جست‌وجوی استاد و پژوهشگر

Nicolas Papernotمشاهده پروفایل

دانشیار

• Machine Learning Security
• Privacy-Preserving Machine Learning
• Adversarial Examples
• +۴ مورد دیگر

Nicolas Papernot is an Associate Professor at the University of Toronto , affiliated with the Edward S. Rogers Sr. Department of Electrical & Computer Engineering (Faculty of Applied Science & Engineering), the Department of Computer Science (Faculty of Arts & Science), and the Faculty of Law . He holds a Canada CIFAR AI Chair at the Vector Institute and serves as a Co-Director of CIFAR’s Canadian AI Safety Institute (CAISI) Research Program , with an International Chair at Inria (2025–2027). His work bridges security, privacy, and machine learning , focusing on trustworthy AI systems. Education : PhD in Computer Science and Engineering from Pennsylvania State University (Google PhD Fellow), followed by a year at Google Brain. His research explores privacy-preserving machine learning (e.g., differential privacy, cryptographic auditing), security vulnerabilities (model extraction, adversarial examples), and machine unlearning . Recent projects include generative model collapse , verifiable unlearning , and privacy bounds in collaborative learning . His publications span top venues like NeurIPS, IEEE S&P, and ICML, with best paper awards at ICLR 2017 and outstanding paper at ICLR 2022 . Scientific awards include the Alfred P. Sloan Research Fellow (2022) , Member of the Royal Society of Canada’s College of New Scholars (2023) , Schmidt Sciences AI2050 Early Career Fellow (2024) , McCharles Prize for Early Career Research Distinction (2024) , and ACM SIGSAC Outstanding Early-Career Researcher Award (2024) . He co-founded the IEEE Conference on Secure and Trustworthy ML (SaTML) and chairs the IEEE Symposium on Security and Privacy (2026–2027) . His research group, the CleverHans Lab , advises PhD and Master’s students , including Varun Gupta , Andrei Muresanu , and Angela Sha . Collaborative efforts involve institutions like Google DeepMind, CIFAR, and the Schwartz Reisman Institute for Technology and Society . For prospective students, he encourages applications to UofT’s Computer Science or ECE (Software Systems) programs.

• Cognitive Augmentation Technologies
• Computing Education
• AI Security & Safety
• +۴ مورد دیگر

Nanna Inie is an Assistant Professor in Software Engineering at IT University of Copenhagen . Her research focuses on Human-Computer Interaction , with specializations in Cognitive Augmentation Technologies , Computing Education , and AI Security & Safety . She employs mixed-method designs to study human cognition extension through technology and its reciprocal effects. Recent publications highlight her exploration of generative AI ’s cognitive costs, LLM red teaming , and gender representation in educational materials. Her methodological approach combines qualitative and quantitative experiments in real-world settings. Key collaborative projects include DIREC (recruitment of IT students), ATTiKA (adaptive learning tools), and digital reading support systems . Her research has been disseminated through 30 publications and 22 media contributions , including interviews with TV2 News and Danish outlets. Funded by Villum Foundation and Innovation Fund Denmark , her work addresses technological ethics , collaborative dynamics , and environmental impacts of AI systems.

Najmul Islamمشاهده پروفایل

استاد

• Blockchain
• Artificial Intelligence
• Digital Transformation
• +۶ مورد دیگر

Najmul Islam is a tenured Professor of Digital Transformation in the Department of Software Engineering at LUT University's School of Engineering Sciences. He was recently promoted from associate professor to full professor and has established himself as a leading researcher in digital systems, blockchain, and artificial intelligence. Dr. Islam's research spans several critical areas in digital transformation: Design and development of responsible, reliable, and legally compliant digital systems Blockchain applications for sustainable consumption and supply chain transparency Explainable AI for cybersecurity and decision-making systems Human-AI collaboration in organizational contexts Impact of digital systems on individuals and organizations His scholarly work demonstrates a strong trajectory toward creating trustworthy digital ecosystems. Recent publications focus on integrating blockchain with AI for enhanced security and transparency, addressing challenges in human-AI collaboration, and developing sustainable digital solutions. His research bridges technical innovation with practical organizational applications, particularly in understanding how digital systems affect both business operations and individual well-being. Dr. Islam's significant contributions to the field were recognized when he was awarded the Information Systems Scientist of the Year 2022 by the Finnish Computer Science Society. This prestigious award acknowledged both his academic merit and his participation in developing the information systems research community at LUT. As an educator, Dr. Islam contributes to LUT's rapidly growing software engineering program, which has expanded from 30 to 300 student places annually over five years. The program offers six specialization options across LUT's Lappeenranta and Lahti campuses, with software engineering being the largest degree program in Lahti.

Phillip M. Lacasseمشاهده پروفایل

استادیار

• Operations Research
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

LTC Phillip M. Lacasse serves as an Assistant Professor of Operations Research in the Department of Operational Sciences at the Air Force Institute of Technology (AFIT), assigned under a formal Memorandum of Agreement between AFIT and the U.S. Army Functional Area 49 Proponent Office. As an active-duty U.S. Army Lieutenant Colonel, his military career includes tank and scout platoon leadership, company command during Operation Iraqi Freedom, and staff roles at USMEPCOM and West Point. His academic credentials include: B.S. in Mathematics from United States Military Academy M.S. in Industrial Engineering from University of Wisconsin-Madison Ph.D. in Engineering from University of Wisconsin-Milwaukee Dr. Lacasse's research integrates Operations Research , Machine Learning , and Natural Language Processing to solve defense and industrial challenges. His work demonstrates expertise in autonomous swarm behavior for anti-access environments, emotion classification in multilingual social media, and workforce optimization for military applications. Recent publications reveal a strong focus on simulation modeling for military processing systems and defect prediction in smart manufacturing. His scholarly output shows consistent application of advanced analytics to real-world problems, particularly in military logistics (e.g., applicant scheduling simulations) and industrial systems (e.g., PCB defect prediction). Key trends include leveraging large language models for linguistic tasks and developing optimization frameworks for Army force structure analysis.

Wesley Klewerton Guez Assuncaoمشاهده پروفایل

دانشیار

• Software Modernization
• Variability Management
• Software Quality
• +۷ مورد دیگر

Wesley Klewerton Guez Assuncao serves as an Associate Professor in the Department of Computer Science within the College of Engineering at North Carolina State University. Previously, he held positions as a University Assistant/Senior Researcher at Johannes Kepler University Linz in Austria, Postdoctoral Researcher at Pontifical Catholic University of Rio de Janeiro in Brazil, and Assistant/Associate Professor at Federal University of Technology - Paraná in Brazil. His academic journey includes a Ph.D. in Computer Science from the Federal University of Paraná with a visiting period at Johannes Kepler University. Dr. Assuncao's research spans several critical areas in modern software engineering. His primary interests include Software Modernization (reverse engineering, re-engineering, and migration), Variability Management (variability mechanisms, software customization, and software reuse), and Software Quality (technical debt, code smells, and software refactoring). He also investigates Model-Driven Engineering (model inconsistency detection, repair generation, and change propagation), Collaboration in Systems Engineering (change synchronization, tool flexibility, and conflict awareness), Software Testing (regression testing, integration testing, and test case selection/prioritization), and AI4SE (Generative AI, Machine Learning, and Evolutionary Algorithms for Software Engineering). His recent publications reveal a strong focus on software modernization challenges, particularly regarding legacy systems transformation, microservices architecture, and the application of AI techniques in software engineering. The research shows increasing integration of Large Language Models in addressing software evolution problems, with emphasis on empirical validation through industrial collaborations. His work consistently bridges theoretical foundations with practical applications, as evidenced by multiple industry partnerships and real-world case studies. Dr. Assuncao has received numerous prestigious awards including Distinguished Reviewer Awards from FSE and SANER conferences, a Young Researcher Award from Johannes Kepler University, and multiple Best Paper Awards from top software engineering conferences. His research has been recognized with ACM SIGSOFT Distinguished Paper Awards and IEEE Computer Society TCSE Distinguished Paper Awards. In terms of academic service, he serves as Co-editor of the In Practice track at the Journal of Systems and Software and has held various leadership roles in major conferences including ICSE, SANER, MSR, and SPLC. He has successfully secured substantial research funding totaling approximately USD 1.91 million from sources including the Austrian Science Fund, Brazilian National Council for Scientific and Technological Development, and state-level Brazilian research foundations. Dr. Assuncao leads the Wolfpack Innovations in Software Engineering Research (WISER) Lab at NC State University, where he supervises graduate students working on cutting-edge software engineering research problems. His lab maintains strong collaborations with international institutions and industry partners including Dynatrace and ITPRO Consulting & Software GmbH.

Dongkuan Xuمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Computer Vision
• +۷ مورد دیگر

Dongkuan Xu is an Assistant Professor in the Department of Computer Science at North Carolina State University, leading the Generative Intelligent Computing (GIC) Lab. His research spans Artificial Intelligence , focusing on scalable, efficient, and reliable AI systems for large language models, diffusion models, and generative frameworks. Education Ph.D. in Computer Science, Pennsylvania State University (2022) M.S. in Optimization and Data Mining, University of Chinese Academy of Sciences (2017) B.E. in Information Management and System, Renmin University of China (2014) His research explores test-time adaptation, computational efficiency, and trustworthy AI through uncertainty-aware learning and robust generalization. Applications include Education , Robotics , Agriculture , Networking , and Healthcare . Recent work emphasizes hardware-algorithm co-design and domain-specific AI platforms like Synthora and Gentopia. His publications from 2024–2025 highlight advancements in Vision Transformers , Diffusion Models , Edge Computing , and Trustworthy AI , with grants from NVIDIA , Microsoft , and the National Science Foundation . Awards include the AAAI Best Demonstration Award (2025), NVIDIA Academic Grant (2025), and the Carla Savage Award (2024). He actively mentors undergraduate researchers and co-chairs workshops at top venues like DAC , CVPR , and KDD , aiming to democratize AI access and enhance its reliability across real-world domains.

• Information Privacy
• Security
• Machine Learning
• +۵ مورد دیگر

Reza Samavi serves as an Adjunct Assistant Professor in the Department of Computing and Software within McMaster University's Faculty of Engineering. His research bridges computer science, healthcare, and privacy with specialization in information privacy and security, machine learning, and trustworthy AI systems for medical applications. Dr. Samavi's research program demonstrates significant evolution from foundational privacy frameworks to increasingly healthcare-focused AI applications: Information Privacy and Security in healthcare systems Trustworthy Machine Learning for medical decision support Deep learning models for child and youth mental health Blockchain applications for privacy-preserving eHealth Natural language processing for mental health screening Uncertainty quantification in medical AI systems His recent scholarly output (2023-2025) reveals a concentrated research program with strong interdisciplinary collaboration between computer science and healthcare domains, particularly in mental health applications. His work shows increasing focus on practical implementations of AI in clinical settings while maintaining rigorous attention to privacy and trustworthiness. Dr. Samavi completed his Ph.D. at the University of Toronto in 2013 and has maintained consistent scholarly productivity, with publication output accelerating significantly in recent years. His work has been referenced in patents, picked up by news outlets, and shared across academic social networks. As an educator, he has taught multiple courses including Modern Software Technology for eHealth (CAS 757), Information Privacy and Security (CAS 767), and Computer Science Practice and Experience (COMPSCI 2XB3) from 2017 through 2020, demonstrating commitment to training the next generation of computer scientists in healthcare applications.

• Human-Computer Interaction
• User Experience
• Player Experience
• +۶ مورد دیگر

Prof. Dr. Maximilian Altmeyer serves as Professor of Web Development and Mobile Applications Engineering at Saarland University of Applied Sciences within the School of Engineering. He leads the interaction experience group and holds key administrative roles including Member of the Faculty Council, Member of the University Library Advisory Board, and Deputy Head of Production Informatics. His work bridges academic research with practical application in digital systems. Altmeyer's research centers on Human-Computer Interaction with specialized focus on User Experience (UX), Player Experience (PX), Gamification, and Persuasive Technology. His investigations span Digital Health applications, gameful design principles, and interactive system development. Recent work examines voice interfaces, music effects in gaming, and sustainable behavior change through gamified eco-feedback systems. He employs rigorous empirical methods including instrument validation, comparative studies, and large-scale literature reviews. His publication trajectory reveals consistent contributions to top venues like CHI and CHI PLAY, with growing emphasis on user typology validation (Hexad scale), multimodal feedback systems, and cross-cultural adaptation of research instruments. The work demonstrates strong methodological diversity spanning controlled experiments, content analysis of 280+ papers, and longitudinal behavior change studies. Altmeyer actively shapes the research community through leadership roles including Work In Progress Co-Chair for CHI Play 2024/2023, Associate Chair for CHI 2026-2024, and extensive reviewing for premier HCI conferences. He teaches across bachelor's and master's programs covering Web Development, Gamification, Theoretical Computer Science, and Empirical Evaluation of Interactive Systems. The interaction experience group under his direction focuses on designing, implementing, and evaluating interactive systems with particular attention to user-centered gamification techniques and persuasive health technologies. Current projects include voice experience measurement tools, office wellness interventions, and game-based learning frameworks.

• Machine Learning
• Computational Biology
• Protein Structure Prediction
• +۳ مورد دیگر

Wouter Boomsma is a Professor in the Machine Learning section of the Department of Computer Science at the University of Copenhagen. He leads the BioML research group, which focuses on developing machine learning techniques to address fundamental challenges in biology, particularly in the realm of protein science. His research interests center around understanding the relationship between protein sequence, structure, and function through advanced machine learning approaches. Key research areas include representation learning of protein sequences and structures, 3D structure prediction, and systematic protein optimization using techniques like Bayesian optimization. His work has significant applications in protein engineering, basic biological research on mutation effects in protein interaction networks, and health-related studies on human disease mutations. Boomsma's research program recently received substantial funding from the Novo Nordisk Foundation to establish the Center for Basic Machine Learning Research in Life Science (MLLS), a collaborative effort involving six machine learning research groups in the Copenhagen area. This initiative underscores the significance and potential impact of his work at the intersection of machine learning and life sciences. His publication record demonstrates a consistent focus on applying machine learning to protein science challenges, with recent work emphasizing protein structure prediction, variant effect analysis, and Bayesian optimization for protein engineering. The research output shows strong interdisciplinary connections between computer science, biology, and medicine. Boomsma maintains active collaborations with industry partners and research institutions, with his work gaining recognition through citations and media coverage. His group's research has been featured in high-impact journals including Nature Methods, Nature Communications, and PLOS Computational Biology. The BioML group operates within the Department of Computer Science's Machine Learning section at the University of Copenhagen, with physical location at Universitetsparken 1, Copenhagen Ø. The group maintains an active presence through their website (https://ku-bioml.github.io) and continues to expand its research portfolio in machine learning for biological applications.

Xiaoxuan Yangمشاهده پروفایل

استادیار

• Processing-in-Memory Systems
• Neuromorphic Computing
• Hardware Accelerators
• +۳ مورد دیگر

Xiaoxuan Yang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Virginia . Her research focuses on processing-in-memory-based system design , biologically plausible systems , and hardware accelerators for emerging applications. She has held postdoctoral positions at Stanford University's Robust Systems Group and served as a research scientist at the University of Virginia. Ph.D. : Electrical and Computer Engineering, Duke University M.S. : Electrical Engineering, University of California, Los Angeles B.S. : Electrical Engineering, Tsinghua University Her research integrates neuromorphic computing , LLM acceleration , and hardware-software co-design , with specific interests in ReRAM crossbars , photonic neural networks , and memristor synapses . Current projects address stochastic noise resilience , quantization optimization , and energy-efficient AI . The 15 most recent publications explore PIM architectures (38%), neuromorphic systems (30%), ML hardware (25%), and optical computing (7%). Key trends include large language model acceleration , hardware robustness , and emerging memory technologies . Third Place ACM Student Research Competition (ICCAD) Best Research Award ACM SIGDA Ph.D. Forum (DAC) Best Paper Award GLSVLSI 2025 Rising Star in EECS NSF iREDEFINE Fellow Machine Learning and Systems Rising Star Rising Scholars Postdoc Fellow

• Artificial Intelligence
• Explainable AI (XAI)
• Computational Argumentation
• +۸ مورد دیگر

Antonio Rago is a Researcher at Imperial College London's Department of Computing (Faculty of Engineering). His work focuses on explainable AI (XAI), computational argumentation, and their applications in engineering design, Formula One strategy, and healthcare. He is affiliated with the ADIX, XAI, and Computational Logic & Argumentation research groups. Education: PhD (Computational Logic & Argumentation) from Imperial College London (2019), MEng in Automotive Engineering from Loughborough University (2012) Professional Experience: Former Race Strategy Engineer at Mercedes AMG Petronas F1 Team (2012-2014), Quantitative Development roles in industry Research interests span argumentation frameworks, counterfactual explanations, and ethical AI systems. He organizes international workshops like ArgXAI and Arg&App, and co-leads tutorials on Argumentative Explanations in AI at conferences including KR and ECAI. His publications emphasize robust AI systems, human-centric explanations, and applications in high-stakes domains like motorsport strategy and clinical decision-making. He collaborates with institutions such as Sapienza University of Rome under the UK-Italy Trustworthy AI Programme.

Majid Afsharمشاهده پروفایل

دانشیار

• Machine Learning in Healthcare
• Critical Care Medicine
• Clinical Deterioration Prediction
• +۳ مورد دیگر

Majid Afshar is an Associate Professor in the Division of Pulmonary and Critical Care Medicine at the University of Wisconsin–Madison's Department of Medicine. He leads the ICU Data Science Lab, focusing on AI-driven solutions for critical care and clinical decision support. His research integrates machine learning, natural language processing (NLP), and electronic health records (EHR) to improve outcomes in areas like clinical deterioration prediction, substance use disorder identification, and personalized treatment planning. Key research themes include multimodal deep learning models for early detection of critical events, large language model applications in healthcare documentation and bias detection, and development of diagnostic tools for infection and opioid use. He has pioneered the use of AI digital twins for medication prediction in ICUs and created validated clinical prediction scores like eCARTv5 for early warning systems. His work emphasizes real-world implementation, including EHR-integrated AI tools for opioid risk screening and patient message response automation. Awards and recognitions are not explicitly listed in the provided text. However, his contributions to TRIPOD-LLM reporting guidelines for large language models highlight his leadership in AI ethics and methodological rigor. Collaborative projects involve multi-center validations and partnerships with state agencies for substance misuse data dashboards. His lab actively develops tools to reduce healthcare disparities and improve clinical workflows through NLP and machine learning.

David Wongمشاهده پروفایل

استاد

• Spatial Analysis
• Geographic Information Systems
• Demography
• +۴ مورد دیگر

David Wong is a Professor in the Department of Geography and Geoinformation at George Mason University's College of Science. His research profile indicates strong affiliations with geospatial analysis and social-demographic studies. Contact information includes his institutional email and faculty webpage. Research Focus Professor Wong's research spans multiple dimensions of spatial analysis with particular emphasis on: Spatial segregation patterns across racial, economic and age dimensions Geographic Information Science methodologies for social and health applications Urban dynamics including transportation systems and infrastructure development Demographic analysis using spatial statistics and emerging data sources His recent work shows growing integration of computational methods like machine learning with traditional geographic approaches. Publication Trends Analysis of recent publications reveals three dominant themes: (1) Advanced spatial analysis of social segregation patterns and health disparities, particularly through multidimensional frameworks; (2) Methodological innovations in GIS and spatial statistics; (3) Emerging applications of AI/ML in geographic problem-solving, including natural language processing for geographic data extraction and transportation modeling. Temporal analysis shows increasing focus on contemporary issues like EV infrastructure, pandemic spread, and computational social science. Awards and Recognition No specific scientific awards or honors are documented in the provided materials. Academic Contributions While no student advising information is available, Professor Wong maintains an extensive publication record with consistent contributions to spatial methodology development and applications across urban, demographic, and health domains. Grant activities aren't specified in available documentation. Research Infrastructure The text provides no explicit information about laboratories, research teams, or specialized facilities associated with Professor Wong's work.

Michael Reissمشاهده پروفایل

پژوهشگر ارشد

• Political Communication
• News Consumption
• Generative AI
• +۴ مورد دیگر

Dr. Michael Reiss is a Research Fellow and Postdoc at the Leibniz Institute for Media Research | Hans Bredow Institute (HBI) since November 2023. He leads the BMBF-funded research project 'Generative Artificial Intelligence for Information Navigation,' investigating the societal implications of generative AI in political information contexts. His work is part of Research Program 1 on the 'Transformation of Public Communication.' Education: Bachelor's in Sociology and Economics, University of Heidelberg Master's in Socio-Ecological Economics and Policy (Vienna University of Economics and Business) Master's in Social Research Methods (London School of Economics and Political Science) Cumulative Dissertation (University of Zurich, Faculty of Arts): 'News Must Die for News to Live' (2023) His research focuses on political communication, news consumption patterns, computational social science methodologies (e.g., large language models), and the societal impact of digital media. Recent projects explore generative AI's role in information navigation and public opinion formation. Grants & Projects: Leading the BMBF-funded 'Generative AI for Information Navigation' project Contributions to the HBI's Research Program 1 Labs/Teams: Embedded within the Hans Bredow Institute's interdisciplinary research environment.

Chang Luمشاهده پروفایل

استاد

• Microfluidic Biotechnology
• Precision Medicine
• Epigenomics
• +۵ مورد دیگر

Chang Lu is the Fred W. Bull Professor of Chemical Engineering and Graduate Chair at Virginia Tech's College of Engineering. He holds a Ph.D. (2002), M.S. (2001) from the University of Illinois at Urbana-Champaign and a B.S. (1998) from Peking University. His research focuses on microfluidic biotechnology, precision medicine, epigenomics, and single-cell analysis with applications in cancer, immunology, and brain development. Recent work explores AI-driven healthcare solutions, spatiotemporal data modeling, and ethical urban digital twins. His publications span machine learning for healthcare, cybersecurity, and environmental systems. He advises on interdisciplinary projects at the intersection of engineering and life sciences. Education: Ph.D. Chemical Engineering, University of Illinois at Urbana-Champaign, 2002 M.S. Chemical Engineering, University of Illinois at Urbana-Champaign, 2001 B.S. Chemical Engineering, Peking University, 1998 Research Interests: Microfluidic systems for biomedical applications Epigenetic regulation in disease contexts Single-cell omics technologies AI in precision medicine Network science for urban and biological systems Recent publications highlight innovations in healthcare AI (e.g., VHC-Bot chatbots), multimodal housing datasets (HouseTS), and ethical frameworks for urban planning. His work bridges chemical engineering with data science, addressing challenges in healthcare, sustainability, and cybersecurity. Collaborations span academia and industry, focusing on translational research. Grants & Awards: Not explicitly listed in provided text. Labs/Teams: Leads interdisciplinary teams at Virginia Tech's College of Engineering, collaborating on projects involving machine learning, genomics, and systems biology.