جست‌وجوی استاد و پژوهشگر

Srirangaraj Setlurمشاهده پروفایل

پژوهشگر

• AI
• Machine Learning
• Pattern Recognition
• +۶ مورد دیگر

Srirangaraj (Ranga) Setlur is a Principal Research Scientist and Co-Director of the Center for Unified Biometrics and Sensors (CUBS) at the University at Buffalo. He also serves as the Associate Director of Community Engagement at the Institute for Artificial Intelligence and Data Science. His research focuses on AI, Machine Learning, Pattern Recognition, Computer Vision, and Information Retrieval with applications in biometrics, document analysis, and healthcare. Education: MS in Industrial Engineering, University at Buffalo (1995) Research Interests: Development of AI-driven systems for biometric authentication (e.g., fingerprint, facial recognition) Design of datasets for chart analysis (CHART-Info), gait recognition (DIOR), and cross-domain fingerprint analysis Applications in healthcare diagnostics (e.g., dyslexia screening via handwriting analysis) Advancements in multimodal fusion (e.g., audio-visual, physiological signals) Recent Research Trends: His recent work emphasizes cross-domain learning (e.g., Ridgeformer), sparse feature aggregation (Proxyfusion), and AI applications in social robotics (AutoMisty). He consistently addresses challenges in unconstrained environments and under-represented data scenarios. Awards: Senior Member, IEEE 2019 ICDAR Best Student Paper Award (F. Xu) 2010 IBM Best Student Paper Award (X. Peng) UB Visionary Innovator Award Labs/Teams: Leads research teams in CUBS and the Institute for AI & Data Science, focusing on biometric systems, surveillance optimization, and multimodal AI applications.

Ifeoma Nwoguمشاهده پروفایل

دانشیار

• Human Behavior Modeling
• Sign Language Understanding
• Probabilistic Modeling
• +۵ مورد دیگر

Ifeoma Nwogu is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo. She holds an appointment in the School of Engineering and Applied Sciences, specializing in research areas including human behavior modeling, sign language understanding, and probabilistic modeling. Her work spans computer vision, robotics, and AI with applications in healthcare, education, and social signal processing. Her research focuses on advancing technologies for sign language interpretation, infant vocal analysis, and human-robot interaction. Key projects include developing automated code generation frameworks for social robots, 3D motion synthesis systems for sign language avatars, and methodologies for emotion recognition in facial interactions. She teaches courses such as CSE 455/555 (Pattern Recognition), CSE 610 (Special Topics), and CSE 706 (Seminars), reflecting her expertise in AI and machine learning. Nwogu's recent publications emphasize cross-disciplinary applications of machine learning, including medical imaging anonymization, multi-agent systems, and ethical considerations in data privacy. Her work bridges computational methods with societal needs, particularly in education accessibility for deaf communities and stroke rehabilitation technologies.

• Linguistics
• Sign Language Research
• Computational Linguistics
• +۳ مورد دیگر

Vadim Kimmelman is a Professor at the Department of Linguistic, Literary and Aesthetic Studies at the University of Bergen . His research integrates computational methods with sign language analysis, focusing on formal properties of nonmanual markers and depicting constructions. He collaborates internationally with institutions like the University of Amsterdam (UvA), NTNU, and the University of Western Norway. Active Project : Fundamentals of formal properties of nonmanuals: A quantitative approach (University of Bergen) Completed Projects : Whole-entity classifiers: a multiperspective approach (Center for Basic Research); Signed Language Depiction as an Engine for Promoting Inclusion, Communication, and Translation (DEPICT) (University of Western Norway) Research Trends : Recent publications highlight his interdisciplinary work combining linguistics , computer vision , and machine learning to analyze sign language syntax (e.g., classifier constructions), head pose estimation algorithms, and eye blink detection in sign language corpora. His work addresses both theoretical questions (e.g., language-specific perception of depicting signs) and technological applications. Affiliations : University of Bergen (Department of Linguistic, Literary and Aesthetic Studies), University of Amsterdam (collaborative research), NTNU, and University of Western Norway (DEPICT project).

Lin Geng Fooمشاهده پروفایل

پژوهشگر

• Video Analysis
• Activity Understanding
• Diffusion Models
• +۲ مورد دیگر

Lin Geng Foo is a postdoctoral researcher at the Max-Planck Institute for Informatics under the Visual Computing and Artificial Intelligence (VCAI) Department, supervised by Prof. Christian Theobalt. He earned his PhD and Bachelor's from the Singapore University of Technology and Design (SUTD) with the President's Graduate Fellowship and top honors. His research focuses on video analysis , activity understanding , diffusion models , and dynamic neural networks , particularly for 3D human generation/editing . His work includes groundbreaking papers on adversarial attacks, human mesh recovery, and pose estimation. Key trends in his recent publications include applications of diffusion models dynamic networks 3D human modeling video analysis action recognition neural rendering for tasks like mesh recovery, controllable video editing, and sign language translation. Scientific awards include PREMIA Best Student Paper 2023 Commendation Prize Keppel Awards of Excellence (2018, 2019) EY Business Analytics Award He serves as a reviewer for major conferences (CVPR, ICCV, NeurIPS, ICML) and has presented talks at ICME 2024 Metaverse Workshop and SDSC Industry & Demo Day 2024.

Niels Peekمشاهده پروفایل

استاد

• Health Informatics
• Translational Data Science
• Risk Prediction
• +۸ مورد دیگر

Niels Peek is Professor of Health Informatics in the Division of Informatics, Imaging & Data Sciences within the Faculty of Biology, Medicine and Health at the University of Manchester. He serves as Director of the Christabel Pankhurst Institute for Health Technology Research and Innovation, Co-director of the International Centre for Translational Digital Health, and Digital Infrastructure Lead for the NIHR Manchester Biomedical Research Centre. His leadership extends to roles as Academic Lead for the North West Secure Data Environment and Theme Lead for Health and Care within Digital Futures. Peek holds a PhD in Computer Science from Utrecht University (2000) and MSc degrees in both Computer Science and Artificial Intelligence from the same institution (1994). His methodological expertise spans the design and evaluation of informatics interventions, computerized decision support systems, statistical modeling, machine learning, data mining, qualitative research methods, and technology acceptance models. His research program focuses on translational data science applications in healthcare, particularly in risk prediction, personalized and precision medicine, patient safety, and managing patients with multiple conditions (multimorbidity). Analysis of his recent publications reveals a strong emphasis on practical applications of AI and data science in clinical settings, with particular attention to electronic health records, clinical decision support, and the evaluation of digital health interventions across various healthcare contexts including primary care, hospital settings, and chronic disease management. Peek has received significant recognition for his contributions to the field: Fellow of the Alan Turing Institute Fellow of the American College of Medical Informatics Fellow of the International Academy of Health Sciences Informatics President of the Society for Artificial Intelligence in Medicine (2013-2017) His editorial leadership includes serving as Associate Editor for BMJ Health & Care Informatics and on the editorial boards of npj Digital Medicine, Journal of the American Medical Informatics Association, Artificial Intelligence in Medicine, and Health Data Science journals. He has secured substantial research funding including participation in the BHF Centre for Research Excellence 2024-2029 and leadership roles in multiple active projects focused on digital health, AI applications in medicine, and opioid management research. Peek leads the Christabel Pankhurst Institute for Health Technology Research and Innovation and co-directs the International Centre for Translational Digital Health, creating collaborative environments that bridge computer science, statistics, and clinical practice to develop practical healthcare solutions. His work contributes significantly to UN Sustainable Development Goals through the University of Manchester's research beacons in Digital Futures, Data Science and AI, Sustainable Futures, and Healthier Futures.

Raúl Aranovichمشاهده پروفایل

استاد

• Theoretical Linguistics
• Syntax
• Morphology
• +۱۴ مورد دیگر

Raúl Aranovich is a Professor in the Department of Linguistics at the University of California, Davis, within the College of Letters and Science. He has been a faculty member at UC Davis since 2001 and previously held academic positions at Ohio State University and the University of Texas at San Antonio. He earned his Ph.D. in Linguistics from UC San Diego in 1996 under the supervision of Professors S.-Y. Kuroda and John Moore. His research centers on theoretical linguistics, particularly the interfaces between syntax, morphology, and semantics, with a focus on grammatical mismatches across these domains. He specializes in Romance languages—especially Spanish—and lesser-studied languages such as Fijian and Shona. His work increasingly integrates computational methods, including corpus linguistics, natural language processing, and knowledge representation via ontologies. He has also contributed to interdisciplinary research in cybersecurity communication and neural machine translation. The recent publications of Raúl Aranovich span a wide range of subfields, from historical linguistics and construction grammar to computational modeling and cross-linguistic typology. A strong trend in his work is the use of formal and empirical methods to analyze syntactic variation, auxiliary selection, impersonal constructions, and morphological complexity, often with a comparative approach across languages. His interdisciplinary collaborations, especially with computer scientists, reflect a growing interest in applying linguistic theory to real-world digital communication and security challenges. Fellow, Linguistic Society of America Raúl Aranovich has mentored numerous students and collaborators, though specific advisees are not listed in the provided text. He has secured research funding through collaborative projects, particularly in computational linguistics and cybersecurity, as evidenced by co-authored publications with researchers in computer science. His work on low-resource machine translation and semantic web applications suggests active grant-supported research. He is involved in modeling grammars using OWL ontologies and has explored language use in digital communities such as GitHub and cybersecurity forums. His research group likely includes students and postdocs working at the intersection of linguistics and computer science, particularly in NLP and knowledge representation.

Kalin Stefanovمشاهده پروفایل

پژوهشگر ارشد

• Affective Computing
• Artificial Intelligence
• Accessible Computing
• +۵ مورد دیگر

Kalin Stefanov is an ARC DECRA Fellow and Research Fellow in the Department of Human Centred Computing at Monash University. He holds a PhD in Computer Science from KTH Royal Institute of Technology and an MSc in Artificial Intelligence from the University of Amsterdam. His research focuses on Affective Computing, exploring systems that recognize and simulate human affects, with applications in social robotics, neurodiverse communication, and multimodal interaction. He has led projects on sign language translation and large-scale deepfake detection datasets. Key collaborations include work at the University of Southern California’s Institute for Creative Technologies and National Institute of Informatics. He has received accolades such as the Discovery Early Career Researcher Award (2023) and Best Paper Awards (2019, 2024). His research also contributes to UN SDG 4 (Quality Education) through accessible technologies for neurodiverse groups and visually impaired learners. Projects include the 'Active Generation of fingerspelling in Australian Sign Language' and 'Research Towards automated Australian Sign Language translation,' funded by the Australian Research Council. Stefanov’s work spans AI ethics, multimodal data platforms (e.g., OpenSense), and systems for social signal processing in human-robot interaction.

Laurence Meurantمشاهده پروفایل

پژوهشگر

• Sign Language Linguistics
• Contrastive Linguistics (French-LSFB)
• Corpus Linguistics
• +۳ مورد دیگر

Laurence Meurant is a Qualified Researcher at the University of Namur since 2011, leading the LSFB-Lab - the first and only research team dedicated to French Belgian Sign Language (LSFB). She has been instrumental in establishing sign language linguistics in the French-speaking Belgium region since her 2006 PhD thesis on gaze in LSFB. Her work bridges theoretical linguistics with applied research in bilingual education, serving as scientific expert for Sainte-Marie Inclusive School since 2004. Education: Doctor of Letters (2006) from University of Namur Awards: Van Goethem-Brichant Prize (2004) Beloeil Clinic Prize (2006) Namur Citizen of the Year (2012) Best Educational Book Prize (2022) Her research focuses on LSFB linguistics through corpus studies (including the first representative LSFB corpus), contrastive analysis with French, and multimodal approaches to discourse markers. She leads an 11-member interdisciplinary team working on parallel corpora development and sign language video recognition systems. Recent publications emphasize bilingual education frameworks , sign language technology , and discourse reformulation across spoken and signed modalities. Her scientific activities demonstrate continuous collaboration with educational institutions and technology developers to improve accessibility for deaf communities. As academic spokesperson for technological platforms and member of various university committees, she promotes methodological innovation in human sciences while maintaining active involvement in deaf education policy and sign language recognition .

• Natural Language Processing
• Dialogue Systems
• Machine Translation
• +۲ مورد دیگر

Luísa Coheur is an Associate Professor at the Department of Computer Science , Instituto Superior Técnico (University of Lisbon), and a researcher at INESC-ID 's Human Language Technologies group. She served on the Management Committee of IST-Taguspark (2020-2023), overseeing pedagogical organization and library services. Education: Ph.D. in Natural Language Processing (IST/Université Blaise-Pascal) Postgraduate in Higher Education Pedagogy (University of Lisbon, 2023) Degree in Applied Mathematics and Computer Science (IST) Research Interests: Specializes in NLP with focus areas in: Dialogue systems and conversational AI Machine translation (including Portuguese Sign Language) Question answering architectures Educational technology and accessibility Cyberbullying detection in social media Her work integrates linguistic theory with machine learning for real-world applications. Publication Trends: Over 120 publications emphasizing machine translation evaluation (e.g., fine-grained error detection), NLP for social good (cyberbullying datasets), sign language processing, and educational tools. Recent works leverage active learning and transformer models for low-resource scenarios. Awards: IST Outstanding Teaching Award (2023) Recognized as 'Excellent Professor' >20 times via IST QUC Advising & Projects: Supervised 10+ PhD and 80+ Master's students. Secured participation in 17 national/international projects (e.g., EU-funded initiatives in NLP). Leads pedagogical innovation like educational escape games for STEM courses. Labs/Teams: Core member of INESC-ID's Human Language Technologies group , developing resources for Portuguese NLP. Collaborates with clinicians on assistive tech (e.g., VITHEA-Kids for autism language skills).

• Digital Signal Processing
• Image Processing
• Computer Vision
• +۲ مورد دیگر

Dimitrios Kosmopoulos serves as Professor in the Computer Engineering and Informatics Department at the University of Patras, Greece, with extensive experience across multiple academic institutions including National Technical University of Athens (NTUA), Rutgers University, and University of Texas at Arlington. His research bridges theoretical computer science with practical applications in accessibility, agriculture, and cultural heritage preservation. Education: B.Eng. in Electrical and Computer Engineering, National Technical University of Athens (1997) PhD in Electrical and Computer Engineering, National Technical University of Athens (2002) Professor Kosmopoulos' research integrates computer vision, machine learning, and signal processing to solve real-world problems. His primary focus areas include sign language recognition systems for museum accessibility, precision agriculture applications for crop monitoring and disease detection, and digital restoration of ancient scripts like Mycenaean Linear B. His methodological innovations frequently involve geometric analysis, time-series modeling, and multimodal data fusion techniques that advance both theoretical frameworks and practical implementations. Analysis of his recent publications (2023-2025) reveals three dominant research thrusts: accessibility technologies for deaf communities (particularly museum navigation systems), agricultural automation using computer vision (olive grading, tomato disease detection), and computational archaeology (Linear B tablet restoration). His work consistently employs cutting-edge approaches including geometric knowledge distillation, coupled learning architectures, and 3D motion analysis, demonstrating strong interdisciplinary connections between computer science, agriculture, and humanities. No scientific awards were documented in the provided source materials. While specific advising details and grant information were not explicitly stated, his leadership in projects like HealthSign (sign language healthcare systems) and MuseLearn (museum accessibility platforms) indicates substantial research funding and collaborative supervision activities spanning computer vision, robotics, and assistive technology domains. Professor Kosmopoulos operates within the Division of Hardware and Computer Architecture at the University of Patras, collaborating with the Computer Technology and Architecture Laboratory, VLSI Microelectronics Laboratory, Signals and Telecommunications Laboratory, and Computer Communications Networks Laboratory. His current research integrates these facilities to develop systems like the SignGuide project for museum tours and frameworks for early pest detection in greenhouse crops, emphasizing practical implementations of machine learning in constrained environments.

Aaron Kaatمشاهده پروفایل

دانشیار

• Neurodevelopmental Disorders
• Patient-Reported Outcome Measures
• Cognitive Assessment Tools
• +۳ مورد دیگر

Aaron Kaat is an Associate Professor in the Department of Medical Social Sciences at Northwestern University's Feinberg School of Medicine. His research focuses on neurodevelopmental disorders, patient-reported outcome measures (PROMs), cognitive assessment tools, and psychometrics. He is affiliated with institutions such as the Institute for Innovations in Developmental Sciences, the Center for Patient-Centered Outcomes, and the Northwestern University Clinical and Translational Sciences Institute (NUCATS). Kaat's work involves developing and validating standardized tools like the NIH Baby Toolbox® and Mobile Toolbox for assessing neurodevelopmental domains including executive function, memory, language, motor skills, and social-emotional functioning. His recent publications examine pandemic impacts on early intervention systems, cross-cultural psychometric validation, and clinical trial readiness for rare disorders like SCN2A-related syndromes and Duchenne muscular dystrophy. He employs mixed-methods approaches, simulation studies, and novel psychometric methodologies such as bookmarking and measurement invariance analysis. Research also includes technology-based assessments (iPad, smartphone platforms), caregiver-mediated interventions, and addressing biases in developmental measurements across diverse populations.

Robert Mikael Östlingمشاهده پروفایل

دانشیار

• Computational Linguistics
• Natural Language Processing
• Multilingual NLP
• +۳ مورد دیگر

Robert Mikael Östling is a Docent (Associate Professor) in Computational Linguistics at Stockholm University's Department of Linguistics. His research spans natural language processing with a multilingual perspective, computational typology, and applications of NLP to psycholinguistic investigations. Position: Docent (Associate Professor) Institution: Stockholm University Department: Linguistics Workplace: Room C 338, Universitetsvägen 10 C, plan 2-3 Östling's research focuses on natural language processing applications including part-of-speech tagging, machine translation, and word alignment from a multilingual perspective. He also utilizes NLP tools in computational typology to understand global language structures and to automate psycholinguistic investigations. His work bridges theoretical linguistics with practical NLP applications. His recent publications reveal consistent focus on multilingual language models, language representations, and cross-linguistic patterns. The research demonstrates strong connections between computational approaches and linguistic theory, with applications ranging from sign language analysis to machine translation systems. Östling serves as principal investigator for the Swedish Research Council funded project "Structured multilinguality for natural language processing" and is involved in the National Graduate School in Digital Philology (DigPhil) and Swe-CLARIN initiatives. His technical expertise spans neural language models, multilingual processing, and computational analysis of linguistic structures across diverse language families.

Archie Singh Khumanمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Computational Intelligence
• Machine Learning
• +۵ مورد دیگر

Dr. Archie Singh Khuman serves as Associate Head of Education and Senior Lecturer at De Montfort University's School of Computer Science and Informatics. An award-winning academic and proud DMU alumnus (BSc 2009, MSc 2011, PhD 2017), he specializes in Artificial Intelligence, Computational Intelligence, and Uncertainty Modelling with extensive leadership in curriculum development and Universal Design for Learning. His educational background demonstrates deep institutional commitment: PhD in Computer Science (2017, De Montfort University) MSc in Intelligent Systems (2011, De Montfort University) BSc (Hons) in Computer Science (2009, De Montfort University) Dr. Khuman's research pioneers hybrid uncertainty frameworks, notably the R-fuzzy grey analysis framework (RfGAf), bridging Grey Theory, Fuzzy Logic, and Rough Sets. His work focuses on quantifying perception-based uncertainty for applications in healthcare diagnostics, robotic swarm intelligence, and risk assessment systems, emphasizing real-world implementation through industry partnerships. His publication trajectory reveals increasing interdisciplinary convergence, particularly in healthcare AI applications since 2021. Recent outputs integrate Grey-Fuzzy methodologies for medical diagnostics (diabetes, heart disease), sentiment analysis of pandemic communications, and adaptive systems in automotive/industrial contexts, demonstrating strong translation from theoretical foundations to societal impact. Major recognitions include: Five consecutive Students' Choice Teaching Awards (2018-2024) De Montfort Teacher Fellowship (2022) Senior Fellow of the Higher Education Academy (2022) Leverhulme Trust-funded international Grey Systems network (£124,997) Best Paper Award at Grey Systems and Uncertainty Analysis (2016) As academic lead for the Data Analytics Project, he mentored students in developing machine learning solutions that generated £2.5m savings for retail partners. His grant portfolio includes Royal Society funding for IoT-based scheduling (£12,000) and leadership of the Race Equality Network. Current initiatives focus on transnational education partnerships and SI-PASS peer learning programs. Dr. Khuman co-leads DMU's Institute of Artificial Intelligence research group and serves as School Governor for Inglehurst Junior School, maintaining active collaborations with institutions in China, Canada, Romania, and Spain through the International Network on Grey Systems.

• Artificial Intelligence in medicine
• natural language processing
• deep learning
• +۴ مورد دیگر

LCDR Gregory J Booth is an Assistant Professor at the Uniformed Services University of the Health Sciences (USUHS) School of Medicine, Department of Anesthesiology. He serves as Program Director of the Anesthesiology Residency and Vice Chair of the Institutional Review Board at Naval Medical Center Portsmouth (NMCP), while cofounding the Naval Biotechnology Group for medical device innovation. His educational trajectory includes: M.S. (candidate) in Data Science, Drexel University (2020-present) Anesthesiology Residency, McGaw Medical Center, Northwestern University (2014-2018) M.D., Feinberg School of Medicine, Northwestern University (2010-2014) B.S. in Biomedical Engineering (magna cum laude), Washington University in St. Louis (2006-2010) Booth's research bridges artificial intelligence and clinical medicine, with emphasis on natural language processing for orthopedic data, deep learning for surgical outcome prediction, and computer vision applications in critical care. His work in hemodynamic monitoring pioneers non-invasive techniques for hemorrhage detection in military settings, while his quality improvement initiatives target opioid stewardship and surgical complication prevention through algorithmic approaches. His 2020-2021 publications reveal a cohesive trajectory in machine learning-driven clinical tools, with military-relevant applications dominating 70% of output. Key themes include predictive modeling for surgical complications (fascial dehiscence, opioid dependence), hemodynamic monitoring innovations, and low-cost medical devices for austere environments—demonstrating consistent integration of data science with military medical challenges. Major recognitions include: Dr. A. Lawrence and Ellen S. Wilson Award for Advancing Clinical Education through Innovation (2017) NMCP Anesthesiology Teacher of the Year Award (2020) Navy-wide Academic Research Competition 1st Place for AI Hemodynamic Prediction (2020) NMCP Medical Corps Junior Officer of the Quarter (Fall 2019) FAER Resident Scholar designation Booth actively mentors USUHS and Eastern Virginia Medical School students alongside NMCP residents, with research funding secured through Navy channels supporting his Naval Biotechnology Group's work on the Portsmouth Ventilator and 3D-printed laryngoscopes. His grant portfolio emphasizes translational projects with direct battlefield applicability. The Naval Biotechnology Group operates as a cross-functional military-civilian team developing field-deployable medical solutions, with current focus on AI-integrated monitoring systems and rapid-manufacture devices for combat casualty care in resource-constrained environments.

Jorge Isaac Chairez-Oriaمشاهده پروفایل

دانشیار

• Biomedical Engineering
• Neural Networks
• Robotic Control Systems
• +۴ مورد دیگر

Jorge Isaac Chairez-Oria is an Associate Professor in the Department of Biomedical Engineering at Tecnológico de Monterrey's School of Engineering, Campus Guadalajara. He maintains an active research program bridging biomedical engineering, neural networks, and robotic control systems with significant contributions to rehabilitation technology and sustainable manufacturing. His research focuses on neural network applications for system identification and control , particularly in constrained dynamical systems. His work spans medical rehabilitation robotics (including exoskeletons and orthotic devices), bioprinting and tissue engineering , and sustainable environmental technologies utilizing neural network approaches. Recent publications demonstrate strong interdisciplinary work connecting engineering principles with biological and medical applications. Analysis of his 2024-2025 publications reveals consistent focus on barrier Lyapunov functions, constrained state systems, and neural network identification methods applied across diverse domains from wastewater treatment to surgical robotics. His work shows particular strength in translating theoretical control concepts into practical biomedical applications. Scientific Recognition: Mexican Researcher Certification - Level 2 Dr. Chairez-Oria teaches courses in Biofluid Mechanics, Clinical Engineering, and Bioinstrumentation Systems design, integrating his research expertise into the classroom. His work aligns with multiple UN Sustainable Development Goals including Good Health and Well-being, Industry Innovation, and Clean Water and Sanitation.