جست‌وجوی استاد و پژوهشگر

Andrew Lanمشاهده پروفایل

دانشیار

• Artificial Intelligence in Education
• Educational Data Mining
• Personalized Learning
• +۵ مورد دیگر

Andrew Lan is an Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, where he also serves as the CS Undergraduate Program Director. He was granted tenure by the UMass Board of Trustees in June 2025 and is currently on leave through Spring 2026. His research focuses on developing human-in-the-loop machine learning methods to enable scalable, effective, and personalized learning experiences in education. Dr. Lan received his BS in Physics and Mathematics from the Hong Kong University of Science and Technology, followed by his MS (2014) and PhD (2016) in Electrical and Computer Engineering from Rice University. He completed postdoctoral research at Rice University (2016) and Princeton University's EDGE Lab (2017-2018). His research spans artificial intelligence for education, with particular expertise in educational data mining, knowledge tracing, personalized learning systems, and human-AI collaboration in educational contexts. Dr. Lan's work leverages massive and multimodal learner and content data collected from both traditional classrooms and online learning platforms to develop systems that deliver high-quality, affordable, and personalized learning experiences. He has made significant contributions to areas including computerized adaptive testing, math word problem generation, student affect detection, and automated grading systems. His recent work increasingly focuses on leveraging large language models for educational applications while maintaining rigorous scientific validation of these approaches. Best Student Paper Award at the 2024 AIED Conference (with Alexander Scarlatos) Best Paper Nominee at LAK 2021 Best Student Paper Award at IEEE Big Data 2020 NAEP Math Automated Scoring Challenge Grand Prize Winner Dr. Lan actively mentors graduate students and postdoctoral researchers, with several of his advisees receiving recognition for their work. He has secured substantial funding from the National Science Foundation, including a $90M grant for the SafeInsights project, a secure cyberinfrastructure for educational research. His research group collaborates with institutions including Worcester Polytechnic Institute, University of Pennsylvania, and Rice University. He teaches undergraduate and graduate courses including COMPSCI 240 (Reasoning under Uncertainty) and COMPSCI 590OP (Applied Numerical Optimization), with a focus on the practical application of theoretical concepts in machine learning and artificial intelligence. His educational philosophy emphasizes bridging the gap between theoretical foundations and real-world implementation in educational technology.

Hengrui Caiمشاهده پروفایل

استادیار

• Causal Inference
• Reinforcement Learning
• Graphical Models
• +۷ مورد دیگر

Dr. Hengrui Cai is an Assistant Professor of Statistics at the University of California Irvine (UCI), affiliated with the Donald Bren School of Information and Computer Sciences. She holds a Ph.D. in Statistics from North Carolina State University (NCSU) and a B.S. in Statistics from Zhejiang University. Her research focuses on causal inference, reinforcement learning, and graphical models, with applications in precision medicine, healthcare analytics, and epidemiology. She develops interpretable solutions for individualized decision-making, particularly in healthcare settings such as ICU patient treatment optimization and pandemic analysis. Notable achievements include the NSF CDS&E-MSS Award (2024), ICS Research Awards (2023–2024), and recognition for contributions to causal discovery and policy evaluation. Dr. Cai advises graduate and undergraduate students on projects involving causal AI, machine learning, and healthcare data analysis. She teaches courses like 'Causal Machine Learning' and 'Introduction to Probability and Statistics,' emphasizing interdisciplinary approaches to real-world problems. Her work integrates statistical theory with practical applications, exemplified by software tools like ANOCE-CVAE for causal mediation analysis and the Sepsis EHR Benchmark Environment for reinforcement learning. Dr. Cai collaborates widely, contributing to projects such as quantifying the impact of the 2020 Hubei lockdowns on virus spread in China through causal graph analysis.

• Data Mining
• Machine Learning
• Clustering Algorithms
• +۶ مورد دیگر

Claudia Plant is a Professor in the Faculty of Computer Science , leading the Research Group Data Mining and Machine Learning . Her research focuses on clustering algorithms, data mining, and machine learning applications in areas like biomedical data, wind energy, and causality inference. She has contributed to projects such as Knowledge-infused Deep Learning for Natural Language Processing (2020–2028) and Hybrid Computational Sciences (2021–2021). Plant has authored over 160 publications, with recent work emphasizing deep learning, anomaly detection, and GPU-optimized algorithms. She actively engages in academic activities, including talks on clustering methods and interdisciplinary projects like Governing Algorithms: The Politics of Data and Decision-Making . Her research interests span clustering algorithms , graph neural networks , causality discovery , and ethical digital transformation . Notable projects include causal analysis of wind farm dynamics and AI-enhanced education tools. Plant’s work bridges computational methods with societal challenges, such as empowering marginalized communities through ethical technology adoption.

Alan Ritterمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Alan Ritter is an Associate Professor at the School of Interactive Computing, Georgia Institute of Technology (Georgia Tech), affiliated with the Machine Learning Center (ML@GT). His research focuses on natural language processing (NLP), machine learning, and robust computational models. He completed his Ph.D. at the University of Washington and a postdoctoral fellowship at Carnegie Mellon University's Machine Learning Department. Education : - Ph.D. in Computer Science, University of Washington - Postdoctoral Research, Machine Learning Department, Carnegie Mellon University Research Interests : Ritter's work emphasizes developing models that operate across domains and languages with minimal supervision. His projects include systems analyzing social media data for cybersecurity threats, cultural bias in LLMs, and privacy-preserving dialogue agents. His group also explores efficient fine-tuning of language models and cross-lingual information extraction. Recent Activities & Awards : - NSF CAREER Award - Amazon Research Award - Best Social Impact Paper Award (ACL 2024) - Program Chair for NAACL 2025 Advising & Students : Ritter advises Ph.D. and M.S. students in Georgia Tech's ML and CS programs. Notable advisees include Yang Chen (Ph.D. 2024, now at NVIDIA) and Fan Bai (Ph.D. 2023). Labs & Affiliations : - Machine Learning Center (ML@GT) - Collaborations with institutions like AI2, Stanford, and Microsoft Research

Gamze Z. Daneمشاهده پروفایل

استادیار

• Urban Planning
• GIS and Decision Making
• Urban Informatics
• +۳ مورد دیگر

Gamze Z. Dane is a tenured Assistant Professor at the Department of Built Environment of Eindhoven University of Technology (TU/e), affiliated with EAISI Mobility and EAISI Health. She leads the Digital City Program (2020-2024) and specializes in decision-support systems, GIS, urban informatics, and data analytics for sustainable urban development. Her research integrates citizens into urban decision-making using digital tools like VR twins and data-driven approaches. Education: PhD in Urban Planning, MSc in Geographical Information Systems (GIS) and Decision Making. Research Interests: Focuses on human-environment interaction, transdisciplinary urban projects, and the impact of digitalization on cities. She develops tools for public participation and uses big data to analyze citizen behavior and urban experiences. Projects: Principal Investigator for EU/national projects involving cities like Eindhoven, Bologna, and Lisbon. Notable projects include UBeX Urban Behavior eXtended reality lab (2024-2026) and ROCK (2017-2020). Awards: Cuperusprijs 2020 (2nd place for student thesis) Drivers of Change Exhibition 2021 ISPRS International Journal Cover Story (2020) Teaching & Innovation: Coordinates courses like Smart Cities and Urban Redevelopment. Developed online teaching materials using VR, drones, and mobile apps. Guest lectures at Istanbul Technical University and visiting scholar at National University of Singapore. Labs & Networks: Leads the UBeX lab exploring immersive technologies for urban analysis. Active in academic networks including Urban Planning journals and international conferences.

Jacob Krügerمشاهده پروفایل

استادیار

• Software Engineering
• Software Evolution
• Variant-Rich Systems
• +۵ مورد دیگر

Jacob Krüger is an Assistant Professor at Eindhoven University of Technology , specializing in the development and evolution of variant-rich software systems. He holds a PhD from Otto-von-Guericke University Magdeburg (2021) and has held academic and research positions at institutions including Ruhr-University Bochum, Chalmers University of Technology, and the University of Toronto. His research focuses on the interplay between human cognition and software quality, particularly in complex systems requiring frequent adaptation. Education: PhD in Computer Science, Otto-von-Guericke University Magdeburg (2021) MSc Business Informatics, Otto-von-Guericke University Magdeburg (2016) Research Interests: Variant-Rich Systems Program Comprehension Software Product Lines Human Factors in Software Engineering Architecture Smells and Quality Assurance Articles Trends: Recent work emphasizes fork ecosystem visualization (VisFork tool), the impact of AI on scientific practices, and crisis-driven software development (e.g., Corona-Warn-App). Key themes include empirical studies, tool development, and industry collaboration. Awards: Best Dissertation Award (2022) Frank Anger Memorial Award (2019) Multiple conference best-paper and review awards Advising & Grants: Supervises 12+ PhD students across multiple institutions. Active in funding projects like INKleSS (German Research Foundation) and FOSD Meeting 2024 (NWO). Leads collaborations with ASML, Danfoss, and Axis AB. Labs/Teams: Member of the Software Engineering and Technology (SET) group at TU Eindhoven, focusing on industrial-strength software systems and cognitive aspects of development.

Mykola Pechenizkiyمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Predictive Analytics
• +۵ مورد دیگر

Mykola Pechenizkiy is a Full Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), holding the Data Mining Chair. He also serves as an Adjunct Professor in Data Mining for Industrial Applications at the University of Jyväskylä. His research focuses on predictive analytics, data mining, and responsible AI, addressing real-world challenges in industry, healthcare, and education. He leads the Customer Journey research program at the Data Science Center Eindhoven, emphasizing ethical and transparent analytics. Academically, he holds a PhD from the University of Jyväskylä (2005) and has held visiting researcher positions at institutions like Columbia University and NYU. He has co-authored over 300 peer-reviewed publications and serves on editorial boards and committees for leading conferences (e.g., AAAI, IJCAI). He is the President of the International Educational Data Mining Society (IEDMS). His research interests include concept drift adaptation, sparsity techniques in neural networks, and fairness-aware AI. He has led projects such as the TKI PPS KPN Smart Two initiative and collaborates with industries like ASML, Philips, and Rabobank. His work contributes to UN SDGs, particularly in sustainable development through AI-driven solutions. Awards: Best Demo Paper Award (IEEE ICDE 2023), Best Paper Awards (ALA 2022, LoG 2022), and SensorKDD 2009 recognition. Grants/Projects: Active projects include TKI PPS KPN Smart Two (2019–2025) and Smart One W&I TKI KPN Flagship (2018–2022). Labs/Teams: Affiliated with EAISI Health, SIKS Scientific Board, and the University of Waikato’s AI Institute.

Fethiye Irmak Doganمشاهده پروفایل

پژوهشگر

• Human-Robot Interaction
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Fethiye Irmak Dogan is a Postdoctoral Research Associate at the University of Cambridge's Department of Computer Science and Technology, working in the Affective Intelligence and Robotics Laboratory. She holds a Ph.D. in Computer Science from KTH Royal Institute of Technology (2023), an M.Sc. and B.Sc. in Computer Engineering from Middle East Technical University (METU). Her research focuses on human-robot interaction, continual learning, and socially appropriate robot behaviors leveraging explainability. She has conducted robotics research at KTH's Division of Robotics, Perception and Learning and collaborated internationally, including a visiting scholar stint at Georgia Institute of Technology. Education highlights include: B.Sc., Computer Engineering, METU (2015) M.Sc., Computer Engineering, METU (2018), with research at Kovan Robotics Lab Ph.D., Computer Science, KTH (2023), with visiting research at Georgia Tech Research interests emphasize deploying autonomous robots in human environments, resolving ambiguous user instructions through explainability, and enabling socially intelligent robot behaviors. Recent work explores continual learning for context adaptation, multimodal frameworks for human-robot collaboration, and vision-language models for wellbeing assessment in children. Key projects include BT-ACTION (modular instruction understanding), GRACE (LLM-driven socially appropriate actions), and STREAK (continual learning for household tasks). Her contributions span robotics, AI ethics, and human-centered design, with a focus on real-world applications in healthcare and education.

Suranga Chandima Nanayakkaraمشاهده پروفایل

دانشیار

• Intelligent Systems
• Human Augmentation
• Human-Computer Interaction
• +۳ مورد دیگر

Suranga Chandima Nanayakkara is an Associate Professor in the Department of Computer Science at the National University of Singapore (NUS), leading the Centre for Holistic Inquiry into Lifelong Learning (CHILL) and the Smart Systems Institute (SSI). He holds roles such as AI+HCI Theme Lead at NUS+CNRS IPAL Lab and Residential Fellow at NUS College. His research focuses on assistive human-computer interfaces, emphasizing technologies that enhance perceptual and cognitive capabilities for individuals with sensory deficits. He earned his PhD and BEng from NUS, followed by postdoctoral work at MIT Media Lab. Notable projects include the Augmented Human Lab, iTILES for rehabilitation, and the FingerReader device for visually impaired shoppers. His work has garnered awards like the MIT TR35, TOYP, and INK Fellowship. Research interests include Intelligent Systems, Human Augmentation, and Design Science. He explores applications in assistive technologies, healthcare informatics, and educational tools. Over 15 publications highlight innovations in wearables, stress management, and multimodal interaction. Awards: 40+ accolades, including MIT TR35, TOYP, and multiple design awards for projects like Kiwrious and FingerReader. Grants: Led projects funded by institutions like NUS and Singapore’s innovation initiatives. Labs: Augmented Human Lab (founded 2011), focusing on humanizing technology through natural interfaces.

Liyi Zhouمشاهده پروفایل

مدرس

• Systems Security
• Blockchain Technology
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Liyi Zhou is a Lecturer in the School of Computer Science at the University of Sydney, specializing in systems security, blockchain, and AI. His research focuses on developing automated and adaptive security tools using machine learning and reinforcement learning. He co-founded D23E.ch, a platform addressing blockchain security and privacy challenges. Research interests include AI-driven vulnerability detection, large security models, real-time intrusion prevention, advanced program analysis (fuzzing/symbolic execution), and privacy-preserving systems. He actively recruits PhD students for projects advancing AI in cybersecurity. Notable achievements include pioneering 'sandwich attacks' discovery in DeFi protocols, contributing to Ethereum Foundation grants, and receiving bug bounties from Flashbots and Ethereum Foundation for vulnerability disclosures. His work has been published in venues like IEEE S&P, USENIX Security, and SIGMETRICS. Teaching includes the course INFO2222. He seeks collaborations and funding to bridge academic research with real-world industry problems, emphasizing practical impact.

Lara Buchakمشاهده پروفایل

استاد

• Decision Theory
• Ethics
• Epistemology
• +۶ مورد دیگر

Lara Buchak is a Professor of Philosophy at Princeton University, where she returned after 12 years at UC Berkeley. Her work bridges decision theory, ethics, epistemology, and the philosophy of religion. She holds a Ph.D. from Princeton (2009). Her research focuses on risk analysis in decision-making, ethical implications of risk attitudes (e.g., climate policy, biomedical ethics), and the rationality of faith. Notable contributions include her 2013 book *Risk and Rationality*, which argues that diverse risk preferences can be rational. Current projects explore faith as commitment despite counterevidence and the role of worst-case arguments in ethics. Her recent articles emphasize normative theories beyond expected utility, applications of decision theory to climate and biomedical ethics, and philosophical analyses of faith and tradition. She has also engaged with free will, group decision-making, and religious definitions. Buchak’s work intersects formal philosophy with practical ethics, offering frameworks to address complex societal challenges through rigorous theoretical foundations.

Jose Suchمشاهده پروفایل

استاد

• Cybersecurity
• Artificial Intelligence
• User Privacy
• +۵ مورد دیگر

Prof. Jose Such is a Professor of Computer Science at King's College London (KCL), affiliated with the KCL Cybersecurity Centre and the Informatics Security Hub. His research focuses on cybersecurity, AI ethics, privacy engineering, and conversational systems. He leads major projects such as REPHRAIN (Phase I & II) and SAIS, funded by EPSRC, addressing privacy, adversarial influence, and secure AI assistants. His work contributes to UN Sustainable Development Goals related to privacy and digital security. Key research areas include large language models (LLMs), smart home security, multi-user privacy conflicts, and ethical AI governance. Prof. Such has published over 80 peer-reviewed papers, with recent emphasis on mitigating privacy risks in conversational AI and developing safety benchmarks for LLMs. He oversees research projects involving multi-disciplinary teams, integrating technical solutions with legal and ethical frameworks. Notable outputs include the MalProtect malware defense system and the CASE-Bench evaluation framework for AI safety. His datasets and tools, such as SkillVet and ELVIRA, address privacy risks in voice assistants and cloud services. Prof. Such collaborates globally, with recent work exploring cross-cultural privacy practices in smart homes and the security challenges faced by marginalized groups. His research bridges technical innovation with societal impact, advocating for transparent and accountable AI systems.

Bart De Keijzerمشاهده پروفایل

مدرس ارشد

• Algorithmic Game Theory
• Mechanism Design
• Financial Networks
• +۳ مورد دیگر

Bart De Keijzer is a Senior Lecturer in Computer Science at King's College London, within the Department of Informatics. He serves as Deputy Director of the Distributed AI Research Group and is the Finance Hub Champion. His research focuses on the intersection of economics and theoretical computer science, particularly algorithmic game theory, mechanism design, and financial networks. Education includes a PhD in Algorithmic Game Theory from Centrum Wiskunde & Informatica (CWI) and Vrije Universiteit Amsterdam, and a Computer Science degree from Delft University of Technology. Prior to King's, he held roles at the University of Essex, Sapienza University of Rome, and the University of Liverpool. His research interests span financial networks, systemic risk mitigation, auction design, and multi-agent systems. Recent work explores strategies to reduce systemic risk in financial networks and the development of strategyproof mechanisms. Awards: EPSRC New Investigator Award (2024). Labs/Teams: Distributed AI Research Group, Informatics Finance Hub.

Gunnar Blohmمشاهده پروفایل

استادیار

• Sensorimotor Neuroscience
• Clinical Medicine
• Neurology
• +۳ مورد دیگر

Gunnar Blohm is an Assistant Professor in the Department of Biomedical and Molecular Sciences at Queen's University, affiliated with the School of Medicine and Faculty of Health Sciences. His research focuses on sensorimotor neuroscience, particularly 3D sensorimotor control, eye-hand coordination, and computational modeling of neural processes. He holds a Ph.D. from Université Catholique de Louvain and has held postdoctoral positions at York University and his alma mater. Cross-appointed to the School of Computing, Department of Psychology, and Department of Mathematics and Statistics, he is also Vice-Director of the Connected Minds initiative. His research integrates behavioral experiments, brain imaging (MEG/EEG), and patient studies to understand how sensory information is transformed into goal-directed actions. Key areas include visuomotor transformations, multisensory integration, and Bayesian processes in neural computations. Blohm leads the Computational Sensorimotor Neuroscience Lab, emphasizing collaborative projects like Neuromatch Academy and contributions to open science initiatives. Affiliated with Queen's Centre for Neuroscience Studies and Ingenuity Labs, his work bridges computational approaches with clinical applications, aiming to develop frameworks for understanding brain dysfunction and clinical tools. His recent articles explore topics like saccade dynamics, pupil responses, and generative adversarial collaborations in scientific discourse.

Hasan Davulcuمشاهده پروفایل

استاد

• Sociocultural Modeling
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Hasan Davulcu is a Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU). He holds a B.S. in Mathematics from Middle East Technical University (Turkey) and M.S./Ph.D. in Computer Science from Stony Brook University (NY). His research focuses on sociocultural modeling, AI, machine learning, and behavioral analytics for fraud detection. He leads the CIPS-AI Lab, developing data mining tools for semantic information extraction from social media and web data. Affiliations: Senior Global Futures Scientist (Global Futures Scientists and Scholars Program), Co-founder & CIO of ARTIS MAGI (AI-driven behavioral analysis startup). Education: Ph.D. Computer Science (Stony Brook, 2002), M.S. Computer Science (Stony Brook, 1995), B.S. Mathematics (METU, 1993). Research interests include: Sociocultural modeling and persuasive AI. Web/social media mining, information extraction, and database systems. Behavioral analytics for fraud detection and countering extremist influence. Key achievements: 2011 HSCB Focus Exceptional Scientific Achievement Award for work on sociocultural modeling in the DOD Minerva project. Principal Investigator on NSF and DoD grants, including behavioral analytics for financial fraud and social influence analysis of extremist groups. Grants & Projects: NSF PFI:BIC Grant (2014-2019): Behavioral analytics for fraud detection via visual analytics infrastructure. DoD Minerva (2015-2019): Measuring social influence of extremist groups. ONR Projects (2018-2021): Modeling polarization, adversarial framing, and disinformation tracking. Labs/Teams: Cognitive Information Processing Systems (CIPS-AI) Lab, which pioneers data mining techniques for unstructured social media data and semantic representation systems.