جست‌وجوی استاد و پژوهشگر

• AI Ethics
• Computational Creativity
• Philosophy of Technology
• +۴ مورد دیگر

Alice C Helliwell is an Assistant Professor in Philosophy at Northeastern University London, specializing in AI art and computational creativity. Her research addresses philosophical questions about machine creativity, AI ethics, and the aesthetics of AI-generated art. She holds a PhD from the University of Kent and has held roles including Interim Director of the Doctoral Research School at NU London (2023-24). Her current projects include a Visiting Fellowship at Friedrich-Alexander-Universität exploring AI responsibility frameworks. Education includes: PhD in History and Philosophy of Art (University of Kent) MA in History and Philosophy of Art (University of Kent, Paris) PGCert in Occupational Psychology (Birkbeck, University of London) MA(Hons) in Philosophy and Psychology (University of Edinburgh) Her research spans: Philosophy of technology Creativity theory Applied AI ethics Landscape of AI ethics in creative industries Recent publications focus on AI alignment problems, cluster accounts of art, and Wittgensteinian analyses of AI governance. She co-edited volumes on Wittgenstein and AI (2024) and authored key works linking Darwinian creativity models to computational systems. Awards include the Paris Scholarship and Vice Chancellor’s Scholarship. Professional roles include Associate Editor at AI & Society (2024–present) and Trustee of the British Society of Aesthetics. Teaching focuses on AI/data ethics and aesthetics at NU London, with supervision of dissertations in AI art, technology ethics, and aesthetics. She holds Fellowship of Advance HE (2024).

• Machine Learning
• Artificial Intelligence
• Genomics
• +۴ مورد دیگر

Haohan Wang serves as Assistant Professor at the School of Information Sciences, University of Illinois Urbana-Champaign, with additional appointments as Affiliate at the Carl R. Woese Institute for Genomic Biology and Assistant Professor at the National Center for Supercomputing Applications (NCSA). His interdisciplinary work bridges machine learning, genomics, and AI security, focusing on trustworthy systems for biomedical applications and foundational AI research. Wang's research centers on robust and secure artificial intelligence, with emphasis on large language model vulnerabilities (jailbreaking, safety evaluation), federated learning personalization, and genomic data analysis. He develops techniques for privacy-preserving dataset distillation, confounding factor correction in genome-wide studies, and multi-agent frameworks for scientific discovery. His fingerprint highlights expertise in Machine Learning (94%), Linear Mixed Models (87%), and Confounding Factor Correction (41%), reflecting his focus on methodological rigor in complex data environments. Analysis of his 2025 publications reveals dominant trends in AI security (jailbreak evaluation frameworks like GuardVal, adversarial attacks such as InfoFlood), biomedical AI (transcriptomic analysis, wearable data privacy), and foundational methods (federated learning optimization, synthetic data generation). These works consistently address real-world challenges in model trustworthiness while advancing computational techniques for genomics and healthcare. Through NCSA's high-performance computing resources and the Institute for Genomic Biology's collaborative ecosystem, Wang integrates supercomputing capabilities with biological research to tackle data-intensive problems in disease modeling and AI safety testing, as evidenced by media coverage of his team's AI security testing methods.

Yingfei Xiongمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages

Yingfei Xiong is an active Associate Professor at Peking University, China, specializing in software engineering and programming languages. With a consistent research trajectory from 2013 through 2026, Xiong has established themselves as a prominent figure in the software engineering research community, regularly contributing to top-tier conferences including SPLASH, ICSE, ASE, and PLDI. Dr. Xiong's research primarily focuses on program synthesis, automated program repair, and software analysis techniques. Their work bridges theoretical programming language concepts with practical software engineering applications, particularly in developing novel approaches for code generation, bug fixing, and program optimization. The research demonstrates strong interdisciplinary connections between traditional software engineering, programming languages theory, and emerging AI techniques. Analysis of Xiong's publication trends reveals a clear evolution in research focus, beginning with foundational work in API transformations and program adaptation around 2013-2016, shifting toward program repair techniques from 2017-2020, and most recently incorporating machine learning and neural approaches into program synthesis and repair (2021-2026). The work consistently addresses practical challenges in software development while maintaining theoretical rigor, with increasing integration of AI techniques in recent years. Dr. Xiong has served in various leadership roles across the software engineering conference ecosystem, including program committee membership and session chair positions at major conferences. Their extensive service demonstrates recognition by peers as a subject matter expert in software engineering and programming languages research. While specific grant information isn't detailed in the provided text, the sustained publication record suggests successful research funding.

John Baslمشاهده پروفایل

دانشیار

• Applied Ethics
• AI Ethics
• Machine Learning Ethics
• +۳ مورد دیگر

John Basl is an Associate Professor of Philosophy at Northeastern University and Associate Director of the Northeastern Ethics Institute, where he leads initiatives in AI and data ethics. He holds affiliate roles at Khoury College of Computer Science, the Institute for Experiential AI, and Harvard's Edmond and Lily Safra Center for Ethics. His research focuses on applied ethics, particularly in AI, machine learning, data ethics, and environmental ethics. He is the author of The Death of the Ethic of Life (Oxford UP) and has contributed to interdisciplinary projects on AI governance, including the NSF-funded AI & Data Ethics (AIDE) Graduate Training Program and the National Internet Observatory. His work explores ethical challenges posed by emerging technologies, such as moral obligations to AI systems, transparency in automated decision-making, and the ethical implications of autonomous vehicles. Basl’s research also engages with environmental ethics, addressing questions about species partiality, ecological justice, and the moral status of non-human entities. He actively collaborates with technologists, policymakers, and ethicists to ensure ethical considerations are integrated into technological development. His media engagements include talks on AI ethics at institutions like the University of Toronto and the Web Summit, and articles in outlets like Aeon Magazine. Current projects include grants focused on training the next generation of AI ethics scholars and analyzing the societal impacts of generative AI. Basl advocates for accessible, interdisciplinary approaches to ethics in technology, emphasizing practical solutions to real-world challenges.

Naren Ramakrishnanمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Urban Analytics
• +۲ مورد دیگر

Naren Ramakrishnan is the Thomas L. Phillips Professor of Engineering in the Department of Computer Science at Virginia Tech, where he directs the Sanghani Center for AI and Data Analytics. He also serves as AI and Machine Learning Lead for the Virginia Tech Innovation Campus. His research spans data science, machine learning, urban analytics, forecasting, and computational epidemiology. Recent publications (2024-2025) focus on language model optimization, AI applications in government and environmental conservation, and spatiotemporal data analysis. Work demonstrates strong emphasis on real-world AI deployments in regulatory compliance, supply chain verification, and network optimization. Methodological innovations include prompt engineering techniques, world models for reinforcement learning, and specialized embedding methods. Dr. Ramakrishnan has received prestigious fellowships from ACM, AAAS, and IEEE. His research has been supported by numerous agencies including DARPA, NSF, NIH, and industry partners like Amazon and Boeing, with 36 PhD students mentored to completion.

Patrick T. Brandtمشاهده پروفایل

استاد

• Political Dynamics
• Time Series Analysis
• Machine Learning
• +۸ مورد دیگر

Patrick T. Brandt is a Professor of Political Science, Public Policy, and Political Economy at the University of Texas at Dallas , affiliated with the School of Economic, Political and Policy Sciences . His work integrates advanced statistical methods with political science, focusing on time series analysis, machine learning, and Bayesian modeling to study political dynamics. His research spans international relations , political economy , terrorist targeting , and conflict forecasting . He specializes in developing novel models for event count time series, including the Bayesian Poisson Vector Autoregression and MS-BVAR packages for R. His NSF-funded projects focus on event data generation and real-time conflict forecasting. Recent publications emphasize domain-specific language models (ConfliBERT variants), graph neural networks for conflict prediction, and machine translation challenges in political text analysis. He maintains the OpenEvent Data Repository and develops software like MSBVAR and PESTS for academic use. Scientific Awards : Robert H. Durr Award for Best Methodology Paper, Midwest Political Science Association (2006)

Tommy Löfstedtمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۴ مورد دیگر

Tommy Löfstedt is an Associate Professor at Umeå University , affiliated with the Department of Computing Science and the Department of Mathematics and Mathematical Statistics. His research focuses on machine learning , computer vision , and medical image analysis , with applications in life sciences, radiation therapy, and biomedical imaging. He leads multiple research projects, including AI-driven delineation in radiation therapy, quantitative MRI for radiotherapy, and machine learning for plant nutrient uptake. Current research emphasizes structured regularization methods to improve model interpretability and robustness. Key applications include medical image segmentation , Alzheimer's classification , and uncertainty estimation in MRI . Recent publications highlight his work on morphological regularization , adversarial attack mitigation , and multi-task learning in medical imaging contexts. His projects span 2022–2026 with funding for pediatric oncology automation and gynecological cancer staging. Affiliated with both computing and mathematical departments, he bridges algorithm development with applied mathematical frameworks in medical and life science domains.

• Artificial Intelligence
• Machine Learning
• Knowledge Graphs
• +۵ مورد دیگر

Dr. Madhushi Bandara is a Lecturer at the School of Computer Science, University of Technology Sydney (UTS), specializing in knowledge representation, complex system modeling, and data analytics. She leads the data management research stream at the UTS DigiSAS lab and is a core member of the Biomedical Data Science Laboratory within the UTS Australian Artificial Intelligence Institute. Her industry collaborations include Telstra, Cancer Australia, and Capsifi, focusing on AI integration in healthcare and finance. She coordinates the Business Information Systems major in UTS's Master of Information Technology program and convenes the Future Generation Enterprise Architecture Community of Practice. Education PhD in AI Systems Engineering, University of New South Wales (2020) BSc (Hons) in Engineering, University of Moratuwa, Sri Lanka (2015) Research Interests Madhushi's work bridges machine learning, knowledge graphs, and enterprise architecture to address challenges in data governance for SMEs, ESG metric management, and healthcare pathway analysis. Her research emphasizes translating cutting-edge AI into industry solutions through contextual domain knowledge integration. Scientific Awards UNSW-UTS Trustworthy Digital Society Scholarship Teaching & Leadership She teaches enterprise information systems, digital strategy, and AI for enterprises in UTS's online postgraduate programs. Her service roles include co-chairing tracks at the Australasian Conference on Information Systems and reviewing for Expert Systems with Applications.

Philippe Schwallerمشاهده پروفایل

استادیار

• Artificial Intelligence in Chemistry
• Machine Learning for Molecular Discovery
• Sustainable Chemistry
• +۶ مورد دیگر

Philippe Schwaller is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), School of Basic Sciences, within the Institute of Chemical Sciences and Engineering. He leads the Laboratory of Artificial Chemical Intelligence (LIAC), a research group focused on leveraging artificial intelligence to accelerate molecular discovery and sustainable chemistry. He is also a core Principal Investigator of the NCCR Catalysis, a national Swiss research center. His research lies at the intersection of chemistry, materials science, and computer science, with a strong emphasis on developing machine learning models for molecular design and synthesis. LIAC's work is driven by real-world sustainability challenges, aiming to reduce the time and cost of discovering new functional molecules and materials. The recent publications and projects from his lab highlight a strong trend in generative AI for chemistry, including memory-augmented models, hypergraph neural networks, and large language models tailored for scientific discovery. These efforts are complemented by educational initiatives such as the 'AI for Chemistry' course and practical programming resources for chemists. He actively supervises a diverse group of PhD students and contributes to multiple doctoral programs at EPFL, including EDCH and EDPY. His teaching portfolio includes courses on computational chemistry, AI applications in chemistry, and scientific machine learning. Philippe Schwaller is deeply involved in advancing AI-driven scientific discovery through both research and education, positioning his lab at the forefront of artificial chemical intelligence. The lab maintains active open-source contributions on GitHub, fostering collaboration and transparency in scientific AI development.

Martin Henzمشاهده پروفایل

دانشیار

• Programming Languages
• Software Engineering
• Artificial Intelligence
• +۸ مورد دیگر

Martin Henz is an Associate Professor at the National University of Singapore , affiliated with the School of Computing and its Department of Computer Science . His academic journey includes an M.Sc. in Computer Science from Stony Brook University (1993) and a Dr.rer.nat. in Computer Science from Saarland University (1997). He has also worked as a Research Scientist at the German Research Centre for Artificial Intelligence. Research Focus : Scalable Experiential Learning, Systems for Teaching/Learning, AI in Education, Programming Languages, Algorithms, and Constraint Programming. Key Projects : Source Academy (immersive programming environment), Deep Teaching (LMS enhancements), and NUS Seafarers (maritime experiential learning). Publications span education technology, programming languages, and sustainable engineering, with recent works focusing on JavaScript-based pedagogy, automated question generation, and electric vehicle conversions. He supervised Rahul Singhal 's PhD, leading to the educational startup Cerebry, and co-founded Workforce Optimizer Pte Ltd with Alan Sevugan. Awards : NUS Annual Digital Education Award (2021) NUS Annual Teaching Excellence Award (2016/17) Fulbright Scholarship (1990) Startup @ Singapore Champion (2001)

David R. Kauchakمشاهده پروفایل

استاد

• Natural Language Processing (NLP)
• Text Simplification
• Machine Learning
• +۳ مورد دیگر

David R. Kauchak is a Professor of Computer Science at Pomona College, part of The Claremont Colleges. He has been affiliated with the institution since 2014. His academic background includes a Ph.D. (2006) and M.A. (2006) in Computer Science from the University of California, San Diego, and a B.S. (2005) in Computer Science from the University of Utah. His research focuses on Natural Language Processing (NLP) , particularly text simplification , aiming to reduce text complexity while preserving content. Applications include improving health literacy through simplified medical text and enhancing accessibility of technical documents. He has also contributed to machine learning methodologies and information retrieval systems. Key awards include the Distinguished Poster Award at AMIA 2016 and the Best Undergraduate Paper Award at SocalNLP 2018. His work frequently addresses interdisciplinary challenges in healthcare communication, education technology, and software tool development. Teaching highlights include courses in Artificial Intelligence , Algorithms , Natural Language Processing , and foundational computer science modules. He has advised numerous projects in computational linguistics and health informatics, though specific student names are not explicitly listed in the provided materials. His collaborations span academic and medical domains, including joint research with institutions like the University of Utah and projects funded through initiatives like the German Climate Modeling Initiative (though this appears unrelated to his primary work). He maintains an active presence in academic conferences, publishing in venues such as ACL, AMIA, and IEEE journals.

Andrew M. Olneyمشاهده پروفایل

استاد

• Natural Language Processing
• Intelligent Tutoring Systems
• Dialogue Systems
• +۵ مورد دیگر

Andrew M. Olney is Professor in both the Institute for Intelligent Systems and Department of Psychology at the University of Memphis. His work bridges artificial intelligence, cognitive science, and education, with a primary focus on natural language interfaces for learning. His educational background includes a Ph.D. in Computer Science from the University of Memphis (2006), an M.S. in Evolutionary and Adaptive Systems from the University of Sussex (2001), and a B.A. in Linguistics with Cognitive Science from University College London (1998). Dr. Olney's research centers on intelligent tutoring systems and natural language processing applications in education. His specific interests include vector space models, dialogue systems, unsupervised grammar induction, and computational representations of meaning. He has made significant contributions to conversational intelligent tutoring systems and task-oriented natural language interfaces, with particular emphasis on dyadic interaction and engagement in learning contexts. Analysis of his recent publications reveals a consistent focus on leveraging AI for educational enhancement, particularly through automated content creation, question generation, and adaptive learning systems. His work spans multiple disciplines including computer science, cognitive science, linguistics, and education, demonstrating strong interdisciplinary integration. Scientific Awards and Recognition: Best Paper Award, Proceedings of Empowering Education with LLMs (2023) Associate Editor, International Journal of Artificial Intelligence in Education (Impact Factor 4.9) Dr. Olney has been actively involved in mentoring graduate students and securing research funding. He has served as Principal Investigator on $6M of federal grants within a total of $18M in federal grant funding. His service includes former editorship of the Journal of Educational Data Mining (2017-2022) and former leadership roles as Director/Associate Director of the Institute for Intelligent Systems (2006-2017). He currently coordinates both the Cognitive Science Graduate Certificate and Undergraduate Minor in Cognitive Science at the University of Memphis. His BrainTrust project, funded by the NSF, addresses the knowledge engineering bottleneck for intelligent tutoring systems through innovative approaches to virtual student simulation and knowledge representation.

Kourosh Davoudiمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Kourosh Davoudi is an Associate Professor of Computer Science at Ontario Tech University's Faculty of Science. He holds a PhD in Computer Science from York University with a focus on Machine Learning and Data Mining. Prior to joining Ontario Tech in 2019, he was a postdoctoral research fellow at the University of Waterloo's Department of Management Sciences. His research interests span Natural Language Processing, Deep Learning, Reinforcement Learning, Graph Mining, and Machine Learning. He actively supervises graduate students in these areas and teaches courses such as Data Mining and Artificial Intelligence. His research emphasizes practical applications of AI techniques in areas like outbreak detection, sentiment analysis, and automated grading systems. Recent work includes innovations in neural document segmentation, vision-language models, and hybrid outbreak detection using social media data. His publications consistently address challenges in algorithm design, explainable AI, and domain-specific NLP applications. Dr. Davoudi has contributed to conferences such as COLING, EMNLP, and IEEE transactions, focusing on interdisciplinary applications of machine learning. His work bridges theoretical advancements with real-world problems in healthcare, education, and social media analysis.

Jack Mostowمشاهده پروفایل

استاد پژوهشی

• Educational Technology
• Artificial Intelligence
• Natural Language Processing
• +۳ مورد دیگر

Jack Mostow is a Research Professor affiliated with the Human-Computer Interaction Institute at Carnegie Mellon University. His work focuses on educational technology, artificial intelligence, and natural language processing. He is renowned for developing Project LISTEN's automated reading tutor that listens to children's oral reading, integrating speech recognition and machine learning to improve literacy. His research addresses challenges in child literacy, automated assessment, and the application of AI in education. Key contributions include advancing speech recognition accuracy for children’s voices, designing adaptive tutoring systems, and exploring EEG and visual features to gauge learning states. He has contributed to global education initiatives, such as the XPRIZE field study, and developed tools for iterative improvement of intelligent tutoring systems. His work emphasizes scalable educational technologies for under-resourced regions and data-driven approaches to enhance learning outcomes. Mostow’s publications span over two decades, addressing topics like reinforcement learning for instructional policies, semi-supervised affect perception, and difficulty-controllable question generation. His research bridges AI, education, and human-computer interaction, aiming to create systems that adapt to learners’ needs while advancing computational methods for educational analytics.

Jakub Macinaمشاهده پروفایل

پژوهشگر ارشد

• Natural Language Processing
• Learning Sciences
• Dialogue Systems
• +۶ مورد دیگر

Jakub Macina is a Doctoral Fellow at the ETH AI Center and a PhD Candidate at ETH Zürich . He works in the intersection of Natural Language Processing and Learning Sciences as part of the Language, Reasoning and Education Lab (led by Prof. Mrinmaya Sachan) and the Professorship for Learning Sciences and Higher Education (led by Prof. Manu Kapur). Forbes 30 Under 30 in Science & Education 2023 Recipient of ETH AI Center Fellowship ( Co-founder of a health-tech startup with seed investment Research Interests : Focus on generative large language models (LLMs), dialogue tutoring systems , pedagogical alignment of AI models, and mathematical reasoning . His work explores: Reinforcement learning for pedagogical steering LLM evaluation frameworks Socratic question generation Stepwise error detection and remediation Student-teacher interaction modeling Publications span top conferences like EMNLP , ACL , NeurIPS , and RecSys , with particular emphasis on educational applications of LLMs and dialogue-based learning systems . Scientific Awards : Forbes 30 Under 30 in Science and Education (2023) 2nd Place in ACM IT SPY Computer Science Master's Thesis Competition (2017) ETH AI Center Fellowship (2021) Leadership & Teaching includes: Managing team of 6 data scientists Teaching Assistant for Machine Learning and NLP courses at ETH Zurich Developing large-scale ML pipelines for recommender systems Open-source contributions to Discourse and Google Summer of Code projects