جست‌وجوی استاد و پژوهشگر

Joshua Wilsonمشاهده پروفایل

دانشیار

• Writing Education
• Educational Technology
• Automated Writing Evaluation
• +۳ مورد دیگر

Dr. Joshua Wilson is an Associate Professor in the School of Education at the University of Delaware, specializing in improving writing instruction through automated evaluation systems. His research focuses on leveraging technology to enhance teaching and learning, particularly in K-12 settings, and he has secured over $3.4 million in grants from federal, foundation, and industry sponsors. He coordinates the M.Ed. in Exceptional Child and Youth program and teaches courses on elementary special education methods. Education: Ph.D. in Special Education (University of Connecticut, 2014), M.S. in Special Education (Southern Connecticut State University, 2005), B.A. in Religion (Wesleyan University, 2000). Professional Experience: Joined University of Delaware in 2014 as Assistant Professor, promoted to Associate Professor in 2020. His research interests include automated writing evaluation systems, formative assessment, and the intersection of technology with writing pedagogy. He has published in top journals such as Computers & Education and sits on the editorial boards of Gifted Child Quarterly and Journal of Educational Psychology . Recent grants include a $1.49M project on Efficacy of “MI Write” Automated Writing Evaluation (Bill & Melinda Gates Foundation, 2020) and a $399,999 study on researcher-practitioner partnerships in writing assessment (U.S. Department of Education, 2017–2021). Awards: 2019 Gerard J. Mangone Young Scholars Award (Social Sciences/Humanities), 2017 Dean’s Faculty Research Award, 2015 Harris Kahn Dissertation Award. Wilson’s work emphasizes the practical application of AI tools to address writing proficiency gaps and improve educational outcomes for diverse student populations.

Jon Moreno-Medinaمشاهده پروفایل

استادیار

• Urban economics
• Public economics
• Media economics
• +۶ مورد دیگر

Jon Moreno-Medina is an Assistant Professor of Economics at the University of Texas at San Antonio (UTSA), affiliated with the Alvarez College of Business. He holds a Ph.D. in Economics from Duke University, an M.S. from Université Catholique de Louvain (Belgium), and a B.A. from Universidad Nacional de Colombia. His research focuses on urban, public, and media economics, with additional expertise in development economics and political economy. He also employs Natural Language Processing (NLP) and machine learning tools in his work. Education: Ph.D. in Economics, Duke University M.S. in Economics, Université Catholique de Louvain B.A. in Economics, Universidad Nacional de Colombia Research interests span urban economics (e.g., housing markets, segregation), crime economics (weather impacts, media framing of police violence), and migration policy. His recent work includes analyzing the Venezuelan migrant crisis, media bias in crime reporting, and the interplay between weather, religion, and criminal activity. Teaching includes graduate courses on causal inference and machine learning, alongside undergraduate classes in public economics and econometrics. No scientific awards are explicitly listed, but his research has been published in top journals like the Review of Economics and Statistics . Advising and grants: No formal student advisee list provided, though his teaching and research imply active involvement in mentoring. No grants mentioned in the text. He is based at the Alvarez College of Business, UTSA, with a focus on applied microeconomics at the intersection of crime, urban development, and media analysis.

Pau Batlle Franchمشاهده پروفایل

پژوهشگر ارشد

• Statistics
• Applied Mathematics
• Uncertainty Quantification
• +۴ مورد دیگر

Pau Batlle Franch is a Research Fellow in the Computing and Mathematical Sciences Department at California Institute of Technology (Caltech), working with Professor Houman Owhadi. He holds a PhD from Caltech (June 2025) and was a research affiliate at NASA Jet Propulsion Laboratory (JPL). His research focuses on the intersection of statistics and applied mathematics, including frequentist confidence intervals in inverse problems, game-theoretical uncertainty quantification, and Gaussian processes. He has applied his work to domains like remote sensing, biology, earthquake prediction, and telecommunications engineering. Education : PhD in Computing and Mathematical Sciences (Caltech, 2025); Double undergraduate degree in Mathematics and Engineering Physics from Universitat Politècnica de Catalunya (CFIS program); Research visitor at NYU's Center for Data Science. His research interests include optimization-based statistical methods, Gaussian process frameworks for scientific computing, and uncertainty quantification in physical systems. Notable contributions include resolving the Burrus conjecture and developing computational hypergraph discovery techniques applied to NASA JPL projects. His work bridges theory and application, addressing challenges in ill-posed inverse problems and robust statistical inference. Recent activities include presenting at SIAM conferences and workshops on inverse problems in Earth science. His Gaussian process methods have been published in journals like PNAS and SIMODS, with applications ranging from PDE solving to RNA classification. Collaborations include JPL and the Groningen seismic study. Grants & Collaborations : Ongoing work with NASA JPL on lunar rover control and computational graph discovery; Seismic modeling in the Groningen gas field with epistemic/aleatoric uncertainty frameworks. He maintains an active GitHub profile showcasing projects in machine learning and scientific computing, including repositories like DarwinProjectAnalytics and emb4class .

Bestoun S. Ahmed Al-Beywaneeمشاهده پروفایل

استاد

• Quality Assurance of Machine Learning Systems
• Software Testing
• Trustworthy AI Systems
• +۵ مورد دیگر

Bestoun S. Ahmed Al-Beywanee is a Professor in AI and Software Engineering at the Department of Mathematics and Computer Science, Karlstad University, Sweden. He joined the university as a Senior Lecturer in 2019, was promoted to Associate Professor in 2020, and to Professor in 2023. His roles include teaching advanced courses such as AI Engineering, Automated Software Engineering, and Software Testing Fundamentals. His research focuses on software quality assurance, trustworthy AI systems, and MLOps, with a strong emphasis on combinatorial testing, IoT systems, and applied optimization techniques. Education: BSc (Electrical and Electronic Engineering, University of Salahaddin-Erbil, 2004); MSc (University Putra Malaysia, 2009); PhD (Software Engineering, University Sains Malaysia, 2012). Postdoctoral research at the Swiss AI Lab IDSIA (2015) and positions at Salahaddin University and Czech Technical University further enriched his expertise. Research interests span Quality Assurance of machine learning systems, software testing methodologies, trustworthy AI, IoT system reliability, and optimization algorithms. He has pioneered frameworks like PatrIoT for IoT testing and contributed to MLOps robustness. His work integrates machine learning with anomaly detection, adaptive systems, and industrial applications. Recent articles highlight advancements in data-driven heat pump management, MLOps robustness, and edge-cloud AR/VR optimization. Collaborations include projects on digital twins, smart manufacturing, and industrial IoT. His contributions bridge theoretical research with practical industrial solutions, emphasizing system reliability and AI ethics.

Minsu Parkمشاهده پروفایل

استادیار

• Computational Social Science
• Human-centered Data Science
• Cultural Consumption
• +۹ مورد دیگر

Minsu Park is an Assistant Professor of Social Research and Public Policy at New York University Abu Dhabi (NYUAD), with affiliate status at the Center for Data Science at New York University (NYU). He holds a PhD in Information Science from Cornell University, where he was advised by Michael W. Macy and Mor Naaman. His academic work bridges computational techniques and social science theory, focusing on cultural consumption, social networks, and human-centered data science. PhD in Information Science, Cornell University His research centers on understanding how individuals form cultural preferences through social and psychological mechanisms, particularly in domains like music, food, fashion, and science. He leverages large-scale digital trace data—including social media, streaming platforms, and physiological signals from smart devices—to model behavior. His interdisciplinary approach draws from sociology, social computing, and data science, aiming to uncover both individual and global patterns in cultural dynamics. Key themes include variety-seeking behaviors, affective preferences, and the interplay between social position and taste. The 15 most recent publications reflect a strong trend toward using computational methods to explore sociocultural phenomena. Topics span affective preference rhythms in music, testing sociological theories like cultural omnivory, imputing user attributes from digital behavior, and enhancing model interpretability. These works are published in high-impact venues such as Nature Human Behaviour and ICWSM, indicating recognition in both computer and social sciences. Minsu Park is deeply committed to responsible data science, addressing issues like algorithmic bias, transparency, fairness, data privacy, and research ethics. He emphasizes mixed-methods approaches and critical reflection on data generation and usage. Published in Nature Human Behaviour Presented at ICWSM Focus on ethical and interpretable AI Advocate for reproducibility and data curation He mentors students through capstone projects in computer science and teaches courses such as Human-Centered Data Science and Textual Analysis for the Social Sciences. While specific grants are not detailed in the text, his research program suggests involvement in interdisciplinary, data-intensive projects likely supported by academic or scientific funding bodies. His technical expertise includes Python, R, machine learning frameworks (TensorFlow, Keras), and tools like Gephi and AWS. He leads or contributes to research initiatives involving smartwatch-based data collection, surname-based ethnicity matching, and global music consumption analysis, as evidenced by his public GitHub repositories. These projects highlight his commitment to open science and collaborative research.

• Distributed Computing
• Federated Learning
• Cloud Computing
• +۱۲ مورد دیگر

Salman Zubair Toor is a researcher at Uppsala University, Sweden, specializing in distributed computing, federated learning, and cloud/edge infrastructure optimization. His work spans resource scheduling, data streaming, and secure anomaly detection.

Vincent Sitzmannمشاهده پروفایل

استادیار

• Computer Vision
• 3D Reconstruction
• Neural Scene Representations
• +۷ مورد دیگر

Vincent Sitzmann is an Assistant Professor at the Massachusetts Institute of Technology (MIT) in the Department of Electrical Engineering and Computer Science (EECS), where he leads the Scene Representation Group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on building machines that learn to understand and interact with the world autonomously through 'world models' - mental simulators that enable agents to predict environmental outcomes and the consequences of their actions. His educational background includes a PhD from Stanford University under Gordon Wetzstein and a Bachelor's degree from the Technical University of Munich. Sitzmann's research spans computer vision, graphics, and robotics, with pioneering contributions to neural scene representations. He introduced Scene Representation Networks (SRNs) that enable continuous 3D-structure-aware scene modeling from 2D images. His work on implicit neural representations with periodic activation functions has become foundational to the field. Recent research focuses on scaling 3D reconstruction techniques, improving generative models for visual content, and developing methods for robot control through neural Jacobian fields. His approach emphasizes both theoretical rigor and practical applications across multiple domains. His publication record shows a clear progression toward more sophisticated diffusion models applied to video generation, robotics, and 3D reconstruction. The 2025 Nature paper on robot control via Jacobian fields demonstrates his expanding influence beyond traditional computer vision into robotics. His work consistently bridges theoretical advances with practical implementations, as evidenced by the CVPR 2023 Best Paper Runner-Up for pixelSplat, which offers scalable solutions for 3D reconstruction. His notable scientific achievements include: CVPR Best Paper Runner-Up (2023) for 'pixelSplat' Multiple papers with 'Spotlight' or 'Oral' presentations at NeurIPS and CVPR 2023 Amazon Research Award for '2D and 3D Animation via Image-Conditional Generative Flow Models' NeurIPS Outstanding New Directions Honorable Mention (2019) As leader of the Scene Representation Group, Sitzmann mentors researchers working at the intersection of computer vision, graphics, and AI. The group has secured funding from prestigious sources including Amazon Research Awards. Their work has practical applications in virtual reality, robotics, and content creation industries. Sitzmann teaches advanced courses at MIT, including 'Advances in Computer Vision' (6.8300). The Scene Representation Group focuses on developing novel methods for 3D scene understanding and manipulation. Current projects include research on neural radiance fields, diffusion models for 3D content creation, and methods for autonomous scene understanding. The group maintains active collaborations with industry partners and academic institutions to advance visual computing research.

• Geometric Data Processing
• Computational Geometry
• Optimal Transport
• +۷ مورد دیگر

Justin Solomon is an Associate Professor in the Department of Electrical Engineering & Computer Science at Massachusetts Institute of Technology, where he serves as Principal Investigator of the Geometric Data Processing Group. He maintains dual affiliations with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT Center for Computational Science and Engineering (CCSE), reflecting his interdisciplinary research bridging theoretical mathematics with practical applications in graphics and machine learning. His research interests center around geometric data processing, computational geometry, and optimal transport theory, with significant contributions to computer graphics, machine learning, and computer vision. Solomon's work spans fundamental mathematical theory to practical implementations, particularly in shape analysis, 3D reconstruction, and geometric deep learning. His research demonstrates consistent innovation in developing algorithms that bridge discrete and continuous geometry with applications in graphics, vision, and AI. The publication trends reveal Solomon's evolving research trajectory from foundational work in geometry processing toward increasing integration with modern machine learning techniques. His recent work shows strong emphasis on diffusion models, geometric deep learning, and applications of optimal transport in AI, with significant contributions to SIGGRAPH, NeurIPS, and ICML proceedings. The research demonstrates both mathematical rigor and practical impact, with applications spanning character animation, 3D reconstruction, and generative AI. Amazon Research Award (2017) for Large-Scale Geometrically-Structured Sampling Amazon Research Award (2023) for Lightweight Algorithms for Generative AI Ben Wegbreit Prize for Best Undergraduate Honors Thesis Firestone Medal for Excellence in Undergraduate Research Boothe Prize for Excellence in Writing 2nd place, SGP best paper awards (2010) Solomon has secured substantial research funding through awards like the Amazon Research Awards and maintains active collaborations across academia and industry. His group has produced numerous influential publications with students and collaborators, contributing significantly to both theoretical foundations and practical implementations in geometric data analysis. His textbook "Numerical Algorithms" demonstrates his commitment to education alongside research. As Principal Investigator of the Geometric Data Processing Group, Solomon leads a research team focused on developing mathematical foundations for analyzing and processing geometric data. The group maintains strong connections with both theoretical mathematics and practical applications, working at the intersection of computer graphics, machine learning, and computational geometry. Their work has significant implications for fields ranging from computer animation to medical imaging and scientific computing.

Piotr Gmytrasiewiczمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Multiagent Systems
• Decision Theory
• +۷ مورد دیگر

Piotr Gmytrasiewicz is an Associate Professor at the Department of Computer Science, University of Illinois at Chicago (UIC). He leads the Multiagent Systems Group within the Artificial Intelligence Laboratory at UIC. His research focuses on rationality in artificial agents , particularly in environments with multiple interacting agents. Key areas include Interactive Decision-Making Bayesian Modeling for Agent Communication Recursive Belief Frameworks Time Pressure and Computational Trade-offs Evolution of Agent Communication Languages Dynamic Resource Allocation His recent work examines the rationality of insincere communication and methods to discount potentially insincere information. Past projects include modeling emotions in agent design and developing decision-theoretic approaches to game theory. He has secured funding from prestigious institutions such as the National Science Foundation (NSF) , Office of Naval Research (ONR) , and DARPA . Current and past projects emphasize Automated Linguistic Competence Evolution Emergent Communication Protocols Scalable Multiagent Learning Strategic Coordination under Uncertainty He earned his Ph.D. from the University of Michigan, Ann Arbor (1992) and has previously collaborated with the Department of Computer Science and Engineering (CSE) at the University of Texas at Arlington on DARPA-funded research.

• Personnel Economics
• Microeconomics
• Organizational Design
• +۴ مورد دیگر

Michael Gibbs is a Clinical Professor of Economics at the University of Chicago Booth School of Business. His research focuses on personnel economics, organizational design, and productivity analysis, with notable contributions to understanding incentive compensation, remote work dynamics, and innovation management. He co-authored the textbook Personnel Economics in Practice , translated into five languages. PhD in Economics, University of Chicago Research Fellow, Institute of Labor Economics (IZA) His research examines how job design affects intrinsic motivation and productivity, particularly in remote/hybrid work environments. Key findings include reduced innovation quality during remote work and increased communication costs in hybrid models. He also investigates incentive systems in auto dealerships, mergers, and public sector workforce challenges. Recent publications analyze productivity shifts during the pandemic, the role of reputation in global markets, and the evolution of performance measurement systems. He has contributed to journals like Nature Scientific Reports , Quarterly Journal of Economics , and Review of Economics & Statistics . Notable Contribution to Management Accounting Literature (American Accounting Association) Hillel Einhorn Excellence in Teaching Award (x4) As former Faculty Director of Booth’s Executive MBA program (2012-2015), he influenced executive education design. His work bridges economic theory with practical organizational challenges, offering insights into incentive structures and workforce management.

Onet-Marian Zsuzsannaمشاهده پروفایل

مدرس

• Software Engineering
• Machine Learning
• Data Mining
• +۳ مورد دیگر

Dr. Onet-Marian Zsuzsanna is a Lecturer in the Department of Computer Science at Babes-Bolyai University in Cluj-Napoca, Romania. Her research focuses on applying machine learning techniques to software engineering challenges, particularly software defect prediction and restructuring. Academic Rank: Lecturer Affiliation: Babes-Bolyai University Department: Computer Science Email: zsuzsanna.onet@ubbcluj.ro Research Interests : Dr. Marian specializes in developing machine learning models (clustering, association rules, reinforcement learning) for software defect detection, package-level restructuring, and test order optimization. She also explores applications of Formal Concept Analysis in text summarization and music pattern discovery. Recent Publications highlight her work in source-code embeddings, unsupervised learning for software analysis, and comparative studies of online/traditional learning environments. Her methods often integrate domain-specific metrics and AI-driven optimization. Contact : zsuzsanna.onet@ubbcluj.ro | Office: Teodor Mihaly street, Room 440

Roy E. Welschمشاهده پروفایل

استاد

• Statistics
• Data Science
• Statistical Learning
• +۱۰ مورد دیگر

Dr. Roy E. Welsch is the Eastman Kodak Leaders for Global Operations Professor of Management and a Professor of Statistics and Data Science at the MIT Sloan School of Management and the MIT Center for Statistics and Data Science . He currently serves as Director of the MIT Center for Computational Research in Economics and Management Science . Education : AB in Mathematics from Princeton University (1969), MS and PhD in Mathematics from Stanford University Dr. Welsch's research spans advanced statistical methodologies and their interdisciplinary applications. His work focuses on: Robust statistical methods for regression and covariance modeling Applications in financial markets, biomedical imaging, and drug repurposing Machine learning algorithms for high-dimensional data analysis Statistical computing and computational finance Uncertainty quantification in experimental design His recent publications emphasize cross-domain applications of machine learning, particularly in financial forecasting, biomedical diagnostics, and network behavior modeling. Notable trends include: Development of robust statistical frameworks Integration of NLP techniques in financial analysis Biomedical image processing algorithms for disease quantification Time-series and high-content analysis Portfolio optimization under uncertainty Scientific Recognition : Fellow of the Institute of Mathematical Statistics Fellow of the American Statistical Association Fellow of the American Association for the Advancement of Science Eastman Kodak Leaders for Global Operations Professorship As a dedicated educator, Dr. Welsch teaches Data Analysis and Applied Statistics courses focusing on regression modeling, experimental design, and quality control with applications in finance and marketing.

• Artificial Intelligence
• Explainable AI
• Computational Argumentation
• +۳ مورد دیگر

Antonio Rago is a Researcher in the Department of Computing at Imperial College London. He specializes in Explainable Artificial Intelligence (XAI) with a focus on computational argumentation frameworks and their integration with data-driven AI systems. His work bridges symbolic AI and machine learning to enhance transparency and societal benefit in AI applications. Education: PhD in Computing (2019) from Imperial College London, supervised by Prof. Francesca Toni and Dr. Marco Aurisicchio, with an MEng in Automotive Engineering from Loughborough University (2012). Research: Explores explainable AI through argumentation semantics, counterfactual reasoning, and hybrid symbolic-statistical methods. Application domains include e-learning, Formula One race strategy, healthcare, and mechanical engineering. Workshops: Organizer of international workshops like Arg&App 2025 and ArgXAI-25, and co-organizer of previous events at KR, ECAI, and COMMA conferences. Publications: Active in top AI venues (KR, IJCAI, AAAI, AAMAS) with over 25 publications since 2016, emphasizing argumentation-based explanations and robust AI systems. Industry Experience: Former Race Strategy Engineer at Mercedes AMG Petronas F1 Team (2012-2014) and Project Manager at Green Lifting Ltd. (2014-2017).

• Machine Learning
• Information Fusion
• Data Clustering
• +۳ مورد دیگر

Julien Ah-Pine is a lecturer at Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS) under Université Clermont Auvergne , with affiliations at Institut national polytechnique Clermont Auvergne and École des Mines de Saint-Étienne . He also holds a Researcher position at CNRS. Research Interests His work spans machine learning , information fusion , aggregation functions , and multi-criteria decision support , with a focus on complex data types like graphs , functional data , and multi-view datasets . Recent publications emphasize anomaly detection in spectral data streams , online learning , and interpretable AI for industrial applications. Selected Publications 2025 work on OnlineBootKNN introduces a novel framework for real-time spectral anomaly detection, while 2024 research explores multiple kernel methods in functional data classification. Earlier studies cover graph-based clustering , relational data mining , and linguistic network models for NLP tasks. Laboratory & Collaborations Works within LIMOS laboratory at Université Clermont Auvergne, collaborating with institutions like Mines Saint-Étienne and CNRS. Key partnerships include Nicolas Rojas Varela and Engelbert Mephu Nguifo on data stream analysis projects.

• Data science
• Machine learning
• Artificial intelligence
• +۱۰ مورد دیگر

Dr. Xiaobai (Bob) Li is a Professor in the Department of Operations and Information Systems at the Manning School of Business, University of Massachusetts Lowell. His work bridges data science, machine learning, and business analytics with specialized focus on data privacy and information economics. Ph.D. in Management Science, University of South Carolina MBA, University of New Hampshire BS in Civil Engineering, Chongqing University Research Interests span data science, machine learning, business analytics, and data privacy. His work explores Privacy-preserving analytics Healthcare data anonymization Information economics Optimization techniques Big Data applications Text analytics with particular emphasis on balancing data utility and privacy protection. Scientific Awards include: Manning Research Excellence Award (2023) Best Associate Editor Award (2021) Best Paper Awards at NEAIS, WITS, and ICIS Teaching Excellence Awards Grants from NIH and NSF supported his work on Consumer Electronic Privacy Protection and New Technology to Preserve Patient Privacy . He has served as Associate Editor for journals like Information Systems Research and Decision Support Systems .