جست‌وجوی استاد و پژوهشگر

• Human-Computer Interaction
• Accessibility
• User Interface Design
• +۵ مورد دیگر

Junko Shirogane is an Associate Professor in the Department of Computer Science and Engineering at Waseda University's School of Fundamental Science and Engineering. With over 25 years of academic research spanning from 1998 to 2024, she has established herself as a prominent researcher in human-computer interaction, accessibility, and user interface design. Her extensive publication record demonstrates consistent contributions to the fields of software engineering and human-computer interaction. Dr. Shirogane's research interests focus on Human-Computer Interaction, Accessibility, User Interface Design, Web Accessibility, Software Engineering, and Requirements Engineering. Her work particularly emphasizes designing systems that accommodate users with visual impairments and developing methods to evaluate and improve software usability. She has pioneered approaches for analyzing webpage layouts, visualizing importance areas, and converting color schemes for color-impaired users. Her research bridges theoretical software engineering concepts with practical applications that directly impact user experience. Her publication trends reveal a strong focus on accessibility challenges, with recent work (2020-2024) concentrating on supporting visually impaired users through innovative techniques like reading voice modification, webpage validation visualization, and text highlighting methods. Earlier work (2014-2019) focused more on GUI consistency, scenario generation, and requirements engineering. The breadth of her research spans from fundamental software engineering principles to applied user interface solutions. Dr. Shirogane has maintained a long-standing research collaboration with Yoshiaki Fukazawa (27 joint publications), Hajime Iwata (22 joint publications), and other researchers at Waseda University. This collaboration network has produced numerous publications in prestigious venues including IEICE Transactions on Information Systems, IEEE Access, and major conferences like JCKBSE, ICSOFT, and IUI.

Jurriaan Hageمشاهده پروفایل

استاد

• Static Analysis
• Type Error Diagnosis
• Functional Programming
• +۵ مورد دیگر

Jurriaan Hage is a Full Professor and Head of the Department of Computer Science at Heriot-Watt University, serving concurrently as Deputy Executive Dean of the School of Mathematical and Computer Sciences. His academic journey includes a PhD in Computer Science from Leiden University under the supervision of Grzegorz Rozenberg and Tero Harju, focusing on switching classes of graphs. While currently focused on administrative leadership, his research expertise spans static analysis, type error diagnosis in functional languages, and program plagiarism detection. He is actively involved in academic services, including steering committees for IFL and TFP symposia, and maintains the Helium compiler for Haskell. Education: Studied computer science at Leiden University, completing a PhD on graph switching theory. Research Interests: Static analysis methodologies, type systems, functional programming, and software architecture. Notable contributions include advancements in type error diagnosis for Haskell and compiler design. Key Projects: Funded research includes DOMSTED (type error diagnosis), Higher-Ranked Polyvariance, and ServiciFi (software modernization). His work emphasizes practical applications in legacy system analysis and compiler tooling. Grants: NWO-funded projects on type error diagnosis and software architecture. Advising: Supervises MSc students on programming language topics. Teams: Maintains the Helium compiler, contributes to Haskell Prime Committee.

Darrell Worthyمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Decision-Making
• Categorization
• +۱۱ مورد دیگر

Dr. Darrell Worthy is an Associate Professor in the Department of Psychology at Texas A&M University, affiliated with the Neuroscience & Personality Processes research cluster. His work integrates behavioral, computational, and neuroscience methods to study learning and decision-making. Key research areas include reinforcement learning, decision-making under uncertainty, and the impact of clinical issues like substance abuse and depression on cognitive processes. He explores both basic cognitive mechanisms and individual differences, bridging sub-disciplines through neuroscientific approaches. His research employs formal mathematical models and fMRI to test hypotheses about cognitive processes. Notable contributions include studies on frequency-based decision-making, the inverse base-rate effect, and the effects of acute stress on decision strategies. He is actively involved in mentoring students, with current openings for 2024-2025 academic year. Scientific achievements include the 2016 Best Article award in Cognitive, Affective, and Behavioral Neuroscience. His lab (Worthylab) focuses on strategic decision-making models, with recent work on reward segmentation and category learning. Collaborative projects address topics like zoonotic disease communication and dopamine's role in decision-making.

Elsa L Gunterمشاهده پروفایل

استاد پژوهشی

• Formal Methods
• Programming Languages
• Software Engineering
• +۷ مورد دیگر

Elsa L Gunter is a Research Professor and Senior Lecturer at the University of Illinois at Urbana-Champaign's Department of Computer Science. Her academic background includes a Ph.D. in Mathematics from the University of Wisconsin, Madison. She leads research in formal methods, programming languages, and human-computer systems. Research Interests: Her work spans formal verification, programming language semantics, automated theorem proving, and security. She develops tools for compiler optimization verification, human-automation system safety, and concurrent program analysis. Key projects include the VeriF-OPT framework for parallel program transformations and Tutela for human-computer system protection analysis. Publications Focus: Her recent research emphasizes compiler verification, concurrency models, and human-system interaction. Work includes symbolic analysis for CSP, dependently-typed session systems, and robustness verification for safety-critical interfaces. Awards: Most Influential 10 Year Paper award at Requirements Engineering (RE 2010) EASST Best Software Science Paper at ETAPS 2001 Best Paper award at Fourth International Conference on Requirements Engineering (2000) Funding and Labs: Secured NSF grants for projects on parallel program verification ($450K) and human task analysis ($500K). Leads the Formal Methods and Verification Lab, collaborating with NASA on NextGen aviation systems. Student Advising: Mentored 7 PhD graduates and 12+ Master's students. Current PhD candidates work on secure distributed programming (Dennis Griffith) and formal methods for concurrent systems (Liyi Li, Susannah Johnson).

• Educational Psychology
• Computer-Based Assessment
• Digital Learning Environments
• +۹ مورد دیگر

Prof. Dr. Marlit Annalena Lindner is Professor for Digitisation and Education at the University of Tübingen and Head of the Leibniz Research Group Digitisation and Education at the Leibniz-Institut für Wissensmedien (IWM). She leads cutting-edge research on digital learning and assessment, focusing on computer-based formative assessment, feedback design, and multimedia testing environments in schools and higher education. PhD in Psychology, Christian-Albrechts-University Kiel Head of Leibniz Junior Research Group COMET (2019–2023) Research visits at ETS Princeton and UCSB Funded research via Leibniz SAW competition Her research lies at the intersection of cognitive and motivational psychology in educational contexts, particularly in how digital feedback and multimedia design affect learning, engagement, and test performance. She employs advanced methodologies such as eye tracking, process data analysis, and micro-longitudinal designs to uncover real-time learning dynamics. Her recent publications reveal a strong trend in optimizing digital assessment through evidence-based design principles. Key themes include the affective impact of feedback, the role of visual representations in testing, time-of-day effects on disengagement, and the potential of generative AI in education. Her work bridges theory and practice, aiming to improve both the validity and user experience of digital assessments. Funding for Leibniz Junior Research Group COMET via Leibniz SAW competition She actively mentors doctoral researchers and leads a dynamic team at IWM. Her work includes advising on exam didactics, organizing national workshops on digital university assessments, and exploring the implications of AI tools like ChatGPT in educational settings. She collaborates internationally and contributes to shaping policy through expert statements, such as on large language models in education. She leads the research lab on digital assessment at IWM, focusing on process data, multimedia effects, and feedback design. Her team integrates cognitive science with educational technology to innovate assessment practices in the digital age.

Amey Karkareمشاهده پروفایل

استاد

• Compilers
• Functional Programming
• Program Analysis
• +۳ مورد دیگر

Amey Karkare is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur). He holds a PhD from IIT Bombay and has been affiliated with IIT Kanpur since 2010, progressing through academic ranks to his current role since 2021. Education: PhD, IIT Bombay (2003-2008); B.Tech., IIT Kanpur (1994-1998) Research Interests: His work spans compilers , functional programming , program analysis , code optimization , and programming education . He focuses on improving compiler error messages, automated error repair, and educational tools like Prutor for programming courses. Recent Publications highlight advancements in pedagogical error repair , intelligent problem indicators , error localization , and GPU energy optimization . Notably, his 2023 paper on automated error repair received a Best Paper Award. Awards & Recognitions: Poonam and Prabhu Goel Chair Fellowship (2022) IIT Kanpur 1989 Batch Faculty Award for innovative teaching (2019) Best Faculty of the Year by Computer Society of India (2018) P. K. Kelkar Young Faculty Fellowship (2013-2016) Teaching & Advising : He supervises PhD, M.Tech, and B.Tech students, offering courses like Advanced Compiler Optimizations and Principles of Programming Languages . His projects integrate AI and functional programming in education.

Bella Struminskayaمشاهده پروفایل

دانشیار

• Survey Methodology
• Smartphone Surveys
• Data Donation
• +۶ مورد دیگر

Bella Struminskaya is an Associate Professor at Utrecht University’s Department of Methodology & Statistics, Faculty of Social and Behavioural Sciences. She is also an affiliated researcher at Statistics Netherlands and previously held a senior researcher role at GESIS - Leibniz Institute for the Social Sciences. Her work bridges survey methodology with digital data collection techniques, focusing on smartphone sensors and passive data donation. Education: PhD in Survey Methodology, Utrecht University M.A. in Sociology, University of Mannheim B.A. in Sociology, Novosibirsk State University Her research explores innovative data collection methods, including smartphone surveys, mixed-mode designs, and ethical frameworks for data donation. She investigates how mobile technologies can reduce survey burden while addressing nonresponse bias and measurement errors. Recent work emphasizes integrating digital trace data with traditional surveys to enhance accuracy and scope. Key article trends highlight applications of smartphone sensors in health studies, GDPR compliance for data access, and methodological advancements in smart surveys. Her contributions span technical tools like the Port software and theoretical insights on panel conditioning and ethical data augmentation. Scientific Awards: GOR Thesis Award 2015 She serves as a board member for the German Society for Online Research, Program Chair for the General Online Research Conference (GOR), and holds advisory roles in organizations like the European Social Survey, SHARE ERIC, and ODISSEI. She is an associate editor for multiple journals, including the Journal of Survey Statistics and Methodology and Survey Research Methods.

Karl Norrmanمشاهده پروفایل

پژوهشگر

• Cryptography
• Formal Methods
• Network Security
• +۴ مورد دیگر

Karl Norrman is an Industry doctoral student and Researcher at the Department of Theoretical Computer Science (TCS) at KTH Royal Institute of Technology, concurrently working as a Security Researcher at Ericsson Research since 2001. His academic appointment at KTH is part-time (20% time allocation), while he spends the majority of his time (80%) at Ericsson, where he holds the formal title of Expert Mobile Network Security. He is supervised by Professor Mads Dam for his doctoral studies and receives partial research funding from the Wallenberg AI, Autonomous Systems and Software Program (WASP). His educational background includes: PhD candidate in Computer Science at KTH Royal Institute of Technology, Department of Theoretical Computer Science (ongoing). Research focuses on formal modeling and proofs for cryptographic protocols using pen-and-paper proofs and mechanized proof-support tools such as Tamarin and EasyCrypt. Master's degree in Computer Science from Stockholm University, Department of Mathematics (2001). Thesis: "RTP Security in 3G Networks." During this work, he contributed to the development of the Secure Real-time Transport Protocol (SRTP), standardized in IETF as RFC 3711. Norrman's research centers on formal methods for security protocol verification , with particular expertise in cryptographic protocol analysis, modeling, and mechanized proof techniques. His work bridges theoretical computer science and practical security applications, focusing on making formal verification tools accessible for industrial adoption. Key research areas include 5G/6G security architectures, privacy-preserving mechanisms for mobile networks, authentication and key agreement protocols, and software security. He advocates for "goal oriented and motivated security designs" that balance theoretical rigor with practical implementation constraints while specializing in translating complex security requirements into implementable solutions for telecommunications infrastructure. Analysis of Norrman's publication history reveals a consistent evolution from foundational work on SRTP (2002-2007) through LTE security analysis (2014-2015) to pioneering 5G security research (2016-2020) and now 6G security exploration (2024). His work demonstrates methodological progression from analyzing existing protocols to designing novel security mechanisms and developing verification frameworks like OpenSAW. A distinctive pattern is his focus on industrial applicability —ensuring theoretical security models translate to real-world implementations, particularly evident in his work on USIM-compatible protocols and error-correcting authentication for noisy wireless channels. Recent publications increasingly address cross-domain challenges like secure federated learning in mobile networks, reflecting the expanding scope of telecommunications security. Professional engagement: Reviewer for ACM CCS 2023, EURO S&P 2023, ACM CCS 2022, NordSec 2022, Vietcrypt 2006, IEEE Telecommunications Journal Active contributor to 3GPP security standardization processes Co-author on multiple Ericsson whitepapers shaping industry security practices Norrman maintains a unique dual affiliation that enables direct translation of academic research into industrial security solutions. At KTH, he contributes to the Theoretical Computer Science group's formal methods research, while at Ericsson he applies these techniques to real-world security challenges in mobile network development. His work on OpenSAW exemplifies this bridge between academia and industry, creating practical tools for automated security testing of component-based software systems. This position allows him to identify emerging security challenges in next-generation networks while maintaining theoretical rigor in his approach.

• Cybersecurity
• Social Media Analytics
• Software Quality
• +۳ مورد دیگر

Dr. Manish Agrawal is a Professor in the School of Information Systems and Management at the University of South Florida (USF) , and currently serves as the Academic Director for Education at Cyber Florida . He has previously held the role of Chair of the Department of Information Systems and Decision Sciences (ISDS) at USF. His expertise lies in cybersecurity, social media analytics, software quality, offshoring, and extreme event response . Education: PhD in Information Systems – SUNY Buffalo Undergraduate studies – Indian Institute of Technology, Kanpur Research Interests: Dr. Agrawal’s research is deeply interdisciplinary. He investigates how cybersecurity systems can be improved through social media analytics , particularly in the context of crisis response and extreme events . His work also explores software quality assurance in outsourced and offshored IT projects, and how decision fusion can be used to improve information retrieval and knowledge discovery in high-stress environments. His recent publications reflect a consistent focus on information systems in crisis , social media behavior during disasters , and outsourcing economics . These works are published in top-tier journals such as MIS Quarterly , Management Science , and IEEE Transactions on Software Engineering . Grants and Funding: NSF Grant #IIS1651475 (2016–2018): Co-PI, $199,999 – “EAGER: An Investigation of the Propagation of Error-Resistant and Error-prone Messages over Large-Scale Information Networks” NSF Grant #1043919 (2014–2018): Co-PI, $299,468 – “Developing Open Authentic Case Studies for an MS in Cybersecurity Capstone Course” Service and Leadership: Department Chair, ISDS, USF (2016–Present) Conference Co-Chair, IFIP Dewald Roode Workshop (2017) Member, USF General Education Council (2011–2013) USF Faculty Senate Representative (2009–2012) Faculty Advisory Committee, Center for India Studies, USF (2010–Present) Teaching: Dr. Agrawal teaches courses in information security , business data communications , and web application development at both undergraduate and graduate levels. His teaching philosophy emphasizes real-world application and case-based learning .

Yi Liuمشاهده پروفایل

استادیار

• geometric deep learning
• AI for Science
• LLMs for Science
• +۳ مورد دیگر

Yi Liu is an Assistant Professor of Data Science jointly appointed in the Department of Applied Mathematics & Statistics and the Department of Computer Science at Stony Brook University. Previously, he served as an Assistant Professor in the Department of Computer Science at Florida State University. His academic background includes a Ph.D. in Computer Science from Texas A&M University (2022), an M.E. in Biomedical Engineering (2015), and a B.E. in Electronic Engineering (2012), both from the University of Science and Technology of China. Dr. Liu's research focuses on cutting-edge developments in artificial intelligence and computational science, with particular emphasis on: Geometric deep learning architectures for molecular and material systems AI-driven scientific discovery (AI4Science) Large language models for scientific applications 3D graph neural networks for molecular modeling Neural operators for physical system simulations His recent publications demonstrate a strong focus on developing novel AI methodologies for scientific applications, particularly in computational chemistry and materials science. Research trends include geometric deep learning for 3D molecular graphs, neural operators for physical modeling, explainable AI for chemical systems, and transformer-based approaches for material property prediction. The work consistently bridges fundamental AI research with applications in drug discovery, materials design, and scientific computing.

• Medication Safety
• Health Informatics
• Patient-Provider Communication
• +۵ مورد دیگر

Bruce L Lambert is a Professor in Medical Social Sciences (Intervention Science) at Northwestern University's Feinberg School of Medicine and concurrently holds a Professor position at the School of Communication. His work bridges clinical medicine, health informatics, and communication science with appointments across multiple institutes including the Center for Diabetes and Metabolism, Institute for Public Health and Medicine (IPHAM), and Northwestern University Clinical and Translational Sciences Institute (NUCATS). His research focuses on medication safety systems, electronic health record (EHR) design, and health communication. Key interests include preventing drug name confusion errors, optimizing indication-based prescribing, and developing clinical decision support tools. Lambert employs psycholinguistic testing to predict real-world medication errors and investigates secure messaging networks in healthcare settings. His work demonstrates how EHR interface design directly impacts wrong-patient and wrong-drug error rates. Analysis of his recent publications reveals strong emphasis on opioid safety in emergency departments, Veterans Health Administration prescribing patterns, and multimodal approaches to medication error prevention. His scholarship consistently addresses system-level interventions rather than individual provider blame, advocating for human factors engineering solutions in healthcare technology. Lambert actively contributes to medication safety policy, having published on Tall Man lettering limitations and the need for comprehensive immune monitoring in vaccine trials. His research directly informs EHR design standards and clinical workflow improvements across multiple healthcare systems. As an educator, he integrates principles of conservative prescribing and health literacy into medical training. His work on pharmacy deserts in minority communities highlights health equity concerns in medication access. Lambert's PhD from University of Illinois at Urbana-Champaign (1992) established his foundation in communication science applied to healthcare contexts.

• Authentic leadership
• Leadership development
• Workplace safety
• +۷ مورد دیگر

Hannes Leroy is a Professor in Leadership Development at the Department of Organisation and Personnel Management, Rotterdam School of Management (RSM), Erasmus University Rotterdam. He maintains significant additional roles including Distinguished Research Professor at Exeter Business School (2022-2025), advisory positions at EMPATHOS BVBA (2022-2025) and BOLD expeditions (2025-2028), and executive education activities at RSM BV. Leroy's research critically examines authentic leadership and leadership development, exploring outcomes like safety and work engagement, antecedents like mindfulness training, and relationships to concepts like behavioral integrity. His work challenges simplistic notions about authenticity, demonstrating through research like his 2024 paper with Choi that 'Honesty Is Not Always the Best Policy' in leadership contexts. He investigates how leadership development programs can move beyond superficial approaches to create genuine change, arguing in his 2024 MIT Sloan Management Review article that 'Leadership Development Is Failing Us. Here's How to Fix It.' His publication record shows consistent high-impact contributions, with recent work spanning authentic leadership (2025), configural leadership approaches (2025), social-symbolic work (2025), and mindfulness in leadership development (2025). These publications reveal a research trajectory focused on understanding the nuanced, context-dependent nature of effective leadership rather than promoting one-size-fits-all solutions. Leroy's scholarship has generated significant media attention, with coverage in CHRO.nl, IMD.org, Talent Canada, The Conversation, and Workplace Insight. His research on authenticity at work received widespread coverage in late 2019 across numerous international outlets, demonstrating the practical relevance of his academic work to organizational practitioners. His teaching at RSM aligns closely with his research interests, focusing on global leadership and professional competencies. Through both his scholarship and teaching, Leroy emphasizes that genuine leadership development requires moving beyond comfort zones and developing leaders who can strategically apply authenticity based on contextual understanding rather than following simplistic 'just be yourself' advice.

Masud Rahmanمشاهده پروفایل

دانشیار

• Software Engineering
• Artificial Intelligence for Software Engineering (AI4SE)
• Software Maintenance and Evolution
• +۱۱ مورد دیگر

Masud Rahman is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Canada, where he leads the RAISE Lab. Previously a tenure-track Assistant Professor, he completed his Ph.D. in Computer Science/Software Engineering from the University of Saskatchewan and a postdoctoral fellowship at Polytechnique Montreal. His academic career demonstrates strong progression with significant research impact in software engineering. Faculty of Computer Science, Dalhousie University (Current) University of Saskatchewan (Ph.D. studies) Polytechnique Montreal (Postdoctoral research) Dr. Rahman's research focuses on the intelligent automation of software maintenance and evolution, particularly targeting software debugging, code search, and code reviews. His work strategically combines Software Engineering with Artificial Intelligence techniques including Machine/Deep Learning, Information Retrieval, Mining Software Repositories, and Natural Language Processing. His industry experience as a professional developer for three years significantly shaped his research direction toward solving practical software maintenance challenges that cost the global economy billions annually. His research program addresses critical problems in software bug detection, diagnosis, explanation, and reproduction, with increasing focus on AI-powered and simulation modeling software. His publications demonstrate consistent output in top-tier venues including 7 papers at ICSE (A*), 3 at FSE (A*), 3 at ASE (A*), 8 at EMSE (A), 6 at ICSME (A), and 9 at MSR (A). The research trends show increasing focus on deep learning applications for software engineering problems, with particular attention to code search, bug localization, and debugging automation. His work has evolved from traditional information retrieval approaches to incorporate advanced neural network techniques and generative AI. Governor General's Gold Medal 2019 U of S Doctoral Thesis Award 2019 CS Best PhD Thesis Award 2019 TCSE Distinguished Paper Award Most Influential Paper Award Dr Keith Geddes Award Dalhousie Belong Research Fellowship President's Gold Medal (Bangladesh) Dr. Rahman has secured $500K+ in competitive research funding as Principal Investigator, including an NSERC Discovery Grant, Mitacs Accelerate International, and Climate Action and Awareness Fund. He actively collaborates with industry partners including Metabob Inc., Mozilla Firefox, and Vendasta Technologies. His service to the community includes extensive program committee work for major conferences and journal reviewing. He leads the RAISE Lab, which focuses on developing AI-powered solutions for software maintenance challenges, with current projects emphasizing sustainable software innovation and sustainable AI as part of Dalhousie's strategic goals.