جست‌وجوی استاد و پژوهشگر

Nata Stulovaمشاهده پروفایل

پژوهشگر

• Software Engineering
• Formal Methods
• Natural Language Processing
• +۳ مورد دیگر

Nata Stulova is a Researcher currently working as a staff research scientist at MacPaw's Technological R&D Center, focusing on software engineering research involving formal and informal program specifications. Formerly, she held academic roles including senior postdoctoral researcher at the University of Bern (SCG), researcher at EPFL's LARA Lab, and PhD work at the Technical University of Madrid (UPM). Her research spans software documentation consistency, NLP applications, runtime verification, and low-code tools. She has led multiple projects addressing code comment quality, empirical studies on documentation practices, and tool development for requirements engineering. Despite transitioning to industry in 2022, she maintains active collaboration with academia, publishing in top conferences like ICSE and IEEE Transactions. She holds a PhD in Computer Science from UPM and has authored numerous peer-reviewed articles and conference papers. Her work emphasizes bridging academic research with industry applications, particularly in software analysis and developer productivity. Education: PhD in Computer Science, Technical University of Madrid (2018) MSc in Artificial Intelligence, Technical University of Madrid (2013) BSc in Applied System Analysis, NTUU "KPI" (2012) Research Interests: Her work focuses on improving software quality through automated documentation tools, runtime verification techniques, and low-code solutions for requirements engineering. She explores NLP methods for detecting inconsistent or outdated comments and optimizing static analysis efficiency. Recent projects include SwiftEval (LLM-generated code evaluation) and RepliComment (cloned comment detection). She also emphasizes practical applications of formal methods in dynamic languages like Prolog and Python. Grants & Funding: ASA: Agile Software Assistance TRACES: Resource-Aware Software Tools N-GREENS: Energy-Efficient Software Labs/Teams: Core member of the Ciao system development team at IMDEA Software Institute, contributor to the Software Composition Group (SCG) at UniBe, and collaborator with MacPaw's TR&D team on industry-focused R&D.

• Theorem Proving
• Runtime Verification
• Automated Reasoning
• +۳ مورد دیگر

Giles Reger is a Lecturer in the Formal Methods Group within the School of Computer Science at the University of Manchester. His primary roles include research and teaching in theorem proving, runtime verification, and formal methods. He has been actively involved in projects such as the Centre for Digital Trust and Society and the SCorCH project focusing on secure code verification. Educational background: Giles holds a BA in Computer Science from the University of Cambridge (2009), an MSc in Advanced Computer Science from the University of Manchester (2010) with the Highest Achiever of the Year Award, and a PhD from the University of Manchester (2014) titled Automata based monitoring and mining of execution traces . Research interests focus on two core areas: theorem proving (quantifier reasoning, first-order theories, collaborative proof search, and integration of SAT/SMT solvers) and runtime verification (temporal specifications, monitoring algorithms, violation explanation, and benchmarking). His work emphasizes practical applications in tool development and real-software analysis. Notable awards include the ACM SIGSOFT Distinguished Paper Award (2024) for his paper on LLM-generated invariants. His research contributions span over 50 publications, with active involvement in funded projects like SCorCH (Secure Code for Capability Hardware) and the Centre for Digital Trust and Society. Advising and grants: Reger welcomes PhD/MSc students in theorem proving, program verification, and related fields. He has supervised multiple research students and leads collaborative projects involving international researchers. His lab, the Formal Methods Group, develops tools like the Vampire theorem prover and contributes to runtime verification frameworks.

Sebastian Schusterمشاهده پروفایل

مدرس

• Computational Linguistics
• Natural Language Processing (NLP)
• Large Language Models Evaluation
• +۵ مورد دیگر

Sebastian Schuster is a Lecturer in Computational Linguistics at University College London (UCL), effective January 2024. He will transition to the University of Vienna in mid-2025 to lead a WWTF-funded research group. Previously, he held postdoctoral positions at Saarland University and NYU (via the 2020 Computing Innovation Fellowship), and completed his PhD in Linguistics at Stanford University, affiliated with the ALPS Lab and Stanford NLP Group. His research focuses on evaluating large language models (LLMs), computational semantics, and pragmatics. Key themes include improving NLU systems through entity tracking analysis, understanding scope ambiguities, and modeling pragmatic inferences. Recent work explores how pretraining affects LLM capabilities and how context influences scalar implicature processing. Notable contributions include datasets like SIGA for scalar implicatures and SpreadNaLa for code generation evaluation. He has presented at ETH Zurich, Oxford, and served as Area Chair for *SEM2023's psycholinguistics track. His fellowship and invited talks reflect recognition of his interdisciplinary work bridging computational and experimental linguistics. Professional activities include contributions to Universal Dependencies projects, software tools like Open Linguistics, and collaborations on child language development. His work integrates methodologies from NLP, experimental psychology, and cognitive science to advance both theoretical and applied linguistic research.

Niklas Elmqvistمشاهده پروفایل

استاد

• Data Visualization
• Immersive Analytics
• Human-Computer Interaction
• +۳ مورد دیگر

Niklas Elmqvist is a Professor in the Department of Computer Science at Aarhus University. His research focuses on immersive analytics, data visualization, and human-computer interaction, emphasizing user-centered design and interdisciplinary approaches. He leads projects on interactive visualization systems, collaborative platforms, and accessibility in data analysis. Key research areas include agentic visualization systems, attention-aware interfaces, and ubiquitous analytics environments. He explores novel interaction techniques, such as bimanual gestures and multimodal feedback, to enhance data exploration experiences. His work bridges computer science with domains like epidemiology, cybersecurity, and creative writing. Elmqvist has contributed to influential platforms like DashSpace (collaborative immersive analytics) and Datamancer (gesture-driven analytics). He investigates challenges in accessible visualization for visually impaired users and the use of large language models for automated design feedback. His recent work addresses human-centered AI integration in visualization tools and the ethics of automated decision-making systems. He collaborates internationally on projects such as Riverside (cybersecurity visualization) and Lodestar (data science workflow recommendations). His research has been published in top journals like IEEE Transactions on Visualization and Computer Graphics, emphasizing both theoretical advancements and practical system implementations.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۶ مورد دیگر

Shwai He is a PhD student and Affiliate Assistant Professor at the University of Maryland, advised by Ang Li. Their research focuses on advancing AI systems through innovative approaches in large language models (LLMs), fairness in machine learning, and efficient neural network architectures. Key areas include multi-agent systems, causal modeling for bias mitigation, and optimization techniques for mixture-of-experts models. Research interests span artificial intelligence, machine learning, and natural language processing with emphasis on practical applications like healthcare diagnostics and social pairing systems. Notable work includes developing GNWT-based multi-agent digital twins for social platforms and improving LLM transparency through token analysis. Publications highlight contributions to counterfactual fairness, dynamic-depth transformers, and parameter-efficient methods. Current efforts explore computational efficiency in vision-language models and bio-inspired antibody prediction systems. No scientific awards have been mentioned. Advising and grants: Currently a PhD student under Ang Li's supervision. Research involves collaborations across computer science and bioinformatics domains.

Christine Herlihyمشاهده پروفایل

دانشیار مدعو

• Artificial Intelligence
• Machine Learning
• Health Informatics
• +۵ مورد دیگر

Christine Herlihy holds the academic rank of Adjunct Associate Professor in the Department of Computer Science at the University of Maryland. She is also a PhD Candidate advised by John Dickerson, located in IRB 2108. Her research focuses on algorithmic fairness, health informatics, and natural language processing with applications in socially consequential domains like public health and agriculture. She explores optimization techniques such as restless bandits and develops tools for scientific model augmentation, including a Julia package named SemanticModels.jl. Education: Christine is pursuing her PhD in Computer Science at the University of Maryland under the guidance of John Dickerson. Earlier academic qualifications are not detailed in the provided information. Research Interests: Christine’s work bridges computer science and societal impact. She investigates how AI systems can improve health literacy through personalized smartphone applications and ensure fairness in algorithmic decision-making. Her studies also address challenges in clinical NLP, resource allocation for small farmers, and longitudinal analysis of LLM data contamination. She advocates for transparent and equitable algorithmic frameworks in domains ranging from healthcare to policy evaluation. Advising & Grants: Christine is advised by John Dickerson in her doctoral studies. No grants or independently advised students are explicitly listed in the provided materials.

Julia Mendelsohnمشاهده پروفایل

استادیار

• Computational Linguistics
• Political Communication
• Sociolinguistics
• +۶ مورد دیگر

Julia Mendelsohn is an incoming Assistant Professor at the University of Maryland's College of Information and Department of Government and Politics, starting August 2025. She is currently a postdoctoral scholar at the University of Chicago Data Science Institute. Her research focuses on the intersection of language, politics, and computation, with expertise in computational linguistics, political communication, and sociolinguistics. Education: PhD in Information (University of Michigan), BA in Linguistics and MS in Computer Science (Stanford University) Her research explores computational models of political rhetoric, dehumanization in discourse, and the societal impacts of language technologies. Notable projects include analyzing framing in immigration debates, Russian wartime media, and developing tools to combat antisemitism. Recent work includes studies on metaphor usage in immigration discourse (2025), AI agent truthfulness trade-offs (2024), and the role of multilingual users in social media (2023). She has been recognized with awards such as the Google PhD Fellowship and the 2023 Outstanding Methodology Award. Awards: Google PhD Fellowship, NSF Honorable Mention, ICWSM Outstanding Methodology Award (2023) Julia is actively recruiting PhD students for Fall 2025, emphasizing interdisciplinary approaches in NLP, computational social science, and political communication. She collaborates with research centers like the Computational Linguistics and Information Processing (CLIP) Lab and the Global Elections and Information Security (GEIS) group.

Meiyappan Nagappanمشاهده پروفایل

دانشیار

• Empirical Software Engineering
• Mining Software Repositories
• Software Security
• +۳ مورد دیگر

Academic Profile Dr. Meiyappan (Mei) Nagappan is an Associate Professor at the University of Waterloo's Cheriton School of Computer Science. He holds a Ph.D. (2011) and M.Sc. (2008) from North Carolina State University, and a B.Sc. from Anna University, India (2006). Research Focus His research spans empirical software engineering, mining software repositories, and mobile app store analysis. Current investigations focus on software security, static analysis techniques, and diversity in software engineering. Recent work critically examines AI programming assistants like GitHub Copilot, evaluating their performance, security implications, and impact on developer workflows. Key Research Areas Diversity analysis in open-source contributions and academic publishing Vulnerability detection using machine learning Bug localization and automated debugging Cross-platform app analysis Publications Research demonstrates strong focus on empirical validation of developer tools, with recent emphasis on AI-assisted programming. Work appears in top software engineering venues covering security, testing, and developer productivity.

Sirisha Rambhatlaمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Representation Learning
• +۴ مورد دیگر

Sirisha Rambhatla is an Assistant Professor at the University of Waterloo, holding appointments in the Management Sciences Department (Faculty of Engineering), Systems Design Engineering, and the David R. Cheriton School of Computer Science (Faculty of Mathematics). She leads the CriticalML lab, focusing on reliable AI for healthcare, aviation, and climate change, leveraging deep learning and spatiotemporal analysis. Her work bridges theory and practice, with publications at top venues like NeurIPS, ICLR, and clinical journals. Affiliations include the Waterloo AI Institute and the Waterloo Institute for Sustainable Aeronautics. Education: Ph.D. and M.S. in Electrical Engineering, University of Minnesota (2019, 2012) B.Tech in Electronics and Telecommunication Engineering, College of Engineering Roorkee (2010) Research Interests: Representation Learning, Spatiotemporal Data Analysis, AI for Healthcare, Computer Vision, and provable algorithms. Her work emphasizes interpretable AI and applications in surgery, intelligent automation, and climate modeling. Awards: 2021 Merit Award for Excellence in Postdoctoral Research E. Bruce Lee Memorial Fellowship University Bronze Medalist (2010) Teaching: Courses include Advanced Machine Learning (MSCI 546/MSE 546), Big Data Analytics (MSCI 623), and Special Topics in Management Engineering (MSCI 598/MSE 598). Labs/Teams: Leads the CriticalML lab, focusing on trustworthy AI systems for critical applications.

• Compiler Testing
• Program Analysis
• Static Binary Taint Analysis
• +۵ مورد دیگر

Chen Sun is an Adjunct Assistant Professor at the Department of Data and Systems Engineering, The University of Hong Kong. His research focuses on compiler testing, program analysis, static binary taint analysis, and software engineering methodologies. Notable areas include leveraging large language models (LLMs) for compiler testing baselines, probabilistic delta debugging, and enhancing program reduction techniques across multiple languages. Recent work addresses ransomware defense mechanisms, fuzzing countermeasures, and database management system (DBMS) bug discovery through configuration transformations. Research interests span compiler optimization, fault localization, cybersecurity, and algorithmic efficiency. His publications from 2023-2025 highlight trends in combining AI with traditional software engineering challenges, such as improving type inference for Java code and developing syntax-guided program reduction frameworks. Articles frequently address practical security concerns like protecting embedded devices from protocol fuzzing and mitigating memory leaks through sanitizer-based localization. No scientific awards are explicitly mentioned. Advising and grants sections remain underdeveloped in available data. Current affiliations emphasize academic-industrial collaboration through adjunct roles focused on cutting-edge software reliability and security research.

• Computational Sustainability
• Cloud Computing
• Edge Computing
• +۳ مورد دیگر

Ivona Brandic is a Full Professor of High Performance Computing Systems at TU Wien's Institute of Information Systems Engineering, leading the HPC Research Group. She specializes in Computational Sustainability, Cloud/Edge Computing, and Quantum-Classical Systems. Her roles include Head of the Computational Sustainability Research Unit and membership in TU Wien's Faculty Council. She teaches courses such as AI/ML in Climate Change and Hybrid Quantum-Classical Systems. Her research focuses on sustainable IT, energy-efficient systems, and hybrid quantum-classical workflows. Projects include computational sustainability initiatives funded by the Austrian Science Fund (FWF) and industry partnerships like the Virtual Shepherd project. She has contributed to over 50 publications, emphasizing edge computing, quantum algorithms, and HPC optimization. Notable contributions include developing frameworks like RIGOLETTO for hybrid scientific workflows and FRESCO for edge offloading. Her work bridges theoretical advancements with practical applications in environmental monitoring and energy efficiency.

Thomas Bäckمشاهده پروفایل

استاد

• Artificial Intelligence
• Evolutionary Computation
• Machine Learning
• +۶ مورد دیگر

Thomas Bäck is a Full Professor at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, and Chief Scientist at NORCE Norwegian Research Center, Norway. His career spans roles as Director of the Center for Applied Systems Analysis (Germany) and Vice Scientific Director at LIACS (since 2017). He holds additional positions like Adjunct Professor at the University of Calgary and Visiting Professor at Xi'an Jiaotong University. Bäck's research focuses on evolutionary computation, machine learning, and their applications in sustainable smart industry and health. He leads interdisciplinary initiatives like the Society, Artificial Intelligence, and Life Sciences (SAILS) program, with expertise in natural computing, data-driven optimization, and quantum computing challenges. IEEE Fellow (2022) Member of KNAW (2021) IEEE CIS Pioneer Award (2015) Fellow of International Society of Genetic and Evolutionary Computation (2003) Best PhD Thesis Award from German Society of Computer Science (1995) Bäck has graduated 22 PhD students and currently supervises 14, with 62 MSc students mentored. His editorial roles include Editor-in-Chief of the Evolutionary Computation Journal and Associate Editor for multiple journals. He has secured grants from the Dutch Research Council and The Research Council of Norway for projects like ECOLE and CIMPLO.

• Medical Statistics
• Artificial Intelligence in Healthcare
• Machine Learning
• +۳ مورد دیگر

Lei Clifton is a Research Fellow and Official Fellow in AI & Machine Learning at Reuben College, University of Oxford. She leads a team of researchers in the Nuffield Department of Population Health and contributes to the Pandemic Science Institute’s preparedness efforts. With a PhD in Statistical Machine Learning (2007, University of Manchester), her expertise spans large-scale clinical data analysis and healthcare AI deployment. She advises the NIHR Research Support Service on study design and collaborates with Prof David Clifton’s 'AI for Healthcare' CHI Lab. Her research focuses on medical statistics and AI applications in observational studies and electronic health records, including synthetic data generation, immune protein biomarker discovery, and scalable AI implementation in healthcare systems. She co-leads projects like the Implementation framework for AI deployment and dysregulated immune protein prediction in Multiple Myeloma. Key areas of contribution include healthcare AI ethics, cross-institutional model validation, and LLM applications in medicine. Her work bridges statistical rigor with real-world clinical challenges, emphasizing generalizability and fairness in machine learning systems.

Antti Oulasvirtaمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• User Interfaces
• Human Factors
• +۴ مورد دیگر

Antti Oulasvirta is an Associate Professor at Aalto University, leading the User Interfaces research group. He previously held positions at the Max Planck Institute for Informatics and the Helsinki Institute for Information Technology (HIIT). His doctoral degree in Cognitive Science from the University of Helsinki (2006) was followed by a Fulbright Scholarship at UC Berkeley (2007-2008). He has received the ERC Starting Grant (2015-2020) for computational user interface design research. Education: Doctorate in Cognitive Science from the University of Helsinki (2006), postgraduate studies at UC Berkeley’s Neuropsychology Lab (2002-2003), and an internship at T-Labs, Berlin (2006). Research Interests: Focus on human-computer interaction, user interfaces, and computational modeling of human behavior. His work bridges HCI with cognitive science, emphasizing optimal decision-making and interaction design. Key areas include reinforcement learning agents, eye movement control, and human-AI collaboration. Recent Articles: Focus on LLM limitations in problem reframing, touchscreen typing errors, and task-driven eye movement models. His work integrates computational rationality to explain and enhance human-computer interactions. Scientific Awards: Multiple CHI Best Paper Awards (12x between 2008-2018), ERC Starting Grant, and recognition at major conferences like NordiCHI and CoDIT. His research contributes to UN Sustainable Development Goals through inclusive and efficient interface design. Advising & Grants: Principal investigator on projects funded by EU Horizon Europe, Academy of Finland, and Business Finland. Leads initiatives like the Finnish Center for Artificial Intelligence (FCAI) and the Artificial User project. Supervises numerous theses and mentors researchers globally. Labs & Teams: Director of the User Interfaces group at Aalto, collaborating internationally on HCI and AI. Active in organizing conferences (e.g., ACM SIGCHI) and reviewing for journals like the International Journal of Human-Computer Studies.

• Computing Education Research
• Educational Technology
• Large Language Models in Education
• +۳ مورد دیگر

Arto Hellas is a Senior Lecturer at the Department of Computer Science, University of Helsinki, where he leads the Lecturer Hellas Arto group. His work focuses on computing education research (CER) and educational technology, particularly leveraging large language models (LLMs) to enhance programming education and automated assessment tools. He also explores web development optimization through edge computing and serverless architectures. Research Interests: Hellas's research spans the integration of AI into education, programming pedagogy, and educational technology. He investigates how LLMs can generate feedback, analogies, and exercises for students, as well as their impact on MOOCs and classroom dynamics. His work addresses challenges in student performance analysis, synthetic data generation for educational purposes, and the ethical implications of AI in learning environments. His studies on web development emphasize distributed systems and edge computing applications to improve web service delivery. Scientific Awards: ICER 2022 Best Paper Award for work on LLM-generated programming exercises SIGCSE TS 2022 Best Paper Award in Computing Education Research UKICER 2023 Best Paper Award Advising & Grants: Hellas currently leads the CoCoAI: Human-Centered AI for Computer Science Education project (2025–2027), focusing on personalized learning through LLMs. He has supervised 2 theses but no student names were explicitly provided. His grants and projects highlight contributions to educational technology, including datasets like the IDE Action Log Dataset and Morning or Evening? circadian rhythm study. Labs & Teams: Active within the Lecturer Hellas Arto group, his collaborations span computing education, AI ethics, and web development. His team's work has been featured in media, such as a 2023 article discussing AI's role in developing a chess website in days.