جست‌وجوی استاد و پژوهشگر

Earl T. Barrمشاهده پروفایل

استاد

• Software Engineering
• Program Analysis
• Type Systems
• +۶ مورد دیگر

Earl T. Barr is a Professor of Software Engineering at University College London (UCL), where he heads the System Software Engineering Group and is a member of the Centre for Research on Evolution, Search and Testing (CREST). His academic journey began with a Ph.D. in Computer Science from the University of California, Davis in 2009, after which he joined UCL as faculty. His research spans multiple domains within software engineering, with particular focus on program analysis, type systems, automated program repair, and the emerging field of dual channel analysis that examines the interplay between natural language and formal programming language in source code. Barr's work on the 'naturalness of software' has been influential in understanding how code differs from natural language while exhibiting statistical regularities. Barr's publication record shows a strong trend toward integrating machine learning with traditional software engineering techniques, particularly in type inference (Typilus), program repair, and code understanding. His recent work increasingly focuses on dual channel analysis, exploring how the natural language elements in code (identifiers, comments) interact with the formal programming language to create a richer communication channel for developers. MSR 2019 Most Influential Paper Award Multiple ACM SIGSOFT Distinguished Paper Awards Best Paper Award at IEEE Conference on E-Commerce Technology (2005) Barr actively supervises numerous Ph.D. students and postdocs, with current projects focusing on dual channel program analysis, AI for code, and software security applications. He has established long-term collaborations with researchers at institutions including Royal Holloway and the University of Luxembourg. His teaching portfolio at UCL includes core courses in Malware, Compilers, and Validation and Verification, reflecting his broad expertise across the software engineering spectrum. Barr leads the System Software Engineering Group at UCL, which focuses on practical applications of software engineering research with strong connections to industry problems. The group's work bridges theoretical foundations with real-world software development challenges, particularly in the areas of program analysis and automated software maintenance.

Kristin Linnمشاهده پروفایل

استادیار

• Predictive Modeling
• Causal Inference
• Sequential Multiple Assignment Randomized Trials (SMARTs)
• +۴ مورد دیگر

Kristin Linn, MStat, PhD is an Assistant Professor of Biostatistics at the University of Pennsylvania and Associate Director of PennSIVE, a biomedical imaging group. She is affiliated with CHIBE (Center for Health Incentives and Behavioral Economics) and focuses on methodological and applied research in precision medicine, causal inference, and health equity. Key affiliations: CHIBE, PennSIVE Expertise: Statistical methods for personalized interventions, algorithmic fairness, health policy evaluation Dr. Linn's research bridges biostatistics and behavioral economics to address critical healthcare challenges: Developing statistical methods for sequential multiple assignment randomized trials (SMARTs) to optimize long-term health behaviors Quantifying racial bias in healthcare algorithms (e.g., VA risk scores) Designing machine learning fairness frameworks for health applications Biomedical imaging research involving scanner harmonization and personalized TMS treatments Policy work evaluating Medicaid waivers and primary care payment systems Her methodological rigor has been applied across diverse domains including: Physical activity promotion Gun safety interventions Clinician behavior modification Medicaid policy evaluation Dr. Linn collaborates extensively with interdisciplinary teams across Penn Medicine, including: Kevin Volpp, MD, PhD Amol Navathe, MD, PhD Atheen Venkataramani, PhD, MD, MPhil Ravi Parikh, MD, MPP

Anca Draganمشاهده پروفایل

دانشیار

• AI Safety and Alignment
• Robotics
• Machine Learning
• +۵ مورد دیگر

Anca Dragan is an Associate Professor in the EECS Department at UC Berkeley and currently leads AI Safety and Alignment at Google DeepMind. She specializes in human-AI interaction, focusing on aligning AI systems with human goals through algorithmic methods. Her work spans robotics, machine learning, and cognitive science, emphasizing safety, collaboration, and understanding human preferences. Affiliations: UC Berkeley EECS, Center for Human-Compatible AI, Google DeepMind Education: B.Sc. (Jacobs University Bremen), Ph.D. (Carnegie Mellon University) Research: Her lab (InterACT) develops algorithms for robots and AI to collaborate safely with humans. Key areas include reward learning from diverse feedback (e.g., demonstrations, corrections), game-theoretic coordination, and mitigating risks in advanced AI systems. Notable contributions include Inverse Reward Design and cooperative inverse reinforcement learning. Publications: Over 100+ papers in top venues (ICRA, CoRL, NeurIPS) on topics like human-robot collaboration, safety-critical systems, and alignment challenges. Recent work addresses risks in large models and scalable alignment techniques. Awards: Sloan Fellowship, MIT TR35, Okawa Award, NSF CAREER, PECASE. Recognized for teaching excellence (McEntyre Award) and contributions to AI ethics. Grants & Leadership: NSF and ONR grants, co-PI of Human-Compatible AI. Advised Waymo on safety for autonomous systems. Active in professional roles (e.g., conference chairs, steering committees). Labs/Teams: InterACT Lab (UC Berkeley), leading safety teams at DeepMind. Collaborations with BAIR Lab and academic-industry partnerships.

Yu Luoمشاهده پروفایل

مدرس

• Hidden Markov models
• Bayesian theory and applications
• Causal inference
• +۲ مورد دیگر

Yu Luo is a Lecturer in Statistics at the Department of Mathematics, King’s College London, affiliated with the Faculty of Natural, Mathematical & Engineering Sciences. He holds a PhD in Biostatistics from McGill University (Canada) and previously worked at Imperial College London and Lancaster University. His research focuses on Bayesian methodologies, hidden Markov models, causal inference, and applications in healthcare and transportation. Key projects include developing longitudinal Bayesian frameworks for causal analysis and evaluating policy impacts through statistical models. Education: PhD in Biostatistics, McGill University, Canada Research Interests: Hidden Markov models and Bayesian computation Causal inference methodologies Medical and transportation engineering applications Grants & Projects: EPSRC-funded project: Bayesian causal estimation via model misspecification (2024-2025) Recent Publications: Focus on Bayesian statistical methods applied to health systems, policy evaluation, and environmental interventions. Notable topics include causal dose-response modeling, stop-and-search policy analysis, and global climate change interventions.

Tomer Sagiمشاهده پروفایل

دانشیار

• Data Integration
• Artificial Intelligence
• Computer Science
• +۳ مورد دیگر

Tomer Sagi is an Associate Professor in the Department of Computer Science at Aalborg University (AAU), Denmark. He is affiliated with The Technical Faculty of IT and Design and leads projects in the AI for the People and BLUE – Marine & Maritime Research groups. His research focuses on data integration, ontology engineering, artificial intelligence applications in healthcare and environmental science, and knowledge graph development. PhD in Information Systems from Technion-Israel Institute of Technology (2015) Former Lecturer at University of Haifa (2017–2022) Principal Investigator/Co-PI in projects like ODINI (AI-based Data Integration), MEHDIE (Middle Eastern Heritage Knowledge Graph), and DarkScience (Microbial Data Science) Research Interests: Data Integration, AI for Ocean Science, Medical Informatics, Ontology Evaluation, Multilingual Knowledge Systems, and Explainable AI. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure. Key Projects (2022–2025): DarkScience: Metagenomic data analysis funded by Villum Foundation ODINI: AI-driven ocean data fusion and 3D reconstruction MEHDIE: Multilingual historical knowledge graphs for Middle Eastern heritage Awards: Received NLP4KGC Best Paper Award (2023) and AIME 2020 Best Paper Nomination. His contributions span 46+ publications, 8 datasets, and media coverage on AI applications in healthcare and environmental science. Labs/Teams: Active in AI for the People (applied AI solutions) and BLUE (marine data science). Collaborations include work on virtual twin technology for stroke management and medical data analytics.

Lin Qikaمشاهده پروفایل

پژوهشگر ارشد

• Natural Language Processing
• Artificial Intelligence in Healthcare
• Multi-modal Representation Learning
• +۳ مورد دیگر

Lin Qika is a Research Fellow at the Saw Swee Hock School of Public Health, National University of Singapore (NUS). His research focuses on advancing natural language processing (NLP) and AI applications in healthcare, particularly leveraging large language models (LLMs) for robust healthcare solutions. He holds a PhD from Xi’an Jiaotong University (2023), an M.S. from Beijing Institute of Technology (2019), and a B.S. from the same institution (2016). His expertise spans multi-modal representation learning, neuro-symbolic systems, and logical reasoning applied to healthcare challenges. Notable research includes developing frameworks like TECHS for explainable extrapolation reasoning and integrating knowledge graphs with LLMs. His work emphasizes practical healthcare applications, such as depression detection and medical diagnostics. Lin Qika’s recent publications (2022–2025) explore cutting-edge topics like contrastive graph representations, knowledge graph completion, and adversarial attacks on knowledge embeddings. He actively contributes to conferences like ACL, SIGIR, and KDD, showcasing innovations in AI-driven healthcare and multimodal reasoning. No scientific awards or funded grants are explicitly mentioned in the provided information.

Ian Lundbergمشاهده پروفایل

استادیار

• Quantitative Methodology
• Causal Inference
• Social Stratification
• +۳ مورد دیگر

Ian Lundberg is an Assistant Professor in the Department of Sociology at the University of California, Los Angeles (UCLA) . He holds a PhD in Sociology and Social Policy from Princeton University (2021) and an AB in Sociology and Statistics from Harvard College. His research focuses on stratification and inequality , emphasizing computational methods and causal inference to address societal disparities. Lundberg is a faculty organizer of the Inequality Data Science Lab and an affiliate of the California Center for Population Research . His research interests include quantitative methodology, causal inference frameworks, and the application of machine learning to social science problems. He has developed the gap-closing estimand – a causal approach to evaluate interventions that reduce disparities across social categories such as race, class, and gender. Lundberg also investigates housing policy, eviction prevention, and the predictability of life outcomes through large-scale collaborations. Awards/Grants include a National Science Foundation grant (2021–2022) for computational tools in income mobility research and recognition from the ASA Methodology Section. His work bridges theory, methodology, and policy, with contributions to sociology, demography, and interdisciplinary data science. Lundberg’s methodological innovations include R packages like gapclosing , which operationalizes causal decomposition analysis. His recent projects explore adaptive experimentation in conjoint surveys and the origins of unpredictability in life trajectory predictions. He has held prior positions at Cornell University’s Department of Information Science and UCLA’s Sociology Department as a postdoctoral scholar.

Andreas Andreouمشاهده پروفایل

استاد

• Neuromorphic Engineering
• CMOS Microsystems
• Sensory Information Processing
• +۶ مورد دیگر

Andreas Andreou is a Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), with secondary appointments in Computer Science and the Whitaker Biomedical Engineering Institute. He co-founded the JHU Center for Language and Speech Processing (CLSP) and co-directs the Andreou Lab, focusing on brain-inspired microsystems, neuromorphic engineering, and biomedical sensors. His research spans CMOS-based neuromorphic processors, event-based vision systems, and wearable health monitoring devices like the StethoVest. Key contributions include silicon retinas, polarization-sensitive imagers, and algorithms for pattern analysis. Research Interests: Neuromorphic Computing: Designing energy-efficient brain-inspired chips using 3D CMOS, FETs, and memristive technologies. Biomedical Microsystems: Wearable acoustic sensors for cardiac monitoring and vestibular prosthetics. AI Hardware: Neuromorphic accelerators for edge computing, leveraging LLMs for automated circuit design. Notable Achievements: IEEE Fellow (since 2020) Recipient of the 3rd Best Paper Award at IEEE BioCAS 2018 $2M DARPA grant for bio-inspired event cameras Labs/Teams: The Andreou Lab collaborates with the Kavli Neuroscience Discovery Institute and NSF-funded neuromorphic projects. Ongoing work includes neuromorphic Ising machines, LLM-driven chip design, and quantum sensing for medical applications.

• Autonomous Systems
• Bayesian Statistics
• Big Data
• +۷ مورد دیگر

Professor Simon Maskell is a Professor of Autonomous Systems at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science and the Faculty of Science and Engineering. He holds degrees including MEng, MA, PhD, and is a Fellow of the Institution of Engineering and Technology (FIET) and Chartered Engineer (CEng). His research focuses on algorithmic solutions at the intersection of Computer Science, Engineering, and Statistics. Key areas include Bayesian statistics for decision-making under uncertainty, particle filters for high-dimensional parameter estimation, and applications in autonomous systems, big data analytics, and data fusion for sectors like defense, healthcare, and maritime surveillance. He leads the Liverpool Big Data Network and contributes to the Stone Soup open-source tracking framework. Maskell has secured grants from the Home Office, European Commission, and EPSRC, addressing challenges like combating cyber threats, improving drug interaction detection, and enhancing sensor technologies. His notable award includes an Honorary Research Fellow title from Imperial College (2010). In teaching, he oversees modules such as Instrumentation & Control and supervises research on topics like Bayesian methods in proteomics, traffic signal control, and antimicrobial resistance. He serves as an Associate Editor for IEEE Transactions on Aerospace and Electronic Systems and IEEE Signal Processing Letters. Professional activities include editorial roles and administrative leadership in interdisciplinary initiatives, emphasizing ethical AI deployment and data-driven healthcare solutions.

Mel Chekolمشاهده پروفایل

استادیار

• Knowledge Graphs
• Artificial Intelligence
• Data Science
• +۴ مورد دیگر

Mel Chekol is an Assistant Professor at Utrecht University, affiliated with the Data Intensive Systems group within the Science faculty. He holds a PhD from INRIA Rhône-Alpes and a double MSc from Vienna University of Technology and Free University of Bozen-Bolzano. His research focuses on knowledge graphs, spatio-temporal data integration, probabilistic inference, and scalable machine learning applications. Previously, he worked at institutions including INRIA Nancy Grand Est, University of Mannheim, and the National Institute of Informatics in Tokyo. Key research interests include reasoning in knowledge graphs, temporal data modeling, and applying language models to enhance knowledge representation. He contributes to projects like the Utrecht Platform for Applied Data Science and has collaborated on frameworks such as the EXMO and WAM teams. His work emphasizes practical applications of AI and data science in governance and sustainability. Mel has published extensively in venues like VLDB Journal, ISWC, and AAAI, focusing on topics like rule learning, temporal knowledge graphs, and scalable inference systems. His research bridges theoretical advancements with real-world data challenges.

• In-Memory Databases
• Data Stream Processing
• Database Systems
• +۱۲ مورد دیگر

Matthias Uflacker is a Professor at the Hasso Plattner Institute (HPI), University of Potsdam, Germany, where he conducts research in database systems, in-memory computing, and data-driven enterprise applications. His work bridges systems research and practical applications in Industry 4.0, education, and bioinformatics. University: University of Potsdam School: Hasso Plattner Institute Department: Department of Digital Engineering Research Focus: In-Memory Databases, Stream Processing, Causal AI, and Database Education His research interests span in-memory database systems , real-time data stream processing , GPU-accelerated analytics , and causal structure learning . He also contributes to computer science education , particularly in agile software engineering and interactive learning tools for K-12. The recent publications reflect a strong trend in enterprise data management , with a focus on benchmarking (e.g., ESPBench), performance optimization (e.g., Kafka, Hyrise), and intelligent decision support using causal models in industrial contexts. His work increasingly integrates AI for monitoring and automation in manufacturing and e-commerce. He has co-authored numerous publications in top-tier venues such as VLDB, ICDE, EDBT, and KDD, often in collaboration with Hasso Plattner and other HPI researchers. Matthias Uflacker leads and contributes to research projects on autonomous database systems , dynamic pricing simulations , and educational technology platforms for programming exercises. His team develops tools for real-time data integration, causal analysis, and classroom software engineering assessment.

Xibin Zhangمشاهده پروفایل

استاد

• Bayesian inference
• Financial econometrics
• Nonparametric estimation and testing
• +۷ مورد دیگر

Xibin Zhang is a Professor in the Department of Econometrics and Business Statistics at Monash Business School, Monash University, Australia. He has been a faculty member since 2004, progressing from Lecturer to full Professor in 2021, and has established a strong research profile in econometrics and applied statistics. Educational Background: PhD in Econometrics, Monash University, Australia Doctoral Degree in Systems Engineering, Tianjin University, China MSc in Probability and Statistics, Nankai University, China BSc in Mathematical Statistics, Nankai University, China Research Interests: Professor Zhang’s research focuses on advanced econometric methodologies, including Bayesian inference, semiparametric and nonparametric estimation, panel data models, and hypothesis testing. His work extends into financial econometrics, actuarial studies, and energy economics, with a strong emphasis on empirical applications related to climate policy, energy transition, and economic sustainability. He applies sophisticated statistical techniques to real-world data, contributing to both methodological development and policy-relevant insights. Publication Trends: His recent publications (2019–2025) reveal a consistent focus on nonparametric and Bayesian methods in econometrics, with applications in energy economics, stochastic frontier analysis, and financial modeling. He frequently publishes in top-tier journals such as Journal of Econometrics , Energy Economics , and Economic Modelling , often in collaboration with leading researchers. A notable trend is the integration of kernel-based methods and Bayesian bandwidth selection across various modeling frameworks. Scientific Awards: Dean's Award for Excellence in Research (2009) Dean's Commendation for Excellence in Research Publication (2007) Advising and Grants: He has supervised over 15 PhD and Master’s students to completion and currently supervises multiple PhD candidates. His research is supported by competitive grants, including four Australian Research Council (ARC) Discovery Projects (2006–2016) and a DFAT-funded project (2017–2018) on Chinese tourism dispersal in regional Australia. Labs and Research Teams: While no formal lab is mentioned, Professor Zhang collaborates extensively within econometrics and business statistics research groups at Monash, particularly with colleagues like M.L. King, Jiti Gao, and Russell Smyth. He also engages in interdisciplinary work involving energy policy, finance, and actuarial science.

• Machine Learning
• Natural Language Processing
• Multimedia Systems
• +۳ مورد دیگر

Liao Lizi is an Assistant Professor and Lee Kong Chian Fellow at Singapore Management University's School of Computing and Information Systems. She received her Ph.D. from the National University of Singapore under Professor Tat-Seng Chua. Her research explores the intersection of Machine Learning, Natural Language Processing, and Multimedia, focusing on developing conversational AI systems. Key areas include: Proactive conversational agents that anticipate user needs Multimodal conversational search and recommendation systems Task-oriented dialogue frameworks Human-machine collaborative systems Emotional support and empathetic response generation Her publications demonstrate a strong focus on advancing conversational AI through novel architectures for dialogue planning, multimodal understanding, and knowledge-enhanced generation. Recent work shows increasing emphasis on LLM optimization, financial reasoning benchmarks, and emotional intelligence in dialogue systems. Scientific Awards: Lee Kong Chian Fellowship Google South Asia & Southeast Asia Research Awards 2023 She leads the CoAgent Lab and advises multiple PhD students including recipients of the SMU Presidential Doctoral Fellowship. Current teaching includes Visual Analytics for Business Intelligence (IS428) and Text Analytics and Application (ISSS609).

• Artificial Intelligence
• Data Science
• Data Management & Mining
• +۵ مورد دیگر

Steven Hoi is a full-time Professor of Computer Science at the School of Computing and Information Systems (SCIS) within Singapore Management University (SMU) . He is currently on leave but remains actively engaged in academic research. His primary research domains include Artificial Intelligence , Data Science , and Machine Learning , with specific emphasis on Vision-Language Models , Time Series Forecasting , and Human-Machine Collaborative Systems .

• Computer Networks
• Machine Learning
• Reinforcement Learning
• +۶ مورد دیگر

Alessio Sacco is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy. He's affiliated with the NETGROUP - Computer Networks Group and actively contributes to research in computer networks, machine learning, and software-defined networking. Current academic position: Assistant Professor Research group: NETGROUP Key collaborations: Guido Marchetto, Flavio Esposito Research Interests focus on: Computer networks and communications Dynamic programming and reinforcement learning Machine learning applications in network management Software-defined networks Cybersecurity and privacy analysis Intelligent Internet of Things systems Recent Publications demonstrate expertise across: Network security verification (PathSafe) AI-driven agricultural optimization (FertilizeSmart) Cross-layer congestion control (Flecto) Reinforcement learning for computing continuum scheduling Explainable AI for network transparency (ClearNET) Multi-agent exploration systems (MARS) Scientific Contributions include: Commercial research on network security and anomaly detection ERC sector expertise in distributed systems and machine learning Teaching in computer engineering programs since 2019 Advising involves: Supervising Federico Rinaudi (ongoing) Co-supervising Doriana Monaco (ongoing)