جست‌وجوی استاد و پژوهشگر

Xingjie Weiمشاهده پروفایل

دانشیار

• Business Analytics
• Unstructured Data Mining
• Social Computing
• +۱۲ مورد دیگر

Dr. Xingjie Wei is an Associate Professor in Business Analytics and Machine Learning at the Centre for Decision Research (CDR), Leeds University Business School, University of Leeds. Her research bridges data science and business management, focusing on understanding human behavior through unstructured data such as images, text, and digital footprints using machine learning and big data analytics. She leads and advises on multiple funded research initiatives and supervises PhD students in related areas. PhD in Computer Science, University of Warwick Research Associate, Psychometrics Centre, Cambridge Judge Business School, University of Cambridge Lecturer, University of Bath Visiting Researcher, National Lab of Pattern Recognition (NLPR), Chinese Academy of Sciences Her research interests include business analytics, unstructured data mining, social computing, human trait analysis, multimodal data, financial risk analytics, and initial coin offerings. She develops algorithms to extract psychological and behavioral insights from digital footprints, with applications in policy, finance, and public services. Her work emphasizes human-centered decision-making and service optimization. The recent publications reflect a strong trend in using machine learning to analyze human traits from visual and textual data, with applications in finance, marketing, and behavioral science. Topics include CEO image analysis, emotion from EEG, facial similarity in films, and tabular data visualization. Her work spans computer vision, affective computing, and behavioral finance, demonstrating interdisciplinary depth. Winner of the 2014 IGI Global's Excellence in Research Journal Award Published in Journal of Corporate Finance , IEEE Transactions on Affective Computing , Annals of Operations Research , and International Marketing Review Active editorial roles in Sensors and Discover Analytics Xingjie Wei has secured and led multiple research grants, including ESRC, Innovate UK, and LUBS Challenge Fund projects. She supervises PhD students and hosts postdoctoral researchers. Her collaborative work includes partnerships with SR Mailing, Katchr, KAIST, and interdisciplinary teams across Leeds. She actively mentors early-career researchers and contributes to academic service through reviewing, conference organization, and public engagement. She is involved in the Centre for Decision Research and leads projects on digital footprints for policy, climate change impacts, and AI-driven decision support. Her team includes current PhD students and research associates working on topics like credit risk, problem gambling, and life quality measurement. She promotes interdisciplinary collaboration and knowledge transfer between academia and industry.

Alex Doboliمشاهده پروفایل

استاد

• Electronic Design Automation (EDA)
• Cyber-Physical Systems
• Mixed-Domain Embedded Systems
• +۵ مورد دیگر

Alex Doboli is a Professor in the Department of Electrical and Computer Engineering at Stony Brook University, State University of New York. He is also affiliated with SUNY Korea’s Department of ECE. His academic journey includes a Ph.D. in Computer Engineering from the University of Cincinnati (2000) and earlier degrees from Politehnica University Timisoara, Romania (B.Eng. 1990, Doctorate 1997). He served as junior faculty at Politehnica University before joining Stony Brook in 2000. His research focuses on Electronic Design Automation (EDA) , Cyber-Physical Systems , and Human-inspired Machine Learning . Key areas include analog/mixed-signal design, innovation methodologies, and data-driven design approaches. He has authored over 170 peer-reviewed publications and co-authored a textbook on mixed-signal design. His lab, the Mixed-Domain Embedded Systems Laboratory , explores team behavior modeling, IoT integration, and cognitive architectures. Dr. Doboli has advised 17 Ph.D. and 12 M.S. students. Notable awards include the IBM Partnership Award (2001) and the Traian Lalescu Award (1987). He serves as an Associate Editor for Integration, the VLSI Journal and holds leadership roles in professional organizations like the IEEE Long Island Circuits and Systems Society. His teaching spans undergraduate and graduate courses in programming, algorithms, VLSI design, and machine learning. Current courses include ESE 327: Fundamental Algorithms for Machine Learning and ESE 589: Learning Systems for Engineering Applications . Recent research highlights include automated dialog systems (diaLogic), IoT-human behavior integration frameworks, and studies on artistic understanding in neural networks. His work bridges EDA, machine learning, and embedded systems to advance design innovation and human-centric technologies.

Lili Jiangمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Privacy Protection
• Fairness in AI
• +۴ مورد دیگر

Lili Jiang is an Associate Professor at the Department of Computing Science, Umeå University, where she serves as Director of Studies and Programme Director for the MSc in Computing Science. She holds a PhD in Computer Science with expertise in AI trustworthiness, data mining, and natural language processing. Her research bridges computer science and artificial intelligence, focusing on privacy, fairness, and federated learning systems. Her research interests center on: Developing trustworthy AI systems with emphasis on privacy-preserving techniques Ensuring algorithmic fairness in socio-technical decision-making Advancing few-shot learning methods for NLP tasks Creating robust data federation frameworks for cross-database analysis Recent publications demonstrate strong focus on few-shot learning (particularly in NLP), privacy-preserving AI , and applied data mining for industrial/environmental systems. Her work consistently addresses real-world challenges through multimodal learning and explainable AI techniques. She leads the Deep Data Mining research group and collaborates with the Interpresence institute. Key projects include: Explainable, Safe, Contact-Aware Planning for Heavy Machinery (XSCAVE, 2024-2028) Assessment and Engineering of Equitable AI Systems (2022-2024) Privacy-aware Federated Database Infrastructure for Micro-Data Analysis (2018-present)

Gullal Singh Cheemaمشاهده پروفایل

پژوهشگر

• Multimodal Learning
• Natural Language Processing
• Computer Vision
• +۳ مورد دیگر

Gullal Singh Cheema is a Researcher at the Leibniz Information Centre for Science and Technology (TIB) within the Visual Analytics research group (Program Area C - Research and Development). His work bridges computer vision and natural language processing to advance multimodal analysis in digital media contexts. His research spans multimodal learning, natural language processing, and computer vision with emphasis on fact-checking systems, claim detection, and social media analytics. Key contributions include the MM-Claims dataset for multimodal verification and leadership in the CLEF CheckThat! lab series addressing political bias and news source authority. His work integrates visual and textual evidence for applications in news analysis, event-centric knowledge graphs, and wildlife conservation. Recent publications (2021-2023) reveal a concentrated focus on multimodal news verification, with significant output in CLEF and SemEval evaluation campaigns. His research demonstrates consistent innovation in fusing transformer architectures with syntactic features while expanding into multilingual sentiment analysis and misogynistic meme detection. Cheema actively shapes the research community through workshop organization (MUWS'22) and dataset development. His Visual Analytics group at TIB drives methodological advances in multimodal understanding with direct applications for social media monitoring and digital humanities.

Ian Laneمشاهده پروفایل

دانشیار

• Natural Language Processing
• Speech Recognition
• Machine Translation
• +۵ مورد دیگر

Ian Lane is an Associate Professor in the Computer Science and Engineering Department at the University of California, Santa Cruz's Baskin Engineering school, serving as Program Director for the Natural Language Processing Professional Master's Degree Program. He joined UCSC in Fall 2022 after an extensive career spanning academia and industry. His research centers on computational systems that understand spoken human language, spanning speech recognition, transcription, meaning interpretation, and contextually appropriate responses. Key research areas include: Natural Language Processing for real-world applications Conversational AI systems development Speech-to-speech translation technologies Multimodal interaction (audio-visual integration) Language technologies that learn through real-world interaction His recent publications demonstrate strong focus on hallucination detection in LLMs, tabular data understanding, explainable AI, and robust speech recognition systems. Current work emphasizes "in the wild" language technologies that adapt through user interaction. Dr. Lane has received recognition through impactful industry applications including Jibbigo (the first mobile speech translation app) and military translation systems deployed in Iraq and Afghanistan. He actively mentors students and collaborates across UCSC's Silicon Valley Campus programs including Games and Playable Media and Human-Computer Interaction. His vision includes integrating NLP with virtual environments for language learning and skill acquisition.

Yiwei Wangمشاهده پروفایل

استادیار

• Natural Language Processing
• Large Language Models
• Graph Machine Learning
• +۳ مورد دیگر

Yiwei Wang is an Assistant Professor at the Department of Computer Science, University of California, Merced, leading the UC Merced NLP Lab . He holds a Ph.D. from National University of Singapore (2023), M.Phil from Hong Kong University of Science and Technology (2019), and B.S. from Southeast University (2017). His research focuses on natural language processing , large language models , and graph machine learning , with emphasis on trustworthy AI systems.

• Augmented Reality
• Computational Geometry
• Computer Vision and Pattern Recognition
• +۱۲ مورد دیگر

Andrea Bottino is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) at the Polytechnic University of Turin. He has been actively involved in the Computer Graphics and Vision Group and leads various VR@POLITO initiatives. Chair of the Master HUMANAIZE program Coordinator of multiple Machine Learning for Vision and Multimedia courses Research Focus : Augmented and Virtual Reality for education and safety Computer Vision with applications to medical imaging and kinship analysis Human-Computer Interaction in immersive environments Multimodal Learning systems XR for Cultural Heritage Publication Trends : Recent works focus on AI applications , XR training systems , and computer vision techniques applied to medical diagnostics and cultural preservation . Leadership Roles : Scientific Director for MEI - Interactive Egyptian Museum Coordinator of PNRR Mission 4 projects Principal Investigator for Holo-BLSD and ALPTECH initiatives Labs and Collaborations : Active in Visionary LAB and VR@POLITO Collaborates with Balletto Teatro di Torino for cultural XR applications Partners with Fondazione Museo Egizio and Robin Studio

• Biomedical Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Hoifung Poon is General Manager at Microsoft Health Futures and affiliated faculty at University of Washington Medical School. He leads Real-World Evidence (RWE) research focusing on AI applications for precision health. Poon earned a B.S. with Distinction in Computer Science from Sun Yat-Sen University and a Ph.D. in Computer Science and Engineering from University of Washington. Specializes in biomedical AI research Focuses on structuring unstructured medical data Co-PI for DARPA Big Mechanisms projects Research strength lies in biomedical multimodal learning (text, radiology, pathology, genomics) and causal learning for real-world evidence generation. His team develops methods for LLM self-verification , multi-modal fusion , and biases correction in observational data. Publications show expertise in Nature , Nature Methods , and NEJM AI , covering topics from digital pathology to clinical text analysis. Scientific recognition includes: Best Paper Awards at NAACL, EMNLP, and UAI Winner of ACM Health Best Paper Award Named Technology Champion 2022 by Puget Sound Business Journal

Karsten Borgwardtمشاهده پروفایل

استاد

• Machine learning
• Systems biology
• Bioinformatics
• +۳ مورد دیگر

Prof. Dr. Karsten Borgwardt is Director of the Research Department of Machine Learning and Systems Biology at the Max Planck Institute of Biochemistry in Martinsried, Germany. A leading figure in the intersection of machine learning, bioinformatics, and systems biology, he heads a multidisciplinary team that develops novel computational methods to extract knowledge from large biomedical data sets. Research Mission: The Borgwardt lab converges big data analytics and biomedical research . Two overarching goals drive their work: (1) Automatically generating new biological and medical knowledge from massive data via state-of-the-art machine-learning algorithms. (2) Understanding the molecular underpinnings of biological system function, with emphasis on personalized medicine and biomarker discovery. Their methodological toolbox spans graph neural networks, kernel methods, conformal prediction, deep learning on sequences and structures, and topological data analysis . Application domains include antimicrobial resistance prediction, protease engineering, acute-kidney-injury forecasting, coronary-artery-disease diagnostics, single-cell spatial proteomics, and Long-COVID immune profiling. Recent Publication Landscape (2023-2025): The group’s latest articles demonstrate a clear trend toward translationally relevant machine learning . High-impact venues such as Nature Communications , Science , ICLR , and RECOMB feature their work on: Data-driven protein engineering using DNA-recorded deep mutational scanning. Guaranteed antimicrobial resistance detection from MALDI-TOF spectra via conformal prediction. Graph-based biomarker discovery with theoretical guarantees. Deep phenotyping of human iPSC-derived neuronal networks to study disease mutations. Multi-modal learning that fuses genomics, proteomics, and clinical data for patient stratification. These contributions collectively advance both the theoretical foundations and real-world deployment of machine learning in medicine. Scientific Awards & Honors: While no explicit award list is provided, the breadth and impact of publications, invited book chapters, and keynote-level conference presentations (ICLR, RECOMB, ISMB/ECCB) testify to sustained international recognition. Laboratory & Collaboration Ecosystem: The Borgwardt lab operates at the Max Planck Institute of Biochemistry —a world-leading biomedical research campus. Collaborations span multiple Max Planck centers, university hospitals across Europe, and international consortia such as the EyeConic study on optogenetics therapy. The lab’s open-source footprint includes the Multi-SConES R package for multi-task network-regularized feature selection, fostering reproducible science across the community.

Slavko Žitnikمشاهده پروفایل

دانشیار

• information retrieval
• information extraction
• natural language processing
• +۷ مورد دیگر

Slavko Žitnik is an Associate Professor and Vice-dean at the Faculty of Computer and Information Science, University of Ljubljana, where he is a member of the Laboratory for Data Technologies. His academic career spans multiple research projects and international collaborations focusing on data technologies and natural language processing. His primary research interests include information retrieval, information extraction, natural language processing, entity extraction, relationship extraction, coreference resolution, data merging, redundancy elimination, and ontologies. Dr. Žitnik's work often bridges theoretical computer science with practical applications in various domains including education, healthcare, and smart city ecosystems. Dr. Žitnik has led and participated in numerous significant research projects including P2-0359 on Ubiquitous Computing (2023-2027), PoVeJMo on Adaptive Natural Language Processing with Large Language Models (2023-2026), and the GOBLIN COST Action for building global networks of large-scale knowledge graphs. His recent work demonstrates a strong focus on adapting natural language processing techniques with large language models and creating practical applications of these technologies. His scientific contributions span multiple domains including: Natural Language Processing and Information Extraction techniques Knowledge graph construction and integration Applications in education, healthcare, and smart city ecosystems Development of practical tools and systems for data processing Dr. Žitnik has established international collaborations with institutions including Harvard University's Department of Biomedical Informatics (where he conducted a research visit from July to October 2022), the University of South Florida, and various European partners through COST Actions and other collaborative frameworks.

• Natural Language Processing
• Applied Data Science
• Computational Linguistics
• +۳ مورد دیگر

Dr. Pablo Mosteiro Romero is an Assistant Professor at Utrecht University's Faculty of Social and Behavioural Sciences within the Department of Methodology and Statistics . His work bridges Natural Language Processing (NLP) , Applied Data Science , and Human-centered Artificial Intelligence , focusing on language change, morphology-syntax trade-offs, and applications in healthcare and open societies. Education: B.Sc. in Physics, Rutgers University (Summa Cum Laude, 2007) Ph.D. in Computer Science, Princeton University (2014, Centennial Fellowship) Research Interests revolve around computational linguistics, information-theoretic approaches to linguistics, and ethical AI. He investigates synonym evolution, morphosyntactic interactions, and fairness in mental health-focused AI systems. Recent work includes de-identification in medical texts and taxonomy induction via reinforcement learning. Publications span top venues like ACL, IEEE, and Springer, covering topics such as topic modeling interpretability, bias discovery in ML, and multimodal negation processing. Collaborations with colleagues like Scheepers, Spruit, and Kaymak highlight interdisciplinary applications in electronic health records. Awards include the Centennial Fellowship at Princeton and Summa Cum Laude at Rutgers. As a University Teaching Qualification holder, he teaches courses in data science, Python programming, and research methods, supervising theses in AI and Business Informatics. His work aligns with Utrecht's Institutions for Open Societies (IOS) theme, addressing gender diversity and global justice in data-driven systems.

Abraham Bernsteinمشاهده پروفایل

استاد

• Semantic Web
• Data Mining
• Machine Learning
• +۷ مورد دیگر

Abraham Bernstein is a Full Professor of Informatics at the University of Zurich (UZH), where he serves as Head of the Dynamic and Distributed Information Systems Group and Director of the UZH Digital Society Initiative. He leads a university-wide initiative with over 180 faculty members investigating the interplay between society and digitalization. His work bridges social science foundations (organizational psychology/sociology/economics) and technical disciplines (computer science, artificial intelligence), creating a unique interdisciplinary approach to digital transformation challenges. Education: Diploma in Computer Science from ETH Zurich Ph.D. in Management with concentration in Information Technologies from MIT's Sloan School of Management Professor Bernstein's research spans the Semantic Web, data mining/machine learning, recommender systems, crowd computing, and collective intelligence. His work uniquely integrates social science perspectives with technical computer science approaches, examining how social and technical elements interact in digital systems. Recent work focuses on explainable AI, ethical decision-making with AI systems, and the societal implications of digital transformation, reflecting his commitment to addressing both technical challenges and their broader societal context. His publication record shows a strong trajectory in multimodal information retrieval, knowledge representation, and human-AI collaboration, with increasing focus on ethical considerations and societal impact of AI technologies. The research demonstrates consistent innovation in bridging technical AI capabilities with human-centered design principles, particularly in areas like explainable recommender systems and democratic applications of AI. Scientific Recognition: Nominated Digital Shaper by Bilanz magazine (2017) Professor Bernstein has supervised over 30 PhD students whose work spans semantic technologies, data mining, recommender systems, and human-AI interaction. His research group has secured significant funding for projects related to digital society, knowledge representation, and AI ethics. As Director of the Digital Society Initiative, he coordinates cross-disciplinary research across UZH's faculties, bringing together scholars from humanities, social sciences, law, economics, and STEM fields to address complex digital transformation challenges. He leads the Dynamic and Distributed Information Systems Group at UZH, which maintains strong international collaborations and contributes significantly to both theoretical advances and practical applications in information systems. The group's work has influenced standards in semantic web technologies and continues to shape discourse on responsible AI development and deployment in society.

Kenney Ngمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Biomedical Informatics
• +۶ مورد دیگر

Kenney Ng is a Researcher at IBM Research and the science program manager for the MIT-IBM Watson AI Lab . He focuses on applying data mining, machine learning, and AI techniques to real-world healthcare data, particularly in cardiovascular disease, diabetes, and genomic risk modeling. His career spans roles at IBM Software Group, iPhrase Technologies, and MIT-affiliated labs. Bachelor's, Master's, and PhD in Electrical Engineering and Computer Science from Massachusetts Institute of Technology Kenney Ng’s research interests include biomedical informatics , machine learning , and genomic data analysis . His work connects AI with clinical applications, such as predicting disease progression, analyzing cardiometabolic risks, and developing causal inference frameworks. His publications emphasize AI in healthcare (e.g., drug screening, diabetes prediction, cardiovascular risk modeling) and methodological advancements in machine learning (e.g., bias formulas, latent space exploration). Fellow of the American Medical Informatics Association Senior Member of IEEE Kenney Ng collaborates with institutions like the Broad Institute , Massachusetts General Hospital , and Cleveland Clinic . He co-authored over 100 publications and 25 patents, with leadership roles in IBM’s Center for Computational Health and the MIT-IBM Watson AI Lab .

Dokyoon Kimمشاهده پروفایل

دانشیار

• Bioinformatics
• Data Integration
• Translational Genomics
• +۳ مورد دیگر

Dokyoon Kim is an Associate Professor of Informatics and Director of the Center for Artificial Intelligence (CATI). His academic work focuses on bioinformatics, data integration, and translational genomics. Research Interests Developing integrative multi-omics approaches for disease prediction Creating AI-driven models for biological age assessment Analyzing genetic pleiotropy across biobank-scale datasets Building knowledge graphs for genomic networks Recent Publications (2025) demonstrate expertise in disease risk prediction through proteomic/genomic integration, AI applications in biobank research, and cross-ancestry genetic studies. Key domains include Alzheimer's disease , cardiovascular diseases , cancer genomics , and ophthalmology . His work also addresses computational challenges in polygenic risk scoring, overfitting mitigation, and multimodal data fusion for clinical applications.

MEHMET KILIÇمشاهده پروفایل

دانشیار

• English Language Teaching
• Second Language Acquisition
• Phonetics and Pronunciation
• +۵ مورد دیگر

Mehmet Kiliç serves as an Associate Professor in the Department of Foreign Language Education at Gaziantep University's Faculty of Education. His academic journey includes a Doctorate in English Language Education from Çukurova University (2008-2013), a Master's from Gaziantep University (2005-2007), and undergraduate studies at Boğaziçi University (2001-2005). Current Rank: Associate Professor (2020-present) Previous Positions: Doctor Lecturer (2013-2020), Lecturer (2005-2013) Administrative Roles: Department Head (2018-2020), Institute Deputy Director (2018-2020), Erasmus Coordinator (2013-present) His research spans English Language Teaching with emphasis on second language acquisition mechanics, particularly phonetic phenomena like pausing patterns and lexical stress in cross-linguistic contexts. Key interests include teacher identity construction, language learning strategies, foreign language anxiety, and the impact of L1 on L2 acquisition. His work integrates psycholinguistic analysis with educational applications, examining how speech patterns correlate with proficiency and how teacher development affects classroom outcomes. Kiliç's publication trends reveal sustained focus on empirical linguistic analysis (40% of recent work), teacher development (30%), and language learning psychology (30%). His research increasingly incorporates cross-cultural comparisons (Turkish, Iraqi, Swahili, Hausa speakers) and examines technology's role in language education. The 2020-2024 period shows growing emphasis on AI integration in ELT and refugee language acquisition. As a thesis supervisor, he has guided 17 master's students to completion and currently oversees 2 doctoral candidates. His administrative contributions include journal editorships for Gaziantep University Journal of Social Sciences and Educational Sciences. Current teaching responsibilities include Linguistics, Teaching Language Skills, and Psychological Foundations of Language Education courses at graduate and undergraduate levels. Professional activities include frequent conference presentations at international venues like GlobELT and ULEAD Congress. His editorial work focuses on advancing Turkish scholarship in language education through national journal development.