جست‌وجوی استاد و پژوهشگر

• Remote Sensing
• Geographic Information Systems (GIS)
• Environmental Monitoring
• +۷ مورد دیگر

Giorgos Mountrakis is a Professor in the Department of Environmental Resources Engineering at SUNY College of Environmental Science and Forestry (ESF). His research focuses on environmental monitoring using remote sensing, environmental modeling through geographic methods, and decision support systems for ecological and urban challenges. He holds a Dipl. Eng. from the National Technical University of Athens (1998), an M.S. (2000), and Ph.D. (2004) from the University of Maine. His work integrates advanced technologies like satellite imagery, LiDAR, and machine learning to address land cover dynamics, climate impacts, and wildlife conservation. Current advisees include Atef Amriche (PhD candidate in Geospatial Information Science), Babak Haji Seyed asadollah (PhD in Environmental Resources Engineering), Ahmadreza Safaeinia (PhD in Environmental Resources Engineering), and Zhixin Wang (PhD in Geospatial Information Science). Key research themes include: land use/cover classification using deep neural networks, climate change impacts on forests and rangelands, and optimizing spatial-temporal models for large-scale environmental analysis. His projects span global datasets (e.g., Landsat, MODIS) and regional case studies in the US, Mongolia, and Algeria. Publications emphasize methodological advancements in remote sensing, such as fusion of multisensor data, accuracy assessment frameworks, and applications in biodiversity conservation. His work bridges technical innovation with practical environmental decision-making, addressing issues like urban growth prediction and wildlife-vehicle collision mitigation.

Nalin Arachchilageمشاهده پروفایل

دانشیار

• Cyber Security
• Human-Computer Interaction (HCI)
• Software Engineering
• +۵ مورد دیگر

Associate Professor Nalin Arachchilage is a leading academic in Cyber Security at RMIT University's School of Computing Technologies. He holds an honorary role at the University of Warwick and advises DEFSAFE Cyber Security Inc. His research focuses on usable security, privacy engineering, and AI-driven cybersecurity solutions. Key roles include revamping RMIT's Master of Cyber Security program and leading the Usable Security Engineering group at UNSW ADFA. Education: PhD in Cyber Security (Brunel University), Postdocs at Oxford and UBC. Research spans interdisciplinary areas including HCI, machine learning for threat modeling, and serious games for security education. He has published extensively in top venues like ACM CCS and SOUPS, contributing to global standards like OWASP. Leadership: Previously Assistant Head of School (Research) at the University of Auckland, and Director of the MProfStuds in Digital Security. Active in program committees for major conferences (ACM CCS, SOUPS). Current supervision includes 5 PhD students across RMIT and Auckland. Media Impact: Featured in Sky News Australia, ABC, and TVNZ. Collaborations with HP and Facebook. Awards include impactful contributions to security frameworks and standards. Teaching: Coordinates courses like INTE2625 (Cyber Security) and COSC2738 (Human-Centric Cyber Security). Supervises projects on topics like blockchain security and responsible AI. Non-Academic Roles: Chair of the Academic Board at Canberra Business & Technology College (2020-2021). Advisor to DEFSAFE, focusing on novel cyber-security products.

• Machine Learning
• Deep Learning
• Video Classification
• +۳ مورد دیگر

Reza Bosagh Zadeh is an Adjunct Professor at the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning, deep learning, and their applications in video classification, healthcare analytics, and distributed algorithms. He specializes in developing scalable computational methods for real-time data processing and has contributed to advancements in neural networks and optimization techniques. Reza's work spans theoretical and applied domains, with notable contributions to TensorFlow frameworks, video summarization systems, and medical imaging analysis. His research often integrates interdisciplinary approaches, leveraging both academic and industrial collaborations. Notable projects include developing machine learning models for glaucoma detection and creating efficient algorithms for large-scale data processing in environments like Apache Spark. His publications emphasize real-time video stream analysis, distributed computing architectures, and practical implementations of deep learning. Reza holds a strong presence in both academic and tech sectors, with contributions to platforms like Twitter's Who-to-Follow system and innovations in edge computing for video surveillance.

Aftab Ahmadمشاهده پروفایل

استاد

• Machine Learning
• Cyber Security
• Public Health Prediction
• +۵ مورد دیگر

Aftab Ahmad is a Professor in the Department of Computer Science at the City University of New York (CUNY), specializing in cybersecurity and machine learning applications. He holds a Doctor of Science from George Washington University. His research focuses on developing machine learning algorithms for cyber threat intelligence (CTI) and public health prediction, along with designing secure generative deep learning models resistant to reverse-engineering. Key research areas include: Cybersecurity frameworks and privacy-preserving architectures Generative adversarial networks (GANs) with embedded security features Biomedical signal analysis and human body channel modeling Secure wireless protocols for critical infrastructure His publication trends emphasize: Privacy metrics and data protection mechanisms Smart grid and IoT security Neuroscience-inspired machine learning models Wireless network vulnerability assessments No scientific awards or grants were explicitly mentioned in the provided texts. He teaches advanced courses in computer security and network forensics at undergraduate and graduate levels. No advising relationships or lab affiliations were detailed in the available information.

• Computer Vision
• Machine Learning
• Image and Video Processing
• +۳ مورد دیگر

Fouad Khelifi is an Associate Professor in the Department of Computer and Information Sciences at Northumbria University. His research focuses on computer vision, machine learning, image/video processing, biometrics, multimedia forensics, and medical image analysis. He obtained his PhD in Computing Science from Queen's University Belfast (2007) and held prior research roles at the University of Bradford (2007–2009) before joining Northumbria in 2010. He supervises PhD students in cybersecurity applications and palm-vein recognition systems. Education: PhD in Computing Science, Queen's University Belfast (2004–2007) Fellow of the Higher Education Academy (FHEA, 2014) Research Interests: Khelifi’s work spans advanced deep learning techniques for medical imaging (e.g., cancer detection, retinal disease analysis), source camera identification in digital forensics, and biometric authentication systems. He develops novel algorithms for feature extraction, fusion networks, and transformer-based models in healthcare and multimedia security. Advising: Supervising Egallekanda Perera (PhD, 2019–2025): Efficient Keypoint-based Palm-vein Recognition Co-supervising Ikechukwu Ikpeama (PhD, 2024–): Cybersecurity for Industrial Control Systems Labs/Teams: Active in Northumbria’s Digital Media and Systems Research groups, contributing to interdisciplinary projects in AI-driven medical imaging and multimedia forensics.

Kasper Green Larsenمشاهده پروفایل

استاد

• Machine Learning
• Theoretical Computer Science
• Algorithms and Data Structures
• +۳ مورد دیگر

Kasper Green Larsen is a Professor in the Department of Computer Science at Aarhus University. His research focuses on theoretical computer science, machine learning, algorithms, and data structures. He has made significant contributions to boosting algorithms, PAC learning theory, and computational geometry. His work often bridges algorithm design with complexity theory, addressing challenges in optimization, memory efficiency, and lower bounds analysis. Key research areas include: Algorithmic Learning Theory (e.g., boosting, bagging, and PAC learners) Data Structure Design (e.g., invertible Bloom tables, succinct representations) Computational Complexity (e.g., lower bounds for dynamic and oblivious algorithms) Geometric Algorithms (e.g., hierarchical searching, range queries) Recent publications emphasize foundational advancements in learning theory (e.g., optimal weak-to-strong learning) and data efficiency (e.g., memory-reduced Bloom filters). His work frequently appears in top conferences like IJCAI, ICALP, and SODA, reflecting rigorous theoretical contributions with practical implications.

• Radiation Detection for Nuclear Nonproliferation
• Fission Fragment Measurement
• Muon Imaging
• +۷ مورد دیگر

Adam Hecht is a Professor in the Department of Nuclear Engineering at the University of New Mexico since 2008. He holds a Ph.D. in Physics (Yale University, 2004) and has led multidisciplinary research in radiation detection, nuclear nonproliferation, and detector development. His educational background includes Ph.D., M.Phil., and M.S. in Physics from Yale University (2001-2004) and a B.S. in Physics from the University of California, Irvine (1997). Ph.D. in Physics (Yale, 2004) M.Phil. in Physics (Yale, 2001) M.S. in Physics (Yale, 1999) B.S. in Physics (UC Irvine, 1997) His research focuses on Radiation Detection for nuclear nonproliferation, Fission Fragment Measurement via spectrometers at LANSCE, Muon Imaging for spent fuel casks, and Novel Detector Development (perovskites, AlSb semiconductors). He directs the Radiation Research and Detector Development (R2D2) Laboratory , collaborates with LANL/INL, and advises UNM's Institute of Nuclear Materials Management (INMM) student group. Key article trends show expertise in Muon Tomography (2025), Neuromorphic Computing for radiation detection (2024), AlSb Semiconductor Detectors (2016), and Fission Data Analysis (2015-2022). His work spans Physics of Atomic Nuclei , Nuclear Instruments and Methods , and Environmental Geochemistry . Awards include the J.W. Gibbs Fellowship (Yale, 1997-1998). Students advised include Daniel Poulson (Muon Imaging), Richard Blakeley (Fission Spectrometer), Erin Vaughan (AlSb Detectors), and Joseph Morris (Refractive Index Analysis). Collaborations with electrical engineering, Portland State University, and LANL/INL are frequent.

Matthias Oliver Wilhelmمشاهده پروفایل

دانشیار

• Quantum Field Theory
• Mathematical Physics
• Special Functions
• +۳ مورد دیگر

Matthias Oliver Wilhelm is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, affiliated with the Quantum Mathematics research group. His work focuses on advanced theoretical physics topics including scattering amplitudes in gauge/gravity theories, Feynman integrals, special functions, and applications of machine learning in physics. He has contributed to groundbreaking research at the intersection of quantum field theory and mathematical physics, particularly in understanding gravitational wave phenomena and high-energy particle interactions. Research Interests: His research combines quantum field theory with algebraic geometry and computational methods, exploring topics like elliptic Feynman integrals, post-Minkowskian expansions, and machine learning-driven amplitude calculations. Recent work includes leveraging Calabi-Yau manifolds for gravity-related Feynman integrals and developing transformer-based algorithms for scattering amplitude computations. Awards: He received the Velux Grant - Villum Young Investigator in 2018, recognizing his innovative contributions to theoretical physics. Projects: Leveraging Algebraic Geometry for High-Precision Fundamental Physics (2024-2028, DFF-funded) Thermodynamics of strongly coupled Quantum Field Theory (2019-2027, private foundation-funded) Key Themes in Recent Work: His articles emphasize novel computational techniques (e.g., machine learning for integration-by-parts reduction), formal developments in scattering amplitude theory, and geometric approaches to quantum gravity problems. Notable contributions include classifying Feynman integral geometries for black-hole scattering and advancing elliptic function methodologies in perturbative QFT.

• Linguistics
• Cognitive Linguistics
• Corpus Linguistics
• +۵ مورد دیگر

Laura Alexis Janda is a Professor of Russian Linguistics at the UiT The Arctic University of Norway , where she has worked since 2008. She is a member of the CLEAR research group (Cognitive Linguistics: Empirical Approaches to Russian) and leads the THREAT-DEFUSER project funded by the Norwegian Research Council. Her academic profile spans Cognitive Linguistics , Construction Grammar , and Corpus Linguistics , with a focus on Russian, Slavic, and Sámi languages . Education: PhD in Slavic Linguistics & Certificate in Teaching ESL, UCLA (1984) Visiting Fellowships at Princeton University and UNC Chapel Hill Training in Persian, Czech, and Russian methodologies Research Interests: Janda’s work bridges constructional networks and grammatical profiling in Slavic languages, with emphasis on aspect , case semantics , numeral classifiers , and political discourse analysis . She explores how morphological units like Russian prefixes function as verb classifiers and investigates language change in Sámi and Russian contexts. Publication Trends: Recent articles analyze ChatGPT’s grammatical interpretation , constructicon architecture , and ideological messaging in Putin’s speeches . Her research integrates quantitative methods and FAIR data principles to address both theoretical and pedagogical challenges. Awards & Grants: 2021 Honorary Doctorate (University of Turku, Finland) 2018 Member of the Norwegian Academy of Science and Letters 2017 Open Data Champion (SPARC Europe) 2013-2015 Norwegian Research Council grant for 'Birds & Beasts: Shaping Events in Old Russian' 2007-2011 Norwegian Research Council grant for 'Exploring Emptiness: Russian Verbal Morphology and Cognitive Linguistics' Collaborative Networks: Janda mentors international researchers and collaborates with linguists in Russia, Norway, and the U.S. She co-developed the Russian Constructicon and Construxercise! pedagogical tool, emphasizing interdisciplinary applications of linguistic theory. Labs & Projects: She co-founded the Tromsø Construction Lab , advancing constructicon resources for Russian and Sámi languages. Her THREAT-DEFUSER project examines perceived threats in Russian and Norwegian political discourse.

• Ethnomusicology
• Hindustani Classical Music
• Rhythmic Analysis
• +۴ مورد دیگر

Professor Martin Clayton is a leading scholar in Ethnomusicology at Durham University's Department of Music. He holds a BA in Music/Hindi (SOAS, 1988) and a PhD in Ethnomusicology (SOAS, 1993). His work focuses on Hindustani classical music, rhythmic analysis, and embodied musical interaction. He has directed major research projects like the EU-funded EnTimeMent and AHRC-funded 'Interpersonal Entrainment in Music Performance.' Clayton is a Fellow of the British Academy (2020) and former editor of the British Journal of Ethnomusicology. His research explores cross-cultural musicology, computational analysis of performance, and rhythmic entrainment across global traditions. Education: Bachelor of Arts in Music and Hindi (SOAS, University of London, 1988) Doctor of Philosophy in Ethnomusicology (SOAS, University of London, 1993) Research Interests: Clayton’s work bridges ethnomusicology with computational methods, focusing on rhythmic structures in Indian classical music, embodied performance practices, and cross-cultural musical interactions. His studies analyze gesture-sound relationships, temporal frameworks in rag performance, and the social dimensions of musical entrainment. Recent projects integrate machine learning and computer vision to classify ragas and analyze performer movements in ensemble contexts. Publications Overview: His 15+ years of research span theoretical works (e.g., Time in Indian Music , 2000), edited volumes (e.g., Experience and Meaning in Music Performance , 2013), and interdisciplinary studies combining ethnography with computational analysis. Recent work emphasizes multimodal data collection to study synchronization in global music traditions, including Japanese gagaku and Indian khyal. Awards: British Academy Fellowship (2020), Leverhulme Trust Major Research Fellowship Advising & Grants: Supervised doctoral students focusing on Indian music, global guitar practices, and embodied music interaction. He led a £1.2M AHRC grant (2016–2018) on interpersonal entrainment and co-investigated projects on Khyal music and South Asian cultural networks. Labs/Teams: Collaborates with interdisciplinary teams on projects like the Interpersonal Entrainment in Music Performance (IEMP) initiative, developing tools such as the movementsync R package for synchrony analysis. Active in global music research networks, including ESEM and the British Forum for Ethnomusicology.

Sabirat Rubyaمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Social Computing
• Health Informatics
• +۳ مورد دیگر

Dr. Sabirat Rubya is an Assistant Professor in the Department of Computer Science at Marquette University, co-directing the Social and Ethical Computing Lab. Her research focuses on designing mobile health technologies for vulnerable populations at risk of mental health disorders, emphasizing human-centered computing and health informatics. Education: BSc in Computer Science from Bangladesh University of Engineering and Technology (BUET), PhD in Computer Science from the University of Minnesota. Research Interests: Human-Computer Interaction, Social Computing, Health Informatics, and Human-Aided Machine Learning. She explores technologies supporting marginalized communities through participatory design and mixed-method approaches. Publications highlight user-centric evaluations of mental health apps, predictive analytics for veteran crises, and community-based data validation in recovery contexts. Recent work includes a grant from Advancing a Healthier Wisconsin Endowment to commercialize a veteran support app (as co-Investigator). Service: Served as CHI 2021 Late-Breaking Work Program Committee member, PC member for CHI 2022, and Web Co-Chair for CSCW 2023. Holds leadership roles in HCI conferences and lab co-direction. Labs/Teams: Co-directs the Social & Ethical Computing Lab at Marquette, focusing on ethical tech design and health equity.

Yaser S. Abu-Mostafaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Medical Applications of AI
• +۴ مورد دیگر

Yaser S. Abu-Mostafa is a Professor of Electrical Engineering and Computer Science at the California Institute of Technology (Caltech), with a distinguished career spanning over four decades. He earned his B.Sc. from Cairo University in 1979, an M.S.E.E. from Georgia Tech in 1981, and a Ph.D. from Caltech in 1983. Holding roles such as Garrett Research Fellow (1983), Assistant Professor (1983-89), Associate Professor (1989-94), and Professor since 1994, he has cemented his reputation as a pioneer in artificial intelligence and machine learning. Educational Background B.Sc., Cairo University (1979) M.S.E.E., Georgia Institute of Technology (1981) Ph.D., California Institute of Technology (1983) Abu-Mostafa’s research bridges AI/ML with medical diagnostics and computational finance. His projects include non-invasive blood pressure measurement via ultrasound, AI-based detection of congestive heart failure through video analysis, and early stroke prediction using clot-detection algorithms. These innovations leverage machine learning to address biological variability and real-world complexity. His recent publications focus on predictive modeling for epidemiology (2022, 2021), theoretical advancements in machine learning (2015, 2006, 2005), and foundational work in swarm robotics and financial mathematics (2004). Trends in his work emphasize robustness, adaptability, and interdisciplinary applications. Scientific Awards Clauser Prize (Caltech’s most original Ph.D. thesis) ASCIT Teaching Awards (1986, 1989, 1991) GSC Teaching Awards (1995, 2002) Richard P. Feynman Prize for Excellence in Teaching (1996) Abu-Mostafa Fellowship established by Hertz Foundation (2005) Abu-Mostafa has advised numerous students and led research teams at Caltech, while also serving on scientific advisory boards and consulting for industries. His Caltech MOOC on machine learning has garnered over 8 million views, and his textbook Learning from Data is an Amazon bestseller. He collaborates extensively, including a major project with UCSF on heart failure detection and partnerships in computational finance. His work on a Caltech patent for stroke prediction received a US patent in 2025, alongside other medical AI patents.

Daniel Paulusmaمشاهده پروفایل

استاد

• Structural Graph Theory
• Algorithmic Graph Theory
• Computational Complexity
• +۶ مورد دیگر

Daniel Paulusma is a Professor in the Department of Computer Science at Durham University, where he serves as Head of the Algorithms and Complexity research group (ACiD). He obtained his PhD and Master's degrees from the University of Twente in 2001 and 1997 respectively, and has been at Durham University since 2004, progressing from Lecturer (2004-2011) to Senior Lecturer (2011-2013), Reader (2013-2015), and finally to Professor in 2015. He previously served as Director of Research for the School of Engineering and Computing Sciences from 2014 to 2017. Paulusma's research spans two main areas: structural and algorithmic graph theory, with a focus on computational complexity of graph problems under input restrictions; and cooperative game theory, particularly matching games related to kidney exchange. His work has resulted in numerous survey papers and significant contributions to the field. He has received several prestigious awards including the CIAC 2025 Best Paper Award, ISAAC 2024 and 2023 Best Paper Awards, and the Glover-Klingman Prize for his work on graph contractions. His recent publications demonstrate a strong focus on complexity frameworks for forbidden subgraphs, matching cuts, graph coloring, and kidney exchange algorithms. The research shows a consistent pattern of exploring computational complexity within specific graph classes, particularly H-free graphs, and developing frameworks to classify problems based on graph structure constraints. CIAC 2025 Best Paper Award for "Atoms versus avoiding simplicial vertices" ISAAC 2024 Best Paper Award for "Complexity framework for forbidden subgraphs II: Edge subdivision and the \"H\"-graphs" ISAAC 2023 Best Paper Award for "Matching cuts in graphs of high girth and H-free graphs" Glover-Klingman Prize for "The Computational Complexity of Graph Contractions I, II" Paulusma has successfully supervised numerous PhD students including Yilin Li, Tala Eagling-Vose, Xin Ye, Siani Smith, Giacomo Paesani, Anthony Stewart, Carl Feghali, Jian Song, and Pim van 't Hof. He has secured substantial research funding including grants from the Leverhulme Trust, EPSRC, and Royal Society for projects on graph coloring, kidney exchange algorithms, and network structure analysis. His current projects include "Algorithmic Meta-classifications for Graph Containment" (2025-2028) and "KidneyAlgo: New Algorithms for UK and International Kidney Exchange" (2023-2025). As Head of the Algorithms and Complexity research group (ACiD), Paulusma leads a vibrant research team focused on theoretical computer science problems with applications in network analysis, optimization, and game theory. The group regularly organizes workshops and collaborates with international researchers in the field.

Danica M Ommenمشاهده پروفایل

دانشیار

• Forensic Statistics
• Computational Statistics
• Handwriting Analysis
• +۳ مورد دیگر

Danica M Ommen is an Associate Professor at Iowa State University, specializing in forensic statistics and computational methodologies. Her research bridges machine learning, handwriting analysis, and source identification frameworks. Education: Ph.D. in Computational Science and Statistics (2017), M.S. in Mathematics (2014), B.S. in Mathematics (2012), all from South Dakota State University. Affiliations: Chair of the OSAC Statistics Task Group; Vice-Chair of the ASA Advisory Committee on Forensic Science. Her work focuses on statistical modeling for forensic evidence , particularly in handwriting identification, aluminum powder analysis, and digital device forensics. Recent publications explore interpretable deep learning, synthetic data anchoring, and ensemble methods for likelihood ratios. The 15 most recent articles (2023–2025) span forensic machine learning, handwriting kinematics, multi-camera smartphone identification, and Bayesian frameworks. Keywords include Forensic Science , Machine Learning , and Computational Statistics , with subfields like Score-Based Likelihood Ratios and Smartphone Forensics .

Chris Cornelisمشاهده پروفایل

استاد

• Machine Learning
• Fuzzy Sets
• Rough Sets
• +۳ مورد دیگر

Chris Cornelis is a full-time Professor in fuzziness and uncertainty modelling at Ghent University's Department of Applied Mathematics, Computer Science and Statistics. His research integrates fuzzy logic and rough set theory to advance machine learning methodologies for complex data analysis. Education: M.Sc. in Computer Science, Ghent University (2000) Ph.D. in Computer Science, Ghent University (2004) Research Focus: Cornelis pioneers fuzzy-rough hybrid systems for uncertainty handling in machine learning. His work spans theoretical foundations (e.g., implication operators, granular approximations) and practical applications including emotion detection, medical diagnosis, and imbalanced data classification. Key innovations include FRNN-OWA classifiers and polar encoding for missing values, demonstrating exceptional versatility in bridging abstract mathematics with real-world AI challenges. Publication Trends: Recent work (2023-2025) reveals intensified exploration of topological data analysis (Mapper-based rough sets), advanced granular computing (disjoint/adjacent fuzzy granules), and ethical AI ("No Imputation Without Representation"). His research shows consistent progression from foundational fuzzy-rough theory toward multi-disciplinary applications while maintaining mathematical rigor, particularly in Choquet integration and quantifier-based frameworks. Scientific Awards: No specific awards were documented in the provided sources. Research Support: Cornelis has secured competitive funding including FWO postdoctoral mandates, a Ramón y Cajal contract at the University of Granada, and an FWO Odysseus Type II project at Ghent University. These grants enabled foundational work in fuzzy-rough set theory and its applications to complex data problems. Research Unit: He leads research within Ghent University's Computational Web Intelligence (CWI) unit, focusing on intelligent data analysis systems that leverage fuzzy-rough methodologies for web-scale information processing.