جست‌وجوی استاد و پژوهشگر

• Software Metrics
• Model Driven Software Development
• Empirical Software Engineering
• +۵ مورد دیگر

Miroslaw Staron is a Professor of Interaction Design and Software Engineering at Chalmers University of Technology. He maintains a unique 50/50 work arrangement, spending half his time on field research at Ericsson while holding his academic position. His research bridges academic theory with industrial practice through collaborations with major companies including Volvo Car Corporation and Volvo Information Technology. His research spans several key areas in software engineering: Software metrics and measurement systems in industry Model driven software development and empirical studies Defect prediction in software projects Requirements engineering in model-based development Applications of AI and machine learning in software engineering Automotive software development and security Staron's recent work demonstrates a strategic shift toward integrating AI technologies into software engineering processes, with particular focus on automotive applications. His publications from 2024-2025 reveal expertise in generative AI applications for code review automation, testing methodologies, and requirements engineering, showing how these technologies can transform traditional software development practices while addressing domain-specific challenges in automotive systems. Current research projects include: Kvantdatorer för framtidens mobilitetslösningar (2025-2027) Automatiserad och designoptimerad programvarukonstruktion/kodgenerering (2025-2029) Förvandla fordonsarkitektur med hjälp från AI (2021-2023) Arkitektonisk design och verifiering/validering av system med maskininlärning komponenter (2020-2024) With 78 publications documented in Chalmers' research database, Staron has established himself as a significant contributor to evidence-based software engineering research with strong industrial relevance.

Demetriadis Stavrosمشاهده پروفایل

استاد

• Learning Technologies
• Educational Technology
• Conversational Agents in Education
• +۷ مورد دیگر

Full Professor at the School of Informatics, Aristotle University of Thessaloniki (AUTh), Greece. Previously served as Associate Professor (2015-2020), Assistant Professor (2008-2015), and Lecturer (2002-2008) at the same institution. Also worked as an Informatics Teacher in Greek Secondary Education from 1989-2002. His research focuses on Learning Technologies with emphasis on Conversational Agents in Education, Learning Analytics, Computer-Supported Collaborative Learning, Computational Thinking, Teaching Machine Learning at School, and Massive Open Online Courses (MOOCs). His recent work heavily investigates the application of AI, particularly conversational agents and large language models, in educational contexts. His publications show a strong trend toward AI applications in education, particularly focusing on conversational agents, learning analytics, and automated grading systems. The research spans multiple educational contexts from K-12 to higher education, with particular attention to student self-regulation, collaborative learning, and ethical considerations in AI implementation. 3 Best paper awards at international conferences Interview by Educational Technology Magazine (2013) Cubes Coding project - Winners of Open Education Challenge 2014 Cubes Coding project - Winners of NUMA Competition 2014 Has supervised 5 completed PhD theses, 4 ongoing PhD theses, over 60 Master's theses, and over 120 undergraduate theses. Led the colMOOC project (2018-2020), a €999,000 EU-funded project on integrating conversational agents and Learning Analytics in MOOCs. Also coordinated the T4E project (2020-2022) on Teachers' Fast-paced Distance Training on Tele-education and a MOOC in Greek on Introduction to Programming with Python. Previously served as Director of the Software and Interactive Technologies Laboratory (SWITCH Lab) until 2020, Member of AUTH Educational Policy Committee until 2020, and Chair of the Scientific Supervisory Board of the 2nd Experimental Junior High School in Thessaloniki until 2020.

Carolyn Conner Seepersadمشاهده پروفایل

استاد

• Design for Additive Manufacturing
• Engineering Design
• Additive Manufacturing
• +۴ مورد دیگر

Carolyn Conner Seepersad serves as the J. Mike Walker Professor of Mechanical Engineering at the University of Texas at Austin and directs the Center for Additive Manufacturing and Design Innovation. She holds membership in the U.T. System Academy of Distinguished Teachers and maintains active leadership in the additive manufacturing community through roles such as co-organizer of the Solid Freeform Fabrication Symposium and ASME Design Engineering Division Executive Committee membership. Her academic credentials include: PhD in Mechanical Engineering from Georgia Tech (2004) MA/BA in Philosophy, Politics and Economics from Oxford University (1998, Rhodes Scholar) BS in Mechanical Engineering from West Virginia University (1996) Dr. Seepersad's research centers on computational design methodologies and additive manufacturing innovation , with particular expertise in simulation-based design of complex systems, environmentally conscious product development, and materials engineering. Her work bridges theoretical design frameworks with practical manufacturing applications, emphasizing sustainability and performance optimization across aerospace, automotive, and energy systems. Current projects explore reactive extrusion additive manufacturing, negative stiffness materials, and machine learning integration for process-aware design. Analysis of her 15 most recent publications reveals a dominant focus on process innovation in additive manufacturing (70%), particularly stereolithography and selective laser sintering, with growing emphasis on data-driven design approaches (20%) and sustainable engineering applications (10%). Her work demonstrates consistent progression from fundamental material design toward integrated system optimization and industrial scalability. Her scientific recognition includes: International Outstanding Young Researcher Award in Freeform and Additive Manufacturing (2009) UT System Regents’ Teaching Award (2010) ASME Design Automation Committee Outstanding Young Investigator Award (2010) ASEE Outstanding New Mechanical Engineering Educator Award (2013) Multiple ASME and ASEE best paper awards U.T. System Academy of Distinguished Teachers membership Dr. Seepersad maintains an extensive advising portfolio with 48 graduate students (16 PhD, 24 MS, and 8 current) plus 2 postdoctoral researchers, reflecting sustained research productivity and educational impact. Her Product, Process, and Materials Design Lab fosters interdisciplinary collaboration between mechanical engineering, materials science, and computational design teams.

• Data Journalism
• Digital Journalism
• Algorithmic Accountability
• +۷ مورد دیگر

Christina Elmer is Professor for Digital Journalism and Data Journalism at the Institute of Journalism, Technical University of Dortmund. Prior to her academic career, she held significant positions at DER SPIEGEL as Deputy Head of Development, Member of the Editorial Board of SPIEGEL ONLINE, Head of the Data Journalism Department, and Science Editor (2013-2021). She is recognized as a leading expert in data journalism, AI in media, and digital transformation of journalism. Elmer's research focuses on data journalism, algorithmic accountability, and the integration of artificial intelligence in journalistic workflows. Her work explores how digital transformation affects media production, distribution, and reception, with particular attention to user-centered journalism, ethical dimensions of digital media, and the development of editorial products. She has pioneered approaches to structured journalism and modular content creation that adapt to changing audience needs. Her recent publications examine AI's impact on journalism, methods for combating disinformation, and strategies for maintaining journalistic integrity in algorithmically mediated information ecosystems. Her work shows a clear trend toward investigating how journalism can maintain societal relevance while adapting to technological changes, with increasing focus on AI systems as both tools and challenges for quality journalism. scoop award of the nextMedia.Hamburg initiative, 2023 Helmut Schmidt Journalist Prize (second prize) for 'Blackbox Schufa', 2019 Philip Meyer Award (third place) for 'Hanna and Ismail', 2018 dpa-infografik Award for 'Die Pendlerrepublik', 2018 Journalistin des Jahres, Fachkategorie Wissenschaft, 2016 Deutscher Journalistenpreis Forst & Holz (Print), 2007 Elmer actively contributes to the journalism community as a board member of Netzwerk Recherche (serving as second chair 2021-2023), shareholder of AlgorithmWatch, and member of various advisory boards including Science Media Center Germany and MIP.labor. She frequently collaborates with students through the KURT student editorial team, guiding them in applying design thinking to develop new journalistic formats. Her approach emphasizes the importance of user-centered thinking while maintaining journalistic integrity in the digital age.

Anne Elisabeth Haxthausenمشاهده پروفایل

دانشیار

• Formal methods
• Specification languages
• Semantics
• +۶ مورد دیگر

Anne Elisabeth Haxthausen is an Associate Professor at the Software Systems Engineering section within DTU Compute , Technical University of Denmark . Her work focuses on formal methods, railway control systems, and safety-critical software engineering. Founder and leader of the DTU Railway Verification Group Member of European Technical Working Group on Formal Methods in Railway Control Editorial board member for Springer Formal Aspects of Computing Journal Active in the Overture Language Board Her research emphasizes formal verification of railway interlocking systems, particularly through compositional approaches and automated tools. She has contributed to projects like RobustRailS, Overture, and RAISE, focusing on model-based development and verification. She serves as a tutor for bachelor students and contributes to the advisory committee for DTU's Computer Science and Engineering MSc program. Her recent publications explore challenges in verifying autonomous and AI-driven railway technologies.

Lianne Lefsrudمشاهده پروفایل

دانشیار

• Risk Management
• Sustainability
• Organizational Theory
• +۵ مورد دیگر

Lianne Lefsrud serves as Associate Professor and Risk, Innovation, and Sustainability Chair (RISC) in the Department of Chemical and Materials Engineering at the University of Alberta's Faculty of Engineering. Her interdisciplinary research bridges engineering, social sciences, and policy to transform risk management practices across energy, mining, construction, and railroading industries, directly influencing regulations, building codes, and industry operations for sustainable development. Her academic credentials include: BSc in Civil Engineering (Cooperative Program), University of Alberta (1994) MSc in Interdisciplinary Civil & Environmental Engineering and Sociology, University of Alberta (1996) PhD in Strategic Management and Organization, Alberta School of Business (2014) Dr. Lefsrud's research centers on risk management frameworks for sustainability challenges. She examines hazard identification, social license to operate, and technology adoption drivers in high-hazard industries, with emphasis on prospective risk assessment (e.g., hydrogen infrastructure design) and retrospective analysis (e.g., microplastic pollution impacts). Her work integrates circular economy principles into energy systems while addressing unintended consequences across UN Sustainable Development Goals. Recent publications (2024-2025) demonstrate heavy focus on machine learning applications for rail and construction safety, hydrogen infrastructure risk analysis, and science denial mitigation. Key patterns show cross-industry adaptation of AI for incident prediction, regulatory gap analysis for emerging energy systems, and socio-technical approaches to reconcile sustainability goals with operational realities. Scientific recognition includes: Erb Post-Doctoral Fellowship (University of Michigan) Dow Sustainability Research Fellowship (Ross School of Business) Dr. Lefsrud mentors graduate students through industry-integrated projects like her Sustainable Design course where teams generated patents and city solutions. Her research secures Alberta Innovates funding with 1:4 industrial-to-federal matching, collaborating with Suncor, Transport Canada, and Canadian Standards Association. Grants target practical implementations including railcar inspection systems and hydrogen safety protocols. She co-founded Insight Risk Systems and leads the Lefsrud Lab, prioritizing inclusive teams with under-represented groups (women, Indigenous, LGBTQ2S+, neurodiverse) to tackle 'wicked problems' in sustainability. The lab leverages interdisciplinary partnerships across engineering, computer science, psychology, and environmental sociology for real-world risk management solutions.

Daniel Fremontمشاهده پروفایل

دانشیار

• Formal Methods
• Automated Reasoning
• Cyber-Physical Systems
• +۶ مورد دیگر

Daniel Fremont is an Associate Professor of Computer Science and Engineering at the University of California, Santa Cruz, where he conducts research at the intersection of formal methods and autonomous systems. His work focuses on developing mathematical techniques to improve the reliability of software, hardware, and cyber-physical systems through precise specification, formal verification, automatic synthesis, and principled testing approaches. Dr. Fremont's research interests center on applications of logic in computer science, particularly using automated reasoning to enhance system reliability. His work spans formal methods for cyber-physical systems (CPS), especially autonomous systems that incorporate machine learning. Key research areas include algorithmic improvisation for creating systems with controlled randomness, probabilistic programming through the Scenic language for environment modeling, and formal verification techniques applicable to safety-critical autonomous systems. His group has successfully applied these methods to autonomous vehicles, aircraft systems, and robotics, with significant contributions to both theoretical foundations and practical implementations. The publication record reveals a strong trajectory from theoretical foundations of control improvisation toward practical applications in autonomous systems verification. Early work established the theoretical framework of control improvisation, while recent publications focus on applying these techniques to real-world challenges in autonomous driving, aircraft systems, and AI-based autonomy. A consistent theme across his research is the integration of formal methods with machine learning to address the verification challenges posed by complex, learning-based systems operating in uncertain environments. Best Paper Award at IoTDI 2016 for 'Control Improvisation with Probabilistic Temporal Specifications' Dr. Fremont leads a research group focused on formal methods for autonomous systems, with significant contributions to the development of tools like Scenic (a probabilistic programming language for scenario specification) and VerifAI (a toolkit for formal design and analysis of AI-based systems). His work bridges theoretical computer science with practical engineering challenges in safety-critical autonomous systems, receiving funding from various sources supporting research at the intersection of formal methods and artificial intelligence. The group's approach combines theoretical algorithm development with practical implementation and testing, often collaborating with industry partners working on autonomous vehicle technology. The research group maintains active development of several open-source tools, including the Scenic language for scenario specification and VerifAI for formal analysis of AI systems. They have demonstrated applications across multiple domains including autonomous vehicles, aircraft systems, and robotics, with particular emphasis on simulation-based testing and verification approaches that can provide formal guarantees about system behavior.

Naren Ramakrishnanمشاهده پروفایل

استاد

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

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

Cong Puمشاهده پروفایل

استادیار

• Network Security
• Data Privacy
• Applied Cryptography
• +۴ مورد دیگر

Dr. Cong Pu is an Assistant Professor in the Department of Computer Science at Oklahoma State University (OSU), Stillwater, Oklahoma. He holds a Ph.D. and M.S. in Computer Science from Texas Tech University and a B.S. in Computer Science and Technology from Zhengzhou University, China. His primary research focuses on network security, data privacy, applied cryptography, wireless networking, and mobile computing. He leads the Security & Networking Lab at OSU and has secured grants from NSF, NSA, and other agencies. His work emphasizes secure IoT and drone networks, privacy-preserving protocols, and AI-driven cybersecurity solutions. Education Ph.D. and M.S. in Computer Science, Texas Tech University, USA B.S. in Computer Science and Technology, Zhengzhou University, China Research Interests Dr. Pu's research spans network security , data privacy , applied cryptography , wireless networking , and mobile computing . He specializes in designing lightweight authentication protocols for IoT and drone networks, enhancing privacy in distributed systems, and developing AI-driven cybersecurity frameworks. Recent efforts include blockchain-assisted authentication, fault-tolerant data aggregation, and resource-efficient cryptographic protocols. Grants & Awards NSF SaTC Award ($288,398) for securing Internet of Drones systems (2024) NSA Cybersecurity Training Program Grant ($127,128) (2023) Best Paper Award at IEEE CCNC 2024 Listed in Stanford/Elsevier Top 2% Scientists (2025) Advising & Training He mentors Ph.D., M.S., and undergraduate students in areas like network security and applied cryptography. Offers research assistantships, independent studies, and visiting scholar collaborations. Has developed training programs for K-12 educators in cybersecurity via NSF/OSRHE grants. Labs & Infrastructure Leads the Security & Networking Lab , focusing on secure IoT/drones, privacy-preserving systems, and AI-driven security tools. Collaborates on projects involving blockchain, reinforcement learning, and hardware-software co-design for defense mechanisms.

• Intellectual Property Law
• Copyright and Artificial Intelligence
• IP and Right to Repair
• +۵ مورد دیگر

Kanchana Kariyawasam serves as Associate Professor in the Department of Accounting, Finance and Economics within Griffith University's Business School. Holding a PhD in IP Law from Griffith University, an LL.M (Advanced) in IP Law from The University of Queensland, and an LL.B (Hons) from the University of Colombo, she maintains active research affiliations with the Law Futures Centre (2009-2024) and Griffith Asia Institute (2019-present). Her research spans Intellectual Property Law with specialized focus on Copyright and Artificial Intelligence, IP and Right to Repair, Patent Law in Biotechnology, and Gender Equality in IP systems. Current projects examine AI-generated works, digital exhaustion doctrine, and NFT legality under Australian copyright frameworks, reflecting strong alignment with Sustainable Development Goals 4 (Quality Education) and 9 (Industry Innovation). Professor Kariyawasam's funded research portfolio includes significant projects such as the WIPO Study on Illegal Retransmission of Live Broadcasts (2023), Queensland Government Ewaste initiatives (2022), and Griffith University's Right to Repair Workshop (2020). Her publications demonstrate consistent output in high-impact journals including European Intellectual Property Review and International Journal of Law and Information Technology , with recent works analyzing AI copyright challenges, spatial data protection, and medical device repair rights. Deputy Vice-Chancellor's 2024 Student Experience of Teaching Survey Commendation Green Impact Gold Award Recipient (2023, Griffith Repair Cafe) Highly Commended Griffith Award for Excellence in Teaching (Large Classes) Two GBS teaching citations She currently supervises five doctoral candidates researching AI data privacy, patent law intersections, and agricultural IP issues, while having successfully completed supervision of four doctoral theses including works on female entrepreneurship and agribusiness marketing. As Coordinator of Griffith Repair Cafe and Steering Committee Member of Australian Repair Network, she bridges academic research with community engagement on repair rights and e-waste reduction.

• Model-based Systems Engineering
• Safety-Critical Systems
• Systems Engineering
• +۳ مورد دیگر

Dr. Siyuan Ji is a Reader in Model-based Systems Engineering (MBSE) at Loughborough University, serving as Deputy Head of the Manufacturing, Systems & Management Academic Community and Deputy Director of the Doctoral Training Centre in MBSE. He previously held a Senior Lecturer position in Systems Engineering at the University of York, where he led the MSc Programme in Safety-Critical Systems Engineering. His academic journey includes a PhD and MSc in Physics from the University of Nottingham, followed by research roles in model-based systems engineering at Loughborough University. His research focuses on advancing model-based techniques for systems engineering, particularly in safety-critical systems, formal methods, and complex system design. He has contributed to areas such as hazard management (e.g., BSafeML framework), response time analysis in real-time systems, and model synchronization for requirements engineering. His work bridges theoretical foundations with practical applications in automotive systems, embedded software, and educational technology. Dr. Ji holds the title of Fellow of the Higher Education Academy and has published extensively on topics ranging from quantum technology reporting to conversational tutoring systems. His research emphasizes interdisciplinary collaboration, evident in projects like the EPSRC-funded analysis of vehicles as complex systems. He actively contributes to both academic and industrial advancements in systems engineering methodologies and education innovation. His professional roles include managing doctoral training programs, overseeing academic communities, and advancing systems engineering education. Collaborations span industry partnerships and international academic networks, reflecting his commitment to impactful research and training the next generation of systems engineers.

• Intellectual Property Law
• Artificial Intelligence and Copyright
• Blockchain and Smart Contracts
• +۷ مورد دیگر

Dr. Andres Guadamuz is a Reader in Intellectual Property Law at the University of Sussex, affiliated with the School of Law, Politics and Sociology. He serves as Editor in Chief of the Journal of World Intellectual Property . His research focuses on intersections of law and technology, including artificial intelligence and copyright, open licensing, cryptocurrencies, smart contracts, and blockchain. He has authored over 40 articles, book chapters, and two books, alongside regular blog posts on technology regulation. Research Interests: Artificial Intelligence and Copyright Blockchain Technology and IP Cryptocurrency Regulation Open Source Licensing Internet Governance Data Mining Legal Frameworks Publication Trends: His recent work addresses AI's impact on copyright law, NFTs, and legal challenges in smart contracts. He critically explores EU AI legislation, liability frameworks for AI inputs/outputs, and NFT authenticity issues. Teaching: Teaches courses on Digital Intellectual Property Law, Internet Law, and Information Technology IP Law, including developing the specialized LLM in IT and Intellectual Property Law. Grants/Advising: Supervises PhD/postgraduate students in his research areas. His work is widely cited in legal, academic, and policy circles, with over 100 Mendeley readers for key papers.

• Graph Theory
• Graph Databases
• Knowledge Graphs
• +۵ مورد دیگر

Carlos R. Rivero is an Associate Professor in the Department of Computer Science at the Rochester Institute of Technology (RIT), located within the Golisano College of Computing and Information Sciences. His primary research focuses on graph theory applications in knowledge graphs, graph databases, and computer-aided program comprehension. He holds a PhD from the University of Seville (Spain), completed in 2012, with postdoctoral work at the University of Idaho (USA). His teaching responsibilities include courses such as Principles of Data Management, Data Mining, and Big Data exploration. Rivero has advised numerous PhD and Master’s students, contributing to research projects in link prediction, knowledge graph completion, and educational technology. He actively serves on program committees for conferences like The Web Conference and SIGKDD, and has reviewed for journals including the VLDB Journal and Communications of the ACM. His research emphasizes evaluating knowledge graph embeddings, improving link prediction methodologies, and developing tools for educational feedback in programming. He has contributed to projects like AYNEXT, which streamlines link prediction evaluation, and CAFE, a neighborhood-aware knowledge graph completion tool. Rivero’s work bridges theoretical advancements with practical applications in education and industry. Notable contributions include frameworks for automated feedback in programming courses and methodologies for assessing inference patterns in knowledge graphs. His grants and service roles reflect a commitment to advancing computational methods and fostering academic collaboration in data science and education.

Jeff Lundeenمشاهده پروفایل

دانشیار

• Quantum Optics
• Photonics
• Quantum Computing
• +۳ مورد دیگر

Dr. Jeff Lundeen is an Associate Professor in the Department of Physics at the University of Ottawa's Faculty of Science. His research focuses on experimental and theoretical quantum physics, particularly in photonics and quantum computing. He leads the Lundeen Lab, developing methods to manipulate single photons and entangled photon pairs for quantum logic, communication, and metrology applications. Research interests include experimental photonics, quantum-enhanced sensors, quantum metrology, and quantum optics. His work addresses challenges in quantum device development, such as ultra-thin imaging systems and quantum state tomography. Key contributions include direct measurements of quantum wave functions and density matrices, weak value amplification techniques, and space-compressing optics. His recent publications (2023–2025) explore neural adaptive quantum tomography, quantum metrology in noisy environments, and reconfigurable optical systems. Dr. Lundeen collaborates internationally on projects like quantum state estimation and photon pair generation in fibers. His lab emphasizes practical applications of quantum principles in sensors, communication, and imaging technologies.

Xiajun Jiangمشاهده پروفایل

استادیار

• Adaptive AI Computing
• Physics-Informed Deep Learning
• AI for Healthcare
• +۳ مورد دیگر

Xiajun Jiang is an Assistant Professor in the Department of Computer Science at the University of Memphis, joining in Fall 2024. He holds a PhD in Computing and Information Sciences from Rochester Institute of Technology (2024), an M.S. in Computer Science from the University of Southern California (2018), and a B.S. in Electrical Engineering and Automation from Zhejiang University (2016). His research focuses on adaptive AI computing, physics-informed deep learning, and their applications in healthcare, particularly in medical imaging and cardiac simulation. Key contributions include hybrid neural state-space modeling for electrocardiographic imaging and physics-informed frameworks for bi-ventricular electrophysiological simulations. Education: PhD, Rochester Institute of Technology, 2024 M.S., University of Southern California, 2018 B.S., Zhejiang University, 2016 Research Interests: Machine learning for healthcare Adaptive computing in AI models Physics-informed deep learning His work bridges machine learning and biomedical engineering, with applications in cardiac imaging and electrophysiology. Recent articles highlight advancements in hybrid models for ECGI and meta-learning approaches for personalized cardiac simulations. He has reviewed for top conferences like ICLR, NeurIPS, and MICCAI, and contributed to projects like the Computational Biomedical Lab (CBL).