2.2M; as CI: £14M+). Funders include EPSRC, MoD, EU, and industry partners like BBC/Huawei. Key Projects: AudioTurbo (fast text-to-audio), SemantiCodec (ultra-low bitrate audio), DCASE Challenge contributions. Lab Leadership: Machine Audition Lab explores intersections of audio perception and AI. Recent work emphasizes large audio models , cross-modal generation, and real-world applications in aquaculture monitoring and urban soundscapes.

Leon BergenView profile

Associate Professor

• Computational Linguistics
• Pragmatics
• Machine Learning
• +3 more

Leon Bergen is an Associate Professor of Linguistics at the University of California, San Diego, with a courtesy appointment in Computer Science. His research focuses on computational linguistics, pragmatics, and machine learning, bridging cognitive science and AI. He explores topics such as language processing, contextual reasoning, and the application of LLMs to scientific challenges. His work spans algorithmic tasks in NLP, biomedical informatics, and climate science, with notable contributions to benchmarks like EvidenceBench and ClimaQA. He also investigates neural mechanisms underlying language comprehension, including brain region activity in sublexical processing. Bergen’s recent articles highlight trends in data-centric AI for climate modeling, bias detection in biomedical reports, and the cognitive foundations of pragmatic reasoning. His interdisciplinary approach addresses foundational questions in linguistics while advancing practical tools for scientific problem-solving. He is affiliated with the UC San Diego Linguistics Department and maintains an active research lab, though specific grants or advisees are not listed here. His contact information includes lbergen@ucsd.edu and a profile on the department website.

Ana OzakiView profile

Associate Professor

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Learning Theory
• +7 more

Ana Ozaki is an Associate Professor at the Department of Informatics, University of Bergen, Norway. Her research focuses on the intersection of Artificial Intelligence (AI), knowledge representation and reasoning, and learning theory. She explores formal methods to analyze learnability, complexity, and reducibility in logical frameworks like description logics. Ozaki leads projects on ontology learning from neural networks, knowledge graph embeddings, and theoretical guarantees for machine learning systems. She has organized international conferences like AIB 2022 and DL 2024, and serves on editorial boards for Journal of Machine Learning Research and Journal of Web Semantics . Ozaki supervises PhD and master’s students in topics ranging from query-based learning to traffic prediction with graph neural networks. Her work emphasizes bridging theoretical foundations with practical AI applications, including ethics in autonomous systems and formal verification of neural networks.

Damiano SpinaView profile

Senior Lecturer

• Information Retrieval
• Text Analytics
• Interactive Information Retrieval (IIR)
• +7 more

Dr. Damiano Spina is a Senior Lecturer at RMIT University's School of Computing Technologies, specializing in Information Retrieval (IR), Text Analytics, and Human-AI Interaction. He leads research on fairness-aware evaluation, interactive search systems, and conversational AI. His work includes the Walert chatbot, EXIST projects on sexism detection, and contributions to the Australian Internet Observatory. Research interests span IR evaluation, voice-enabled assistants, and ethics in AI. Awards include an ARC DECRA (2020-2023) and RMIT's Research Impact Award (2021). He supervises PhD/Master’s students on topics like neurophysiological approaches to IR and misinformation detection. Teaching includes Capoeira and Samba, reflecting his commitment to cultural engagement. Key affiliations include the ARC Centre of Excellence for Automated Decision-Making and Society (ADM+S) and the International Panel on the Information Environment (IPIE).

John H. DrakeView profile

Associate Professor

• Metaheuristics
• Evolutionary Computation
• Operational Research
• +3 more

Dr. John H. Drake is an Associate Professor in Computer Science at the University of Leicester, affiliated with the School of Computing and Mathematical Sciences. His research focuses on metaheuristic and evolutionary computation methods, particularly in solving real-world optimization problems such as scheduling, routing, and combinatorial optimization. He has contributed to advancements in hyper-heuristics, memetic algorithms, and selection mechanisms within evolutionary computation frameworks. Dr. Drake's work spans applications in workforce scheduling, vehicle routing, and network design optimization. His research emphasizes cross-domain applicability of heuristic methods and the integration of machine learning techniques like reinforcement learning and neural networks into optimization processes. He has collaborated extensively with researchers in operational research and computer science, publishing widely in top-tier journals and conferences. Key areas of contribution include algorithm design, optimization under uncertainty, and the development of adaptive search strategies. His publications highlight innovations in both theoretical foundations and practical implementations of computational intelligence methods.

Vladimir ZadorozhnyView profile

Professor

• Networked information systems
• Complex adaptive systems
• Heterogeneous data fusion
• +7 more

Professor Vladimir Zadorozhny is a faculty member at the University of Pittsburgh's School of Computing and Information, holding a PhD from the Institute for Problems of Informatics, Russian Academy of Sciences. His roles include Adjunct Professor at the University of Agder, Norway, and Core Faculty in Pitt's Biomedical Informatics Training Program. He leads projects in data science, medical informatics, and social inequality analysis, funded by NSF, NIH, and others. His research focuses on networked information systems, data fusion for medical and societal challenges, and scalable architectures. Education: PhD, Russian Academy of Sciences (Moscow) Research emphasizes historical data integration (e.g., CHIA project), medical decision-making (PREDICT initiative), and causal reasoning systems (CaReLearner). Awards include the Fulbright Scholarship and Leiv Eiriksson Fellowship. His work spans 100+ peer-reviewed articles, with recent contributions in Tsetlin Machine applications, fake news detection, and IoT credibility frameworks. Labs include the Center for Artificial Intelligence Research (CAIR) and ADMT Lab. Grants include NIH-funded cardiac arrest recovery prediction and NSF-funded horizontal inequality analysis. Collaborations span biomedical, engineering, and social science domains.

• Deep Learning
• Speech Analysis
• Image and Video Analysis
• +6 more

Ali Shariq Imran is an Associate Professor at the Department of Computer Science, Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. He holds a Ph.D. from the University of Oslo and a Master's from NUST, Pakistan. His research focuses on deep learning applications in speech analysis, image/video processing, semantic web technologies, and eLearning. He has published extensively in journals like IEEE Access, Engineering Applications of Artificial Intelligence, and Frontiers in Computational Neuroscience, with notable work on hate speech detection, biomedical signal analysis, and sentiment analysis in social media contexts. His awards include the HEC and UIUC scholarships and a 2015 Best Paper Award at HCI Intl. He serves on the board of the HCI International Conference and is an IEEE member. Teaching includes courses on coding/compression, software engineering, and web development. His competencies span AI, digital signal processing, and multimedia systems. Research interests include contextual understanding, OCR, and object identification in multimedia, alongside eLearning innovations like MOOCs and LMS integration. He has co-edited special issues on healthcare systems and software standards in journals like JMIHI and IEEE Access. His work frequently employs explainable AI (XAI) to enhance transparency in medical diagnostics and social media analysis.

Jeffrey A. BilmesView profile

Professor

• Machine Learning
• Optimization
• Graphical Models
• +6 more

Jeffrey A. Bilmes is a Professor in the Department of Electrical and Computer Engineering at the University of Washington, Seattle, with adjunct roles in Computer Science & Engineering and Linguistics. He founded the MELODI Lab, focusing on machine learning, optimization, and data interpretation. Bilmes holds a Ph.D. from UC Berkeley and a Master's from MIT. His research spans graphical models, speech recognition, bioinformatics, and submodular optimization, with notable contributions like the GMTK toolkit and pioneering work in submodularity. He has received prestigious awards including the NSF Career Award (2001), NAE Gilbreth Lectureship (2008), and best paper awards at ICML/NIPS (2013). Bilmes has held leadership roles in UAI and NeurIPS conferences, and his work bridges theoretical foundations with practical applications in computational systems and human-computer interaction. Education: Ph.D., Computer Science, UC Berkeley; M.S., MIT. Research Interests: Machine learning, temporal graphical models, submodularity, speech interfaces, and algorithmic optimization. His work on submodular functions has been recognized with multiple awards, including the 25-year ICS award for his 1997 matrix optimization research. He actively contributes to academic service through conference organization and editorial roles at JMLR. The MELODI Lab develops cutting-edge tools like GMTK, PhiPAC, and Vocal Joystick for real-world applications. Recent Activities: Invited lectures at Yale (2016), Harvard (2015), and IIT Bombay. Co-organized NIPS workshops on discrete optimization (2013–2016). Authored influential papers on submodular optimization, semi-supervised learning, and parallel computing. Current research emphasizes submodular applications in large-scale data management and distributed systems.

• Artificial Intelligence
• Machine Learning
• Autonomous Systems
• +4 more

Loo Junn Yong is a Lecturer at the Malaysia School of Information Technology, Monash University, specializing in artificial intelligence, machine learning, and autonomous systems. His research focuses on applying advanced algorithms to real-world challenges in robotics, brain network analysis, and traffic safety. He leads projects such as 'Efficient and Interpretable Multi-view Deformable Transformer with 3D Position Embeddings and Intelligent Queries for Autonomous Driving' and collaborates on ASEAN NCAP assessment protocols for vehicle safety. His work spans generative models, trajectory prediction, and ethical decision-making in autonomous systems. Notable contributions include frameworks for brain disorder identification using dynamic graph representation and energy-efficient trajectory planning for electric vehicles. He actively publishes in top conferences like IJCAI and IEEE SMC. His research aligns with UN SDGs through innovations in sustainable transport and health diagnostics.

Sanjiban ChoudhuryView profile

Assistant Professor

• Artificial Intelligence
• Robotics
• Reinforcement Learning
• +5 more

Sanjiban Choudhury is an Assistant Professor at Cornell University's Ann S. Bowers College of Computing and Information Science and a Machine Learning Researcher at Aurora. He leads the PoRTaL group, focusing on interactive AI agents that self-align through few-shot human interactions. His research emphasizes reinforcement learning (RLHF), imitation learning (IRL), and foundation models for robotics, planning, and code generation. Key achievements include receiving the 2025 ONR Young Investigator Award for multistep robot task learning, the OpenAI Superalignment Award (2024), and a Google Research Award for LLM-based planning. His group develops modular robotics foundation models (MOSAIC), earning best paper awards at ICRA 2024 workshops. Research projects aim to bridge AI language models with robotic execution, enabling robots to interpret manuals/videos and perform complex tasks like engine repairs in hazardous environments. Lab members include doctoral students Gonzalo Gonzalez, Yuki Wang, Kushal Kedia, and master’s student Prithwish Dan. Ongoing work focuses on task super-alignment, human-robot transfer learning, and open-source training models for the robotics community. Current funding supports developing robots capable of fluid, multi-step tasks through integrated AI systems.

• Web Search Engines
• Text-to-SQL Systems
• Online Social Network Analysis
• +5 more

Özgür Ulusoy is a Professor of Computer Engineering at Bilkent University, Turkey. He has held academic positions since 1993, including Assistant Professor (1993–2003) and Associate Professor (1998–2003) before becoming a full Professor in 2003. He earned his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 1992, following an M.S. (1988) and B.S. (1986) in Computer Engineering from Bilkent University and Middle East Technical University, respectively. His research focuses on web search engines, text-to-SQL systems, social network analysis, multimedia databases, and wireless/mobile systems. Notable projects include developing domain-specific web portals, optimizing query result caching, and exploring semantic relationships for search engine efficiency. He has directed over 20 sponsored projects funded by TÜBITAK, EU, and international collaborations, involving topics like data mining in mobile networks and real-time database scheduling. Ulusoy’s work bridges theoretical contributions with practical implementations, including prototyping video database systems and educational search engines. His research often emphasizes scalability, efficiency, and user-centric design. He has advised graduate students such as Arif Usta and Akifhan Karakayalı. His team’s contributions span algorithm development, system prototyping, and interdisciplinary applications in multimedia, social networks, and cloud computing. Key grants include projects on information retrieval paradigms in SQL translation (TÜBITAK, 2019–2021), refreshment strategies for web search caching (TÜBITAK, 2010–2011), and semantic relationships in search engines (TÜBITAK, 2008–2010). His recent work addresses challenges in IoT sensor networks, cloud resource scheduling, and mitigating misinformation in online platforms.

Yi Ava WuView profile

Professor

• Financial Accounting
• Financial Analysis
• Integrated Reporting
• +1 more

Dr. Yi Ava Wu is a ZJU100 Young Professor at the School of Management, Zhejiang University, where she serves as a doctoral supervisor. Her research focuses on Financial Accounting, Financial Analysis, Integrated Reporting, and Audit Quality. She is based in Hangzhou, China, and can be contacted at avayi_wu@zju.edu.cn. Her work bridges accounting practices, regulatory frameworks, and organizational behavior, with recent studies examining auditor litigation risk, analyst behavior, and the impact of integrated reporting on corporate strategy. Education details are not explicitly provided, but her role as a doctoral supervisor suggests advanced academic credentials in accounting or finance. Her research emphasizes empirical studies on market efficiency, regulatory effectiveness, and the application of technology in education and healthcare, as seen in her work on automated feedback systems and mobile healthcare surveillance. Publications highlight trends in audit quality evaluation, analyst decision-making under regulatory changes, and cross-disciplinary innovations in information systems. While no awards are noted, her prolific output across accounting, finance, and technology underscores her contributions to multiple academic domains. She advises doctoral students and has engaged in diverse research collaborations spanning both theoretical and applied fields.