جست‌وجوی استاد و پژوهشگر

• Computer Vision
• Face Recognition
• Machine Learning
• +۶ مورد دیگر

Jinho Shin is an active researcher and likely faculty member specializing in computer vision, machine learning, and their applications across various domains including biometrics, fraud detection, and semiconductor manufacturing. His work spans from theoretical foundations in queueing theory to cutting-edge applications of deep learning and federated learning systems. Dr. Shin's research interests focus on computer vision (particularly face recognition and anti-spoofing systems), machine learning algorithms, federated learning frameworks, and applications in healthcare fraud detection and semiconductor manufacturing optimization. His work demonstrates expertise in both theoretical foundations and practical implementations of AI systems. His recent publication trend shows a strong focus on computer vision security applications, particularly face anti-spoofing techniques using advanced neural network architectures like Vision Transformers. He has also made significant contributions to federated learning through his ProtoFL framework. Earlier in his career, he focused more on industrial applications in semiconductor manufacturing and queueing theory. Dr. Shin has established productive collaborations with researchers including Youngjun Kwak, Minyoung Jung, Changick Kim, and James R. Morrison, indicating active participation in research communities across computer vision, machine learning, and industrial engineering domains.

• Health Informatics
• Digital Health
• Mental Health
• +۳ مورد دیگر

Jonas Danielson is a Senior Lecturer in Nursing at Arcada University of Applied Sciences, affiliated with the Academy for Master's Education and Research. His work focuses on health informatics, digital health, and mental health, particularly exploring temporal patterns in health information seeking behaviors using infodemiology approaches. He holds a Ph.D. and contributes to academic publishing through editorial roles. Research interests include analyzing online health discussions (e.g., Suomi24 forums), social media sentiment, and the application of IoT in healthcare. His studies often utilize longitudinal data from platforms like Twitter and Google Trends to understand behavioral rhythms in mental health contexts. He also investigates emerging technologies like extended reality (XR) and computer vision for healthcare monitoring. Publications span topics from ADHD information trends to real-time media consumption patterns, emphasizing the intersection of technology and health. Collaborations include international multidisciplinary student teams developing digital health solutions. No awards or grants are explicitly listed in the provided texts. His teaching and research bridge nursing education with digital innovation, covering e-learning strategies and secure IoT systems for healthcare environments. He actively explores future trends in healthcare technology, including streaming media's temporal impacts and wearable device challenges.

Chris Schwiegelshohnمشاهده پروفایل

دانشیار

• Machine Learning
• Algorithm Design
• Data Mining
• +۳ مورد دیگر

Chris Schwiegelshohn is an Associate Professor at the Department of Computer Science, Aarhus University, specializing in theoretical computer science and algorithm design. His research focuses on clustering algorithms, coresets, approximation algorithms, and fairness in machine learning. He has contributed significantly to the development of efficient algorithms for large-scale data analysis and privacy-preserving techniques. Key research interests include optimization in k-means clustering, distributed privacy protocols, and fair recommendation systems. His work bridges theoretical foundations with practical applications in data mining and machine learning. Publications span topics such as PAC learning for k-means, dynamic facility location, and fair projections for balanced recommendations. He has explored tradeoffs between computational efficiency and accuracy in big data clustering, as well as low-distortion clustering techniques for ordinal data. No scientific awards or grants are explicitly listed in the provided texts. His advising record remains unspecified.

Simon Bonnerمشاهده پروفایل

دانشیار

• Ecological Statistics
• Bayesian Methods
• Mark-Recapture Models
• +۷ مورد دیگر

Simon Bonner is an Associate Professor in Environmetrics in the Department of Statistical and Actuarial Sciences at The University of Western Ontario. Previously, he held positions at the University of Kentucky (Assistant Professor), University of British Columbia (Postdoc), and completed his PhD at Simon Fraser University. His research focuses on developing statistical models for ecological data, particularly Bayesian hierarchical methods applied to wildlife population monitoring via mark-recapture experiments. He has contributed to studies on salamanders impacted by mountaintop removal mining, predator-prey dynamics, and conservation challenges in diverse ecosystems. Education: PhD in Statistics, Simon Fraser University Postdoc, University of British Columbia Research Interests: Ecological statistics, Bayesian modeling, and hierarchical models Applications in wildlife population dynamics, conservation, and environmental impact studies Methodological advancements in capture-recapture and occupancy modeling Advising & Grants: Supervises PhD/MSc students in statistical ecology and applied data science Focus on interdisciplinary projects with collaborators in biology and environmental science Labs & Teams: Lead of the Ecological Statistics and Applied Data Science Laboratory at Western University Collaborations with researchers on salamander ecology, predator behavior, and conservation biology

Paras Mehtaمشاهده پروفایل

پژوهشگر

• Spatial Data Mining
• Geoinformatics
• Information Retrieval
• +۳ مورد دیگر

Dr. Paras Mehta is a Researcher at the Department of Databases and Information Systems, part of the Computer Science faculty at Freie Universität Berlin. His work focuses on spatial data mining, geoinformatics, and spatio-temporal analysis with applications in disaster response systems and social media analytics. He contributes to projects such as MoveSafe and CliniScale, addressing challenges in real-time data processing and location-based services. Research interests include geotagged post analysis, spatio-textual data retrieval, and distributed computing for large-scale spatial datasets. His recent work emphasizes continuous summarization of streaming data and hotspot detection using frameworks like Apache Spark. Notable projects involve developing systems like μTOP for trending topic detection in microblogs and LocXplore for urban region profiling. His publications span spatial keyword queries, trajectory aggregation, and disaster response frameworks. Paras Mehta holds a PhD and has contributed to both academic and applied research in database systems. Despite no listed awards or grants in the provided texts, his research portfolio demonstrates strong engagement with spatial computing challenges. He collaborates within the Databases and Information Systems group at Freie Universität Berlin, contributing to both theoretical advancements and practical software solutions.

Diana Maynardمشاهده پروفایل

عضو هیئت علمی

• Information extraction
• Social media analysis
• Sentiment analysis
• +۶ مورد دیگر

Dr. Diana Maynard is a Senior Research Fellow and Deputy Head of the Natural Language Processing (NLP) research group at the University of Sheffield's School of Computer Science. She leads computational linguistics efforts in the GATE research team, focusing on NLP tools and semantic analysis. Her work spans social media analytics, misinformation detection, and ontology development. She has held key roles in EU-funded projects like KNOWMAK and RISIS2, advancing research-policy linkages and social media analysis frameworks. Education and affiliations include extensive NLP research with a focus on GATE tool development, collaborations with organizations like the Centre for Freedom of the Media, and advisory roles in digital policy projects. Research interests emphasize applying NLP to address societal challenges like media freedom, environmental sustainability, and political discourse analysis. She has supervised numerous PhD students and delivered training courses on sentiment analysis and GATE. Her research output includes over 150 peer-reviewed publications, with recent focus on multimodal fake news detection, online abuse datasets, and climate change communication. She has secured £4M+ in research grants, including projects addressing journalist safety and environmental policy analysis. Her work bridges academic NLP with real-world applications in media monitoring, crisis response, and policy-making. Grants include leadership in EU Horizon2020 initiatives and UK research councils, emphasizing interdisciplinary NLP applications. She actively engages in public speaking and policy advisory roles, promoting the societal impact of NLP technologies.

Faisal Quaderمشاهده پروفایل

استاد مدعو

• Cybersecurity
• Data Science
• Artificial Intelligence/Machine Learning
• +۶ مورد دیگر

Faisal Quader is an Adjunct Professor of Information Science at the University of Maryland's College of Engineering & Information Technology. He holds a PhD in Cybersecurity & Data Science (UMD), MS in Computer Science & Engineering (Johns Hopkins), and BS in Computer Science (University of Wisconsin). As an industry leader with 29 years of experience, he specializes in large-scale product development, federal/commercial service delivery, and R&D optimization. His research focuses on cybersecurity, data science, AI/ML, human-computer interaction, and smart cities. He is President & Cofounder of Technuf LLC and former senior manager at GE and Lockheed Martin. Education: PhD in Cybersecurity & Data Science, University of Maryland (2020) MS in Computer Science & Engineering, Johns Hopkins University (2003) BS in Computer Science, University of Wisconsin (1995) Research Interests: Dr. Quader's work integrates technical innovation with societal impact through: Cybersecurity frameworks for human-centric systems Data-driven predictive models for public safety AI/ML applications in smart infrastructure RPA automation for enterprise efficiency Health informatics for clinical decision support Cloud computing security protocols Awards: GE Leadership Awards for global team management "Rookie of the Year" at Lockheed Martin International Scholarship Recipient (90% coverage) "Best & the Brightest" student award (1994) Professional Roles: President of AABEA Central Community advocate through cultural and nonprofit leadership Active IEEE/ACM participant

Lukasz Golabمشاهده پروفایل

استاد

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

Lukasz Golab is a Professor in the Department of Management Science and Engineering at the University of Waterloo, cross-appointed to the David R. Cheriton School of Computer Science. He holds a BSc from the University of Toronto (2001) and a PhD from the University of Waterloo (2006, Alumni Gold Medal recipient). Previously, he was a Senior Member of Research Staff at AT&T Labs (2006–2011) and held the Canada Research Chair (2014–2024). His research focuses on data-centric systems with societal impact, including explainable AI, blockchain systems, and applications in sustainability and education. Key research areas include: Explainable AI and model transparency High-speed data systems (blockchains, stream processing) Hate speech detection via multi-modal analysis Education data mining (co-op programs, mental health) Sustainability analytics (climate policy, energy systems) Notable achievements include Best Demo Award at EDBT 2025 and Best Short Paper at SSDBM 2023. His work spans 180+ publications in top venues like VLDB, ICDE, and AAAI. Current projects emphasize societal impact through data-driven solutions.

Patrik Lundellمشاهده پروفایل

استاد

• Media History
• Political History
• Public Discourse
• +۳ مورد دیگر

Patrik Lundell is a Professor of History at Örebro University, specializing in media history and political history. He previously held professorships at Mid Sweden University (2016–2018) and earned his PhD in History of Sciences and Ideas from Lund University in 2002. His research focuses on the evolution of print media, including their marketing strategies and public discourse dynamics, with particular attention to parallels between historical media phenomena and modern social media. Lundell also explores right-wing extremist advocacy in interwar Europe and WWII, analyzing how intellectual elites engaged with Nazi Germany. He emphasizes historical perspectives to critically evaluate contemporary media environments. Key research areas include the transformation of media systems from the 18th century to present, the role of media in shaping public opinion, and the intersection of media with political movements. His work challenges simplistic narratives about media progress, advocating for nuanced historical analysis to understand modern media dynamics. Lundell has developed databases tracking textual migrations between Sweden and Finland, and his methodologies incorporate digital humanities tools for computational history analysis. His academic trajectory includes a Docent title from Lund University (2009), and his research has been disseminated through numerous articles, books, and collaborative projects. While no specific grants or awards are listed, his contributions to media historiography and critical analysis of far-right media rhetoric have positioned him as a leading scholar in these fields.

Magnus Löfstrandمشاهده پروفایل

استاد

• Mechanical Engineering
• Product Development
• Production Development
• +۷ مورد دیگر

Magnus Löfstrand is a Professor of Mechanical Engineering at Örebro University, Sweden. He holds a PhD in Computer-Aided Design (2007) from Luleå University of Technology and a Docent (2015) in Product and Production Development from Chalmers University of Technology. His academic career spans multiple institutions, including Uppsala University and Umeå University prior to his current role. Research focuses on industrial system availability, digital twin technology, and data-driven approaches for optimizing production systems. Key areas include simulation-driven design, predictive maintenance, and integrating mechanical engineering with information technology. His work emphasizes sustainability through resource-efficient production processes and competitive industrial advantages. Teaching responsibilities include courses in the Industrial Engineering and Management program at Örebro University, covering topics like Process Development, Reliability Engineering, and Academic Writing. He supervises bachelor's and master's theses across engineering disciplines. Collaborations with institutions like the University of Nottingham and Umeå University's Logic and Applications group highlight his interdisciplinary approach. He is a member of the American Society of Mechanical Engineers (ASME) and actively participates in international conferences and research projects, including the AVANS MAXI program and digital twin initiatives in mining innovation.

Edward Moskalمشاهده پروفایل

دانشیار

• Cyber Security
• Digital Forensics
• Blockchain Technology
• +۵ مورد دیگر

Edward Moskal serves as Associate Professor of Computer & Information Sciences at Saint Peter's University, where he has taught for 18 years and previously chaired the department (2015-2018). During his leadership, student enrollment doubled from 70 to 145. He founded the Master's program in Cyber Security and established the University Cyber Security Center, Advisory Board, and internship program placing 24 students annually. His academic journey includes a B.S. from Saint Peter's University, M.M.S. from Stevens Institute of Technology, and M.S. from University of Notre Dame. Moskal's research spans cyber security, digital forensics, and workplace analytics through multimodal sensing. His work with the $7.9M IARPA MOSAIC project explored productivity measurement using wearables, smartphones, and machine learning across nine institutions. Current initiatives include managing the $100M CyberNYC workforce development project with NYC's Economic Development Council. His scholarly output focuses on integrating social media, sensor data, and behavioral modeling to predict job performance and well-being. Key contributions include establishing cyber security concentrations in undergraduate, MBA, and School of Professional Studies programs, plus the graduate Blockchain Certificate. His publications demonstrate expertise in crime analytics, personality sensing, and educational frameworks for cybersecurity centers. Moskal actively shapes industry standards through affiliations with the U.S. Secret Service Electronic Crimes Task Force and National Cyber Security Alliance. 2014 ISECON Conference Meritorious Paper Award Elected to Journal of Information System Educators Editorial Board (2014) Elected to International Journal of Cyber Research and Education Editorial Board (2018) Scholar-in-Residence at NYU (5 summers: 2005, 2008-2010, 2013) Notre Dame Fellowship developing NSA/DHS curriculum alignment (2014/2015) As Project Manager for iQ4's CyberNYC initiative, Moskal bridges academia and industry to build NYC's cybersecurity workforce capacity. His $7.9M IARPA grant managed technical infrastructure for multisensor workplace studies across nine institutions. Teaching philosophy emphasizes real-world case studies, team projects, and multimedia integration across 20+ courses including Cyber Security Capstone and Digital Forensics Lab. He maintains active industry connections through the Cybersecurity Workforce Alliance and EMC Academic Alliance. Moskal directs Saint Peter's Cyber Security Center with its dedicated Advisory Board and internship pipeline. The center partners with government agencies through the New Jersey Cybersecurity and Communications Integration Cell and National CyberWatch Center. His lab infrastructure supports hands-on learning in blockchain, digital forensics, and network security through specialized courses with $75 lab fees for equipment access.

• Databases
• Database Theory
• Complexity Theory
• +۲ مورد دیگر

Christoph Koch is an Associate Professor at Technische Universität Wien specializing in databases and artificial intelligence. His research spans database theory, complexity theory, and logic in computer science, with a focus on XML processing, query optimization, and parallel data science techniques. Lead projects: KnowledgeGraph (2020–2028), HINT (2012–2017), Weblearn (2005–2008) Developed the Lixto visual extraction system and contributed to DLV knowledge representation framework Supervised T. Lukasser's diploma thesis on XPath query processing

• Data Mining
• Machine Learning
• Computational Sustainability
• +۵ مورد دیگر

Stefan Kramer is a Full Professor at the Institute of Computer Science, Johannes Gutenberg University Mainz. He holds leadership roles including Spokesperson of the DeCoDeML network and involvement in initiatives like the Plattform Lernende Systeme. His research focuses on data mining, machine learning, computational sustainability, and bioinformatics, with over 200 peer-reviewed publications. Kramer has supervised numerous theses and secured significant funding from EU, DFG, BMBF, and industry. Awards include Best Paper recognitions at KDD, ICDM, and ICBK conferences. He actively contributes to academic governance and interdisciplinary collaborations, leading the Institute of Computer Science from 2016–2018. His work emphasizes privacy-preserving techniques, interpretable models, and applications in social good. Education: PhD in Technical Sciences (Dr. techn.) from TU Wien (1999) Positions at Austrian Research Institute for Artificial Intelligence, Siemens AG, and TU München (2003–2011). Research Interests: Kramer’s work spans structured data mining (sequences, graphs), privacy-aware algorithms, computational sustainability, and bioinformatics applications. Notable projects include CheS-Mapper for chemical data visualization and enviPath for biodegradation pathway prediction. Grants & Collaborations: Over 30 projects funded by EU, DFG, BMBF, and industry. Key collaborations include OpenTox Euro, the Predictive Toxicology Challenge, and interdisciplinary networks like the Rhein-Main Universities’ DeCoDeML initiative. Labs/Teams: Leads the Data Mining Group at Mainz, contributing to tools like CheS-Mapper and enviPath. Active in organizing conferences (ECML/PKDD 2021) and editorial roles for Machine Learning journals.

Miodrag Mihaljevicمشاهده پروفایل

استاد پژوهشی

• Cryptology
• Information Security
• Blockchain Technology
• +۱۲ مورد دیگر

Miodrag Mihaljević is a Research Professor at the Mathematical Institute , Serbian Academy of Sciences and Arts (Belgrade, Serbia) since 1999, and serves as Deputy Director of the Institute and Head of the National Center for Cyber Security and Privacy . He has held visiting positions at institutions including The University of Tokyo (1997-2023), AIST (2006-2013), and Chuo University (2013-2014), with a focus on cryptology and information security. BSEE (1979), University of Belgrade MS (1981) and Ph.D. (1990) in Electrical Engineering His research spans Cryptology , Blockchain Technology , Information Security , and Coding Theory , with significant contributions to symmetric key cryptography, stream ciphers, LPN problem analysis, broadcast encryption, and lightweight authentication protocols. His work addresses applications in IoT, cloud computing, and smart grid security, often integrating blockchain for data integrity and access control. Recent publications highlight blockchain-enabled solutions for smart contract vulnerability detection (2025), cross-chain identity authentication (2025), and secure inference in IoT (2025). Earlier works focus on cryptographic attacks (e.g., fast correlation, algebraic attacks) and security frameworks for software-defined radio and cloud systems. Scientific recognition includes the Serbian Academy of Sciences and Arts Award (2013), JSPS Fellowship (1998), and consistent inclusion in the World's Top 2% Scientists (2020-2024). He has led projects on cryptographic techniques for information security (2002-2019) and holds multiple international patents in encryption/decryption systems.

Volkan Atalayمشاهده پروفایل

استاد

• Machine Learning
• Bioinformatics
• Data Stream Analysis
• +۳ مورد دیگر

Volkan Atalay is a Professor in the Department of Computer Engineering at Middle East Technical University (METU), Ankara, Turkey. His research focuses on machine learning applications in bioinformatics, data stream analysis, and drug discovery. He has held leadership roles including Vice President for Research at METU (2010–2016) and chaired the Department of Computer Engineering (2007–2008). Education: Ph.D. in Computer Science from Université Paris Descartes (1993), followed by postdoctoral work at Virginia Tech (2004–2005). Research Interests: Machine learning for bioinformatics (drug-target interaction prediction, protein function annotation), data stream clustering algorithms, and deep learning applications in drug discovery. Key projects include CROssBAR (biomedical knowledge graphs) and DEEPScreen (drug-target interaction prediction tools). Recent publications (2020–2023) emphasize hybrid deep learning models, data fusion for energy forecasting, and open-source protein annotation benchmarks. Awards include the Best Poster Award at GLBIO 2017 for iBioProVis visualization work. Current grants: SmartHome Consortium (data stream analytics) and projects on biomedical relation curation. Labs/Teams: Leads the Cancer Systems Biology Laboratory and collaborates with international consortia like the CAFA challenge.