جست‌وجوی استاد و پژوهشگر

• Time series analysis
• Changepoint analysis
• Signal processing
• +۶ مورد دیگر

Xueheng Shi is an Assistant Professor in the Department of Statistics at the University of Nebraska-Lincoln (UNL), with a joint appointment in Biological System Engineering . He joined UNL in August 2022 after postdoctoral positions at UC Santa Cruz (2020–2021) and UC Davis (2021–2022) under mentors Robert Lund, Alexander Aue, and Thomas Lee. Dr. Shi teaches graduate courses including mathematical statistics, asymptotic theory, probability, stochastic processes, and time series analysis. Research Interests: Dr. Shi's work focuses on developing statistical methodologies for time series analysis , changepoint detection , and high-dimensional data . Key applications include climate science, signal processing, and machine learning. His research emphasizes computational algorithm development and theoretical foundations across domains like climate modeling and stochastic optimization. Publications: Recent articles (2019–2024) concentrate on climate change detection , particularly global warming trends and temperature series analysis. Methodological innovations include autocovariance estimation around changepoints, comparative studies of detection algorithms, and reviews of best practices in climatological statistics. Dominant themes are changepoint techniques, environmental data diagnostics, and statistical computing. Advising: Dr. Shi actively mentors graduate students in statistics and invites prospective applicants interested in his research areas to contact him directly.

• Data Quality
• Database Systems
• Data Profiling
• +۷ مورد دیگر

Felix Naumann is a Professor at the Hasso Plattner Institute in Potsdam, Germany, specializing in data management and database systems. His research focuses on data quality, data profiling, entity resolution, functional dependencies, and database optimization. He has authored over 300 publications in top-tier conferences and journals, including VLDB, SIGMOD, and ICDE. His work bridges theoretical foundations with practical systems, such as TASHEEH for data cleaning and PRISMA for privacy-preserving schema matching. He serves as an editor for the ACM Journal of Data and Information Quality and has led initiatives on hybrid conference formats and inclusive computing. Key contributions include: Data Quality: Pioneered studies on data quality's impact on machine learning and developed tools like AutoTSAD for anomaly detection. Data Dependency Discovery: Advanced functional and inclusion dependency mining algorithms, including Hitting Set Enumeration methods. Schema Matching & Integration: Created systems like BrewER and Frost for entity resolution and schema alignment. Educational Impact: Led massive open courses in data engineering with over 10,000 participants. His work emphasizes practical applications in cultural heritage data (ReCLAIM), Wikipedia table analysis, and cross-platform data systems (RHEEMix). He collaborates extensively with industry and academia, addressing challenges in dynamic datasets and data governance.

• Cybersecurity
• Computer Security
• Information Security
• +۶ مورد دیگر

Matthew Allen Bishop is a Professor in the Department of Computer Science at the University of California, Davis. His primary affiliation is with the College of Engineering. Bishop's research focuses on cybersecurity, including secure programming, insider threat detection, malware analysis, and cybersecurity education. He has contributed extensively to curricular guidelines (e.g., CSEC 2017) and frameworks for cyber defense. His work spans theoretical advancements (e.g., intrusion detection models) and applied systems (e.g., secure voting platforms). Notable research areas include: Cybersecurity Education: Developing curricula and pedagogical frameworks for secure coding and ethical practices. Insider Threat Mitigation: Declarative approaches and behavioral analysis for detecting and preventing attacks. Malware Mitigation: Techniques leveraging uncertainty principles and defensive programming. Election Security: Analyzing vulnerabilities and designing secure voting systems. Bishop has collaborated with institutions like the Department of Homeland Security (DHS) and National Security Agency (NSA) on critical infrastructure protection. His publications span conferences like IEEE Security & Privacy, HICSS, and NSPW, emphasizing real-world applications of cybersecurity principles.

Radek Paluszynskiمشاهده پروفایل

دانشیار

• International Economics
• Macroeconomics
• Time Inconsistency
• +۳ مورد دیگر

Radek Paluszynski is an Associate Professor in the Department of Economics at the University of Houston. His research focuses on International Economics, Macroeconomics, and Time Inconsistency, with notable work on sovereign debt dynamics, globalization effects, and financial crises. He holds a mailing address at 3581 Cullen Boulevard Suite 230, Houston, TX 77204. Recent research trends include analyzing global macroeconomic linkages, sovereign default risks, and the interplay between fiscal and monetary policy. His work often combines theoretical models with empirical evidence to address policy-relevant questions. Notable contributions include the 2025 paper Globalization Accounting , presented at the NBER Summer Institute. No scientific awards are explicitly listed. Advising and grants information is not detailed here. His research interests span interdisciplinary topics such as public finance mechanisms, shadow financing systems, and the effects of intangible capital on economic growth.

Tamara Sumnerمشاهده پروفایل

استاد

• AI in Education
• Classroom Discourse Analysis
• Educational Technology
• +۴ مورد دیگر

Tamara Sumner is a Professor at the Institute of Cognitive Science , University of Colorado. Her research focuses on leveraging AI and educational technology to improve teaching practices, particularly in STEM education, and fostering equitable learning opportunities. She co-leads the Institute for Student-AI Teaming (iSAT), reimagining AI's role in education. Her work emphasizes classroom discourse analysis, teacher professional development, and rural STEM pathways. Key research areas include: AI tools for automating feedback on teacher-student interactions Equity-focused learning analytics and visualizations Rural youth engagement in STEM through community partnerships Integration of computational thinking and sensor technologies in K-12 curricula Her recent articles highlight advancements in automated discourse analysis, equity-driven tools like the SEET system, and AI-augmented tutoring models. She has contributed to grants such as the BIGDATA: IA initiative (2018) and co-designed programs like DaSH Home for remote learning. Her work bridges research and practice, involving educators and communities in co-design processes. Current initiatives aim to address systemic inequities through technology, such as visual learning analytics for classroom reflection and STEM career pathways for underserved rural populations.

Shivanjali Khareمشاهده پروفایل

استادیار

• Internet of Things (IoT) Data Security
• Hybrid Cryptography
• Wireless Sensor Technologies
• +۳ مورد دیگر

Dr. Shivanjali Khare is an Assistant Professor in the Computer Science department at the University of New Haven, affiliated with the Tagliatela College of Engineering. She leads the SARI (Security and ARtificial Intelligence) Research Lab and teaches courses in Artificial Intelligence and Data Mining. Her research focuses on enhancing cybersecurity through hybrid cryptography, IoT data security, machine learning, and wireless sensor technologies to empower user protection in the digital age. Education: Ph.D. and M.S. in Computer Science from the University of Louisiana at Lafayette (2021 and 2016, respectively). Research Interests: IoT Data Security Hybrid Cryptography Systems Wireless Sensor Networks Big Data Sharing Mechanisms AI-Driven Cybersecurity Solutions Publications highlight contributions in IoT security protocols, cryptographic algorithm optimization, and machine learning applications. Notable work includes energy-efficient secure IoT drone systems (ESIoD) and ensemble learning for anomaly detection in smart homes. Recent outreach includes conducting cybersecurity seminars for senior citizens through the New Haven Free Public Library, demonstrating her commitment to societal impact and public education.

Franziska Pannachمشاهده پروفایل

استادیار

• Computational Linguistics
• Digital Humanities
• Semantic Web Technology
• +۴ مورد دیگر

Dr. Franziska Pannach is an Assistant Professor at the Faculty of Arts of the Rijksuniversiteit Groningen (Netherlands), affiliated with the Centre for Language and Cognition (CLCG) . She specializes in Computational Linguistics , Digital Humanities , and Semantic Web Technology , focusing on modeling narratives and mythological texts using NLP and ontologies. Her work emphasizes Open Science and cross-cultural comparisons of folktales and myths. Education : Dr. rer. nat. (PhD) in Applied Computer Science (Digital Humanities/Computational Linguistics), University of Göttingen, Germany (2024) MSc in Applied Computer Science (Digital Humanities), University of Göttingen (2019) BSc in Applied Computer Science (Computational Neuroscience), University of Göttingen (2013) Research Interests include computational approaches to mythological and folkloristic analysis, shallow ontologies for narrative comparison, and semantic web applications in cross-cultural studies. She leads the GOLEM (Graph Ontologies for Literary Evolution Models) project and contributed to the STRATA (Stratification Analyses for Mythic Narrative Materials) project. Teaching : Semantic Web Technology (MSc), Database-driven Web Technology (BSc), Digital Humanities: Tools and Methods (MSc), Analysing Data (MSc) Supervision : PhD students Pritha Majumdar (RUG) and Kristina Schneider (RUG/University of Mainz) Publications focus on narrative ontologies, mythological event annotation, and NLP for under-resourced languages. Her recent work includes the GOLEM Triple Store and shallow ontologies for myth comparison. Networks & Memberships : Member, Digital Humanities Association of Southern Africa (DHASA) since 2019 Co-founder and editor of the Journal of Digital Humanities Association of Southern Africa (JDHASA) Collaborations with institutions in Germany, South Africa, and Denmark

Ahmed Sherifمشاهده پروفایل

دانشیار

• Cybersecurity
• Cryptography
• Autonomous Vehicles
• +۲ مورد دیگر

Ahmed Sherif is an Associate Professor specializing in cybersecurity and privacy-preserving systems, likely affiliated with the University of Southern Mississippi. He holds a Ph.D. from Tennessee Technological University (2017) and M.S. from Egypt Japan University of Science & Technology (2014). His research focuses on cryptographic solutions for autonomous vehicles, IoT networks, and smart grid security. Research interests span privacy-preserving systems across transportation and critical infrastructure, with emphasis on authentication protocols and hardware security. Teaches courses including Digital Logic, Network Security, and Advanced Security Techniques. Publications show strong focus on privacy engineering in emerging technologies (2021-2025), particularly in vehicular/drone networks and medical AI. No awards listed. Research includes federated learning for medical diagnosis and hardware-accelerated security frameworks.

Ayman El-Hagمشاهده پروفایل

دانشیار

• Electrical Engineering
• High Voltage Engineering
• Power Systems
• +۵ مورد دیگر

Ayman El-Hag is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He is affiliated with the Outdoor Insulation and Condition Monitoring Research Group, focusing on advancing technologies for high-voltage insulation systems and smart grid infrastructure. His work integrates machine learning, signal processing, and materials science to improve condition monitoring and diagnostic methods for power equipment. Research interests include partial discharge detection using UHF and acoustic sensors, machine learning applications for defect classification in outdoor insulators, and the development of non-invasive sensing techniques for real-time monitoring. He also explores energy management systems leveraging fuzzy logic and smart meter data analysis for residential and grid-level applications. Recent publications highlight advancements in capsule networks for insulator discharge prediction, deep learning-based hydrophobicity classification, and novel antenna designs for partial discharge localization. His work emphasizes practical solutions for power system reliability and environmental resilience of insulation materials under extreme conditions. El-Hag is a Full-time faculty member and holds Adjunct faculty status, contributing to interdisciplinary research projects. He actively engages in promoting condition monitoring methodologies through educational initiatives and industry partnerships.

Urs Hengartnerمشاهده پروفایل

دانشیار

• Information Privacy
• Computer Security
• Network Security
• +۳ مورد دیگر

Urs Hengartner is an Associate Professor at the Department of Computer Science, University of Waterloo. His research focuses on information privacy, computer and network security with emphasis on smartphones, IoT, and machine learning-based authentication systems. He holds a Ph.D. (2005) and M.Sc. (2003) from Carnegie Mellon University, and a Diploma from ETH Zürich (1997). His work spans Adaptive security attacks on ML systems Implicit user authentication frameworks Privacy-preserving technologies for location and genomic data Secure authentication systems resilient to voice/spoofing attacks Recent publication trends show a strong focus on adversarial attack detection (e.g., watermarking evasion, diffusion model attacks) and context-aware authentication systems . His frameworks like MRAAC and SHRIMPS address multi-stage authentication challenges in mobile ecosystems. Key contributions include frameworks for evaluating multi-user authentication systems (SHRIMPS), risk-aware access control (MRAAC), and novel defense strategies against collaborative robot traffic fingerprinting. His work bridges security mechanisms with user-centric design principles.

Katerina Sotirakiمشاهده پروفایل

استادیار

• Theoretical Cryptography
• Post-Quantum Cryptography
• Complexity Theory
• +۴ مورد دیگر

Katerina Sotiraki is an Assistant Professor in the Department of Computer Science at Yale University, associated with the Archimedes AI research group. Her work focuses on theoretical cryptography, post-quantum cryptography, complexity theory, and secure computation. She holds a Ph.D. (2020) and M.S. (2016) in Computer Science from MIT, and a B.S. in Applied Mathematics (2013) from the National Technical University of Athens. Prior to Yale, she was a postdoc at UC Berkeley working with Raluca Ada Popa and Alessandro Chiesa, under the guidance of Vinod Vaikuntanathan at MIT. Research Interests : Design and analysis of cryptographic protocols resilient to quantum computing Efficient zero-knowledge proofs and succinct arguments Algorithmic game theory and fair division problems Lattice-based cryptography and post-quantum security Awards & Honors : Winner of 7th iDASH Competition (Track 3) - 2018 Chateaubriand Fellowship - 2018 Paris C. Kanellakis Fellowship (MIT EECS) - 2013 Key Contributions : Developed HOLMES - a system for secure collaborative learning Pioneered lattice-based succinct arguments with polylogarithmic verification Advanced understanding of modulo-p arguments through topological methods Labs & Teams : Active member of the Archimedes AI group at Yale, focusing on secure computation and cryptographic protocols.

• Biometrics
• Biometric Systems
• Computer Vision
• +۵ مورد دیگر

Raul Sanchez Reillo is a Full Professor at Universidad Carlos III de Madrid (UC3M), affiliated with the Grupo Universitario de Tecnologías de Identificación (GUTI). His research focuses on biometric systems, mobile authentication, and security technologies. Key areas include presentation attack detection, vein recognition, and ECG biometrics. He leads projects involving smartphone-based biometric solutions, 3D printed markers, and standards development for biometric interoperability. Research interests span multiple modalities: fingerprint authentication, dynamic signature verification, gait recognition, and vascular biometrics. He emphasizes usability and accessibility in mobile environments, exploring ergonomics and user interaction challenges. His work integrates machine learning (transformers, RNNs) with hardware solutions like FPGA-based systems. Publications highlight innovations in spoofing detection, medical applications (ECG/vein analysis), and low-cost hardware implementations. He contributes to European standards (BioAPI, Hand Data Interchange Format) and evaluates security practices for R&D compliance with EU data protection regulations. Current initiatives include enhancing biometric systems for critical infrastructure security and improving accessibility for elderly users. Active in interdisciplinary collaborations, he leads the Mobile Pass project evaluating user interaction in biometric systems. His lab (GUTI) develops open testing methodologies for biometric performance under Common Criteria and environmental stressors. Recent work addresses vulnerabilities in mobile fingerprint sensors and the ethical implications of biometric-as-a-service models.

• Algorithms for Big Data Analytics
• Computational Geometry
• Machine Learning
• +۴ مورد دیگر

Jeff M Phillips is a Professor in the Kahlert School of Computing at the University of Utah, specializing in algorithms for big data analytics, computational geometry, and machine learning. He holds a BS in Computer Science and Mathematics from Rice University (2003) and a PhD in Computer Science from Duke University (2009). He serves as Director of the Utah Center for Data Science, Director of the Data Science Program in the Kahlert School of Computing, and Faculty Co-Director of the One U Data Science Hub. His research focuses on geometric data analysis, coresets, sketches, and handling uncertainty in data. Education: BS/BA (Rice University, 2003), PhD (Duke University, 2009) CI Postdoctoral Fellow at University of Utah (2009–2011) His research interests include algorithms for big data analytics, computational geometry, machine learning, spatial statistics, and AI. He has led NSF-funded projects on spatial data analysis, cosmic origins via AI, and reactive flow data modeling. Phillips has advised numerous PhD and master’s students, contributing to topics like trajectory classification and bias mitigation in word embeddings. His publications span computational geometry, data science, and machine learning. Notable work includes coresets for kernel density estimates, bias mitigation in language models, and scalable spatial scan statistics. Phillips is also active in academic service, serving as co-PC chair for SoCG 2024 and on program committees for major conferences like NeurIPS and ICML.

Paul Rosenمشاهده پروفایل

دانشیار

• Scientific Visualization
• Topology-Based Methods
• Network Visualization
• +۳ مورد دیگر

Paul Rosen is an Associate Professor at the University of Utah, affiliated with the Scientific Computing and Imaging Institute and the Kahlert School of Computing. He holds a Ph.D. in Computer Science from Purdue University (2010). Prior to his current role, he was an Assistant/Associate Professor at the University of South Florida (2015–2022) and a Research Assistant Professor at the University of Utah's SCI Institute (2010–2015). Research Focus: Rosen specializes in topology-based visualization techniques, with emphasis on network visualization, uncertainty quantification, and perceptual studies. His work bridges computational methods with human perception, aiming to enhance data understanding through effective visual design. Awards & Recognition: National Science Foundation CAREER Award (2019) Best Paper Awards at PacificVis 2016, IVAPP 2016, and multiple other conferences Honorable Mentions for IEEE VIS and VAST Challenge submissions Leadership: As General Chair of IEEE VIS 2024, Rosen led the planning for this flagship visualization conference, emphasizing community-driven design and in-person collaboration. Education Contributions: His research includes pedagogical innovations, such as predictive modeling for student feedback and peer review analysis in visual literacy courses.

• Human-Robot Interaction
• Social Robotics
• Group Dynamics
• +۳ مورد دیگر

Sarah Gillet is a Postdoctoral Researcher at the Division of Robotics, Perception, and Learning at KTH Royal Institute of Technology, where she focuses on developing social robot behaviors to foster collaboration and inclusion in human groups. Her research addresses challenges like in-group favoritism through computational approaches to shape group interactions. She holds a Doctoral Thesis (2024) titled Computational Approaches to Interaction-Shaping Robotics . Her work emphasizes group dynamics , robot-mediated inclusion , and pedagogical robotics , particularly in children and adolescent populations. Key areas include gaze behavior analysis, equitable participation promotion, and social robot roles such as mediators in educational settings. Dr. Gillet teaches the Social Robotics (DD2413) course and supervises master theses. Her recent publications explore robot gaze behaviors for participation balance, socially appropriate listening, and influence prediction models like RoSI. She actively participates in conferences like ACM/IEEE HRI and IEEE RO-MAN. Her research integrates computational methods with social science insights to design robots that actively improve human group interactions, with applications in education, collaboration, and bias mitigation.