جست‌وجوی استاد و پژوهشگر

Danfeng Wuمشاهده پروفایل

پژوهشگر ارشد

• syntax-semantics interface
• syntax-prosody mapping
• ellipsis
• +۸ مورد دیگر

Dr. Danfeng Wu is a Research Fellow at Magdalen College, University of Oxford, conducting interdisciplinary research in syntax-semantics and syntax-prosody mappings. Her work focuses on ellipsis, focus in coordination, and the empirical interplay between syntactic structure and prosody, employing experimental phonetic methods. She investigates languages like English and Mandarin Chinese to explore the cognitive and computational aspects of linguistic structure. Her research themes include phonetics, prosody, semantics, and syntax, with a particular emphasis on how syntactic configurations interact with prosodic phrasing and stress patterns. She has contributed to studies on corrective constructions, clitic climbing in Wolof, and vowel lengthening in Zulu, demonstrating a broad methodological approach combining theoretical linguistics with experimental and computational methods. Dr. Wu is affiliated with the Phonetics Lab and Language & Brain Lab within the Faculty of Linguistics, Philology & Phonetics. She has organized interdisciplinary events such as the Oxford workshop on AI language models and developed linguistics courses for computer scientists and cognitive scientists at MIT. Her work bridges formal linguistics with cognitive science and computational linguistics, emphasizing realistic models of language acquisition and evolution.

Giovanna Fassettaمشاهده پروفایل

مدرس ارشد

• Educational Inclusion
• Multilingualism
• Refugee Education
• +۳ مورد دیگر

Dr. Giovanna Fassetta is a Senior Lecturer in Social Inclusion at the University of Glasgow's School of Education, specializing in the Culture, Literacies, Inclusion & Pedagogy department. With over 20 years of teaching experience across Italy, Eritrea, and the UK, she focuses on inclusive education for migrant, refugee, and asylum-seeking youth, linguistic diversity, and crisis-affected contexts. She teaches on the MSc Education for Sustainable Future and MEd Inclusive Education programs, leading courses like Approaches to Inclusive Learning and Dissertation supervision. Research Interests: Her work explores multilingualism's role in inclusion, resilience in conflict zones, and decolonial pedagogies using qualitative and participatory methods. She emphasizes visual and arts-based methodologies, particularly in marginalized communities. Grants & Projects: Co-Investigator in Education in Conflict (2024) and LINEs4Palestine (2024) Principal Investigator of Welcoming Languages 2 (2023) and AHRC-funded projects on Palestinian Arabic education Contributor to the Culture for Sustainable and Inclusive Peace (CUSP) Network Teaching & Supervision: Supervises PhD students researching refugee education, trauma, and inclusion. Leads courses on inclusive pedagogy and dissertation methodologies. Additional Roles: Member of the School of Education Ethics Committee and co-convenor of Glasgow Refugee Asylum and Migration Network (GRAMNet).

Joshua Loftusمشاهده پروفایل

استاد

• Algorithmic Fairness
• Causal Inference
• Data Science
• +۴ مورد دیگر

Joshua Loftus is a Professor of Statistics and Data Science at the London School of Economics (LSE), Department of Statistics. His research focuses on improving data science practices to reduce bias and enhance fairness in algorithms, particularly addressing social harms and scientific reproducibility. He develops methods for statistical inference post-model selection and uses causality to analyze algorithm fairness and interpretability. His work bridges high-dimensional statistics, causal inference, and ethical AI, with a strong emphasis on practical applications using R in data science education. Before joining LSE, Loftus earned his PhD in Statistics at Stanford University, served as a Research Fellow at the Alan Turing Institute (affiliated with the University of Cambridge), and was an Assistant Professor at New York University (2017–2020). His research interests extend to the societal implications of technology, advocating for systems that prioritize human values over technical efficiency. Key research themes include counterfactual fairness, causal reasoning in algorithmic systems, and disaggregated interventions to reduce inequality. His recent work explores temporal aspects of fairness, model-agnostic auditing, and the integration of ethical frameworks into machine learning pipelines. While no scientific awards are explicitly listed, his contributions to foundational AI ethics and statistical methodology are widely recognized in academic circles. Advising and grant details are not provided in the source text, but his leadership in interdisciplinary research collaborations, such as the Turing Institute affiliation, highlights active engagement in research networks. Loftus is part of the LSE’s vibrant data science community, contributing to both theoretical advancements and applied solutions for equitable technology deployment.

Bernadette Bucherمشاهده پروفایل

استادیار

• Embodied AI
• Vision-Language Grounding
• Mobile Manipulation
• +۳ مورد دیگر

Bernadette Bucher is an Assistant Professor in the Robotics Department (primary) and Computer Science and Engineering Department at the University of Michigan. Her research focuses on embodied AI, vision-language grounding, and mobile manipulation, with an emphasis on interpretable visual representations and uncertainty estimation for robotics tasks. She previously worked at Boston Dynamics AI Institute, NVIDIA Research, and Lockheed Martin Corporation. Her academic background includes a PhD in Computer Science from the University of Pennsylvania (GRASP Lab) under advisors Kostas Daniilidis and Nikolai Matni, alongside MA degrees in Mathematics and Economics from the University of Alabama (2014). Research interests include robotics, computer vision, and machine learning intersections, particularly autonomous mobile manipulation. Her work emphasizes uncertainty-aware systems and deployable learning-based methods. Notable achievements include the Best Paper in Cognitive Robotics at ICRA 2024. Her research spans projects like EVORA for off-road autonomy and ASHiTA for hierarchical task analysis. She has contributed to open-source projects like RoboNet and actively publishes in top conferences (CVPR, CoRL, ICRA). Key projects: EVORA, ASHiTA, Vision-Language Frontier Maps (VLFM) Grants and funding: Honda Research Institute (Curious Minded Machines project) Labs/Teams: Active participation in robotics labs at University of Michigan and prior collaborations with industry partners

Gunel Jahangirovaمشاهده پروفایل

مدرس

• Software Testing
• Software Engineering for AI
• Search-Based Software Engineering
• +۶ مورد دیگر

Dr. Gunel Jahangirova is a Lecturer in Computer Science within the Department of Informatics, Faculty of Natural, Mathematical & Engineering Sciences at King's College London. Her research focuses on software testing, software engineering for AI, and search-based software engineering. She earned her PhD through a joint program at Fondazione Bruno Kessler (Italy) and University College London (UK), followed by postdoctoral work on the ERC-funded 'Precrime' project at Università della Svizzera italiana (Switzerland). Software Testing AI Engineering Search-Based Optimization Deep Learning Verification Her recent publications explore fault localization in neural networks, ethical testing of autonomous systems, and environmental impacts of AI code development. Current projects include ITEA GENIUS and ITEA GreenCode , focusing on AI testing and sustainable software practices.

Hassan Sajjadمشاهده پروفایل

دانشیار

• Natural Language Processing
• Deep Learning
• Safe and Trustworthy AI
• +۷ مورد دیگر

Hassan Sajjad is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He is also the Director of HyperMatrix, a research group focused on AI and deep learning. His work centers on Natural Language Processing, Safe and Trustworthy AI, interpretability, and robustness of language models. PhD - University of Stuttgart, Germany (2012) Masters - National University of Computer and Emerging Sciences, Pakistan (2007) BSc - National University of Computer and Emerging Sciences, Pakistan (2005) Hassan Sajjad's research focuses on making AI systems more interpretable, robust, and safe. He investigates how deep learning models, particularly transformers, encode linguistic and conceptual knowledge. His work spans language generation , model editing , interpretability , and generalization . He develops tools like NeuroX and NxPlain to analyze neuron-level behavior in NLP models. His research also extends to crisis informatics and multilingual NLP, especially Arabic and Urdu. The recent publications show a strong trend in analyzing and interpreting deep NLP models, with a focus on neuron interpretation , latent concept discovery , and model robustness . His work appears in top-tier venues like NeurIPS, ICLR, ACL, and EMNLP, indicating high impact in the AI and NLP communities. There is a clear emphasis on developing practical tools and frameworks for model analysis. No formal scientific awards are mentioned in the provided text. Hassan Sajjad mentors students and has fellowship opportunities available. While specific grants are not listed, his extensive publication record and leadership roles suggest active research funding. He advises students in AI, NLP, and deep learning, and collaborates widely across institutions. He leads the HyperMatrix research group at Dalhousie University, which focuses on AI, deep learning, and NLP. The group develops tools for model interpretation and works on safe and trustworthy AI. Collaborations extend to institutions like MBZUAI, NYU Abu Dhabi, and various international research centers.

• Federated Learning
• Optimization for Machine Learning
• Privacy-Preserving Machine Learning
• +۱۲ مورد دیگر

Sai Praneeth Karimireddy is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), with a courtesy appointment in the Ming Hsieh Department of Electrical and Computer Engineering. He previously held an SNSF postdoctoral fellowship at UC Berkeley under Michael I. Jordan and earned his PhD at EPFL advised by Martin Jaggi. He co-leads the Federated Learning and Data Quality working group at MONAI (NVIDIA) and collaborates with researchers at Apple Research. His research lies at the intersection of optimization, machine learning, statistics, and economics, with a strong focus on federated learning, privacy-preserving machine learning, data valuation, and AI for healthcare. He investigates how data quality, privacy, and incentives shape collaborative ML systems, especially in high-stakes domains like medicine. His work has been deployed at companies such as Meta, Google, OpenAI, and Owkin. His recent publications span top-tier venues including NeurIPS, ICML, ICLR, and JMLR, with influential contributions such as the SCAFFOLD algorithm for federated learning. His research shows a consistent trend toward building robust, private, and incentive-compatible collaborative learning systems, with increasing emphasis on real-world deployment in healthcare and decentralized data markets. 2023 SNSF Mobility Fellowship 2022 Patrick Denantes Memorial Prize for best thesis in computer science 2022 EPFL thesis distinction (top 8%) 2021 Chorafas Foundation Prize for exceptional applied research Capitol One Fellow (2025) He is actively mentoring PhD students and leads a research group focused on foundational and applied challenges in federated and privacy-preserving ML. He teaches graduate courses at USC, including CSCI 599 on Optimization for Machine Learning and CSCI 699 on Privacy-Preserving Machine Learning. He serves as an area chair for ICLR 2025 and co-organizes major workshops on incentives in data sharing and federated learning. His lab collaborates with institutions like NVIDIA, Apple, and Argonne National Laboratory, and he is building a research program centered on sustainable, equitable, and trustworthy AI ecosystems.

• Machine Learning
• Explainable AI (XAI)
• Deep Learning
• +۸ مورد دیگر

Haoyi Xiong is an active academic researcher in artificial intelligence, machine learning, and data science, with extensive publications in top-tier journals and conferences including IEEE TPAMI, NeurIPS, ICML, KDD, and AAAI. His work spans explainable AI, graph neural networks, diffusion models, remote sensing, and large language models. Research Interests: Explainable AI (XAI) and model interpretability Graph Neural Networks and contrastive learning Diffusion models and generative AI Medical and remote sensing image analysis Large language models and autonomous agents Learning to rank and web search His recent publications (2023–2025) show a strong trend toward self-supervised learning , model robustness , and integration of LLMs with structured data and knowledge graphs . He frequently collaborates with researchers from major tech and academic institutions. Scientific Awards: No explicit awards mentioned in the provided text. Advising and Grants: While no direct mention of students or grants, his role as a senior author on numerous papers suggests he advises graduate students and likely leads funded research projects in machine learning and AI. His work on frameworks like COLTR , GS2P , and MUSCLE indicates leadership in developing scalable AI systems. Labs and Teams: Though not explicitly stated, his frequent collaboration with Jiang Bian, Dejing Dou, and Dawei Yin suggests affiliation with a well-established AI research lab or industry-academia partnership focused on data mining, intelligent systems, and large-scale learning.

• Digital circuits and systems
• Electronic systems
• Modeling
• +۷ مورد دیگر

Alberto Macii is a Full Professor in the Department of Control and Computer Science (DAUIN) at Polytechnic University of Turin. He is also a member of the Interdepartmental Center IAM@PoliTo - Integrated Additive Manufacturing and serves on two colleges: College of Computer, Film and Mechatronics Engineering and College of Mechanical, Aerospace and Automotive Engineering. His research interests include: Digital circuits and systems Electronic systems, modeling, and simulation Energy-efficient circuits and systems Energy management and battery systems Embedded systems and cyber-physical systems Low energy building technologies His work aligns with several Sustainable Development Goals including affordable and clean energy (Goal 7), industry innovation and infrastructure (Goal 9), and sustainable cities and communities (Goal 11). His publication record shows a consistent focus on energy efficiency in electronic systems spanning over two decades, with particular emphasis on battery modeling and power management. Recent work has expanded into environmental applications with transformer neural networks for flood forecasting, demonstrating the evolution of his research into new application domains while maintaining core expertise in energy optimization. His scientific awards include: Best paper award ACM/IEEE Great Lakes Symposium on VLSI (2008) IEEE Fellow (2007-) Senior Member IEEE He has served as Editor-in-Chief for the Journal of Embedded Computing (2005) and as Program Chair for EUC2005: Embedded & Ubiquitous Computing. Professor Macii has advised PhD students including Alberto Bocca (2019-2023) whose thesis focused on compact modeling techniques for energy analysis and optimization of complex systems. He has led numerous research projects including CANP (2018-2020), R3-PowerUP (2017-2021), SERENA (2017-2020), AMable (2017-2021), and STAMP (2016-2019), demonstrating sustained research leadership across multiple funding mechanisms. He is active in the EDA - Electronic Design Automation research group and LAB 4 research laboratory at DAUIN, with research spanning VLSI-CAD, Smart City technologies, and Industry 4.0 applications. His work bridges theoretical computer engineering with practical applications in energy conservation across multiple domains.

Shweta Agarwalمشاهده پروفایل

استاد بالینی

• Pathology
• Head and Neck Pathology
• Cytopathology
• +۳ مورد دیگر

Shweta Agarwal is a board-certified clinical pathologist specializing in Head and Neck pathology and Cytopathology. Currently affiliated with Mayo Clinic in Jacksonville, Florida, she focuses on diagnosing salivary gland, thyroid, oral cavity, and sinonasal cancers. Her research integrates artificial intelligence for metastasis prediction and explores PD-L1 expression in rare tumors. 2018 Fellowship in Cytopathology - University of California San Francisco Medical Center 2017 Fellowship in Head and Neck Pathology - Massachusetts General Hospital 2016 Residency in Anatomic Pathology - University of Oklahoma Health Sciences Center 2011 Senior Residency in Oncopathology - Tate Memorial Hospital 2003 MBBS - GSVM Medical College, Kanpur University Her research bridges clinical pathology with computational tools, investigating salivary gland cancers, thyroid neoplasms, and oral squamous cell carcinoma. Key publications include AI-driven nodal metastasis predictions and PD-L1 analysis in rare cancers. Scientific awards include the 2025 Mayo Clinic 'Healing' recognition, 2022 Presidential Teaching Fellow nomination, and multiple best paper awards. She serves on committees for the College of American Pathologists and Papanicolaou Society of Cytopathology.

Santa Slokenbergaمشاهده پروفایل

دانشیار

• Medical Law
• Administrative Law
• Biobanking
• +۷ مورد دیگر

Santa Slokenberga, LL.D., serves as an Associate Professor in Medical Law and Senior Lecturer in Administrative Law at Uppsala University's Department of Law within the Faculty of Law. She maintains an active research profile focusing on the intersection of law, medicine, and technology, with particular expertise in biobanking, genetic privacy, and healthcare data governance. Her research interests span several critical areas in contemporary medical law, including the human genome and biobanking, AI applications in healthcare, privacy and personal data protection frameworks, quality of pediatric healthcare, and governance of rare diseases. Slokenberga's work demonstrates a strong focus on European regulatory frameworks and their implementation across different jurisdictions, particularly examining how GDPR interacts with medical research and biobanking practices. Analysis of her recent publications reveals a clear trajectory toward increasingly complex issues at the intersection of law, technology, and healthcare. Her scholarship consistently addresses regulatory challenges in genomic data sharing, European health data spaces, and the ethical implications of emerging technologies in medicine. Notably, she has contributed significantly to understanding how European data protection law applies to medical research contexts. Slokenberga holds leadership positions as a board member of EAHL and the Nordic Permed Law, reflecting her recognized expertise in health policy at both national and international levels. Her collaborative research projects often involve multinational teams, demonstrating her engagement with global health law challenges. Her academic supervision and teaching activities focus on preparing future legal professionals to navigate the complex landscape of medical law, administrative law, and data protection regulations as they apply to healthcare systems and biomedical research.

Pierre Marquisمشاهده پروفایل

استاد

• Artificial Intelligence
• Knowledge Representation
• Automated Reasoning
• +۶ مورد دیگر

Pierre Marquis is a distinguished Professor of Computer Science at Université d'Artois , affiliated with the Centre de Recherche en Informatique de Lens (CRIL-CNRS, UMR 8188) . Since December 2024, he has served as the vice-president for research and doctoral studies at Université d'Artois. His research focuses on Artificial Intelligence , particularly knowledge representation , automated reasoning , inconsistency handling , and knowledge compilation , with recent emphasis on Explainable AI (XAI) . Research Interests: Marquis's work spans foundational AI topics including abduction , induction , belief revision , and preference modeling . He has pioneered knowledge compilation techniques to optimize AI tasks and developed frameworks for reasoning under inconsistency through paraconsistent logics and argumentation. His EXPEKTATION chair (2020-2026) under France's national AI program drives his current focus on interpretable machine learning models. Scientific Awards: 2025: CNRS Silver Medal 2022: AAIA Fellow 2017: Senior Member of Institut Universitaire de France (IUF) 2009: EurAI (ECCAI) Fellow Doctoral Students: Mentoring Clément Lens (critical patient monitoring systems) and Mehdi Sabiri (data-knowledge integration for AI explanations). Collaborating with students like Louenas Bounia (formal XAI models) and Romain Wallon (pseudo-Boolean constraints). Grants & Projects: Leads the EXPEKTATION research chair (2020-2026) and participates in ANR PING/ACK (2019-2023), ANR THEMIS (2021-2025), CNRS IRP MAKC (2020-2024), and H2020 TAILOR (2020-2024). Previously led PIA4 MAIA (2023-2032) and Pint (2022-2023). Labs & Teams: Active in CRIL-CNRS, contributing to PyXAI (Python XAI library) and d4 (model counting), while mentoring teams on consensus belief merging and dynamic constraint processing .

Chaopeng Shenمشاهده پروفایل

استاد

• Hydrology
• Deep Learning
• Physics-Informed Machine Learning
• +۹ مورد دیگر

Chaopeng Shen is a Professor in the Department of Civil and Environmental Engineering at Pennsylvania State University. His research bridges hydrology with state-of-the-art deep learning and differentiable modeling techniques, focusing on advancing our understanding of hydrologic cycles and their interactions with ecosystems, energy, and carbon cycles. He leads the Multi-scale Hydrology, Processes and Intelligence group (MHPI) and has developed the Process-based Adaptive Watershed Simulator (PAWS) for large-scale hydrologic modeling. Shen's work emphasizes physics-informed machine learning , where deep learning components are integrated with process-based equations through differentiable modeling. This approach enables training neural networks using big data while respecting physical laws, leading to improved generalizability and robustness. His group has demonstrated advantages of differentiable models in rainfall-runoff prediction, routing, ecosystem modeling, and water quality studies. Notably, his team's deepLDB project addresses landslide prediction using AI and big datasets. Recent publications highlight his contributions to global water modeling (grid-LSTM, differentiable Muskingum-Cunge routing), extreme flood forecasting (probabilistic diffusion models), and hydrologic uncertainty quantification . Shen actively engages in interdisciplinary collaborations through the PRISM Cooperative Institute, which aims to integrate multi-domain data for systemic risk assessment. His group has advised students including Dapeng Feng, Wen-Ping Tsai, Kuai Fang, Xinye Ji, and Tasnuva Mahjabin. Shen's research is supported by the National Science Foundation (NSF), Department of Energy (DoE), USGS, Google.org, and the Gates Foundation. He serves as Editor for the Journal of Geophysical Research - Machine Learning & Computation and Chief Editor for Frontiers in Water: Water & AI. His open-source software tools like PAWS and deepLDB are available through dedicated project websites.

• Artificial Intelligence
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Zachi Attia, Ph.D., M.B.A., is an Associate Professor at the Mayo Clinic College of Medicine and Science, Rochester, Minnesota. His research focuses on applying artificial intelligence (AI) and machine learning to cardiac biosignals, particularly for early disease detection and prediction. He holds primary and joint appointments as Consultant in AI within the Department of Cardiovascular Medicine, collaborating with the Center for Digital Health and the Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery. Education: Ph.D. in Electrical Engineering from the University of Minnesota, Rochester; BSc and MSc in Electrical Engineering from Ben Gurion University, Israel. Dr. Attia's work centers on developing AI models that analyze multimodal cardiac data (ECGs, echocardiograms, angiograms) to detect silent diseases. His research includes pragmatic clinical trials to validate AI's impact on patient outcomes, explainable AI for biological insights, and integrating AI dashboards into medical records for clinical usability. Recent publications highlight AI applications in detecting atrial fibrillation, hypertrophic cardiomyopathy, and pulmonary hypertension via ECG analysis. Scientific Awards: No explicit awards mentioned in the text. Email: attia.itzhak@mayo.edu

• Terminology Applied to Translation
• Specialized Languages (English-Spanish)
• Corpus Linguistics
• +۳ مورد دیگر

Chelo Vargas Sierra is a Professor at the University of Alicante, affiliated with the Department of English Philology within the Faculty of Philosophy and Letters I (Philology). As a founding member and director since 2021 of the Interuniversity Institute of Applied Modern Languages (IULMA), she leads innovative research in terminology and translation technologies. Her academic background includes a PhD in Translation and Interpreting, a Master's in Audiovisual Translation (University of Cádiz), and a Master's in Terminology (Universitat Pompeu Fabra). PhD in Translation and Interpreting (University of Alicante) Máster en Traducción Audiovisual (University of Cádiz) Máster en Terminología (Institut Universitàri de Llingüística Aplicada) Her research focuses on terminology applied to translation, specialized languages (English-Spanish), corpus linguistics, and gender-sensitive terminology. She directs the DIGITENDER project (TED2021-130040B-C21) for digitizing multilingual terminological resources. She has participated in over 90 international conferences and chaired events like enTRetextos 2021 and AESLA 2016. Recent publications highlight trends in metaphor analysis in medical discourse, gender-sensitive terminology, and cross-linguistic sentiment studies in financial journalism. She chairs the Technical Standardisation Committee 191 for Terminology and has delivered 50+ international seminars for institutions like the European Union. Doctorate Extraordinary Award She serves on editorial boards of journals like Ibérica and Sendebar , contributes to translation technology training, and collaborates in international academic programs. Her work combines theoretical research with practical applications in translation technologies and terminological standardization.