جستجوی استاد راهنمای دانشگاهی

Julie Mederoمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Community-Engaged Research
• +۳ مورد دیگر

Julie Medero is an Associate Professor and Associate Chair of the Computer Science Department at Harvey Mudd College. Her research focuses on applying computing technologies to community-engaged environmental projects, natural language processing (NLP), and educational tools. She holds a PhD in Electrical Engineering from the University of Washington (2014), an MS from the same institution (2010), and a BA in Computer Science and Linguistics from Swarthmore College (2003). Her work includes developing low-cost air quality sensors for community research and exploring machine learning techniques for text simplification, kinetic typography, and hyperpartisan news detection. She has taught multiple courses including 'STEM and Social Impact: Climate Change' and 'Data Structures & Program Development,' collaborating with colleagues across disciplines. Medero's publications span NLP, computational linguistics, and interdisciplinary projects. She actively mentors students through independent studies and has contributed to open-source annotation tools like the ACE Annotation System at the Linguistic Data Consortium. Her advising and grants include undergraduate research support for environmental computing projects. She emphasizes community partnerships in her work, such as the Active Transportation project encouraging sustainable commuting among schoolchildren.

Joseph Suمشاهده پروفایل

استاد

• Cancer Epidemiology
• Nutritional Epidemiology
• Environmental Exposure
• +۵ مورد دیگر

Joseph Su is a Professor of Epidemiology and Associate Dean for Academic Affairs at the Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center. He also serves as co-director of the Simmons Comprehensive Cancer Center Office of Training and Education. Dr. Su received his educational training from several prestigious institutions: Ph.D. in Nutritional Epidemiology from the University of North Carolina at Chapel Hill M.P.H. in Public Health Nutrition from the University of Minnesota, Minneapolis Undergraduate training in Nutritional Sciences from the University of Minnesota, St. Paul Additional undergraduate training in Chemistry from Chung-Yuan University in Taiwan As a cancer epidemiologist, Dr. Su specializes in assessing nutritional and environmental exposures and their interactions with genes in relation to cancer development and progression. His research spans several key areas: Evaluating the relationship between diet, heavy metal exposure, and adverse health outcomes Investigating genomics, epigenomics, and metabolomics in population-based studies Developing expertise in minority recruitment and follow-up methodologies Applying optical character recognition (OCR) data processing and validation techniques Designing survey studies and managing analytical chemistry laboratories for human biological sample processing His laboratory is equipped with state-of-the-art instrumentation including ICP-MS and an HPLC system. Dr. Su's publication record demonstrates consistent research focus on cancer epidemiology, particularly prostate, breast, and colorectal cancers. His work examines how nutritional factors, environmental exposures (including pesticides and heavy metals), and genetic factors interact to influence cancer risk and progression. A notable aspect of his research is the attention to racial disparities and minority populations, with numerous studies specifically addressing African American and other minority communities. His methodological approaches span from molecular techniques to population-based survey designs and geospatial analysis. Dr. Su has held significant leadership positions in federal agencies: Branch Chief at the Division of Epidemiologic Research of the Center for Medical Devices and Radiological Health (FDA) Program Director at the Epidemiology and Genomic Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute In these roles, he managed grant portfolios and oversaw post-approval medical devices in the U.S. market.

Marcos Zampieriمشاهده پروفایل

استادیار

• Computational Linguistics
• Natural Language Processing (NLP)
• Machine Learning
• +۷ مورد دیگر

Marcos Zampieri is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University's School of Computing. He holds a PhD from Saarland University, where he was affiliated with the German Research Center for Artificial Intelligence (DFKI). His research focuses on computational linguistics and natural language processing (NLP), emphasizing robust NLP system development through large-scale text analysis from diverse sources like social media and news. Key research areas include multilingual benchmarking for code generation (e.g., mHumanEval), offensive language detection across languages, and lexical simplification (ALEXSIS-PT). He has co-edited influential volumes such as Similar Languages, Varieties, and Dialects: A Computational Perspective . Over 100 peer-reviewed publications and multiple conference proceedings underscore his prolific output. Recent work explores ethical implications of AI in education, cross-lingual NLP challenges, and leveraging large language models (LLMs) for tasks like mental health analysis and educational tools. Collaborations include projects on code-mixed emotion detection (EmoMix-3L) and privacy-preserving offensive language identification via federated learning. Zampieri advises on shared tasks like the BEA 2024 Lexical Simplification Pipeline and leads workshops such as VarDial. His datasets (e.g., Sold for Sinhala offensive language) and benchmark frameworks have become industry standards. Despite no explicitly listed grants or labs, his work spans interdisciplinary collaborations in computing, linguistics, and healthcare.

• Business Process Management
• Process Mining
• Data Science
• +۳ مورد دیگر

Pnina Soffer is a Professor at the University of Haifa. She specializes in Business Process Management (BPM), Process Mining, and Information Systems. Her research focuses on process-aware systems, data impact analysis, and decision support mechanisms. She has contributed to advancing techniques for analyzing business processes, including workarounds detection, data inaccuracy mitigation, and process model quality improvement. Her work integrates interdisciplinary approaches, combining computer science with organizational behavior and healthcare informatics. She frequently collaborates with leading researchers in BPM and process mining, contributing to conferences like CAiSE and BPMDS. Her recent efforts emphasize the intersection of process mining with cybersecurity and human factors, exploring how cyber risks propagate through business processes and how facial expressions can predict task performance in process mining tasks. Key research areas include conceptual modeling, event log analysis, and the application of database principles to normalize process logs. Her work aims to bridge theoretical frameworks with practical applications, such as improving clinical decision support systems through layered guideline models and enhancing process robustness against data inaccuracies.

Rebecca Hunting Pomponمشاهده پروفایل

دانشیار

• Post-stroke aphasia
• Neurorehabilitation
• Psychosocial factors in aphasia recovery
• +۴ مورد دیگر

Dr. Rebecca Hunting Pompon is an Associate Professor in the Department of Communication Sciences & Disorders at the University of Delaware’s College of Health Sciences. She directs the Aphasia & Rehabilitation Outcomes (ARO) Lab and serves on the editorial board of Aphasiology and the board of directors of the National Aphasia Association. Ph.D., Speech & Hearing Sciences, University of Washington (2013) B.S., Speech & Hearing Sciences, University of Washington (2007) M.A., Education Counseling, Seattle University (1997) B.A., Education Human Services, Western Washington University (1993) Her research focuses on psychosocial and cognitive factors in aphasia recovery, including chronic stress, depression, and resilience. She develops patient-reported outcome measures and integrates counseling skills into aphasia rehabilitation. Recent publications examine stress biomarkers, treatment response predictors, and resilience frameworks in poststroke aphasia. She applies the Rehabilitation Treatment Specification System to standardize aphasia interventions. NIH R01 grant (2023-2028) for patient-reported outcomes in stroke recovery VA Career Development Award (2016-2018) for stress measurement in aphasia NIH predoctoral fellowship (2012-2013) for inhibition studies in aphasia The ARO Lab investigates the interplay of working memory, attention, and psychosocial factors in aphasia recovery, aiming to improve clinical assessment and treatment planning.

Christopher McCombمشاهده پروفایل

دانشیار

• Sociotechnical Systems
• Machine Learning for Engineering Design
• Human-AI Collaboration
• +۱۷ مورد دیگر

Christopher McComb is an Associate Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering. He leads research in sociotechnical systems, machine learning for engineering design, and human-AI collaboration. He is affiliated with the Block Center for Technology and Society, Manufacturing Futures Institute, NextManufacturing Center, and Wilton E. Scott Institute for Energy Innovation. Previously, he was an assistant professor at Penn State, where he directed the Center for Research in Design and Innovation and led the Technology and Human Research in Engineering Design Group. Ph.D., Mechanical Engineering, Carnegie Mellon University M.S., Mechanical Engineering, Carnegie Mellon University B.S., Civil Engineering and Mechanical Engineering, California State University-Fresno His research centers on human-AI teaming , sociotechnical systems , and computational design , with applications in additive manufacturing, STEM education, and energy systems. He explores how machine learning can enhance engineering design processes, particularly through human-centered AI, generative design, and agent-based modeling. His work emphasizes the integration of human cognition and behavior into AI systems to improve collaboration and innovation. The 15 most recent publications (2025) demonstrate a strong trend in AI-driven design automation , neural surrogate modeling , human-AI interaction , and data generation for engineering simulations . Topics span from using large language models for material selection and design concept generation to developing datasets and benchmarks for advanced manufacturing and CAD systems. There is a clear emphasis on real-world applications in aerospace, finance, and global manufacturing, particularly in Africa. National Science Foundation Graduate Research Fellow McComb has received research funding from NSF, DARPA, and private corporations, and has collaborated with Boeing through their Visiting Professorship Program. He advises students in mechanical engineering and design, and leads the Human+AI Design Initiative and the Design Research Collective. His research has been applied in partnerships with NASA and in addressing manufacturing challenges in Africa. He leads or contributes to interdisciplinary research teams focused on AI in design, additive manufacturing, and energy systems. His labs and initiatives include the Human+AI Design Initiative and the Design Research Collective, which foster collaboration between human-centered design and artificial intelligence.

Sergio Rodriguez Mendezمشاهده پروفایل

پژوهشگر ارشد

• Knowledge Graphs
• Ontology Engineering
• Linked Data & Semantic Web
• +۷ مورد دیگر

Sergio Rodriguez Mendez is a Research Fellow in Knowledge Graph Engineering at the School of Computing, Australian National University. His work focuses on advancing ontology engineering, linked data, semantic web technologies, and their applications in domains like astronomy, brain-computer interfaces, and IoT. He is a member of the Australian Government Linked Data Working Group (AGLDWG) and the W3C Knowledge Graph Construction Community Group, and holds a senior role at the Software Innovation Institute (SII). His research interests include Knowledge Graphs, Ontology Engineering, Linked Data, Data Science, Machine Learning, Natural Language Processing, Brain-Computer Interfaces, IoT, Cloud/Fog/Edge Computing, and Software Engineering. He has contributed to frameworks like pathfinder for astronomical literature review and Doc-KG for document-to-KG conversion. Recent work emphasizes integrating large language models (LLMs) with knowledge graphs, such as AstroLLaVA for astronomical data unification and hybrid frameworks for entity linking. His publications span conferences like WWW, JCDL, and the ACM/IEEE Joint Conferences. He actively supervises research students and is involved in initiatives like the ASKG project for enriching scholarly knowledge graphs. His work addresses challenges in semantic data representation, automated query processing, and syntactic complexity reduction, with a focus on domain-specific applications in science and cultural heritage.

Tien N. Nguyenمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Software Security
• +۷ مورد دیگر

Dr. Tien N. Nguyen is a Professor in the Computer Science Department at the Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas. His research focuses on applying artificial intelligence, machine learning, and natural language processing to solve challenges in software engineering and software security. His primary research interests include: AI/ML4Code: Integration of AI and machine learning in software engineering Software security and program analysis Software testing methodologies Software mining and repository analysis Software evolution and maintenance techniques Professor Nguyen's recent publications (2024-2025) demonstrate strong emphasis on large language models for software engineering tasks, including code analysis, testing automation, program behavior prediction, and repository-level code completion. His work frequently appears in premier venues like ICSE, FSE, and ASE. His research achievements have been recognized with multiple prestigious awards: ACM SIGSOFT Distinguished Paper Award (FSE 2024, FSE 2016, ESEC/FSE 2009, ASE 2012, ASE 2014) IEEE Computer Society TCSE Distinguished Paper Award (SANER 2022) ASE Best Paper Award (2014) FOSS Impact Award - Special Mention (MSR 2019) Professor Nguyen has secured substantial research funding through NSF grants including NSA NCAE-C-002-2021, CNS-2120386, CCF-1723215, CCF-1723432, CNS-1723198, and others. He currently has open positions for TA/RA and encourages interested students to contact him.

• Accounting
• Managerial Accounting
• Financial Accounting
• +۳ مورد دیگر

Dr. Zhimin Yu is an Assistant Professor in the Department of Accounting and International Business at the University of Houston-Downtown. He holds a Ph.D. in Accounting from the University of Calgary, an MS in Accounting from Brock University, an MBA from Shanghai University of Finance and Economics, and a BE in Engineering from Northern Jiaotong University. His teaching focuses on managerial accounting, cost accounting, and intermediate accounting, with extensive experience delivering both in-person and online courses. Ph.D. in Accounting, University of Calgary MS in Accounting, Brock University MBA, Shanghai University of Finance and Economics BE (Engineer), Northern Jiaotong University Dr. Yu’s research spans accounting, corporate governance, and strategic management. His work explores cost behavior in industries like aviation, financial report readability, and institutional investment dynamics. His publications and presentations highlight asymmetric cost behavior, resource commitment, and methodological challenges in financial modeling. Dr. Yu has received the AY 2024 Excellent Research Award from the University of Houston-Downtown and secured sponsored research funding for studies on accounting conservatism and financial report readability. He actively participates in academic conferences, including the American Accounting Association (AAA) and ORCA Symposium, and serves on multiple university and departmental committees related to faculty development, scholarship, and tenure reviews.

Mike Waldمشاهده پروفایل

استاد

• Accessibility Technology
• Artificial Intelligence
• Human-Computer Interaction
• +۴ مورد دیگر

Professor Mike Wald serves as a Professorial Fellow in Education at the University of Southampton's Faculty of Engineering and Physical Sciences, Department of Electronics and Computer Science. His work bridges cutting-edge technology with real-world accessibility challenges through the Web and Internet Science research group. Wald's research spans Accessibility Technology , Artificial Intelligence , and Human-Computer Interaction , with particular focus on assistive technologies for disabled communities. His work integrates natural language processing for text simplification, recommender systems for outdoor tourism, and AI-driven adaptations for augmentative communication. Current projects examine autonomous vehicle trust perception and context-aware climbing recommendation systems, demonstrating his interdisciplinary approach spanning computer science, health informatics, and social inclusion. His recent publications reveal strong trends in applied AI for social good , particularly in disability inclusion and outdoor accessibility. The research consistently combines technical innovation with user-centered evaluation , often focusing on visually impaired and communication-disabled populations. Key methodological themes include practical metric development, contextual integration in recommender systems, and real-world deployment challenges. Professor Wald actively supervises postgraduate researchers including PhD candidate Yue Zheng in Computer Science. His research is supported by major funders including EPSRC, European Union (H2020/FP7), Qatar National Research Fund, and the Alan Turing Institute through projects like SemanticNews, Net4Voice, SlideWiki, and Michelangelo. As a recognized expert, Wald has delivered keynote addresses including "Building Rating System for Indoor Navigation by Blind People" (2018) and "How AI can overcome barriers to inclusion" (2021), demonstrating his leadership in translating research into practical accessibility solutions.

• Empirical Software Engineering
• Code Quality Analysis
• Software Maintenance
• +۷ مورد دیگر

Rocco Oliveto is a Professor in the Department of Computer Science at the University of Salerno, Italy, with a distinguished research career spanning over two decades in empirical software engineering. His work bridges theoretical software engineering principles with practical applications, with recent expansion into healthcare informatics and machine learning applications. His research interests focus on code quality assessment, software maintenance practices, developer behavior analysis, and empirical studies of software engineering phenomena. He has made significant contributions to understanding code smells, bug prediction, API compatibility issues, and more recently, container technologies and smart contract analysis. His recent work demonstrates a strategic expansion into healthcare applications, leveraging software engineering techniques for medical diagnostics and rehabilitation systems. Oliveto's publication pattern shows consistent productivity with multiple high-impact publications each year across top venues including IEEE Transactions on Software Engineering, ACM Transactions on Software Engineering and Methodology, and Empirical Software Engineering journal. His recent articles (2023-2025) reveal a growing interest in applying software engineering techniques to healthcare domains while maintaining strong contributions to core software engineering topics. The research demonstrates sophisticated methodological approaches combining empirical studies with machine learning techniques. His collaborative network includes prominent researchers such as Simone Scalabrino, Gabriele Bavota, and Andrea De Lucia, with whom he has co-authored numerous high-impact publications. This collaboration spans both traditional software engineering topics and emerging interdisciplinary applications in healthcare.

Paolo Falcarinمشاهده پروفایل

دانشیار

• Cybersecurity
• Software Engineering
• Artificial Intelligence
• +۵ مورد دیگر

Paolo Falcarin is an Associate Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Computer Science and Statistics. His research focuses on cybersecurity, software engineering, and AI compliance, with notable contributions to software protection mechanisms, reverse engineering defenses, and cyber-physical systems. He actively participates in editorial roles for journals like Computers & Security and serves on program committees for IEEE/ACM conferences. Key Projects: ASPIRE (EU FP7), SERICS (PNRR), and Flexymob (Currant s.r.l.) Research Interests: Cybersecurity Knowledge Graphs, Runtime Security (Falco), AI Act Compliance, and Software Renewability His work bridges theoretical cybersecurity advancements with practical applications in distributed systems and IoT. Recent publications emphasize anomaly detection in large-scale systems and regulatory compliance frameworks for AI.

Heman Shakeriمشاهده پروفایل

استادیار

• Machine Learning
• Learning-Based Control
• Network Science
• +۱۵ مورد دیگر

Heman Shakeri is an Assistant Professor at the School of Data Science, University of Virginia (UVA), where he leads the DYNAMO Lab. He is also a member of the UVA Center of Diabetes Technology and the UVA Cancer Center, reflecting his interdisciplinary research in biomedical applications of machine learning and control systems. Ph.D. in Electrical and Computer Engineering, Kansas State University M.S. in Mechanical Engineering, Amirkabir University of Technology – Tehran Polytechnic B.S. in Mechanical Engineering, Amirkabir University of Technology Dr. Shakeri's research focuses on algorithm development in machine learning , learning-based control , and network science , with applications in diabetes management, single-cell signaling, glaucoma risk assessment, and complex dynamical systems. His work bridges artificial intelligence, control theory, and data science to solve real-world biomedical challenges. The 15 most recent publications highlight a strong trend in dynamical systems modeling , stochastic flow matching , Koopman operator theory , and reinforcement learning for biomedical control . His papers frequently appear in top-tier venues such as ICML, NeurIPS, ICLR, and IFAC, demonstrating consistent innovation in both methodological and applied domains. Dr. Shakeri has received numerous scientific awards and grants, including: NIH R01 Grant ($1.6M) for optimizing renal cancer treatment Capital One Data Design Fellowship ($55k) LaunchPad Grant ($200k) for artificial pancreas research UVA Research Communications Fellow (2024) Multiple pilot awards from UVA Cancer Center, Brain Institute, and Ivy Foundation He actively mentors graduate students and postdoctoral researchers in the DYNAMO Lab, supporting translational research from algorithm design to clinical applications. His lab has secured funding from NIH, NCI, Oracle, and industry partners, indicating strong external validation of his research vision. The lab collaborates extensively with clinical and computational teams across UVA, particularly in diabetes and cancer research. The DYNAMO Lab is a hub for advancing machine intelligence for dynamical systems , with current projects including smart artificial pancreas systems, explainable AI for glaucoma, operator-theoretic methods, and complex network analysis. The lab emphasizes interdisciplinary collaboration, real-world impact, and methodological rigor.

Petar Jovanovicمشاهده پروفایل

پژوهشگر

• Big Data Management
• Data Governance
• Data Integration
• +۳ مورد دیگر

Petar Jovanovic is a researcher at the Department of Service and Information Systems Engineering within the Barcelona School of Computer Science at Polytechnic University of Catalonia (UPC). His work focuses on Big Data management , data governance , and user-centered data integration platforms . PhD (2016) from UPC and Université Libre de Bruxelles Software Engineering degree from University of Belgrade Jovanovic created the Quarry platform, enabling non-technical users to perform data analysis for global health initiatives like WHO disease eradication programs. His 2017 SCIE/BBVA award recognized innovations in applying Big Data to combat diseases such as Chagas in underprivileged countries. Recent research emphasizes: Knowledge graph-based data governance frameworks (2024) Web API evolution prediction models (2024) Automated FAIR data lifecycle systems (2023-2024) He has co-authored 15+ high-impact publications and holds one patent as primary innovator. His work spans European Union research programs and WHO collaborations.

Furkan Soyمشاهده پروفایل

مدرس

• Orthopedics
• Traumatology
• Developmental Dysplasia of Hip
• +۳ مورد دیگر

Furkan Soy serves as a Lecturer in the Department of Orthopedics and Traumatology within the Faculty of Medicine, specializing in orthopedic surgery with emphasis on hip and knee pathologies, pediatric orthopedics, and trauma management. His clinical and academic work bridges surgical practice with evidence-based research. Dr. Soy's research focuses on developmental dysplasia of the hip (DDH), meniscal disorders, Haglund deformity, and osteonecrosis. He investigates vitamin D deficiency in Turkish populations, biomarkers for ACL injuries, and reliability of online orthopedic information. His work includes bibliometric analyses of Turkish orthopedic literature and public health interventions for DDH in Kirikkale province. Analysis of his 15 most recent publications reveals strong trends in diagnostic accuracy improvement, public health implementation of orthopedic screening programs, and interdisciplinary approaches connecting orthopedics with infection control. His studies consistently address practical clinical challenges in Turkish healthcare settings while contributing to global orthopedic knowledge through case reports, cross-sectional analyses, and bibliometric evaluations.