جست‌وجوی استاد و پژوهشگر

• Cybersecurity
• Machine Learning
• Data Analytics
• +۵ مورد دیگر

Dr Zhiyuan (Thomas) Tan is an Associate Professor at Edinburgh Napier University’s School of Computing, Engineering and the Built Environment . He is internationally recognised for his cybersecurity research and has been listed among Stanford University’s Top 2% Scientists for 2021–2023. Education BEng (2005) with high distinction – North-eastern University, China MEng (2008) – Beijing University of Technology, China PhD in Computer Systems (2014) – University of Technology Sydney, Australia Research Interests Dr Tan’s research integrates cybersecurity with machine learning and data analytics. His core areas include: Intrusion detection and defence of critical service systems Adversarial machine learning for malware and anomaly detection Virtualisation security through non-parametric behaviour modelling IoT and vehicular network security—cloud/edge/cloudlet frameworks Privacy-preserving AI and federated machine unlearning Smart-city digital forensics and cyber-physical system resilience Research Output Trends His recent articles (2020–2025) reveal a strong focus on federated learning, edge & mobile computing, UAV coordination, and AI-driven security. Topics span advanced steganography, metamorphic malware, graph injection attacks, and trustworthiness in vehicular platoons, all anchored in real-world IoT and transportation applications. Awards & Distinctions Stanford University Top 2% Scientists List (2021, 2022, 2023) National Research Award 2017 – Research Council of the Sultanate of Oman Best Paper Awards (three instances) Kaspersky Lab Student Cyber Security Conference – Finalist Award SICSA Supervisor of the Year 2019 – Honourable Mention Grants & Leadership Dr Tan has attracted over £200k in external funding including: Carnegie Trust (£73,564) – Federated Machine Unlearning ENU Development Trust (£29,998) – Machine Unlearning Royal Society (£12,000) – VANET Security & Privacy SICSA & other awards for MemoryCrypt, AI Secrets, behaviour biometrics, and visiting-fellow schemes. Supervision & Mentoring Since 2013 he has supervised or co-supervised 16 doctoral candidates and several master’s students; six PhDs have successfully graduated. Roles range from Director of Studies to additional supervisor across diverse topics from malware evolution to VR olfactory interfaces. Research Groups & Collaboration He is affiliated with the Centre for Artificial Intelligence and Robotics , the Centre for Distributed Computing, Networking and Security , and the Centre for Cybersecurity, IoT and Cyber-physical Systems at ENU, fostering interdisciplinary collaboration with national and international partners.

Hemant Purohitمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Data Mining
• Natural Language Processing
• +۵ مورد دیگر

Hemant Purohit is an Associate Professor in the Department of Information Sciences and Technology at George Mason University, and Director of the Humanitarian Informatics Lab. He focuses on developing interactive intelligent systems to support emergency services and humanitarian organizations by analyzing non-traditional data sources like social media, web, and IoT using data mining, NLP, and human-centered computing. His work integrates social-psychological theories to enhance human capabilities in crisis contexts. Purohit holds a PhD in Computer Science and Engineering from Wright State University. His research has been recognized through prestigious awards including the ITU Young Innovator Award (2014), NSF CRII Award (2017), and a best paper award at IEEE/WIC/ACM Web Intelligence (2018). His lab is supported by grants from NSF and international agencies. Key research interests include crisis informatics, social computing, and AI ethics. He has led projects on adversarial scam detection, inclusive cybersecurity, and human-AI teaming for disaster response. Purohit serves on editorial boards for journals like Elsevier's Information Processing & Management and Frontiers in Big Data, and actively contributes to international conferences in his field. His work emphasizes real-world impact, bridging technical innovation with societal needs through collaborations between researchers and practitioners. Current projects address challenges in multilingual data analysis, social media activism, and resilience data repositories.

• Artificial Intelligence
• Recommender Systems
• Information Systems
• +۲ مورد دیگر

Dietmar Jannach is a Full Professor at the University of Klagenfurt, Austria, affiliated with the Institute for Artificial Intelligence and Cybersecurity where he leads the Research Group for Information Systems. His academic roles include membership in the university's Senate and Curricular Commissions for Liberal Arts and Information Management. His research spans: Core Areas : Artificial Intelligence, Recommender Systems, and Software Engineering. Methodological Focus : Algorithm reproducibility, fairness in AI, sequential recommendations, and hybrid learning models. Emerging Interests : Generative AI for group decision support, ethical recommender systems, and foundation model applications. Jannach's recent publications critically evaluate reproducibility challenges in AI research, advocate for calibrated recommendations to mitigate bias, and explore agentic paradigms in group recommender systems. He emphasizes real-world validation, with studies on deployment challenges and developer experiences in software processes. He actively contributes to academic governance and mentors through research groups, though specific student advisees are not listed. Contact via Dietmar.Jannach@aau.at .

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Prof. Margret Keuper is a Professor in the Department of Computer Vision and Machine Learning at the Max Planck Institute for Informatics. Her research focuses on advancing machine learning and computer vision techniques, with an emphasis on model fairness, adversarial robustness, and multimodal interactions. She leads interdisciplinary projects exploring topics such as dataset analysis, generative models, and climate action through visual narrative analysis. Research Interests: Her work bridges theoretical foundations and practical applications in domains like adversarial training, image classification robustness, and robotics perception. She explores how vision-language models can be steered to align with human biases and develops methods for data-efficient learning and interpretability. Recent Contributions: Recent work includes FAIR-TAT (model fairness via adversarial training), VSTAR (video synthesis), and TikZero (zero-shot graphics program generation). Her publications in top venues like CVPR, ICCV, and ICLR highlight contributions to both methodological innovation and real-world impact. Collaborations: Works closely with researchers across Max Planck and academic partners, focusing on projects such as sensor layout optimization, climate discourse analysis via social media imagery, and domain-aware foundation model fine-tuning.

• Human-Centered Systems
• Human-Computer Interaction
• Artificial Intelligence
• +۹ مورد دیگر

Alessio Malizia is a Professor of User Experience Design at the University of Hertfordshire, UK, and concurrently holds a full Professorship at the Computer Science Department of the University of Pisa, Italy. His research focuses on Human-Centered Systems, Human-Centered AI, and Design Fictions, emphasizing ethical AI, user participation, and the integration of visual programming languages. He has held roles at institutions including Sapienza University of Rome, IBM, Silicon Graphics, and Xerox PARC, where he researched neural networks and gestural interfaces. His work bridges academia and industry, addressing challenges in healthcare, agriculture, and education. Malizia’s career includes visiting research at Xerox PARC, associate professorship at the University Carlos III of Madrid, and senior lecturer roles at Brunel University London. He is an ACM Distinguished Speaker and actively promotes democratizing AI through tools like BlocklyBias and PyFlowML. His research spans touchless interfaces, collaborative design methodologies, and AI explainability. Key contributions include frameworks for socio-technical process modeling in agriculture, tools for bias detection in AI data, and visual languages fostering user participation in ML systems. His work on telemedicine and healthcare AI emphasizes co-design and trusted AI systems. Recent studies explore end-user development for smart environments, digital agriculture, and computational thinking education through tools like TAPASPlay. Awards: ACM Distinguished Speaker Key Interests: Human-AI Interaction, UX Design, AI Ethics, IoT Ecosystems, and Co-Creation Methodologies Notable Projects: Figmant, ModeLLer, and the Chatbot Usability Scale validation His grants and collaborations focus on improving AI transparency, user-centered design processes, and participatory innovation in technology adoption across sectors.

• Computer Science
• Machine Learning
• Data Science
• +۴ مورد دیگر

Yong Zhang is affiliated with Tsinghua University's Research Institute of Information Technology in Beijing, China. His research focuses on machine learning, optimization algorithms, edge computing, and their applications in areas like time series analysis, federated learning, and sensor networks. He has collaborated on projects involving neural networks, scheduling problems, and privacy-preserving techniques. Education: Yong Zhang earned a PhD in Computer Science and Engineering from Fudan University in 2007. His academic career includes roles at institutions like the Chinese Academy of Sciences and the University of Hong Kong, reflecting a strong interdisciplinary background. Research Contributions: His work spans theoretical computer science, algorithm design, and applied machine learning. Notable areas include developing efficient scheduling algorithms for energy systems, creating robust federated learning frameworks for industrial demand forecasting, and advancing methods for sentiment analysis using multimodal data. He has also contributed to biomedical engineering through smartphone-based health monitoring systems. Collaborations: He frequently collaborates with researchers at institutions like the University of Electronic Science and Technology of China, Nanyang Technological University, and The Hong Kong Polytechnic University. Key projects involve data caching optimization in edge computing, distributed algorithms for dynamic networks, and combinatorial optimization problems. Labs & Future Work: His team explores cutting-edge topics in AI-driven systems, including trust-aware machine learning, distributed resource allocation, and real-time data processing for IoT applications. Current research emphasizes scalable solutions for complex optimization challenges in both academic and industrial settings.

Martin Wynnمشاهده پروفایل

دانشیار

• Digitalisation
• Digital Transformation
• Emerging Technologies
• +۴ مورد دیگر

Martin Wynn is an Associate Professor of Information Technology at the University of Gloucestershire's School of Computing and Engineering. With 20 years of industry experience as an IT professional and senior manager at Glaxo Pharmaceuticals and HP Bulmer Drinks, he transitioned to academia with a focus on knowledge transfer partnerships and post-doctoral supervision. His research interests span digital transformation, emerging technologies, sustainability, IT strategy, and risk management. He holds a PhD in Town and Country Planning and a PGCFHE in Education. Education: BA (Hons) Geography (Durham, 1973), MA Geography (Durham, 1977), PhD Town and Country Planning (Nottingham Trent, 1981), PGCFHE Education (Gloucestershire, 2003). Research focuses on leveraging technology for sustainable development, including digitalisation in manufacturing, cybersecurity, and the interplay between technology and urban planning. Over 20 doctoral students have been supervised in areas like digitalisation, analytics, and sustainability. Publications span e-business adoption in developing countries, ERP systems in SMEs, and ICT utilization in Libyan universities. He is a PRINCE2 Practitioner and Certified Management Consultant (CMC).

Chi-Chun Leeمشاهده پروفایل

استاد

• Speech and Language Processing
• Affective Computing
• Health Analytics
• +۳ مورد دیگر

Chi-Chun Lee (Jeremy) is a Professor and Associate Chair in the Department of Electrical Engineering at National Tsing Hua University (NTHU), Taiwan. He also serves as Director of the NVIDIA-NTHU Joint Innovation Center and leads the Behavioral Informatics & Interaction Computation (BIIC) Lab. His academic journey includes a B.S. (magna cum laude) and Ph.D. in Electrical Engineering from the University of Southern California (USC), USA (2007 and 2012), followed by roles as a data scientist at id:a lab and technical consultant for companies like E.Sun Bank and Allianz Taiwan. Research focuses on speech processing, affective computing, health analytics, and behavior signal processing. He is an IEEE Senior Member and holds editorial roles in top journals such as IEEE Transactions on Affective Computing and Multimedia. Key contributions include leading teams to international competitions (e.g., 1st place in INTERSPEECH 2009 Emotion Challenge) and developing AI frameworks for clinical applications like respiratory sound classification and tumor image synthesis. Recipient of prestigious awards including the NTHU-Novatek Distinguished Talent Chair (2024), National Science and Technology Council Outstanding Research Award (2023), and multiple best paper awards. His work bridges academia and industry, with collaborations extending to NVIDIA and startups like AHEAD Medicine. Research has been featured in major media outlets including Scientific American and Discovery.

• Large-Scale Optimization
• Machine Learning
• Optimization Methods
• +۳ مورد دیگر

Jelena Diakonikolas is an Assistant Professor at the Department of Computer Sciences at the University of Wisconsin-Madison, with a courtesy appointment in the Department of Statistics. She is also an affiliate of the Data Science Institute at UW-Madison. Her research focuses on large-scale optimization and its applications in machine learning. Prior to UW-Madison, she held postdoctoral positions at UC Berkeley and Boston University. She completed her Ph.D. in Electrical Engineering at Columbia University. Her academic journey includes a postdoctoral fellowship at the Simons Institute for the Theory of Computing and affiliations with leading institutions such as the Fields Institute and the Qualcomm Innovation Fellowship program. She has organized numerous workshops, including sessions on optimization and sampling at the Simons Institute and NeurIPS. Key research interests span optimization algorithms, distributed systems, wireless networking, and energy-efficient systems. Her work has been recognized with prestigious awards like the NSF CAREER Award and AFOSR Young Investigator Program Award. She co-founded the WISCERS program, which supports underrepresented undergraduates in research, earning Google’s exploreCSR award. Jelena has advised multiple Ph.D. students, including Dr. Lin and Dr. Cai. Her contributions to mentoring earned her the Award for Mentoring Undergraduates in 2023. She remains active in academic service, serving on program committees for ICML, ICLR, and other conferences.

Katerina Sotirakiمشاهده پروفایل

استادیار

• Theoretical Cryptography
• Post-Quantum Cryptography
• Complexity Theory
• +۴ مورد دیگر

Katerina Sotiraki is an Assistant Professor in the Department of Computer Science at Yale University, associated with the Archimedes AI research group. Her work focuses on theoretical cryptography, post-quantum cryptography, complexity theory, and secure computation. She holds a Ph.D. (2020) and M.S. (2016) in Computer Science from MIT, and a B.S. in Applied Mathematics (2013) from the National Technical University of Athens. Prior to Yale, she was a postdoc at UC Berkeley working with Raluca Ada Popa and Alessandro Chiesa, under the guidance of Vinod Vaikuntanathan at MIT. Research Interests : Design and analysis of cryptographic protocols resilient to quantum computing Efficient zero-knowledge proofs and succinct arguments Algorithmic game theory and fair division problems Lattice-based cryptography and post-quantum security Awards & Honors : Winner of 7th iDASH Competition (Track 3) - 2018 Chateaubriand Fellowship - 2018 Paris C. Kanellakis Fellowship (MIT EECS) - 2013 Key Contributions : Developed HOLMES - a system for secure collaborative learning Pioneered lattice-based succinct arguments with polylogarithmic verification Advanced understanding of modulo-p arguments through topological methods Labs & Teams : Active member of the Archimedes AI group at Yale, focusing on secure computation and cryptographic protocols.

• AI and Literary Translation
• Corpus Linguistics
• Translation Theory
• +۴ مورد دیگر

Prof. Dorothy Kenny is a full professor of Translation Studies at Dublin City University, affiliated with the School of Applied Language & Intercultural Studies. She holds a BA in French and German from DCU, an MSc in Machine Translation, and a PhD in Language Engineering from the University of Manchester. Her research focuses on AI in literary translation, corpus linguistics, machine translation ethics, and translation pedagogy. She led the EU-funded MultiTraiNMT project (2019–2022), developing materials for MT education. She co-edits the journal Translation Spaces and is an Honorary Fellow of the Chartered Institute of Linguists (UK). Education: BA in French and German, Dublin City University MSc in Machine Translation, University of Manchester PhD in Language Engineering, University of Manchester Research Interests: Her work bridges theoretical and applied translation studies, emphasizing corpus-based methods, machine translation ethics, and the impact of AI on literary translation. Recent projects explore customization of MT for literary texts and ethical workflows in MT. Awards & Recognition: 2020: Co-edited Fair MT: Towards Ethical, Sustainable Machine Translation 2017: Edited Human Issues in Translation Technology 2020: Honorary Fellowship from the Chartered Institute of Linguists Grants & Projects: Principal Investigator of MultiTraiNMT (EU-funded, 2019–2022) Recipient of Ulysses Travel Grant (2018–2019) for collaboration with Université Grenoble-Alpes Labs & Teams: She directs research at the Centre for Translation and Text Studies (CTTS) , fostering interdisciplinary work on translation technology and corpus linguistics.

• Data Science
• Machine Learning
• Natural Language Processing
• +۱۲ مورد دیگر

Xiuzhen Jenny Zhang is a Professor of Data Science at RMIT University , affiliated with the School of Computing Technologies. Her research bridges artificial intelligence, machine learning, and social media analysis, with a focus on text mining and trustworthy data science. Her recent publications highlight expertise in point-of-interest recommendation , misinformation detection , multi-task learning , and transformer-based NLP . Key trends include applications of large language models for social good, fairness in recommendation systems , and adversarial learning for robustness. Best Paper Award at TrustCom’12 Best Short Paper Award at ADCS’2009 She leads the Text And LanguagE (TALE) research group and has supervised over 20 PhD students. Research grants include Australian Research Council and Victoria state government funding.

• Algorithms for Big Data Analytics
• Computational Geometry
• Machine Learning
• +۴ مورد دیگر

Jeff M Phillips is a Professor in the Kahlert School of Computing at the University of Utah, specializing in algorithms for big data analytics, computational geometry, and machine learning. He holds a BS in Computer Science and Mathematics from Rice University (2003) and a PhD in Computer Science from Duke University (2009). He serves as Director of the Utah Center for Data Science, Director of the Data Science Program in the Kahlert School of Computing, and Faculty Co-Director of the One U Data Science Hub. His research focuses on geometric data analysis, coresets, sketches, and handling uncertainty in data. Education: BS/BA (Rice University, 2003), PhD (Duke University, 2009) CI Postdoctoral Fellow at University of Utah (2009–2011) His research interests include algorithms for big data analytics, computational geometry, machine learning, spatial statistics, and AI. He has led NSF-funded projects on spatial data analysis, cosmic origins via AI, and reactive flow data modeling. Phillips has advised numerous PhD and master’s students, contributing to topics like trajectory classification and bias mitigation in word embeddings. His publications span computational geometry, data science, and machine learning. Notable work includes coresets for kernel density estimates, bias mitigation in language models, and scalable spatial scan statistics. Phillips is also active in academic service, serving as co-PC chair for SoCG 2024 and on program committees for major conferences like NeurIPS and ICML.

Suresh Venkatasubramanianمشاهده پروفایل

استاد

• Algorithmic Fairness
• Automated Decision Making
• Computational Geometry
• +۴ مورد دیگر

Suresh Venkatasubramanian is a Professor at Brown University, previously at the University of Utah's School of Computing. His research focuses on algorithmic fairness, automated decision systems, computational geometry, and the societal impacts of AI. He co-founded the FAT* conference and sits on the ACLU of Utah board. He holds a B.Tech from IIT Kanpur and a Ph.D. from Stanford University. Education : B.Tech (Computer Science, IIT Kanpur), Ph.D. (Stanford University). Key Roles : Member of Computing Community Consortium Council, Research Advisory Council for NYC's FTA Tool, and First Judicial District of Pennsylvania. Research interests emphasize fairness, accountability, and transparency in algorithms. Notable contributions include work on predictive policing biases, Shapley-value critiques, and information access gaps. Awards include an NSF CAREER Award and an ICDE Test-of-Time Award. Grants : Mozilla Foundation, NSF BIGDATA, DARPA A4V. Teaching : Advanced Algorithms, Ethics of Data Science, and courses on algorithmic fairness. Service roles include organizing conferences like FAT* and ALENEX, and advising on algorithmic governance in criminal justice systems.

Shahin Jabbariمشاهده پروفایل

استادیار

• Machine Learning
• Algorithmic Fairness
• Game Theory
• +۷ مورد دیگر

Shahin Jabbari is an Assistant Professor in the Computer Science Department at the College of Computing & Informatics, Drexel University, where he is a member of the EconCS research group. His research lies at the intersection of machine learning, game theory, and algorithmic fairness, with a focus on ethical AI and its societal implications. Prior to Drexel, he was a CRCS postdoctoral fellow at Harvard University's School of Engineering and Applied Sciences, hosted by Milind Tambe, and affiliated with the EconCS group. Education: PhD in Computer and Information Science, University of Pennsylvania (2013–2019), advised by Michael Kearns Master's in Computing Science, University of Alberta, advised by Robert Holte and Sandra Zilles Bachelor's in Computer Engineering, Sharif University of Technology His research interests center on machine learning, algorithmic fairness, and game theory, particularly focusing on how AI systems can be designed to be more equitable, interpretable, and robust. He investigates ethical aspects of algorithmic decision-making, aiming to ensure AI technologies contribute positively to society. His work often integrates human behavior modeling and experimental validation, especially in cybersecurity and public health domains. His recent publications span top venues including ICML, NeurIPS, AAAI, AAMAS, PNAS, and TMLR. The research trends show a consistent focus on fairness in AI, explainability, robustness, and strategic interactions in complex systems. Topics include fair influence maximization, adaptive phishing training, cyber deception games, and ethical machine learning frameworks. These works reflect a multidisciplinary approach combining theoretical rigor with real-world applicability. Scientific Awards and Recognitions: Best Paper Finalist, AAMAS 2021 Best Paper, GameSec 2020 Spotlight Presentation, ICML 2021 Best Paper, KI 2012 Shahin Jabbari actively contributes to the academic community through advising, teaching, and service. He teaches graduate courses such as CS 589: Responsible Machine Learning and CS 590: Privacy. He has served on the senior program committees of ICML and NeurIPS, is an Action Editor for TMLR, and has reviewed for numerous top-tier conferences and journals. He mentors students through research projects and invites prospective PhD candidates to apply through Drexel’s formal channels. He is involved in the Drexel Computer Science Theory Reading Group and contributes to advancing responsible AI practices. He is affiliated with the EconCS group at Drexel, which focuses on economic and computational aspects of AI, including game theory, mechanism design, and multi-agent systems. His lab integrates tools from machine learning, behavioral modeling, and optimization to develop AI systems that are not only intelligent but also fair and trustworthy. Future work is expected to further explore human-AI collaboration, ethical AI deployment, and policy-aware algorithm design.