جست‌وجوی استاد و پژوهشگر

Naim U. Rashidمشاهده پروفایل

دانشیار

• Precision medicine
• Genomics
• Statistical computing
• +۷ مورد دیگر

Naim U. Rashid, PhD, is an Associate Professor with tenure in the Department of Biostatistics at the UNC Gillings School of Global Public Health and holds a joint appointment as Research Associate Professor at the Lineberger Comprehensive Cancer Center. He serves as Associate Director of the Lineberger Biostatistics Shared Resource and co-directs the Biostatistics Cores of the UNC Pancreatic and Breast Cancer SPOREs. His work bridges statistical methodology development with collaborative cancer research, focusing on translating genomic discoveries into clinical applications. Dr. Rashid's research spans precision medicine, genomics, statistical computing, and machine learning with specific applications to pancreatic and breast cancers. His lab develops novel statistical methods for high-throughput genomic data analysis, cancer subtyping, missing data problems in deep learning, and clinical trial design. Recent work includes developing an AI tool that recommends optimal clinical trials to pancreatic cancer patients, funded by a $311,000 Department of Defense grant in 2024. His methodological contributions focus on improving replicability in gene signature selection and clinical prediction, with emphasis on addressing racial disparities in cancer outcomes. His publication record shows consistent output in top statistical and medical journals, with recent work focusing on high-dimensional statistics, missing data methods, and cancer genomics. His research demonstrates a clear trajectory from methodological innovation to clinical implementation, particularly in pancreatic cancer where his PurIST classifier has gained recognition. The work increasingly incorporates machine learning approaches while maintaining strong statistical foundations. Delta Omega Faculty Award (2021, UNC Chapel Hill) IBM and R.J. Reynolds Junior Faculty Development Award (2017, UNC Chapel Hill) Barry H. Margolin Dissertation Award (2013, UNC Chapel Hill) Training Grant recipient (2006-2011, Genomics and Cancer) Dr. Rashid actively mentors graduate students and serves as trial statistician on multiple cancer clinical trials. He teaches BIOS 735, a doctoral-level course on statistical computing, and is involved with the Translational Breast Cancer Research Consortium Statistical Working Group. His lab collaborates extensively with clinicians at UNC Lineberger and beyond, with recent work including the PROCLAIM Study examining mHealth apps to improve diverse recruitment in pancreatic cancer trials. The Rashid Lab focuses on developing computational tools that directly impact clinical decision-making while addressing methodological challenges in genomic data analysis.

Brian Surgenorمشاهده پروفایل

استاد

• Mechanical Engineering
• Machine Vision Systems
• Autonomous Vehicles
• +۳ مورد دیگر

Professor Brian Surgenor is a faculty member at Queen's University's Department of Mechanical and Materials Engineering, part of the Smith Engineering faculty. He holds a B.Sc. (1977), M.Eng. (AECL/Whiteshell), and Ph.D. (1983) in Mechanical Engineering from Queen's University. His research focuses on machine vision systems for automation, autonomous vehicle navigation, and mechatronic system design education. He has held key administrative roles including Department Head (1993-2002), Associate Dean (2008-2013), and Vice-Dean (2013-2016). His work emphasizes interdisciplinary innovation, such as the Mitchell Hall design project and contributions to Ingenuity Labs. Education: B.Sc. Mechanical Engineering, Queen's University (1977) M.Eng. Engineering Physics, McMaster University (AECL/Whiteshell) Ph.D. Mechanical Engineering, Queen's University (1983) Research interests include: - Pneumatic servosystems - Intelligent algorithms for machine vision - Off-road autonomous vehicle systems - Mechatronics education methodologies - Hybrid powertrain systems for vehicles His recent publications (2017–2024) explore autonomous systems, machine vision applications, and fuel cell hybrid technologies. Notable trends include advancements in UAV-based infrastructure inspection, terrain-adaptive autonomous driving, and low-cost machine vision solutions for small part sorting. His work bridges theoretical control systems with practical industrial automation challenges. He has contributed to laboratory design for CDIO curricula and pioneered mechatronics education through problem-based learning. His administrative leadership has shaped Queen's engineering graduate programs and research infrastructure. Currently involved in Ingenuity Labs, fostering cross-disciplinary innovation.

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

Goran Oreški is an Associate Professor and Head of the Laboratory at the Faculty of Informatics in Pula (University Jurja Dobrile, Croatia), where he has been employed since 2019. He teaches courses on databases, object-oriented programming, data warehousing, and artificial intelligence at both undergraduate and graduate levels. Education: Ph.D. in Informatics (2016), Faculty of Organization and Informatics Industry Experience: 9 years as software architect and programmer in banking sector Research Focus: Artificial Intelligence systems, classical machine learning algorithms, and deep learning architectures. His work bridges theoretical advancements with practical applications in autonomous vehicles, traffic monitoring, and financial risk assessment. Recent Publication Trends: 2023-2025 works emphasize generative AI for synthetic credit data, traffic object segmentation with monocular cameras, and context-aware detection models (YOLO*C). Earlier works focus on genetic algorithms and ensemble learning for imbalanced datasets. Awards: Google RFP Award for autonomous vehicle research Highly Cited Paper (Web of Science, top 1%) Best Paper at CECIIS conference Leadership: Director of FIPU Laboratory since 2022, leading projects like ai.Shuttle (autonomous mini-bus) and CenAI (industry collaboration with Cenosco).

Jessica J. Fridrichمشاهده پروفایل

استاد

• Steganography
• Digital Forensics
• Signal Processing
• +۴ مورد دیگر

Jessica J. Fridrich is a Distinguished Professor in the Department of Electrical and Computer Engineering at Binghamton University, part of the State University of New York (SUNY) system. She is affiliated with the T. J. Watson School of Applied Science and Engineering. Her research focuses on steganography, steganalysis, digital forensics, and machine learning, with notable contributions to secure data hiding and patented camera fingerprinting techniques approved for legal evidence. Education: PhD in Electrical and Computer Engineering from Binghamton University Her research interests include steganography and steganalysis of digital images, digital forensics for linking photos to cameras via sensor fingerprints, signal estimation and detection, and applications of machine learning. Earlier work explored chaotic nonlinear dynamical systems and encryption. Her methods have led to over 150 refereed publications and seven successfully commercialized patents. Her articles emphasize advancements in batch steganography, JPEG compatibility, and adaptive embedding strategies. Recent work leverages machine learning for steganalysis and explores security trade-offs in high-dimensional feature spaces. 2006-2007 Chancellor's Award for Excellence in Scholarship and Creative Activities 2002 Chancellor's Award for Outstanding Inventor Narrative on advising and grants: She mentors graduate students and leads projects funded by AFOSR, NSF, and AFRL. Her research addresses challenges in data hiding security, forensic analysis, and optimizing steganographic algorithms. The Digital Data Embedding Lab, which she directs, focuses on algorithmic innovation and empirical validation in steganography and forensics. Labs/Teams: Digital Data Embedding Lab

YooJung Choiمشاهده پروفایل

استادیار

• Probabilistic Machine Learning
• Tractable Probabilistic Modeling
• Algorithmic Fairness
• +۶ مورد دیگر

YooJung Choi is an Assistant Professor at the School of Computing and Augmented Intelligence, Arizona State University. Her research focuses on probabilistic machine learning, trustworthy AI, and tractable probabilistic modeling. She holds a PhD in Computer Science from UCLA and has been recognized with awards like the Cisco Research Award and Simons-Berkeley Research Fellowship. Education: PhD in Computer Science (University of California, Los Angeles). Research interests include probabilistic reasoning, fairness, robustness, and interpretability. Her work bridges theoretical foundations and practical applications, with contributions to probabilistic circuits and optimal transport. Notable achievements include co-organizing TPM 2024 and presenting at venues like NeurIPS and AAAI. She actively promotes fairness in AI through frameworks that address label bias and discrimination patterns. Awards: Cisco Research Award, Simons-Berkeley Fellowship, AAAI 2023 New Faculty Highlights. Teaching: Courses on artificial intelligence, machine learning, and research supervision. Service: Tutorial leadership on probabilistic circuits, workshop organization, and academic talks globally.

• Speech Communication Technology
• Speech Signal Processing
• Machine Learning Applications in Speech Analysis
• +۳ مورد دیگر

Paavo Alku is a Professor of Speech Communication Technology at Aalto University's Department of Information and Communications Engineering. With academic credentials from Helsinki University of Technology (M.Sc. 1986, Lic.Tech. 1988, Dr.Sc.Tech. 1992), he has held academic positions at Asian Institute of Technology (1993) and University of Turku (1994-1999). Current research focuses on speech production analysis, parametric speech synthesis, and speech-based biomarkers for health monitoring Actively develops machine learning models for voice disorder detection and Parkinson's disease classification Principal investigator for projects including SymptoSonic (2024-2025) and HEART (2020-2024) His recent publications emphasize: Wavelet scattering for neurological speech analysis Fisher vector representations in voice disorder classification Machine learning approaches to vocal intensity categorization Respiratory aerosol emission during speech production Formant tracking through hybrid neural network/LP methods Awarded: IEEE Fellow (2020) Academy Professor (2015-2019) Multiple best student paper awards at ICASSP and Interspeech

Xiaobo Liمشاهده پروفایل

استاد

• Neuroscience
• Medical Imaging
• Machine Learning Applications in Neurology
• +۳ مورد دیگر

Xiaobo Li is a Professor in the Department of Bio-Medical Engineering at New Jersey Institute of Technology. Holding a Ph.D. in Computer Aided Geometric Design from the University of Birmingham and a B.S. in Automation from Nanjing University of Aeronautics, their research bridges computational methods with neuroimaging and psychiatric disorder analysis. Ph.D., University of Birmingham (Computer Aided Geometric Design, 2004) B.S., Nanjing University of Aeronautics (Automation, 1999) Dr. Li’s work focuses on applying machine learning and graph theory to understand brain network abnormalities in conditions like ADHD , schizophrenia , and traumatic brain injury . Their studies analyze structural-functional connectivity , reward processing , and gut-brain axis interactions using fMRI , fNIRS , and diffusion tensor imaging . Recent publications highlight their development of tools like the GAT-FD MATLAB toolbox for brain network analysis and their exploration of multimodal MRI in schizophrenia diagnosis. They also investigate the neurobiological effects of photobiomodulation and vision therapy interventions.

Gabriella Casalinoمشاهده پروفایل

استادیار

• Computational Intelligence
• eHealth
• Data Stream Mining
• +۵ مورد دیگر

Gabriella Casalino is an Assistant Professor at the University of Bari Aldo Moro, Department of Computer Science, and a key researcher at CILAB - Computational Intelligence Lab. Her work focuses on Computational Intelligence methods for interpretable data analysis, particularly in eHealth, Data Stream Mining, and eXplainable Artificial Intelligence (XAI) within medical and educational domains. She has contributed to innovative approaches in smartphone-based health monitoring, fuzzy logic applications, and remote vital sign detection via photoplethysmography. Education : Ph.D. in Computer Science, with advanced training at institutions like Universitat de Girona and Université de Mons. Research Trends : Recent publications highlight applications of evolving granular computing, neuro-fuzzy systems, and explainable AI in hypertension prediction, bipolar disorder monitoring, and educational data analysis. Key subfields include remote health monitoring, medical data streams, and hybrid AI models. Grants : Research funded by AIRC (Italian Cancer Research Foundation), focusing on computational methods for healthcare challenges. Labs & Collaborations : Active in CILAB, collaborating on projects involving mHealth solutions, cardiovascular risk assessment, and intelligent educational systems.

• Algorithmic Fairness
• Responsible AI
• Deep Learning
• +۵ مورد دیگر

Golnoosh Farnadi is an Assistant Professor at McGill University's School of Computer Science , an Adjunct Professor at Université de Montréal , and a Visiting Faculty Researcher at Google Research . She holds the Canada CIFAR AI Chair and is a core academic member of Mila – Quebec AI Institute . Her research focuses on algorithmic fairness , responsible AI , and optimization . She founded the EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms) to address bias and discrimination in AI systems. Key publications explore fairness in kidney exchange programs, generative model geometry, multilingual LLM de-biasing, and prototype-based recommender systems. Her work bridges causal inference, adversarial robustness, and ethical AI. Google Award for Inclusion Research (2023) Women in AI Awards North America Finalist (2023) Facebook Privacy Enhancing Technologies Award (2021) IVADO Postdoctoral Fellowship (2018–2021) She has supervised over 15 PhD and Master's students, including Prakhar Ganesh (McGill) and William St-Arnaud (Université de Montréal). Her teaching includes Responsible AI and Machine Learning courses at McGill and HEC Montréal.

Didier Meuwlyمشاهده پروفایل

استاد

• Forensic Science
• Biometrics
• Probabilistic Evaluation
• +۳ مورد دیگر

Didier Meuwly is a Full Professor of Forensic Biometrics at the University of Twente (since 2013) and Principal Scientist at the Netherlands Forensic Institute (NFI). His work focuses on automating and validating probabilistic evaluation of forensic evidence, particularly biometric traces. He has contributed to international standards via ISO Technical Committee 272 and served as Associate Editor for Forensic Science International . PhD in Forensic Speaker Recognition (University of Lausanne, 2000) Research spans forensic biometrics, likelihood ratios, AI validation, and gait/body analysis from surveillance footage. Recent work addresses ISO standards (21043), forensic AI explainability, and multimodal evidence evaluation. His publications emphasize empirical validation and statistical rigor. Key awards include: ENFSI Distinguished Forensic Scientist Award (2022) University of Lausanne Law Faculty Prize (2002) Active in global forensic networks, he chairs the ENFSI R&D Committee and collaborates across disciplines on digital evidence, biometric security, and forensic methodology.

Charles Lingمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۴ مورد دیگر

Charles Ling is a Professor of Computer Science at Western University, holding the title of Science Distinguished Research Professor. He also serves as Director of the Data Mining and Business Intelligence Lab and Associate Scientist at the Lawson Health Research Institute. His academic background includes a B.Eng. (CS and EE) from Shanghai Jiao Tong University and MSc/PhD from the University of Pennsylvania (UPenn). Research interests span machine learning, deep learning, AI, and healthcare informatics, with notable contributions to the GlucoGuide diabetes management system. He has authored over 220 peer-reviewed papers and a book titled Crafting Your Research Future , focusing on academic career development. Awarded Fellow of the Canadian Academy of Engineering (CAE) and recipient of the First Prize for Best Clinical Research Presentation (2011). Active in grants (NSERC, FedDev, Mitacs) and organizational roles in top conferences (KDD, ICDM). Supervises 5 PhD and 4 MSc students, with notable advisees including Harry Zhang and Victor Sheng. Leverages AI in education to enhance children's cognitive abilities through video-based programs like Power Thinking , approved by Curriculum Services Canada. His work integrates machine learning with healthcare, finance, and software engineering.

• Speech Processing
• Voice Disorders
• Machine Learning
• +۴ مورد دیگر

Farhad Javanmardi is a Researcher at Aalto University within the Department of Information and Communications Engineering. His work focuses on applying advanced computational methods to speech and biomedical signal analysis. Research Interests: Speech processing, voice disorders, machine learning, deep learning, biomedical signal processing, computational linguistics, and health informatics. His recent research trends emphasize the use of transformer-based models and wav2vec2 for robust detection of heart failure and voice pathologies in telephony environments. He investigates database-independent approaches, severity classification, and data augmentation techniques to improve model generalizability. Publications appear in journals like Speech Communication and Computer Speech and Language , as well as conferences including ICASSP and INTERSPEECH .

• Languages and literature
• reading-research
• immigrant-achievement
• +۶ مورد دیگر

Dr. Lee Paul Gunderson is a Professor in the Faculty of Education at the University of British Columbia , affiliated with the Department of Language & Literacy Education . His research focuses on second language literacy, immigrant student achievement, and multicultural education approaches. Key affiliations: Department of Language & Literacy Education, UBC Research programs: Language and Literacy Education (PhD/MA), Teaching English as a Second Language (PhD/MA) Research Interests : Dr. Gunderson investigates how home literacy environments , English acquisition , and multicultural inclusion impact immigrant students’ academic success. His work critiques English-only policies and emphasizes culturally responsive pedagogy in K-12 settings. Recent Trends : Recent publications analyze Chinese-Canadian bilingualism , PPVT-5 assessment tools , and systemic barriers for ESL learners. Collaborative work with Li, D’Silva, and Murphy Odo highlights family literacy practices and policy implications . Scientific Awards : 2023: Education Advocate Award 2021: Reading Hall of Fame inductee 2010: Honorary Lifetime Membership (Learning Disabilities Association) 2009: BC TEAL Lifetime Contributors Award Grants & Collaborations : Dr. Gunderson leads projects like Predictors of Immigrant Achievement in English-Only Environments and has developed assessment frameworks including the Burnaby English Reading Assessment . His work bridges theory and practice through partnerships like the Lower Mainland ESL Assessment Consortium.

Nikitas Karanikolasمشاهده پروفایل

استاد

• Natural Language Processing and Understanding
• Computational Linguistics
• Computational Phonology
• +۷ مورد دیگر

Nikitas Karanikolas serves as Professor in the Department of Informatics and Computer Engineering at the University of West Attica since March 2018, following a distinguished career progression from Assistant Professor (2004) to Associate Professor (2010) and Professor (2014) at the Technological Educational Institute of Athens. His professional trajectory includes significant roles as Systems Head of TEI Athens Library (1996-1997) and Chief of Informatics at Aretaieio University Hospital (1997-2004), alongside leadership positions in the Greek Computer Society as Board Member (2004-2006) and Secretary General (2006-2008). His academic foundation includes: Bachelor's in Statistics and Informatics from Athens University of Economics and Business (1988) PhD in Applied Informatics from Athens University of Economics and Business (1994) with thesis "Technological and Linguistic approaches in Natural Language Understanding" Dr. Karanikolas maintains an exceptionally broad research portfolio spanning Natural Language Processing , Computational Linguistics , Medical Informatics , and Green Energy systems. His work consistently bridges theoretical computational frameworks with practical healthcare applications, particularly evident in recent dementia care technologies and Greek language processing systems. The interdisciplinary nature of his research connects computational phonology with medical diagnostics and e-government applications. Analysis of his 15 most recent publications reveals a pronounced shift toward AI-driven healthcare solutions (particularly dementia patient monitoring), multilingual NLP systems (Greek and Polish), and urban safety applications . His work demonstrates consistent methodology development in ontological representations and multimodal fusion techniques, with increasing emphasis on real-world clinical and governmental implementations since 2023. No scientific awards were documented in the source materials. With 16 journal papers, 68 conference publications, and six authoritative Greek university textbooks, Dr. Karanikolas maintains an active research trajectory. His advising capacity is evidenced through extensive publication mentorship, particularly in medical informatics and NLP projects. While specific grant details are unavailable, his hospital information system implementations and textbook authorship suggest successful research funding acquisition. Current research activities focus on multimodal aggression prediction systems for dementia care, Greek language ontological frameworks, and urban navigation safety applications, primarily conducted through the University of West Attica's informatics infrastructure.

Fraser Kingمشاهده پروفایل

استادیار

• Climate and Climate Change
• Radiation and Remote Sensing
• Synoptic Meteorology
• +۱۳ مورد دیگر

Fraser King is an incoming Assistant Professor in the Department of Atmospheric and Oceanic Sciences (AOS) at the University of Wisconsin–Madison, starting in Winter 2026. He holds a PhD in Machine Learning and Remote Sensing of Precipitation from the University of Waterloo (2022) and is currently a postdoctoral research associate at NASA Goddard Space Flight Center. His research integrates machine learning with atmospheric physics to advance precipitation and snowfall retrieval, cloud microphysics, and climate modeling. He has held research positions at the University of Michigan and NASA Jet Propulsion Laboratory. His research interests include: Climate and Climate Change Radiation and Remote Sensing Synoptic Meteorology Atmospheric and Cloud Physics Large Scale Dynamics Machine Learning and Model Interpretability Arctic Snowfall Prediction His recent publications reflect a strong trend in applying deep learning (e.g., U-Net, CNNs) and unsupervised methods (PCA, t-SNE, UMAP) to radar and satellite data for precipitation and snow microphysics. Key themes include radar gap inpainting, melting layer detection, and dimensionality reduction for physical interpretation. His work bridges geoscience and AI, aiming for interpretable models that enhance physical understanding. Scientific awards and professional service include: Finalist for the 2023 Governor General's Gold Medal, University of Waterloo Associate Editor, Journal of Atmospheric and Oceanic Technology (AMS) Member, AMS Committee on Artificial Intelligence Applications to Environmental Science Executive Council Member, AGU Precipitation Technical Committee Executive Member, Eastern Snow Conference Research Board Fraser King has mentored students through research projects and led educational initiatives such as a 12-week course on machine learning for land cover classification. He has secured research experience through internships at Aquanty Inc. and multiple NASA-affiliated institutions. He founded MapsByFraser, a company combining cartography and satellite data, and has collaborated with Google's Quantum AI team. His technical skills span Python, deep learning frameworks, and high-performance computing platforms. He leads several major research projects: Towards Interpretable Physical Models : Using sparse autoencoders and nonlinear dimensionality reduction to interpret geoscience models. Microphysical Dimensionality Reduction : Applying PCA, t-SNE, and UMAP to identify physical modes in precipitation data. BlindPaint : A U-Net for radar gap inpainting in spaceborne systems. DeepPrecip : A deep learning model for surface precipitation retrieval. iPhone LiDAR : Using consumer smartphones for snow depth measurement via drones. NRCan Machine Learning Land Cover Classifier : Training ML models on Sentinel-2 data. Climate Model Calibration : Using ML to correct biases in snow-related climate variables. CloudSat Snowfall Validation : Validating high-latitude snowfall estimates. Snow Modelling : A Rust-based physical/temperature-index snow model.