جست‌وجوی استاد و پژوهشگر

Hyeunjung (Elina) Hwangمشاهده پروفایل

دانشیار

• Crowdsourcing
• Data Mining
• Economics of Information Systems
• +۵ مورد دیگر

Hyeunjung (Elina) Hwang is an Associate Professor of Information Systems at the Foster School of Business, University of Washington , where she has been since 2015. She holds the Evert McCabe Endowed Fellowship in Private Enterprise . Ph.D. in Business Technologies, Carnegie Mellon University MBA, University of California, Los Angeles MS in Computer Science, Yonsei University BA in Computer Science, Sogang University Her research focuses on social and business implications of emerging technologies like AI, particularly examining how these technologies transform information sharing, social interactions, and learning. Key areas include crowdsourcing , misinformation diffusion , health IT , open innovation , and economics of information systems . Hwang's publications span top journals such as Management Science , Information Systems Research , and Manufacturing & Service Operations Management , often analyzing large-scale field data using econometrics, network analysis, and machine learning. Her work intersects information systems, economics, sociology, and operations management. Recent research trends include: Misinformation mitigation strategies on social media Healthcare technology adoption patterns Crowdsourcing innovation dynamics Online-offline retail integration Social network effects in professional platforms Telemedicine equity implications Key honors include the INFORMS Information Systems Society Sandy Slaughter Early Career Award (2024) , Dean’s Excellence Award for Undergraduate Teaching (2022) , and Gordon B. Davis Young Scholar Award (2021) . She serves as Associate Editor at Information Systems Research and actively participates in academic governance. She teaches core courses in data management, database systems, and information technology at undergraduate, MBA, and MSBA levels.

• Smart City
• Middleware
• Wireless Sensor Networks
• +۵ مورد دیگر

Félix Jesús Villanueva Molina is an Associate Professor at the University of Castilla-La Mancha, specifically in the Department of Information Technologies and Systems. His academic career demonstrates a strong focus on the intersection of computer science, smart technologies, and practical applications for improving urban and healthcare environments. His educational background includes a Ph.D. in Computer Science from the University of Castilla-La Mancha (2009), along with research stays at Friedrich-Alexander-Universität Erlangen-Nürnberg in Germany and the University of Twente in the Netherlands. These international experiences have contributed to his interdisciplinary research approach. Villanueva Molina's research spans multiple domains within computer science and technology. His primary interests focus on smart city infrastructure, middleware development, wireless sensor networks, and Internet of Things applications. More recently, his work has expanded into healthcare technologies specifically designed for elderly care and active aging. His publication record reveals a clear progression from foundational work in distributed systems and ambient intelligence toward increasingly applied solutions addressing real-world challenges. His scientific contributions demonstrate significant expertise in developing middleware solutions for smart environments, with a particular emphasis on creating practical implementations that bridge theoretical computer science with tangible applications. His work often involves interdisciplinary collaboration, particularly with healthcare professionals when developing technologies for elderly care. Throughout his career, Villanueva Molina has maintained an active research profile, with consistent publication output spanning over a decade. His recent work shows a strategic shift toward applications with direct societal impact, particularly in the domains of smart cities and healthcare technologies for aging populations. He has been involved in developing innovative solutions for urban management, environmental monitoring, and healthcare support systems. His technical expertise in distributed systems, sensor networks, and data processing frameworks has enabled the creation of comprehensive solutions addressing complex real-world problems.

Simon Prestonمشاهده پروفایل

مدرس

• Statistical inference on manifolds
• Shape analysis
• Spherical data analysis
• +۶ مورد دیگر

Simon Preston is a Lecturer at the School of Mathematical Sciences , University of Nottingham. He obtained his MMath and PhD in Mathematics from Nottingham, supervised by Sarah Waters and Oliver Jensen. His research focuses on statistical inference for manifold-valued data , including shape analysis, spherical distributions, and stochastic differential equations. He has contributed to methods like ESAG distribution for anisotropic spherical data and shapePCA for analyzing shape variation via principal components. Recent publications address network analysis on manifolds , non-parametric regression , and Bayesian model selection in biological and urban contexts. His work spans applications in immunology (T-cell motility), plant architecture, and computational linguistics. Scientific Awards : RCUK Academic Fellowship EPSRC Statistics Mobility Fellowship He has collaborated extensively with Andy Wood, Sarah Waters, and Oliver Jensen, and his technical work includes MATLAB toolboxes for shape analysis and stochastic differential equation inference .

Daniele Bernardiniمشاهده پروفایل

پژوهشگر

• Robotics
• Neurorehabilitation
• Machine Learning
• +۳ مورد دیگر

Daniele Bernardini is a researcher at the School of Engineering and Design, Technical University of Munich (TUM), contributing to Healthcare and Rehabilitation Robotics under Prof. Cristina Piazza. He co-founded and leads Cognivix, a startup focused on industrial automation for high-variability/low-volume production. Education : M.Sc. in Theoretical Physics, University of Florence (1997) Ph.D. candidate in Computation, Information and Technology, TUM (since 2022) Research Focus : Robotic perception for manipulation tasks Deep learning in grasping systems 6D pose estimation techniques Photovoltaic energy management frameworks Recent Publication Trends : His 2022-2024 work spans robotics (6D pose estimation, bionic hands), machine learning (deep reinforcement learning), and energy systems (photovoltaic optimization), reflecting interdisciplinary efforts between AI and industrial applications.

• Bioinformatics
• Computational Biology
• Protein Structure Prediction
• +۳ مورد دیگر

Tobias Olenyi is a researcher at the Department of Informatics, Technical University of Munich (TUM), working under the Chair for Bioinformatics led by Prof. Dr. Burkhard Rost. Holding an M.Sc. degree, he contributes to computational biology research with focus on protein structure analysis and machine learning applications. His office is located at Boltzmannstr. 3, 85748 Garching bei München (Room 5609.01.055) with office hours Tuesday and Wednesday 10:00-17:00. Education: M.Sc. in relevant computational/biological field Research Interests: Olenyi's work centers on bioinformatics and computational biology , specifically protein structure prediction, visualization, and functional analysis. He leverages deep learning (protein language models, embeddings) and structural bioinformatics to develop tools for protein space visualization (FlatProt, ProtSpace), transmembrane protein analysis (TMVisDB), and phenotype prediction (LambdaPP). His research bridges computational methods with biological applications, emphasizing accessibility through interactive platforms and crowdsourced data collection. Publication Trends: From 2018-2025, Olenyi's work shows a clear trajectory toward protein-centric machine learning . Early projects focused on crowdsourced data platforms (VoiLA, 2018; gamified collection, 2019), while 2021-2022 publications established his expertise in protein embeddings for variant effect prediction and Gene Ontology annotation. His 2025 output demonstrates maturation into specialized visualization tools (FlatProt, TMVisDB), reflecting a cohesive research program that integrates AlphaFold2 structures and deep learning to solve protein analysis challenges. Laboratory and Team Affiliation: Olenyi operates within the Rost Lab at TUM—a globally recognized bioinformatics group specializing in protein structure prediction. The lab maintains strong industry/academic collaborations and emphasizes open-source tool development (e.g., PredictProtein server). His work directly supports the chair's mission to translate computational advances into biological insights, particularly in protein function and disease mechanisms.

Erik Andersenمشاهده پروفایل

استادیار

• Information Science
• Cognitive Science
• Educational Technology
• +۷ مورد دیگر

Erik Andersen is an Assistant Professor at the Department of Computer Science in the College of Engineering at Cornell University . His research focuses on Educational Technology , Human-Computer Interaction , and Game Design . He develops interactive systems that use games for education, particularly in programming and language learning domains. Teaching: CS/INFO 6360 (Educational Technology), CS/INFO 4154 (Analytics-driven Game Design) Key Collaborations: Working with Zoran Popović and Ross Knepper on educational game frameworks Research Themes include gamified learning, adaptive educational systems, and AI-driven progress analysis. His recent work explores progression synthesis in Korean language games and implicit communication in human-AI teams. He uses techniques from machine learning and data visualization to create effective educational tools. Scientific Recognition: Mr. and Mrs. Richard F. Tucker '50 Teaching Award (2017) Minister of Education Japan Prize (2011) Grand Prize at Disney Learning Challenge (2010) Best Paper Nomination at AIIDE (2012)

• Data Management
• Mobile Data Management
• Sensor Networks
• +۹ مورد دیگر

Demetris Zeinalipour is a Professor of Computer Science at the University of Cyprus, where he founded and directs the Data Management Systems Laboratory (DMSL). He holds a Ph.D. (2005) and M.Sc. (2003) in Computer Science and Engineering from the University of California - Riverside, CA, USA and a B.Sc. in Computer Science from the University of Cyprus (2000). His career includes research positions at Akamai Technologies, University of Athens (as a Marie-Curie Fellow), University of Pittsburgh, and the Max Planck Institute for Informatics (as a Humboldt Fellow). He is an ACM Distinguished Speaker (2017-2020), a Senior Member of ACM and IEEE, and serves on the editorial boards of ACM Transactions on Spatial Algorithms and Systems and Distributed and Parallel Databases. His primary research spans Data Management in Computer Systems and Networks, with expertise in Mobile, Sensor and Spatio-Temporal Data Management; Big Data Management in Parallel and Distributed Architectures; Network, Blockchain and Telco Data Management; Crowd, Web 2.0 and Indoor Data Management; Data Privacy Management; Data Management for Sustainability; and AI and Data Management. His work bridges theoretical foundations with practical applications, resulting in numerous systems including Anyplace Indoor Information Service, ChatUCY, GreenCap, EcoCharge, and Triabase. His research has evolved from early work in sensor networks to current focus on sustainable computing applications addressing climate change challenges. Professor Zeinalipour's publication record shows a strong trajectory toward sustainability-focused data management, with recent work on energy management for smart homes, electric vehicle charging, and solar self-consumption. His research integrates multiple disciplines including spatial databases, mobile computing, IoT systems, and blockchain technologies, with increasing emphasis on addressing real-world environmental challenges through computing solutions. Scientific Recognition IEEE ICDE'24 Outstanding Reviewer Award IEEE MDM'21 Best App Paper Award IEEE MDM'18 Best of MDM Award and Best Demo Award Humboldt Fellow (2016) ACM Distinguished Speaker (2017-2020) ACM and IEEE Senior Member status He has advised numerous PhD students and postdocs, many of whom have established successful careers in both academia and industry. His laboratory maintains strong industry connections and contributes to open-source development. Professor Zeinalipour is deeply committed to addressing real-world challenges through computing research, with recent work focusing on climate change mitigation and sustainable energy management solutions.

• Spatiotemporal Data Management
• Spatial Crowdsourcing
• Routing Algorithms
• +۳ مورد دیگر

Mario Nascimento is a Professor at the University of Alberta 's Department of Computing Science within the Faculty of Science. He previously served as department Chair from 2014-2021 (with a 2019-2020 leave) and holds affiliations with institutions including the National University of Singapore , Aalborg University , and others. His current leave-of-absence status allows focusing on specific research initiatives.

Heike Hofmannمشاهده پروفایل

استاد

• Data Visualization
• Statistical Computing
• Machine Learning
• +۴ مورد دیگر

Heike Hofmann is a Professor at the University of Nebraska-Lincoln. Her work focuses on data visualization, statistical computing, and forensic statistics, with a strong emphasis on developing open-source software tools for data exploration in R. She actively contributes to academic research and software development. Current Institution: University of Nebraska-Lincoln Email: hofmann@iastate.edu Her research spans topics such as forensic toolmark analysis , machine learning explainability , and interactive visualization frameworks . She designs algorithms for bullet comparison and error rate assessment in forensic science, leveraging 3D imaging and statistical diagnostics . Recent publications highlight her work on human factors in visual inference , automated forensic matching , and reproducible pipelines for functional data. Notably, she creates tools like the cmcR package for bullet land scan comparison and nullabor for statistical significance calculations. Heike Hofmann contributes to software development with R packages including ggparallel (parallel coordinate plots), gglogo (sequence logos), and rotations (3D rotation analysis). Her GitHub activity shows consistent commits to statistical computing projects.

Austin R. Bensonمشاهده پروفایل

استادیار

• Computer Science
• Data Science
• Applied Mathematics
• +۵ مورد دیگر

Austin R. Benson is an Assistant Professor of Computer Science at Cornell University , with field memberships in Applied Math and Data Science. His research develops computational frameworks for analyzing complex data to understand network connectivity and decision-making patterns. Research supported by the Army Research Office and National Science Foundation , including an NSF CAREER Award . Core areas: Higher-order Network Analysis , Hypergraph Clustering , Discrete Choice Modeling , and Graph Neural Networks . Key contributions: theoretical advancements in hypergraph properties , submodular optimization , and temporal network modeling . His 15 most recent publications focus on higher-order structures in networks, hypergraph learning, and discrete choice models. Awards include the Stanford ICME Gene Golub Doctoral Dissertation Award , KDD Best Paper (2019), and ASONAM Best Paper Runner-up (2019).

Mor Naamanمشاهده پروفایل

استاد

• AI-Mediated Communication
• Computational Social Science
• Data Science
• +۵ مورد دیگر

Mor Naaman is the Don and Mibs Follett Professor of Information Science at Cornell Tech, where he leads the Social Technologies research group and serves as Associate Dean for Faculty Affairs at the Jacobs Technion-Cornell Institute. His career spans academia, industry research (Yahoo! Research Berkeley), and entrepreneurship (startup co-founder). Cornell Tech, 2012-present Rutgers School of Communication and Information, 2008-2012 Yahoo! Research Berkeley, 2005-2008 Stanford University, Ph.D. in Computer Science His research focuses on Technology, Media and Democracy , particularly AI-mediated communication's societal impact and information ecosystem trustworthiness. The lab employs multidisciplinary methods (machine learning, experiments, qualitative analysis) to address misinformation, algorithmic bias, and digital governance challenges. Recent work examines AI-generated content moderation Visual misinformation tracking Language model cultural bias Workplace AI communication dynamics Political misinformation campaigns Notable scientific awards include NSF Early Faculty CAREER Award Microsoft Research Award LinkedIn-Bowers CIS Strategic Partnership award Vannevar Bush Best Paper Awards (JCDL 2007, JCDL 2004) Best Paper Honorable Mentions (CHI 2023, CHI 2016) As an advisor , he has mentored numerous PhD students in areas like computational social science and digital democracy. His research is supported by grants from National Science Foundation Microsoft Meta (Facebook) Google The Social Technologies Lab (founded 2012) examines AI-Mediated Communication implications Healthy information ecosystems Trust, credibility and misinformation Collaborative fact-checking systems

Emma Piersonمشاهده پروفایل

استادیار

• Machine Learning
• Data Science
• Health Equity
• +۳ مورد دیگر

Emma Pierson is an Assistant Professor of Computer Science at the University of California, Berkeley, and holds a parallel appointment as Assistant Professor at Cornell Tech (Jacobs Technion-Cornell Institute) with a secondary joint position in Population Health Sciences at Weill Cornell Medical College. She is affiliated with the Berkeley AI Research Lab, Computational Precision Health, and the Center for Human-Compatible AI. Education & Career Path: PhD in Computer Science, Stanford University (Jure Leskovec lab) MSc in Statistics, University of Oxford (as a Rhodes Scholar) Former Senior Researcher, Microsoft Research New England Former Data Scientist, 23andMe and Coursera Research Focus: Professor Pierson develops machine-learning techniques to advance health and social science, with a strong emphasis on reducing inequality and improving healthcare delivery . She leverages large language models, sparse autoencoders, and other modern tools to uncover hidden biases, design fairer algorithms, and generate novel scientific hypotheses from complex datasets. Across more than 50 peer-reviewed publications, her work repeatedly combines computational innovation with real-world impact —from documenting racial inequities in pain management ( Nature Medicine , 2021) to modeling COVID-19 disparities ( Nature , 2021) and quantifying urban segregation via mobility networks ( Nature , 2023). Recent trends show an intensified focus on LLM-driven discovery and evidence-based AI policy , with forthcoming papers in Science (2025) and New England Journal of Medicine AI (2025). Scientific Honors: NSF CAREER Award Rhodes Scholarship & Hertz Fellowship MIT Technology Review 35 Innovators Under 35 Forbes 30 Under 30 in Science AI2050 Early Career Fellowship Samsung AI Researcher of the Year (2023) Best Paper Awards – KDD, AISTATS, CHIL Advising & Funding: Prof. Pierson is actively recruiting PhD students for Fall 2026 and welcomes post-doctoral researchers, master’s students, and undergraduates. Her group is supported by the NSF, the AI2050 program, and various industry and philanthropic partners. Labs & Teams: She leads an interdisciplinary research group spanning UC Berkeley EECS and Cornell Tech. Collaborations include clinicians at UCSF and Weill Cornell, social scientists at Stanford, and policy experts at the Berkeley Center for Human-Compatible AI.

Cyril Faucherمشاهده پروفایل

دانشیار

• Temporal Knowledge Modeling
• Model-Driven Engineering
• Semantic Web
• +۳ مورد دیگر

Cyril Faucher is an Associate Professor at the University of La Rochelle , associated with both the L3i Laboratory and the IUT of La Rochelle 's Computer Science department. He has served since 2013 as the head of the Integrator Developer (ID) course within the DUT Informatique program. His research focuses on Temporal Knowledge Modeling Model-Driven Engineering Semantic Web Applications Periodic Event Simulation Human Activity Modeling He has contributed to multiple research projects including ANR DéAIS (2014-2016), HOSEN (2013-2016), and Tourinflux (2013-2015). His work has been implemented in the RelaxMultiMedias ANR project (2009-2012) for cultural event modeling. Professor Faucher teaches across various IT domains including Database Systems (Oracle, MySQL) Web Development (PHP, JavaScript, jQueryMobile) Object-Oriented Programming (Java) Enterprise Architecture (UML, TOGAF) Software Project Management (Agility, SCRUM) Model-Driven Engineering He has developed several open-source tools for model engineering including Kermeta, OpenEmbeDD, and MDE Utils.

Maik Thieleمشاهده پروفایل

استاد

• Database Systems
• Data Engineering
• Big Data Analytics
• +۴ مورد دیگر

Prof. Dr.-Ing. Maik Thiele is a Professor at the Faculty of Computer Science and Mathematics , Dresden University of Applied Sciences. He leads the Professorship for Database Systems , focusing on advanced database technologies, big data platforms, and data engineering. Academic Leadership: Dean of Master's program in Applied Computer Science (since 2021), elected member of Faculty Council (since 2024) Research: Data analysis along the data value chain from acquisition to AI-driven processing, with emphasis on Active Learning , Transformer Models , and Mathematical Information Retrieval Applied Projects: Director of Dresden Database Forum , member of Digital Transformation Development Workshop (EDiT) His recent work analyzes Active Learning strategies for biomedical image classification and synthetic data, develops time series matching techniques , and explores ethical AI evaluation for e-commerce chatbots. He supervises student theses on topics ranging from PDF data extraction to Smart Grid monitoring and Speech-to-Text optimization . Professor Thiele serves on program committees for leading conferences including ADBIS , DAWAK , and DOLAP , and contributes to journals like Transactions on Knowledge and Data Engineering and International Journal on Very Large Data Bases .

Nathan B. Gawمشاهده پروفایل

استادیار

• Machine Learning
• Multimodal Data Fusion
• Healthcare Telemonitoring
• +۷ مورد دیگر

Dr. Nathan B. Gaw is an Assistant Professor of Data Science at the Air Force Institute of Technology (AFIT), where he develops advanced statistical machine learning algorithms for multimodal data fusion in defense, healthcare, and remote sensing applications. He holds a Ph.D. in Industrial Engineering from Arizona State University (2019) and completed postdoctoral research at both the ASU-Mayo Clinic Center for Innovative Imaging (2019-2020) and Georgia Institute of Technology's School of Industrial and Systems Engineering (2020-2021). Research Interests: His work focuses on three core areas: Feature extraction/selection using tensor decomposition and functional data analysis Spatio-temporal neural networks with attention-based ConvLSTMs Image registration/fusion through convolutional neural networks Key application domains include aviation human factors, weather remote sensing, and cybersecurity. Scientific Awards: Dr. Gaw has received 14 major scientific awards including: AFWERX Datathon Challenge (5 awards) INFORMS Principal Cup (2nd place) Harold Wolff-John Graham Award (American Academy of Neurology) Tau Beta Pi Fellowship Dean's Fellowship, ASU Professional Engagements: He serves as chair of the INFORMS Data Mining Society and council member of IISE Data Analytics Division, while maintaining IEEE membership. His 16 peer-reviewed publications (12 Q1, 4 Q2) include a U.S. patent and book chapter contributions.