جستجوی استاد راهنمای دانشگاهی

Jodi Schneider is an Associate Professor at the University of Illinois Urbana-Champaign , with affiliate appointments at the Beckman Institute , Health Care Engineering Systems Center , European Union Center , and Center for Health Informatics . She directs the Information Quality Lab and focuses on the science of science through argumentation and evidence analysis. PhD in Informatics (National University of Ireland, Galway) M.S. in Library and Information Science (UIUC) M.A. in Mathematics (UT-Austin) B.A. in Liberal Arts (St. John's College) Her research examines how scientific controversies persist through citation patterns, the role of knowledge brokers in public policy, and information quality in biomedical contexts. She has developed semantic frameworks for micropublications and knowledge maintenance in digital libraries. Recent publications include citation integrity studies in Scientometrics , retraction indexing in STI Conference , and argumentation mining in Human Language Technologies . Collaborative projects span institutions like Harvard Radcliffe Institute and RWTH Aachen . NSF CAREER Award IMLS Early Career Award Senior Member, Association of Computing Machinery Marie Curie Fellow She advises graduate students in information quality and knowledge representation , with funding from the Alfred P. Sloan Foundation , NIH , and European Commission . Her lab develops tools to combat scientific misinformation and improve public health informatics .

• Controversies in Science
• Science of Science
• Argumentation and Evidence
• +۱۲ مورد دیگر

Bo Chen is a postdoctoral researcher at the Siebel School of Computing and Data Science and the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. His work focuses on AI-system co-design for immersive computing, particularly in extended reality (XR) and multi-modal content delivery over wireless networks. Ph.D. in Computer Science (2022), advised by Klara Nahrstedt B.S. in Computer Science from Shanghai Jiao Tong University (2016) His research integrates AI techniques with system-level optimizations to address challenges in XR infrastructure , including multi-view video streaming , NeRF-based content delivery , and uncertainty management in video transmission . He has pioneered methods like Loose Frame Referencing for learned codecs and Context-Aware NeRF Serving for mobile XR applications. Bo Chen's recent publications span top venues like ACM MobiSys, ACM SenSys, and USENIX NSDI. Key themes include AI-driven compression , dynamic 3D rendering , and reliable streaming over mobile networks . He has received recognition including the Rising Star Best Presentation Award (ACM MobiSys 2025) and Best Student Paper Award (ACM MMSys 2022). Bayesian optimization for XR systems Multi-view video aggregation at edge networks 3D Gaussian Splatting for immersive media

• Networking
• Machine Learning Systems
• Virtual Reality
• +۵ مورد دیگر

Samuel Jean Bassetto is an Associate Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He serves as Director of the Continuous Improvement Laboratory (LABAC) and holds membership in multiple prestigious research groups including the Research Group on Globalisation and Management of Technology (GMT), Poly-Industries 4.0 Laboratory, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), and Institute for Data Valorization (IVADO). Dr. Bassetto's research spans multiple disciplines, focusing on continuous improvement through the integration of engineering, artificial intelligence, cognitive science, psychology, and design. His primary sphere of excellence is in New Frontiers in Information and Communication Technologies, with secondary expertise in Modeling and Artificial Intelligence and Human Health. He develops tools that place humans at the center of technology to enhance organizational performance while respecting human rhythms and cognitive limitations. His recent publication portfolio reveals a strong interdisciplinary approach, with research bridging industrial engineering, cognitive neuroscience, and AI ethics. His work addresses practical challenges in lean manufacturing assessment, racial bias in medical AI systems, cognitive data collection in natural environments, and condition monitoring for industrial machinery. The research consistently demonstrates a commitment to developing practical solutions that integrate human factors with technological innovation. NSERC Synergy Prize for Innovation recipient Principal investigator on multiple research grants from NSERC, FRQ, and MITACS Collaborations with over a dozen institutions across multiple countries Supervision of over 150 highly qualified personnel throughout his career Dr. Bassetto teaches specialized courses including CAP7011 (Creativity in Research), IND8444 (Continuous Improvement), IND8203 (Industrial Launch), and previously taught IND8178 (Production). His teaching philosophy emphasizes practical application, with courses featuring hands-on exercises, real-world scenarios, and gamification techniques to enhance learning. His supervision portfolio includes numerous Ph.D. and Master's students working on topics ranging from human-technology collaboration to reinforcement learning for production management. Through LABAC, Dr. Bassetto leads research initiatives focused on developing human-centered tools for continuous improvement in organizational settings. The laboratory conducts projects related to industrial IoT applications, cognitive aspects of process improvement, and the development of practical frameworks for organizations to enhance performance while maintaining respect for human rhythms and cognitive capabilities.

• Industrial Engineering
• Artificial Intelligence
• Cognitive Science
• +۴ مورد دیگر

Arash Joorabchi is an Assistant Professor at the Department of Electronic and Computer Engineering, Faculty of Science and Engineering, University of Limerick, Ireland. His research focuses on the intersection of machine learning, educational technology, and digital library systems, with particular emphasis on automated assessment, text mining, and knowledge organization techniques. Research Trends: Analysis of his publications reveals sustained contributions to automated short-answer grading, Arabic text classification, and semantic integration of Wikipedia with academic resources. Key methodologies include sentence transformers, hybrid text representation models, and citation-based indexing techniques. Technical Domains: His work spans natural language processing, educational data mining, metadata management, and semantic web technologies. Specific applications include Q&A platform analysis, library resource discovery, and curriculum development systems.

• Machine Learning
• Natural Language Processing
• Educational Technology
• +۴ مورد دیگر

Mark S. Handcock is a Distinguished Professor in the Department of Statistics and Data Science at the University of California, Los Angeles (UCLA), where he leads research at the intersection of statistical methodology and applied problems in social sciences, epidemiology, and environmental science. His work bridges theoretical statistics with real-world challenges through innovative methodological development. His primary research interests encompass statistical models for social networks, network inference, methodology for hard-to-reach population surveys, spatial processes, demography, and environmetrics. Handcock has pioneered advances in exponential-family random graph models (ERGMs) and developed foundational R packages like ergm and tergm within the statnet suite, enabling sophisticated network analysis across disciplines. Analysis of his recent publications (2023-2025) reveals three dominant research thrusts: (1) Antarctic sea ice modeling using Bayesian reconstruction and temporal variability analysis, (2) epidemiological modeling of infectious disease transmission dynamics (particularly COVID-19), and (3) methodological innovations in network inference and causal analysis over stochastic networks. His work consistently integrates advanced computational statistics with domain-specific applications in climate science, public health, and social systems.

• Statistics
• Social Network Analysis
• Survey Sampling
• +۳ مورد دیگر

Professor Dragan Jovcic is the Chair in Engineering at the University of Aberdeen's School of Engineering , where he has been a faculty member since 2004 and a full professor since 2012. Concurrently he serves as Director of the Aberdeen HVDC Research Centre , a role he has held since 2015. Education: PhD in Electrical Engineering, University of Auckland, 1999 Diploma Engineer in Control Systems, University of Belgrade, 1993 Postgraduate Certificate in University Teaching, University of Ulster, 2003 Research Interests: Professor Jovcic’s research centres on high-power electronics and HVDC transmission systems , with particular emphasis on the development of DC transmission grids that will enable large-scale integration of offshore wind energy . His work spans DC/DC converters , DC circuit breakers , modular multilevel converters (MMC) , flexible AC transmission systems (FACTS) , and advanced power system modelling and control . The overarching goal is to underpin the transition from fossil-fuel generation to renewable-dominated power systems, especially in the North Sea and European contexts. Publication Trends: Recent publications (2020-2023) reveal a strong focus on DC protection technologies —notably circuit breakers and energy absorbers—and on modelling methodologies for HVDC grids and offshore wind integration. The works address both theoretical advances (phasor and state-space models) and experimental validation (kV-level prototypes), reflecting a balanced portfolio of fundamental research and practical demonstration. Scientific Awards & Recognition: IEEE Fellow (2021) IEEE PES Distinguished Lecturer (since 2015) IET Fellow (2019) and Chartered Engineer (2018) Research Funding & Supervision: With over £5.5 million in external research income, Professor Jovcic is principal investigator or work-package leader on numerous EU Horizon Europe and EPSRC projects. He has supervised 9 PhDs to completion and is currently mentoring 2 PhD students and 2 post-doctoral fellows . Major grants include the €35 million PROMOTioN project on multiterminal DC networks and the €4 million MoWiLife project on wide-bandgap power electronics. Laboratory & Facilities: The Aberdeen HVDC Research Centre hosts a 0.9 kV DC grid demonstrator , 30 kW thyristor- and IGBT-based DC/DC converters , and 5 kV, 2 kA DC circuit breaker test benches , providing a world-class platform for experimental research and student training.

• High Voltage Direct Current (HVDC) Transmission
• DC Transmission Grids
• High Power Electronics
• +۴ مورد دیگر

Joshua J. Coon is a Professor at the University of Wisconsin-Madison with appointments in the Department of Biomolecular Chemistry and the Department of Chemistry. He leads the Coon Group, focusing on advancing mass spectrometry technologies for proteomics, metabolomics, and lipidomics. His research addresses fundamental questions in cell biology, including stem cell differentiation, epigenetic regulation, and cancer biomarker discovery. Affiliations : Director of the NIGMS National Center for Quantitative Biology of Complex Systems. Research Emphasis : Instrumentation development, data analysis software, ion chemistry, and biological applications of proteomics. Laboratory : Located in the Genome Center of Wisconsin with a dozen hybrid mass spectrometers, including Orbitrap systems. Collaborations : Long-term partnership with Thermo Fisher Scientific and the Wisconsin Alumni Research Foundation (WARF) for technology commercialization. Training : Mentored 27 Ph.D. students since 2009, emphasizing interdisciplinary research and professional development.

• Bioanalytical Chemistry
• Mass Spectrometry
• Proteomics
• +۴ مورد دیگر

Marc Sachon is a Full Professor and Director of the Department of Operations, Information, and Technology at IESE Business School, University of Navarra . He serves as Academic Director for Advanced Management Programs (AMP) and specialized courses like Successful Change Management and Industry 4.0 . His leadership extends to the annual IESE Automotive Industry Conference (since 1986) and consulting engagements with global firms such as BMW Group , Phoenix Group , and Traton Group . PhD in Industrial Engineering and Engineering Management from Stanford University MBA from IESE Business School Master's in Aerospace Technology from University of Stuttgart His research focuses on operations strategy , particularly in the automotive industry , and the impact of Industry 4.0 on manufacturing and logistics. He explores how digital transformation reshapes value chains, emphasizing human-machine collaboration and supply chain resilience. His work spans academic journals like IEEE Transactions and business publications like IESE Insight , with case studies on companies such as Porsche and Netflix . Professor Sachon has received multiple teaching awards and maintains an active consulting practice across industries including airlines , pharmaceuticals , and logistics . He previously worked at Airbus and IBM , and currently advises a mobility startup board. His recent publications highlight trends in electric mobility , 3D printing , and supply chain sustainability .

• Operations Strategy
• Industry 4.0
• Supply Chain Management
• +۳ مورد دیگر

Cécile Mailler is a Reader in Probability at the University of Bath, where she is a member of the probability group Prob-L@B. She has held significant research positions including an EPSRC postdoctoral fellowship (2018-2021) titled "Random trees: analysis and applications" and previously worked as a postdoc at Prob-L@B (2013-2016) as part of Peter Mörters' EPSRC project "Emergence of Condensation in Stochastic Networks". She earned her PhD under the supervision of Brigitte Chauvin and Danièle Gardy at the Laboratoire de Mathématiques de Versailles. Her research focuses on probability theory with emphasis on branching processes, random trees, reinforcement mechanisms, Pólya urns, stochastic approximation, random networks, and statistical physics. She has made significant contributions to understanding preferential attachment models, zero-range processes, and random Boolean trees. Her work bridges theoretical probability with applications in statistical physics and combinatorics. Analysis of her recent publications shows a strong focus on random tree structures, branching processes, and reinforcement learning algorithms, with applications spanning from network theory to statistical mechanics. Her research demonstrates sophisticated mathematical techniques applied to complex stochastic systems, particularly those with reinforcement mechanisms and memory effects. Associate Editor of the Applied Probability Trust (since October 2020) Associate Editor of Stochastic Processes and Their Applications (since March 2022) Author of a general introduction to Pólya urns for the LMS Newsletter (November 2020) Co-organizer of the "Random Walks: Applications and Interactions" conference at CIRM (January 2026) She actively supervises PhD students working on topics including the multi-city ants process, Pólya urns with growing initial composition, large deviations for the Monkey walk, and competing growth processes. She has secured research funding through EPSRC fellowships and has been involved in multiple collaborative projects with prominent researchers in probability theory. Mailler regularly teaches mini-courses on advanced probability topics at international summer schools and workshops, demonstrating her commitment to knowledge dissemination in the field.

• Branching processes
• Random trees
• Reinforcement processes
• +۹ مورد دیگر

Cesare Franchini is a full Professor at the University of Vienna's Faculty of Physics, leading the Computational Materials Physics research group. His work focuses on theoretical understanding and computational modeling of quantum materials using first principles methods, particularly VASP. He maintains an active research program with numerous postdocs, PhD students, and collaborations across multiple institutions including the University of Bologna. Professor Franchini's research centers on quantum materials with many interacting degrees of freedom (lattice, spin, and electron orbital) that enable novel electronic and magnetic phases. His specific interests include metal-insulator transitions, polaron physics (electron-phonon interactions), non-collinear spin orderings, topological Dirac/Weyl phases, multiferroism, and superconductivity. He has increasingly incorporated machine learning data-driven tools and diagrammatic Monte Carlo techniques into his computational approaches. Analysis of his recent publications (2024-2025) reveals a strong focus on polaron physics across multiple material systems, with significant work on hematite, titanium dioxide, and quantum paraelectrics like KTaO3. His research increasingly integrates machine learning with traditional first-principles methods, particularly for studying hydrogen diffusion, surface science phenomena, and electronic structure calculations. There's also substantial work on single-atom catalysis and the application of advanced computational techniques to understand fundamental charge transport mechanisms in energy materials. Professor Franchini actively supervises numerous PhD students and postdocs, including Andrea Angeletti, Viktor Birschitzky, Lorenzo Celiberti, and several others working on diverse aspects of computational materials physics. He leads or participates in major research projects including TACO (Taming Complexity in Materials Modeling), DCAFM (Doctoral College Advanced Functional Materials), and the recently launched Spin-orbit entangled anharmonic polarons project. His group maintains strong collaborations with experimentalists at Charles University, Technical University of Vienna, and other international institutions.

• Quantum Materials
• Computational Physics
• First Principles Methods
• +۷ مورد دیگر

Associate Professor Dan Dongseong Kim is Deputy Director of UQ Cybersecurity and an Associate Professor at The University of Queensland (UQ), Australia. Previously, he held permanent academic positions at The University of Canterbury (UC), New Zealand (2011-2018) as a Senior Lecturer and Lecturer. His research focuses on Cybersecurity and Dependability for AI, IoT, Autonomous Vehicles, Cloud Computing, and Moving Target Defenses (MTD). Doctor of Philosophy in Computer Engineering from Korea Aerospace University Postdoctoral Research at Duke University (2008-2011) Visiting Scholar at University of Maryland (2007) Dan's work explores Graphical Security Models , Moving Target Defense for proactive resilience, and AI-Driven Cybersecurity with emphasis on adversarial robustness and interpretable models. His recent publications (2024-2025) span journals like IEEE Transactions on Dependable and Secure Computing and conferences such as DSN , addressing automated defense, evolving attacks, and hardware-aware security frameworks. He has advised 15 Ph.D. graduates, including researchers now at institutions like RMIT University, La Trobe University, and CSIRO's Data61. Current supervision includes projects on Automated Penetration Testing , AI-Based Intrusion Response , and Moving Target Defense . Dan's research is funded by agencies including the Republic of Korea's Agency for Defence Development and US Army Research Lab . His professional roles include Associate Editor for IEEE Communications Surveys and Tutorials and Steering Committee Chair for IEEE PRDC .

• Cybersecurity
• Cybersecurity Modeling and Analysis
• Moving Target Defense
• +۷ مورد دیگر

Mannes Poel is a researcher specializing in Datamanagement & Biometrics with a focus on applied machine learning. His work spans healthcare analytics, sensor technology optimization, and meta-learning frameworks for missing data. ORCID: 0000-0002-3813-9732 Active Domains : Artificial Intelligence, Medical Predictive Modeling, Industrial Sensor Analytics Collaboration Network : Interdisciplinary work with institutions in healthcare (e.g., Diagnostics journal) and engineering (e.g., IEEE MEMS conference) Scientific Achievements : Best paper award at Intetain 2107 (2017) Research Trends : His recent publications (2024-2025) emphasize explainable AI for missing data in clinical contexts and machine learning-enhanced sensor systems for industrial fluid dynamics. These works combine traditional ML with real-time data processing and cross-domain model adaptation.

• Artificial Intelligence
• Machine Learning
• Health Informatics
• +۳ مورد دیگر

Parisa Hosseinzadeh is an Assistant Professor in the Department of Bioengineering at the University of Oregon. Her research focuses on computational protein design and structure-guided rational protein/peptide engineering, with applications in enzyme design, biosensors, and biomedical solutions. She holds a B.Sc. from the University of Tehran, a Ph.D. from the University of Illinois (advisor: Yi Lu), and a postdoc at the University of Washington in David Baker's lab. Her lab emphasizes interdisciplinary approaches at the intersection of computer science, chemistry, and biology, prioritizing diversity and inclusion in STEM. Key projects include designing cyclic peptides as enzyme inhibitors, developing methods for tuning redox potentials in metalloproteins, and creating tools to combat biomedical challenges. Lab members include postdocs, graduate students (e.g., Noora Azadvari, Andrew Powers), and undergraduates. Notable achievements include NSF grants, the Baxter Foundation Award, and the Hans Horse Meyer Award. The lab also emphasizes mentorship, collaborative culture, and outreach initiatives.

• Protein Design
• Computational Biochemistry
• Biosensors
• +۳ مورد دیگر

Alexander Russell is a Professor of Computer Science and Mathematics at the University of Connecticut, serving as Director of Graduate Affairs in the School of Computing and Director of the UConn Voting Technology Research Lab. He holds a Ph.D. in Mathematics and an S.M. in Computer Science from MIT, alongside dual B.A. degrees in Mathematics and Computer Science from Cornell University. His research focuses on cryptographic protocols, blockchain security, quantum computing, algorithms, and election auditing. Key areas include consensus algorithms, complexity-theoretic cryptography, and applied cryptography in voting systems. Recent work emphasizes low-variance risk-limiting audits and adaptive security mechanisms for blockchains. Notable contributions span provably secure blockchain protocols (e.g., Ouroboros), election integrity methods, and smartphone-based depression prediction models. His articles address topics like settlement bounds in longest-chain consensus, Byzantine-resilient gossip protocols, and energy-efficient neighbor discovery in mobile networks. Russell advises on interdisciplinary projects at the Voting Technology Research Center and collaborates on grants involving quantum-resistant cryptography and healthcare analytics. His work bridges theoretical computer science with practical applications in secure systems and public infrastructure.

• Cryptography
• Blockchain Protocols
• Quantum Computing
• +۴ مورد دیگر

Jennifer Dy is a Distinguished Professor at Northeastern University with joint appointments in Electrical and Computer Engineering and Khoury College of Computer Sciences. As Director of AI Faculty at the Institute for Experiential AI, she leads research in machine learning, computer vision, and explainable AI. Her work spans biomedical applications (COPD phenotyping, neuroimaging) and fundamental algorithms (active learning, continual learning). She holds a PhD from Purdue University and is an AAAI Fellow. Research Focus: Dy develops methodologies for robust and interpretable machine learning, including techniques for model stability in continual learning, dependency-aware active learning, and axiomatic explanation frameworks. Her applied research advances diagnostic tools using Raman spectroscopy, CT imaging, and multi-omics biomarker discovery. Awards: Recognized with the NSF CAREER Award, Faculty Research Team Award, and AAAI Fellowship for contributions to unsupervised learning and medical AI. Publication Trends: Recent articles demonstrate strong cross-disciplinary integration, combining theoretical advances in explainability/robustness with applications in healthcare, wireless systems, and particle physics. Methodological themes include optimal transport theory, probabilistic modeling, and transformer architectures.

• Machine Learning
• Computer Vision
• Explainable AI
• +۲ مورد دیگر