جست‌وجوی استاد و پژوهشگر

Stephen Lee-Urbanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Game AI
• +۳ مورد دیگر

Stephen Lee-Urban is a Teaching Associate Professor in the Department of Computer Science & Engineering at Lehigh University, affiliated with the Rossin College of Engineering. He holds a Ph.D., M.S., and B.S. in Computer Science and Engineering from Lehigh University, all completed with summa cum laude distinction. His research focuses on fundamental and applied artificial intelligence, machine learning, game AI, cognitive systems, and automated planning. He has contributed to innovative projects such as HuManIC (human-machine interpretive control), CORA (cognitive systems framework), and crowdsourced narrative generation systems. His academic career includes significant work in cybersecurity through intelligent agent modeling of malware, as well as contributions to game AI for strategy games and military training simulations. Notable awards include summa cum laude honors for all three of his university degrees. Lee-Urban's scholarly output spans over 20 publications since 2000, with recent emphasis on AI applications in collaborative storytelling, adaptive planning systems, and human-computer interaction. His research integrates machine learning techniques with sociocultural analysis, crowd-powered content creation, and hierarchical task networks. Current work explores autonomous systems capable of leveraging crowd intelligence for generating interactive narratives and optimizing military training scenarios. While no specific grants or advising roles are listed, his interdisciplinary approach bridges computer science with game design, cybersecurity, and cognitive modeling.

• Agent-Based Modelling
• Computational Social Science
• Natural Language Processing (NLP)
• +۳ مورد دیگر

Dr. Sjoukje Osinga is an Assistant Professor in the Information Technology group at Wageningen University's Department of Social Sciences. Her research focuses on computational social science, natural language processing (NLP), and big data applications in agriculture. She holds a PhD from Wageningen University on agent-based modelling of knowledge management in the pig sector, with fieldwork in China. She contributed to EU H2020 projects like Cybele (big data in agriculture) and Dragon (knowledge transfer of ABM tools). She is a member of the SiLiCo Centre, specializing in simulating complex systems through agent-based simulations. Education: Artificial Intelligence and Cognitive Science (Groningen and Leuven, 1991) Research interests include agent-based modelling, big data analytics for agriculture, machine learning, and knowledge management. She explores topics like digital twins in health and agriculture, and sentiment analysis in policy-making. Her work bridges technical innovation with societal challenges, such as sustainable farming practices and compliance strategies in regulatory environments. Publications span agent-based models for pork supply chains, machine learning applications in crop forecasting, and digital twin frameworks for agriculture. She actively engages in interdisciplinary projects addressing data integration and policy implications of emerging technologies.

Wenqi Shiمشاهده پروفایل

استادیار

• Artificial Intelligence in Healthcare
• Precision Medicine
• Translational Medicine
• +۳ مورد دیگر

Dr. Wenqi Shi serves as an Assistant Professor at the Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center. Her research focuses on the integration of artificial intelligence with healthcare, particularly advancing algorithms and systems for precision medicine. She specializes in working with multi-modal patient data including EHRs, medical notes, imaging, and genomics, with dedicated applications in pediatric healthcare, cancer, and rare diseases. Her research interests include developing large language models for translational medicine, creating agentic AI and generative models for biomedical discovery, and establishing responsible AI practices to enhance clinical outcomes. Publication trends show extensive work in explainable AI, clinical decision support systems, and multi-modal data integration, with recent emphasis on retrieval-augmented language models and causal inference methodologies. Dr. Shi obtained her Ph.D. from the Georgia Institute of Technology prior to joining UT Southwestern.

Matthew Hertzمشاهده پروفایل

استاد آموزشی

• Computer science education
• runtime systems
• dynamic memory management

Matthew Hertz is a Teaching Professor in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. His research focuses on computer science education, runtime systems, and dynamic memory management. Education: PhD, Computer Science, University of Massachusetts Amherst, 2006 MS, Computer Science, University of Massachusetts Amherst, 2001 BA, Computer Science, Carleton College, 1997 Research interests span computer science education and systems optimization. His educational research investigates learning factors in introductory programming courses, develops pedagogical tools like CloudCoder for programming exercises, and analyzes failure rates in CS1 courses. In systems research, he focuses on memory management innovations including garbage collection algorithms, adaptive resource allocation, and performance optimization in shared environments. Publications show dual focus: recent work emphasizes educational data analysis and programming pedagogy while earlier research concentrates on memory management efficiency and runtime systems. Trends include automated assessment tools and adaptive algorithms for resource-constrained environments. No scientific awards reported. No advising or grant information available. No labs or teams mentioned in available data.

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Prof. Margret Keuper is a Professor in the Department of Computer Vision and Machine Learning at the Max Planck Institute for Informatics. Her research focuses on advancing machine learning and computer vision techniques, with an emphasis on model fairness, adversarial robustness, and multimodal interactions. She leads interdisciplinary projects exploring topics such as dataset analysis, generative models, and climate action through visual narrative analysis. Research Interests: Her work bridges theoretical foundations and practical applications in domains like adversarial training, image classification robustness, and robotics perception. She explores how vision-language models can be steered to align with human biases and develops methods for data-efficient learning and interpretability. Recent Contributions: Recent work includes FAIR-TAT (model fairness via adversarial training), VSTAR (video synthesis), and TikZero (zero-shot graphics program generation). Her publications in top venues like CVPR, ICCV, and ICLR highlight contributions to both methodological innovation and real-world impact. Collaborations: Works closely with researchers across Max Planck and academic partners, focusing on projects such as sensor layout optimization, climate discourse analysis via social media imagery, and domain-aware foundation model fine-tuning.

Jingchao Niمشاهده پروفایل

استادیار

• Machine Learning
• Time Series Analysis
• Cross-Modal Learning
• +۹ مورد دیگر

Jingchao Ni is an Assistant Professor in the Department of Computer Science at the University of Houston. He previously worked as a researcher at NEC Labs America (2018-2022) and AWS AI Labs (2022-2024). He earned his Ph.D. in Computer Science from The Pennsylvania State University's College of Information Sciences and Technology in 2018 under Prof. Xiang Zhang. Research Interests: Machine Learning, Time Series Analysis (Cross-Modal/Multimodal Integration, LLM Reasoning), Graph Learning, Anomaly Detection, Generative Models, and applications in Healthcare (personalized systems, Cyber-Physical Systems, AIOps). His recent publications focus on multimodal time series analysis, vision models for temporal data, and interpretable graph neural networks, with deployments in AWS cloud systems. He has advised students on projects involving LLM agents, causal discovery, and robust forecasting. Awards include a AAAI 2019 Most Influential Paper (PaperDigest) and an ICLR 2022 Spotlight Presentation. He leads the Data-Driven Intelligence (D2I) Group and has contributed to tutorials at KDD 2025 and IJCAI 2025.

Christopher V. Trinactyمشاهده پروفایل

دانشیار

• Classical Studies
• Latin Literature
• Ancient Philosophy
• +۳ مورد دیگر

Christopher V. Trinacty is an Associate Professor of Classics at Oberlin College, affiliated with the College of Arts and Sciences. He holds a PhD from Brown University (2007), MA from the University of Arizona (2000), and BA from Pitzer College (1996). His primary research focuses on Seneca the Younger, particularly his tragedies and Naturales Quaestiones , exploring Stoic philosophy and intertextuality with Augustan poets like Horace, Virgil, and Ovid. He has published a commentary on Seneca’s Natural Questions and contributed to volumes such as C.H. Sisson Reconsidered . Trinacty teaches Latin and Greek language, Roman history, and courses integrating public humanities projects, such as an Ancient Science class website . He curated an exhibition on the island of Aegina at Oberlin’s Terrell Library and actively presents at international conferences (e.g., University of Athens, University of Cincinnati). His work bridges classical literature with modern interpretations, as seen in studies linking Seneca to contemporary media like Batman. Education: PhD in Classics, Brown University, 2007 MA in Classics, University of Arizona, 2000 BA in Classics, Pitzer College, 1996 Research Interests: Senecan tragedy and philosophy, Augustan poetry reception, Stoic natural science, intertextuality in Roman literature, ancient science communication, and digital humanities. Recent Scholarly Work: Explores Seneca’s engagement with Augustan literary tradition, the impact of natural disasters on Stoic thought, and cross-media adaptations of classical myths. Key publications include Senecan Tragedy and the Reception of Augustan Poetry (2014) and commentary on Seneca’s Natural Questions (2022). Labs/Teams: Leads Oberlin Classics’ public humanities initiatives, including student-driven commentaries and digital projects like Horace’s Epistles website.

• Human Aspects of Cyber Security
• Cyber Awareness and Hygiene
• Serious Games (Cyber Defence Exercises)
• +۵ مورد دیگر

Dr. Kaie Maennel is a Lecturer at the School of Computer and Mathematical Sciences, part of the Faculty of Sciences, Engineering and Technology at the University of Adelaide. Her research focuses on Human Aspects of Cyber Security, including Cyber Awareness and Hygiene, Serious Games (Cyber Defence Exercises), and Learning Analytics in Cyber Training. She also investigates Usable Security, Information Security Culture, and Cybersecurity Risk Management. With over 20 years of corporate experience in Audit and Assurance at Deloitte, she holds professional certifications: ACCA, CIA, and Estonian CPA. Her research interests emphasize bridging cybersecurity education with practical applications, leveraging experiential learning and advanced analytics. She actively participates in high-profile cybersecurity initiatives like the NATO CCDCOE’s Locked Shields workshop, contributing to exercise design and assessment frameworks. Dr. Maennel is eligible to supervise Masters and PhD students as a Co-Supervisor, focusing on cybersecurity education, exercise methodologies, and human-centric security strategies. She prioritizes interdisciplinary approaches and real-world impact through collaborations with industry and academic partners. Her work spans cyber defense exercise ontology development, cultural adaptation of cybersecurity programs, and leveraging behavioral genetics insights. Recent trends in her publications highlight the integration of AI into cybersecurity training and the critical role of human factors in mitigating cyber risks.

• Biomedical Informatics
• Natural Language Processing
• Large Language Models
• +۳ مورد دیگر

Weipeng Zhou is a Postdoctoral Associate at the Yale School of Medicine within the Department of Biomedical Informatics and Data Science . Working under Professor Hua Xu , he specializes in pre-training and evaluating large medical language models using electronic health records and medical claims data. His work bridges Natural Language Processing , Biomedical Data Science , and Clinical Informatics to address critical healthcare challenges. PhD in Medical Informatics from the University of Washington (2025) Bachelor's in Computer Science and Statistics from the University of Wisconsin (2019) His research involves NLP/LLM applications in healthcare domains such as: Long COVID characterization and prediction Cardiovascular disease analysis Suicide prevention through clinical text mining Clinical note section identification Emerging water contaminant detection via PubMed article analysis His publications focus on model transferability , automated cohort discovery , and contextual health research tools . Notable collaborations include work with teams at University of Washington and Yale .

Shahin Jabbariمشاهده پروفایل

استادیار

• Machine Learning
• Algorithmic Fairness
• Game Theory
• +۷ مورد دیگر

Shahin Jabbari is an Assistant Professor in the Computer Science Department at the College of Computing & Informatics, Drexel University, where he is a member of the EconCS research group. His research lies at the intersection of machine learning, game theory, and algorithmic fairness, with a focus on ethical AI and its societal implications. Prior to Drexel, he was a CRCS postdoctoral fellow at Harvard University's School of Engineering and Applied Sciences, hosted by Milind Tambe, and affiliated with the EconCS group. Education: PhD in Computer and Information Science, University of Pennsylvania (2013–2019), advised by Michael Kearns Master's in Computing Science, University of Alberta, advised by Robert Holte and Sandra Zilles Bachelor's in Computer Engineering, Sharif University of Technology His research interests center on machine learning, algorithmic fairness, and game theory, particularly focusing on how AI systems can be designed to be more equitable, interpretable, and robust. He investigates ethical aspects of algorithmic decision-making, aiming to ensure AI technologies contribute positively to society. His work often integrates human behavior modeling and experimental validation, especially in cybersecurity and public health domains. His recent publications span top venues including ICML, NeurIPS, AAAI, AAMAS, PNAS, and TMLR. The research trends show a consistent focus on fairness in AI, explainability, robustness, and strategic interactions in complex systems. Topics include fair influence maximization, adaptive phishing training, cyber deception games, and ethical machine learning frameworks. These works reflect a multidisciplinary approach combining theoretical rigor with real-world applicability. Scientific Awards and Recognitions: Best Paper Finalist, AAMAS 2021 Best Paper, GameSec 2020 Spotlight Presentation, ICML 2021 Best Paper, KI 2012 Shahin Jabbari actively contributes to the academic community through advising, teaching, and service. He teaches graduate courses such as CS 589: Responsible Machine Learning and CS 590: Privacy. He has served on the senior program committees of ICML and NeurIPS, is an Action Editor for TMLR, and has reviewed for numerous top-tier conferences and journals. He mentors students through research projects and invites prospective PhD candidates to apply through Drexel’s formal channels. He is involved in the Drexel Computer Science Theory Reading Group and contributes to advancing responsible AI practices. He is affiliated with the EconCS group at Drexel, which focuses on economic and computational aspects of AI, including game theory, mechanism design, and multi-agent systems. His lab integrates tools from machine learning, behavioral modeling, and optimization to develop AI systems that are not only intelligent but also fair and trustworthy. Future work is expected to further explore human-AI collaboration, ethical AI deployment, and policy-aware algorithm design.

• Data Science
• Concept-Drift Management
• Database Systems
• +۱۲ مورد دیگر

Tania Cerquitelli is a Full Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, where she leads research in data science, concept-drift management, and inclusive AI technologies. She is a member of SmartData@PoliTO, the GEDI Observatory for Gender Equality, and serves in leadership roles related to social affairs and community policies at the university level. She also acts as a scientific advisor for the partnership with Accenture. Her research interests span Data Science , Concept-Drift Management , Database Systems , Conversational Data Science , and Industry 4.0 . She applies AI and machine learning to industrial, societal, and ethical challenges, particularly in promoting inclusive communication and gender equality in research. The most recent publications highlight her work in explainable AI, concept drift detection, multimodal diagnostics, and AI for social good. Her research integrates machine learning, natural language processing, and computer vision to address real-world problems in manufacturing, healthcare, agriculture, and education. She is an Associate Editor for several prestigious journals including Expert Systems with Applications , Computer Networks , Future Generation Computer Systems , and Knowledge and Information Systems . She has served on the program committees of major conferences such as ECML PKDD, EDBT/ICDT, and ACM KDD, and has been a reviewer and selection committee member for ETH Zurich and EMPA. She actively supervises PhD students and teaches a wide range of courses including Data Science and Database Technologies, Business Intelligence for Big Data, and Gender and Diversity in Research. She is involved in multiple national and international research projects such as E-MIMIC, WEBFARE, and EnABLES, focusing on inclusive AI, smart data, and industrial applications. Her lab affiliations include the DBDM - Database and Data Mining Group (DAUIN) and the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory , where she contributes to advancing data science methodologies and their societal impact.

• Natural Language Processing
• Multilingual Language Modeling
• Neuro-Symbolic AI
• +۴ مورد دیگر

Miryam de Lhoneux is an Assistant Professor at the Department of Computer Science , Faculty of Engineering Science , KU Leuven . She is affiliated with interdisciplinary institutes including Leuven.AI and LILI (KU Leuven Interdisciplinary Language Institute). Active in Multilingual Natural Language Processing and Fair AI research Member of the Faculty of Engineering Science Council and Computer Science Department Council Her research focuses on neuro-symbolic methods , language adaptation , and typological diversity in NLP. Recent projects include: Neuro-Symbolic Methods for Fair Multilingual NLP (2023-2027), Graph-Based Instance Selection for Multi-Modal Data (2024-2028), and Unit of Representation in Multilingual Modeling (2025-2028). Her publications span topics including code-mixed data analysis , English bias in LLMs , and sociolinguistically informed interpretability . She teaches courses in Advanced Natural Language Processing and Language Engineering Applications .

Stefan Marrمشاهده پروفایل

پژوهشگر

• Concurrent Programming
• Language Implementation
• Virtual Machines
• +۶ مورد دیگر

Stefan Marr is a faculty member at the University of Kent, United Kingdom, focusing on Concurrent Programming , Language Implementation , and Virtual Machines . His research spans projects like optimizing interpreters, improving concurrency models, and enhancing profiling tools for dynamic languages. Projects : Royal Society Industry Fellowship (FastStart), EPSRC-funded CaMELot, Oracle Labs-funded Automatic Superinstructions. Collaborations : With students and researchers across institutions, including Shopify, Vrije Universiteit Brussel, and Johannes Kepler University Linz. His recent work emphasizes profiling reliability for Java, interpreter performance , and tooling for complex concurrent systems . Projects like SOM (Simple Object Machine) and its variants (SOM++, TruffleSOM, PySOM) highlight his commitment to language implementation research and education. Scientific Awards include fellowships and grants from the Royal Society, EPSRC, and Oracle Labs. His contributions to conferences as program committee member , steering committee , and workshop organizer further underscore his academic leadership.

• Software Engineering
• Model Driven Engineering
• Software Product Lines
• +۷ مورد دیگر

Jean-Marc Jezequel is a Professor of Software Engineering at University of Rennes , affiliated with CNRS , Inria , IRISA , and Institut Universitaire de France (IUF) . His research focuses on Model-Driven Engineering , Software Product Lines , Dynamic Adaptation , and Executable Meta-languages . Key Contributions : Pioneering work in aspect-oriented and model-driven approaches for software evolution Foundational research on model transformations (e.g., UMLAUT framework) Advances in testing and validation of distributed systems Research Trends from his recent publications include: Intelligent modeling assistance integrating machine learning Contextual variability modeling for complex systems Runtime model execution for self-adaptive systems Formal methods and constraint resolution for UML validation Collaborations include researchers from Luxembourg, Montreal, Colorado State University, and INRIA.

• International Law
• Comparative Labor Law
• Labor Mediation
• +۳ مورد دیگر

Charles-Francis-Xavier Szymanski is a Professor at the Faculty of Law of Vytautas Magnus University , specializing in the Department of Private Law . He is a renowned expert in international labor law, comparative legal systems, and ethical dimensions of legal practice, with a focus on globalization's impact on legal frameworks. Languages: English (native), Polish (native), Spanish (basics) Key affiliations: Faculty of Law (32 publications), Department of Private Law (12 publications) His research spans Labor Law , Contract Law , and Applied Ethics , particularly examining: Good faith doctrine in U.S. labor negotiations Platform economy worker classification Historical implications of Chiune Sugihara's actions Comparative analysis of European and U.S. mediation systems Social clauses in trade agreements Union roles in modern labor disputes Recent publications reveal expertise in gig economy regulation , collective bargaining , and judicial impacts on labor movements . While no explicit awards are documented, his extensive international collaborations (Italy, Poland, UK) demonstrate academic influence.