جست‌وجوی استاد و پژوهشگر

Greg Gangerمشاهده پروفایل

استاد

• Cloud Computing
• Storage Systems
• Operating Systems
• +۳ مورد دیگر

Greg Ganger is the Jatras Professor of Electrical and Computer Engineering at Carnegie Mellon University and Director of the Parallel Data Lab (PDL). His research focuses on computer systems, including cloud computing, storage systems, distributed systems, and machine learning infrastructure. He holds a Ph.D. in Computer Science and Engineering from the University of Michigan and completed postdoctoral work at MIT. Education: Ph.D., M.S., and B.S. in Computer Science from the University of Michigan (1991–1995). Research Interests: Ganger leads projects in cloud computing, storage/file systems, operating systems, and systems for big data and large-scale machine learning. Recent work includes optimizing cloud resource scheduling, developing sustainable storage solutions, and improving ML cluster efficiency. The PDL explores storage system architecture, file systems, and leveraging new storage technologies like non-volatile memory (NVM). Awards: 2021 OSDI Best Paper, 2021 SOSP Best Paper, 2021 SoCC Test of Time Award, and 2021 R&D 100 Award. His team's work on Kangaroo caching and MACARON cloud caching exemplifies cutting-edge contributions. Advising & Grants: Advises graduate students in ECE and Computer Science. Active in grants related to distributed storage, cloud systems, and ML infrastructure. Collaborates with industry partners like Los Alamos National Lab on storage systems. Labs/Teams: Directs the Parallel Data Lab (PDL), a leading research group in storage and distributed systems. Collaborates with CMU’s CyLab on security aspects of storage systems and ML infrastructure.

Xiaojun Ruanمشاهده پروفایل

دانشیار

• Energy-Efficient Systems
• Cloud Computing
• Storage Systems Optimization
• +۷ مورد دیگر

Xiaojun Ruan is an Associate Professor in the Department of Computer Science at California State University, East Bay. He holds a Ph.D. in Computer Science from Auburn University (2011) and a B.E. in Computer Science and Technology from Shandong University (2005). His primary research focuses on energy-efficient systems, cloud computing optimization, storage systems, and security-aware resource management. He has extensive experience in thermal modeling, parallel I/O performance, and distributed deep learning frameworks. Dr. Ruan’s work emphasizes balancing energy efficiency, reliability, and performance in storage and cloud environments. Notable projects include DuoFS (hybrid storage system), energy-aware VM allocation strategies, and securing cloud infrastructure against co-residence attacks. His research bridges hardware-software co-design principles with practical system optimizations. His publications span topics from NVMe SSD performance optimization to text augmentation for spam detection, reflecting a blend of storage systems and machine learning applications. He has actively contributed to improving Shuffle I/O in big data processing, thermal management in clusters, and secure virtualization techniques. Dr. Ruan collaborates on interdisciplinary projects involving distributed computing, cybersecurity, and real-time systems. His lab focuses on deploying energy-efficient solutions while maintaining robust reliability, evidenced by over 50 peer-reviewed articles and ongoing contributions to academic conferences.

János Kertészمشاهده پروفایل

استاد

• Statistical Physics
• Complex Networks
• Financial Analysis
• +۹ مورد دیگر

János Kertész is a Professor at the Department of Network and Data Science at Central European University (CEU) since 2012, and previously held the position of Professor at the Budapest University of Technology and Economics (1992–2018). He obtained his PhD in Physics from Eötvös University (1980) and DSc from the Hungarian Academy of Sciences (1989). His research spans statistical physics applications, complex networks, and financial analysis. He has authored over 280 papers and served on editorial boards of journals like Journal of Physics A and Physical Review E . His research focuses on interdisciplinary topics including social network dynamics, systemic risk in economic systems, and algorithmic bias in digital environments. Notable awards include the Széchenyi Prize (Hungary’s highest scientific honor) and the Finland Distinguished Professorship. He has led projects such as SAI (Socially Explainable AI) and HUMANE-AI-NET, addressing algorithmic bias and AI ethics. His work bridges physics-based modeling with real-world social and economic systems, emphasizing computational approaches to corruption, opinion formation, and innovation diffusion. Key contributions include modeling cascading failures in interdependent networks and analyzing attention dynamics on platforms like Sina Weibo during the pandemic. He advises on systemic risk mitigation strategies and collaborates internationally, with visiting roles in Germany, the U.S., France, Italy, and Finland.

Per Lynggaardمشاهده پروفایل

استاد

• Integrated Circuit Design
• Wireless Sensor Networks (WSN)
• Machine Learning
• +۸ مورد دیگر

Per Lynggaard is a Professor of Electronics at the Technical University of Denmark (DTU) , leading the B.Eng. program in Electronics. Previously, he held an Associate Professor role at Aalborg University, combining academic excellence with a robust industrial career in technical-scientific research and development. Education: M.Sc. in Electrical Engineering and Information Technology (EE and IT) Ph.D. in Electronics from Aalborg University Research Interests: Focus on Integrated Circuit Design, Wireless Sensor Networks (WSN), Machine Learning, IoT, and Smart City Technologies . His work emphasizes energy-efficient systems, cybersecurity in IoT, AI-driven interference mitigation, and sustainable energy harvesting solutions. He has contributed to UN Sustainable Development Goals through projects addressing smart infrastructure and environmental monitoring. Projects & Collaborations: Leads and participates in EU-funded initiatives such as InnoTech (2023–2025) for green transition solutions and TransportTech (2023–2026) for Industry 4.0 logistics. Active in cybersecurity research via projects like Jamming Against Critical Wireless Communication , aiming to protect critical infrastructure. Awards: Recognized with multiple honors and rewards during his industrial career, though specific names are not listed. His work has been cited widely, with notable impact in IoT security and energy-efficient systems. Advising & Grants: Supervises Turnip T.N. in a PhD project on 6G security protocols. Engaged in securing funding for projects like F2D2: The Community for Dynamic Data (2021–2030), focusing on dynamic data systems and cybersecurity. Labs & Teams: Collaborates in interdisciplinary teams such as the InnoTech TaskForce and F2D2 Community , advancing IoT and AI integration. His research bridges academia and industry, with outputs spanning smart cities, healthcare IoT, and sustainable energy systems.

• Artificial Intelligence
• Fintech
• Big Data
• +۳ مورد دیگر

Andrea Kő is a researcher at the Corvinus University of Budapest's Institute of Data Analysis and Informatics, with a focus on artificial intelligence, fintech, and big data applications. She has held positions in the Department of Information Systems until 2022 before transitioning to her current role. Her work emphasizes investment recommenders, Industry 4.0 readiness, and e-government solutions. Her research spans financial technologies, manufacturing optimization, and organizational resilience in SMEs. Notable contributions include hybrid AI models for production systems and frameworks for digital transformation assessment. She actively contributes to international conferences like EGOVIS and BiDEDE, editing proceedings and presenting on topics such as robo-advisors and smart manufacturing. Key projects include the CCMS2.0e maturity model for Industry 4.0 adoption and studies on pandemic impacts on SMEs. Her work integrates machine learning (ANFIS, MMNN) with domain-specific challenges, addressing both theoretical and practical aspects of digital innovation across sectors.

Carlos Castilloمشاهده پروفایل

استاد پژوهشی

• Algorithmic Fairness
• Social Computing
• Artificial Intelligence Ethics
• +۳ مورد دیگر

Carlos Castillo is an ICREA Research Professor at Universitat Pompeu Fabra, leading the Social and Responsible Computing Research Group. Their work focuses on algorithmic fairness, social computing, and mitigating bias in AI systems across criminal justice, education, healthcare, and hiring. Key projects include the Horizon Europe FINDHR initiative to detect discrimination in algorithmic hiring, causal inference studies on recidivism risk prediction, and analyzing gender biases in student evaluations. Research Interests: Algorithmic fairness, bias mitigation in AI systems, social data ethics, criminal justice analytics, education technology, and health informatics. Notable contributions include the FA*IR fair ranking algorithm and foundational work on auditing algorithms in digital health. Recent Trends in Articles: Recent work emphasizes interdisciplinary applications of algorithmic fairness—e.g., improving representativeness in hiring datasets, analyzing disparities in medical AI models, and addressing bias in student satisfaction surveys. Publications also explore radicalization pathways via recommendation systems and the societal impact of algorithmic decision-making. Awards: Best Paper Awards in CIKM 2022 (Francesco Fabbri), ICAIL 2019 (Marius Miron), and ISCRAM 2013 (Muhammad Imran). Grants: Leads the Horizon Europe FINDHR project and collaborates with the European Commission on algorithmic discrimination research. Labs/Teams: Directs the Social and Responsible Computing Group, focusing on interdisciplinary projects with PhD students and industry partnerships.

Amitabh Mishraمشاهده پروفایل

استاد مدعو

• Computer-Communication Networks
• Wireless Networks
• Network Security
• +۵ مورد دیگر

Amitabh Mishra is an Adjunct Professor at the University of Delaware. His research focuses on three core areas: computer-communication networks (wireless architectures, cross-layer design, mobile cloud computing), network performance analysis (stochastic models, numerical optimizations), and network security (vulnerability assessments, authentication protocols). He has contributed to interdisciplinary fields including IoT security, smart healthcare frameworks, and socio-technical systems analysis. His work spans technical domains like wireless sensor networks, tactical network management, and quantum dot material studies, alongside applied research in tourism economics, healthcare data analytics, and educational technology. Notable contributions include frameworks for energy-efficient physiological monitoring, secure IoT configurations, and machine learning-driven security protocols. Recent research highlights include: Developing secure mobile cloud computing paradigms Modeling Multipath TCP capacity bounds using stochastic theory Investigating AI applications for deepfake ethics and tourism marketing His publications span technical journals in computer networks, medical IoT systems, and interdisciplinary studies in cultural tourism and climate change resilience.

• Real Estate Economics
• Housing Economics
• Urban Economics
• +۸ مورد دیگر

Rainer Schulz is a Senior Lecturer at the Business School of the University of Aberdeen, where he conducts research and teaches in real estate economics, housing markets, and financial economics. He is actively accepting PhD students and supervises research in real estate. His affiliations include the Gillmore Centre for Financial Technology at Warwick Business School and the Berlin Center for AI Research in Economics and Management. Research Interests: Dr. Schulz's research focuses on urban real estate markets using econometric and machine learning methods. Key areas include automated valuation modelling (AVM), real estate investment vehicles, fund management, and location choice. He investigates the impact of teleworking on housing demand and the application of real options in real estate development. His work is interdisciplinary, combining urban economics, financial theory, and data science. Publication Trends: His recent publications (2022–2024) emphasize the effects of teleworking on housing markets, machine learning applications in real estate, and local housing market information systems. Earlier works focus on land value mapping, hurdle rates in investment, and the relationship between house prices and macroeconomic variables. His research consistently applies advanced quantitative techniques to real-world property market problems. Scientific Awards: Poster award for Semiparametric Estimation of Land Values, 2015 Aareal Award of Excellence in Real Estate Research, 2012 ARES Foundation Award, 2006 Deutsche Immobilien Akademie Research Prize for doctoral thesis, 2003 Advising and Grants: Dr. Schulz is currently accepting PhD students in Real Estate. He leads the compilation of the Aberdeen Housing Market Report, a knowledge exchange project with the Aberdeen Street Property Company (ASPC). While specific grants are not listed, his research is supported through institutional collaborations and publications in high-impact journals. Labs and Teams: He is affiliated with the Centre for Real Estate Research at the University of Aberdeen Business School. He collaborates with researchers at Humboldt University (Berlin) and participates in international research networks such as the Berlin Center for AI Research. His work involves interdisciplinary teams focusing on urban data, housing policy, and financial technology.

• Behavioral Economics
• Big Data
• Computational Social Science
• +۶ مورد دیگر

Giuseppe Alessandro Veltri is a Full Professor at the University of Trento's Department of Sociology and Social Research. His expertise spans Behavioral Economics, Big Data, Computational Social Science, and Social Psychology. He currently teaches courses such as 'Big Data' and 'Social Dynamics Lab' within the Data Science and Previsione Sociale programs. His research focuses on understanding human behavior through digital methodologies, including studies on vaccine hesitancy, darknet markets, and the psychological aspects of pandemics. Veltri is also actively involved in designing computational tools like TextualLLMap to analyze societal biases in AI systems. His work bridges theoretical frameworks with empirical data, emphasizing interdisciplinary approaches to social science challenges. Research interests include the application of computational methods to study risk perception, online behavior, and policy design. Recent studies explore how behavioral insights can improve public health strategies during crises and how transparency in digital platforms influences consumer decisions. Veltri's contributions also extend to methodological innovations, such as optimizing online experiments and addressing gaps in cognitive sociology.

Rakesh Nagiمشاهده پروفایل

استاد

• Data Science & Analytics
• Machine Learning & AI
• Operations Management
• +۷ مورد دیگر

Rakesh Nagi is a Professor and Head of the Engineering Systems and Design Pillar at Singapore University of Technology and Design (SUTD), where he joined in August 2023. He concurrently holds the Donald Biggar Willett Professorship at the University of Illinois, Urbana-Champaign (UIUC), on leave. His academic leadership includes serving as Department Head of Industrial and Enterprise Systems Engineering at UIUC (2013–2019) and as Interim Director of the Illinois Applied Research Institute (2016–2018). Previously, he was Chair of Industrial and Systems Engineering at the University at Buffalo (SUNY) from 2006 to 2012. Education: PhD (1991) and MS (1989) in Mechanical Engineering from the University of Maryland, College Park, with work at the Institute for Systems Research and INRIA, France. BE (1987) in Mechanical Engineering from the University of Roorkee (now IIT Roorkee), India. Research focuses on Data Science, Machine Learning, Operations Research, GPU-accelerated computing, and military applications. Key areas include Big Graphs, High-level Information Fusion, Production Systems, and Multi-Agent Systems. His work often leverages parallel computing and optimization techniques. Recipient of prestigious awards: IISE David F. Baker Award (2022), INFORMS Koopman Award (2021, 2018), and multiple DARPA Graph Challenge recognitions. Contributions span over 100 peer-reviewed articles in top journals (e.g., Operations Research, IEEE Transactions) and conferences. Research projects include Hybrid AI/ML-Optimization for cloud workflows, GPU-accelerated algorithms for multi-target tracking, and interventions against illicit supply chains. Active collaborations with IBM-Illinois and NSF-funded initiatives address strategic resource allocation and network analysis. Labs/Teams: Leads the Engineering Systems and Design Pillar at SUTD and coordinates interdisciplinary projects at UIUC’s Coordinated Science Laboratory.

Anna Monrealeمشاهده پروفایل

دانشیار

• Privacy-Preserving Data Mining
• Big Data Analytics
• Social Network Analysis
• +۷ مورد دیگر

Anna Monreale is an Associate Professor in the Department of Computer Science at the University of Pisa and a key member of the Knowledge Discovery and Data Mining Laboratory (KDD-Lab), a joint research group with the Information Science and Technology Institute of the National Research Council (ISTI-CNR) in Pisa. Her academic career is rooted in the University of Pisa, where she completed her Bachelor's, Master's, and Ph.D. in Computer Science. Her research focuses on privacy-preserving data analytics, with core interests in big data analytics, social network analysis, spatio-temporal mining, and explainable AI. She is particularly known for her work on privacy-by-design in data mining and evaluating privacy risks in analytical processes. Her research bridges technical innovation with ethical and legal considerations in data science. Her recent publications reveal a strong trend toward explainable AI, privacy in federated learning, and risk assessment in mobility and health data. She actively contributes to developing methods for explaining black-box models, assessing privacy exposure, and balancing privacy, utility, and fairness in AI systems. Privacy by Design Ambassador (2014) ISTI-CNR Young++ Researcher Award (2014) Monreale has advised and co-chaired several international workshops, including PriSMO, PinSoDa, and MoKMaSD, and serves on editorial boards such as Transactions on Data Privacy. She teaches advanced data mining, big data ethics, and database systems across multiple graduate and undergraduate programs. She is involved in major EU projects like SoBigData, XAI, TAILOR, and HumMingBird, reflecting her leadership in data science and AI ethics. She is affiliated with the KDD-Lab, a prominent research group focused on knowledge discovery, social mining, and big data analytics, contributing to both theoretical advances and real-world applications in privacy-aware data science.

Sandra Wachterمشاهده پروفایل

استاد

• AI Ethics
• Algorithmic Bias
• Data Privacy
• +۳ مورد دیگر

Professor Sandra Wachter is a Professor of Technology and Regulation at the Oxford Internet Institute, University of Oxford. She leads the Governance of Emerging Technologies (GET) Research Programme, focusing on legal, ethical, and technical aspects of AI and robotics. Her affiliations include Harvard’s Berkman Klein Center, the World Economic Forum, and the European Commission’s Expert Group on Autonomous Cars. She advises governments and tech companies globally on AI governance. Education: Studied at the University of Oxford and the University of Vienna’s Law Faculty, with prior roles at the Royal Academy of Engineering and the Austrian Ministry of Health. Research interests include algorithmic bias, explainable AI, privacy law, and ethical design. Her work on counterfactual explanations and the CDD bias test has been implemented by Google, IBM, and Amazon. She explores AI’s societal impacts, including predictive policing, health tech, and cyber-security. Awards include the Privacy Law Scholar Award (2019) and the ‘AI Superhero Award’ (2017). She leads the British Academy project on algorithmic inferences and contributes to standards for AI transparency and accountability.

Mingming Chengمشاهده پروفایل

استاد

• Social Media Analytics
• Sustainable Tourism
• Sharing Economy
• +۴ مورد دیگر

Professor Mingming Cheng is a faculty member at Curtin University, serving as Professor in Digital Marketing and Director of the Social Media Research Lab within the School of Management and Marketing (Faculty of Business and Law). His research focuses on social media analytics, sustainable tourism, and Chinese outbound tourism, with a strong emphasis on data science and the sharing economy. He holds a PhD from the University of Technology Sydney and previously worked at the University of Otago. Professor Cheng has secured over AUD 650,000 in research funding and serves on editorial boards of top journals like Tourism Management and Annals of Tourism Research . He has been recognized with prestigious awards, including the 2021 Australia's Top 40 Early Career Researchers and the 2023 Young Tall Poppy Science Award. Research Interests: Social media analytics, sustainable tourism, sharing economy, digital transformation Key Roles: Editor for Tourism Management Perspectives and Tourism Geographies Affiliations: Collaborations with institutions like the Japan Foundation and Western Australian Department of Agriculture His teaching includes courses on digital marketing, big data analytics, and social media management. He actively engages with media, contributing insights to ABC News and South China Morning Post.

Mostafa Milaniمشاهده پروفایل

استادیار

• Data Management
• Databases
• Data Cleaning
• +۷ مورد دیگر

Mostafa Milani is an Assistant Professor in the Department of Computer Science at Western University. His research focuses on data management, databases, and their applications in data cleaning, privacy, provenance, and fairness. Before joining Western, he held postdoctoral positions at the University of British Columbia and McMaster University, and earned his Ph.D. from Carleton University under Dr. Leopoldo Bertossi. Education: Ph.D. in Computer Science from Carleton University (supervised by Leopoldo Bertossi), Postdoctoral Fellowships at University of British Columbia and McMaster University. Research Interests: Data Quality, Privacy, Provenance, Fairness, Entity Matching, Query Optimization, and Database Systems. His work emphasizes ethical data practices and integrates machine learning for improved database interactions. He has contributed to projects like Building Trust in Data (privacy/fairness integration) and Unified Data Exploration (provenance and query recommendations). Courses taught include Databases I/II, Applied Logic, and Web Systems. Current advisees include 7 MSc and 1 PhD student. Former students have graduated across MSc and undergraduate programs. His research is supported by grants and collaborations, and he actively participates in program committees for top conferences like SIGMOD and VLDB.

Dollas Apostolosمشاهده پروفایل

استاد

• Reconfigurable Computing
• Hardware Architecture
• Embedded Systems
• +۴ مورد دیگر

Professor Dollas Apostolos serves as a Professor in the School of Electrical and Computer Engineering at the Technical University of Crete (TUC), where he has held leadership roles such as Department Chairman. He directs the Microprocessor and Hardware Laboratory, focusing on reconfigurable computing, embedded systems, and high-performance digital systems. His work emphasizes rapid prototyping and real-world implementation of computational solutions. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign (1987) M.Sc., Computer Science, University of Illinois at Urbana-Champaign (1984) B.Sc., Computer Science, University of Illinois at Urbana-Champaign (1982) Research Interests: Reconfigurable computing architectures FPGA-based acceleration for bioinformatics and genomics Embedded systems and real-time processing Hardware-software co-design for high-performance computing His research bridges theoretical innovation with practical applications, such as FPGA implementations for genome assembly and aquaculture monitoring systems. Publications: Recent work highlights FPGA-based solutions for bioinformatics (e.g., genome assembly acceleration), real-time embedded systems (e.g., fish cage net monitoring), and scalable data processing frameworks. His articles often explore the intersection of FPGA technology with computational biology, embedded vision, and distributed systems. Awards and Affiliations: Senior Member, IEEE and IEEE Computer Society Recipient of IEEE Computer Society Golden Core and Meritorious Service Awards Twice honored with the University of Illinois Teaching Excellence Award He is a co-founder of IEEE conferences like FCCM and RSP, reflecting his leadership in the reconfigurable computing community. Teaching and Labs: Teaches courses on computer architecture, logic design, and VLSI design. The Microprocessor and Hardware Lab under his direction drives advancements in FPGA-based systems, with projects ranging from bioinformatics hardware accelerators to embedded vision systems.