جست‌وجوی استاد و پژوهشگر

• Human-AI Collaboration
• Deepfake Detection
• Medical Diagnosis
• +۴ مورد دیگر

Matt Groh is an Assistant Professor at Northwestern University's Kellogg School of Management, with a courtesy appointment in the McCormick School of Engineering's Computer Science department. He serves as Principal Investigator of the Human-AI Collaboration Lab, focusing on interdisciplinary research bridging social science and machine learning. His work investigates human-AI collaboration dynamics, particularly in domains like deepfake detection, medical diagnosis, and empathic communication. Research Interests: Groh's research integrates computational methods with social science to address challenges in human-AI partnerships. Key areas include: Deepfake detection across modalities (text, audio, video) AI ethics and transparency in healthcare applications Human perception of AI-generated media Computational empathy and emotional AI Lab & Collaborations: The Human-AI Collaboration Lab (HAIC) develops large-scale digital experiments to study hybrid human-machine systems. Recent work includes analyzing photorealism in AI-generated images and evaluating LLM reliability in subjective tasks. Groh collaborates with experts in dermatology, computer vision, and social computing. Teaching & Mentorship: He teaches Human and Machine Intelligence at Kellogg, emphasizing interdisciplinary approaches to AI's societal impact. Current openings include a postdoc fellowship with Dashun Wang exploring Human-AI Collaboration and Science of Science.

• Speech Recognition
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Tien Ping Tan is an experienced researcher specializing in speech recognition , natural language processing , and machine learning applications. With a PhD in Automatic Speech Recognition for Non-Native Speakers from Joseph Fourier University (2008), his career spans two decades of impactful contributions across multiple domains.

Nori Jacobyمشاهده پروفایل

استادیار

• Cognitive Psychology
• Neuroscience
• Machine Learning
• +۳ مورد دیگر

Nori Jacoby is an Assistant Professor in the Department of Psychology at Cornell University and a Research Group Leader at the Max Planck Institute for Empirical Aesthetics in Frankfurt. Her research bridges cognitive science, neuroscience, and machine learning to explore how internal representations shape sensory and cognitive abilities, with a focus on universality/diversity in perception and collective behavior. Education: PhD from Hebrew University of Jerusalem (ELSC), postdocs at MIT, UC Berkeley, and Columbia University Lab: Directs the CoCoCo Lab (Cornell Computational Cognition Lab) Funding: NSF-funded postdoctoral program collaborating with UC Davis, CUNY, and Princeton Research interests include: High-dimensional perceptual spaces using adaptive sampling (e.g., Gibbs sampling with people) Cross-cultural studies of music and perception via global experiments Human-AI hybrid systems for collective creativity and decision-making Recent work explores mechanisms of cultural diversity in urban populations, neural correlates of rhythm in stroke patients, and LLM alignment with human sensory judgments. Current projects include large-scale music evolution experiments and NSF-funded studies on collective intelligence. Recruitment: Actively hiring postdocs and PhD students for interdisciplinary work.

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

Dr. Suman Kumar Ghosh is a Lecturer in Data Science and Computer Science at the London campus of York St John University, affiliated with the York Business School. His research focuses on computer vision, machine learning, natural language processing, and large language models, with over a decade of experience in these domains. His work spans applications in handwritten character recognition, scene text analysis, and document processing. His research contributions include advancements in word spotting techniques, segmentation-free CNN models, and the development of datasets for historical document analysis. He has published in leading conferences such as ICDAR and CVPR, as well as journals like Pattern Recognition Letters and the International Journal on Document Analysis and Recognition (IJDAR). Dr. Ghosh’s expertise bridges academic research and practical industry applications, particularly in areas such as scene text understanding for advertisements and cultural heritage digitization. His teaching includes modules on machine learning and artificial intelligence concepts.

• Digital Humanities
• Artificial Intelligence Ethics
• Machine Learning Epistemology
• +۳ مورد دیگر

Fabian Offert is an Assistant Professor in the Department of History and Theory of the Digital Humanities at the University of California, Santa Barbara (UCSB). He serves as Director of the Center for the Humanities and Machine Learning and Principal Investigator of the AI Forensics Project, funded by the Volkswagen Foundation. His research focuses on the epistemology, aesthetics, and politics of artificial intelligence, with a current book project exploring how AI models represent cultural concepts. Offert’s academic background includes roles at German institutions such as ZKM Karlsruhe and Goethe-Institut New York. His work bridges humanities and technical disciplines, advocating for methodologically rigorous critical approaches to AI. Key themes in his research include the limitations of existing critical frameworks, the material grounding of AI critiques, and the intersection of machine learning with visual and cultural studies. Recent projects include the Critical Machine Learning Studies initiative, funded by UCHRI, which emphasizes situated analyses of specific machine learning systems. His pedagogical efforts aim to counteract misinformation about AI, equipping students with tools to critically engage technical systems through close reading of models and technical papers. Offert’s upcoming engagements include lectures at institutions like UC Berkeley, MIT, and UW Seattle, addressing topics such as the cultural politics of computational imagery and ethical dimensions of AI. His work consistently advocates for interdisciplinary collaboration and technically informed humanities scholarship.

• Cancer
• Clinical Trials
• Cardiovascular Disease
• +۳ مورد دیگر

Professor John Simes AO is a Professor of Clinical Epidemiology at the University of Sydney's Faculty of Medicine and holds leadership roles at the NHMRC Clinical Trials Centre (CTC), including Senior Associate Director and Co-Director of Cancer Trials. He has led over 100 multicenter clinical trials, notably the LIPID and FIELD trials, contributing to global guidelines in cardiovascular and diabetes care. His research spans cancer, cardiovascular disease, neonatal medicine, and clinical trial methodology. Education: BSc (Med), MBBS, FRACP, SM (Harvard), MD. He is a practicing Medical Oncologist at Royal Prince Alfred Hospital and Chris O'Brien Lifehouse. Research Interests: Large-scale clinical trials in oncology (e.g., gastrointestinal, genitourinary cancers), cardiovascular risk reduction, and neonatal medicine. Notable contributions include demonstrating pravastatin's survival benefits (LIPID trial) and fenofibrate's microvascular benefits (FIELD trial). Current trials include INTEGRATE (gastric cancer) and INSPIRE (aspirin in VTE). Awards: Order of Australia (2020), MOGA Cancer Achievement Award (2010), Harvard Biostatistics Alum Award (2009), and R.T. Hall Prize (1999). Advising: Supervises students like Janene Dalrymple on translational research. Grants: Holder of NHMRC Senior Principal Research Fellowship and Program Grants. Labs/Teams: Leads CTC's oncology trials group and chairs INSPIRE Steering Committee.

• Transportation Engineering
• Intelligent Transportation Systems
• Infrastructure Resilience
• +۷ مورد دیگر

Sean Qian is a Professor at Carnegie Mellon University (CMU), jointly appointed in the Department of Civil and Environmental Engineering, Heinz College of Information Systems and Public Policy, and the Department of Electrical and Computer Engineering. He directs the Mobility Data Analytics Center (MAC) and co-founded TraffiQure Technologies to commercialize AI-driven infrastructure solutions. His research focuses on dynamic network modeling, intelligent transportation systems, climate resilience, and infrastructure interdependency. Supported by NSF, U.S. DOT, and industry partners, his work integrates AI, big data, and policy analysis to address urban mobility challenges. Education: PhD in Civil Engineering (UC Davis, 2011), MS in Statistics (Stanford, 2012), and dual MS/BS in Civil Engineering (Tsinghua University, 2006/2004). Research emphasizes smart cities, EV integration, cybersecurity for infrastructure, and equity in mobility policies. He serves on editorial boards for Transportation Research journals and TRB committees. Awards include the NSF CAREER Award (2018) and Greenshields Prize (2017). Grants and collaborations include projects with Fujitsu, IBM, and state agencies, addressing curbside management, climate adaptation, and rural mobility. His lab develops tools like the Rural Access Mobility Platform and social digital twin technologies for infrastructure resilience.

Nguyen Thanh Sonمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Multimodal Deep Learning
• Knowledge Base Systems
• +۳ مورد دیگر

Nguyen Thanh Son is a Research Scientist at the Artificial Intelligence Initiative under the Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore. He holds a PhD in Information Systems from the School of Information Systems, Singapore Management University (SMU), where he was advised by Associate Professor Hady Lauw. His research focuses on natural language processing, opinionated text mining, and multimodal deep learning. Previously, he completed a visiting PhD program at Carnegie Mellon University (CMU) and an internship at IBM Research Lab in Dublin, Ireland. He also earned a Bachelor of Information Systems from the University of Engineering and Technology (UET), Vietnam National University, Hanoi, with academic distinctions in research. Research Interests: His work spans natural language processing, emotion recognition, knowledge base systems, and agentic AI. He has contributed to advancements in large language models, multimodal encoding, and retrieval-augmented systems. Grants & Awards: He secured a Singapore Aerospace Programme grant (SGD 360,000) as PI and co-led an A*STAR grant (SGD 6 million). His accolades include the SMU Presidential Doctoral Fellowship and top research prizes during his undergraduate studies. Labs & Teams: Active in IHPC's AI initiatives and collaborated with CMU and IBM on projects like sentiment analysis and ontology building. His work bridges academic research and industrial applications in AI.

Ivan Adriyanov Nikolovمشاهده پروفایل

استادیار

• Computer Graphics
• Computer Vision
• Augmented Reality
• +۶ مورد دیگر

Ivan Adriyanov Nikolov is an Assistant Professor at the Department of Architecture, Design and Media Technology within Aalborg University's Technical Faculty of IT and Design. He specializes in Computer Graphics, Computer Vision, and Augmented Reality, with a focus on 3D reconstruction techniques like Structure-from-Motion (SfM). His work bridges academic research and industrial applications, particularly in wind turbine blade inspection and educational technology. His educational background includes contributions to computer science education through innovative teaching methods. He has led projects like 'Drone Application for Pioneering Reporting in Wind Turbine Blade Inspection' (2017–2019) and 'Leading Edge Roughness - Wind Turbine Blades' (2015–2019), advancing drone-based inspection and 3D modeling for wind energy sectors. Research interests include synthetic data generation, environmental monitoring datasets (e.g., BrackishMOT, DigiWeather), and improving VR/AR user experiences. He has developed tools for dynamic lighting in pixel art games and multimodal guardian systems in VR. His datasets, such as Sewer Defect Point Clouds and Wind Turbine Blade SfM Reconstructions, are publicly available for academic use. He actively contributes to educational innovation, such as flipped classroom strategies to boost programming class engagement. His interdisciplinary approach spans computer graphics, AI-driven NPC interactions, and collaborative mixed-reality games for trust-building. Labs/Teams: Member of the Computer Graphics Group and Visual Analysis and Perception team at Aalborg University. Collaborates with industry partners on drone technology and environmental surveillance systems.

Hans-Peter Seidelمشاهده پروفایل

استاد

• Computer Graphics
• 3D Image Analysis
• Digital Geometry Processing
• +۳ مورد دیگر

Hans-Peter Seidel is a leading academic in computer graphics, serving as Director of the Max Planck Institute for Informatics and Full Professor at Saarland University since 1999. Previously held roles include Full Professor at University of Erlangen (1992–1999) and Assistant Professor at University of Waterloo (1989–1992). Holds a PhD in Mathematics (1987) and Habilitation in Informatics (1989) from University of Tübingen. Research focuses on 3D image analysis, digital geometry processing, visual computing, and free viewpoint rendering. Key achievements include pioneering work in surface editing, motion capture, and multi-view video processing. Has organized major conferences like Eurographics and SIGGRAPH, serving as editor for journals including IEEE TVCG and Computer Aided Geometric Design. Recipient of the Eurographics Distinguished Career Award (2012), Gottfried Wilhelm Leibniz Prize (2003), and numerous fellowships. Led initiatives such as the Cluster of Excellence on Multimodal Computing and Interaction (M2CI) and the Max Planck Center for Visual Computing and Communication (MPC-VCC). Active in academic leadership roles including Eurographics Chair and DFG committees. Publications span over 30 SIGGRAPH and 50 Eurographics contributions, with an h-index of 62 and 14,000+ citations. Recognized as a top-cited researcher in computer graphics. Current research explores advanced visualization techniques and geometric modeling innovations.

• Behavioral Pharmacology
• Epilepsy Research
• Neuropharmacology
• +۶ مورد دیگر

Dr. Misty Smith is a Research Associate Professor at the University of Utah's School of Dentistry and holds a joint appointment in the Department of Pharmacology and Toxicology. Her research focuses on epilepsy and antiseizure drug development, particularly investigating cannabidiol interactions, behavioral comorbidities of epilepsy, and neuropeptide roles in neurological conditions. She leads NIH-funded studies in the Anticonvulsant Drug Development Program, ensuring regulatory standards and preclinical model rigor. Dr. Smith's work emphasizes therapeutic potential evaluation using isobolographic analysis and pharmacokinetic studies. Educations: BS in Biology, Muskingum University, 1996 PhD in Physiology, University of South Carolina School of Medicine, 2001 Research Interests: Dr. Smith's research integrates behavioral pharmacology with preclinical epilepsy models to address comorbidities like neuropsychiatric conditions. She explores cannabidiol's efficacy in combination therapies and investigates neuropeptide systems (e.g., neurotensin, galanin) and steroid hormones' roles in seizure modulation. Her work highlights sex differences in neurological responses and drug interactions to improve treatment safety and efficacy. Key Awards: Targeted Research Initiative for Cannabidiol in Epilepsy (TRICE) Award (2015) Distinguished Doctorate Alumni Award (2013) Elected Member-At-Large of ASPET's Neuropharmacology Division (2012) Grants & Funding: Isobolographic Analyses of Cannabidiol Interactions with Antiseizure Drugs (Epilepsy Foundation, 2014–2016) Multiple grants focused on epilepsy drug screening and cannabidiol's mechanisms Labs & Teams: Core member of the NIH-sponsored Anticonvulsant Drug Development (ADD) Program, coordinating preclinical research and regulatory compliance.

Alexander Lexمشاهده پروفایل

دانشیار

• Data Visualization
• Human-Computer Interaction
• Reproducible Research
• +۳ مورد دیگر

Professor Alexander Lex is an Associate Professor of Computer Science at the University of Utah, affiliated with the Scientific Computing and Imaging (SCI) Institute and the Kahlert School of Computing. He directs the Visualization Design Lab, focusing on interactive visual data analysis for scientific fields like biology and medicine. His work emphasizes reproducibility, accessibility for visually impaired users, and infrastructure for crowd-sourced user studies. He co-founded Datavisyn, a startup for pharmaceutical visualization solutions. Education: Lex holds a PhD, master’s, and undergraduate degrees from Graz University of Technology. He was a postdoctoral researcher at Harvard University and a visiting researcher at Harvard Medical School. Research Interests: His areas include data visualization, human-computer interaction, reproducible workflows, and accessibility. Recent work explores mitigating data misinformation through visualization guardrails and scalable evaluation frameworks like reVISit. Articles Trends: Recent publications address ethical visualization (e.g., cherry-picking mitigation), accessibility (text descriptions for UpSet), and multimodal visualization (Aardvark). His work spans journals like IEEE TVCG and conferences like CHI/IEEE VIS, often focusing on user-centric design and system integration. Awards: NSF CAREER Award, 10-year InfoVis Test of Time Award (2024), multiple best paper awards at IEEE VIS and CHI. Advising & Grants: Supervises 4 current PhD students and has graduated 15+ students. Leads grants including NSF-funded reVISit and NIH projects on cell microscopy visualization. Serves as Director of Graduate Studies for the Kahlert School. Labs & Teams: Visualization Design Lab (VizLab) develops tools like UpSet, reVISit, and MultiNet. Collaborates with industry (e.g., Datavisyn) and academic partners on visualization infrastructure.

• AI and Robotics
• Human-Robot Interaction (HRI)
• Autonomous Agents
• +۷ مورد دیگر

Dr. Abolfazl Zaraki is a Senior Lecturer in AI and Robotics at the University of Hertfordshire's Department of Computer Science, part of the School of Physics, Engineering & Computer Science. He leads the Robotics Research Group and previously held roles at Cardiff University's School of Engineering and the IROHMS Research Centre. His academic journey includes a Master's in Mechatronics from University Technology Malaysia (2010) and a PhD in Automatic Robotic and Bioengineering from the University of Pisa (2014). He has held Research Fellow positions in Italy and the UK until 2019. Dr. Zaraki's research focuses on AI-driven autonomous systems, social robotics, and assistive technologies. Key projects include the EASEL, BabyRobot, and JAMES EU initiatives, alongside the Innovate UK-funded InSight project. His work emphasizes Human-Robot Interaction (HRI), trusted autonomy, and applications in healthcare and industrial contexts. Notable contributions include the development of the Kaspar humanoid robot for autism therapy and advancements in reinforcement learning for robotic control. His recent publications (2021–2025) explore agentic AI, memory-driven systems, and personalized LLMs for HRI, alongside technical advancements in robotic control, communication systems, and bio-inspired robotics. His research bridges theoretical AI innovation with practical applications in healthcare, education, and industrial automation. Zaraki has collaborated internationally across institutions and industry partners, contributing to 35+ research outputs. His work aligns with global trends in ethical AI, explainable systems, and human-centric robotics design.

• Audio Signal Processing
• Music Information Retrieval
• Natural Language Processing (NLP)
• +۳ مورد دیگر

Panagiotis G. Zervas is an Associate Professor at the Department of Electrical and Computer Engineering, University of Peloponnese (since 2020). His expertise spans audio signal processing, music information retrieval, and natural language processing for knowledge extraction. He teaches courses including Signals & Systems, Digital Signal Processing, and Machine Learning. His research focuses on AI-driven applications in sound analysis, music feature extraction, and multimodal information processing. Education: PhD (2007) in Electrical Engineering from the University of Patras, specializing in Greek prosody modeling for text-to-speech systems. Previous roles include Assistant Professorships at Hellenic Mediterranean University (2015–2020) and Technical Educational Institute of Crete (2008–2015). Research Interests: Natural Language Processing (NLP) for text analysis and large language models (LLMs) Audio signal processing, voice analysis, and embedded systems AI applications in job market analytics and skills frameworks Machine learning for music information retrieval Notable Projects: Principal Investigator in EU projects EU-ALMPO (2025–), Train4Blue (2025–), GROWTH4BLUE (2024–), and MICROIDEA (2024–) World Bank consultant (2023–) for AI-driven employment systems in Greece and Pacific Islands Publications in journals like 'Acoustics' and conferences like WAC 2022 and Forum Acusticum 2023 Office: Building K, Office K2.07 | Contact: pzervas@uop.gr

• Computer Vision
• Multimodal Learning
• Generative Models
• +۳ مورد دیگر

Dr. Yuanhong Chen is a Postdoc Researcher at the Australian Institute for Machine Learning (AIML) within the University of Adelaide's Division of Research and Innovation. His work focuses on advancing computer vision, multimodal learning, and generative models, particularly in medical image analysis and audio-visual perception. He explores integrating large language models for cross-modal understanding to enhance AI systems' interpretability and robustness. Research interests include: Interpretable AI frameworks for medical imaging Semi-supervised and self-supervised learning techniques Audio-visual contextual learning and binaural audio generation Applications in breast cancer screening and disease classification Recent publications highlight innovations in prototype-based learning for medical diagnosis, cross-modal segmentation, and unsupervised anomaly detection. His work on Braix risk score and AutoCumulus demonstrates contributions to automated medical biomarker development. Collaborations within AIML and interdisciplinary projects at the University of Adelaide underscore his commitment to bridging foundational AI research with real-world healthcare applications.