جست‌وجوی استاد و پژوهشگر

• Human-Centered Design
• Digital Ethics
• Social Technology
• +۶ مورد دیگر

Thomas Olsson is a **Professor in Human-Centered Design** at Tampere University, affiliated with the Faculty of Information Technology and Communication Sciences and the Computing Sciences department. He leads the *Technology x Social Interaction Research Group* and the *Digital and Sustainability Transitions in Society* research platform. His work focuses on designing ICT systems that prioritize digital ethics, social sustainability, and environmental responsibility. **Research Interests**: Social technologies, sustainable design of ICT, AI-driven recruitment systems, cultural integration through technology, and the ethical implications of digital media. He employs qualitative methods and design-through-research approaches, often collaborating across disciplines like computer science and behavioral/social sciences. Current roles: Academic Director of Digital/Sustainability Transitions Platform; Head of Master's Program in Computing Sciences and Electrical Engineering Key projects: AIdience (AI in Journalism), Trust-M (Migrant Services), Big Match (Team Assembly), Emotions in Digital Media Teaching: Coordinates Human-Technology Interaction studies and develops courses like *Sustainable Design* and *Ethics of Technology* **Lab/Team**: Leads the *Technology x Social Interaction* group (collaboration opportunities available). Active in the *Rajapinta* interdisciplinary research community.

Ninghao Liuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Explainable AI (XAI)
• +۶ مورد دیگر

Dr. Ninghao Liu is an Assistant Professor of Computer Science in the School of Computing at the University of Georgia, part of the Franklin College of Arts & Sciences - Division of Physical & Mathematical Sciences. He holds a Ph.D. in Computer Science from Texas A&M University (2021) and an M.S. in Electrical and Computer Engineering from Georgia Institute of Technology (2015). His research focuses on Explainable AI (XAI), Graph Mining, Model Fairness, Recommender Systems, and Outlier Detection, with notable contributions to foundational AI techniques and their applications in education, healthcare, and environmental sciences. Dr. Liu has secured significant funding, including a three-year NSF grant (2022–2025) for 'Graph-Oriented Usable Interpretation' and a five-year $10 million grant from the U.S. Department of Education (2024–2029) for the GenAI Empowered National Initiative for STEM+C Education. He has also been honored with the Outstanding Paper Award at ICML 2022, Best Paper Award Shortlist at WWW 2019, and other distinctions. His work emphasizes interpretable machine learning, graph neural networks, and addressing algorithmic bias. He collaborates across disciplines, contributing to radiology AI, climate-smart forestry, and pandemic prediction through knowledge-enhanced deep learning. His lab is based at the Boyd Research and Education Center, where he advances research in trustworthy AI systems and data-centric solutions.

Akhtar Hussainمشاهده پروفایل

استادیار

• Power Systems
• Smart Grids
• Energy Storage
• +۶ مورد دیگر

Akhtar Hussain serves as an Assistant Professor in the Department of Electrical and Computer Engineering at Laval University, Quebec. His research centers on AI-driven optimization of power and energy systems, with emphasis on microgrid resilience, distributed energy resource integration, and electric vehicle-grid interactions. He actively contributes to advancing grid reliability through innovative resource allocation and consumer satisfaction frameworks. Ph.D. in Electrical Engineering, Incheon National University, South Korea (2019) M.Sc. in Electrical Engineering, Myongji University, South Korea (2014) B.Sc. in Electrical Engineering, National University of Sciences and Technology, Pakistan (2011) Dr. Hussain's research spans power systems resilience, smart grid technologies, and equitable energy access. His work integrates artificial intelligence with traditional power engineering to address challenges in microgrid operation, electric vehicle integration, and renewable energy management. Key focus areas include developing algorithms for optimal resource utilization, enhancing grid stability during contingencies, and designing frameworks for fair energy distribution in diverse communities. His recent publications (2023-2025) reveal a strong trajectory toward AI-enhanced grid management, with recurring themes of resilience optimization, equity-focused resource allocation, and electric vehicle-grid synergies. The research demonstrates increasing sophistication in handling uncertainty through machine learning while addressing socio-technical dimensions of energy transition. Dr. Hussain currently supervises one Master's student and has guided five Ph.D. candidates to completion. His research is funded by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant ($160,000/year) for the project 'Grid Condition and Resilience-Aware Incentivization and Deployment of Distributed Energy Resources' (2024-2029), supplemented by a Springboard to Discovery award ($40,000) for 2024-2025. As an IEEE member, he collaborates with industry partners on real-world grid modernization initiatives, focusing on practical implementation of resilience strategies through microgrids and mobile energy resources.

• Networking and Communications
• Blockchain Technology
• Smart Grids and Energy Systems
• +۵ مورد دیگر

Roberto Rojas-Cessa is a Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology (NJIT), affiliated with the School of Applied Engineering and Technology. His research focuses on networking, blockchain applications in smart cities, energy systems, wireless communications, and high-performance switching. He has led multiple National Science Foundation (NSF)-funded projects, including initiatives on controlled delivery power grids and next-generation network quality of service. Notably, his work explores blockchain for energy metering, sustainable environmental measures, and smart grid optimization. He is also a Senior Member of the National Academy of Inventors (2024). His research interests span network protocols, distributed systems, and IoT applications. Recent projects include AMI-Chain (a blockchain-based power metering system) and studies on indirect free-space optical communications for vehicular networks. He has contributed to advancements in medium access control for crowded networks and energy packet switches for digital microgrids. Rojas-Cessa’s work integrates machine learning for network management and flood impact analysis. He has developed tools for time-lapse analysis of urban data and agent-based models to evaluate electric vehicle adoption. His publications emphasize scalability, security, and efficiency in both traditional and emerging technologies. Grants: Collaborative Research on Power Grids (NSF, 2016–2018), NeTS-NR: Quality of Service Networks (NSF, 2004–2008) Awards: Senior Member of the National Academy of Inventors (2024) His lab activities include experimental evaluations of digital microgrids and blockchain implementations for carbon footprint tracking. He actively collaborates on projects addressing emergency communications and resilient energy distribution systems.

• Credit Scoring
• Consumer Credit Risk Modelling
• Predictive Analytics
• +۳ مورد دیگر

Christophe Mues is a Professor of Data Science and Information Systems at the University of Southampton's School of Management, within the Department of Decision Analytics and Risk. His research focuses on credit scoring, consumer credit risk modeling, and applications of predictive analytics, including machine learning techniques for credit risk assessment. He leads the Information Systems & Business Analytics section and supervises multiple PhD students in Business Studies and Management. His work spans advanced statistical methods for predicting Probability of Default (PD), Loss Given Default (LGD), and loan profitability. He is actively involved in the academic community, serving on the organizing committee for the Credit Scoring and Credit Control conference. His teaching includes topics in information systems and business analytics. Contact: C.Mues@soton.ac.uk. Research Interests: Credit Scoring and Consumer Credit Risk Modelling Predictive Analytics in Finance Machine Learning Applications (Deep Learning, Graph Neural Networks) Non-Traditional Data Integration Credit Model Transparency and Fairness Debt Collection Optimization PhD Supervision: Currently guiding four students in Business Studies & Mngt: Kameswara Rao Korangi, Sarthak Gurnani, Pablo Casas, and Nora Agyei-Ababio. Professional Activities: Leads research groups and contributes to international conferences. His work bridges academic research with practical financial risk solutions, emphasizing ethical AI and regulatory compliance in credit modeling. Biography: Holds a PhD in Applied Economics from KU Leuven (Belgium). Joined the University of Southampton in 2004, advancing from researcher to his current leadership role in Decision Analytics and Risk.

Jaap Zevenbergenمشاهده پروفایل

استاد

• Land Administration
• Geo-Information Management
• Property Rights
• +۵ مورد دیگر

Jaap Zevenbergen is a Full Professor at the University of Twente's Faculty of ITC, specializing in Land Administration and Geo-Information Management. He holds a PhD from Delft University of Technology (2002) and degrees in Geodetic Engineering (Delft) and Law (Leiden University). His research focuses on international land governance, digital transformation of property institutions, and pro-poor land tools, with projects in Africa, Southeast Asia, and Eastern Europe. He has contributed to UN Habitat initiatives and World Bank programs, emphasizing sustainable development goals (SDGs 1, 11, 13). Key roles: Theme leader at TU Delft's OTB Institute (2003–2010), Portfolio Manager for MSc Land Administration at ITC. Teaching: MSc programs in Land Administration, GIMA, and International Land Management. Research interests include: Land Administration Domain Model (LADM) implementation, legal-technical integration in geo-ICT systems, and post-disaster/post-conflict land governance. He has authored/co-authored over 340 publications and edited books like Real Property Transactions . Notable achievement: Co-winner of the 2018 FIG-Survey Review Prize for land policy analysis. Current projects explore 3D cadastral systems, UAV applications in land registration, and ethical geospatial practices. He advises on land reforms in Greece and Egypt and collaborates with institutions like UN Habitat and the World Bank.

Adrian Perrigمشاهده پروفایل

استاد

• Network Security
• Cybersecurity
• Distributed Systems
• +۳ مورد دیگر

Adrian Perrig is a Full Professor at the Department of Computer Science at ETH Zürich. He leads research in network security, distributed systems, and internet architecture, focusing on projects like the SCION secure internet architecture and its commercialization through Anapaya Systems. His work emphasizes secure communication, denial-of-service defense, and public key infrastructure (PKI) innovations. Affiliations: ETH Zürich, Institute for Information Security Key Contributions: SCION, SAGE, RHINE, F-PKI Research interests include path-aware networks, cryptographic protocols, and resilient systems. His publications span over 295 results since 2005, with notable awards including the Best Paper Award (CoNEXT 2021) and ANRP 2023. He has contributed to foundational work in secure routing, DNS security, and GPU attestation. Scientific awards include Best Paper Awards at CoNEXT and ACM SIGCOMM, as well as recognition for applied networking research. His work bridges academia and industry, addressing challenges in global network security and scalability.

Nigel Boschمشاهده پروفایل

استادیار

• Machine Learning
• Human-Computer Interaction
• Educational Technology
• +۳ مورد دیگر

Nigel Bosch is an Assistant Professor in the School of Information Sciences (iSchool) at the University of Illinois Urbana-Champaign, with a joint appointment in the Department of Educational Psychology. He is also a faculty affiliate at the National Center for Supercomputing Applications (NCSA) and Illinois Informatics. His primary research focuses on machine learning and human-computer interaction applications in education, with particular emphasis on affective computing, metacognition, and online learning environments. Bosch holds a PhD in Computer Science from the University of Notre Dame, followed by a postdoctoral research position at the National Center for Supercomputing Applications. His research explores machine learning applications in education, including automatic emotion measurement in programming education, metacognition analysis through natural language processing, and ethical implications of AI in learning. He also investigates wearable technologies for health monitoring and algorithmic bias mitigation in educational data. Bosch’s work is supported by grants from the National Science Foundation (NSF), the Institute of Education Sciences (IES), and the University of Illinois. He leads the (Human + Machine) Learning lab, which develops innovative technologies for educational analytics, AI ethics, and human-centered computing.

Giuseppe Colangeloمشاهده پروفایل

دانشیار

• Innovation Policy
• Intellectual Property Law
• Competition Policy
• +۳ مورد دیگر

Giuseppe Colangelo is an Associate Professor of Law and Economics at the University of Basilicata and holds adjunct roles at LUISS Guido Carli (Rome, Italy) and the Transatlantic Technology Law Forum (TTLF). He specializes in innovation policy, competition law, and economic analysis of legal frameworks. His research bridges EU and U.S. antitrust approaches, focusing on digital markets, data governance, and transnational litigation. Education includes a Law degree from LUISS Guido Carli, an LL.M. in Competition Law from Erasmus University Rotterdam, and a Ph.D. in Law and Economics from LUISS. He has been a TTLF Affiliate since 2017. Key research interests include AI-driven collusion, antitrust implications of data accumulation, and interoperability policies. Over 15 publications analyze topics like anti-suit injunctions in SEP litigation, EU-US regulatory divergence in digital platforms, and privacy-antitrust intersections. His work emphasizes transatlantic comparative law and policy recommendations for addressing market power in tech sectors.

Matt Garrattمشاهده پروفایل

استاد

• Robotics
• Swarm Robotics
• Unmanned Aerial Vehicles
• +۵ مورد دیگر

Professor Matt Garratt is a faculty member at the University of New South Wales (UNSW Canberra), School of Engineering and IT, serving as AI theme lead for the Defence Trailblazer Universities initiative with over $200 million in funding. His primary research focuses on sensing, guidance, and control for autonomous systems within robotics and unmanned aerial vehicles. Garratt's research spans robotics, swarm intelligence, and autonomous systems with emphasis on bio-inspired navigation techniques and adaptive flight control. His work addresses critical challenges including terrain following using vision systems, landing UAVs on moving platforms, and developing self-organizing swarms. He integrates artificial intelligence, computer vision, and machine learning to advance unmanned systems capabilities in complex environments. Analysis of his recent publications reveals strong trends in bio-inspired UAV navigation (particularly honeybee behavior modeling) and swarm robotics applications. His work increasingly incorporates deep learning for perception tasks while addressing real-world challenges like gas plume detection and adversarial robustness in 3D vision systems. The research demonstrates consistent progression toward practical implementation of autonomous systems in dynamic environments. Professor Garratt has secured over $7.7 million in external research funding as Chief Investigator on 33 grants. He actively mentors graduate students with scholarships available for Masters and PhD research in robotics and AI, focusing on: UAV path planning and adaptive control systems Swarm robotics collective motion optimization Bio-inspired autonomous navigation techniques Computer vision for robotic perception He co-founded the UNSW Canberra AIR (AI and Robotics) Group (AIR Lab), which drives research in trusted autonomy, swarm intelligence, and AI integration for defense applications. The lab develops practical solutions for autonomous systems operating in complex, real-world environments while maintaining ethical AI frameworks.

Abdolmajid Erfaniمشاهده پروفایل

استادیار

• Construction Economics
• Data-driven Infrastructure Management
• Project Delivery and Procurement
• +۵ مورد دیگر

Dr. Abdolmajid Erfani is an Assistant Professor in the Department of Civil, Environmental, and Geospatial Engineering at Michigan Technological University. He holds a PhD in Civil Engineering from the University of Maryland, College Park (2023), an MSc in Construction Engineering and Management from the University of Tehran (2019), and a BSc in Civil Engineering from the same university (2017). His research focuses on construction economics, smart construction technologies, data-driven infrastructure management, workforce development, and AI applications in project delivery. Dr. Erfani leads studies on workforce equity in transportation and construction industries, leveraging big data and natural language processing. He has published over 30 peer-reviewed papers and received prestigious ASCE awards including the 2024 Arthur M. Wellington Prize and Thomas Fitch Rowland Prize. He serves on the editorial board of the ASCE Journal of Management in Engineering. His recent grants include a National Cooperative Highway Research Program project (PI, 2024–2027) on price adjustment clauses for construction risk sharing and a Minnesota DOT-funded initiative (PI, 2025–2027) on leveraging transportation investments for economic equity. He also co-leads a Federal Railway Administration project (Co-PI, 2025–2028) aimed at promoting railroading careers. Research interests span equity analysis, AI modeling, and predictive analytics for infrastructure resilience. He integrates LinkedIn data and machine learning to study gender disparities in career progression, achieving groundbreaking insights in leadership dynamics within construction sectors.

Christopher Brooksمشاهده پروفایل

استادیار

• Educational Technology
• Data Science Education
• Learning Analytics
• +۳ مورد دیگر

Christopher Brooks is an Assistant Professor at the University of Michigan's School of Information, specializing in educational technologies and data science education. He directs the Educational Technology Collective (etc), a multidisciplinary research group focused on learning analytics, educational data mining, and collaborative learning systems. His work bridges computer science and education, with a focus on improving teaching methods through AI-driven tools and platforms. Research Interests: Development and impact assessment of educational technologies Predictive modeling for student success Data science pedagogy Privacy in smart home technologies Publications reflect a focus on learning analytics, MOOC design, and educational AI, with notable contributions to conferences like CHI, LAK, and AIED. Awards include multiple best paper recognitions. Teaching includes applied data science courses at UMich and Coursera. He leads the Master of Applied Data Science (MADS) program and collaborates with institutions like Microsoft to build AI-driven educational tools.

Fenglong Maمشاهده پروفایل

دانشیار

• Data Mining
• Health Care Informatics
• Machine Learning
• +۵ مورد دیگر

Fenglong Ma is an Associate Professor at Pennsylvania State University, affiliated with the Institute for Computational and Data Sciences and the Center for Socially Responsible Artificial Intelligence. His research focuses on data mining, healthcare informatics, machine learning, natural language processing, and multimodal learning. He holds a Ph.D. from the University at Buffalo (2019) and degrees from Dalian University of Technology. His work addresses challenges in federated learning, medical AI, adversarial robustness, and multimodal systems. Key contributions include innovations in quantization for large language models, federated knowledge injection, and medical vision-language benchmarking. Recent publications explore topics like collaborative fairness in federated learning, robust medical vision-language models, and adversarial attack mitigation. His research bridges theory and practical applications in healthcare, cybersecurity, and personalized recommendation systems. He leads the PSU Data Science Lab and collaborates on projects involving AI ethics, multimodal data integration, and scalable medical foundation models.

Alvaro Fernandez Quilezمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Responsible AI
• AI Ethics
• +۱۳ مورد دیگر

Alvaro Fernandez Quilez is an Associate Professor in Artificial Intelligence at the Department of Electrical Engineering and Computer Science, Faculty of Science and Technology, University of Stavanger. He leads the Stavanger AI Laboratory (SAIL), fostering interdisciplinary AI research with a focus on healthcare and education applications. Research Interests: His work centers on responsible AI, emphasizing ethics, fairness, transparency, and uncertainty in AI systems. He applies deep learning and machine learning techniques to medical imaging, particularly in prostate cancer and neurodegenerative diseases like Alzheimer’s and Parkinson’s. His research integrates algorithmic innovation with clinical relevance, addressing challenges in data scarcity, bias, and model interpretability. The recent publications highlight a strong trend in developing and evaluating AI models for diagnostic support in radiology and neurology. Key themes include uncertainty quantification, self-supervised learning, synthetic data generation via GANs, and fairness analysis across gender and centers. The work spans from foundational AI methods to their clinical translation in multi-center studies. Teaching and Academic Leadership: He coordinates the course DAT105 - AI for everyone and has contributed as a guest lecturer in bioinformatics, technological foundations, and PhD ethics, particularly on AI and ethics. He is also enrolled in a PhD supervisory qualification program, underscoring his growing role in graduate education. Advising and Grants: While specific students and grants are not listed in the text, his leadership of SAIL and active publication record suggest involvement in research supervision and project funding. His collaborations span multiple institutions and disciplines, indicating strong team-based research efforts. Laboratories and Teams: He leads the Stavanger AI Laboratory (SAIL), which serves as the central hub for AI research at the University of Stavanger, promoting collaboration across departments and with external partners in healthcare and technology.

Jean F. Honorio Carrilloمشاهده پروفایل

استاد مدعو

• Machine Learning
• Artificial Intelligence
• Optimization Theory
• +۵ مورد دیگر

Jean F. Honorio Carrillo is an Adjunct Professor at Purdue University's Department of Computer Science and a Senior Lecturer at the University of Melbourne's School of Computing and Information Systems. He specializes in machine learning theory, optimization, and their applications to combinatorial and non-convex problems. His research focuses on developing algorithms with theoretical guarantees for structured prediction, robustness, fairness, and federated learning. He has advised numerous students across multiple institutions and holds adjunct roles at Purdue's Statistics Department and MIT CSAIL. Roles: Senior Lecturer (Melbourne), Adjunct Professor (Purdue), Adjunct at MIT CSAIL Research Areas: ML Theory, Non-Convex Optimization, Fairness, Federated Learning Key contributions include breakthroughs in exact inference for structured prediction, optimization frameworks for NP-hard problems, and theoretical foundations for modern ML challenges. His work has been published in top venues like NeurIPS, ICML, and JMLR. He has secured grants from NSF and industry partners, including a 2021 NSF DMS grant for deep learning research. His students have gone on to postdoctoral roles at NUS and UChicago/CMU.