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Mingwei LiuView profile

Associate Professor

• Software Engineering
• Artificial Intelligence
• AI4SE
• +4 more

Dr. Mingwei Liu is an Associate Professor at the School of Software Engineering, Sun Yat-Sen University, China. Previously, he completed postdoctoral research at Fudan University in 2024 and received both his Ph.D. (2022) and B.E. (2017) in Software Engineering from Fudan University under the mentorship of Xin Peng. Dr. Liu's research focuses on the intersection of Software Engineering (SE) and Artificial Intelligence (AI), specifically in the domains of AI4SE (AI for Software Engineering) and SE4AI (Software Engineering for AI). His primary interest lies in leveraging advanced AI technologies, particularly large language models (LLMs) and knowledge graphs (KGs), to address complex software engineering challenges and tackle system engineering problems in AI applications. His research portfolio demonstrates a strong focus on practical applications of AI in software development, with particular emphasis on code generation, vulnerability detection, test generation, and knowledge representation for software artifacts. Through his work, Dr. Liu has contributed significantly to understanding how LLMs perform in realistic software development scenarios beyond simple function-level code generation. Dr. Liu has published over twenty papers in top international journals and conferences including TSE, TOSEM, ICSE, FSE, and ASE. His research has been recognized with prestigious awards including the IEEE TCSE Distinguished Paper Award (ICSME 2018) and the ACM SIGSOFT Distinguished Paper Award (FSE 2023). IEEE TCSE Distinguished Paper Award (ICSME 2018) ACM SIGSOFT Distinguished Paper Award (FSE 2023) Dr. Liu actively contributes to the academic community as a program committee member for major conferences including ASE, ESEC/FSE, ICSME, and SANER. He leads the SYSUSELab research group, which has developed notable resources including the ClassEval benchmark for evaluating LLMs on class-level code generation tasks.

• Urban Geography
• Economic Geography
• Regional Development
• +3 more

Dr. Bruce W. Smith serves as Professor of Geography in the School of Earth, Environment and Society at Bowling Green State University and as Research Fellow for the Center for Regional Development. His expertise spans economic geography, regional development, and geospatial analysis with applications in cluster-based economic development. His academic credentials include: Ph.D. in Economic Geography, University of Illinois at Champaign-Urbana (1970) M.A. in Geography, University of Illinois at Champaign-Urbana (1967) B.A. in Social Studies Education, Potsdam State College of New York (1965) Smith's research focuses on cluster-based economic development, particularly in northwest Ohio's floriculture industry (a top 5% U.S. production region). He integrates geospatial analysis and social network methodologies to study economic clusters, regional development, and university economic impacts. His work bridges theoretical geography with practical community development applications, examining how century-old family businesses navigate collaboration through embedded social relationships. Analysis of his 2004-2007 publications reveals consistent application of spatial statistics to economic geography problems. Key trends include identifying economic clusters through spatial autocorrelation, assessing university economic impacts, and modeling social networks in local development. His work demonstrates strong methodological innovation using ArcGIS tools and social network analysis. As Co-PI, Smith secured significant funding including USDA grants totaling $1.4+ million for Ohio greenhouse industry impact studies (2005-2007) and Economic Development Administration funding ($158k/year) for University Center Programs. These projects support applied research in regional economic development across northwest Ohio. Smith co-founded the international E-4 network connecting Bowling Green State University, University of Toledo, University of Auckland, Birmingham University, and University of Calgary. This collaboration conducts comparative studies on local economic development using geospatial tools, structural equation modeling, and social network analysis, facilitating global research partnerships across three continents.

Wei HuView profile

Assistant Professor

• Deep Learning Theory
• Machine Learning
• Neural Networks
• +2 more

Wei Hu is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan's College of Engineering. His research focuses on uncovering the theoretical and scientific foundations of deep learning, aiming to open the black box of neural networks through a combination of theoretical and empirical approaches. Dr. Hu received his PhD in Computer Science from Princeton University, where he was advised by Sanjeev Arora. Prior to his PhD, he completed his undergraduate studies at Tsinghua University as a member of the prestigious Yao Class. He also served as a FODSI postdoc at UC Berkeley before joining the University of Michigan faculty. His research interests center around understanding the fundamental mechanisms of deep learning, particularly focusing on training dynamics, generalization properties, and the theoretical underpinnings of neural networks. His work spans both clean, controlled problems and complex real-world models, with recent emphasis on transformer architectures, grokking phenomena, and the implicit biases in neural network training. Analysis of his recent publications reveals a strong focus on theoretical deep learning with particular attention to transformer models, grokking phenomena, and generalization theory. His work bridges the gap between theoretical understanding and practical deep learning applications, with significant contributions to understanding abrupt learning transitions, representation learning, and the dynamics of neural network training. AAAI New Faculty Highlights, 2024 Google Research Scholar Award, 2023 Siebel Scholar, 2021 Best paper award at ICML Workshop on Modern Trends in Nonconvex Optimization for Machine Learning, 2018 Gordon Y.S. Wu Fellowship, 2016 Gold medal (1st place), The 27th Chinese Mathematical Olympiad, 2012 Dr. Hu currently advises three PhD students: Pulkit Gopalani, Zhiwei Xu (co-advised with Yixin Wang), and Yongyi Yang. His research group has received substantial funding through awards including the Google Research Scholar Award. He teaches courses including Introduction to Machine Learning (EECS 445) and specialized topics in machine learning theory and large language models (EECS 598/CSE 598). His research group maintains an active presence in top machine learning conferences, with publications appearing regularly in venues such as NeurIPS, ICML, ICLR, and others. The group's work has gained significant recognition in the theoretical machine learning community for its rigorous approach to understanding deep learning phenomena.

Sarah GrayView profile

Associate Professor

• Developmental Psychopathology
• Early Childhood Mental Health
• Parent-Child Relationships
• +4 more

Dr. Sarah Gray is an Associate Professor in the Department of Psychological Sciences at the University of Connecticut. She earned her Ph.D. in 2013 from the University of Massachusetts Boston. As a licensed clinical psychologist, Dr. Gray directs the Child and Family Lab, where she investigates developmental psychopathology and early childhood mental health through integrated behavioral, physiological, and narrative methodologies. Her research program centers on intergenerational trauma transmission, parent-child relationships under stress, and preventive interventions for vulnerable families. Dr. Gray's research examines multilevel pathways of risk and resilience across generations, with emphases on: Developmental consequences of early trauma/adversity Biological embedding of stress through psychophysiological mechanisms (e.g., respiratory sinus arrhythmia) Cultural and contextual influences on family processes Adaptation of attachment-based interventions to telehealth formats Analysis of her recent publications reveals dominant themes in: COVID-19's impact on family stress and child behavioral outcomes Physiological synchrony in caregiver-child dyads Protective effects of emotion-focused parenting practices Intergenerational transmission of trauma in marginalized communities Telemental health adaptations of evidence-based interventions The Child and Family Lab employs multimodal assessment strategies to understand how adult caregivers support children's stress regulation. Current work explores neurobiological mechanisms linking maternal early adversity to child outcomes, culturally responsive intervention models, and pandemic-related family adaptation.

• Distributed Artificial Intelligence
• Sensor Networks
• Multi-Agent Systems
• +3 more

Dr. Conor Muldoon is a Senior Lecturer in Software Engineering at the Department of Computing and Mathematics, Manchester Metropolitan University. His research focuses on distributed artificial intelligence, sensor networks, and multi-agent systems with applications in environmental monitoring and smart home technologies. Previously, he held postdoctoral positions at University College Dublin (UCD) and the University of Oxford, supported by prestigious fellowships including the INSPIRE Marie Curie and Government of Ireland Embark awards. He earned a Ph.D. in Computer Science from UCD, along with a first-class B.Sc. in Computer and Software Engineering and a Postgraduate Certificate in Higher Education Teaching. His research portfolio includes developing water quality forecasting systems, citizen science platforms (e.g., COBWEB), and autonomic energy management solutions. He has published extensively in journals and conferences, addressing topics such as sensor network optimization, incentive mechanisms for crowdsourcing, and adaptive middleware for ambient intelligence. Dr. Muldoon is a Fellow of the Higher Education Academy and supervises PhD candidates in areas like multi-agent systems and environmental informatics. Education: Ph.D. in Computer Science, University College Dublin B.Sc. (Honours) in Computer and Software Engineering, University College Dublin Postgraduate Certificate in Teaching and Learning in Higher Education His work bridges theoretical computer science with real-world applications, emphasizing sustainability and participatory technologies. Key projects include the Dublin Bay Water Quality Modelling and the MERA Data Extraction Toolkit. Awards include recognition for his contributions to agent-based systems and environmental data science.

• Geometry
• Topology
• Mathematical Analysis
• +3 more

Alexey BALITSKIY is a Postdoctoral Researcher at the Department of Mathematics within the Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg. His research focuses on geometric analysis, topology, and convex geometry, with particular attention to systolic inequalities, geometric bounds, and billiard dynamics. He explores topics such as Urysohn width, waist inequalities, and symplectic geometry, contributing to both theoretical advancements and applications in geometric optimization. His work often bridges pure mathematics with computational approaches, addressing problems in geometric measure theory, metric embeddings, and discrete geometry. Notable contributions include generalizations of classical plank inequalities and studies on equality cases in conjectures like Viterbo’s and Mahler’s. Alexey’s research is disseminated through high-impact journals such as the Journal of Topology and Analysis and collaborations on topics like flip cycles in plabic graphs and geometric complexity of planar drawings. Alexey’s publications reflect a strong focus on geometric inequalities, with recent work emphasizing codimension 2 width estimates, systolic vertex counting, and multi-plank generalizations. His work often intersects with symplectic geometry, convex billiards, and computational topology, demonstrating interdisciplinary reach. Despite no listed awards or grants, his prolific output since 2016 highlights sustained academic engagement and innovation in geometric analysis.

Philipp GrohsView profile

Professor

• Mathematical Data Science
• Deep Learning Algorithms
• High-Dimensional Partial Differential Equations
• +7 more

Prof. Dr. Philipp Grohs is a full professor of Mathematical Data Science at the University of Vienna and head of the Mathematical Data Science group at RICAM (Austrian Academy of Sciences). He holds a MSc from TU Vienna (2006) and a PhD from the same institution (2007). After postdoc positions at TU Graz, KAUST, and ETH Zurich, he became an assistant professor at ETH Zurich in 2011 before moving to the University of Vienna in 2016. His research focuses on designing efficient algorithms for signal processing, computational sciences, and finance, with recent contributions to understanding deep learning algorithms and solving high-dimensional PDEs using machine learning. He has received the ETH Latsis Prize (2014) and was selected for an Alexander von Humboldt Professorship (2019). Research Interests: Mathematical foundations of deep learning High-dimensional PDEs and their numerical solutions Signal and image processing (phase retrieval, Gabor systems) Approximation theory and function spaces Computational finance and mathematical modeling Recent Research Trends: His work explores theoretical guarantees for neural network performance, particularly in overcoming dimensionality challenges. Notable contributions include phase retrieval algorithms, analysis of DNN expressivity, and applications of deep learning in quantum chemistry and epidemiology modeling. Awards: ETH Latsis Prize (2014) Alexander von Humboldt Professorship (2019) Advising & Projects: Leads research initiatives such as the 'Data Science' hub at Vienna, and has coordinated projects on hybrid computational sciences and explainable AI models. Active in supervising interdisciplinary research teams across mathematics and computer science. Labs/Teams: Directs the Mathematical Data Science group at RICAM and oversees computational research collaborations with institutions like KAUST and ETH Zurich. Engages in applied projects like group testing strategies for SARS-CoV-2 and neural network-based electronic structure calculations.

• Statistical Data Analysis
• Computer Vision
• Digital Health
• +5 more

Mirko Birbaumer is a Professor at the Lucerne School of Engineering and Architecture (HSLU), specializing in data science and machine learning applications. He holds a PhD in computer-assisted image analysis from ETH Zurich and has completed advanced executive education at MIT in Innovation and Strategy. PhD in Systems Biology (ETH Zurich, 2010) Executive Certificate in Innovation and Strategy (MIT, 2022) Advanced Executive Certificate in Innovation, Strategy, and Technology (MIT, 2024) His research integrates statistical data analysis , computer vision , and digital health , with applications spanning industrial quality control, medical imaging, and environmental monitoring. Recent work focuses on physics-informed neural networks, interpretable anomaly detection, and causal machine learning. His projects include AI-driven solutions for medical diagnostics, predictive maintenance in manufacturing, and environmental anomaly detection. He has supervised over 20 student theses on topics like deep learning for knee osteoarthritis detection, monocular depth estimation, and AI-assisted logo similarity analysis. He leads the Data Science profile in the Master of Science and Engineering program and teaches courses on deep learning, Bayesian machine learning, and computer vision. His methodological expertise includes agent-based modeling, statistical analysis, and explainable AI.

Reinhard RauscherView profile

Professor

• Computer Science
• Digital Systems Design
• VLSI Design
• +3 more

Prof. Dr. habil. Reinhard Rauscher is a retired professor at the Osnabrück University of Applied Sciences, affiliated with the Faculty of Management, Culture and Technology. He holds a Diplom in Informatics from the Christian-Albrechts-Universität Kiel (1982), a PhD in Computer Science from the University of Hamburg (1987), and a habilitation in Computer Science focusing on 'Design and Methodology of Application-Specific Digital Systems' (1998). His teaching responsibilities include courses such as Algorithms, Programming, and Databases. Research Interests: His work spans digital system design, VLSI architecture, CAD tools for hardware design, microprogramming optimization, and real-time image processing ASICs. Notable contributions include methodologies for microcode transformation verification, CAD systems for digital circuit design, and research on numerical stability in systolic arrays. Publications: Over 30 peer-reviewed papers in conferences like EUROMICRO, CONPAR, and journals such as Microprocessing and Microprogramming. His research emphasizes practical applications of theoretical computer science in hardware-software co-design. Grants & Labs: Involved in projects like CAMILOD (correctness-proven hardware design), BOSSDIS (automated hardware description transformation), and collaborations on ASIC development for image processing and SCHEME coprocessors. His work integrates algorithmic theory with embedded system implementation challenges.

• Metric Measure Spaces
• Optimal Transport
• Geometric Analysis
• +3 more

Tapio Rajala is an Associate Professor at the University of Jyväskylä. His research focuses on geometric analysis, metric measure spaces, optimal transport, and functional analysis. He has contributed to areas such as Sobolev extensions, BV spaces, and geometric measure theory in non-smooth settings. His work often intersects with calculus of variations, partial differential equations, and the study of geometric properties in metric spaces. Recent publications explore topics like Laplacian bounds in metric measure spaces, entropy-transport problems, and dimension estimates for fractal-like structures. Rajala has organized academic events such as the Sub-Riemannian Geometry workshops (2017–2019) and the Optimal Transport and Applications conferences (2011–2018). His research trends emphasize interdisciplinary approaches to geometric analysis and functional inequalities.

Leman AkogluView profile

Associate Professor

• Data Mining
• Graph Mining
• Machine Learning
• +7 more

Leman Akoglu is the Heinz College Dean's Tenured Associate Professor at Carnegie Mellon University's Heinz College of Information Systems and Public Policy. She holds courtesy appointments in the Machine Learning Department (MLD) and the Computer Science Department (CSD) within the School of Computer Science (SCS). Her research focuses on anomaly detection, graph mining, machine learning, and data mining, particularly in identifying anomalies in large, dynamic datasets. She is currently on academic leave as a full-time Amazon Scholar until Spring 2026. Education: Ph.D. in Computer Science from Carnegie Mellon University (2012), B.S. in Computer Science from Bilkent University (2003-2007). Research Interests: Data Mining, Graph Mining, Machine Learning, Anomaly Detection, Self-Supervised Learning, Outlier Detection, Fraud Detection, and Healthcare Analytics. She leads the DATA Lab at Heinz College, exploring scalable computational methods for anomaly detection across domains like healthcare, finance, and cybersecurity. Recent Work Highlights: Contributions include trajectory anomaly detection, self-supervised time series analysis, and fair outlier detection. Her work appears in top conferences like KDD, SIAM SDM, AAAI, and NeurIPS. She has received prestigious awards such as the SDM/IBM Early Career Award and NSF CAREER Award. Teaching: Courses include Machine Learning for Problem Solving and Big Data and Large Scale Computing at CMU. She emphasizes practical machine learning processes, feature design, and scalable methods.

• Geometric Group Theory
• Hyperbolic Groups
• Hierarchically Hyperbolic Groups
• +7 more

Carolyn R. Abbott is an Assistant Professor in the Department of Mathematics at Brandeis University, where she holds the Landsman Career Development Chair in the Sciences. She is currently on sabbatical in Spring 2025. Her research is centered in geometric group theory, with a focus on group actions on metric spaces, particularly hyperbolic and hierarchically hyperbolic structures. Ph.D., University of Wisconsin–Madison (2017), advised by Tullia Dymarz NSF Postdoctoral Fellow, Columbia University (2019–2021) NSF Postdoctoral Fellow, UC Berkeley (2018–2019) Morrey Visiting Assistant Professor, UC Berkeley (2017–2018) Her research interests include geometric group theory , acylindrically hyperbolic groups , hierarchically hyperbolic groups , mapping class groups , and group actions on hyperbolic spaces . She has made significant contributions to the understanding of acylindrical actions, hyperbolic structures on groups, and the geometry of infinite-type surfaces. She co-organizes the Brandeis Topology Seminar and serves as the Undergraduate Advising Head II. The most recent publications reflect a sustained focus on the interplay between large-scale geometry and algebraic structure in groups. Her work frequently explores classification of group actions, invariants under quasi-isometry, and structural properties of hierarchically hyperbolic and acylindrically hyperbolic groups. Several recent papers investigate the dynamics of big mapping class groups and the geometry of 3-manifold groups. Landsman Career Development Chair in the Sciences NSF Postdoctoral Fellow (twice) Morrey Visiting Assistant Professor She advises multiple graduate students and has taught a wide range of courses, from calculus to advanced topology. She has previously taught at Columbia University, UC Berkeley, and the University of Wisconsin–Madison. Her advising includes both current and graduated students, and she is actively involved in mentoring and academic leadership. She is a co-organizer of the Brandeis Topology Seminar, contributing to a vibrant research community. Dr. Abbott maintains an active research program with recent publications in top journals such as Algebraic & Geometric Topology , Mathematische Zeitschrift , and Transactions of the AMS . Her collaborative work spans multiple institutions and reflects deep engagement with the geometric group theory community.

• Partial Differential Equations
• Mathematical Physics
• Stochastic Analysis
• +2 more

Dr. Martin Spitz is a researcher in the Faculty of Mathematics at Bielefeld University, Germany. He is affiliated with the Department of Mathematics and holds an office in UHG V4-233. His research focuses on nonlinear partial differential equations (PDEs), stochastic analysis, and mathematical physics, with particular emphasis on systems such as the nonlinear Schrödinger equation, Zakharov system, Maxwell-Dirac equations, and quasilinear Maxwell equations. His work addresses well-posedness, scattering behavior, and regularity theory for both deterministic and stochastic PDEs. Spitz's research interests span energy-critical and supercritical phenomena, modified scattering in quantum field theory, and boundary value problems in electromagnetism. His recent studies explore laser-plasma interactions, stochastic regularization effects, and inverse scattering for three-dimensional systems. He has contributed to advancing understanding of probabilistic well-posedness and long-time dynamics in nonlinear dispersive equations. His publications cover theoretical developments in PDE analysis, with applications to plasma physics, quantum systems, and nonlinear wave dynamics. Despite no awards explicitly listed, his active publication record reflects significant contributions to these fields. No information on advising students or grants is provided in the source material.

• Software Engineering
• Parallel Algorithms
• Computer Networks
• +7 more

Prof. Hasan BULUT is a full-time faculty member at Ege University's Faculty of Computer and Information Sciences, Department of Computer Engineering. His primary research focuses on software engineering, parallel algorithms, computer networks, artificial intelligence, and algorithm design. He has contributed to fields like distributed systems, data structures, and bioinformatics through innovative algorithmic solutions. His academic work spans over two decades, with notable contributions to machine learning applications in energy forecasting, DNA sequence analysis, and cloud computing optimization. Key areas of expertise include hybrid machine translation models, real-time data clustering, and optimization techniques for computational problems. Prof. BULUT's recent publications emphasize interdisciplinary approaches, combining deep learning with traditional methods to solve challenges in healthcare informatics, financial prediction, and bioengineering. His work on network slicing techniques for 5G and beyond networks highlights cutting-edge contributions to modern communication systems. Despite an extensive publication record and collaborations within Ege University, no formal scientific awards or grant information is explicitly mentioned in the provided texts. His academic career includes supervising numerous research projects but specific student advisee details are not documented here.

Ken M. L. YiuView profile

Professor

• Databases
• Query Processing
• Spatiotemporal Data
• +7 more

Ken M. L. Yiu is a Professor in the Department of Computing at Hong Kong Polytechnic University , Faculty of Engineering. He received his PhD and Bachelor's degree from the University of Hong Kong in 2006 and 2002, respectively, and was previously affiliated with Aalborg University (2006–2009). He is a leading researcher in databases, with a focus on spatiotemporal data, query processing, and multidimensional data management. PhD, University of Hong Kong (2006) Bachelor of Computer Engineering, University of Hong Kong (2002) His research interests lie at the intersection of database systems and spatial analytics. He investigates efficient indexing, query optimization, and privacy-preserving techniques for large-scale spatial and temporal datasets. His recent work explores learned index structures, GPU-accelerated query processing, and high-dimensional data retrieval. He has made significant contributions to spatial query processing, trajectory analytics, and location-based services. The trends in his recent publications (2021–2025) reflect a strong focus on high-performance database systems, including GPU acceleration (GHive), perfect hashing on GPUs (GPH), and learned cardinality estimation. His work increasingly integrates machine learning with traditional database techniques, as seen in AlayaDB for LLM inference and learning-based query optimization. He also continues to advance core database problems such as spatial indexing, trajectory analysis, and similarity search. SSTD 2025 10-Year Impact Award Ken Yiu has successfully led multiple competitive research projects funded by the Hong Kong GRF, including grants on learned index structures (2024–2026), smart memory for vector data mining (2021–2023), and efficient spatial data management (2017–2019). He has supervised numerous PhD and MPhil students, many of whom now hold academic positions (e.g., Bo Tang at SUSTech, Yu Li at HDU) or work in top tech companies (e.g., Huawei, Alibaba). His professional service is extensive, including roles as PI for major grants, area chair (ICDE 2024), and program committee member for top conferences like SIGMOD, VLDB, and ICDE. He is actively involved in research groups and projects related to database systems, particularly in spatiotemporal data management and efficient query processing. His lab collaborates closely with students and co-supervisors like Bo Tang on topics such as trajectory mining, spatial indexing, and learned databases. The research group maintains strong ties with international institutions and contributes to major open problems in database performance and scalability.