جست‌وجوی استاد و پژوهشگر

Kent Jonesمشاهده پروفایل

استاد

• Computer Science
• Electrical Engineering
• Software Engineering
• +۳ مورد دیگر

Kent Jones is a Professor in the Department of Math/Computer Science at Whitworth University since 1995. His academic career includes roles as site director for the ACM Intercollegiate Pacific Northwest Programming Competition and judge/problem writer since 2000. He has collaborated on medical technology grants and taught over 15 courses ranging from introductory programming to advanced topics in computer architecture and graph theory. Education: Ph.D./M.S. from Washington State University, B.S. from Walla Walla College Research focuses on machine learning applications in medical technology, genetic algorithms, and computational networks. His work includes developing an endoscopic guidance system for neurosurgery and exploring fuzzy retrieval systems in spatial databases. Jones emphasizes interdisciplinary collaboration, demonstrated through international presentations in India and Australia. Grants include $15K for neurosurgery technology (2000) and $4.4K for hardware/software (2001). He co-organized computing outreach seminars for high-school students (2007) and maintains industry ties through prior roles at Boeing and McDonald's.

• Cryptology
• Game Theory
• Blockchain Security
• +۳ مورد دیگر

Sanjay Bhattacherjee is a Lecturer in Cyber Security at the School of Computing , University of Kent. He serves as the Information Services Liaison Lead for the Institute of Cyber Security for Society (iCSS) and leads the Undergraduate Year I module on Blockchain and Distributed Systems. His research focuses span cryptology, blockchain security, algorithm design, and game theory, as detailed on his research webpage . Research Interests Cryptology and Lattice Reduction Algorithms Game Theory Applications in Blockchain Security Algorithm Design for Broadcast Encryption Network Security and Proportional Representation in Financial Systems Teaching Role Highlights Undergraduate and Postgraduate Lectures on Blockchain, Cryptography, and Algorithms Module Leadership in "Maths for Computing" and "Distributed Systems" Contributions to Pedagogical Essays on Depth vs. Breadth in Education and Assessment Practices

• Artificial Intelligence
• Natural Language Processing
• Vision-Language Models
• +۶ مورد دیگر

Chitta Baral is a Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU), part of the Ira A. Fulton Schools of Engineering. He joined ASU in 1999 as an Associate Professor and was promoted to Full Professor in 2002. His research focuses on artificial intelligence, natural language processing, vision-language systems, and neuro-symbolic approaches. He directs the Cognition and Intelligence Lab (COGINT Lab) and has authored influential works like the book Knowledge Representation, Reasoning and Declarative Problem Solving (Cambridge University Press). His academic journey includes a B.Tech from IIT Kharagpur, and M.S./Ph.D. from the University of Maryland, College Park. Baral's work spans theoretical contributions (e.g., Answer Set Programming, logical reasoning) and applied domains like cybersecurity, biomedical informatics, and robotics. He has held editorial roles at top AI journals, led KR Inc., and collaborated with organizations like the Mayo Clinic. His recent research emphasizes LLM instruction engineering, bias mitigation, and multimodal reasoning benchmarks. He teaches advanced courses in NLP and advises students in AI-related areas.

• Information Extraction
• Natural Language Processing
• Data Privacy
• +۳ مورد دیگر

Besat Kassaie is a Postdoctoral Researcher collaborating with Renée Miller. His research focuses on information extraction, natural language processing, and data privacy. Key areas include improving unstructured data quality through updatable extracted views, mathematical information retrieval (MathIR), and ontology matching techniques. His work spans theoretical advancements in information systems and practical applications in healthcare analytics. Notable contributions include frameworks for automated view maintenance, differential privacy methods for text data, and systems for detecting math answers using neural networks like Tangent-L. Kassaie's articles demonstrate a strong emphasis on database systems optimization, dynamic data processing, and the intersection of AI with mathematical problem-solving. His research bridges foundational computer science principles with real-world challenges in data privacy and interpretability.

Johannes Pernaaمشاهده پروفایل

مدرس

• Chemistry Education
• Teacher Training
• Educational Technology
• +۳ مورد دیگر

Johannes Pernaa serves as a University Lecturer in the Department of Chemistry at the University of Helsinki's Faculty of Science, specializing in chemistry teacher education. He holds positions as Teachers' Academy Supervisor in the Doctoral Programme in Chemistry and Molecular Sciences and maintains a docentship in the Department of Chemistry. Pernaa also serves as editor-in-chief of the LUMAT journals and holds external appointments as Docent in Chemistry Education at the University of Ljubljana (2022-2027) and Visiting Professor at Universidad Metropolitana de Ciencias de la Educación (since 2021). His educational background includes a PhD in Chemistry Teaching (2011), multiple MSc degrees in Chemistry Teaching (2008, 2010), and an Engineer degree in Media Technology (2015). Pernaa's research focuses on the research-based development of chemistry teacher education with special expertise in modern technology applications for chemistry instruction. His scholarly work demonstrates strong emphasis on educational technology integration, particularly regarding AI applications in chemistry education, systems thinking pedagogy, and sustainable chemistry teaching approaches. Recent publications reveal growing interest in the psychological and identity aspects of technology adoption among teacher education students, alongside continued development of evidence-based chemistry teacher training frameworks. Lifetime membership in Teachers' Academy (2025) Bronze merit award (2022) Pernaa actively supervises theses and participates in numerous research projects including LUMA Stars (2025-present), ORCheSSE ER (2024-2027), and the EAKR.Jakamisella viisautta-JAVIST project (2023-2025). His leadership extends to directing the Keminformatiikka kemian opetuksessa research project since 2021. He maintains significant editorial responsibilities as editor-in-chief of LUMAT-B: International Journal on Math, Science and Technology Education and serves on multiple editorial boards. Through his Chemistry Teacher Education Unit work and media engagement including interviews explaining chemistry's relevance to everyday life, Pernaa actively connects academic research with practical teaching applications and public understanding of science.

Haodong Wangمشاهده پروفایل

دانشیار

• Wireless and mobile computing
• Security and privacy in pervasive computing
• Information retrieval
• +۵ مورد دیگر

Haodong Wang is an Associate Professor in the Department of Electrical Engineering and Computer Science at Cleveland State University, where he maintains his office in FH 219. He previously served as an Assistant Professor in the Department of Math and Computer Science at Virginia State University before joining Cleveland State. Wang received his educational foundation through a Bachelor of Engineering in Electronic Engineering from Tsinghua University in Beijing, China, followed by a Master of Science in Electrical Engineering from Penn State University, culminating in a Ph.D. in Computer Science from the College of William and Mary in August 2009. His research primarily focuses on wireless and mobile computing security, with particular expertise in cryptographic implementations for resource-constrained sensor networks. Wang's WM-ECC project represents one of the most efficient publicly available ECC implementations for wireless sensor motes, adopted by numerous institutions including USC, UCLA, and Siemens Research Corporation. His work bridges theoretical security concepts with practical implementations for pervasive computing environments. Analysis of his 15 most recent publications reveals a progression from foundational wireless sensor network security work toward broader applications in cloud computing, virtualization, and web security, while maintaining his core expertise in wireless communications. His research consistently demonstrates a practical engineering approach to solving real-world security and performance challenges. Wang has served on technical program committees for major conferences including GLOBECOM 2014 and ICCCN 2014, reflecting his standing in the networking research community. He teaches a comprehensive range of courses at Cleveland State University from foundational programming and data structures to advanced topics in information security, blockchain, and artificial intelligence, demonstrating both breadth and depth in computer science education.

Behrooz Mansouriمشاهده پروفایل

استادیار

• Information Retrieval
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Behrooz Mansouri is an Assistant Professor of Computer Science at the University of Southern Maine. He holds a Ph.D. (2022) in Computer Science from the Rochester Institute of Technology (Rochester, NY), an M.Sc. (2017) in Software Engineering from the University of Tehran, and a B.Sc. (2013) in Software Engineering from Shahid Beheshti University. His roles include Director of the Michael E. Dubyak Center, Director of the Artificial Intelligence and Information Retrieval (AIIR) Lab, and Graduate Program Director. He previously worked on the Parsijoo Persian search engine project and co-organized the ARQMath lab during his Ph.D. Education: Ph.D., Computer Science, Rochester Institute of Technology, USA (2022) M.Sc., Software Engineering, University of Tehran, Iran (2017) B.Sc., Software Engineering, Shahid Beheshti University, Iran (2013) His research focuses on Information Retrieval (IR) , Natural Language Processing (NLP) , and Mathematical Information Retrieval (MIR) . He leads the AIIR Lab, which develops systems like MathMex (a conversational math search engine) and explores cross-lingual math retrieval, legal question classification, and cloud detection in satellite imagery. Recent projects include NSF-funded work on conversational math search and participation in CLEF SimpleText labs. His lab's work has been recognized, including a Best Lab Paper at SimpleText'24 and an NSF CRII Grant (2024). He advises students on projects like legal case search, math definition extraction, and AI ethics in STEM education. Grants & Awards: NSF CRII Grant: 'Towards Conversational Search Systems for Math' (2024) Best Lab Paper at SimpleText'24 (2024) Outstanding Reviewer at ECIR 2024 Doctoral Consortium Co-chair at SIGIR 2025 The AIIR Lab collaborates on interdisciplinary projects, including MathMex (math definition search), Cloud Detection in Satellite Images , and Tree Classification . Prospective students are encouraged to align with these research themes and apply via specific guidelines outlined on his website.

• Cybersecurity
• Mobile Technologies
• Wireless Sensor Networks
• +۵ مورد دیگر

John J Yoon is a Professor of Cybersecurity in the Math/Computer Sciences Department at Mercy College's School of Liberal Arts. He serves as Director of the Mercy College Edge Computing (MCEC) initiative. His research focuses on cybersecurity, mobile technologies, wireless sensor networks, and machine learning applications in network security. Key areas include IoT security, secure coding practices, and distributed data services. His work bridges theoretical advancements with practical implementations, such as deep-learning approaches to IoT threat detection and steganography techniques in Python. Recent publications emphasize edge computing trustworthiness, real-time packet analysis, and collaborative sensor calibration. Notable contributions include an intelligent distance learning framework for heterogeneous online students (2021), a deep-learning IoT attack handling system (2020), and studies on sensor trustworthiness in mobile edge computing (2017-2018). His research spans over three decades with foundational work in XML security (2006) and telecommunications fault management (1993).

• Internet of Things
• Smart Homes / Building Automation
• Embedded Systems
• +۵ مورد دیگر

Aliaksei Andrushevich is a Senior Research Associate at the Institute of Electrical Engineering IET iHomeLab within the Lucerne School of Engineering and Architecture (HSLU T&A). He holds a PhD in Information Technologies (2021–2024) and has extensive experience in IoT, smart homes, and embedded systems. He has served as a guest lecturer at the Hogeschool van Amsterdam (2015–2018) and contributed to academic memberships including IEEE and AAATE. Education highlights include an MSc in Embedded Systems from the University of Lugano (2005–2007) and a Dipl.-Math from Belarusian State University (2001–2006). His research focuses on IoT security, ambient assisted living (AAL), energy-efficient systems, and smart city technologies. Notable projects include AgeWell Companion, WorkLifeCabin, and FEEB&D (Future Energy Efficient Buildings). His recent publications emphasize edge computing for public building monitoring, conversational AI in elderly care, and semantic IoT frameworks. Awards include the Young Researcher Award (2014) and SenZations Prize (2013). He actively collaborates on projects like RecoveryFun (mental health monitoring) and Ella4Life (AAL solutions), leveraging interdisciplinary approaches to improve quality of life through technology.

Yunshi Lanمشاهده پروفایل

دانشیار

• Knowledge Bases
• Question Answering
• Information Extraction
• +۶ مورد دیگر

Yunshi Lan is an Associate Professor at the School of Data Science and Engineering, East China Normal University (ECNU). He holds a Ph.D. from Singapore Management University (SMU) and a Bachelor's degree from Southwest University (SWU). His research focuses on knowledge bases, question answering systems, deep learning, and large language models. He has led multiple grants, including projects from the National Science Foundation of China and the Shanghai Pujiang Talent Program. Lan has published extensively in top venues like ACL, EMNLP, and AAAI, with notable contributions to visual question answering, grammatical error correction, and educational NLP. He serves on program committees for ACL, EMNLP, and AAAI, and has given invited talks on large language models and knowledge-based systems. His teaching includes undergraduate and graduate courses on deep learning, emphasizing practical applications and LLM integration. Lan mentors a vibrant research group, with students contributing to impactful projects like MWPToolkit and FlaCGEC datasets. Education: Ph.D., SMU (201X); B.Sc., SWU (201X). Key grants include 'Knowledge Base Question Answering System in Interactive Environments' (NSFC, 2023-2025) and 'Intelligent Low Carbon Oriented Theories for Cross-Border Electric Power Trading' (NSFC, 2024-2028). Awards include the First Prize in the Financial GraphRAG Competition (2024) and the Shanghai Pujiang Talent Program (2022). His research highlights include benchmarking Visual Chinese GEC, developing TreeEval for LLM evaluation, and creating educational NLP tools like 中介语智能批改 for CFL learning.

Eugene Santos Jr.مشاهده پروفایل

استاد

• Artificial Intelligence
• Decision-Making Systems
• Adversarial Modeling
• +۶ مورد دیگر

Eugene Santos Jr. is the Sydney E. Junkins 1887 Professor of Engineering and Director of the Master of Engineering Program at Dartmouth College's Thayer School of Engineering. He holds adjunct roles in Computer Science and serves on the Department of the Air Force Scientific Advisory Board. His research bridges artificial intelligence, decision science, and social systems, focusing on computational intent, adversarial modeling, and explainable AI applications in healthcare, cybersecurity, and societal resilience. Education: BS/MS in Math/CS (Youngstown State), ScM/PhD in Computer Science (Brown University). Awards include AAAS and IEEE Fellowships. Key projects include AI ethics frameworks, insider threat detection, and federated biomedical knowledge graphs. Research interests span Bayesian knowledge systems, human-machine teaming, and nonlinear decision-making. Over 150 peer-reviewed publications include advancements in reward function analysis, ontology fusion, and trust in AI systems. Advised 10+ PhD students and leads the DI²AG research group. Active in policy roles, including Vermont's AI Taskforce and editorial leadership for IEEE Transactions on Cybernetics.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۸ مورد دیگر

Rohini Srihari is a Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York, where she also serves as Associate Chair. Additionally, she holds an Adjunct Professor position in the Department of Linguistics. Her academic journey includes a PhD in Computer Science from the University at Buffalo (1992) and a B. Math degree from the University of Waterloo (Canada). Dr. Srihari's research spans multiple cutting-edge areas in artificial intelligence, with a strong focus on Natural Language Processing, Information Retrieval, and Text Mining. Her work encompasses both theoretical foundations and practical applications, particularly in Conversational AI systems, disinformation detection, and social unrest prediction. She has pioneered approaches in multilingual text mining for less commonly taught languages and developed innovative methods for social media analysis. Her recent research emphasizes AI for Social Good, addressing critical societal challenges through technology. Her publication record shows a clear evolution from foundational work in multimedia information retrieval and document analysis toward increasingly socially impactful applications. The recent publications demonstrate a strong focus on using AI to address misinformation, predict civil unrest, and develop empathetic conversational agents. Her work increasingly integrates multiple data sources and leverages state-of-the-art deep learning techniques while maintaining a focus on real-world applicability and social impact. Awards & Recognition: Bronze prize in the Alexa Prize Socialbot Grand Challenge 4 (2021) Two US patents in text mining and image analysis Extensive publication record with over 100 papers and an h-index of 40 Dr. Srihari has secured significant research funding from major agencies including the National Science Foundation (NSF), DARPA, and IARPA. She has directed multiple research projects focused on AI applications for social good, including an NSF-funded project on Purposeful Conversational Agents based on Hierarchical Knowledge Graphs. She supervises students across both Computer Science and Linguistics departments, fostering interdisciplinary research. Her research group, focused on Conversational AI for Social Good, addresses critical issues like combating disinformation and assisting the disabled through trustworthy socialbots. She also directs the Center of Excellence for Document Analysis and Recognition (CEDAR) and has led interdisciplinary student teams in major competitions. Her industry experience includes founding and directing technology startups focused on big data analytics, two of which were acquired by larger media analytics companies.

Siwen Luoمشاهده پروفایل

مدرس

• Natural Language Processing
• Multimodal Learning
• Explainable Artificial Intelligence
• +۴ مورد دیگر

Siwen Luo is a Lecturer in the Department of Computer Science and Software Engineering within the School of Physics, Maths and Computing at The University of Western Australia. She holds a PhD in Computer Science from The University of Sydney (2023) and specializes in multimodal AI systems with emphasis on explainability and robustness. Education PhD in Computer Science, The University of Sydney, 2023 Research Focus Dr. Luo's work centers on Natural Language Processing and Vision-Language Integration , with significant contributions to Explainable AI , Visual Question Answering , and Visually-rich Document Understanding . Her research improves AI interpretability for scientific applications and mental health domains, aligning with UN Sustainable Development Goals for quality education and industry innovation. Key methodologies include knowledge distillation and multimodal large language models. Publication Trends Her 2023-2025 publications reveal strong focus on knowledge distillation techniques for multimodal tasks, particularly visual question answering and mental health detection. Works appear in top venues including AAAI, CIKM, IJCAI, and ACM Computing Surveys, demonstrating expertise in bridging computer vision with language understanding through datasets like MMVQA and PDF-VQA. Mentoring and Collaboration Dr. Luo actively mentors PhD and HDR students at UWA while collaborating across disciplines to develop responsible AI technologies. She currently accepts new research students for projects in NLP and multimodal learning.

Marc Coutancheمشاهده پروفایل

دانشیار

• Cognitive Neuroscience
• Memory
• Learning
• +۳ مورد دیگر

Marc Coutanche is an Associate Professor in the Department of Psychology at the University of Pittsburgh, affiliated with the School of Computing and Information. He holds a PhD from the University of Pennsylvania and completed a postdoctoral fellowship at Yale University. His research focuses on the cognitive neuroscience of memory, learning, and perception, with a particular emphasis on neuroimaging analysis methods. His work integrates techniques like fMRI multivariate pattern analysis to explore how the brain encodes and retrieves information across different cognitive tasks. Dr. Coutanche’s educational background includes advanced training in cognitive neuroscience and psychology. His research interests span the neural mechanisms underlying memory retrieval, the impact of context on recall, and the development of novel neuroimaging methodologies. He has contributed to understanding how symbolic and non-symbolic numerical information is processed, as well as the role of sleep in memory consolidation. His recent publications highlight advancements in functional lateralization studies, the application of virtual reality for memory research, and the analysis of neural representations in brain regions like the intraparietal sulcus. These studies underscore his interdisciplinary approach, combining cognitive psychology with cutting-edge neuroimaging techniques. While no specific awards or grants are listed, his extensive publication record reflects a commitment to advancing knowledge in cognitive neuroscience and neuroimaging methodologies. He is actively involved in training future researchers and contributes to academic discourse through peer-reviewed articles and edited volumes like the APA Handbook of Research Methods in Psychology.

Eduardo Sany Laberمشاهده پروفایل

دانشیار

• Algorithm Design
• Machine Learning
• Optimization
• +۵ مورد دیگر

Eduardo Sany Laber is an Associate Professor in the Computer Science Department at Pontifical Catholic University of Rio de Janeiro (PUC-Rio), with over two decades of academic and industrial experience. He completed his undergraduate (1996), master's (1997), and doctorate (1999) at PUC-Rio, focusing on data compression. Since 2001, he has been a faculty member, with visiting roles at Carnegie Mellon University (2001-2002). Education : PhD in Computer Science (1999), PUC-Rio Master in Computer Science (1997), PUC-Rio BSc in Computer Engineering (1996), PUC-Rio His research centers on designing algorithms with theoretical guarantees for problems in data compression , machine learning , and finance , combining optimization, information theory, and industrial applications. His recent work explores connections between k-means and decision trees , alongside information-theoretic clustering . The 15 most recent publications highlight his focus on algorithm efficiency, spanning machine learning , information retrieval , and combinatorial optimization . Notable venues include ICML , ECAI , and ISAAC , with applications in finance , data analysis , and web engineering . Scientific Awards : Gold Medal, Brazilian Math Olympiad (1990) Bronze Medal, International Math Olympiad (1991) First Prize, Brazilian Computer Society PhD Thesis Contest (2000, 2004 co-advisor) Affiliated Member, Brazilian Academy of Sciences (2008) Best Paper Award, ISAAC (2015) Laber supervises PhD and Master's students intensively, including recipients of national awards like the Brazilian Computer Society's First Prize for Marco Molinaro's Master's Dissertation (2009) and Second Prize for Renato Carmo's PhD Thesis (2006) . He is a CNPq Level 1 Researcher and collaborates with the Greyhounds Laboratory .