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Markus VenohrView profile

Researcher

• Nutrient fluxes
• River system modelling
• Water quality management
• +5 more

Dr. Markus Venohr is a Senior Scientist and Research Group Leader at the Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB) in Berlin, Germany, where he leads the River System Modelling research group within Department 1 (Ecohydrology and Biogeochemistry). He holds a PhD from Humboldt-University Berlin and has been with IGB since completing his doctorate, first as a Post-doc (2006-2008) and then as a Senior Scientist since 2009. Dr. Venohr received his PhD from Humboldt-University Berlin (2001-2005), following studies in Geography, Meteorology and Oceanography at Christian-Albrechts University Kiel (1996-2000), and initial Geography studies at Ruhr-University Bochum (1994-1996). His research focuses on nutrient fluxes from land to sea , nutrient transport and transformation , biotic-abiotic coupling , river system modelling , climate change and management adaptation , and recreation ecology . Dr. Venohr specializes in understanding how human activities impact freshwater systems, particularly through nutrient pollution from agricultural and urban sources, and develops models to assess and predict these impacts. His work bridges the gap between scientific research and practical water management solutions. Dr. Venohr's recent publications demonstrate a strong focus on nutrient modeling (particularly the AGRUM-DE framework), water quality assessment across scales from Berlin waterways to the Danube basin, and the impacts of human activities on freshwater ecosystems. His research increasingly addresses urban pollution sources, recreation ecology impacts, and climate adaptation strategies, with significant international collaborations evident in his work on topics ranging from the Kakhovka Dam destruction to nicotine contamination in surface waters. AQUATAG (2019-2022): Water-based recreational activities analysis BAUM (2015-2018): Climate adaptation measures in Brandenburg MARS (2014-2018): Multiple stressors in aquatic ecosystems OSCAR (2017-2020): Riparian buffer strips optimization RESI (2015-2018): Ecosystem services of rivers and floodplains He leads the River System Modelling group at IGB, which develops and applies advanced modeling approaches including MONERIS and AGRUM-DE to understand human impacts on freshwater systems. The group works closely with water management authorities across Germany and Europe to translate scientific findings into practical management solutions, particularly regarding nutrient pollution reduction and climate adaptation strategies.

Jonathan L. AuerbachView profile

Assistant Professor

• Data Science
• Causal Inference
• Survey Methods
• +6 more

Jonathan L. Auerbach is an Assistant Professor in the Department of Statistics at George Mason University . His work bridges statistics and public policy , focusing on causal inference , longitudinal data analysis , and official statistics . He has contributed to understanding urban myths (e.g., New York City rat populations, building height trends), election fraud , and policy evaluation for initiatives like Vision Zero. Education : PhD in Statistics (Columbia University, 2020), BA in Economics (Cornell University, 2010) Research Interests include data science , causal inference , survey methodology , and urban analytics . His recent work addresses climate change impacts on cherry blossom seasons, federal data security , and street vendor demographics in New York City. Scientific Awards include the 2024-2025 President of Washington Statistical Society , 2020-2021 American Statistical Association Science Policy Fellow , and 2019 Howard Levene Outstanding Teaching Award . He has served as an instructor for courses like Capstone in Statistics and Causal Inference , emphasizing technical communication and data ethics .

Laura T. PeriniView profile

Associate Professor

• Philosophy of Science
• Philosophy of Biology
• Scientific Representation
• +5 more

Laura T. Perini is an Associate Professor of Philosophy at Pomona College and serves as the Coordinator of the Science, Technology, and Society program. She has been a member of the faculty since 2007 and is on leave during Spring 2026. Her office is located in Pearsons Hall 204, and she can be reached at laura.perini@pomona.edu. Education: Ph.D., University of California, San Diego Master of Arts, University of California, San Diego Master of Arts, University of California, Los Angeles Bachelor of Science, University of California, Los Angeles Laura Perini's research centers on the philosophical analysis of visual representations in science. Her work explores how diagrams, graphs, photographs, and imaging technologies such as MRI contribute to scientific reasoning, education, and public communication. She investigates the epistemic and aesthetic dimensions of scientific visuals, addressing questions about truth, explanation, and confirmation in pictorial forms. Her interdisciplinary approach draws from philosophy of science, philosophy of biology, cognitive science, and aesthetics. Her publications reveal a sustained focus on the role of two-dimensional representations in scientific practice. Key themes include the semiotics of pictures, the cognitive function of diagrams, and the use of visual evidence in theory confirmation. Her work bridges abstract philosophical analysis with concrete examples from biological and medical sciences. Scientific Awards: Philosophy of Science Association prize for best paper published in Philosophy of Science by a recent Ph.D., 2006 Visiting Fellow, Dartmouth College Humanities Institute (2005–2006, Spring 2006) Virginia Tech Humanities Symposium Award, 2006 Fellow, Center for Philosophy of Science, University of Pittsburgh, Fall 2004 Virginia Tech Humanities Summer Stipend, Summer 2004 NEH Summer Institute “Art, Mind, and Cognitive Science”, Summer 2002 Laura Perini has taught a range of courses including Aesthetics, Logic, Philosophy of Science: Historical and Topical Surveys, and Philosophy of Biology. While there is no public information about graduate students she may have advised or research grants she has led, her contributions to the philosophy of scientific representation are significant and widely cited. She has held multiple prestigious fellowships, reflecting the impact and quality of her scholarly work.

Bo LiView profile

Associate Professor

• Artificial Intelligence
• Computer Vision
• Computer Graphics
• +2 more

Dr. Bo Li serves as an Associate Professor at the University of Southern Mississippi, where he teaches core computer science courses including Artificial Intelligence, Computer Graphics, and Database Management Systems. His academic foundation spans institutions across three countries, reflecting a globally oriented research perspective in visual computing and machine learning. His educational background includes: PhD in Computer Science from Nanyang Technological University (2012) MS in Computer Science from Texas State University (2015) MS in Computer Science from Xi'an Jiaotong University (2005) BS in Computer Science from Xi'an Jiaotong University (2005) Dr. Li's research centers on 3D shape retrieval systems, where he pioneers methods for sketch-based and image-based 3D model search. His work bridges computer vision, graphics, and machine learning through innovative approaches to 3D scene analysis, semantic modeling, and cross-modal translation. Recent investigations extend into social media analysis and speech emotion recognition, demonstrating methodological versatility within artificial intelligence. Analysis of his 15 most recent publications reveals a sustained focus on 3D shape retrieval benchmarking through SHREC competitions, evolving from traditional descriptor methods to deep learning frameworks. Key trends include multimodal query processing, large-scale dataset handling, and applications in real-world image denoising. His research consistently addresses challenges in partial/non-rigid model matching and semantic scene understanding. Dr. Li has not been documented with scientific awards in the provided information. Regarding academic mentorship and funding, no details about student supervision, research grants, or sponsored projects are available in the source material. Similarly, information about laboratory facilities, research teams, or collaborative groups is not provided in the current documentation.

• Machine Learning
• Data Visualization
• Cybersecurity
• +3 more

Duen Horng (Polo) Chau is an Associate Professor of Computing at Georgia Institute of Technology, where he co-directs the MS Analytics program. He serves as Director of Industry Relations for The Institute for Data Engineering and Science (IDEaS) and Associate Director of Corporate Relations for The Center for Machine Learning. Ph.D. in Machine Learning from Carnegie Mellon University His research group integrates machine learning and visualization to develop scalable interactive tools for analyzing massive datasets, interpreting AI models, and addressing challenges in cybersecurity, human-centered AI, graph visualization and mining, and social good. His work has led to open-sourced or deployed technologies by Intel (e.g., ShapeShifter, SHIELD), Google, Facebook, Symantec (Polonium, AESOP), and Atlanta Fire Rescue Department, with Symantec's AESOP protecting 120 million users from malware. Scientific Awards & Fellowships: Carnegie Mellon University Computer Science Dissertation Award (Honorable Mention) Intel Outstanding Researcher Award Raytheon Faculty Fellowship Edenfield Faculty Fellowship Outstanding Junior Faculty Award The Lester Endowment Award Symantec Fellowship (twice) Best Student Paper (SDM'14) Runner-up Best Student Paper (KDD'16) Runner-up Best Demo (SIGMOD'17) Chinese CHI'18 Best Paper ACM TiiS 2018 Best Paper (Honorable Mention) He has received research grants from NSF, NIH, NASA, DARPA, Google, NVIDIA, IBM, Yahoo, Amazon, Microsoft, eBay, and LexisNexis. His leadership roles include steering committee membership for ACM IUI, Associate Editor for ACM TIIS, and organizing chairs for ACM KDD'14, ACM WSDM'16, and the IDEA workshop at KDD.

Leila De FlorianiView profile

Professor

• Geometric Modeling
• Data Visualization
• Spatial Data Representation and Processing
• +6 more

Leila De Floriani is a Professor at the University of Maryland, with appointments in the Department of Geographical Sciences and the University of Maryland Institute for Advanced Computer Studies (UMIACS). She previously served as a professor at the University of Genova (Italy) since 1990, where she developed Italy's first undergraduate and graduate curricula in computer graphics and directed the Ph.D. program in Computer Science for eight years. Her professional activities include serving as the 2020 President of the IEEE Computer Society and currently as IEEE Division VIII Director for 2023-24. Professor De Floriani's research spans geometric modeling, data visualization, spatial data representation and processing, computer graphics, shape analysis, and topological data analysis. Her work focuses on developing mathematical models and algorithms for representing, analyzing, and visualizing complex spatial data, particularly through hierarchical models, mesh-based representations, and topology-based approaches. Her research group, the GeoVis group, investigates applications in terrain modeling, environmental data analysis, and forest structure mapping using LiDAR technology. Analysis of her recent publications reveals a strong focus on terrain representation and processing, with increasing emphasis on topological data analysis, machine learning integration, and efficient algorithms for large-scale spatial data. Her work bridges theoretical foundations in computational topology with practical applications in geospatial sciences, demonstrating consistent innovation in data structures and visualization techniques. Scientific Awards & Recognitions Fellow of IEEE (2016) for contributions to geometric modeling and scientific visualization Fellow of International Association for Pattern Recognition (IAPR) (1998) for contributions to geometric modeling and image analysis Fellow of Eurographics Association (2020) for outstanding contributions to computer graphics and visualization Pioneer of Solid Modeling Association (2017) for seminal work in solid and feature-based modeling Inducted Member of IEEE Visualization Academy (2020) IEEE Computer Society Golden Medal Award (2018) Inducted Member of IEEE Honor Society Eta Kappa Nu (2019) Multiple best paper awards at major conferences including Shape Modeling International (2015), IEEE/EG Symposium on Volume and Point-Based Graphics (2008), and ACM SIGSPATIAL (2008) Professor De Floriani has successfully advised numerous PhD students including Xin Xu, Yunting Song, and Noel Dyer, whose recent dissertations focused on topology-based individual tree mapping, efficient terrain analysis, and bathymetric data visualization respectively. Her research has been funded by prestigious agencies including the National Science Foundation, NASA, and the European Commission. As the leader of the UMD GeoVis group, she oversees a research program that develops open-source tools for spatial data analysis available on GitHub, with current projects focusing on forest point cloud processing and topology-based geospatial data visualization. The GeoVis group, affiliated with the Department of Geographical Sciences, UMIACS, and the Center for Geospatial Information Sciences, maintains a strong collaborative environment with ongoing projects in geometric modeling, spatial data structures, topology-based machine learning, and mesh-based terrain modeling. The group has received recent funding from NASA's HPOSS program for developing an open-source library for forest point cloud processing based on topological data analysis.

Andreas NieklerView profile

Researcher

• Natural Language Processing
• Computational Social Science
• Digital Humanities
• +7 more

Andreas Niekler is a research associate and lecturer in Computational Humanities at the Institute of Computer Science, Leipzig University, Faculty of Mathematics and Computer Science. He develops computational methods for semantic language analysis and applies them to computational social science and humanities research, with focus on machine learning and data management methodologies. His research interests span Natural Language Processing , Computational Social Science , Digital Humanities , Text Mining , Machine Learning , and Conversational AI . Niekler specializes in developing algorithms including Bayesian Models, Topic Models, Deep Learning, and Support Vector Machines for text analysis applications. He is actively involved in the development of the interactive Leipzig Corpus Miner platform and researches how semantic representations can be applied to literary studies and scientometrics. Niekler's recent publications demonstrate expertise across computational linguistics, social science methodology, and practical applications of text mining. His work shows strong interdisciplinary connections between computer science, linguistics, and social sciences, with particular emphasis on developing robust methodologies for automated content analysis. As an educator, Niekler has extensive teaching experience in computational methods for humanities and social sciences, offering courses on text mining, computational linguistics, and programming in R and Python. He serves as a scientific staff member in the Computational Humanities research group led by Prof. Dr. Manuel Burghardt and represents scientific staff in the Faculty Council of Mathematics and Computer Science at Leipzig University.

• Digital Health
• Human-Computer Interaction (HCI)
• User-Centered Design
• +11 more

Professor Marilyn Lennon is a leading academic in the Department of Computer and Information Sciences at the University of Strathclyde , holding the title of Professor in Digital Health and Care. She founded and directs the Digital Health and Wellness Group (DHaWG) , focusing on multidisciplinary research for designing, evaluating, and implementing digital technologies that enhance individual and population health and wellbeing. First class BSc in Psychology (University of Glasgow, 1998) PhD in Computing Science (University of Glasgow, 2002) Postgraduate diploma in academic practice (Fellow of Higher Education Academy) With over two decades of experience in Human-Computer Interaction (HCI) , usability, and user-centered design, her research spans wearable and mobile technologies for home healthcare, including remote monitoring (predictive falls monitoring), remote diagnosis (colon capsule technology evaluations), and smart home solutions for social care. She specializes in addressing technical and social barriers to real-world health technology implementation, emphasizing robust yet rapid evaluation methods. Recent publications highlight her work in 3D healthcare visualization, telehealth implementation, and medication management systems. Her research combines lab-based usability testing with real-world ('in the wild') evaluations of technologies like smart home systems and telehealth solutions. Key trends include cross-disciplinary collaboration, accessibility for sensory-impaired individuals, and AI-driven healthcare innovations. Scientific Awards MyCity: Glasgow - Gamechanger Award (Gold Medal) 2025 Images of Research 2025 Shortlist Emerald Outstanding Paper Award 2013 Best Paper Award (most replicable scientific paper) 2013 As course director for the Masters in Digital Health Systems , she has supervised 5 PhD students (2 completed) and over 100 honors projects. Current projects include the No Need to Fall initiative funded by the Health Foundation (£400k) and the PREMIO project co-creating clinical trials in advanced breast cancer. She maintains active collaborations with NHS, charities, and international institutions like the University of Waterloo.

David SepkoskiView profile

Professor

• History of Science
• Paleobiology
• Data Science
• +4 more

David Sepkoski is the Thomas M. Siebel Chair in History of Science and a Professor in the Department of History and School of Information Sciences at the University of Illinois at Urbana-Champaign. His research focuses on the transnational history of biological, environmental, and information sciences within cultural contexts, with particular attention to data collection, visualization, and the evolution of paleobiology. PhD in History of Science and Technology, University of Minnesota MA in Social Sciences, University of Chicago BA, Carleton College His work examines the interplay between scientific objectivity and cultural biases in debates about human nature. He was awarded the prestigious Guggenheim Foundation Fellowship (2020-2021) for his project on biological determinism. Recent publications analyze sociobiology, environmental history, and the historiography of data. Key research areas include: History of data and information systems Science and technology studies Digital humanities Extinction and biodiversity debates His 2025 article in Historical Studies in the Natural Sciences explores sociobiology's political dimensions, while his 2024 Handbook of the Historiography of the Earth and Environmental Sciences redefines interdisciplinary approaches to ecological science history. Scientific contributions Guggenheim Foundation Fellowship (2020) Author of two monographs on paleobiology and extinction Editorial work on data histories and bioinformatics

Nan ChenView profile

Associate Professor

• Applied Mathematics
• Uncertainty Quantification
• Data Assimilation
• +7 more

Nan Chen is an Associate Professor at the Department of Mathematics, University of Wisconsin-Madison, and a faculty affiliate of the Institute for Foundations of Data Science (IFDS), a multi-University TRIPODS Phase II Initiative. His research spans applied mathematics with applications in atmosphere-ocean science, climate dynamics, and data science. Education: PhD from Courant Institute of Mathematical Sciences (CIMS) and Center of Atmosphere and Ocean Science (CAOS), New York University (NYU), May 2016 Postdoc research associate at CIMS, NYU (June 2016-May 2018) Master's degree from School of Mathematical Sciences, Fudan University, Shanghai Undergraduate in Mechanical Engineering, Fudan University, Shanghai Visited Department of Scientific Computing at Florida State University working with Dr. Max Gunzburger and Dr. Xiaoming Wang Nan Chen's research focuses on contemporary applied mathematics, particularly modeling complex systems, stochastic methods, numerical algorithms, and data science. He specializes in uncertainty quantification (UQ), data assimilation, and developing statistically accurate algorithms to address the curse of dimensionality in large-dimensional complex dynamical systems with strong non-Gaussian features. His work has significant applications in atmosphere-ocean science, including predicting phenomena such as the Madden-Julian Oscillation (MJO), monsoons, El Niño Southern Oscillation (ENSO), and sea ice dynamics. He has also extended his research to material science, neuroscience, and other complex systems. His recent publications demonstrate expertise in inverse problems, wave equations, numerical methods, and data compression techniques that blend mathematical theory with practical applications. Dr. Chen has authored a book titled "Stochastic Methods for Modeling and Predicting Complex Dynamical Systems --- Uncertainty Quantification, State Estimation, and Reduced-Order Models" published by Springer, and a tutorial paper "Taming Uncertainty in a Complex World: The Rise of Uncertainty Quantification — A Tutorial for Beginners" in the Notices of the AMS. Professional Activities: Organizing "Data Meets Dynamics: Workshop on Data Assimilation for Complex Systems and Applications" (August 21-22, 2025) Author of two articles in Elsevier's Reference Module in Earth Systems and Environmental Sciences Participant in Wisconsin Science and Computing Emerging Research Stars (WISCERS) program Judge for Outstanding Student Paper Award (OSPA) program at American Geophysical Union (AGU) fall meetings Involved in Madison Experimental Mathematics Lab (MXM Lab) Dr. Chen actively mentors undergraduate students for research during semesters and summers, encouraging them to present at the UW undergraduate symposium. He also offers reading and independent study courses for interested undergraduates. He is currently seeking highly motivated PhD students to join his research group with possible Research Assistantship support.

• Geochemistry
• Environmental Geochemistry
• Carbon Cycling
• +5 more

Katherine Romanak is a Research Professor at the Bureau of Economic Geology, The University of Texas at Austin, specializing in geochemistry and carbon capture/storage (CCS) technologies. She pioneered the process-based soil gas monitoring approach for detecting CO2 leakage, which has become a global standard. Her work bridges scientific research and policy, with contributions to UNFCCC COPs and U.S. Class VI CCS regulations. Ph.D. in Geology (1997), UT Austin M.S. in Geology (1988), UT Arlington B.S. in Geology (1984), Southern Methodist University Her research focuses on: Geochemistry of carbon cycling in vadose zones and aquifers Development of environmental monitoring protocols for CCS sites CO2 leakage attribution and stakeholder engagement Integration of machine learning with field monitoring techniques Recent publications emphasize simplifying monitoring complexity, offshore CCS applications, and machine learning for anomaly detection. She holds two U.S. patents and collaborates globally on projects in Japan, Australia, Canada, and the U.S. Gulf Coast. Scientific awards include: 2015 BEG publication award 2017 U.S. patents for CO2 leakage detection Her team seeks partnerships with vadose zone modelers and microbiologists to advance industrial-scale monitoring systems. Romanak also co-developed UT Austin’s online CCS certification program and provides technical training for petroleum professionals transitioning to CCS roles.

• GPU Computing
• Parallel Computing
• Domain-Specific Languages
• +5 more

Prof. Dr.-Ing. Richard Membarth is a Research Professor for System-on-a-Chip and AI at the Edge Computing at Technische Hochschule Ingolstadt (THI). He is affiliated with the Hardware-Software Co-Design group and holds a secondary position at the German Research Center for Artificial Intelligence (DFKI) Saarbrücken. Co-creator of DSL frameworks like AnyDSL and Hipacc Key contributor to MetaDL (AI metaprogramming) and PRIME (predictive rendering) His research bridges GPU computing , domain-specific languages , and compiler technology , with recent work on Vulkan SPIR-V compilation and device-driven SpMV algorithms . Notable awards include the HiPEAC Paper Award (2018) and multiple Best Paper Awards for his compiler frameworks.

Jing YangView profile

Professor

• Data Visualization
• Visual Analytics
• High Dimensional Data Analysis
• +7 more

Jing Yang is a full Professor and Director of MS CS Program in the Computer Science Department at the University of North Carolina at Charlotte (UNCC). She has been actively involved in data visualization and visual analytics research since joining UNCC in 2005 after completing her PhD at Worcester Polytechnic Institute. Dr. Yang earned her Bachelor's degrees in Engineering Mechanics and Computer Science from TsingHua University in 1997 and completed her Ph.D. in Computer Science from Worcester Polytechnic Institute in May 2005 under advisors Matthew O. Ward and Elke A. Rundensteiner. Her research focuses on developing visual analytics techniques for abstract data including multidimensional data, time-oriented data, networks, hierarchies, text documents, and trajectory data. She conducts design studies for application domains such as sports, bioinformatics, finance, network security, and health, while exploring fundamental visualization topics like interactions, insight management, clustering-based approaches, and animations. Her recent work emphasizes sports analytics, urban data, and multivariate time series visual analytics. Analysis of Dr. Yang's publication record reveals a strong focus on practical applications of visualization techniques across diverse domains. Her work consistently bridges theoretical visualization frameworks with real-world data challenges, particularly in transportation (taxi trajectories), sports (tennis match analysis), financial transactions, and bioinformatics. The publications demonstrate an evolution from foundational visualization techniques to increasingly sophisticated domain-specific applications. Dr. Yang has secured substantial research funding from NSF, EPA, DHS, and industry partners including Google and Bank of America. Her grants portfolio includes significant projects like TrajAnalytics (NSF, $200,950), Visualizing Event Dynamics (NSF EAGER, $75,317), and Visualizing High Dimensional Categorical Datasets (Google Faculty Research Award, $60,000), among others totaling over $4 million in research support. As an educator, Dr. Yang has taught numerous courses including Visual Analytics, Information Visualization, and Database Design. She directs the Charlotte Visualization Center and has mentored numerous students through research projects. Her collaborative approach is evident in her extensive co-authorship network spanning multiple institutions and disciplines. Current research directions include narrative animation for streaming text visualization and advanced techniques for exploring high-dimensional categorical datasets.

Shabnam Sadeghi EsfahlaniView profile

Associate Professor

• Robotics
• Mechatronics
• Artificial Intelligence
• +3 more

Dr. Shabnam Sadeghi Esfahlani is an Associate Professor in Robotics at the School of Engineering and the Built Environment, Anglia Ruskin University , where she serves as Deputy Leader of the BORI research group and leads the Automation & Robotics MSc program. Her interdisciplinary expertise spans mechatronics, artificial intelligence, virtual reality, and serious games , with a focus on applications for rehabilitation, medical training, and autonomous systems . As a Chartered Engineer and Senior Fellow of the Higher Education Academy , she has secured significant funding from Innovate UK, Horizon 2020, and GCRF , with grants exceeding £3 million. Education PhD in Mechanical Engineering, Anglia Ruskin University BSc (First Class) in Statistics & Mathematical Science, Shahid Beheshty University Her research integrates AI with robotics for societal impact, exemplified by the open-source SROBO ground robot and projects like Rehabgame and the Assistive Feeding Robot . She has published over 45 peer-reviewed articles and contributes to academic communities as a journal guest editor and conference organizer . Key collaborations include IET, IMechE, and the Nuffield Foundation as a mentor for young students. Scientific Awards & Recognitions: Chartered Engineer (CEng), Engineering Council UK Senior Fellow (SFHEA), Higher Education Academy Student-Voted 'Made a Difference Award' (2018) Post-Graduate Certificate in Higher Education

Matias D. CattaneoView profile

Professor

• Mathematical statistics
• Econometrics
• Data science
• +7 more

Matias D. Cattaneo is a Professor in the Department of Operations Research and Financial Engineering at Princeton University , with affiliated roles in the School of Public and International Affairs , Economics Department , Latin American Studies Program , Data-Driven Social Science , AI at Princeton , and Center for Statistics and Machine Learning . He serves as an Amazon Scholar and collaborates with global organizations. Education : Ph.D. in Economics (2008) and M.A. in Statistics (2005) from UC Berkeley, Master in Economics (2003) from Universidad Torcuato Di Tella, Licentiate in Economics (2000) from Universidad de Buenos Aires. Research focuses on interdisciplinary challenges in social, behavioral, and biomedical sciences, combining econometrics, statistics, data science, and causal inference. His methodological work includes regression discontinuity designs, synthetic control methods, and local polynomial estimation, with applications to decision-making under uncertainty. Scientific recognition : Elected Fellow of the American Statistical Association Elected Fellow of the Institute of Mathematical Statistics Elected Fellow of the International Association for Applied Econometrics Elected Member of the International Statistical Institute Software contributions include R packages rdhte , scpi , and lpcde , freely available on GitHub. His GitHub activity includes 344 contributions in the last year, with active repositories on regression discontinuity and synthetic control methods.