جست‌وجوی استاد و پژوهشگر

Naci Mocanمشاهده پروفایل

استاد

• Labor Economics
• Health Economics
• Economics of Crime
• +۴ مورد دیگر

Naci Mocan serves as Professor and Ourso Distinguished Chair in Economics at Louisiana State University's E.J. Ourso College of Business. He maintains significant external affiliations as a Research Associate with the National Bureau of Economic Research (NBER) and Research Fellow at the Institute for the Study of Labor (IZA), while contributing to academic discourse through editorial board membership for the Journal of Labor Research. His educational foundation includes a PhD in Economics from the City University of New York (1989) and a BA in Economics from Bogazici University (1984). This training enabled his distinctive interdisciplinary approach spanning multiple economic subfields. Professor Mocan's research program integrates Labor Economics, Health Economics, and Cultural Economics with groundbreaking work in Law and Economics. His investigations into judicial bias mechanisms—examining racial preferences in court decisions and using NBA referees as experimental analogs—demonstrate innovative methodology. Parallel streams explore refugee-crime dynamics, political economy of deterrence, and cultural determinants of economic prosperity, with his forthcoming MIT Press book representing a major theoretical contribution. This work consistently bridges micro-level behavioral insights with macro-level policy implications. His publication trajectory shows sustained productivity in top journals, with 2023-2025 output focusing on cultural economics foundations and judicial behavior. The concentration of recent work in Journal of Law and Economics, Journal of Policy Analysis and Management, and Review of Economics and Statistics underscores disciplinary influence. His primary recognition is the prestigious Ourso Distinguished Chair at LSU, reflecting institutional esteem for his scholarly impact. External validation comes through continued NBER and IZA appointments, which represent selective recognition within the economics profession. As an active committee member in LSU's business college governance and editorial contributor to field-specific journals, Professor Mocan shapes academic discourse beyond his publications. His media engagements with NPR and The Economist demonstrate public impact, particularly regarding cultural influences on economic outcomes. Though not leading a named laboratory, his research operates through departmental infrastructure at LSU combined with NBER/IZA networks. This collaborative model facilitates international data access and methodological exchange, particularly evident in cross-national studies of prejudice and crime dynamics.

• Data Science
• Internet of Things (IoT)
• Visual Analytics
• +۱۰ مورد دیگر

Professor Dirk J. Lehmann is a Professor of Data Science in IoT at Ostfalia University of Applied Sciences, Faculty of Computer Science, where he has been employed since May 2022. He holds significant leadership roles including Deputy Head of the Institute for Information Engineering (since 2024), Research Officer of the Faculty of Computer Science (since 2023), and membership in multiple committees including the Admissions Committee for Digital Technologies and the Digital Technologies Examination Board. Professor Lehmann's extensive academic journey includes: Part-time professorship in Data Science in IoT at Ostfalia University (2020-2022) Senior Specialist for Digitalization, AI, and Visual Analysis at IAV GmbH (2018-2023) Assistant Professor of Visual Data Analysis at Nazarbayev University, Kazakhstan (2017) Visiting professorships at TU Graz, Austria and Universidad Rey Juan Carlos, Spain (2016-2017) Researcher at Otto-von-Guericke University Magdeburg (2009-2017) His research expertise centers on Visual Analytics and Data Science, with particular emphasis on high-dimensional data visualization, categorical data analysis, and IoT applications. Professor Lehmann leads the Data Science in IoT working group, conducting research across three main areas: visual data analysis, distributed data analysis using AI methods, and applied data analysis in geology, climate data, medicine, and industrial processes. His methodological contributions include innovative visualization techniques for complex datasets across multiple domains. Analysis of Professor Lehmann's 15 most recent publications (2017-2025) reveals a consistent focus on advancing visualization techniques for complex data analysis. His work spans categorical data visualization (CatNetVis), biological data analysis (D. Melanogaster research), optimization of star coordinate systems, and interactive exploration methods for large datasets. These publications appear in top venues including IEEE Transactions on Visualization and Computer Graphics and EuroVis, demonstrating both theoretical rigor and practical application across diverse domains from healthcare to environmental science. As an educator, Professor Lehmann teaches a comprehensive range of courses from foundational mathematics to advanced machine learning and visualization techniques. He actively supervises student projects and theses, emphasizing clear project definitions with measurable acceptance criteria. His international collaborations span institutions in Israel, Saudi Arabia, China, Austria, and Spain, reflecting a global research perspective that bridges academic theory with industry applications, particularly through his previous role at IAV GmbH, a Volkswagen subsidiary.

• Sparse Arrays
• Beamforming
• Sensor Arrays
• +۴ مورد دیگر

Robin Rajamäki is a Visiting Professor in the Department of Information and Communications Engineering at Aalto University, Finland. He is affiliated with the Visa Koivunen Group and holds an ORCID ID (0000-0002-5028-6022). His academic credentials include a Doctor of Technology (Tekn. toht.) in Electrical Engineering (2021), a Master's in Engineering and Technology (2016), and a Bachelor's in Telecommunications Engineering (2014), all from Aalto University. Research interests focus on Sparse Arrays , Beamforming , ISAC (Integrated Sensing and Communications) , and Array Configuration models. His work explores optimal array geometries, waveform design, and identifiability guarantees in active sensing systems, with applications to MIMO radar, millimeter-wave communications, and future 6G networks. Key methodologies include statistical signal processing, machine learning, and computational optimization. Recent publications highlight advancements in generative deep synthesis , array geometry optimization , and sensor array applications in ISAC. His research spans 2015–2025, including 5 projects (e.g., FUN-ISAC, INSTINCT) and collaborations with institutions like the University of California, San Diego (2019–2020), Technion (2017–2018), and University of Pennsylvania (2016). Scientific Awards: Best Student Paper Award (3rd place, 2019) Projects include fundamental limits in ISAC, joint sensing-communications systems for immersive connectivity, and sparse antenna array processing for 6G and millimeter-wave applications. His work has been cited in Scopus and supported by the Academy of Finland and EU Horizon grants.

Işıl Dilligمشاهده پروفایل

دانشیار

• Program analysis
• Program synthesis
• Program verification
• +۴ مورد دیگر

Işıl Dillig is an Associate Professor of Computer Science at the University of Texas at Austin, where she leads the UToPiA research group. Her academic career spans over a decade of significant contributions to programming languages research, particularly in program analysis, verification, and synthesis. Dr. Dillig received all her academic degrees (BS, MS, and PhD) from Stanford University before joining the faculty at UT Austin. Her educational background established the foundation for her innovative research approach that bridges theoretical computer science with practical applications. Her research focuses on developing techniques to make software systems more reliable, secure, and easier to build through advanced program analysis, verification, and synthesis methods. She has pioneered approaches that combine symbolic reasoning with machine learning to tackle complex software engineering challenges across multiple domains including security, databases, and programming language theory. Her work demonstrates exceptional depth in creating practical tools that address real-world software development problems while maintaining strong theoretical foundations. Analysis of Dr. Dillig's publication record reveals a consistent trajectory of innovation in program synthesis, with recent work expanding into neurosymbolic approaches that bridge neural networks with formal methods. Her research shows strong connections between theoretical foundations and practical applications, particularly in security-critical systems, database technologies, and blockchain applications. The evolution of her work demonstrates increasing sophistication in handling complex program structures while maintaining practical usability. Dr. Dillig has received prestigious recognition for her research contributions: Sloan Fellowship NSF CAREER award As a dedicated educator and research leader, Dr. Dillig has served in significant roles including Program Chair for PLDI 2022 and Steering Committee member for PLDI. She has mentored numerous students through her UToPiA research group, guiding research in program synthesis, verification, and analysis. Her work has been supported by substantial research grants that have enabled innovative projects at the intersection of programming languages and security. Dr. Dillig leads the UToPiA (UT Austin Programming, Languages, and Analysis) research group, which focuses on developing novel techniques for program analysis, verification, and synthesis. The group maintains strong collaborations with industry partners and academic institutions worldwide, translating theoretical advances into practical tools that address real software engineering challenges.

Marc Pouzetمشاهده پروفایل

استاد

• Programming Languages
• Synchronous Systems
• Reactive Systems
• +۳ مورد دیگر

Marc Pouzet is a Professor at École Normale Supérieure (ENS) in the Department of Computer Science (DIENS), where he serves as Director of CS studies. He leads the INRIA project-team PARKAS and was a Junior Member of the Institut Universitaire de France (2007–2012). His research centers on synchronous programming languages for safety-critical embedded systems, with contributions to real-time software verification, hybrid systems modeling, and probabilistic reactive programming. Research Focus Pouzet's work bridges theory and practice in: Synchronous Languages : Design/extensions of Lustre, Lucid Synchrone, and Zelus for embedded control Formal Methods : Mechanized semantics (Coq) and verified compilers (Vélus) for correctness guarantees Hybrid Systems : Integrating ODEs with discrete logic (ProbZelus for probabilistic inference) Real-time Systems : Scheduling, latency constraints, and memory-safe compilation Awards & Leadership Inria–Académie des sciences Innovation Award (2016) Program committees: EMSOFT, PLDI, POPL, ECRTS Associate Editor: EURASIP Journal on Embedded Systems Advising & Projects Supervised 19+ PhD students on topics spanning compiler verification (Bourke, Pesin), probabilistic languages (Baudart), and hybrid systems (Pauget). Leads development of open-source tools: Zelus (synchronous language with ODEs) Vélus (verified Lustre compiler) ReactiveML (reactive extension of OCaml)

• Computational Neuroscience
• Perception
• Decision Making
• +۲ مورد دیگر

Ulrik Beierholm is an Associate Professor in the Department of Psychology at Durham University, serving as Department Representative and Fellow of the Durham Research Methods Centre, and holding an Associate Professor position on the Executive Board of the Biophysical Sciences Institute. His research employs computational frameworks to investigate uncertainty processing in perception, decision making, and learning through interdisciplinary methodologies. His core research spans Computational Neuroscience, Perception, Decision Making, Neuroeconomics, and Machine Learning, with emphasis on Bayesian inference and reinforcement learning models validated via psychophysics, fMRI, and pharmacological techniques. Recent work demonstrates how the nervous system optimally handles sensory uncertainty across multisensory contexts, developmental stages, and cognitive tasks. Analysis of his publication trajectory reveals consistent innovation in Bayesian modeling of perceptual processes, with growing emphasis on computational tools development, neuroeconomics applications, and educational initiatives. His work bridges theoretical neuroscience with experimental validation across diverse cognitive domains. Honors include: Facebook Faculty Award for Virtual Reality (2016) Associate Editor for PLoS Computational Biology Editor for Bayesian methods section in Springer's Encyclopedia of Computational Neuroscience Dr. Beierholm supervises postgraduate researchers including Denise Foresteire and has co-organized influential workshops such as the Durham Computational Biology Symposium (2018) and Probabilistic Brain Workshop (2018), establishing himself as a key collaborator in computational neuroscience communities. His dual affiliations with the Biophysical Sciences Institute and Durham Research Methods Centre facilitate cross-disciplinary integration of psychological theory, neural data, and advanced computational modeling to address fundamental questions about human cognition.

• Cognitive Neuroscience
• Computational Modeling
• Predictive Coding
• +۴ مورد دیگر

Ryszard Auksztulewicz serves as Visiting Professor and Group Leader of the Prediction and Memory unit within the Department of Education and Psychology at Freie Universität Berlin. Based at Habelschwerdter Allee 45, Room JK 25/220 in Berlin, he leads research at the intersection of cognitive and systems neuroscience. His research focuses on neural mechanisms of prediction, attention, and memory , employing multimodal approaches including EEG, fMRI, MEG, ECoG, TMS, electrophysiology, and calcium imaging. He integrates these with computational modeling (Bayesian models of neural/behavioral data) and multivariate decoding techniques. Key research themes include: Predictive coding frameworks for understanding sensory processing Neural gain modulation in temporal expectation Auditory cortex representations of memory and prediction Task-dependent modulation of sensory predictions Recurrent processing in perceptual awareness Analysis of his 34 publications (h-index: 18) reveals consistent focus on computational neuroscience approaches to memory systems , with recent work emphasizing cross-species auditory processing and neural representation dynamics. His 2022-2018 publications particularly highlight mechanistic distinctions between "what" and "when" predictions in sensory cortex. Teaching responsibilities include: Probabilistic and Statistical Modeling (Lecture) Introduction to Programming (Seminar) Research Colloquium for Qualification Theses

Timothy Beckerمشاهده پروفایل

استادیار

• Genomics
• Deep Learning
• Generative Modeling
• +۸ مورد دیگر

Timothy Becker serves as the John D. MacArthur Assistant Professor of Computer Science at Connecticut College, where he joined in 2023. His research focuses on transforming complex datasets into functional instruments through advanced computational techniques. His academic background includes: B.M. in Music Production from The Hartt School of Music B.S. in Computer Science from the University of Hartford Ph.D. in Computer Science and Engineering from the University of Connecticut Becker specializes in genomics applications but maintains active interdisciplinary collaborations in ecology and transportation. His core methodology integrates deep learning, generative modeling, and data visualization to develop open-source software tools. He emphasizes practical implementations for real-world data analysis and creates interactive tutorials to foster persistent student learning. His publication record from 2018-2025 reveals consistent contributions to bioinformatics and environmental data science, particularly in structural variation analysis and river connectivity modeling. Key trends include developing neural network frameworks for genomic data integration and creating visualization methods for multi-omic datasets. Becker actively seeks undergraduate research collaborators, encouraging students with domain-specific data or research questions to initiate projects. His interdisciplinary approach connects computer science with life sciences and environmental studies through numerous cross-institutional partnerships.

Yang Liمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Bayesian Nonparametrics
• Scalable Optimization
• +۵ مورد دیگر

Yang Li serves as Associate Professor of Marketing and Associate Dean for the MBA Program at Cheung Kong Graduate School of Business (CKGSB). Holding a PhD in Marketing from Columbia Business School alongside dual master's and bachelor's degrees from Columbia and Peking University respectively, he bridges advanced statistical methodologies with practical business applications. His research centers on statistical machine learning and Bayesian nonparametrics applied to consumer behavior analysis, with specialization in online personalization, text mining, and choice modeling. Recent work demonstrates significant focus on fragmented attention economies, ethical AI frameworks, and NFT network dynamics, reflecting contemporary digital market challenges. Management Science Marketing Science Journal of Marketing Research Journal of Consumer Research Harvard Business Review Professor Li's publications reveal evolving expertise from foundational pricing elasticity studies toward cutting-edge AI applications in consumer contexts. His work increasingly integrates generative models and graph neural networks to decode complex consumer collection behaviors and digital ecosystem dynamics. Scientific recognition includes being a Finalist for the 2021 Paul E. Green Best Paper Award. Industry impact is demonstrated through executive education programs and strategic consultancies with Tencent, Haier, and Tmall. As Associate Dean for MBA Programs, he oversees curriculum development while maintaining active corporate governance roles on boards of publicly traded companies across China and Hong Kong, directly applying his research insights to strategic decision-making in digital transformation initiatives.

Marenglen Bibaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۶ مورد دیگر

Marenglen Biba is a Full Professor and Dean of the Faculty of Engineering and Architecture at the University of New York Tirana, where he also chairs the Department of Computer Science. His academic journey includes a Ph.D. in Computer Science from the University of Bari, Italy (2009), and a Laurea degree cum laude from the same institution (2004). His research spans: Core AI/ML : Machine learning algorithms, statistical relational learning, pattern recognition Language technologies : NLP for Albanian, stemming, morphological analysis Applied domains : Computational biology, social network mining, emotion detection Recent publications (2018–2021) focus on NLP for low-resource languages, deep learning surveys, and emotion-aware data mining. Award highlights include: ICT Academic of the Year 2012 (Albania) Best Paper Award at ILP 2008 Multiple University of Bari research grants He has led EU-funded projects like ADRIATinn (€4.6M budget) and maintains collaborations with the University of Washington, University of Bari, and Asian universities. Administrative leadership includes decade-long oversight of UNYT's Computer Science department and current deanship.