جست‌وجوی استاد و پژوهشگر

Luigi Benedicentiمشاهده پروفایل

استاد

• Software Agents
• Software Process Improvement
• New Media Technology
• +۳ مورد دیگر

Dr. Luigi Benedicenti is a Professor and Dean of the Faculty of Computer Science at the University of New Brunswick. He holds a Laurea in Electrical Engineering and a Ph.D. in Electrical and Computer Engineering from the University of Genoa, Italy. A licensed Professional Engineer in Saskatchewan and an Italian-licensed Engineer, he collaborates extensively with TRLabs and IEEE, engaging in global research partnerships across Europe, Southeast Asia, and North America. His research focuses on three core areas: Software Agents, Software Process Improvement, and New Media Technology. Current work emphasizes unifying distributed digital service platforms, optimizing delivery across devices and locations. Benedicenti's recent studies explore emotional contagion in open-source collaborations, agile methodologies in mission-critical systems, and decision-support frameworks for software development. Publications highlight themes like emotional dynamics in agile teams, optimization in manufacturing processes, and adaptive frameworks for defense software. His work bridges theoretical models with practical applications in security-critical systems and ubiquitous computing environments. Professional contributions include leadership roles in advancing software engineering practices and fostering interdisciplinary collaboration. His research integrates technical innovation with human-centric design principles to address complex challenges in modern software ecosystems.

Francis Palmaمشاهده پروفایل

استادیار

• Empirical Software Engineering
• Software Architecture
• Machine Learning
• +۶ مورد دیگر

Dr. Francis Palma is an Assistant Professor in the Department of Computer Science at the University of New Brunswick (UNB), Fredericton, Canada. He leads the SE+AI Research Lab and holds a Ph.D. in Software Engineering from Ecole Polytechnique de Montreal (2015). His research focuses on Empirical Software Engineering, Machine Learning applications in software testing, and Service-Oriented Architecture. Previously, he worked as an Assistant Professor at Linnaeus University (Sweden), a Postdoctoral Research Fellow at Ryerson University and Concordia University, and a Research Scientist at Screaming Power Inc. Education: Ph.D. (2015) - Ecole Polytechnique de Montreal, M.Sc. (2010) - University of Trento, B.Sc. (2005) - International Islamic University Chittagong. Labs/Teams: SE+AI Lab (UNB), Ptidej Team (PolyMtl), MAGIC Team (Concordia). His work integrates AI techniques into software engineering processes, including requirements prioritization, test case optimization, and architectural quality analysis. Key contributions span antipattern detection in service-based systems and machine learning for energy data management. Awards: Best Thesis 2015 (PolyMtl). Industry Experience: Screaming Power Inc. (mobile energy data solutions).

Nick Nikiforakisمشاهده پروفایل

دانشیار

• Web Security
• Software Security
• Cybersecurity
• +۳ مورد دیگر

Nick Nikiforakis is an Associate Professor in the Department of Computer Science at Stony Brook University, with an affiliation to the National Security Institute. His research focuses on web security, privacy, software security, and intrusion detection, with particular emphasis on analyzing online ecosystems, domain squatting, honeypot deployment, and client-side defense mechanisms. He completed his PhD in Computer Science at KU Leuven (Belgium), and holds an MSc in Parallel and Distributed Systems and a BSc in Computer Science from the University of Crete, Greece. His work has been funded by NSF and ONR grants, including projects addressing vulnerability scanning, social engineering defenses, and mobile browser security. Nikiforakis' awards include a Distinguished Paper Award at NDSS 2017 and a Best Paper Award at ISC 2014. He teaches courses CSE 508 (Computer Security), CSE 509 (Advanced Topics in Security), and CSE 361 (Data Structures and Algorithms). His research explores cutting-edge threats like blockchain naming system abuse, cryptocurrency scams, and adversarial AI-driven misinformation campaigns.

Yifan Sunمشاهده پروفایل

استادیار

• Optimization Algorithms
• Machine Learning
• Scientific Computing
• +۱۰ مورد دیگر

Yifan Sun is an Assistant Professor in the Department of Computer Science at Stony Brook University. She is also affiliated with the AI Institute and the Institute of Advanced Computational Science (IACS) at Stony Brook. Dr. Sun received her PhD in Electrical Engineering from UCLA in 2015, with research focusing on convex optimization and semidefinite programming. Prior to joining Stony Brook, she worked at Technicolor Research and Innovation on machine learning applications and completed postdoctoral research at the University of British Columbia in Vancouver and INRIA in Paris. Her research centers on the design and analysis of optimization algorithms, particularly those arising in large-scale machine learning and scientific computing. Dr. Sun studies how structural properties like sparsity, curvature, and decomposability can be exploited to design faster, more stable, and more interpretable optimization methods. Her work spans both theoretical foundations and practical applications, covering first-order methods, quasi-Newton techniques, and algorithms for convex programming to nonconvex deep models. She also investigates how optimization theory, particularly insights from linear algebra and geometry, can improve understanding of deep learning models, regularization techniques, and representation learning. Dr. Sun's recent publications demonstrate strong trends in large-scale graph learning, advanced optimization techniques (particularly Frank-Wolfe variants), and applications to natural language processing. Her work often bridges theoretical guarantees with practical implementations, as evidenced by her open-source code repositories. She leads the OptML Research lab at Stony Brook, focusing on optimization methods for machine learning challenges. Her publications appear in top venues including NeurIPS, ICML, CVPR, and various optimization journals, reflecting her interdisciplinary approach that connects computer science, applied mathematics, and engineering disciplines.

Prasad A. Kulkarniمشاهده پروفایل

استاد

• Compilers
• Computer Architecture
• Runtime Systems
• +۴ مورد دیگر

Prasad A. Kulkarni is a Professor in the Department of Electrical Engineering and Computer Science (EECS) at the University of Kansas. He leads the Compilers, Architectures, and Runtime Systems (CARS) research group, focusing on compiler optimizations, runtime systems, and cyber-security in embedded systems. His work addresses challenges like efficient parallel execution, compiler phase ordering, and secure software execution on multi-core and heterogeneous architectures. Education: Ph.D. in Computer Science from Florida State University (2007). Awards include the NSF CAREER Award (2010) and the Harry Tally Excellence in Teaching Award (2012, 2017). He has advised numerous graduate students in areas such as compiler transformations, virtual machine optimization, and binary analysis. Research interests span compiler design, computer architecture, and runtime systems. Recent work emphasizes binary analysis for security (e.g., detecting security checks in binaries) and improving performance in embedded systems. His publications explore topics like dynamic binary translation, memory management, and decompiler accuracy. Teaching includes courses on compilers, computer systems design, and security. He actively recruits students interested in solving efficiency and security challenges in modern computing systems.

Hamid Alinejad-Roknyمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Bioinformatics
• Genomics
• +۷ مورد دیگر

Dr. Hamid Alinejad-Rokny is a Scientia Senior Lecturer at UNSW Sydney and Adjunct Associate Professor at Concordia University. He leads the UNSW BioMedical Machine Learning (BML) Lab within the Graduate School of Biomedical Engineering. His research focuses on applying machine learning, bioinformatics, and statistical methods to understand genomic mechanisms underlying diseases like cancer and neurodevelopmental disorders. Dr. Rokny holds a PhD in Biostatistical Machine Learning from UNSW and has secured over $13M in grants as a principal or co-investigator. He has published 80+ papers, including 10 as first author and 45 as senior author. Education: Bachelor’s in Software Engineering (2004-2009), Master’s in Artificial Intelligence (2009-2012), PhD in BioMedical Machine Learning (UNSW, 2014-2018), Postdoc at Harry Perkins Institute (2017-2019). Research interests include medical AI, deep learning, genomic data analysis, and systems biology. He actively supervises 20+ PhD/Master’s students and collaborates with industry partners like CSIRO and 23Strands. Awards include the DECRA 2023, NHMRC MERIT, and International Autism Fellowships. He also serves as a keynote speaker at conferences like HUGO and an Honorary Lecturer at Macquarie University. Grants total $2.75M as lead investigator and $10.6M as co-investigator. Industry partnerships include PORSPA Advance ($4.7M) and Australian Digital Domains ($3.6M). His lab develops tools like MaxHiC and DeepGenePrior for genomic analysis. Labs/Teams: Director of UNSW BML Lab, Health Data Theme Leader at UNSW Data Science Hub. Active in mentoring 13 researchers globally and co-supervising international teams.

Gerui Wangمشاهده پروفایل

مدرس

• AI Ethics
• Art and AI
• Digital Humanities
• +۳ مورد دیگر

Gerui Wang is a Lecturer at Stanford University’s Center for East Asian Studies and the University of California, Santa Cruz. She specializes in AI ethics, digital humanities, and the intersection of art, technology, and environment. Her research includes a focus on governance and ecology in Chinese visual culture, supported by grants from the Chiang Ching-kuo Foundation and Mellon Foundation. She leads the digital humanities project Storytelling with AI , archived by Stanford Libraries, and has published in Journal of Chinese History and Newsletter for International China Studies . Awards include the 2024 Stanford Teaching Advancement Award for her AI-focused courses. Gerui also contributes to AI policy discussions via platforms like the Alan Turing Institute and Forbes, addressing topics like AI’s societal impact and ethical challenges. Her interdisciplinary work spans academia, media, and policy, with affiliations including the NEH-funded Teaching Art History with AI group at the University of Pittsburgh.

Daniel Y Chenمشاهده پروفایل

مدرس

• Data Science Education
• Statistics
• Bioinformatics
• +۳ مورد دیگر

Daniel Y Chen is a Lecturer in the Department of Statistics at the University of British Columbia (UBC), Vancouver Campus. He holds a PhD Candidate status in Genetics, Bioinformatics, and Computational Biology at Virginia Tech, focusing on data science education in the biomedical sciences. His expertise includes statistical computing, open source software development, and reproducible research practices. Education: PhD Candidate (in progress): Genetics, Bioinformatics & Computational Biology, Virginia Tech MSc: Relevant field (implied via career progression) Research Interests: Daniel's work bridges data science pedagogy and biomedical applications. He focuses on improving computational literacy among medical practitioners through accessible education resources, open source tools, and reproducible workflows. His technical contributions span R and Python ecosystems, including packages like grader and pyprojroot , and authoring Pandas for Everyone . He actively promotes best practices in scientific programming and collaborative software development. Grants & Contributions: Contributions to RStudio's gradethis and learnr packages Open source maintenance (e.g., Arch Linux AUR packages) Labs/Teams: Affiliated with UBC's Data Science for Biomedicine (DS4BIOMED) initiative and Virginia Tech's Social Decision Analytics Laboratory. Collaborates with RStudio and GitHub communities on education and tool development.

Kyle Steinfeldمشاهده پروفایل

دانشیار

• Computational Design
• Design Computation
• Artificial Intelligence in Design
• +۳ مورد دیگر

Kyle Steinfeld is an Associate Professor of Architecture and Director of the Master of Design program at the University of California, Berkeley, College of Environmental Design. His work bridges computational design methods and architectural practice, emphasizing critical engagement with software and data. He teaches design studios, visualization courses, and seminars in digital design theories. Master of Architecture, MIT (2004) Bachelor of Design in Architecture, University of Florida (1999) Steinfeld's research explores the intersection of architecture and computational design, focusing on bioclimatic information design, software development, and the social implications of digital tools. His creative work has been exhibited at NeurIPS workshops and published in Towards Data Science . He has received notable awards including the IDEA Studio Fellowship (2013), Hellman Fellowship (2012), and multiple research grants like the Faculty Research Grant for "Decod.es" (2013) and the Research Enabling Grant for "Dhour" (2011). Steinfeld has taught core architectural design courses and contributed to software development for design computation.

Amir Demboمشاهده پروفایل

استاد

• Probability
• Stochastic Processes
• Random Graphs
• +۴ مورد دیگر

Amir Dembo is the Marjorie Mhoon Fair Professor in Quantitative Science at Stanford University, holding appointments in the Department of Mathematics and Statistics, and adjunct status in the Department of Electrical Engineering. His research focuses on probability theory and stochastic processes, with contributions to random graphs, large deviations, spin glasses, and statistical mechanics. He has authored numerous influential papers on topics such as random cluster models, Gibbs measures, and universality in interacting systems. Dembo teaches advanced probability courses (e.g., STAT310/MATH230) and has explored applications of probability in cryptography, including proof-of-stake blockchain protocols. His work bridges theoretical foundations with interdisciplinary applications in physics and computer science. Education details are not explicitly provided, but his academic trajectory is reflected through his professorial roles and research output. His research interests emphasize probabilistic analysis of complex systems, including random walks, graph structures, and non-equilibrium dynamics. Over 30 years of publications highlight his expertise in large deviation principles, asymptotic analysis of stochastic processes, and the interplay between combinatorics and probability. His recent work (2023–2025) explores cutting-edge topics like regular-tree-like graph models, flow-type scaling limits, and geometric area tilts in Brownian polymers. Earlier contributions include foundational studies on Erdős–Rényi hypergraphs, spectral measures of random matrices, and the universality of Langevin spin glass dynamics. While no explicit awards are listed, his prolific publication record and academic leadership reflect significant scholarly impact. Dembo’s advising and grants are not detailed here, but his teaching and research indicate mentorship in probability theory and stochastic modeling. Collaborations with interdisciplinary teams likely extend into areas like theoretical computer science and statistical physics.

Robert Walkerمشاهده پروفایل

استاد

• Software evolution
• Software reuse
• Empirical software engineering
• +۳ مورد دیگر

Robert Walker is a Professor and Assistant Head (Existing Graduate Students) in the Department of Computer Science at the University of Calgary. He holds degrees from the University of British Columbia including a B.S. in Computer Science (1994), B.S. in Geophysics (1992), M.S. in Computer Science (1996), and a Ph.D. in Computer Science (2003). His research focuses on software evolution, reuse, empirical software engineering, and aspect-oriented programming. Key awards include the Italian National Scientific Habilitation (2015) and ACM Distinguished Paper Awards (2012). He teaches courses like SENG 300 (Introduction to Software Engineering) and advanced topics in software evolution. His work addresses challenges in test code reuse from open source software and unanticipated evolution patterns in software product lines.

• Cybersecurity
• Software Engineering
• Machine Learning
• +۳ مورد دیگر

Van Nguyen is a Research Fellow at the Department of Software Systems & Cybersecurity, Monash University. His research focuses on cybersecurity, software vulnerability detection, and machine learning applications in security. He develops tools like AIBugHunter for vulnerability prediction and repair, and explores AI-driven approaches to phishing detection using large language models. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure security. Research interests include deep learning for vulnerability analysis, domain adaptation techniques, and ethical AI applications. Collaborations span cybersecurity, software engineering, and information theory. Recent projects involve frameworks like PEN for analyzing phishing evolution patterns and SAFE for enhancing vulnerability detection via semantic analysis. Publications highlight advancements in automated vulnerability repair, phishing localization, and model interpretability for cybersecurity tasks. His work emphasizes practical tools and theoretical contributions to combating evolving cyber threats.

Antonio Cartaمشاهده پروفایل

استادیار

• Continual Learning
• Deep Neural Networks
• Machine Learning
• +۴ مورد دیگر

Antonio Carta is an Assistant Professor in the Department of Computer Science at the University of Pisa. His primary research focuses on Continual Learning (CL) with deep neural networks, particularly addressing challenges like catastrophic forgetting and knowledge consolidation in online settings. He leads the Computational Intelligence and Machine Learning (CIML) group and contributes to the Pervasive AI Lab (PAILab) . As the lead maintainer of the Avalanche library, he develops end-to-end tools for deep continual learning. His work spans applications in computer vision, NLP, and time series analysis. Research emphasizes distributed CL, efficient knowledge transfer, and replay-free methods. Teaching includes courses on Continual Learning, Collective Machine Intelligence, and Intelligent Systems at the graduate level. Publications highlight adaptive normalization, temporal ensembles, and domain-based Bayesian approaches in CL.

Costas N. Georghiadesمشاهده پروفایل

استاد

• Statistical Communication Theory
• Wireless Communications
• Optical Communication Systems
• +۷ مورد دیگر

Costas N. Georghiades is a Professor and Delbert A. Whitaker Chair in the Department of Electrical & Computer Engineering at Texas A&M University. He has held leadership roles including Interim Vice President for Research, Interim Department Head, and Associate Dean for Research in the College of Engineering. His research focuses on statistical communication theory, wireless communication systems, and receiver design. Education: D.Sc., Electrical Engineering, Washington University (1985) M.S., Electrical Engineering, Washington University (1983) B.E., Electrical Engineering, American University of Beirut (1980) Research Interests: His work spans fading channels, space-time coding, distributed source coding, interference rejection techniques, and optical communication systems . He emphasizes applications of information theory to practical communication challenges, including synchronization and equalization in wireless environments. Publications & Trends: Recent articles address advanced topics like faster-than-Nyquist signaling, unsourced random access in MIMO systems, and visible light communication design. His work often bridges theoretical insights with practical implementation challenges in wireless and optical domains. Awards & Recognition: IEEE Fellow (2002) 2012 IEEE Communication Theory Technical Committee Service Award Multiple endowed professorships including the Delbert A. Whitaker Chair Advising & Grants: He has advised over 50 graduate students, many contributing to foundational research in CDMA systems, MIMO detection, and network coding. His grants include support for projects on cognitive radio, OFDM systems, and secure communication protocols. Labs & Collaborations: Active in Texas A&M's Global Cyber Research Institute (GCRI) and affiliated with the Multidisciplinary Engineering program. Collaborates on initiatives like the IEEE Information Theory Society and Hamming Medal Committee.

Jessie Galasso-Carbonnelمشاهده پروفایل

استادیار

• Software Reverse Engineering
• Knowledge Extraction and Representation
• Variability Management
• +۳ مورد دیگر

Jessie Galasso-Carbonnel is an Assistant Professor in the Department of Electrical and Computer Engineering at McGill University's Faculty of Engineering. She holds a Ph.D. in Computer Science from the University of Montpellier (2018), followed by postdoctoral research at the University of Montréal (2020-2023) under IVADO funding. Her expertise lies in software reverse engineering, knowledge extraction, variability management, and AI-driven pipeline development. Education: Ph.D. in Computer Science (2018), University of Montpellier Postdoctoral Researcher (2020-2023), University of Montréal (Geodes group) Research Focus: Combines symbolic AI, software engineering, and machine learning to address challenges in model extraction, software product line analysis, and low-code solutions for AI pipelines. Current work emphasizes domain-specific AI pipeline automation and accessibility for non-experts. Professional Activities: PC member for IEEE SANER'25, ICSR'24, and MODELS'24 Co-chair of Poster Track (MODELS'24), Doctoral Symposium (SPLC'24), and MODEVAR workshops Invited speaker at OntarioTech, DiverSE (Rennes), and SmartFCA (Nancy) Grants & Funding: Holder of IVADO postdoctoral fellowship (2020) for ML/optimization research in source code recommendation. Labs & Collaborations: Works with Geodes research group and collaborates with institutions like ÉTS and École Polytechnique de Montréal on cross-disciplinary AI/SE projects.