Find Academic Supervisors

Robert West is an Associate Professor at EPFL (École polytechnique fédérale de Lausanne) in the School of Computer and Communication Sciences , leading the Data Science Lab (dlab) . His research focuses on Natural Language Processing , Machine Learning , and Computational Social Science , analyzing human-generated data from the web, social media, and online platforms. Education : PhD in Computer Science (2016) - Stanford University MSc in Computer Science (2010) - McGill University BSc in Computer Science (2007) - Technische Universität München Research Interests : West develops algorithms for analyzing large-scale web data, with emphasis on multilingual NLP , social network analysis , and AI ethics . His work bridges machine learning with social science to understand digital human behavior. Scientific Awards : ICWSM’22 Adamic–Glance Distinguished Young Researcher Award Google Faculty Research Award Facebook Research Award Multiple Outstanding Paper Awards at ICWSM and WWW Advising & Grants : He advises 12 PhD students and has secured funding from the Swiss National Science Foundation , Swiss Data Science Center , and industry partners. His lab maintains collaborations with Microsoft Research and CROSS . Labs & Collaborations : West leads the Data Science Lab at EPFL, which focuses on web-scale data analysis , privacy-preserving machine learning , and AI for social good . The lab develops tools like Wikispeedia and Quotebank for public data exploration.

• Natural Language Processing (NLP)
• Machine Learning
• Computational Social Science
• +4 more

Prof. Luke Zettlemoyer is an Adjunct Professor of Computer Science and Engineering at the University of Washington, with affiliations to the Department of Linguistics. He focuses on machine learning, natural language processing, and multimodal systems, contributing to advancements in large language models, ethical AI, and scalable architectures. His research addresses challenges in model alignment, generalization, and cross-domain integration. Key research interests include multimodal reward models, efficient tokenization strategies, and model optimization techniques. He has explored topics such as neural trajectories for robot learning, content-adaptive image processing, and ethical mitigation of verbatim data reproduction. His publications span 2023–2025, emphasizing practical applications of AI in robotics, vision-language systems, and scalable retrieval-based models. While no formal awards are listed, his work reflects significant contributions to foundational AI research.

• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• +3 more

Arman Cohan is an Assistant Professor in the Department of Computer Science at Yale University, where he leads the Yale NLP Lab since its founding in January 2023. His research spans natural language processing and machine learning with emphasis on language modeling, representation learning, retrieval systems, and specialized domain applications including scientific discovery and AI for science. His primary research interests include: Natural Language Processing Machine Learning Large Language Models Information Retrieval AI for Science Scientific Problem-Solving Recent publications (2025) demonstrate intense focus on evaluating and advancing LLM capabilities across multimodal reasoning, scientific claim verification, financial domain applications, and biological modeling. The lab consistently produces high-impact work accepted at top-tier conferences including ACL, EMNLP, and ICLR, with 11 papers at ACL 2025 alone. Scientific awards include: Best Paper Award at AI4Research Workshop (IJCAI 2024) Outstanding Paper Award at EACL 2023 Best Paper Award at ACL 2024 for Olmo language model research Professor Cohan actively advises PhD students including Kaili Liu, Jacob Dunefsky, Alan Li, Yilun Zhao, and has graduated researchers such as Linyong Nan (now at Zoom) and Ansong Ni (now at Meta). The lab maintains strong industry partnerships and receives substantial research funding as evidenced by its prolific output and conference presence. The Yale NLP Lab hosts the annual New England NLP Workshop and regularly features speakers from leading institutions including Meta AI, Allen Institute for AI, and DeepMind, fostering a collaborative environment for advancing NLP research.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +3 more

Oscar Westlund serves as Professor at the Department of Journalism and Media Studies within the Faculty of Social Sciences at Oslo Metropolitan University (OsloMet), where he co-leads the OsloMet Digital Journalism Research Group. He maintains a secondary appointment at the University of Gothenburg and acts as Editor-in-Chief of Digital Journalism . His research spans digital journalism, fact-checking, platform dynamics, media management, news consumption, and mobile media. Westlund's scholarly focus centers on journalism safety in digital environments, sociotechnical dimensions of misinformation combat, and platform-induced challenges for news organizations. His work examines professional fragilities of journalists, epistemological foundations of data journalism, and legitimacy paradoxes in public service media-platform relationships. Current projects address disinformation countermeasures through the SCAM initiative (Source Criticism And Mediated Disinformation). Analysis of his 2022-2024 publications reveals dominant themes: journalism safety frameworks (33%), platform-journalism tensions (27%), and fact-checking as sociotechnical practice (23%). Key methodological approaches include cross-national comparative studies, epistemological analysis, and practice theory applied to digital news production. Westlund actively contributes to academic discourse through editorial leadership at Digital Journalism and special issue guest editing. His research group coordinates interdisciplinary collaboration on digital journalism studies, while the SCAM project develops practical methodologies for disinformation detection in democratic contexts. Recent work demonstrates increasing emphasis on mobile news ecosystems and journalist safety cultures. He co-leads the OsloMet Digital Journalism Research Group, which investigates platform impacts on news production, mobile journalism evolution, and epistemological shifts in data-driven reporting. The group maintains strong international partnerships, particularly with Swedish institutions through his University of Gothenburg affiliation.

• Digital Journalism
• Fact-checking
• Media Platforms
• +7 more

Byron Wallace is the Sy and Laurie Sternberg Interdisciplinary Associate Professor at Northeastern University's Khoury College of Computer Sciences, where he also serves as Associate Dean for Research and Director of the BS in Data Science Program. His research focuses on Natural Language Processing and Machine Learning applications in healthcare. Education Details of formal education are not explicitly provided in the available text, though he holds a PhD from Tufts University (mentioned in thesis award context). Research Interests His work centers on developing NLP and ML models for health applications, with particular emphasis on: Biomedical evidence synthesis automation Electronic Health Record processing Model interpretability and trustworthiness Human-in-the-loop systems Learning with limited supervision Research Trends Recent publications demonstrate strong focus on large language model applications in healthcare, including factuality evaluation for medical summarization, evidence extraction from clinical trials, and interpretable risk prediction models. Notable contributions include work on GPT-3 applications in medical evidence synthesis and neural methods for EHR analysis. Scientific Awards ACL Outstanding Paper Award (2022) ICLR Spotlight (top 5% acceptance) (2024) Best Student-led Paper Award at AMIA 2021 NSF CAREER Award (2018-2023) Advising and Grants Currently advises 4 PhD students and has mentored numerous others. Major funding includes: NSF CAREER Award ($500K+) NIH R01 grant for EHR summarization NSF Medium grant for healthcare summarization Support from Army Research Office, Amazon, and Seton Hospital Labs and Teams Leads the Evidence Inference project team working on automated biomedical evidence synthesis. Collaborates with Brigham and Women's Hospital, Mass General Hospital, and Reboot Rx for clinical translation of research.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +5 more

Andreas Vlachos is a Professor of Natural Language Processing and Machine Learning at the Department of Computer Science and Technology, University of Cambridge, and holds the Dinesh Dhamija Fellowship at Fitzwilliam College. His research spans dialogue modeling, automated fact-checking, imitation learning, semantic parsing, biomedical text mining, and trustworthiness in AI systems. PhD in Computer Science, University of Cambridge (supervised by Ted Briscoe and Zoubin Ghahramani) Lecturer at University of Sheffield Postdoctoral roles at UCL, University of Cambridge (NLIP group, Stephen Clark), and University of Wisconsin-Madison (Mark Craven) Current research focuses on evaluating and mitigating biases in language models, advancing fact-checking methodologies, and improving model robustness through interpolation, reinforcement learning, and causal reasoning. His work integrates natural logic, knowledge graphs, and multimodal evidence for verification tasks. Recent publications address uncertainty quantification, temporal planning benchmarks, and ethical framing of NLP artifacts. Grants from ERC, EPSRC, Facebook, Google, and the Alan Turing Institute fund his research team. Collaborations include Sebastian Riedel, Stephen Clark, and Mark Craven. Key projects explore disinformation detection, long-form generation, and confidence calibration in AI systems.

• Natural Language Processing
• Machine Learning
• Dialogue Modelling
• +10 more

Dr. Oana Cocarascu is a Senior Lecturer in Artificial Intelligence at the Department of Informatics, Faculty of Natural, Mathematical & Engineering Sciences, King's College London. She holds a PhD and MEng in Computing (Artificial Intelligence) from Imperial College London and conducts applied research focusing on how artificial intelligence can be deployed to support real-world applications, with machine learning and natural language processing as core components of her work. Her research interests span argument mining, explainable AI, machine learning, natural language processing, and symbolic reasoning. She is particularly focused on developing AI systems that can provide transparent, accountable, and ethical decision-making processes. Her work addresses critical challenges in bias mitigation, fairness metrics, fact verification systems, and argumentation-based explanations for complex AI decisions. Analysis of her recent publications (2023-2025) reveals a strong focus on fairness in AI systems, with particular attention to individual fairness metrics and nuanced evaluation frameworks. She has made significant contributions to fact verification systems, especially in multimodal contexts involving charts and tabular data. Her work increasingly integrates argumentation theory with natural language processing to create explainable AI systems that can justify their decisions through structured reasoning. Dr. Cocarascu leads an EPSRC-funded project titled 'A framework for evaluating and explaining the robustness of NLP models' (2024-2027) and is actively involved in the Natural Language Processing Group at KCL. Her research fingerprint shows strong activity in argumentation (100%), decision-making (74%), artificial intelligence (50%), explainable AI (39%), and bias mitigation (35%). She has contributed to numerous high-impact publications in top venues including ACL, EMNLP, AAAI, and the Journal of Artificial Intelligence Research, with a particular focus on making AI systems more transparent, accountable, and aligned with human values. Her work intersects with UN Sustainable Development Goals, particularly those related to reducing inequality and building resilient infrastructure.

• Argument mining
• Explainable AI
• Machine learning
• +5 more

Amy Bruckman is a Regents’ Professor and Senior Associate Chair in the School of Interactive Computing at Georgia Institute of Technology. As a Fellow of The ACM and member of the SIGCHI Academy , she contributes to social computing research focusing on online collaboration, cross-cultural understanding, and content moderation. Her 2022 book "Should You Believe Wikipedia? Online Communities and the Construction of Knowledge" explores digital knowledge creation. Education: Ph.D. from MIT Media Lab (1997), B.A. in Physics from Harvard (1987) Research Affiliations: GVU Center, IRI People and Technology Her work addresses social computing challenges including: AI ethics in human-AI interaction Algorithmic bias in content moderation Epistemic justice in online communities Comparative platform analysis (Reddit, Discord, TikTok) Grassroots technology movements Recent publications examine: AI support tools for online counseling Community-driven research frameworks Racial bias mitigation in toxic language detection Designing teachable social media experiences Human trafficking intervention ethics Scientific recognition includes: ACM Fellowship SIGCHI Academy membership She has contributed to: Georgia Tech's Computing Education initiatives Research ethics frameworks for Human-Computer Interaction

• Social Computing
• Online Collaboration
• Content Moderation
• +3 more

Nasir Memon is a Professor of Computer Science and Engineering at the New York University Tandon School of Engineering and concurrently serves as the Dean of Engineering at NYU Shanghai. He has been a faculty member at NYU Tandon since September 1998. Memon is a co-founder of NYU's Center for Cyber Security (CCS) and NYU Abu Dhabi, and the founder of key initiatives such as the OSIRIS Lab, CyberSecurity Awareness Week (CSAW), the NYU Tandon Bridge program, and the Cyber Fellows program. His work focuses on advancing cybersecurity education and addressing systemic biases in AI-driven systems. Education: Ph.D., Computer Science, University of Nebraska Master of Science, Mathematics, Birla Institute of Technology and Science (BITS), Pilani Bachelor of Engineering, Chemical Engineering, BITS, Pilani Research Interests: Media Forensics and Authentication Biometric Security and Privacy Data Compression and Privacy-Preserving Techniques Network Security and Incident Response AI Ethics and Fairness in Machine Learning Cybersecurity Education and Workforce Development Awards and Honors: IEEE Fellow (2010) SPIE Fellow (2014) Jacobs Excellence in Education Award (2002) NSF CAREER Award (1997) Advising and Grants: Advises Ph.D. students like Anubhav Jain and Govind Mittal, and mentors Master’s students such as Rishit Dholakia. Recipient of NSF grants and funding from NYU Abu Dhabi and Indiana University Bloomington for projects like computational tools for fact-checking and AI-driven bias mitigation. Labs and Teams: Directs the OSIRIS Lab, a leading research group in cybersecurity and AI. Leads initiatives at the NYU Center for Cybersecurity (CCS) and collaborates with NYU Abu Dhabi’s Center for Cyber Security.

• Digital Forensics
• Biometrics
• Authentication
• +6 more

Anders Søgaard is a Professor at the University of Copenhagen , affiliated with both the Department of Computer Science and the Department of Communication. His research bridges Natural Language Processing and Machine Learning with a focus on AI ethics , explainability , and human-AI interaction . Primary Affiliation: Department of Computer Science, University of Copenhagen Secondary Affiliation: Department of Communication, University of Copenhagen Email: soegaard@di.ku.dk, soegaard@hum.ku.dk Research Interests His work spans Natural Language Processing , Machine Learning , and AI ethics , with recent studies addressing: Trustworthiness in AI systems Explainable AI (XAI) frameworks Multilingual model fairness and alignment Human-AI collaboration in reasoning tasks Ethical implications of social robots Mental health analytics using ML Recent Publications His 2025 output highlights trends in: AI ethics (e.g., fairness metrics, trustworthy systems) Multilingual model analysis (knowledge retention, cross-lingual transfer) Human-centric AI (gaze data, cultural considerations) Applications in healthcare and social good

• Natural Language Processing
• Machine Learning
• Artificial Intelligence Ethics
• +3 more

Yonatan Bisk is an Assistant Professor at Carnegie Mellon University within the Language Technologies Institute (with courtesy appointment in Robotics Institute). His research bridges Natural Language Processing , Robotics , and Embodied AI , focusing on language grounding, theory of mind, and multimodal interaction. Education : Ph.D. in Computer Science from University of Illinois at Urbana-Champaign Postdoctoral Experience : USC ISI, University of Washington, Allen Institute for AI Industry Appointments : Microsoft Research, Meta AI His research emphasizes embodied language systems and social intelligence in AI . Recent projects include WebArena for autonomous agents, SOTOPIA for social reasoning, and HomeRobot for open-vocabulary manipulation. He leads the REAL Center (Robotics, Embodied AI, and Learning) to foster interdisciplinary collaboration. Key scientific awards include selection for the DARPA ISAT Study Group (2024). He teaches courses like "Talking to Robots" and "Multimodal Machine Learning" while serving as area chair/editor across NLP, Robotics, and ML communities.

• Embodied Artificial Intelligence
• Natural Language Processing
• Robotics
• +3 more

Sebastian Riedel is a Professor at University College London (UCL) and a Researcher at DeepMind, leading the UCL NLP Lab. His work focuses on teaching machines to read, reason, and write, integrating Natural Language Processing (NLP) with Machine Learning. He holds an Allen Distinguished Investigator award and has held roles at FAIR, UMass Amherst, Tokyo University, and the University of Edinburgh. Education: PhD in Computer Science from the University of Edinburgh (advisor: Ewan Klein), postdoctoral research at UMass Amherst (advisor: Andrew McCallum), and research at Tokyo University (advisor: Tsujii Junichi). Research Interests: NLP, machine learning, information extraction, and multimodal models like Gemini. He develops tools such as UCLEED (BioNLP event extractor), frontlets (Scala map wrappers), and thebibbrag (BibTeX to HTML converter). Awards: Allen Distinguished Investigator. Software contributions include GitHub repositories for NLP, machine learning, and data tools. Contact: s.riedel@ucl.ac.uk | Office: 1st Floor, 90 High Holborn, London WC1V 6LJ | Office Hours: Mondays 11 AM–12 PM.

• Natural Language Processing (NLP)
• Machine Learning
• Artificial Intelligence
• +3 more

Dr. Herb Lin is the Hank J. Holland Fellow in Cyber Policy and Security at the Hoover Institution and Senior Research Scholar for Cyber Policy and Security at the Center for International Security and Cooperation (CISAC), both at Stanford University. He also serves as Chief Scientist, Emeritus for the Computer Science and Telecommunications Board of the National Academies, where he worked from 1990 to 2014, and as Adjunct Senior Research Scholar and Senior Fellow in Cybersecurity at Columbia University's Saltzman Institute for War and Peace Studies. Dr. Lin received his doctorate in physics from MIT, providing him with a strong technical foundation that informs his policy work at the intersection of technology and national security. His academic background bridges the gap between deep technical understanding and policy application in the cybersecurity domain. Dr. Lin's research focuses on the policy dimensions of cybersecurity and cyberspace, with particular emphasis on offensive cyber operations as instruments of national policy, information warfare, and influence operations affecting national security. His work addresses critical challenges at the intersection of emerging technologies like artificial intelligence, quantum computing, and their implications for nuclear security, election integrity, and democratic processes. He has been instrumental in analyzing how cyber capabilities are transforming traditional security paradigms and how policy frameworks must adapt to these changes. His extensive publication record reveals a consistent focus on the evolving relationship between cyber capabilities and national security strategy. Lin's work demonstrates increasing attention to the implications of artificial intelligence for warfare, the vulnerabilities created by quantum computing for current cryptographic systems, and the growing threat of cyber-enabled information warfare to democratic institutions. His research trajectory shows a progression from traditional cybersecurity issues toward the broader implications of digital technologies for national and international security. Member of Science and Security Board of Bulletin of the Atomic Scientists Served on President Obama's Commission on Enhancing National Cybersecurity (2016) Regular congressional testimony on cybersecurity matters Contributor to Stanford Emerging Technology Review As a mentor and collaborator, Dr. Lin works extensively with policymakers, military officials, and technology experts to translate complex cybersecurity concepts into actionable policy recommendations. His approach emphasizes practical solutions to emerging threats while recognizing the technical limitations and policy constraints that shape real-world security outcomes. Dr. Lin is associated with multiple research initiatives including Stanford's Center for International Security and Cooperation and the Stanford Emerging Technology Review, which aim to connect technological advances with policy considerations for national security decision-makers.

• Cybersecurity Policy
• Information Warfare
• Nuclear Security
• +4 more

Zachary Ives is the Adani President's Distinguished Professor and Department Chair of the Computer and Information Science Department at the University of Pennsylvania. He holds affiliations with the ASSET Center for Safe, Explainable and Trustworthy AI, the Warren Center for Network and Data Science, the Center for Neuroengineering and Therapeutics, and serves as a Distinguished Research Fellow at the Annenberg Center for Public Policy. His research focuses on data integration and sharing, data provenance and trustworthiness, and machine learning systems. He develops data science platforms at the intersection of databases, machine learning, and distributed systems, with applications in Web question answering and scientific domains like genetics and neuroscience. His work addresses fundamental challenges in integrating heterogeneous data, ensuring trustworthy results, and facilitating collaborative data science. His recent publications demonstrate a strong focus on data lakes, learned database systems, fine-grained provenance, and question answering systems. These works span top conferences including SIGMOD (where his paper was selected as Best Paper in 2024), VLDB, ACL, and PODS, showing the breadth of his contributions across database systems, natural language processing, and data management. NSF CAREER award recipient Fellow of the ACM Christian R. and Mary F. Lindback Foundation Award for Distinguished Teaching IEEE Technical Committee on Data Engineering Education Award SIGMOD Best Paper Award ICDE 2013 ten-year Most Influential Paper award As Department Chair, Ives has overseen significant departmental growth, hiring 25 new faculty since 2018. He advises numerous PhD students and postdocs, and maintains extensive collaborations across Penn and with external institutions. His research has been funded by NSF, NIH, DARPA, Google, Amazon, and other organizations. He has developed courses including NETS 212 'Scalable and Cloud Computing' and teaches Big Data Analytics. His research group, the Penn Database Group, works on projects including data lake management, data provenance, and collaborative data science platforms. His work with neuroscientists on seizure prediction has received significant attention, including a competition with 504 teams achieving 82% accuracy.

• Data Integration
• Data Provenance
• Machine Learning Systems
• +5 more

Arman Cohan is an Assistant Professor of Computer Science at Yale University, affiliated with the School of Engineering & Applied Science. His research focuses on the intersection of Machine Learning and Natural Language Processing (NLP), particularly in language modeling, representation learning, retrieval systems, and applications in specialized domains such as scientific text processing. He earned his Ph.D. in Computer Science from Georgetown University and has received notable awards, including the Dr. Harold N. Glassman Distinguished Doctoral Dissertation Award (2019) and the EMNLP 2017 Best Long Paper Award. His work emphasizes ethical AI, robustness of LLMs, and interdisciplinary applications in healthcare, science, and education. Cohan's research group, the Yale NLP Lab, develops advanced techniques for multi-document summarization, adversarial fact-checking, and LLM-driven tools for scientific discovery. Recent projects include frameworks like SciBERT, Longformer, and ChemAgent, which enhance domain-specific reasoning and safety in AI systems. His publications address challenges in table reasoning, uncertainty expression, and multimodal reasoning, with applications in medical decision-making and educational problem-solving. He collaborates on initiatives like the Roberts Innovation Fund to advance AI in healthcare and environmental technology.

• Machine Learning
• Natural Language Processing (NLP)
• Language Modeling
• +7 more