جست‌وجوی استاد و پژوهشگر

Lisa Mertenمشاهده پروفایل

پژوهشگر

• Digital Media Studies
• Political Communication
• Algorithmic Personalization
• +۶ مورد دیگر

Dr. Lisa Merten is a Senior Researcher at the Leibniz Institute for Media Research | Hans Bredow Institute (HBI), where she leads computational social science projects like POLTRACK, examining the relationship between information repertoires and political polarization. She also coordinates the DFG-funded 'Public Connection' project, exploring how users engage with diverse publics through media practices. Her research focuses on digital media environments, algorithm-driven personalization, and the impact of social media on news consumption and public opinion. Education : - Studied Communication Science at Universität Leipzig, TU Dresden, Universiteit van Amsterdam, and Boston University - German Academic Scholarship Foundation recipient Research Interests : - Algorithmic publics and personalization - Political information seeking and polarization - Digital methods for media analysis - Journalism and online commentary dynamics Key Projects : - Co-led the POLTRACK project with GESIS and universities of Bremen/Konstanz - Investigated Instagram influencers' reach in 'Media Use and Social Cohesion' with Hannah Immler - Pioneered browser data donation methods in journalism studies Awards : - 2021 Digital Journalism 'Article of the Year' for work on social media news curation Teaching : - Held part-time junior professorship in Computational Social Science at University of Konstanz (2021) - Taught at University of Hamburg, Augsburg, and Kiel University of Applied Sciences Labs/Teams : - Core member of HBI's Computational Social Science team - Collaborates with GESIS, Bremen/Konstanz universities, and international partners

Caroline Ruinerمشاهده پروفایل

استاد

• Digital Transformation and Work
• Sustainability Twin Transformation
• Artificial Intelligence Ethics
• +۴ مورد دیگر

Prof. Dr. Caroline Ruiner is a Professor of Sociology at the University of Hohenheim since 2019. Her research focuses on the impact of digital transformation on work, sustainability transitions, AI ethics, and labor market organizations. Previously, she held positions at the University of Trier and Ruhr University Bochum, where she conducted groundbreaking work on freelance workers and cooperative labor structures. Her interdisciplinary research spans sociology, organizational studies, and digital ethics. Education: PhD in Sociology from the University of Augsburg Studies in Business Administration and Sociology at Goethe University Frankfurt Habilitation in Social Sciences at Ruhr University Bochum (2018) Research Interests: Workplace digitalization and its societal implications Designing ethical AI systems for fair labor practices Twin transformation (digitalization + sustainability) New labor market structures like cooperatives and platform economies Key Projects: AIBBA (AI for Business/ Business for AI) Andromeda (Autonomy in high-reliability organizations) KARAT (AI and driving professions) NewWork4KeyWorker (systemic workforce design) Awards: Recipient of the Teaching Prize from the German Sociological Association for her textbook Arbeits- und Industriesoziologie (UTB 2016). Consultation: Her work bridges academic research and practical applications, addressing labor law challenges, digital ethics frameworks, and sustainable work design. She collaborates internationally with institutions in Europe, Japan, and the U.S.

Kuldeep S. Meelمشاهده پروفایل

دانشیار

• Automated Reasoning
• Formal Methods
• Artificial Intelligence
• +۳ مورد دیگر

Kuldeep S. Meel is the Stephen Fleming Early-Career Associate Professor at Georgia Institute of Technology's School of Computer Science and an Associate Professor at the University of Toronto (currently on leave). His research focuses on the intersection of Formal Methods and Artificial Intelligence, emphasizing scalable automated reasoning techniques. He holds prestigious awards including the 2022 ACP Early Career Researcher Award and the 2019 NRF Fellowship for AI. His work has been recognized with multiple best paper awards at conferences like ICLP, CAV, and IJCAI. Meel's research spans automated reasoning, formal methods, and their applications in AI. He has developed influential tools like ApproxMC and UniGen, advancing model counting and uniform sampling. His academic journey includes roles at NUS and collaborations with institutions globally. Teaching excellence is highlighted by NUS Annual Teaching Awards (2022, 2023). Awards include Distinguished Paper Awards at CAV-23 and CAV-24, and 1st place in Model Counting Competitions. His lab has produced notable advisees securing tenure-track positions worldwide. Current projects explore distribution testing, probabilistic reasoning, and AI verification.

Christina Delimitrouمشاهده پروفایل

دانشیار

• Computer Architecture
• Cloud Computing
• Systems & Networking
• +۶ مورد دیگر

Christina Delimitrou is an Associate Professor at MIT's Department of Electrical Engineering and Computer Science (EECS) and a Principal Investigator at the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on optimizing cloud computing systems, with a strong emphasis on resource management, sustainability, and machine learning-driven solutions. Delimitrou leads projects on carbon-aware scheduling, efficient datacenter operations, and serverless computing frameworks like Ursa and Ditto. Her work bridges theoretical system design with practical deployment challenges, addressing topics such as microservices orchestration, approximation techniques for resource efficiency, and security implications of multi-tenancy in shared cloud environments. Notably, she received the Presidential Early Career Award for her contributions to improving datacenter efficiency through innovative scheduling and resource allocation strategies. Delimitrou's research group develops tools like Sage (ML-driven performance debugging) and Seer (big data analytics for cloud systems), emphasizing reproducibility and scalability. Her lab also explores edge computing, swarm robotics coordination (e.g., Hivemind), and hardware-software co-design for next-generation systems. Her academic affiliations include MIT CSAIL's Systems Community of Research, where she collaborates on large-scale software systems. Key themes in her work include QoS-aware resource management, sustainable computing practices, and leveraging approximation to enhance cloud resource utilization.

Francesco Rampazzoمشاهده پروفایل

مدرس

• Digital Demography
• Computational Demography
• Fertility Studies
• +۳ مورد دیگر

Francesco Rampazzo is a Lecturer in Demography at the University of Southampton, actively contributing to the Leverhulme Centre for Demographic Science. He maintains affiliations as a Non-Stipendiary Research Fellow at Nuffield College and collaborates with the Max Planck Institute for Demographic Research in Rostock and the Centre for Population Change in Southampton. Education: PhD in Social Statistics and Demography, University of Southampton European Doctoral School of Demography diploma Master in Demography, Stockholm University Bachelor in Statistical Science, University of Padova His research focuses on Digital and Computational Demography , leveraging digital traces from advertising platforms like Facebook to study demographic phenomena, particularly fertility and migration patterns. He pioneers methods for real-time migration tracking, crisis response analysis, and innovative survey methodologies. The 2025 article on Global South survey sampling demonstrates his commitment to non-traditional data sources, while his 2024 publications explore Brexit migration impacts, menstrual app fertility data, and cross-European adulthood transitions. His work consistently bridges digital analytics with classical demographic frameworks. Francesco's methodological innovations include validating Facebook's advertising data for social science research (2022), developing migration estimation frameworks combining digital and survey data (2021), and investigating pandemic behavioral impacts through social media surveys (2021). His earlier work (2018) tackled male fertility estimation challenges using digital data.

Aftab Ahmadمشاهده پروفایل

استاد

• Machine Learning
• Cyber Security
• Public Health Prediction
• +۵ مورد دیگر

Aftab Ahmad is a Professor in the Department of Computer Science at the City University of New York (CUNY), specializing in cybersecurity and machine learning applications. He holds a Doctor of Science from George Washington University. His research focuses on developing machine learning algorithms for cyber threat intelligence (CTI) and public health prediction, along with designing secure generative deep learning models resistant to reverse-engineering. Key research areas include: Cybersecurity frameworks and privacy-preserving architectures Generative adversarial networks (GANs) with embedded security features Biomedical signal analysis and human body channel modeling Secure wireless protocols for critical infrastructure His publication trends emphasize: Privacy metrics and data protection mechanisms Smart grid and IoT security Neuroscience-inspired machine learning models Wireless network vulnerability assessments No scientific awards or grants were explicitly mentioned in the provided texts. He teaches advanced courses in computer security and network forensics at undergraduate and graduate levels. No advising relationships or lab affiliations were detailed in the available information.

Houria Oudghiriمشاهده پروفایل

استاد آموزشی

• Computer Science
• Big Data
• Computer Architecture
• +۷ مورد دیگر

Houria Oudghiri is a Teaching Professor in the Department of Computer Science & Engineering at Lehigh University. She earned her PhD in Computer Engineering from McGill University in 1999 and has held academic positions at Ecole Supérieure d’Informatique (Algiers, Algeria), Effat University (Jeddah, Saudi Arabia), and SUNY Polytechnic Institute (Utica, NY). Her career spans 20+ years in academia, with expertise in computer architecture, programming, and data structures. PhD, Computer Engineering, McGill University, 1999 Her research focuses on Big Data discretization techniques, hardware-software co-design for digital systems, and educational assessment frameworks. Her work on threshold analysis and scheduling algorithms reflects interdisciplinary applications in computational theory and system optimization. Current research trends include high-level synthesis of digital systems, data preprocessing for machine learning, and pedagogical evaluation methodologies. She has contributed to understanding design space exploration in embedded systems and weighted scheduling algorithms for resource allocation. Houria works in Packard Lab at Lehigh University, where she teaches foundational computing courses. Her academic journey reflects a global perspective with experiences in Algeria, Saudi Arabia, and the United States.

• Edge Computing
• Fog Computing
• Cloud Computing
• +۷ مورد دیگر

Riccardo Lancellotti is an Associate Professor at the Department of Engineering 'Enzo Ferrari' of the University of Modena and Reggio Emilia. His research focuses on Edge/Fog/Cloud Computing, Cyber Security, and Resource Management in distributed systems. He has extensive contributions in optimizing infrastructure performance, load balancing, and energy efficiency in cloud and fog environments. His work often combines theoretical models with practical simulations, addressing challenges like stale information in edge systems and heterogeneous resource allocation in smart cities. Key research areas include: Fog/Edge computing infrastructure design and optimization Cloud resource provisioning and SLA compliance Security for Industry 4.0 and automotive systems Genetic algorithms for service placement Scalable VM clustering and resource allocation Publications highlight trends in cloud/fog integration, robust game theory for microservices, and distributed load balancing under dynamic conditions. His work emphasizes practical applications, such as pharmaceutical distribution routing and smart city sensor management. No awards are explicitly listed, but his extensive publication record reflects recognition in the field.

Frédéric PASCALمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Adaptive Radar Detection
• +۵ مورد دیگر

Frédéric Pascal is a Full Professor at CentraleSupélec, part of the University of Paris-Saclay, and a member of the Laboratoire des Signaux et Systèmes (L2S). His research focuses on statistical signal processing, machine learning, and robust estimation techniques, with applications in radar detection, covariance matrix estimation, and information geometry. He has held roles including Coordinator of AI activities at CentraleSupélec and Head of the "Signals and Statistics" group at L2S. His academic journey includes a PhD from University Paris X – Nanterre (2006) and an HDR (2012) from University Paris-Sud. His work emphasizes adaptive signal processing in non-Gaussian environments, with contributions to robust covariance estimation, M-estimators, and applications in radar systems and biomedical signal processing. He has authored over 100 journal/conference papers and serves as an Associate Editor for IEEE Transactions on Signal Processing and Elsevier Signal Processing. Current research interests include AI transparency, data-driven methods for industry 4.0, and health-related signal analysis.

Vincent Grégoireمشاهده پروفایل

استاد

• Information Economics
• Market Microstructure
• Financial Big Data Analytics
• +۴ مورد دیگر

Vincent Grégoire is a Full Professor in the Department of Finance at HEC Montréal. He holds a Ph.D. in Finance from the University of British Columbia, M.Sc. degrees in Financial Engineering and Electrical Engineering from Université Laval, and is a Chartered Financial Analyst (CFA). His research focuses on information economics, market microstructure, financial big data analytics, cybersecurity in finance, and machine learning applications in finance. Grégoire is affiliated with the Multidisciplinary Institute for Cybersecurity and Cyber Resilience (IMC²) and IVADO, and collaborates with Fin-ML. He co-chaired the Northern Finance Association in 2024-2025. His recent work includes groundbreaking studies on market microstructure dynamics, passive investing trends, and the implications of cybersecurity on financial systems. His research has been recognized with awards such as the 2022 Best Paper Award in Asset Pricing (Northern Finance Association) and the 2022 Chenelière Éducation/Gaëtan Morin Research Prize from HEC Montréal. Grégoire has supervised over 25 master’s theses and projects, covering topics like cryptocurrency diversification, ESG risk exposure, and fintech-driven solutions for sustainable practices. In teaching, he instructs courses such as Empirical Finance and Investment Analysis. His methodologies emphasize reproducibility and cutting-edge tools like Python for financial data analysis.

Hanna Maohمشاهده پروفایل

استاد

• Transportation Engineering
• Sustainable Transportation
• Freight Transportation Analysis
• +۴ مورد دیگر

Dr. Hanna Maoh is a Professor in the Department of Civil and Environmental Engineering at the University of Windsor. She holds a BSc from Bethlehem University (1996), MSc from McMaster University (1999), and PhD from McMaster (2005). Her research focuses on integrating transportation and land use models, freight transportation analysis, sustainable transportation strategies, and big data applications in GIS. She leads the Traffic Lab, exploring innovative solutions for cross-border logistics, electrification of fleets, and environmental impact mitigation. Key professional affiliations include the Transportation Association of Canada, Professional Engineers Ontario, and the Transportation Research Board. Her work emphasizes policy-relevant research, such as modeling Canada-US border crossing efficiency, optimizing electric vehicle charging networks, and assessing freight infrastructure resilience. Recent publications highlight advancements in truck stop location optimization using GPS data, fleet electrification demand analysis, and border crossing behavior studies. Her research consistently bridges academic insights with practical applications, informing sustainable urban planning and environmental policy.

• Database Systems
• Data Management
• Graph Databases
• +۵ مورد دیگر

Alin Deutsch is a Professor of Computer Science at the University of California, San Diego (UCSD), specializing in database systems, graph databases, and formal verification. He has contributed significantly to research areas including query optimization, data integration, and privacy-preserving systems. His work spans theoretical foundations and practical implementations, such as the Linked Data Benchmark Council (LDBC) and the TigerGraph database system. He co-authored over 100 papers and has been involved in major conferences like SIGMOD and VLDB. Research interests include graph query processing, parallel computing, data-centric business processes, and automated system verification. Recent work focuses on scalable hybrid analytics and graph databases. Deutsch is also active in database education, co-authoring a paper on UCSD's database curriculum. He has led projects in privacy-aware systems, such as policy-aware location-based services, and contributed to tools like CLIDE for interactive query formulation in service-oriented architectures. His collaborations involve industry partners like TigerGraph and academic institutions globally.

Asmus Skar Christiansenمشاهده پروفایل

دانشیار

• Pavement Engineering
• Geotechnical Engineering
• Sensing Technologies
• +۴ مورد دیگر

Asmus Skar Christiansen is an Associate Professor in Pavement Engineering at the Department of Environmental and Resource Engineering, Technical University of Denmark (DTU Sustain). He serves as Head of Study for the Nordic Master in Cold Climate Engineering programme and lectures on pavement engineering, Arctic road construction, and foundation design. His academic career at DTU spans from Postdoc researcher (2017-2019) to Assistant Professor (2020-2023) and current Associate Professor position since 2023. His research centers on pavement technology and geotechnics with specialization in: Development of advanced testing and modeling techniques for pavements Integration of modern sensing technologies in civil infrastructure Computational mechanics for soil-structure interaction Sustainable materials for cold climate engineering Recent work demonstrates a clear shift toward IoT-enabled monitoring systems and data-driven pavement assessment, with 80% of 2023-2025 publications focusing on sensor integration and machine learning applications. Notable scientific contributions include: Creation of open-source datasets (LiRA-CD, RIVA) for road condition modeling Development of thermomechanical models for heated pavements Innovations in waste soil reuse for infrastructure He actively supervises PhD candidates across multiple projects including GREENPIPE (self-sensing pipe systems) and urban pavement analysis, while maintaining industry consultancy through COWI A/S collaborations. Christiansen also contributes to sustainable infrastructure through DTU's alignment with UN SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities).

• New Product Development
• Knowledge Management
• Collaborative Work Environments
• +۵ مورد دیگر

Angelo Corallo is an Associate Professor at the Department of Experimental Medicine, University of Salento, specializing in technologies and methodologies for collaborative processes in industrial systems. His research spans Digital Business Ecosystems , Cybersecurity , and Collaborative Product Design , focusing on the interplay between technology and organizational dynamics. He leads interdisciplinary research divisions in Open Networked Business Management , Learning and Innovation , and Collaborative Product Design . Research Interests : Corallo's work integrates Information and Communication Technologies (ICT) with Business Management, particularly in Digital Twins for healthcare and manufacturing Knowledge Modeling and Ontology Engineering Industry 4.0 and Smart Manufacturing Agri-Food Sustainability through digitalization Scientific Contributions : His recent articles explore trends in Cybersecurity for Industrial IoT Metaverse Applications in business models Traceability Systems in food supply chains Collagen-Based Biomaterials from aquaponics

• Statistics
• Machine Learning
• Bioinformatics
• +۴ مورد دیگر

Ping Ma is a Professor of Statistics with a courtesy appointment in Computer Science at the University of Georgia. His research focuses on developing innovative statistical and machine learning methodologies for complex high-dimensional data, with applications spanning bioinformatics, computational biology, social network analysis, and anomaly detection in power systems. Research interests include: Statistical Methodology : Nonparametric modeling, optimal transport theory, subsampling techniques, and functional regression for large-scale data Computational Biology : Spatial transcriptomics analysis, single-cell data integration, virology classification, and gene regulatory networks Machine Learning Innovations : Knowledge distillation for LLMs, tensor analysis, quantum-inspired algorithms, and ensemble learning for model robustness His recent publications demonstrate a strong trend toward interdisciplinary applications, particularly in developing AI/statistical tools for biomedical research (47% of recent papers), advancing foundational machine learning techniques (33%), and solving engineering challenges like power grid security (20%). Methodologically, 67% focus on novel algorithm development while 33% refine existing techniques for scalability.