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Seth GershensonView profile

Professor

• teacher labor markets
• student absences
• education policy
• +7 more

Seth Gershenson is a Professor of Public Policy in the School of Public Affairs (SPA) at American University, where he serves as Ph.D. Program Coordinator for the Department of Public Administration and Policy (DPAP) and directs the SPA Analytics and Management Institute (SAMI). He holds a Ph.D. in Economics from Michigan State University (2011) and a B.S. in Economics from Drexel University (2005). His academic affiliations include being a Faculty Affiliate at AU's School of Education, Research Fellow at the Institute of Labor Economics (IZA), and Senior Technical Advisor to Johns Hopkins University's Education Policy Institute. Ph.D. in Economics, Michigan State University (2011) M.A. in Economics, Michigan State University (2007) B.S. in Economics, Drexel University (2005) Gershenson's research centers on the economics of education, with specific focus on teacher labor markets, student absences, summer learning loss, racial disparities in education, time use patterns, and the role of expectations in educational outcomes. His work applies economic methodologies to practical policy questions in public education, particularly examining how teacher behavior and demographic representation affect student achievement and school discipline. He has pioneered research on same-race teacher effects, unconscious bias in classrooms, and the causal impact of community traumatic events on student performance. His publication trends reveal deep engagement with racial equity in education, teacher diversity impacts, and methodological innovations in measuring educational outcomes. Recent work increasingly examines teacher mental health, grading standards, and the spillover effects of teacher diversity on colleagues' racial competency. Scholar-Teacher of the Year (2025, AU School of Public Affairs) Thomas A. Downes Award (2016, best article in Education Finance & Policy) Pre-Doctoral New Scholar Award (2010, Association for Education Finance & Policy) Multiple AERA Outstanding Reviewer Awards (2017-2024) Gershenson has advised over 15 Ph.D. students to completion, including notable scholars like Stephen B. Holt and Michael Hayes. His research has secured over $1.5 million in external funding from organizations including the Spencer Foundation, W.E. Upjohn Institute, Institute of Education Sciences (IES), and Google Inc. He currently serves as Co-Editor of the Journal of Policy Analysis & Management and hosts the JPAM's Closer Look podcast. As director of SAMI, he leads an innovative program offering graduate students workshops in GIS mapping, causal inference, data visualization, and policy writing through partnerships with DC-area experts. His work on teacher diversity has directly informed policy discussions at federal and state levels.

Teresa WilcoxView profile

Professor

• Infant Cognition
• Developmental Psychology
• Neuroimaging
• +4 more

Dr. Teresa Wilcox is a Professor in the Department of Psychology at Florida Atlantic University's Charles E. Schmidt College of Science. Her research focuses on infant cognition, perception, and the functional organization of the infant brain, particularly in understanding social and mechanical entities. She employs advanced methodologies such as violation-of-expectation, eye tracking, action tasks, and functional near-infrared spectroscopy (fNIRS). Ph.D., Psychology, University of Arizona (1993) M.S., Child Development, University of California, Davis (1988) B.A., Psychology, Bethel University (1985) Her work explores how infants acquire knowledge about physical, mechanical, and social entities, with a strong emphasis on neural activation patterns and behavioral responses. Recent publications highlight her expertise in fNIRS signal analysis, infant-mother synchrony, and object individuation. Dr. Wilcox leads the Infant Cognition and Language Lab (ICLAB), where her team investigates brain dynamics in early development. Dr. Wilcox's research trends include tensor decomposition for fNIRS data, social perception in neuromarketing contexts, and the role of visual cues in infant cognition. Her work bridges cognitive science, neuroscience, and developmental psychology, contributing foundational insights into early knowledge acquisition.

Adam ButterworthView profile

Professor

• Genomics
• Molecular Epidemiology
• Cardiometabolic Diseases
• +3 more

Professor Adam Butterworth from the University of Cambridge 's Department of Public Health and Primary Care leads a collaborative international research group focusing on genomics , multi-omics , and cardiometabolic diseases . Affiliated with the Cardiovascular Epidemiology Unit, his work emphasizes Large-scale population datasets Reproducible research methodologies Open science initiatives Research interests span genetic risk prediction , molecular epidemiology , and biomarker development for conditions like coronary artery disease, diabetes, and neurovascular disorders. His group specializes in Mendelian randomization techniques Polygenic score optimization Proteogenomic analysis Metabolic pathway modeling Scientific contributions include Developing flashfmZoom for GWAS fine-mapping Advancing multi-ancestry polygenic risk scores Exploring gene-environment interactions in cardiometabolic health Characterizing inflammatory protein pathways in immune-mediated diseases Contact: asb38@cam.ac.uk | Google Scholar: Profile

Brian WeikelView profile

Assistant Professor

• Causal Inference
• Econometric Modeling
• Machine Learning
• +3 more

Dr. Brian Weikel is an Assistant Professor in the Department of Business, Accounting, & Economics at Otterbein University. He brings extensive industry experience from leading analytics teams at Nielsen, Bath & Body Works, and Victoria’s Secret, specializing in causal inference and scalable cloud-based marketing analytics. Ph.D. in Economics (University of Illinois, 2000) M.S. in Statistics (University of Illinois, 1999) M.S. in Economics (Texas Tech University, 1994) B.S. in Economics (The Ohio State University, 1992) His research focuses on causal inference through randomized control trials, econometric methods, and machine learning. Key areas include marketing analytics , data governance , and cloud-based computational scalability . Recent publications address aggregation bias , synthetic control methods , and feature mismeasurement in marketing mix modeling. He has developed enterprise-grade solutions for automotive manufacturing, retail, financial services, and pharmaceutical sectors.

Anastasios GounarisView profile

Professor

• Distributed Databases
• Autonomous data processing
• Big Data Management
• +7 more

Anastasios Gounaris is a Professor at the Department of Informatics, Aristotle University of Thessaloniki, where he has been a faculty member since April 2008. He previously served as a Visiting Lecturer at the University of Cyprus (2007-2008) and held research positions at the University of Manchester and CERTH. His academic journey includes a PhD from the University of Manchester (2005), an MPhil from UMIST (2002), and a degree in Electrical and Computer Engineering from Aristotle University of Thessaloniki (1999). His research spans Distributed Databases, Autonomous Data Processing, Big Data Management, Workflow Optimization, and Data Mining . He has made significant contributions to large-scale data management systems, massive parallelism techniques, and business process analytics. His work bridges theoretical computer science with practical industrial applications, particularly in predictive maintenance and edge computing environments. His recent publications (2024-2025) demonstrate a strong focus on process mining, anomaly detection, predictive maintenance systems, and edge analytics . These works address critical challenges in handling evolving data streams, optimizing task allocation in resource-constrained environments, and developing parameter-free algorithms for real-world applications. His research shows consistent progression from foundational database systems work to cutting-edge applications in industrial IoT and healthcare analytics. Dr. Gounaris has successfully supervised numerous PhD students to completion, including Athanasios Naskos (2017), Georgia Kougka (2017), Christos Bellas (2022), Theodoros Toliopoulos (2022), Anna-Valentini Michailidou (2023), Ioannis Mavroudopoulos (2024), and Konstantinos Varvoutas (2025). His research has been supported by multiple national and European projects including DataflowOpt, PRECognition, NavGreen, CUREX, Rainbow, Lifechamps, and Trineflex. He is an active member of Datalab (formerly Delab) and has contributed to industry applications through collaborations with companies such as Comidor, Gnomon, Istognosis, Atlantis, AFS, Follow-Apps, Sboing, and Upcom. His work demonstrates a strong commitment to transferring research results to real-world applications while maintaining academic rigor.

• Causal inference
• Mendelian randomization
• High-dimensional statistics
• +9 more

Dr. Verena Zuber is an Associate Professor in Biostatistics at the School of Public Health within Imperial College London's Faculty of Medicine (UK). Her research focuses on causal inference methodology applied to life sciences and health data, particularly through genetic instrumental variables (Mendelian randomization). She engages with interdisciplinary collaborations in cardiovascular health, oncology, neurodegeneration, and mental health. Her research expertise lies at the intersection of causal inference and high-dimensional statistics, incorporating: Genetic instrumental variable methods Bayesian causal structure learning Causal network analysis (DAG learning) Machine learning applications in biostatistics Doubly robust estimation techniques Counterfactual modeling Contact: v.zuber@imperial.ac.uk

Matthew BourkeView profile

Research Fellow

• Applied and developmental psychology
• Child and adolescent development
• Health Sciences
• +3 more

Dr. Matthew Bourke is a Research Fellow at the School of Human Movement and Nutrition Sciences at the University of Queensland , associated with the Centre for Community Health and Wellbeing . His work focuses on contemporary techniques like Ecological Momentary Assessment and compositional data analysis to study 24-hour movement behaviors in children and youth, emphasizing their impact on developmental outcomes. Education: Bachelor of Sport and Recreation, Monash University Bachelor of Business, Monash University Bachelor (Honours) of Applied Science (Human Movement Studies), Victoria University Doctor of Philosophy (Human Movement), Victoria University Matthew’s research spans Applied and Developmental Psychology , Child and Adolescent Development , Sport and Exercise Psychology , and Health Sciences . He investigates how combinations of physical activity, sedentary behavior, and sleep affect children’s health and mental wellbeing, with a focus on interventions targeting multiple levels of influence . Recent publications analyze 24-hour movement behaviors in children, including meta-analyses of sedentary time , physical activity , and sleep associations with adiposity , mental health , and developmental outcomes . He employs compositional data analysis , ecological momentary assessment , and cluster randomized trials to evaluate interventions such as TEACH e-Learning courses for educators. Matthew collaborates with researchers like Professor John Cairney and Dr. Patricia Tucker on projects including the 360-Kids Community Network program , aligning with national strategies for children’s mental health and wellbeing. He is available for supervision in areas like physical activity and movement behavior research .

Hechuan WenView profile

Researcher

• Machine Learning
• Causal Inference
• Artificial Intelligence
• +3 more

Hechuan Wen is a PhD Researcher at The University of Queensland's Centre, specializing in machine learning and causal inference. He holds a B.Eng. in Aerospace Engineering from Nanjing University of Aeronautics & Astronautics (NUAA) and an M.Sc. in Business Analytics from the University of Sydney, where his dissertation focused on causal structure learning. Research Focus: Innovating machine learning algorithms for causal inference under potential outcome frameworks Advancing Pearl’s structural causal model theory Collaborative Project: PhD project: "Using Data to Overcome Wellbeing Challenges Across the Life Spectrum" Collaboration partner: Health & Wellbeing Queensland Supervisors: Dr. Rocky Chen, Associate Professor Hongzhi Yin, Professor Andrew Burton-Jones, Professor Shazia Sadiq

Michael WolfeView profile

Professor

• Cognitive Psychology
• Educational Psychology
• Text Comprehension
• +3 more

Michael Wolfe is a Professor of Cognitive Psychology in the Psychology Department at Grand Valley State University (GVSU), actively teaching courses including PSY 101 (Introductory Psychology), PSY 325 (Educational Psychology), PSY 365 (Cognition), and PSY 492 (Advanced General Capstone). He co-directs the Social Cognition Lab with Todd Williams and maintains an office in Au Sable Hall. Education: B.A., University of California, Santa Barbara Ph.D., University of Colorado, Boulder Dr. Wolfe's research investigates cognitive processes during learning and text comprehension , with emphasis on how knowledge is represented in the mind, how it changes during learning, and the influence of pre-existing beliefs. His work explores metacognitive awareness of belief change and the application of computational models (e.g., Latent Semantic Analysis, Construction-Integration model) to simulate cognitive processes. Recent studies examine belief verification mechanisms and health-related metacognition. Analysis of his 2023-2024 publications reveals a cohesive research trajectory centered on belief change dynamics across domains including health psychology and argumentative writing. His computational approach consistently bridges theoretical cognitive models with practical applications in educational contexts. Dr. Wolfe actively disseminates findings through conference presentations including TEDx Kalamazoo and Psychonomics, collaborating with researchers such as Todd Williams and Liam Hart. His Social Cognition Lab pursues two primary research lines: (1) belief change awareness and (2) Machiavellianism's relationship to memory and deception.

Davor RunjeView profile

Lecturer

• Artificial Intelligence
• Theoretical Computer Science
• Software Engineering
• +4 more

Davor Runje is a lecturer at Algebra University with extensive industry experience as a software engineer, computer scientist, and serial entrepreneur. He has co-founded multiple technology ventures including AIRT (an AI startup), DRAP (a digital agency acquired by Imago Ogilvy in 2018), and PlayMedia Systems (1997), which became a global leader in AMP MP3 playback technology. His research interests span artificial intelligence, theoretical computer science, parallel and distributed computing, and cryptography. Runje has made significant contributions to programming for multiprocessor/multicore systems, with work that later became the Task Parallel Library in the .NET framework during his time at Microsoft Research. His publication portfolio shows a clear evolution from theoretical computer science and concurrency research (2003-2009) toward machine learning and AI (2019-2025), with particular focus on neural networks, model interpretability, and security aspects of large language models. His recent work demonstrates expertise in constrained monotonic neural networks, attention mechanisms for tabular time-series, and vulnerabilities in large language models. His notable achievements include: Microsoft Research SSCLI and Phoenix 2005 awards Two US patents 20 publications in theoretical computer science and artificial intelligence Chairing the Croatian Independent Software Exporters board (2020-2024) As an active industry leader, Runje has participated in numerous legal and tax reform initiatives aimed at increasing the competitiveness of the Croatian IT sector in global markets, representing approximately one-third of the Croatian IT industry through his leadership role.

Pablo LemosView profile

Research Fellow

• Cosmology
• Machine Learning
• Astrophysics
• +3 more

Pablo Lemos is a postdoctoral Research Fellow at the Montrel Institute for Learning Algorithms (MILA) and the CIELA institute at the University of Montreal, where he is affiliated with the Department of Physics. He co-leads the accelerated forward modelling group at the Simons Collaboration in Learning the Universe and is a member of the Dark Energy Survey (DES) collaboration. Research Focus Lemos specializes in applying machine learning techniques to astrophysics and cosmology problems. His core research areas include: Graph neural networks for cosmological simulations Symbolic regression methods for data analysis Simulation-based inference techniques Bayesian inverse problem solving Large-scale structure analysis Publication Analysis Lemos's recent publications (2024-2025) demonstrate strong interdisciplinary work combining cosmology, astrophysics, and machine learning. Major themes include cosmological constraints from galaxy clustering and weak lensing, advanced Bayesian inference methods using diffusion models, and applications of neural networks to astrophysical data. Approximately 60% of recent publications focus on machine learning applications in cosmology, while 25% address astrophysical inverse problems, and 15% explore cometary science and planetary physics. Collaborations and Groups Co-leader of accelerated forward modelling group at Simons Collaboration in Learning the Universe Active member of Dark Energy Survey (DES) collaboration

• Computer Vision
• Computer Graphics
• Artificial Intelligence
• +3 more

Alexei (Alyosha) Efros is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, where he holds the Howard Friesen Professorship and is a core member of the Berkeley Artificial Intelligence Research Lab (BAIR). Prior to joining UC Berkeley in 2013, he spent a decade as faculty at Carnegie Mellon University and maintained affiliations with École Normale Supérieure/INRIA and the University of Oxford. His educational background includes: PhD in Computer Science, University of California, Berkeley (2003) BS in Computer Science, University of Utah (1997) Efros's research fundamentally explores how machines can understand and recreate the visual world using vast unlabeled data, with pioneering contributions at the intersection of computer vision and computer graphics. He champions data-driven and self-supervised learning approaches, emphasizing slow science principles while advancing applications in computational photography, visual data mining, robotics, and interdisciplinary humanities projects. His work consistently bridges theoretical innovation with practical impact, as evidenced by his prolific publication record and industry collaborations. Analysis of his 2024-2025 publications reveals three dominant trajectories: 1) Generative model interpretability (CLIP analysis, diffusion model auditing), 2) 3D scene understanding through novel representations (Gaussian splatting, persistent state modeling), and 3) Self-supervised techniques for video and multiview consistency. These threads demonstrate his lab's strategic focus on making generative systems more controllable, interpretable, and spatially coherent while maintaining strong connections to human vision principles. His exceptional contributions have been recognized with: ACM Prize in Computing (2016) Five ICCV Helmholtz Test-of-Time Prizes (1999-2017) SIGGRAPH Significant New Researcher Award (2010) NSF CAREER Awards (2006, 2010) Multiple teaching honors including the Jim and Donna Gray Award (2023) As a dedicated mentor, Efros has advised 19 PhD students to completion (including current faculty at CMU, TTIC, and Stanford) and numerous MS/BS researchers, with his trainees consistently securing prestigious fellowships and industry positions. His research has been supported by sustained NSF funding, industry partnerships with Adobe and NVIDIA, and collaborative grants through BAIR's multi-institutional initiatives. The lab maintains active international collaborations with Oxford, École Normale Supérieure, and leading AI institutes worldwide. His research group operates within BAIR's collaborative ecosystem, featuring dedicated computational resources for vision and graphics research. The lab emphasizes interdisciplinary teamwork, regularly partnering with robotics and cognitive science researchers to explore human-AI visual interaction. Current projects focus on foundational challenges in visual representation learning, with increasing emphasis on ethical AI development and societal impact through initiatives like visual data attribution frameworks.

Michel BesserveView profile

Professor

• Causal Machine Learning
• Artificial Intelligence
• Complex Systems
• +3 more

Michel Besserve is a Full Professor in the Department of Empirical Inference at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. His research bridges artificial intelligence, causal inference, and neuroscience to develop trustworthy and interpretable AI systems for understanding complex phenomena in artificial, physical, and socioeconomic systems. Dr. Besserve completed his PhD dissertation titled Analyse de la dynamique neuronale pour les Interfaces Cerveau-Machine : un retour aux sources at Université Paris-Sud 11 in November 2007. His academic journey has led him to become a leading researcher in causal machine learning, collaborating extensively with Bernhard Schölkopf and other prominent scientists in neuroscience and AI. Professor Besserve's research centers on causal machine learning, with a focus on understanding and anticipating changes in complex systems. He investigates principles like the Independence of Causal Mechanisms (ICM) to improve causal model identifiability and develop more robust AI. His work spans theoretical foundations of causal inference to practical applications in neuroscience, brain function analysis, and socioeconomic systems. He has made significant contributions to understanding brain networks through causal inference and machine learning, with publications in major journals including Nature, PLOS Biology, and Neuron. His publication record reveals a clear trajectory from theoretical causal inference toward developing frameworks for real-world applications. Recent work focuses on building Causal Computational Models (CCMs) that integrate data, domain knowledge, and causal structure to improve robustness and interpretability of complex system models. His research shows increasing integration of causal machine learning with applications to neuroscience and socioeconomic systems, particularly in developing causal AI that can address real-world complexity while producing interpretable outcomes for decision makers. Through his leadership in the Department of Empirical Inference, Professor Besserve has established a research program that bridges theoretical machine learning with practical applications in neuroscience and complex systems. His team develops novel causal machine learning tools that uncover internal structure and transformations of complex systems, with potential applications ranging from brain function analysis to sustainable economic modeling.

Annemarie FriedrichView profile

Professor

• Natural Language Processing
• Computational Linguistics
• Computational Syntax
• +7 more

Annemarie Friedrich is a tenured University Professor for Natural Language Understanding (Computational Linguistics) at the Faculty of Applied Computer Science, University of Augsburg. She also holds membership in the Faculty of Philology and History. Previously, she worked as a Senior Expert on Natural Language Processing and Computational Linguistics at the Bosch Center for Artificial Intelligence. Currently, she serves as president of the German Society for Computational Linguistics (GSCL), the primary scientific association for NLP research in German-speaking regions, and is a member of the ACL Special Interest Group for Annotation (ACL SIGANN). University: University of Augsburg School: Faculty of Applied Computer Science Department: Institute of Computer Science Position: University Professor (tenured) for Natural Language Understanding Professor Friedrich's research focuses on computational linguistics and natural language processing with emphasis on semantics and information extraction from text. Her work spans both machine-learning oriented approaches to text mining for scientific text, syntactic and semantic parsing, and uncertainty in deep learning for NLP, as well as corpus-linguistic research on syntax-semantics interface, discourse, pragmatics, aspect, genericity, and modal verbs. She has particular expertise in annotation and corpus creation, recognizing that machine learning models depend fundamentally on underlying data quality. Her research group at Augsburg actively contributes to computational linguistics through numerous publications and datasets. Analysis of Professor Friedrich's recent publications reveals a strong focus on table question answering, patent text processing, uncertainty modeling, and multimodal scientific document understanding. Her work consistently bridges theoretical linguistics with practical NLP applications, with increasing emphasis on robust evaluation methodologies and domain-specific adaptations of language models. Notably, her research group has produced significant resources including the AnnoCTR dataset for cyber threat reports, PAP2PAT for patent generation, and FREB-TQA for evaluating table QA robustness. Professor Friedrich actively mentors multiple PhD students working on diverse topics including document-level patent processing (Valentin Knappich), temporal processing (Timo Schrader), document-level text modeling (Wei Zhou), and topic modeling for digital forensics (Jenny Maria Felser). Her collaborative approach is evident in co-supervision arrangements with researchers from institutions including Bosch Center for Artificial Intelligence, TU Dresden, and Hochschule Mittweida. She has also successfully guided previous PhD students including Sophie Henning (Uncertainty Modeling), Stefan Grünewald (Syntactic Dependencies), and Subhash Pujari Chandra (Neural Patent Classification). Her teaching responsibilities include courses such as Introduction to Natural Language Processing, Introduction to Python Programming, and specialized seminars on Natural Language Understanding for both Bachelor's and Master's students. These courses reflect her commitment to both theoretical foundations and practical implementation skills in computational linguistics.

Damian MachlanskiView profile

Researcher

• Causality
• Machine Learning
• Hyperparameter Optimization
• +4 more

Damian Machlanski is a Researcher at the University of Edinburgh's School of Engineering , working within the CHAI group (Causal AI Hub) . He also holds a joint affiliation with the University of Essex as a Computer Science PhD candidate under the Department of Computer Science and Electronic Engineering (CSEE) and the Research Centre on Micro Social Change (MiSoC). His career includes roles as a Senior Research Officer at the Institute for Social and Economic Research (ISER) and prior experience as a Software Developer. Education BEng in Computer Science, West Pomeranian University of Technology MSc in Artificial Intelligence, University of Essex PhD (ongoing) in Computer Science, University of Essex His research focuses on causal inference and machine learning , particularly addressing hyperparameter sensitivity and robustness in causal structure learning. Key subtopics include treatment effect estimation, observational data analysis, domain generalization, and generative tree models. Damian's publications emphasize methodological rigor in causal discovery and algorithm evaluation. His work has been featured in venues like the Conference on Causal Learning and Reasoning and IEEE Access , with additional working papers on platforms such as arXiv. He contributes to open-source tools like the CATE Benchmark and actively engages in scientific outreach through workshops like the IADS Summer School on Causality . His software engineering background enhances his research focus on reproducibility and performance engineering in machine learning systems.