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Asli Ozgun-KocaView profile

Professor

• Mathematics Education
• Teacher Education
• Secondary Education
• +7 more

Asli Ozgun-Koca is a Professor of Mathematics Education in the College of Education at Wayne State University. Her work spans teaching, research, and leadership in mathematics teacher education, with a focus on secondary and elementary levels. She is actively involved in national and state-funded initiatives to improve mathematics instruction and equity. B.S. in Mathematics Education, Hacettepe University, Turkiye (1993) M.S. in Mathematics, Middle East Technical University, Turkiye (1996) Ph.D. in Mathematics Education, The Ohio State University (2001) Dr. Ozgun-Koca's research centers on mathematics education, particularly the integration of technology in teaching, professional development for teachers, lesson study, and fostering mathematical knowledge for teaching. She emphasizes equitable practices and the use of real-world, interdisciplinary contexts to enhance learning. Her work explores how teachers analyze tasks and student work to improve instruction, especially in proportional reasoning and statistics. The recent publications and presentations reflect a strong trend in using technology (e.g., GeoGebra) to deepen conceptual understanding, designing integrated STEM and literature-based tasks, and improving feedback and task selection in teacher education. Her scholarship consistently addresses equity, teacher learning, and innovative pedagogies. President's Awards for Excellence in Teaching (2015) COE Faculty Research Award (2014) Career Development Chair Award (2013) Dr. Ozgun-Koca has secured major grants from the NSF and Michigan Department of Education, including SSTEPs, TeachDETROIT, and REALM projects, focusing on evidence-based pedagogies, teacher fellowships, and equity in mathematics education. She advises graduate students and collaborates widely on research. She teaches courses such as Methods and Materials of Instruction, Advanced Studies in Teaching Algebra and Statistics, and Detroit by the Numbers, reflecting her commitment to practical, context-rich teacher preparation. She is a key figure in lesson study adaptations for preservice teachers and leads professional development initiatives that connect theory and practice. Her work is disseminated through national conferences and leading journals in mathematics education.

Shenghua GaoView profile

Associate Professor

• Spatio-temporal Data Analysis
• Machine Learning
• Database Systems
• +4 more

Professor Shenghua Gao is an Associate Professor at the School of Computing and Data Science of the University of Hong Kong (HKU), concurrently serving as Assistant Director for Shanghai Initiatives. He holds a PhD from Nanyang Technological University. His research focuses on integrating machine learning, spatio-temporal data analysis, and database systems to address challenges in mobility prediction, traffic management, and geospatial representation learning. He has contributed significantly to trajectory modeling, indexing frameworks for multi-dimensional data, and the application of large language models (LLMs) in spatio-temporal contexts. Key research interests include: Spatio-Temporal Data Science: Developing frameworks for efficient processing and analysis of point cloud, trajectory, and traffic data. Machine Learning for Databases: Innovating indexing algorithms (e.g., BMTree, MAST) and query optimization techniques leveraging ML. Trajectory and Mobility Prediction: Creating personalized models for next-location prediction and transfer learning across regions. Geographic AI (GeoAI): Enhancing road network representation and urban function inference using physics-guided and foundation models. Recent work highlights include the ST-LLM+ framework for traffic prediction, the MAST system for point cloud analytics, and the exploration of City Foundation Models for urban challenges. His publications span top venues in databases (SIGMOD, VLDB) and AI/data science (ICML, NeurIPS). While no awards are explicitly mentioned, his prolific output and leadership roles indicate significant academic contributions. He is actively involved in teaching and supervising research in the School’s undergraduate and postgraduate programs, including MSc(AI) and MPhil/PhD tracks.

• Machine Learning
• Computer Vision
• Natural Language Processing
• +2 more

Chun-Liang Li is a Research Scientist at Apple MLR and an Affiliate Assistant Professor at the Allen School of Computer Science, University of Washington. He holds a Ph.D. in Machine Learning from Carnegie Mellon University (2014-2019) and a B.S./M.S. in Computer Science and Information Engineering from National Taiwan University (2008-2013). His research spans machine learning, computer vision, and natural language processing, with emphasis on representation learning, efficient model training, and multimodal understanding. Key focus areas include large language model optimization, contrastive learning techniques, document understanding systems, and domain adaptation methods. Recent publications demonstrate strong focus on LLM efficiency (dataset decomposition, distillation techniques), document AI (table reasoning, form extraction), and multimodal alignment (image-text retrieval, prefix conditioning). His work consistently appears in premier venues including NeurIPS, CVPR, ACL, and ICLR. Awards and Honors: IBM Ph.D. Fellowship (2018) Best student paper runner-up, IJCAI (2017) First Place in both tracks of KDD Cup (2011, 2013) He actively mentors students and collaborators, inviting contact for internship opportunities. Current industry-academic position bridges cutting-edge research with practical applications in machine learning systems.

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +4 more

Joelle Pineau is a Professor at the School of Computer Science, McGill University, Montreal, Canada. Her work spans Machine Learning , Artificial Intelligence , and Reinforcement Learning , with significant contributions to causal inference , ethics in AI , and continual learning . She has led initiatives like the NeurIPS 2019 Reproducibility Program and co-authored over 315 publications. Key Research Themes : Algorithmic fairness, robust policy learning, interpretable models, and AI ethics Recent Trends : Focus on uncertainty-aware systems, multi-task learning, and societal implications of foundation models Scientific Awards : NeurIPS 2019 Reproducibility Program Leadership Advancements in AI Ethics Review Practices Her work intersects Computer Science , Biomedical Research , and Societal Policy , with applications in healthcare, robotics, and knowledge graphs.

Jason J. JungView profile

Professor

• Computer Science
• Knowledge Engineering
• Social Media Analysis
• +3 more

Jason J. Jung is a Professor in the Department of Computer Engineering at Chung-Ang University, Seoul, Korea. His academic work focuses on knowledge engineering , social media analytics , and data mining within the Knowledge Engineering Laboratory. Research Interests : Computer Science, Social Knowledge, Data Modeling, Sentiment Analysis Projects : IoT-based cultural systems, real-time social event detection, transmedia storytelling models The 15 most recent publications (2014-2018) demonstrate expertise in social network analysis , multimodal data processing , and context-aware systems applied to urban services, cultural tourism, and digital storytelling. Key trends include real-time analytics , trust modeling , and collaborative frameworks for O2O services. Professional activities include editorial contributions, invited talks, and patent developments. Students at all levels (PhD/MSc/BSc) conduct research under his supervision at the Knowledge Engineering Laboratory. Personal interests include travel, film, painting, literature, and music.

• Algorithmic Sequential Decision-Making
• Robust AI
• Safe AI
• +11 more

Ilija Bogunovic is an Assistant Professor (UK Lecturer) in the Department of Electronic and Electrical Engineering at University College London (UCL), Faculty of Engineering Sciences. His research focuses on algorithmic sequential decision-making for robust, reliable, and safe artificial intelligence, with applications in large language model alignment, reinforcement learning, and human feedback integration. His research interests lie at the intersection of machine learning, optimization, and decision theory. He investigates robustness in Bayesian optimization, bandits, and reinforcement learning under adversarial attacks, model misspecification, and distributional shifts. His work spans both theoretical foundations and real-world applications in energy systems, mobility, and AI safety. A central theme is developing algorithms that are provably robust and efficient in uncertain and potentially corrupted environments. The recent publications highlight a strong trend in robust decision-making, particularly in reinforcement learning and preference optimization. Key themes include adversarial robustness in large models, distributional robustness in RL, safe multi-agent systems, and robust Bayesian optimization. The work frequently involves theoretical analysis of regret and sample complexity, combined with practical algorithm design for high-impact applications. Scientific Awards: Google Research Scholar Program Award (Machine Learning & Data Mining), 2023 EPSRC New Investigator Award, 2023 Ilija Bogunovic actively supervises PhD and MSc students, with several of his advisees leading papers accepted to top venues like NeurIPS and ICLR. He has secured competitive research grants, including the EPSRC New Investigator Award, to support his work on robust decision-making. He is building a research team focused on robust and safe AI. He leads a research team and has initiated an online reading group on modern adaptive experimental design and active learning, fostering a collaborative research environment.

Jean-Cédric ChappelierView profile

Senior Lecturer

• Computational Linguistics
• Natural Language Processing
• Machine Learning
• +3 more

Jean-Cédric Chappelier is a Senior Lecturer and Researcher at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences. He works in computational linguistics and natural language processing, with a focus on robust parsing techniques, semantic indexing, and clustering algorithms. His research interests span computational linguistics, natural language processing, machine learning, tree substitution grammars, and semantic indexing. His publications from 2000-2006 demonstrate expertise in stochastic parsing, ontology-based indexing, and community structure analysis in complex networks. He has supervised multiple EPFL PhD students including Florian Seydoux and Emmanuel Eckard. Jean-Cédric holds an Ms.Sci. and Ph.D. in Computer Science from École Nationale Supérieure des Télécommunications de Paris. He teaches courses on object-oriented programming, computer systems, and natural language processing at EPFL.

• Cybersecurity
• Information Security Governance
• Attack Graph Analysis
• +5 more

Alessandro Palma is a researcher affiliated with Sapienza University of Rome , Italy, focusing on Cybersecurity and Information Security Governance . His work involves attack graph analysis, incident management compliance, and network vulnerability evaluation. Palma collaborates extensively with researchers like Marco Angelini, Silvia Bonomi, and Fabian J. Theis. Recent publications highlight his contributions to: Attack Graph Scalability (2025): Developing frameworks for evaluating network security. Incident Management Compliance (2024): Creating systems for quantitative process assessment. Multi-Modal Single-Cell Generation (2025): Applying machine learning to computational biology. His research integrates graph theory , visual analytics , and machine learning to address cybersecurity challenges in IoT and federated data systems. No scientific awards or student advisement information is publicly available in the provided data.

Feng LiView profile

Professor

• Network Security
• Wireless Networks
• Mobile Ad-hoc Networks
• +6 more

Feng Li is a Professor in the Department of Computer and Information Technology at Indiana University - Purdue University Indianapolis (IUPUI), School of Science. He received his PhD from Florida Atlantic University in 2009 and has since established himself as a leading researcher in network security, wireless networks, and privacy-preserving technologies. His research interests span across multiple domains in computer science, with a primary focus on Network Security , Wireless and Mobile Ad-hoc Networks , Federated Learning Security , Differential Privacy , and Social Network Analysis . Dr. Li's work consistently addresses critical challenges in securing distributed systems while preserving user privacy, with applications ranging from social networks to edge computing environments. His research methodology often combines theoretical frameworks with practical implementations, resulting in solutions that balance security, privacy, and system performance. Dr. Li's publication record reveals a strong trajectory in addressing evolving security challenges in distributed systems. His recent work has focused on securing federated learning systems against sophisticated attacks like backdoors, developing privacy-preserving techniques for social networks using differential privacy, and enhancing malware detection through advanced machine learning approaches. His research shows a clear evolution from traditional network security problems to more complex challenges in modern distributed AI systems. Dr. Li has successfully mentored numerous graduate students who have become active contributors in the field, including Agnideven Palanisamy Sundar, Tianchong Gao, Qin Hu, and Ryan Hosler, who frequently appear as co-authors on his publications. His research has been supported by various funding mechanisms that have enabled his team to tackle significant challenges in network security and privacy. His laboratory focuses on practical implementations of security and privacy solutions, with particular emphasis on real-world applicability of theoretical concepts. Current research directions include enhancing the security of federated learning systems, developing more robust privacy-preserving techniques for social networks, and creating advanced malware detection systems using deep learning approaches.

• Artificial Intelligence
• Machine Learning
• Artificial Neural Networks
• +19 more

Professor Stephan Chalup is a leading academic in Artificial Intelligence and Machine Learning at the University of Newcastle , affiliated with the School of Information and Physical Sciences and the Data Science and Statistics department. He leads the Interdisciplinary Machine Learning Research Group (IMLRG) and the Newcastle Robotics Lab , where his team has achieved global recognition, including two RoboCup world championships. PhD in Computing Science , Queensland University of Technology (2002) Diplom in Mathematics with Neuroscience , University of Heidelberg His research focuses on artificial neural networks , deep learning , and high-dimensional data analysis , with applications in robotics, computer vision, medical imaging, and architectural analysis. He investigates how biological neural systems inspire robust AI models, particularly in topological data analysis and 4D vision . The recent publications highlight a strong trend in topological and geometric machine learning , with a focus on 4D data analysis , multi-agent reinforcement learning , and robot perception . His work bridges theoretical AI with practical industry solutions in transport, healthcare, and robotics. RoboCup World Champion (2008, 2006) Leadership Excellence, CESE (2024) Supervision Research Excellence Award (2015) Multiple Best Student Paper Awards (2019, 2018, 2011) Chalup has supervised numerous students and led significant research projects, including the ARC Discovery Project on estimating topology of low-dimensional data. His lab fosters interdisciplinary collaboration and innovation, with alumni working in top global tech roles. He is an active keynote speaker and program committee member in major AI conferences. His labs, including the Newcastle Robotics Lab , are equipped with state-of-the-art robots and computing systems, supporting cutting-edge research in humanoid robotics, autonomous navigation, and AI-driven data analysis.

Laurent CharlinView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Recommender Systems
• +6 more

Laurent Charlin is an Associate Professor at HEC Montréal and holds an adjunct appointment in Computer Science at Université de Montréal. His research focuses on machine learning for decision-making with applications in recommender systems, reinforcement learning, and optimization.

Daniel BenjaminView profile

Assistant Professor

• Decision Science
• Behavioral Economics
• Judgment and Decision-Making
• +11 more

Daniel Benjamin is an Assistant Professor of Decision Sciences at Nova Southeastern University's H. Wayne Huizenga College of Business & Entrepreneurship. His research explores how complexity influences judgment and decision-making, particularly in interactions with model outputs and technology, applied to policy-relevant domains such as geopolitical prediction, meta-research in social and biomedical sciences, climate change perception, and expert forecasting. Education: Ph.D. in Psychometrics and Quantitative Psychology, Fordham University M.A. in Psychology, Fordham University B.A. in Mathematics and Psychology, University of North Carolina at Chapel Hill Dr. Benjamin's work focuses on quantifying human biases in forecasting, evaluating reproducibility in preclinical research, and analyzing machine influence on human judgment. His recent publications examine clinical trial prediction accuracy, hybrid geopolitical forecasting models, and knowledge graph applications in scientific evaluation. Scientific Awards: NPR coverage for "Can cancer researchers accurately judge whether preclinical reports will reproduce?" Nature coverage for "Can cancer researchers accurately judge whether preclinical reports will reproduce?" PLoS Biology Top Media Hits of 2018 At NSU, he teaches QNT 5000 Foundations of Business Statistics and QNT 5160 Analytical Modeling for Decision Making . His research has been funded by DARPA, IARPA, NSF, CIHR, Genome Canada, and BioCanRx.

Sebastian KempfView profile

Researcher

• Information Extraction
• Table Extraction
• Ontology Generation
• +4 more

Sebastian Kempf is a researcher at the Chair of Computer Science VI (Artificial Intelligence and Knowledge Systems) within the Institute of Computer Science at the University of Würzburg's Faculty of Mathematics and Computer Science. He has been working at the chair since October 2021, focusing on information extraction, particularly table extraction and analysis, as well as ontology-based knowledge extraction. His research spans Information Extraction , Table Extraction , Ontology Generation , Document Analysis , Data Mining , and Knowledge Modeling . These areas drive his development of systems for transforming unstructured and semi-structured data into actionable knowledge, with applications across scientific, industrial, and humanities domains. Publications reveal a consistent trajectory in applying AI to interdisciplinary challenges—from materials science (glass development) to digital humanities (literary analysis) and scientific document processing. His work emphasizes practical AI solutions that bridge theoretical frameworks with real-world data complexities. Kempf actively contributes to teaching through Software Engineering courses (Summer Semesters 2022-2024), Medical Image Processing (Winter Semester 2024/25), and regular supervision of seminar topics and software internships. His project portfolio includes current work on PlastiKG and past involvement in ESF-BuDanu. As a core member of the Center for Artificial Intelligence and Data Science (CAIDAS), he collaborates across Würzburg's AI research ecosystem, leveraging university-wide resources for ontology-driven data infrastructure and knowledge systems development.

Kamesh MunagalaView profile

Professor

• Theoretical Computer Science
• Approximation Algorithms
• Online Algorithms
• +7 more

Kamesh Munagala is a Professor of Computer Science at Duke University's Trinity College of Arts & Sciences since 2016. His primary affiliation is with the Computer Science Department, where he conducts research in theoretical computer science with a focus on algorithm design, fairness, and economic mechanisms. His educational background includes a Ph.D. (2003) and M.S. (2002) from Stanford University. Munagala has maintained a strong research trajectory with continuous publications spanning over two decades. Munagala's research focuses on theoretical computer science, particularly approximation algorithms, online algorithms, and computational economics. His recent work centers on two main themes: (1) persuading optimizers or learners toward specific objectives through information revelation and pricing, and (2) achieving group fairness based on proportionality and stability in resource allocation contexts. His research addresses practical applications including data network design, facility location, data center scheduling, ad slot allocation, ride-share scheduling, and civic budgeting. His publication record shows consistent output with multiple high-impact papers in top conferences like STOC, SODA, and ICML. Recent publications (2024-2025) demonstrate his focus on algorithmic fairness, differential privacy, and mechanisms for resource allocation. His work often intersects computer science with economics, game theory, and social choice theory. Best Paper Award at WINE '18 for 'A simple mechanism for a budget constrained buyer' Best Paper Award at WWW '09 for 'Hybrid keyword search auctions' Munagala has secured significant research funding including an NSF Medium grant (2024-2027) for 'Responsive Optimization for Algorithmic Decision Systems' and an NSF Small grant (2021-2025) for 'Algorithm and Incentive Design for Modern Resource Allocation Platforms'. His teaching portfolio includes graduate and undergraduate courses in algorithm design, randomized algorithms, and algorithmic game theory, with courses scheduled through Fall 2025.

• Healthcare Data Analysis
• Machine Learning
• Deep Learning
• +5 more

Awais Ashfaq is a Postdoctoral Researcher at Halmstad University's School of Information Technology, focusing on healthcare data analytics and AI applications in clinical decision-making. His work bridges the gap between advanced machine learning techniques and practical healthcare solutions, with particular emphasis on emergency department diagnostics and cardiovascular disease analysis. Dr. Ashfaq's research interests center around healthcare data analysis, applying statistics, traditional machine learning, deep learning, and language models to extract insights for informed clinical and management decisions. His work demonstrates strong expertise in interpretable AI models for medical diagnostics, particularly in emergency settings for conditions like dyspnea. He has made significant contributions to the development of evidence-based diagnostic tools that integrate seamlessly with clinical workflows. His publication record shows a consistent focus on leveraging complete healthcare system data to address real clinical challenges. The research trends indicate increasing sophistication in model interpretability while maintaining high diagnostic accuracy. His work spans multiple healthcare domains including emergency medicine, cardiology, and pediatric care, demonstrating the versatility of his methodological approaches. Dr. Ashfaq collaborates extensively with physicians across all care levels to ensure his research translates effectively to clinical practice and improves patient outcomes. His work on regional healthcare information platforms demonstrates commitment to building infrastructure that supports ongoing medical research and quality improvement initiatives.