جستجوی استاد راهنمای دانشگاهی

• Artificial Intelligence
• Graph Neural Networks
• Biomedical Informatics
• +۷ مورد دیگر

Dr. Siwei Liu is an Assistant Professor at the School of Natural and Computing Sciences, University of Aberdeen, UK. He previously held a postdoctoral position at MBZUAI and completed his PhD with the Terrier team under the supervision of Prof. Iadh Ounis and Prof. Craig Macdonald. He is actively involved in research, teaching, and PhD supervision. His research focuses on advancing artificial intelligence methods, particularly in graph neural networks, large language models, and recommender systems, with applications in bioinformatics, biomedical image analysis, and multi-modal single-cell data. He is a co-founder and main contributor to the open-source Beta-Recsys project, promoting reproducibility and evaluation in recommendation systems. Dr. Liu's recent publications (2020–2025) reflect a strong trajectory in deep learning for biomedical applications and intelligent systems. Key themes include GNNs for gene-disease and RNA-disease association prediction, cold-start recommendation using heterogeneous graphs, pre-training strategies, and hybrid Transformer-Mamba architectures for radiology report generation. His work appears in top-tier venues such as IEEE TPAMI, ACM Transactions, and Briefings in Bioinformatics. He teaches courses in Data Mining and Visualisation and Natural Language Processing, contributing to the education of future AI practitioners. While no specific awards or student names are listed, his active research and leadership in open-source initiatives highlight his growing impact in the AI and biomedical informatics communities.

Nicole Karnمشاهده پروفایل

دانشیار

• Analytical Chemistry
• Chemistry Education
• Undergraduate Teaching Labs
• +۴ مورد دیگر

Nicole Karn is an Associate Professor of Teaching and Analytical Lab Supervisor in the Department of Chemistry and Biochemistry at The Ohio State University. Her roles include undergraduate teaching and laboratory supervision, particularly in analytical chemistry. She holds a B.S. from Muskingum University (2004), M.S. and Ph.D. from The Ohio State University (2008, 2011). Before returning to OSU in 2015, she served as an Assistant Professor at Ohio Northern University. Her research focuses on two primary areas: 1) spectroscopic analysis of pyrazolopyrimidine proton transfer mechanisms in varying solvents/pH conditions, and 2) charge transfer dynamics in cross-conjugated organic compounds for molecular electronics and solar energy applications. She employs UV-Vis spectroscopy, time-resolved techniques, and donor-bridge-acceptor molecular models in these investigations. Dr. Karn's educational contributions emphasize improving STEM pedagogy through systemic curriculum reforms, student perception analysis, and innovative lab instruction methods like reciprocal peer teaching. Her work addresses challenges in distance learning, AI integration in education, and equitable teaching practices. Her publications span both analytical chemistry research (e.g., benzo[a]fluorenone photophysics) and education-focused studies (e.g., ChatGPT vs. student performance comparisons). She has extensively studied DNA photocleavage mechanisms using transition metal complexes and pioneered cross-conjugated molecule characterization techniques.

• Interactive Data Visualization
• Intelligence Augmentation
• Machine Learning Interpretability
• +۳ مورد دیگر

Arvind Satyanarayan is an Associate Professor of Computer Science at MIT, leading the Visualization Group within MIT CSAIL. His research focuses on intelligence augmentation through interactive data visualization, exploring how computational tools can amplify human cognition and creativity while respecting user agency. He holds a PhD from Stanford University, advised by Jeffrey Heer. Key research themes include visualization design tools, machine learning interpretability, accessible visualization for visually impaired users, and behavioral principles for information systems. His work has been recognized with an NSF CAREER Award and a 2024 Alfred P. Sloan Fellowship, alongside best paper awards at CHI and IEEE VIS. Education: PhD in Computer Science from Stanford University (Interactive Data Lab) Notable Achievements: Developed widely-used systems like Altair and Vega-Lite, impactful in data science communities Advising: Mentors over 20 graduate and undergraduate researchers, with notable alumni advancing to faculty roles at Brown CS and Utah CS Recent projects include tactile visualization systems (Tactile Vega-Lite), semi-formal programming frameworks (Pluto), and cultural interpretability of AI models. His lab emphasizes collaboration with diverse stakeholders to ensure inclusive design practices.

Michael Wandمشاهده پروفایل

استاد

• Computer Graphics
• Machine Learning
• Geometry Processing
• +۵ مورد دیگر

Michael Wand is a Professor at the Institute of Computer Science , Johannes Gutenberg University Mainz , leading the Visual Computing Group . His research bridges Computer Graphics and Computer Vision , focusing on data-driven methods and structure-aware processing of geometric data. Research Themes Machine learning for geometric data analysis Symmetry and regularity detection in 3D shapes Generative models of visual data Big data in geometry processing Publication Trends : His recent work (2015-2016) explores integrating Markov Random Fields and Convolutional Neural Networks for image synthesis, generative adversarial networks for real-time texture synthesis, and algebraic models for parameterized shape editing. Earlier contributions (2007-2013) focus on point cloud processing , non-rigid shape reconstruction , and interactive editing of complex geometries. Academic Background : Doctoral degree (2004) in Computer Science from University of Tübingen Diploma (2000) in Computer Science from Universität-GH Paderborn

• Large Language Models
• Multilingual Machine Translation
• Vision-Language Models
• +۳ مورد دیگر

Xiaocheng Feng is a prominent researcher in computational linguistics and large language models (LLMs), with extensive contributions to multilingual systems, vision-language integration, and knowledge alignment. His work spans from 2016 to 2025, focusing on advanced topics like Mitigating hallucinations in multimodal models Efficient knowledge editing across multiple models Length-controlled text generation for black-box LLMs Improving contextual faithfulness via retrieval heads Cross-lingual connection mechanisms for fine-tuning His recent publications address critical challenges in LLMs, including knowledge misalignment, entity-level unlearning, and Pareto optimization in multilingual translation. He has explored probability density estimation for text control, adaptive context modeling in visual storytelling, and cross-lingual annotation projection for low-resource languages. Collaborations with researchers like Bing Qin, Lei Huang, and Weitao Ma highlight his central role in advancing NLP methodologies.

• Linguistic Theories
• Philosophical Semantics
• Multimodal Communication
• +۷ مورد دیگر

Dr. Andy Lücking is a postdoctoral researcher at Goethe University Frankfurt, specializing in multimodal communication and cognitive semantics. He serves as Principal Investigator for the GeMDiS project within the ViCom SPP initiative, and previously held research fellowships at Université Paris Cité's Laboratoire de Linguistique Formelle and Frankfurt's Text Technology Lab. Current research focuses on neurocognitive semantics Developed iconic gesture theory with TTR/RTT frameworks Created multimodal corpora (FraGA, DoTT, TGVCorp) Active in computational educational linguistics Contributed to annotation tools (TextAnnotator, DependencyAnnotator) His work combines theoretical modeling with experimental methods, spanning over a decade of contributions to dialogue semantics, gesture-speech integration, and semantic role labeling. Recent projects explore VR-based multimodal research (Va.Si.Li-Lab) and diachronic dependency parsing. He has co-authored 20+ publications including proceedings in SemDial, LREC, and Springer HCI volumes. Collaboration network includes: Jonathan Ginzburg (dialogue theory), Alexander Mehler (computational linguistics), Alexander Henlein (VR research), and Max Planck Institute researchers. Key methodologies involve corpus creation, machine learning, and cognitive modeling of referential phenomena.

• Data Mining
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Philip S. Yu is a distinguished Professor in the Department of Computer Science at the University of Illinois at Chicago's College of Engineering. He received his PhD from Stanford University and has established himself as a leading researcher in data mining, machine learning, and artificial intelligence. His research spans multiple domains including graph neural networks, recommendation systems, fake news detection, and large language models. Professor Yu's research interests primarily focus on Data Mining , Machine Learning , Artificial Intelligence , Graph Neural Networks , Recommendation Systems , Fake News Detection , and Large Language Models . His work addresses fundamental challenges in knowledge discovery from complex data structures, with particular emphasis on developing robust algorithms for real-world applications. His recent publications demonstrate strong interest in the intersection of traditional data mining techniques with emerging large language model architectures. Analysis of Professor Yu's 2025 publication record reveals significant contributions across multiple high-impact venues including IEEE Transactions, ACM journals, and top conferences. His work shows a clear trend toward integrating traditional data mining approaches with large language models, particularly in areas like recommendation systems, fake news detection, and privacy-preserving technologies. Many publications take the form of comprehensive surveys, indicating his role as a thought leader synthesizing emerging trends in the field. While specific awards aren't listed in the current data, Professor Yu's extensive publication record in top-tier venues and his position as senior author on numerous collaborative projects demonstrate significant recognition within the research community. Professor Yu maintains active research collaborations across multiple institutions, as evidenced by his co-authorship on numerous papers with researchers from different universities. His work often involves interdisciplinary approaches, bridging computer science with applications in security, healthcare, and social media analysis.

• Machine Learning
• Artificial Intelligence
• Smart Home Systems
• +۳ مورد دیگر

Paolo Sernani is an active researcher in the fields of Machine Learning , Artificial Intelligence , and Smart Home Systems , with over a decade of collaborative publications in computer science and biomedical informatics. His work appears in journals like IEEE Access , Computers in Medicine and Biology , and Applied Artificial Intelligence , as well as conferences including AmI , CBMS , and ETFA . Key collaborations include Aldo Franco Dragoni , Nicola Falcionelli , and Selene Tomassini .

Bram De Moorمشاهده پروفایل

استادیار

• Transportation Management
• Inventory Management
• Multi-Modal Supply Chains
• +۳ مورد دیگر

Bram De Moor is an Assistant Professor in the Operations, Planning, Accounting & Control group at Eindhoven University of Technology (TU/e), Department of Industrial Engineering & Innovation Sciences. His research focuses on transportation and inventory management, particularly in multi-modal, collaborative, and closed-loop supply chains. Education: B.Sc. (2018) and M.Sc. (2020) in Business Engineering from KU Leuven, Belgium; Ph.D. in Business Economics (2024) from KU Leuven's Research Center of Operations Management. His research applies advanced computational methods like deep reinforcement learning (DRL) to solve complex inventory control problems with multi-dimensional state and action spaces. He explores scalable solutions for practical implementation in sustainable logistics systems. Recent publications demonstrate novel approaches using continuous action representations in DRL, enabling faster training and handling of larger problem instances compared to discrete action methods. These studies bridge theoretical advancements with real-world supply chain applications. Bram is affiliated with the Operations Planning Acc. & Control group at TU/e, contributing to education through courses in Stochastic operations management and Circular service supply chain .

• Music Informatics
• Electronic Composition
• Interactive Sound Systems
• +۳ مورد دیگر

Prof. Thomas Neuhaus is a German Professor of Music Informatics at the Institute for Computer Music and Electronic Media (ICEM) within the College for Music and Art at Folkwang University of the Arts. As Artistic Director of ICEM since 2011 and co-developer of the AUDIAC computer-aided composition system, he bridges computer science and musical artistry through interactive systems. Education: Studied composition with Wolfgang Hufschmidt and electronic composition with Dirk Reith at Folkwang Positions: Faculty 1 member since 1994, full professor since 2004 Research: Focuses on formalization of musical structures and human-machine collaboration His work manifests in algorithmic composition languages and real-time interaction systems , exemplified through 20+ years of collaboration with Theater der Klänge. Key trends include: Interactive sound sculptures (Zollverein installations) Intermedial theatre works (Vanitas, Megalopolis) Digital heritage projects (PPP composition framework) Acoustic-architectural integrations (A Bigger Clock software) Scientific recognition: Student-nominated teaching award (2008/09) Research Prize finalist (FH Dortmund, 2003) His software developments like fkir scheduler and PPP-2 framework demonstrate computational creativity in music. Current projects combine electronic composition with live performance technologies , as seen in the NOW! Festival commissions.

Stefan Heinrichمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Cognitive Psychology
• Computational Neuroscience
• +۷ مورد دیگر

Dr. rer. nat. Stefan Heinrich is an Associate Professor in the Data Science Section at the IT University of Copenhagen , Denmark, and an Affiliate Researcher at the Pioneer Centre for Artificial Intelligence . His work bridges Artificial Intelligence , Cognitive Psychology , and Computational Neuroscience , focusing on computational principles of brain function and their application in AI systems. Education: 20.06.2016 - Doctor of Natural Sciences (Dr. rer. nat.) in Computer Science, Universität Hamburg, Germany 27.10.2009 - Diplom-Informatiker (MSc) in Computer Science, University of Paderborn, Germany Stefan’s research explores temporally dynamic representation and multi-modal integration in the brain, with applications in Neural Networks , Probabilistic Learning , and Machine Learning for tasks like music/language processing , neurodiversity , and cognitive development . His recent work includes Large Language Models (LLMs) for emotion elicitation, EEG neural decoding , and representation engineering to enhance reasoning in AI. Key trends in his publications include Reinforcement Learning , Neural Decoding , Mathematical Reasoning , and Community Analysis of AI research. His projects span REMARO: Reliable AI for Marine Robotics (funded by the European Commission) and the Pioneer Centre for Artificial Intelligence (Danish National Research Foundation). Scientific Collaborations: REMARO : Collaborator with the European Commission (2020–2025) Pioneer Centre for AI : Collaborator (2021–2034) Crossmodal Learning : Postdoctoral Research Associate (2016–2019) Stefan supervises numerous PhD and Master’s students , including Jonsman, Lumholt, Kazlauskaite, and Mortensen (2025), and has led courses like Advanced Machine Learning for NLP and Human-Robot Interaction projects at ITU Copenhagen and Universität Hamburg. He is associated with the brAIn lab and collaborates internationally with institutions such as the University of Tokyo and Tsinghua University Beijing.

Siva Reddyمشاهده پروفایل

استادیار

• Natural Language Understanding
• Representation Learning
• Multimodal AI
• +۶ مورد دیگر

Siva Reddy is an Assistant Professor at McGill University, jointly appointed in the School of Computer Science and Department of Linguistics. He is also a Canada CIFAR AI Chair, core faculty member at Mila (Quebec AI Institute), and a research scientist at ServiceNow Research. His research focuses on advancing natural language understanding through symbolic and deep learning models, emphasizing reasoning, multimodal integration, and safety. Postdoctoral researcher at Stanford University PhD from University of Edinburgh Research themes include: Language representation learning for machine reasoning Conversational AI with retrieval-augmentation for trustworthiness Multi-language and safe AI deployment Integration of language with vision/speech modalities He has received prestigious awards including the Canada CIFAR AI Chair and Google PhD Fellowship. His advising portfolio includes active PhD students like Vaibhav Adlakha, Zichao Li, and Mehar Bhatia, with alumni such as Andreas Madsen (now at Guide Labs) and Edoardo Ponti (Assistant Professor at University of Edinburgh). He contributes to academic leadership as Senior Area Chair for EMNLP 2024 and Ethics Area Chair for COLM 2024.

Soumen Chakrabartiمشاهده پروفایل

استاد

• Graph Neural Networks
• Knowledge Graphs
• Natural Language Processing
• +۳ مورد دیگر

Soumen Chakrabarti is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. His work bridges graph theory, natural language processing, and knowledge representation, focusing on scalable solutions for complex information retrieval problems. He has held visiting positions at Carnegie-Mellon University and Google. Education: B.Tech from IIT Kharagpur, PhD from UC Berkeley Key research areas: Graph Neural Networks, Knowledge Graphs, Code-Switched Text Analysis, Temporal Reasoning His recent publications emphasize neural graph matching , code-switched NLP , and multi-modal QA systems , often integrating symbolic reasoning with deep learning. Awards include the WWW 1999 Best Paper Award and ECML/PKDD 2008 Best Student Paper . He leads projects like CRUSH4SQL for text-to-SQL parsing and CSAW for annotated web search. His lab collaborates with IBM and Google on graph representation learning and temporal knowledge graph completion.

Shangfei Wangمشاهده پروفایل

استاد

• Pattern Recognition
• Affective Computing
• Probabilistic Graphical Models
• +۳ مورد دیگر

Shangfei Wang is a full Professor at the School of Computer Science and Technology, University of Science and Technology of China (USTC). His research focuses on pattern recognition, affective computing, and probabilistic graphical models, with significant contributions to facial expression analysis, emotion recognition, and multimodal human-robot interaction. He leads the Key Laboratory of Computing and Communication Software of Anhui Province and has received multiple international competition awards including placements in the OMG Empathy Prediction Challenge and Detecting Depression with AI Sub-Challenge. PhD in Computer Science, USTC (2002) MSc in Electronic Science and Technology, USTC (1999) BSc in Electronic Engineering, Anhui University (1996) His work combines domain knowledge with advanced machine learning techniques, including adversarial learning, dual learning, and multimodal deep regression Bayesian networks. Research themes include: Emotion-aware medical consultation systems Thermal image-based facial recognition Privileged information learning for emotion detection Spontaneous vs. posed expression differentiation EEG and physiological signal integration AI applications in mental health support Key projects include multiple National Nature Science Foundation of China grants and international collaborations with French institutions. He serves as Associate Editor for IEEE Trans. on Affective Computing and ACM Trans. on Multimedia Computing, and organized several international conference tracks.

• Computer Vision
• Machine Learning
• Healthcare
• +۵ مورد دیگر

Dr. Jianbo Jiao is an Associate Professor in the School of Computer Science at the University of Birmingham , leading the Machine Intelligence + x (MIx) group . He holds a PhD in Computer Science from City University of Hong Kong (Hong Kong PhD Fellowship Scheme) and an MSc from Peking University. He was a visiting scholar at the University of Illinois at Urbana-Champaign and a postdoctoral researcher in the Biomedical Image Analysis (BioMedIA) group and Visual Geometry Group (VGG) at the University of Oxford. Research Focus: Computer Vision, Machine Learning, Medical Imaging, AI for Science Key Themes: Learning from limited supervision (self-supervised, weakly-supervised), multi-modal data analysis (vision-audio-text-chemical spectra), open-world problems (incremental learning, open-vocabulary segmentation), 3D scene understanding Awards & Recognition: Amazon Research Award (2024) Royal Society Short Industry Fellowship (2023) Best Paper Awards at MICAD 2024 and MICCAI 2024 ASMUS Workshop Outstanding Reviewer Awards from CVPR, ECCV, ICCV, NeurIPS, and MICCAI Fellow of the Higher Education Academy (FHEA) Community Contributions: Area Chair for NeurIPS 2025, ACM MM 2025, and ICLR 2025; Senior Program Committee member for IJCAI 2021; Editorial roles at IEEE T-CSVT, Springer The Visual Computer, and TMLR. Collaborations & Labs: Works with Oxford's BioMedIA and VGG groups, Meta Project Aria, and leads the MIx group at Birmingham, which focuses on open-world AI, 3D modeling, and cross-modal healthcare applications.