جست‌وجوی استاد و پژوهشگر

• Ice Mechanics
• Fracture Mechanics
• Arctic Marine Technology
• +۴ مورد دیگر

Jukka Tuhkuri is a Professor at Aalto University's Department of Energy and Mechanical Engineering, specializing in ice mechanics and arctic marine technology . He serves as Editor-in-Chief of Cold Regions Science and Technology and became an Honorary Professor at University College London (Department of Earth Sciences) in 2023. His work spans numerical simulations using the Discrete Element Method (DEM) and experimental research in the Aalto Ice and Wave Tank, with fieldwork in both Arctic and Antarctic regions. Research Focus : Understanding ice fracture mechanics, sea ice ridge formation, and ice-structure interaction processes. He investigates how global warming alters ice conditions and affects loads on ships/marine structures, addressing risks from increased Arctic shipping activity. Scientific Awards 2023 POAC Founders Lifetime Achievement Award Teacher of the Year 2003 Espoo Ambassador 2012 1996 Best Dissertation Stipend from Helsinki University of Technology Collaborative Impact : His research directly informs offshore wind engineering and Arctic risk management frameworks through publications like Challenges with sea ice action on structures for Offshore wind (2023) and A comprehensive approach to scenario-based risk management for Arctic waters (2022).

• Parallel Computing
• High Performance Computing
• Machine Learning
• +۱ مورد دیگر

Zhou Tong serves as an Assistant Professor in the Computer Science Department at Wheaton College in Norton, MA. His academic foundation includes a Ph.D. in Computer Science from Florida State University and a B.S. in Computer Science from Millsaps College. His educational background: Ph.D. in Computer Science, Florida State University B.S. in Computer Science, Millsaps College Dr. Tong specializes in parallel computing and high performance computing (HPC), with significant contributions to performance modeling of HPC applications, workload characterization, and interconnect topology design. His secondary research domains include Machine Learning and Natural Language Processing, where he explores computational efficiency in data-intensive systems. His publication record reveals a concentrated focus on HPC networking innovations from 2016-2021, particularly in adaptive routing algorithms for dragonfly topologies, software-defined networking integration, and MPI application classification using logical clocks. These works consistently address performance optimization challenges in large-scale parallel computing environments. No scientific awards are documented in the available materials. Information regarding student advising, research grants, and laboratory facilities remains unspecified in current records.

• Coding Theory
• Network Coding
• Information Theory
• +۴ مورد دیگر

Chih-Chun Wang is a Professor at the Elmore Family School of Electrical and Computer Engineering , Purdue University, with additional leadership roles as Associate Head for Facilities, Planning, and Staff. He earned his Ph.D. in Information Sciences and Systems from Princeton University in 2005, following an M.S. (2002) and B.S. (1999) in Electrical Engineering from Princeton and National Taiwan University, respectively. Research Interests: His work spans Network coding (graph-theoretic capacity, wireless network coding, feedback mechanisms) Coding theory (LDPC codes, Reed-Solomon decoding, iterative algorithms) Information theory (multi-user detection, network information theory) Signal processing (turbo equalization, space-time codes) Control theory (optimal stopping theory) Scientific Contributions: He has published extensively on Age-of-Information (AoI) minimization, low-latency coding, and multi-hop relay optimization. His research trends include Integrating machine learning with network coding Wireless security for Beyond-5G systems Distributed storage networks with intelligent helper selection Delay-constrained communication protocols Awards: Recognized as an IEEE Fellow in 2024 for contributions to network coding and information theory. Teaching: He teaches undergraduate courses like ECE301: Signals and Systems and graduate courses such as ECE639: Error Control Coding , with a focus on iterative decoding, LDPC codes, and network information theory. Advising: Supervised 15+ Ph.D. students, including current advisees Pin-Wen Su (delay-oriented coding), Wonjun Lee (cyber-physical systems), and Giles Bischoff (low-latency systems). Former students hold prominent roles at institutions like Google, Meta, and Intel.

Matthew Turkمشاهده پروفایل

استادیار

• Data Science
• Computational Astrophysics
• Information Visualization
• +۳ مورد دیگر

Matthew Turk is an Assistant Professor at the University of Illinois' School of Information Sciences and holds a Research Assistant Professor appointment in the Department of Astronomy. His work bridges computational astrophysics and data science, focusing on data analysis tools, human-computer interaction, and the social structures of scientific software communities. PhD in Physics from Stanford University (2009) Postdoctoral work at University of California, San Diego NSF Fellowship in Transformative Computational Science at Columbia University Turk's research centers on data visualization , reproducibility in scientific workflows , and computational infrastructure for astrophysics . He has developed tools like yt, a widely-used astrophysical simulation analysis toolkit, and explores how researchers interact with data through software and visualization techniques. His recent publications highlight trends in scientific software sustainability , machine learning applications in cosmology , and interdisciplinary data sonification . Turk's work demonstrates a consistent focus on integrating computational methods with human-centric approaches to scientific discovery. NSF Fellowship in Transformative Computational Science Turk contributes to scientific education through courses like Data Visualization (IS445ACG) and Independent Study (IS589MJT). His involvement in grants and collaborative projects, including the yt toolkit development and the Data Storytelling Toolkit for Libraries (DSTL), showcases his commitment to expanding computational literacy across domains.

Ken Hendricksمشاهده پروفایل

استاد

• Industrial Organization
• Auction Theory
• Game Theory
• +۶ مورد دیگر

Ken Hendricks is a Professor of Economics at the University of Wisconsin-Madison, with a primary research focus on industrial organization and auction theory. He has held the Laurits R. Christensen Distinguished Chair since 2012 and served as editor of the Journal of Industrial Economics (2000–2005). His career spans institutions like the University of British Columbia (1982–2001), University of Texas at Austin (2001–2010), and visiting roles at Princeton, Harvard, and University of Arizona. Education: B.A., Economics, University of British Columbia (1976) M.A., Economics, University of British Columbia (1977) Ph.D., Economics, University of Wisconsin-Madison (1982) Research interests center on industrial organization , empirical and theoretical auction studies , game theory , and network economics . His work analyzes bidding behavior in auctions, market design, and the economic impacts of mergers, oil drilling, and information asymmetry. Recent publications include studies on sequential auction markets (2024), mortgage supply chains (2023), and competitive dynamics in common value auctions (2022). His articles frequently intersect with fields like energy economics , consumer behavior , and market efficiency . Scientific Awards: Distinguished Fellow, Industrial Organization Society (2024) Fellow, Econometric Society (2004) UBC Killam Research Prize (1993) UBC Alumni Prize for Social Sciences (1992) National Fellow, Hoover Institution (1986–1987) Active in editorial roles (e.g., Journal of Industrial Economics , 2014–present), he has also led academic committees (2015–2020 President, Industrial Organization Society) and contributed to policy and industry grants, including NSF and SSHRC funding.

Colleen Murphyمشاهده پروفایل

استاد

• Political Philosophy
• Jurisprudence
• Ethics
• +۳ مورد دیگر

Colleen Murphy serves as the Associate Dean for Academic Affairs and the Roger & Stephany Joslin Professor of Law at the University of Illinois Urbana-Champaign College of Law, with courtesy appointments in Philosophy and Political Science. She chairs the Illinois Scholars at Risk Program and co-edits the International Journal of Transitional Justice. Her academic credentials include: PhD in Philosophy, University of North Carolina at Chapel Hill BA in Philosophy, University of Notre Dame Murphy's research centers on political philosophy , jurisprudence , and ethics , examining transitional justice mechanisms and moral frameworks for risk analysis. She investigates societal approaches to systemic wrongdoing and develops ethical methodologies for disaster risk mitigation through capability-based approaches. Her recent publications (2022-2025) reveal dominant themes in transitional justice, with concentrated analysis on accountability for historical injustices, racial redress, and post-conflict reconciliation. Concurrently, she explores technology's impact on equality and risk assessment, integrating philosophical ethics with practical disaster management frameworks. No specific scientific awards were detailed in the source material. Information regarding student advising and research grants was not provided in the available documentation. Murphy leads the Illinois Scholars at Risk Program supporting persecuted academics globally and shapes scholarly discourse through her co-editorship of the International Journal of Transitional Justice, fostering interdisciplinary research on justice after systemic oppression.

• Data Journalism
• Digital Journalism
• Algorithmic Accountability
• +۷ مورد دیگر

Christina Elmer is Professor for Digital Journalism and Data Journalism at the Institute of Journalism, Technical University of Dortmund. Prior to her academic career, she held significant positions at DER SPIEGEL as Deputy Head of Development, Member of the Editorial Board of SPIEGEL ONLINE, Head of the Data Journalism Department, and Science Editor (2013-2021). She is recognized as a leading expert in data journalism, AI in media, and digital transformation of journalism. Elmer's research focuses on data journalism, algorithmic accountability, and the integration of artificial intelligence in journalistic workflows. Her work explores how digital transformation affects media production, distribution, and reception, with particular attention to user-centered journalism, ethical dimensions of digital media, and the development of editorial products. She has pioneered approaches to structured journalism and modular content creation that adapt to changing audience needs. Her recent publications examine AI's impact on journalism, methods for combating disinformation, and strategies for maintaining journalistic integrity in algorithmically mediated information ecosystems. Her work shows a clear trend toward investigating how journalism can maintain societal relevance while adapting to technological changes, with increasing focus on AI systems as both tools and challenges for quality journalism. scoop award of the nextMedia.Hamburg initiative, 2023 Helmut Schmidt Journalist Prize (second prize) for 'Blackbox Schufa', 2019 Philip Meyer Award (third place) for 'Hanna and Ismail', 2018 dpa-infografik Award for 'Die Pendlerrepublik', 2018 Journalistin des Jahres, Fachkategorie Wissenschaft, 2016 Deutscher Journalistenpreis Forst & Holz (Print), 2007 Elmer actively contributes to the journalism community as a board member of Netzwerk Recherche (serving as second chair 2021-2023), shareholder of AlgorithmWatch, and member of various advisory boards including Science Media Center Germany and MIP.labor. She frequently collaborates with students through the KURT student editorial team, guiding them in applying design thinking to develop new journalistic formats. Her approach emphasizes the importance of user-centered thinking while maintaining journalistic integrity in the digital age.

Christophe Danjouمشاهده پروفایل

دانشیار

• Industrial Engineering
• Management Information Systems
• Manufacturing
• +۹ مورد دیگر

Christophe Danjou is an Associate Professor in the Department of Mathematical and Industrial Engineering at Polytechnique Montréal . He joined as a professor in January 2018 and serves as Scientific Director of the Poly-Industries 4.0 Laboratory since June 2021. His expertise spans Industrial Engineering , Industry 4.0/5.0 , Manufacturing Systems , and Blockchain . His research focuses on solving interoperability challenges in digital transformation through ontological approaches (OntoSTEP-NC) and blockchain technology. Key themes include strategic positioning frameworks for Industry 4.0/5.0, knowledge management , and smart manufacturing . Recent work explores digital twins for system-of-systems resilience , data integrity in IoT , and AI-driven food processing optimization . He teaches courses like Industry 4.0 and Manufacturing Processes . Under his supervision, 7 PhD and 6 Master’s students are advancing research in areas such as blockchain-based smart maintenance , distributed manufacturing , and carbon emission traceability . He is affiliated with institutions including IVADO (Member), CIRRELT (Member), and Data Intelligence Lab (Member). His publications highlight contributions to digital transformation across construction, agri-food, and SMEs, with 83 total publications (15+ recent articles shown).

• Fog Computing
• Cloud Computing
• Large-scale Distributed Systems
• +۷ مورد دیگر

Guillaume Pierre is a Professor and research leader at Univ Rennes, affiliated with Inria, CNRS, and IRISA, where he leads the Magellan research team. He is based at the Institute of Science and Technology of Information and Communication (ISTIC), Department of Computer Science and Electronics. His research focuses on fog computing, cloud computing, and large-scale distributed systems, with applications in scalable web hosting and edge intelligence. Research Interests: Fog and Edge Computing Cloud Computing and Resource Management Scalable Web Application Hosting Peer-to-Peer and Decentralized Systems Stream Processing and Kubernetes Orchestration Elasticity and Energy Efficiency in Distributed Environments His recent publications highlight a strong trend in geo-distributed systems, particularly focusing on Kubernetes cluster federation, fog-based environmental monitoring, and elasticity in stream processing. His work bridges theoretical advances with practical implementations in real-world fog and cloud infrastructures. Scientific Awards: Best Paper Award, IEEE International Symposium on Applications and the Internet (2005) Best Paper Award, IEEE International Conference on Cloud Engineering (IC2E 2014) Guillaume Pierre has advised numerous PhD students, many of whom now hold positions at Google, Amazon, Ericsson, and Ansys. He has coordinated major research projects such as the H2020 FogGuru initiative and the DiPET project on distributed data stream processing. His work is supported by EU funding and institutional collaborations. Labs and Teams: He leads the Magellan research team at the INRIA/IRISA lab, which is at the forefront of innovation in fog and cloud computing technologies.

Blerim Rexhaمشاهده پروفایل

استاد

• Cybersecurity
• Blockchain Technology
• Machine Learning
• +۵ مورد دیگر

Prof. Blerim Rexha is a full professor at the University of Prishtina's Faculty of Electrical and Computer Engineering, Kosovo. With a Ph.D. in Computer Engineering from Vienna University of Technology (2004), he has led research in cybersecurity, blockchain, machine learning, and electronic voting systems. His teaching portfolio includes data, computer, and internet security courses. Education : Ph.D. in Computer Engineering (Vienna), Electrical Engineer MSc (Prishtina), specialized certifications in software engineering, biometrics, and .NET programming. His research spans cybersecurity (DDoS mitigation, face authentication attacks), blockchain applications (electronic voting bridges, transaction privacy), and machine learning integration (boosted trees for intrusion detection, LSTM for vulnerability scanning). He has contributed to cloud security through novel encryption methods and AI-driven attack detection. Recent publications focus on energy efficiency in cloud vs on-premises systems, XGBoost/CatBoost/LightGBM comparisons for network security, and blockchain bridges for e-voting. His work has addressed privacy preservation in video data, SMS encryption, and eID card pseudo-profiles. Awards include the 2024 Marin Barleti Prize for academic contributions and Best Paper Awards in election security (2015) and Kosovo website vulnerabilities (2013). Honors : Marin Barleti Prize (2024) Cyber Security Ambassador (2018) ICT Academician of the Year (2016) Best Paper Awards (2015, 2013) As academic advisor to the KosovaCyberTeam , he mentors students like Korab Keqekolla and Abian Morina. His leadership extends to Kosovo's Cyber Security State Training Center curriculum development and jury roles in Albanian ICT Awards .

Soroosh Sorooshianمشاهده پروفایل

استاد

• Hydrometeorology
• Water Resources Systems
• Climate Studies
• +۳ مورد دیگر

Soroosh Sorooshian is a Professor at the Samueli School of Engineering , University of California, Irvine, with joint appointments in Civil and Environmental Engineering and Earth System Science . He serves as Founding Director of the Center for Hydrometeorology and Remote Sensing (CHRS) and holds the Samueli Endowed Chair in Engineering . His expertise spans hydrometeorology, climate-water interactions, remote sensing applications, and water resource management in arid regions. Education : Ph.D. in Engineering (1978), Engineer Degree in Systems Engineering (1977), M.S. in Operations Research (1973), B.S. in Mechanical Engineering (1971). Leadership & Affiliations : Member of US National Academy of Engineering , International Academy of Astronautics , and multiple scientific bodies (AAAS, AGU, AMS, IWRA). Former advisor to NASA, NOAA, and UNESCO initiatives. Recent research focuses on machine learning integration for hydrological modeling , satellite precipitation product development , and climate change impact assessments . Key trends include deep learning for bias correction , multi-sensor precipitation fusion , and atmospheric river hydrology in California. Awards include the AGU Horton Medal , NASA Distinguished Public Service Medal , and Prince Sultan Bin Abdulaziz International Water Prize . He consults on urban flooding and surface hydrology challenges. Scientific Honors : Chinese Academy of Sciences Einstein Professorship (2014) UNESCO Great Man-Made River Water Prize (2007) AMS Walter Orr Roberts Lecturer (2009) Multiple Distinguished Educator Awards Advisory Roles : Served on committees for NASA, DOE, and World Climate Research Programme's Hydrology Commission.

Sean B. Anderssonمشاهده پروفایل

استاد

• Optimal Estimation
• System Identification
• Single Particle Tracking
• +۴ مورد دیگر

Sean B. Andersson is a Professor in the Department of Mechanical Engineering at Boston University's College of Engineering. His research focuses on optimal estimation, system identification, single particle tracking, robotics, and control theory. He earned his Ph.D. from the University of Maryland, College Park. Education : Ph.D. in Mechanical Engineering (University of Maryland, College Park) His work integrates control algorithms with applications in microscopy, nanofabrication, and multi-agent systems. Recent research trends highlight persistent monitoring, trajectory optimization, MRI reconstruction, and dip-pen nanolithography. He has mentored numerous graduate and undergraduate students, many of whom now hold positions at institutions like MIT Lincoln Labs, University of Pennsylvania, and Juniper Networks. Scientific Contributions : Developed robust multi-agent control policies for data harvesting Advanced single particle tracking with real-time feedback Innovated in non-raster scanning probe microscopy Optimized sensor scheduling via minimax and semidefinite programming His lab team combines theoretical and applied research in robotics and control systems, with alumni contributing to academia, industry, and research labs globally.

Rasmus Paghمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Big Data
• +۷ مورد دیگر

Rasmus Pagh is a Professor at the Department of Computer Science, University of Copenhagen, specializing in algorithms and complexity. His career includes a 2002 PhD from Aarhus University under Peter Bro Miltersen and a tenure at IT University of Copenhagen until 2020. He leads theoretical research with practical applications in big data, databases, and modern computer architecture parallelism. His research interests span algorithms, data structures, and privacy-preserving computing. Recent work includes the ERC-funded project on Scalable Similarity Search and contributions to the BARC center for basic algorithms research. He has collaborated with Google Research (2019-2020) and focuses on theoretical foundations with real-world impact. Key research trends in his 2023-2024 publications include privacy-preserving data analysis probabilistic data structures distributed secure computation noise-robust coding hashing efficiency continual privacy mechanisms Scientific recognition includes 2024 ACM Fellowship ERC grant leadership multiple top-tier conference publications

Zukui Liمشاهده پروفایل

استاد

• Mathematical Optimization
• Machine Learning
• Process Systems Engineering
• +۴ مورد دیگر

Zukui Li is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta's Faculty of Engineering, where he leads a research group focused on mathematical optimization, machine learning, and process systems engineering. His work spans oil sands extraction, steel production, biomedical applications, and advanced optimization methods. Education: Ph.D. in Chemical Engineering, Rutgers University (2010) M.Sc. in Control Theory and Control Engineering, University of Science and Technology of China (2005) B.Sc. in Automatic Control, University of Science and Technology of China (2002) Postdoctoral Training: Princeton University (2010-2012) Research Focus: Dr. Li's research integrates mathematical optimization and machine learning for complex process systems. His primary areas include: Advanced optimization techniques (robust, stochastic, and distributionally robust optimization) Machine learning applications in process monitoring and biomedical systems Industrial applications in energy, manufacturing, and resource extraction Specific innovations include physics-informed ML for anemia treatment, adaptive optimization for steel production, and distributionally robust methods for uncertainty management. Publication Trends (2019-2023): Recent articles demonstrate a strong focus on uncertainty-aware optimization methods, with increasing integration of machine learning techniques. Dominant themes include distributionally robust optimization, adaptive decision-making under uncertainty, neural network approximations for complex constraints, and applications in industrial process control and biomedical systems. Theoretical advancements are consistently coupled with practical implementations in energy and manufacturing sectors. Research Group: Leads an active team developing optimization frameworks and machine learning solutions for process engineering challenges. Group website: Dr. Zukui Li's Research Group

Le-Nam Tranمشاهده پروفایل

پژوهشگر

• Wireless Communications
• 5G/6G Network Optimization
• Signal Processing
• +۴ مورد دیگر

Dr. Le-Nam Tran is a researcher at the UCD School of Electrical & Electronic Engineering , University College Dublin. His work focuses on optimizing the last hop of 5G/6G wireless networks through mathematical programming, with emphasis on energy efficiency, interference management, and security against eavesdropping. Develops low-cost, low-complexity transmission techniques Projects supported by Science Foundation Ireland Career Development Award Author of over 80 peer-reviewed publications Research Keywords: Wireless Communications Network Security Signal Processing Energy-Efficient Systems Beamforming Optimization Interference Mitigation