جست‌وجوی استاد و پژوهشگر

Saleh Ashkboosمشاهده پروفایل

پژوهشگر

• Deep Learning
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Saleh Ashkboos is a Ph.D. student in the Computer Science Department at ETH Zurich, advised by Professors Torsten Hoefler and Dan Alistarh. He is also a Research Assistant at the Scalable Parallel Computing Lab and an affiliated doctoral student of the ETH AI Center. His research focuses on accelerating deep neural network training and developing systems for large-scale graph processing. Prior to ETH Zurich, he earned his Master's degree in Computer Science from Sharif University of Technology, advised by Professor Amir Daneshgar. His work has led to notable contributions, including the best paper award at SC22 for 'ProbGraph.' Recent research emphasizes efficient LLM training and quantization techniques, with publications on topics like 4-bit inference, quantization-aware training frameworks, and scalable meteorological modeling. He has interned at Apple and Microsoft, and his work is accessible via Google Scholar and GitHub. Key projects include GPTQ (post-training quantization for transformers), SliceGPT (LLM compression), and ProbGraph (high-performance graph mining). His technical contributions span distributed systems, neural network optimization, and climate-related machine learning.

• Artificial Intelligence
• Data Science
• Knowledge Graphs
• +۴ مورد دیگر

Yang Lei is an academic affiliated with the University of Melbourne's Department of Computing and Information Systems. Their research focuses on knowledge graphs, FAIR data principles, large language models (LLMs), and ontology engineering. They contribute to initiatives like the Open Research Knowledge Graph (ORKG) and NFDI4DataScience, emphasizing reproducibility, scholarly metadata, and systematic literature reviews. Recent work includes applications of LLMs for abstract summarization, leaderboard extraction, and entity recognition in scholarly documents. Yang also explores challenges in FAIR Digital Objects, machine-actionable workflows, and interoperability in research data management.

Christian Muiseمشاهده پروفایل

استادیار

• Artificial Intelligence
• Automated Planning
• Machine Learning
• +۶ مورد دیگر

Christian Muise is an Assistant Professor at Queen's University's School of Computing, part of the Faculty of Arts and Science. He holds a PhD (2014) in Artificial Intelligence from the University of Toronto, where he was advised by Sheila McIlraith and J. Christopher Beck. His research focuses on automated planning under uncertainty, combining planning with learning for applications like goal-oriented dialogue systems and multi-agent coordination. He previously held postdoctoral roles at the University of Melbourne's Agentlab and MIT's CSAIL, and was a Research Staff Member at the MIT-IBM Watson AI Lab. Education: PhD in Artificial Intelligence, University of Toronto (2014) MSc in Computer Science, University of Toronto (2009) BSc in Computer Science, Carleton University (2007) Research Interests: His work bridges automated planning and machine learning, emphasizing robustness in uncertain environments. Key areas include non-deterministic planning, model acquisition with large language models (LLMs), and human-aware planning. He explores applications in healthcare (e.g., treatment response prediction), robotics (autonomous navigation), and dialogue systems for safety-critical domains. Current projects include developing explainable planning systems and mitigating bias in AI decision-making. Awards: Scotiabank Scholar (Scotiabank Centre for Customer Analytics) Advising & Labs: Leads the Mu Lab, supervising PhD and Master's students in topics like model acquisition, dialogue systems, and planning bias. Active in open-source tools (e.g., L2P, MACQ library) to democratize planning research. Collaborates on projects like PRP Rebooted and FixMyPlan to advance FOND planning and LLM integration. Labs/Teams: Mu Lab at Queen’s University, focusing on planning under uncertainty, AI safety, and neuro-symbolic systems.

Els Lefeverمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۵ مورد دیگر

Els Lefever is an Associate Professor at Ghent University, where she works with the LT3 (Language and Translation Technology) research team. Her position focuses on computational linguistics and natural language processing research, with strong ties to both theoretical and applied aspects of language technology. Dr. Lefever earned her PhD in Computer Science from Ghent University in 2012 with her dissertation titled "ParaSense: Parallel Corpora for Word Sense Disambiguation." Her academic journey began as a computational linguist at the R&D department of Lernout & Hauspie Speech Products before transitioning to academia. Els Lefever's research spans multiple areas within computational linguistics with particular expertise in multilingual natural language processing. Her work focuses on computational semantics, cross-lingual word sense disambiguation, and multilingual terminology extraction. Recent research directions include automatic detection of irony in online text, argumentation mining in social media, sentiment analysis of financial news, language modeling for low-resourced languages, and computational approaches to Byzantine Greek epigrams. Her research demonstrates a consistent pattern of bridging theoretical computational linguistics with practical applications across diverse language domains and historical periods. Professor Lefever actively supervises PhD research on several cutting-edge topics including terminology extraction from comparable corpora, event extraction and sentiment mining of financial news, language modeling for low-resourced languages, argumentation mining in social media, and the automatic detection of links between Byzantine Greek epigrams. Her supervision portfolio demonstrates her commitment to advancing multiple frontiers of computational linguistics simultaneously. As an educator, Professor Lefever teaches courses in Terminology and Translation Technology, Language Technology, Localisation, Digital Text Analysis, and Digital Humanities. Her teaching reflects her research interests, providing students with both theoretical foundations and practical skills in language technology applications. The LT3 research group, where Professor Lefever is a key member, maintains strong connections with both academic and industry partners. The group has participated in numerous international conferences and shared tasks including SemEval competitions across multiple years, demonstrating consistent contributions to benchmark datasets and evaluation methodologies in natural language processing.

Yousra Aaferمشاهده پروفایل

استادیار

• Mobile and Smart Device Security
• System and Software Security
• Android Security
• +۴ مورد دیگر

Yousra Aafer is an Assistant Professor in the Department of Computer Science at the University of Waterloo. Her research focuses on mobile and smart device security, system security, and software security, particularly in the context of Android and cyber-physical systems. She holds a Ph.D. and M.Eng. from Syracuse University. Education: Ph.D., Syracuse University, United States (2016) M.Eng., Syracuse University, United States (2012) Her research interests include analyzing vulnerabilities in Android systems, binary analysis, IoT security, and fuzzing techniques. She explores methods to enhance security through formalized protocols, probabilistic protection recommendations, and leveraging large language models for vulnerability detection. Her work addresses critical areas such as cross-language buffer overflow detection, residual API audits in custom ROMs, and cyber-physical inconsistency in robotic vehicles. Her recent publications span topics like Android security, binary disassembly (e.g., D-ARM), and IoT security protocols (e.g., ProFactory). She has also contributed to frameworks like Poirot for probabilistic protection recommendations and StochFuzz for efficient binary fuzzing. While no awards are explicitly mentioned, her extensive publication record indicates active recognition in the security research community. She advises on multiple projects but no student names are listed here. Her work often involves collaboration on tools and frameworks for practical security applications.

• Artificial Intelligence in Healthcare
• Health Informatics
• Digital Health
• +۴ مورد دیگر

Professor Sandeep Reddy is a leading academic in healthcare management and digital health at Queensland University of Technology (QUT), where he heads disciplines in Healthcare Management, Health Information Management, and Health, Safety, and Environment. He co-leads the 'Health, Human Biology and MedTech' theme at QUT's Centre for Data Science. With a PhD in Healthcare Management and a medical degree, Reddy holds multiple professional fellowships, including the Australasian Institute of Digital Health and the UK Higher Education Academy. Roles: Professor, Academic Leader, Researcher, Editor Affiliations: QUT, WHO Digital Health Roster, European Academy of Translational Medicine Professionals His research focuses on AI integration in healthcare delivery, governance frameworks, and translational methodologies. Key areas include AI in chronic disease management, medical imaging, and facial recognition technology. Reddy has authored over 80 publications, including a 2024 textbook on translational AI applications. Reddy's work spans global health policy, having advised the WHO and participated in international panels. He teaches courses on health informatics, implementation science, and digital health, with over 20 years of academic and industry experience. Scientific awards and fellowships reflect his expertise: Fellow of AIDH, ACHSM, and Senior Fellow of the UK Higher Education Academy. His editorial roles include PLOS ONE, Computational and Structural Biotechnology Journal, and Frontiers in AI. Current research supervision focuses on AI in medical education, healthcare access, and explainability in AI systems. He advocates for equitable digital health solutions, addressing the rural-urban digital divide through empirical studies.

Stephan Schlöglمشاهده پروفایل

استاد

• Human-Computer Interaction (HCI)
• Artificial Intelligence
• Interaction Design
• +۶ مورد دیگر

Stephan Schlögl is a Full Professor at MCI - The Entrepreneurial School in Austria, leading research and teaching in Human-Computer Interaction (HCI), Artificial Intelligence (AI), and Information Systems. He holds editorial roles at journals like the Springer Discover Artificial Intelligence Journal and MDPI Multimodal Technologies and Interaction Journal. His work spans over two decades, with key roles including Postdoctoral Research Fellow at Télécom ParisTech (2012–2013) and PhD Researcher at Trinity College Dublin (2008–2012). Schlögl’s research focuses on HCI, AI-driven conversational systems, and assistive technologies for aging populations. Education includes a PhD in Computer Science from Trinity College Dublin, an MSc in Human-Computer Interaction from University College London, and a Mag.(FH) in Applied Informatics & Management from MCI. His teaching spans Software Engineering, Business Intelligence, and Research Methods. Research interests emphasize natural language interfaces, AI ethics, and technology’s societal impact. Notable projects include the EU-funded EMPATHIC initiative (2017–2021), developing an empathic virtual coach for elderly care, and the CRYSTAL project (2024–present) on conversational systems for emotional support. Schlögl has supervised numerous theses on AI applications, chatbots, and UX design. He co-organized major conferences like CUI (Conversational User Interfaces) and received awards for best papers in AI-HCI and CHIRA. His work bridges academia and industry, addressing challenges in AI adoption, digital well-being, and ethical technology design.

Barbara Carminatiمشاهده پروفایل

استاد

• Access Control
• Adaptable Collaboration
• Compatibility-driven
• +۱۶ مورد دیگر

Barbara Carminati is a Professor at the Department of Theoretical and Applied Science, University of Insubria, Italy. Her work focuses on security, privacy, and trust management in decentralized systems, particularly online social networks, IoT, and emerging technologies like blockchain and digital twins. She has contributed extensively to access control, risk assessment, and collaborative frameworks. Her research spans trust modeling , privacy-preserving mechanisms , and malware detection , with a strong emphasis on decentralized social networks and UAV security . She actively explores the application of blockchain for secure workflows and information sharing. Recent publications highlight her engagement with large language models for security optimization, IoT botnet detection , and metadata leakage analysis . Her work bridges theoretical foundations with practical implementations in cybersecurity, social network management, and edge computing. Contact: barbara.carminati@uninsubria.it

Xue Linمشاهده پروفایل

دانشیار

• Robust and Secure Machine Learning
• Deep Learning on the Edge
• Cyber-Physical Systems
• +۳ مورد دیگر

Xue Lin is an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a courtesy appointment in Khoury College of Computer Science. She joined Northeastern in 2017 and holds a PhD from the University of Southern California (2016) and a bachelor’s from Tsinghua University. Her research focuses on robust and secure machine learning, deep learning on edge devices, and cyber-physical systems. She leads the High Energy-Efficiency & Performance System Lab, which develops efficient algorithms and systems for applications like autonomous vehicles and medical AI. Dr. Lin’s work is supported by NSF, DARPA, and the U.S. Department of Transportation, among others. Notable achievements include a $1M DARPA grant for adversarial diagnosis systems, a 1st Place ISLPED 2020 Design Contest win, and multiple best paper awards. She has advised students such as Kaidi Xu (PhD’21), Mengshu, and Siyue, who have contributed to impactful projects like adversarial T-shirt attacks and FPGA-based DNN accelerators. Her research also addresses security in autonomous systems and inclusive design challenges for older and visually impaired passengers. Key grants include NSF CPS Small Awards, SaTC Medium Awards, and collaborations with institutions like the University of Maine and Michigan State University. Awards include the 2024 Faculty Fellow Award and recognition in Stanford’s top 2% cited scientists. Her lab’s projects span secure autonomous systems, energy-efficient inference frameworks (e.g., GRIM), and robust neural network verification techniques.

Fabio Crestaniمشاهده پروفایل

استاد

• Information Retrieval
• Text Mining
• Digital Libraries
• +۳ مورد دیگر

Fabio Crestani is a Full Professor of Informatics at the Università della Svizzera italiana (USI) since 2007, serving as Pro-rector for Internationalisation since March 2024. He previously held roles at the University of Strathclyde (UK) and conducted sabbaticals at institutions like UC Berkeley and Xerox PARC. His expertise spans Information Retrieval, Text Mining, and Digital Libraries, with over 250 publications and editorial leadership roles, including Editor-in-Chief of Information Processing and Management (2008–2015). Education: PhD and MSc in Computing Science, University of Glasgow (UK) Degree in Statistics, University of Padova (Italy) Research Interests: Advanced information access systems Conversational search and user interaction models Machine learning for text analysis Early risk prediction (e.g., mental health via social media) Grants & Collaborations: Funded by Swiss National Science Foundation, Hasler Stiftung, and EU projects. Collaborations with institutions in UK, Italy, Spain, USA, and Malaysia. Labs & Teams: Lead the Information Retrieval Group at USI, which focuses on distributed IR, personalization, and mobile information access. The group includes 10+ researchers and has produced influential work in top-tier venues like SIGIR and ACL.

Bowen Xuمشاهده پروفایل

استادیار

• Software Engineering
• Machine Learning
• Program Analysis
• +۱۲ مورد دیگر

Bowen Xu is an Assistant Professor in the Department of Computer Science at North Carolina State University (NC State), College of Engineering. His research focuses on software engineering, machine learning, and program analysis, particularly in securing AI models and improving code quality. He holds a PhD from Singapore Management University (SMU), where he also conducted postdoctoral research. Education: PhD in Computer Science, Singapore Management University (SMU) Postdoctoral Researcher, SMU School of Computing and Information Systems Research Interests: AI for Code, Backdoor Attacks on Code Models, Vulnerability Detection Code Representation Learning, Model Compression, Safety of AI Systems Chatbot Development for Developers, Automatic Code Review Key Contributions: Developed PTM4Tag+, a Stack Overflow tag recommendation system using pre-trained models Explored stealthy backdoor attacks in code and reinforcement learning systems Pioneered work on automatic vulnerability repair using LLMs and broader input analysis Awards: 2022: Honorable Mention Award (ACSAC) 2018: Highly Commended Full Paper Award (ESEM) Service Roles: Editorial Board Member, Empirical Software Engineering Journal Program Committee Co-chair for ICSE/FSE Research Tracks Organized workshops like FORGE, MaLTeSQuE, and SEA4DQ Labs & Teams: Leads the Softmax Lab at NC State, advising 12+ students across PhD, Master's, and undergraduate levels. Alumni include industry professionals at Microsoft, Barclays, and Marvell Semiconductor.

Alexander Miserlis Hoyleمشاهده پروفایل

پژوهشگر

• Computational Social Science
• Natural Language Processing
• Machine Learning
• +۶ مورد دیگر

Alexander Hoyle is a Researcher at the ETH Zürich AI Center, concurrently contributing to natural language processing/machine learning and social science groups. He holds a PhD in Computer Science from the University of Maryland (advised by Philip Resnik) and a Master's in Computational Statistics from University College London (advised by Sebastian Riedel and Jeff Mitchell). His research focuses on computational social science, emphasizing methods for latent construct identification (e.g., topic models, ideal point models) and evaluation frameworks grounded in validity. Key areas include bias/fairness in AI, political science applications, and mental health constructs like suicidality. He pioneered frameworks like PairScale (attitude measurement via pairwise comparisons) and TopicGPT (prompt-based topic modeling). Education: PhD in Computer Science, University of Maryland (2020-2023) MS in Computational Statistics & Machine Learning, University College London (2018) Bachelor's degree (pre-PhD details omitted) Research Interests: Combining NLP with social science needs, particularly in evaluation rigor and interpretability. Active in interdisciplinary work between NLP and computational social science (e.g., measuring attitude evolution on Reddit, improving topic model validity). Advocates for human-in-the-loop approaches to address LLM limitations in tasks like document clustering and sentiment analysis. Grants & Projects: Contributed to a landmark $2.2B DOJ settlement on NYC public housing via econometric modeling at The Brattle Group. Active in graduate labor advocacy (Maryland state legislature testimony) and mentorship (Científico Latino's mentorship program). Labs & Teams: Leads initiatives at the ETH Zürich AI Center, collaborating with groups like Microsoft Research (FATE) and AI2's AllenNLP. Involved in multi-university projects (e.g., University of Maryland's Computational Linguistics lab).

• Mixed Methods
• Digital Methods
• Social Media Research
• +۹ مورد دیگر

Hjalmar Alexander Bang Carlsen is an Associate Professor at the Copenhagen Center for Social Data Science (SODAS), Faculty of Social Sciences, University of Copenhagen. He specializes in mixed digital methods and is deeply engaged in research and teaching related to digital data analysis, particularly in the context of political and civic participation on social media. He is a key contributor to the Social Data Science master's degree program. University: University of Copenhagen School: Faculty of Social Sciences Department: Copenhagen Center for Social Data Science (SODAS) Position: Associate Professor in Mixed Digital Methods Email: hc@soc.ku.dk ORCID: https://orcid.org/0000-0002-2638-0932 His research centers on mixed methods strategies for digital data, with a substantive focus on civic and political engagement via social media. Key areas include informal volunteering during crises, gender inequality in online political participation, and the use of large language models (LLMs) for qualitative interviewing. He leads three major projects: SoMeVolunteer (on crisis volunteering), public participation on Facebook, and AInterviewer (an LLM-based interviewing tool). The recent publications reflect a strong trend in digital sociology, crisis response, and methodological innovation. His work combines large-scale social media data with surveys, interviews, and textual analysis, emphasizing ethical and epistemological considerations in computational social science. Topics span from refugee solidarity and pandemic volunteering to framing contests among climate NGOs and gender disparities in digital political engagement. While no formal scientific awards are listed in the provided text, Carlsen is actively funded by the Velux Foundation and UCPH Data+, and his work is widely disseminated through media and academic outlets. He collaborates closely with researchers like Jonas Toubøl and Snorre Ralund, and his projects often involve interdisciplinary teams. He has secured seed funding for innovative methodological development, indicating strong grant-writing capacity. He is involved in public engagement, with multiple media appearances discussing Danish civic response during the pandemic and refugee crises. His research outputs include journal articles, book chapters, and a co-authored textbook on mixed methods. He also participates in workshops and public lectures, contributing to both academic and public discourse on digital society. Carlsen is affiliated with SODAS and the Social Sciences Datalab, indicating active involvement in data-intensive research infrastructure. His work on AInterviewer suggests leadership in emerging AI-driven qualitative methods, positioning him at the forefront of digital social research innovation.

Maria Del Rio-Chanonaمشاهده پروفایل

استادیار

• Network Science
• Agent-Based Modeling
• Large Language Models
• +۷ مورد دیگر

Maria Del Rio-Chanona is an Assistant Professor in the Department of Computer Science at University College London (UCL), where she conducts interdisciplinary research at the intersection of network science, machine learning, and economic modeling. She is also a member of the External Faculty at the Complexity Science Hub since January 2025, reflecting her ongoing engagement with complex systems research. Her academic background includes a PhD in Mathematics from the University of Oxford, where she was part of the Institute for New Economic Thinking at the Oxford Martin School, and undergraduate studies in Physics at UNAM, Mexico. Her research interests center on understanding socioeconomic transformations using computational methods. She specializes in applying Large Language Models (LLMs), Agent-Based Models (ABMs), and network analysis to study the impacts of generative AI, the net-zero transition, and global crises like the COVID-19 pandemic on labor markets, economic resilience, and public discourse. Her work often involves analyzing large-scale digital trace data, including online labor platforms and social media, to uncover behavioral and structural shifts. The recent publications reveal a consistent focus on labor market dynamics in the face of technological disruption. Her work examines how generative AI reshapes demand for freelance skills, reduces public knowledge sharing on platforms like Stack Overflow, and influences mental health discourse in relation to employment decisions. She also investigates broader economic modeling, including pandemic shock propagation and employment transitions during decarbonization. Among her scientific recognitions is the Emerging Scientific Award from the Complex Systems Society in 2023. She has collaborated with international policy organizations such as the International Monetary Fund (IMF) and the International Labour Organisation (ILO), underscoring the policy relevance of her research. Maria Del Rio-Chanona has held prestigious research positions, including as a James S. McDonnell Foundation (JSMF) Postdoctoral Fellow at the Complexity Science Hub and a Visiting Fellow at the Growth Lab, Harvard Kennedy School. These roles have enabled her to lead and contribute to high-impact interdisciplinary projects. While no current advisees are listed, she actively engages with PhD opportunities and interdisciplinary research networks. She is involved in multiple research initiatives, including the development of HuBERT, an NLP algorithm for extending the Seshat Global History database, and projects studying labor transitions in net-zero scenarios. Her work on the HiST-LLM benchmark for historical knowledge in LLMs highlights her innovative integration of AI with social science.

Bradley J. Ericksonمشاهده پروفایل

استاد

• Quantitative Imaging
• Computer-Aided Diagnosis
• Deep Learning
• +۳ مورد دیگر

Bradley J. Erickson, M.D., Ph.D. Bradley J. Erickson is a Professor of Radiology and Consultant at the Mayo Clinic in Rochester, Minnesota. He holds a joint appointment in the Division of Biomedical Statistics and Informatics. His research focuses on quantitative imaging, computer-aided diagnosis, and deep learning applications in medical imaging. He has pioneered systems for team science integration across imaging, genomics, and clinical data. Education MD/PhD in Biophysics/Biomedical Engineering, Mayo Graduate School Residency in Diagnostic Radiology, Mayo Clinic Research Interests Erickson's work emphasizes extracting diagnostic and prognostic information from medical images using machine learning and AI. Key areas include: Polycystic Kidney Disease (PKD) progression analysis via imaging Development of explainable AI models for cancer diagnosis (e.g., hepatocellular carcinoma) Privacy-preserving LLMs for echocardiography reports Awards & Recognition Team Science Award, Mayo Clinic (2020) Samuel J. Dwyer III, Ph.D., FSIIM Memorial Lectureship (2013) Chair, American Board of Imaging Informatics (2013–2018) Grants & Projects Principal Investigator for NIH-funded projects on synthetic medical images in AI fairness (2024–2025) Mayo Translational PKD Center (2010–2020) Objective decision support for clinical trials (2012–2015) Labs & Affiliations He leads the Imaging and Analysis Core within the Mayo Clinic Pirnie Translational PKD Center. Collaborates with teams in computational biology, radiology informatics, and clinical trials.