جستجوی استاد راهنمای دانشگاهی

• Predictive Maintenance
• Interactive Machine Learning
• Causal Inference
• +۳ مورد دیگر

Sepideh Pashami is an Associate Professor in Machine Learning at Halmstad University's School of Information Technology and a Senior Lecturer at the Center for Applied Intelligent Systems Research . She also serves as a Senior Researcher at RISE Research Institutes of Sweden . PhD in Machine Learning from Örebro University (2016) Technology Area Leader for Aware Intelligent Systems since 2020 Research Interests focus on predictive maintenance, interactive machine learning, causal inference, and representation learning. Her work addresses challenges in: Automotive fault diagnosis Survival analysis for time-to-event prediction Domain adaptation with limited data Explainable AI for industrial systems Feature selection optimization Graph neural networks for anomaly detection Scientific Contributions span 15 recent publications covering: Predictive maintenance for hybrid buses and heavy-duty vehicles Explainable AI frameworks (SurvSHAP, knowledge-based systems) Domain adaptation with geometric constraints Concordance index optimization in survival analysis Feature selection using genetic algorithms Multi-stream regression for industrial data Industry Collaboration includes extensive work with Volvo Group AB on projects like FREEPORT, EVE, In4Uptime, ARISE, and HEALTH. She has served on organizing committees for leading conferences: ECAI 2023, DSAA 2021, ECML/PKDD 2019-2022, and IJCAI-ECAI 2018 industry days.

Anders Munk-Nielsenمشاهده پروفایل

دانشیار

• Empirical Industrial Organization
• Structural Microeconometrics
• Health Economics
• +۵ مورد دیگر

Anders Munk-Nielsen is an Associate Professor in Economics at the Department of Economics, University of Copenhagen, with a focus on Empirical Industrial Organization and Structural Microeconometrics . He is a core member of the Centre for Computational Economics , applying computational methods to analyze pharmaceutical, car, and housing markets. Education : PhD in Economics (2015) and MSc in Economics (2013), both from the University of Copenhagen. His research spans Environmental Economics , Health Economics , and Transportation Economics , emphasizing Household Demand and Discrete Choice . Recent work includes structural models of automobile markets with secondary market complementarities, Danish pharmaceutical price cycles, and income dynamics analyzed via regression trees. Publications include high-impact journal articles in Journal of Political Economy , Journal of Urban Economics , and Economics Letters , alongside working papers on electric vehicle adoption and spatial competition in pharmacies. Articles reflect a blend of Computational Economics and Policy Analysis . Scientific Awards : SAS Analytics Specialepris (2014) Zeuthenprisen (2013) Junior Researcher Paper Prize of ITEA (2014) He actively supervises students, offering guidance on thesis topics, project management, and AI tools. His teaching includes Advanced Microeconometrics , Advanced Industrial Organization , and Game Theory . He has participated in international seminars at Yale, AEA/ASSA, and EEA/ESEM.

Sarra Alqahtaniمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Multi-Agent Reinforcement Learning
• Explainable AI
• +۳ مورد دیگر

Dr. Sarra Alqahtani is an Associate Professor in the Computer Science Department at Wake Forest University. With a Ph.D. from the University of Tulsa (2015) and a two-year postdoc there, she focuses on safety and explainability in reinforcement learning (RL) and multi-agent RL (MARL), particularly for environmental applications. NSF CRII grant for safe MARL research NASA grant for drone navigation in Amazonian rainforests Her work addresses adversarial vulnerabilities in RL systems, develops robust safety policies, and combines local/global explanations to enhance AI interpretability. She applies these techniques to autonomous vehicle platoons, environmental monitoring, and illegal gold mining detection. Recent publications appear in top venues like AAMAS, IJCAI, and Nature. Research emphasizes transitioning MARL algorithms from simulations to real-world deployment while ensuring safety and reliability. Email: sarra-alqahtani@wfu.edu | Personal Site

Vinod Kumar Chauhan Kumarمشاهده پروفایل

مدرس

• Causality
• Healthcare
• Artificial Intelligence
• +۵ مورد دیگر

Vinod Kumar Chauhan Kumar is a Strathclyde Chancellor's Fellow in AI (Lecturer rank) in the Faculty of Science at the University of Strathclyde . He concurrently holds a Visiting Scholar position and the MPLS Enterprise and Innovation Fellowship (2025–26) at the University of Oxford . Education: PhD in Optimisation for Large-Scale Machine Learning, Panjab University, Chandigarh (Awarded 2019) Supported by University Grants Commission’s JRF and SRF Fellowships Research Focus: Dr. Chauhan pioneers data-driven Causal AI for personalized healthcare, bridging Causality , Healthcare , and Artificial Intelligence . His work develops novel methods for treatment effect estimation, bias correction in healthcare ML, and graph-based EHR analysis, with applications in gastric disease prediction and industrial optimization. The UN Sustainable Development Goals framework guides his healthcare impact initiatives. Publication Trends: Recent work (2023-2025) demonstrates three converging thrusts: (1) causal inference frameworks for composite treatments/outcomes in healthcare, (2) graph neural networks for EHR analysis (e.g., CliqueFluxNet), and (3) industrial AI applications. Over 30% of his publications address healthcare bias challenges, while 25% focus on handwriting recognition for Indic scripts. Scientific Recognition: Institute for Manufacturing Postdoctoral Award for Research Excellence (2021) Student Travel Grant Award (2017) Academic Service: Dr. Chauhan actively mentors future researchers and currently seeks PhD students for causal AI projects. He has reviewed 200+ articles across 50+ venues and serves as Associate Editor for PLOS Digital Health and Editorial Board Member for the International Journal of Artificial Intelligence in Healthcare . His NeurIPS AI4Science Area Chair role highlights community leadership. Collaborative Impact: His research integrates academic rigor with real-world validation through partnerships with clinicians and industry leaders including Boeing and Rolls-Royce, developed during six years of postdoctoral work at Oxford and Cambridge.

• Logic
• Epistemology
• Quantum Logic
• +۷ مورد دیگر

Sonja Smets is a full professor of Logic and Epistemology at the Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam, holding a double affiliation between the Faculty of Science and the Faculty of Humanities (Department of Philosophy), with each faculty accounting for 50% of her employment. She joined the ILLC in 2012 and served as its scientific director from June 2016 until July 2021. Previously, she held a Rosalind Franklin Fellowship at the University of Groningen (2008-2012) and conducted postdoctoral research at the Center for Logic and Philosophy of Science in Brussels. Her research spans multiple domains at the intersection of logic, philosophy, and computer science. Smets has made significant contributions to quantum logic, developing logical frameworks for understanding quantum information and computation. She has pioneered work in dynamic epistemic logic applied to social networks, belief revision, and information flow. Her recent work increasingly addresses the philosophical and logical foundations of artificial intelligence, particularly focusing on AI ethics, fairness, and the societal impact of algorithmic decision-making systems. Smets has received numerous prestigious awards recognizing her interdisciplinary contributions, including the Hermann Lotze Prize (2015) and the Birkhoff-Von Neumann Prize (2012). She has secured major research funding through a VIDI award from the Netherlands Science Foundation (2009) and an ERC starters grant for the LogiCIC project (2011-2016). Currently, she serves as co-PI of the University-wide Research Priority Area on Human(e) AI (2019-2023) at the University of Amsterdam, which examines societal consequences of AI development. Corresponding member of Academie Internationale de Philosophie des Sciences (2021) Fellow of Asia-Pacific Artificial Intelligence Association (2021) Member of Academia Europaea (2019) Hermann Lotze Prize (2015) Birkhoff-Von Neumann Prize (2012) Rosalind Franklin Fellowship VIDI award from Netherlands Science Foundation ERC starters grant Smets has been instrumental in building research communities, co-organizing numerous workshops and conferences including the LogiCIC workshops, Quantum Logic workshops, and recent events on Reasoning about Responsible Agency in AI. She serves on multiple editorial boards including the Journal of Logic and Computation, Synthese, and Foundations of Science, and has held leadership positions in professional organizations such as FoLLI (Association for Logic, Language and Information) where she serves as vice-president. Her research group at the ILLC bridges formal logic with practical applications in AI, quantum computing, and social network analysis.

Thorsten Joachimsمشاهده پروفایل

استاد

• Machine Learning
• Counterfactual Learning and Evaluation
• Fairness in AI
• +۶ مورد دیگر

Thorsten Joachims is the Jacob Gould Schurman Professor of Computer Science and Information Science at Cornell University's Ann S. Bowers College of Computing and Information Science, where he serves as Interim Dean. He has held significant leadership roles including Associate Dean for Research and Chair of the Department of Information Science. His academic journey began with a Diplom in Computer Science from the University of Dortmund in 1997, followed by a PhD from the same institution in 2001. Before joining Cornell, he was a PostDoc at GMD's Institute for Autonomous Intelligent Systems and spent time as a visiting scholar at Carnegie Mellon University. Joachims' research focuses on machine learning methods and theory, particularly learning from human behavioral data and implicit feedback, and machine learning applications for search engines, recommendation systems, education, and other human-centered tasks. His work bridges theoretical foundations with practical applications, emphasizing counterfactual evaluation and learning, fairness in AI, ranking algorithms, and bandit methods. His research has significant implications for how we build AI systems that interact with humans in meaningful ways while maintaining ethical standards. The 15 most recent publications demonstrate a strong trajectory toward increasingly complex AI systems, with recent work focusing on large language models, multi-scale learning for long-term objectives, fairness in ranking under uncertainty, and novel approaches to recommendation systems using language-based user profiles. His research group consistently publishes in top venues across machine learning, information retrieval, and AI ethics. ACM Fellow AAAI Fellow Humboldt Fellow SIGIR Academy Member 11 Best Paper Awards 4 Test-of-Time Awards Professor Joachims has advised over 25 PhD students and postdocs who have gone on to successful careers in academia and industry. His research has been supported by multiple NSF grants, including projects on counterfactual learning, fairness in ranking, and AI systems with steerable long-term dynamics. He has been instrumental in establishing Cornell's AI Initiative and Radical Collaboration, positioning the university at the forefront of responsible AI development. His laboratory work focuses on developing machine learning systems that can learn effectively from human behavioral data while addressing critical issues of fairness, transparency, and long-term impact. The group maintains several open-source software projects including Propensity SVM-Rank, POEM, and SVM-struct that have been widely adopted in both research and industry.

Tamir Hazanمشاهده پروفایل

دانشیار

• Machine Learning
• Theoretical Machine Learning
• Computer Vision
• +۶ مورد دیگر

Associate Professor Tamir Hazan is a faculty member at Technion - Israel Institute of Technology, where he joined in 2015. His research focuses on theoretical and practical aspects of machine learning, with applications spanning computer vision, natural language processing, and computational biology. His work bridges mathematical foundations with real-world problem solving in complex systems. Professor Hazan received his Ph.D. from the Hebrew University in 2009. His academic trajectory has established him as a leading researcher in machine learning theory and its applications, with a particular emphasis on developing mathematically rigorous approaches to modern AI challenges. Professor Hazan's research centers on mathematically founded solutions to problems demonstrating non-traditional statistical behavior. His work encompasses perturbation models for efficient learning of high-dimensional statistics, deep learning of infinite networks, and primal-dual optimization for high-dimensional inference problems. His research program spans three major interconnected areas: attention models that improve prediction interpretability, perturbation frameworks that integrate optimization and sampling through extreme value statistics, and convex duality approaches to message-passing in graphical models. His work demonstrates both theoretical depth and practical relevance across multiple domains. Analysis of Professor Hazan's recent publications reveals an evolving research trajectory with increasing emphasis on interpretable machine learning, causal modeling, and applications in medical imaging and behavioral science. His work consistently bridges theoretical foundations with practical implementations, with recent publications showing strong connections between perturbation theory, attention mechanisms, and optimization frameworks. The interdisciplinary nature of his research is evident in applications ranging from pedestrian navigation using smartphone sensors to video-text matching systems and medical image analysis. Professor Hazan has mentored numerous students throughout his career, including: Alex Schwing, now Assistant Professor at UIUC Alon Cohen, now Associate Professor at Tel Aviv University Idan Schwartz, currently Postdoc at Tel Aviv University Current Ph.D. students: Guy Lorberbom, Itai Gat, and Hedda Cohen Multiple M.Sc. students including Adi Manos, Ram Yazdi, and others Professor Hazan's research group maintains an active program with several key focus areas: Attention models for interpretable and improved prediction processes in visual question answering and multimodal applications Perturbation models that enable efficient statistical reasoning in complex systems with exponential configuration spaces Markov random fields, convex duality, and message-passing algorithms for structured prediction and distributed computing

• Grammar Didactics
• Language Reflection
• Semantics
• +۴ مورد دیگر

Helga Gese is a Senior Lecturer at the Department of German Linguistics Didactics, University of Tübingen, focusing on grammar didactics, language reflection, semantics, and pragmatics. She leads the DFG network "i-Funktionen sprachlicher Zeichen im Kontext der Sprachreflexion" and co-leads the project "Praktiken der Wissenschaftskommunikation und die Dynamik des Common Ground" within SFB 1718. Her research bridges theoretical linguistics with pedagogical applications, particularly in adjectival passives and word formation. Education: Second State Exam in German, Philosophy/Ethics, French (2016); PhD in Philosophy (2013) with dissertation on adjectival passives; First State Exam in German, Philosophy (2005). Her research interests include grammar didactics, language reflection, semantics, pragmatics, and sociolinguistics. Recent publications analyze modality, tense semantics, and word formation in educational contexts. She has supervised numerous student theses on topics like language ideologies, corpus studies, and digital learning platforms. Gese actively organizes conferences and workshops on linguistic and didactic innovations. Current teaching includes advanced courses on linguistic variation, sociolinguistics, and grammar didactics for M.Ed. and B.Ed. students. She also leads the "Grammatikpropädeutikum" program for transitioning students into German studies.

Ivana Malenicaمشاهده پروفایل

استادیار

• Causal inference
• Non- and semi-parametric statistics
• Efficiency theory
• +۶ مورد دیگر

Ivana Malenica, PhD, is a tenure-track Assistant Professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill (UNC Gillings School of Global Public Health). Previously, she was a Wojcicki-Troper Data Science Fellow at the Harvard Data Science Initiative and a Postdoctoral Fellow in the Department of Statistics at Harvard University. She earned her Ph.D. in Biostatistics from UC Berkeley in 2022. Education: Ph.D. in Biostatistics, University of California, Berkeley (2022) B.S. in Mathematics, Arizona State University (2013) Research Interests: Dr. Malenica’s methodological research lies at the intersection of causal inference , non- and semi-parametric statistics , efficiency theory , and machine learning . She focuses on challenges in longitudinal and structured dependent data , including adaptive sequential experiments , online learning , reinforcement learning , and applications in personalized and public health. Scientific Awards & Fellowships: Harvard Data Science Initiative (HDSI) Fellowship, Harvard University (2022) Berkeley Wellness Letter Fellowship, UC Berkeley (2020) Wellness Scholarship in Honor of Chin Long Chiang, UC Berkeley (2019) Moore-Sloan Data Science Fellowship, Berkeley Institute for Data Science (2018) Teaching & Courses: In Fall 2025, Dr. Malenica will teach BIOS 760: Advanced Probability and Statistical Inference I at UNC Chapel Hill. Open-Source Software & Leadership: She is an active contributor to the tlverse ecosystem, leading development of R packages such as medltmle (longitudinal mediation analysis), sl3 (super-learning pipelines), origami (cross-validation engine), and tmle3mopttx (optimal individualized treatment estimation).

Maxime Tullingمشاهده پروفایل

استادیار

• Neurolinguistics
• Language Acquisition
• Syntax-Semantics-Pragmatics Interface

Maxime Tulling is an Assistant Professor at the Department of Linguistics and Translation, Faculty of Arts and Sciences, University of Montreal. She is affiliated with the Centre for Research on Brain, Language and Music (CRBLM) and employs diverse experimental methodologies including EEG/MEG, eye tracking, and behavioral paradigms in her research. Current projects focus on neurolinguistic processing of abstract language Specializes in first language acquisition and discourse semantics Collaborates with international research teams at NYU, UCSC, and UMD Uses corpus analysis and electrophysiological measures Her research investigates neural mechanisms underlying the processing of modal expressions, counterfactuals, and discourse particles. Recent projects explore how children develop the ability to interpret hypothetical scenarios and how social interaction influences language learning. Maxime's work reveals distinct neural patterns in processing factual vs. non-actual information, with implications for understanding the syntax-semantics-pragmatics interface. Scientific projects include: Étudier les propriétés représentationnelles de l’expression linguistique de la possibilité (2023-2026) From Repeated Words to Grammar: Brain-based language learning mechanisms (2024-2030) Discourse updating in infants: Social interaction's role in sound learning (2024-2027) She has secured grants from the Social Sciences and Humanities Research Council (SSHRC) and the Fonds de recherche du Québec (FRQSC, FRQNT). Her publications span topics including temporal reference in counterfactuals, prosody of rhetorical questions, and modal flavor distinctions in language acquisition.

Gill Althia Francisمشاهده پروفایل

مدرس

• Developmental Psychology
• Cognitive Psychology
• Educational Psychology
• +۳ مورد دیگر

Dr Gill Althia Francis is a Lecturer in Psychology in Education at the University of York. She is a developmental cognitive psychologist specializing in play and neurodevelopmental diversity, with a focus on autism spectrum disorder and developmental language disorders. Her research employs quantitative methods like latent variable modeling and meta-analysis. Education: PhD in Education, University of Cambridge MPhil in Education and Psychology, University of Cambridge BEd in Educational Testing Measurement and Evaluation, University of West Indies Research Interests: Explore intersections of play, imagination, and cognition in neurodiverse children. Investigates technology's role in inclusive education and uses advanced statistical methods to analyze developmental trajectories. Publications: Key contributions in meta-analytic frameworks for play-based interventions, EdTech efficacy for neurodiverse learners, and the role of imagination in cognitive development. Work spans journals like Autism & Developmental Language Impairments and British Journal of Psychology . Awards: Leverhulme Trust Early Career Fellowship (2021-24) York Open Research Award (2022) White Rose ESRC Postdoctoral Fellowship (2020-21) Collaborations: Works with Dr Umar Toseeb (CANDY Group) and Professor Kathryn Asbury (GOALS Group). Previously collaborated with EdTech Hub at Cambridge.

Stergios-Aristoteles Mitoulisمشاهده پروفایل

دانشیار

• Infrastructure Resilience
• Sustainable Construction
• Digital Transformation of Built Environment
• +۵ مورد دیگر

Stergios-Aristoteles Mitoulis serves as Associate Professor in Engineering and Project Management at University College London's Bartlett School of Sustainable Construction. A Chartered Engineer (CEng MICE) with memberships in ASCE, EAEE, IABSE and FHEA, he directs the MetaInfrastructure.org and BridgeUkraine.org initiatives while coordinating major EU-funded projects including ZEBAI (€5M), RISKADAPT (€2.5M), and PORTAL (€1.8M). His research centers on three pillars: (1) threat-agnostic resilience frameworks for infrastructure under cascading risks; (2) AI-driven decision systems integrating remote sensing and digital twins; and (3) sustainable retrofitting of transport assets using recycled materials. With over £8 million in research funding from UKRI and Horizon Europe, his work directly informs Eurocode standards and national infrastructure policies. Analysis of his recent publications reveals strong emphasis on multi-hazard fragility modeling (particularly bridges), AI-enabled damage identification, and climate-resilient transport networks. His work consistently bridges theoretical innovation with practical implementation, evidenced by 20+ peer-reviewed papers in 2024-2025 alone spanning reliability engineering, structural safety, and sustainable development. Thorpe Medal for outstanding engineering research IStructE Research Award for infrastructure innovation Editor-in-Chief of ICE Journal of Bridge Engineering Regular contributor to Eurocode standardisation committees Mitoulis actively supervises 40+ doctoral and postdoctoral researchers across 10+ international projects. His REF2029 Impact Case Study focuses on digital technologies for infrastructure resilience, while his £8M+ grant portfolio includes major collaborations with Ukrainian institutions through bridgeUkraine.org. He leads the MetaInfrastructure research group developing counterfactual engineering approaches for the metaCity concept, with recent media coverage by BBC on conflict-damaged infrastructure recovery. The MetaInfrastructure team operates through dedicated labs for digital twin development, AI/ML applications, and sustainable materials testing. Current initiatives include the PORTAL project for AI-driven infrastructure monitoring and ReCharged for circular economy in transport networks. Mitoulis' educational contributions feature a FutureLearn MOOC on critical infrastructure resilience and CPD training for ICE professionals.

• Statistical Modeling
• Longitudinal Data Analysis
• Intervention Evaluation
• +۴ مورد دیگر

Prof. Dr. Axel Mayer is a Professor of Psychological Methods and Evaluation at the Faculty of Psychology and Sports Science, University of Bielefeld. His research focuses on advanced statistical modeling for longitudinal data, causal mediation analysis, and developing user-friendly statistical software packages like lsttheory and EffectLiteR . He investigates intervention effectiveness in psychotherapy and health contexts, particularly fatigue in students and cancer survivors, and contributes to latent state-trait models for experience sampling data. Methodological Expertise: Latent growth curve modeling, causal mediation, structural equation modeling Applied Research: Health-related quality of life, educational development, stress response validation Key projects include involvement in the interdisciplinary InChangE initiative studying individualization processes across disciplines, and contributions to the Deutsche Hodgkin-Studiengruppe cancer fatigue research. His recent publications emphasize software tutorials and comparative studies in mental health, education, and public health interventions. Current trends show focus on digital mental health interventions , cross-cultural comparisons , and ecological momentary assessment methodologies . He actively develops R packages for advanced statistical techniques and investigates mediators in clustered trial designs. Axel Mayer serves as Pro-Dean for Research and chairs committees at Bielefeld University. He's President of the European Association of Methodology (2018-2021) and member of the Psychometric Society.

Bertille PICARDمشاهده پروفایل

استادیار

• Econometrics
• Machine Learning
• Policy Evaluation
• +۳ مورد دیگر

Bertille PICARD is an Assistant Professor (tenure track) in economics at CREST-ENSAI since September 2024. Her academic appointment positions her at the intersection of quantitative methods and economic policy analysis within the National School of Statistics and Information Analysis framework. Her research program bridges econometrics and machine learning methodologies, with specific applications in policy evaluation and the study of economic inequalities . This interdisciplinary approach reflects current methodological innovations in empirical economics where traditional econometric techniques integrate with modern computational approaches. Analysis of her limited publication record shows emerging work in digital economy applications, particularly examining how online platforms can improve skills signaling mechanisms. Her methodological focus suggests growing contributions to treatment effect estimation literature using adaptive experimental designs. As a recent PhD graduate from Aix-Marseille School of Economics, her doctoral research investigated machine learning's strengths and limitations for treatment effect and counterfactual estimation, establishing her technical foundation in both economic theory and computational methods. Professional activities indicate active engagement with the CREST research ecosystem, though specific advising relationships, grant funding, or laboratory affiliations aren't documented in the available materials. Her GitHub presence shows technical proficiency in maintaining academic digital infrastructure, including website development and research documentation.

• Data analytics
• Causal machine learning
• Policy evaluation
• +۶ مورد دیگر

Anthony Strittmatter is a Professor of Applied Econometrics at UniDistance Suisse in Brig/Valais, Switzerland. He maintains significant research affiliations with the Center for Research in Economics and Statistics (CREST), the University of Johannesburg, the CESifo Network, the Institut des Politiques Publiques (IPP), and the Swiss Institute for Empirical Economic Research (SEW-HSG). His academic career spans multiple prestigious institutions across Europe and focuses on the intersection of econometrics, machine learning, and economic policy analysis. Strittmatter received his Diploma in Economics from Albert-Ludwigs University Freiburg in 2009, following studies at Albert-Ludwigs University Freiburg, University Karlsruhe (TH), and Corvinus University Budapest. He continued his academic journey at the University of St. Gallen (HSG), where he earned his Ph.D. in Economics and Finance in 2013 under the supervision of Prof. Bernd Fitzenberger and Prof. Dr. Michael Lechner. His research focuses on the innovative integration of causal machine learning with traditional econometric methods to address complex policy evaluation questions. Strittmatter specializes in analyzing heterogeneous treatment effects, with particular applications in labor economics, business economics, and health economics. His methodological expertise in data analytics allows him to tackle challenging questions regarding optimal policy design and implementation using both experimental and non-experimental data. Analysis of Strittmatter's publication record reveals a consistent trajectory of methodological innovation applied to substantive economic questions. His work demonstrates a growing emphasis on causal machine learning techniques, particularly for estimating heterogeneous treatment effects. There's a clear pattern of applying these advanced methods to pressing policy issues, especially in labor market interventions, gender economics, and public policy evaluation. His recent work increasingly leverages big data approaches to address longstanding questions in economics with greater precision. While specific awards are not prominently listed in the available information, Strittmatter's extensive collaboration with leading researchers and his affiliation with prestigious research networks indicate significant recognition within the economics community. His work has been published in top-tier economics journals including the American Economic Review. Strittmatter actively contributes to the academic community through research collaborations and knowledge dissemination. His GitHub presence shows commitment to making research methods accessible through well-documented code repositories. His teaching focuses on causal machine learning, as evidenced by his CML-Course materials which provide practical coding sessions for economic applications. He leads and participates in research teams focused on causal inference methodology and its applications. His work with Michael Lechner, Uwe Sunde, and other collaborators demonstrates a strong commitment to advancing the methodological frontier in econometrics while maintaining relevance to real-world policy questions.