Find Academic Supervisors

• AI Security
• AI Safety & Alignment
• Cooperative AI
• +6 more

Sahar Abdelnabi is an AI Security Researcher at Microsoft and will join the ELLIS Institute Tübingen as a Faculty/Principal Investigator. She is co-affiliated with the Max-Planck Institute for Intelligent Systems and Tübingen AI Center , leading the COMPASS Research Group focused on safe, aligned, and steerable AI agents with emphasis on security, human-AI interaction, and cooperative systems. Her research spans three pillars: (1) Probing AI failures through biases, emergent risks, and misuse scenarios; (2) Developing defenses like white-box control methods and reasoning enhancements; and (3) Leveraging AI for societal good through scientific discovery. Key contributions include coining indirect prompt injection vulnerabilities (2023), pioneering generative AI watermarking (2020), and receiving the ACL2025 Best Paper Award for work on LLM sampling heuristics. PhD in Computer Science (2019-2024) from CISPA Helmholtz Center , advised by Prof. Dr. Mario Fritz MSc in Computer Science from Saarland University Research Highlights Her work bridges AI security and safety with sociopolitical implications, focusing on prompt injection , cooperative multi-agent systems , and contextual integrity . She has been recognized by policymakers and industry leaders, including NIST , OWASP , and Microsoft's AI Bug Bounty Program . Scientific Awards Best Paper Award at ACL2025 Best Paper Award at AISec'23 Workshop Spotlight Paper at NeurIPS Datasets and Benchmarks 2024 Academic Leadership She actively contributes to the AI and security communities through: Program Committee: IEEE S&P (2026) , SaTML (2024-2026), USENIX Security (2025) Organized IEEE SaTML'25 LLMail-Inject Challenge Reviewed for top conferences: ICLR , NeurIPS , CVPR

Nirupam GuptaView profile

Assistant Professor

• Distributed Machine Learning
• Federated Learning
• Privacy-Preserving Algorithms
• +4 more

Nirupam Gupta is an Assistant Professor at the Department of Computer Science (DIKU), University of Copenhagen. His research focuses on robustness, privacy, and fairness in distributed machine learning systems, with particular emphasis on federated learning and Byzantine fault tolerance. He actively contributes to the design of secure, resilient, and privacy-preserving algorithms for collaborative AI systems. He teaches courses in Privacy in Machine Learning (PriMaL) Machine Learning B (MLB) both offered in hybrid formats with full remote participation support. His latest book, Robust Machine Learning: Distributed Methods for Safe AI , provides foundational insights into the field. Key research themes in his work include: Byzantine-Robust Optimization Differential Privacy in Federated Systems Personalization under Adversarial Conditions Secure Aggregation Techniques Contact: nigu@di.ku.dk

Georg RehmView profile

Professor

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence
• +7 more

Georg Rehm is an Honorary Professor for Computational Linguistics and Language Technology at Humboldt University Berlin and a Principal Researcher at the German Research Center for Artificial Intelligence (DFKI) in Berlin, where he serves as Deputy Director of the DFKI Lab Berlin. He is also the Head of the German-Austrian Chapter of the World Wide Web Consortium (W3C) based at DFKI Berlin. Rehm has over 300 scientific publications and extensive experience in leading major research projects in computational linguistics and language technology. His research focuses on Natural Language Processing, Computational Linguistics, and Artificial Intelligence, with specific interests in multilingual language technologies, semantic web, digital humanities, and language data spaces. He has led numerous significant projects including European Language Grid, OpenGPT-X, and NFDI4DataScience. Rehm is particularly active in initiatives promoting digital language equality in Europe by 2030. Rehm's recent work demonstrates a strong focus on large language models, scholarly document processing, scientific knowledge representation, and climate-related fact-checking systems. His publications span across multiple high-impact venues including ACL, ESWC, and LREC, with a notable emphasis on practical applications of language technology in real-world scenarios. DFKI Research Fellow (2018) As an active member of the academic community, Rehm regularly serves as an expert for the European Parliament, reviews EU projects, and organizes numerous scientific conferences and workshops. His leadership extends to multiple European initiatives aimed at advancing language technology infrastructure and promoting digital language equality across Europe.

Zheng YuanView profile

Senior Lecturer

• Educational Technology
• Language Acquisition
• Multilingual NLP
• +3 more

Dr. Zheng Yuan is a Senior Lecturer (Associate Professor) in Natural Language Processing at the School of Computer Science, University of Sheffield. He holds affiliated positions at the University of Cambridge and King's College London, and is a Fellow of Trinity College, Cambridge. His research focuses on NLP applications in education, healthcare, and multilingual systems. Education: PhD in Natural Language Processing, University of Cambridge MPhil in Advanced Computer Science, University of Cambridge BSc(Eng) from Queen Mary University of London Research: Dr. Yuan's work spans educational NLP, multilingual systems under low-resource conditions, and explainable machine learning. His group develops technologies for grammatical error correction, automated assessment, and cross-lingual applications, with significant contributions to computer-assisted language learning and computational creativity. Publications: Recent works (2023-2025) demonstrate strong focus on educational NLP, multilingual systems, and LLM evaluation. Key themes include grammatical error correction for code-switched languages, creativity assessment frameworks, and robust evaluation methods for large language models across diverse linguistic contexts. Awards: Winning systems at SemEval-2021 and CoNLL-2014 Fellowship at Trinity College Cambridge Fellow of Higher Education Academy Grants & Advising: Principal Investigator for Royal Society grant 'Large Language Models as Agents for Intelligent Language Tutoring' (2025-2027). Actively supervises PhD students in NLP and machine learning, welcoming new research collaborations. Affiliations: Member of Alan Turing Institute (Data-Centric Engineering), King's Institute for AI, and ACL committees. Organizes major NLP workshops including ACL/NAACL BEA workshops and AIED tutorials.

Larry P HeckView profile

Professor

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +7 more

Larry P. Heck is a Professor with a joint appointment in the School of Electrical and Computer Engineering and School of Interactive Computing at the Georgia Institute of Technology. He holds the Rhesa S. Farmer Advanced Computing Concepts Chair and is a Georgia Research Alliance Eminent Scholar . Education: BSEE, Texas Tech University (1986) MSEE, Georgia Institute of Technology (1989) PhD EE, Georgia Institute of Technology (1991) His research focuses on conversational AI , dialogue systems , and machine learning applied to natural language processing and speech recognition . He pioneered early industrial applications of deep learning in speech processing and has contributed to advancements in multimodal interaction, knowledge distillation, and real-time question answering systems. Recent publications emphasize moral reasoning in AI , multimodal dialogue , and large-scale dataset creation for conversational systems. His work bridges language modeling , sensor fusion , and ethical AI through innovations in contextual reasoning and interface masking. Scientific Distinctions: IEEE Fellow (2020) IEEE Signal Processing Society Best Paper Award Academy of Distinguished Engineering Alumni, Georgia Tech (2017) Distinguished Engineer Award, Texas Tech University (2017) Fellow, National Academy of Inventors (2025) He has secured significant funding from DARPA and NSA for speaker recognition systems and has led cutting-edge research at institutions including Microsoft, Google, and Samsung. His lab focuses on conversational systems and deep learning for speech and multimodal data.

Shuyin JiaoView profile

Academic

• Computing Education
• Program Analysis
• Code Efficiency Analysis
• +5 more

Shuyin Jiao is an active Associate Teaching Professor in the Department of Computer Science at North Carolina State University's College of Engineering. She serves as course coordinator for CSC 111 Introduction to Computing: Python and teaches multiple core undergraduate courses including CSC 116 (Java) and CSC 246 (Operating Systems), with extensive teaching experience spanning over 9 years at both NC State and the College of William & Mary. Her educational background includes a Ph.D. from the University of Houston (2015), a Diplôme d'Ingénieur from Ecole Central de Lyon, France, and a B.S. from Beihang University, China. She previously held positions as Assistant Teaching Professor (2020-2025) and Lecturer at NCSU, and taught as Lecturer and Adjunct Lecturer at William & Mary from 2015-2020. Dr. Jiao's research focuses on computing education and program analysis, with particular emphasis on developing tools to enhance student learning in computer science. Her work explores pedagogical methods to address diverse learning styles in increasingly large CS classrooms and investigates code inefficiencies through memory profiling and performance analysis. She leads the CERES lab at NCSU and serves as Publications Co-Chair for SIGCSE TS 2026. Her publication record shows consistent output in top venues including SIGCSE, CGO, MobiCom, and ICSE, with recent work spanning from 2025 back to 2012. Her research trajectory demonstrates a clear evolution from materials science (early career) to software engineering and computer science education, with current work heavily focused on educational technology and program analysis tools. Dr. Jiao has secured significant funding including as PI for NSF Grant DUE-#2417469 (2024-2027), NCSU Data Science Academy Seed Grant (2023-2024) as Co-PI, and NCSU GEARS Grants (2023-2024) as PI. She also serves as Co-PI for NSF Grant OAC-#2411136 (2025-2028) focused on computer system research. She actively involves undergraduate students in research through COE REU and CSC 498/499 projects, recruiting at CSC Undergraduate Research Lightning Talks each semester. Her course redesign projects include enhancing operating systems education through multimedia and redesigning CSC 111 with DELTA funding to address challenges in large-class instruction.

Abdelkader MekracheView profile

Researcher

• Artificial Intelligence
• Machine Learning
• 6G Networks
• +3 more

Abdelkader Mekrache is a doctoral researcher in the Communication Systems department at EURECOM. His work focuses on integrating artificial intelligence (AI) and large language models (LLMs) into next-generation (6G) network management frameworks, emphasizing intent-driven systems, zero-touch automation, and explainable AI (XAI) for enhanced trustworthiness. Institution: EURECOM Email: Abdelkader.Mekrache@eurecom.fr Research Interests: Mekrache's research explores AI-driven solutions for telecommunications, including: Intent-based networking for 6G LLM-powered operations support systems Explainable AI (XAI) for network transparency Deep reinforcement learning (DRL) for task scheduling Machine reasoning in FCAPS models Edge computing and network-compute abstraction Publication Trends: His recent work highlights the convergence of AI/LLMs with telecommunications, particularly in automating network management for 6G systems through intent-driven architectures and trustworthy AI paradigms.

• Software Engineering
• Software Testing
• Program Analysis
• +4 more

Alessandro (Alex) Orso is the Dean and Professor at the University of Georgia College of Engineering , starting in 2025. Previously, he spent 25 years at Georgia Institute of Technology , where he served as a Professor in the College of Computing , Director of the Scientific Software Engineering Center , and Associate Dean for Off-Campus and Special Initiatives . Education: Ph.D. in Computer Science – Politecnico di Milano, Italy M.S. in Electrical Engineering – Politecnico di Milano, Italy B.S. in Electrical Engineering – Politecnico di Milano, Italy Research Focus: Alex Orso is a leading expert in software engineering , with a focus on software testing , program analysis , and debugging . His work aims to improve software reliability, security, and trustworthiness , and has been validated on real-world systems. He has led initiatives to integrate AI and large language models into testing workflows, particularly for REST APIs and mobile applications . Scientific Recognition: Distinguished Member of the Association for Computing Machinery (ACM) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) ISSTA Impact Paper Award (2021) Research Funding & Leadership: Orso has secured major funding from Google, IBM, Microsoft , and government agencies. In 2021, he led the acquisition of $11 million from Schmidt Futures to establish the Scientific Software Engineering Center at Georgia Tech, where he served as inaugural director. Teaching & Innovation: He was an early advocate of Georgia Tech’s Online Master of Science in Computer Science (OMSCS) and has taught a virtual software engineering course since its launch in 2014.

Fosca GiannottiView profile

Professor

• Mobility Data Mining
• Social Network Analysis
• Privacy-Preserving Data Mining
• +6 more

Fosca Giannotti is a Full Professor at Scuola Normale Superiore in Pisa, Italy, and leads the Pisa KDD Lab - Knowledge Discovery and Data Mining Laboratory, a joint research initiative of the University of Pisa and ISTI-CNR. Founded in 1994, the Pisa KDD Lab is one of the earliest research labs focused on data mining. Giannotti is a pioneering scientist in mobility data mining, social network analysis, and privacy-preserving data mining. Her educational background includes a Master Degree in Computer Science from the University of Pisa (1982) with 110/100 cum laude. She has held numerous visiting positions including at MCC in Austin, CWI Amsterdam, UCLA, and the Barabasi Lab at Northeastern University. Giannotti's research focuses on social mining from big data, encompassing smart cities, human dynamics, social and economic networks, ethics and trust, and diffusion of innovations. She has authored more than 300 papers and coordinated tens of European projects and industrial collaborations. Her current work increasingly centers on Explainable AI (XAI), as evidenced by her prestigious ERC Advanced Grant for the XAI project focused on "Science and technology for the explanation of AI decision making." Her recent publications reveal a strong emphasis on trustworthy AI, with research spanning privacy-preserving techniques, fairness in machine learning, human-AI collaboration frameworks, and medical applications of explainable AI. The breadth of her work demonstrates how data mining principles are being applied across diverse domains from social sciences to healthcare. ERC Advanced Grant for XAI project Premio Internazionale Tecnovisionarie 2021 Intelligenza Artificiale Giannotti has coordinated numerous significant projects including SoBigData (the European research infrastructure on Big Data Analytics and Social Mining), XAI, TAILOR (Foundations of Trustworthy AI), HumanE-AI-Net, and AI4EU. As former coordinator of SoBigData, she led an ecosystem of ten cutting-edge European research centers providing an open platform for interdisciplinary data science. She leads the Pisa KDD Lab, which serves as a hub for research on knowledge discovery and data mining. The lab has been instrumental in developing techniques for mobility data analysis, social network mining, and privacy-preserving data analytics, with applications ranging from smart cities to pandemic response.

Valeria FilippouView profile

Researcher

• Medical Engineering
• Machine Learning
• Signal Processing
• +5 more

Valeria Filippou is a Researcher at the Computation-based Science & Technology Research Center (CaSToRC) within The Cyprus Institute. She holds an MEng in Medical Engineering, an MSc in Tissue Engineering and Regenerative Medicine, and a PhD in Medical Engineering with a focus on Machine Learning, Signal Processing, and Wearable Technologies from the University of Leeds. Her research spans interdisciplinary applications of AI in healthcare, financial services, and environmental monitoring. Key areas include developing credit risk models, medical diagnostics algorithms, AI-driven data privacy solutions, and consulting on large language models (LLMs) and retrieval-augmented generation (RAG) systems. Her recent publications focus on wearable sensor data analysis for activity recognition, multimodal prediction of psychological conditions like alexithymia, and advancements in medical imaging phantoms via 3D printing. These works bridge computational methods with practical healthcare and environmental monitoring challenges. No scientific awards or grant details are explicitly stated in the provided information.

Zhen XieView profile

Assistant Professor

• High-Performance Computing (HPC)
• Machine Learning
• Parallel Computing
• +3 more

Zhen Xie is an Assistant Professor in the Department of Computer Science at Binghamton University (SUNY), serving as Director of the Parallel Computing and Intelligent System (PCIS) Lab. He holds a PhD from the Chinese Academy of Sciences and a BA from Wuhan University of Technology. His research focuses on high-performance computing (HPC), machine learning, and their intersections, particularly optimizing performance for HPC and AI/DL applications across heterogeneous architectures. Research Highlights: Dr. Xie’s work emphasizes system-level performance optimization for ML and HPC, including GPU acceleration, memory optimization, and AI accelerator selection. His team has won the ACM Gordon Bell Special Prize (2022) for their GenSLMs project predicting SARS-CoV-2 evolution. Recent grants include a 2024 gift from OpenAI for AI testbed initiatives. Awards: ACM Gordon Bell Special Prize (2022), Impact Argonne Awards (2023) Lab: PCIS Lab explores middleware for parallel computing, targeting scientific simulations and big data analytics. Collaborations include Argonne National Lab and Lawrence Berkeley National Lab. Teaching: Teaches Distributed Systems (CS 457/557) and oversees independent studies. Previously trained researchers at Argonne’s ATPESC program. Grants & Collaborations: Subcontract with Lawrence Berkeley Lab (HEVI-LOAD), Argonne testbed expeditions, and OpenAI-funded projects. Active in DOE labs like Summit and Aurora supercomputers.

• Artificial Intelligence and Law
• Contract Law
• Consumer Protection
• +3 more

Yonathan A. Arbel is a Professor of Law at the University of Alabama School of Law, specializing in contractual theory, consumer protection, defamation law, and the impact of artificial intelligence on legal systems. His research bridges legal scholarship with interdisciplinary approaches, focusing on how emerging technologies reshape contractual obligations, consumer rights, and judicial processes. Education: Advanced legal training in contract law and technology policy. Affiliations: Active contributor to legal academia, with publications in top-tier journals like the New York University Law Review and the Chicago Law Review Online. His work on 'Generative Interpretation' explores how AI models like LLMs reinterpret legal texts, challenging traditional textualism. He advocates systemic regulation of AI to balance innovation and societal safety, as seen in 'Systemic Regulation of Artificial Intelligence.' Arbel also addresses the 'no-reading problem' in consumer contracts, proposing solutions using smart readers and transparency tools. Key areas of focus include judicial efficiency in AI-driven systems, fake news mitigation via 'Truth Bounties,' and rethinking defamation law through Bayesian audience models. His research emphasizes practical legal reforms, such as tax levers for AI safety and ex ante consumer contract oversight. Grants & Projects: Not explicitly detailed, but his publications suggest involvement in interdisciplinary research initiatives.

Lonneke van der PlasView profile

Associate Professor

• Cross-lingual NLP
• Computational Semantics
• Computational Creativity
• +4 more

Prof. Lonneke van der Plas is an Associate Professor in Natural Language Processing (NLP) at the University of Lugano (USI) since October 2024. She leads the Computation, Cognition & Language group at Idiap Research Institute (since 2021) and holds adjunct roles in USI's Faculty of Informatics. Previously, she was an Associate Professor at the University of Malta (2014–2020) and Junior Professor at the University of Stuttgart (SFB 732). She earned her PhD in Humanities Computing from the University of Groningen and an M.Phil in Computer Speech and Language Processing from the University of Cambridge. Her research focuses on cross-lingual NLP, computational creativity, and low-resource languages. Key projects include the NCCR Evolving Language (Swiss National Science Foundation, 2024–2028), C-LING (computational models of creativity), and SEM24 (multilingual skill extraction for HR). She has supervised multiple PhD students and postdocs, including notable advisees such as Molly Petersen and Mete Ismayilzada. Her work spans theoretical linguistics and applied NLP, with collaborations in cognitive science, finance, and healthcare. Awards include the ELSI Best Paper Award (2020) for work on societal impacts of AI ethics. Current initiatives include open PhD positions in NLP for cognitive modeling and mental health applications. Education: PhD (University of Groningen), M.Phil (University of Cambridge) Key Projects: NCCR Evolving Language, C-LING, SEM24, LT-BRIDGE Grants: SNSF, Innosuisse, Hasler Foundation Lab/Team: Computation, Cognition & Language Group at Idiap

Vinitra SwamyView profile

Researcher

• Explainable AI
• Machine Learning for Education
• Multimodal Learning
• +4 more

Vinitra Swamy is a Postdoctoral Researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the ML4ED Lab (Machine Learning for Education) and the MLO Lab (Machine Learning and Optimization Group). She holds a PhD in Computer Science from EPFL and a Master's and Bachelor's from UC Berkeley, graduating at 20 as the youngest recipient in UC Berkeley's history. Her research focuses on explainable AI, human-centric machine learning, and education technology. She has held roles as a lead engineer at Microsoft AI (ONNX framework) and served as a lecturer at UC Berkeley and UW Seattle. Vinitra's work bridges technical innovation with societal impact, exemplified by projects like MEDITRON-70B (medical LLMs) and iLLuMinaTE (actionable explanations for students). She has received multiple awards, including the 2024 G-Research PhD Prize and Rising Stars in Data Science recognition. Key contributions include interpretable neural architectures (InterpretCC), bias analysis in LLMs, and multimodal learning systems (MultiModN). Her research emphasizes practical applications in education and healthcare, with a focus on user-centered design and ethical AI practices.

Benjamin C. M. FungView profile

Professor

• Data Mining
• Artificial Intelligence
• Cybersecurity
• +6 more

Benjamin C. M. Fung is a Full Professor at McGill University's School of Information Studies and holds the Canada Research Chair in Data Mining for Cybersecurity. He is also an Associate Member at the School of Computer Science and affiliated with MILA and the McGill Digital Humanities Initiatives. His research focuses on data mining, AI, cybersecurity, privacy protection, and their applications in crime investigation, health informatics, and smart cities. Education: Ph.D. in Computing Science, Simon Fraser University (2007) M.Sc. and B.Sc. in Computing Science, Simon Fraser University Professional Engineer (P.Eng.) in Software Engineering, Ontario Research Interests: Data mining techniques for cybersecurity, AI ethics, privacy-preserving technologies, malware analysis, and foreign interference detection. His work has been supported by NSERC, SSHRC, DRDC, and FRQNT. Key Contributions: Developed the Kam1n0 tool for malware analysis, featured in Montreal's technological innovations. Co-founded the McGill Data Mining and Security (DMaS) Lab. Authored over 180 refereed publications with an h-index of 62. Awards & Recognition: 2022 IEEE CSR Best Research Paper Award ACM & IEEE Senior Member (2013) Top 2% Global Scientists (2021–present) Media & Public Engagement: Frequently comments on cybersecurity, disinformation, and foreign interference for CBC, CBC Radio, OMNI TV, and international outlets. His op-eds address topics like AI ethics and national security.