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Matthew PainterView profile

Professor

• Wealth Inequality
• Social Stratification
• Immigration Studies
• +3 more

Matthew Painter is a Professor and Chair of the Department of Sociology at the University of North Texas. His research focuses on wealth attainment, social stratification, and the intersection of race/ethnicity, immigration, and socioeconomic inequality. He has served as department chair since 2024 and has published widely in top journals including Demography , Social Forces , and Social Problems . His work explores topics such as skin-tone-based disparities, immigrant economic integration, and white ethnic diversity in rural communities. Office hours are by appointment during summer 2025. Education: Ph.D., Sociology, The Ohio State University, 2010 Research Interests: Wealth inequality and its structural determinants Racial/ethnic stratification in asset accumulation Immigrant integration and labor market outcomes Skin tone and health disparities among new immigrants Community dynamics in rural U.S. settings Recent Scholarly Trends: Recent publications emphasize moving beyond binary frameworks in studying gender expression and LGBQ+ identities, while continuing longstanding investigations into immigrant wage channeling and police resource allocation in municipalities with Hispanic populations. His work consistently highlights how race, skin tone, and gender intersect with socioeconomic outcomes. Advising & Grants: While specific student advisees aren’t listed here, Dr. Painter’s research has been supported by grants focusing on immigrant integration and wealth disparities. He advises students pursuing studies in social stratification, migration, and intersectional inequality. Labs/Teams: Engages collaboratively with interdisciplinary teams studying urban/rural inequality and migration policy at the University of North Texas.

Chengnian SunView profile

Associate Professor

• Software Engineering
• Software Reliability
• Software Security
• +5 more

Chengnian Sun is an Associate Professor at the University of Waterloo's David R. Cheriton School of Computer Science. He holds a Ph.D. from the National University of Singapore (2013). His research focuses on software engineering, emphasizing software reliability, security, and developer productivity. His work spans compiler testing, program analysis, cybersecurity, and programming language tools. Key research trends include leveraging large language models (LLMs) for compiler testing and program reduction, probabilistic debugging techniques, and enhancing software security against ransomware and fuzzing attacks. His contributions address challenges in program simplification, fault localization, and vulnerability detection. Notable projects include frameworks like Perses (syntax-guided program reduction), T-Rec (language-agnostic program reduction), and tools like AddressWatcher for memory leak detection. His work bridges theoretical advancements with practical software engineering solutions. Chengnian advises on compiler reliability, cybersecurity, and developer productivity. His research has led to collaborations with industry on testing tools and security frameworks. He maintains an active lab focused on advancing software systems through rigorous analysis and innovation.

Chengnian SunView profile

Associate Professor

• Software Engineering
• Programming Languages
• Compiler Testing
• +4 more

Chengnian Sun is an Associate Professor at the Cheriton School of Computer Science , University of Waterloo, Canada. His research focuses on software engineering and programming languages with an emphasis on software reliability and programming productivity. Education : Ph.D. in Computer Science from National University of Singapore (2013) His work spans compiler testing (EMI, Dfusor, Kitten), program reduction (Perses, Vulcan, PPR), Android testing, and DNN testing. He has received multiple grants including Google Research Scholar Program (2025) and NSERC Discovery Grants (2024-2029). His recent publications focus on LLM-based compiler testing, weighted delta debugging, and ransomware resilience. Scientific Awards : Most Influential Paper Award at SANER (2022) NUS Research Scholarship (2008-2012) ACM SIGSOFT Distinguished Paper Award at ASE (2012) IBM Cup Campus Innovation Contest First Prize (2005) He advises Ph.D. and MMath students in software engineering, compiler testing, and program analysis, including several who have contributed to top-tier conferences like ICSE, ISSTA, and ASPLOS. His service includes program committee roles in ICSE, OOPSLA, and ISSTA.

Rainer KellerView profile

Professor

• Operating Systems
• Distributed Systems
• Parallel Programming
• +4 more

Prof. Dr.-Ing. Rainer Keller serves as Vice Dean of the School of Computer Science and Information Technology at Esslingen University of Applied Sciences. He concurrently holds Laboratory Management roles for both the Operating Systems Laboratory and Information Technology Laboratory, coordinates the Applied Computer Science (Master) program, and serves on the Environmental Committee for his school. His academic journey includes a Doctorate in Engineering (with distinction), Research Associate and Group Leader positions at HLRS, University of Stuttgart (leading the 9-member "Applications, Models and Tools" group), PostDoc tenure at Oak Ridge National Laboratory (ORNL), and prior appointments at Stuttgart University of Applied Sciences. Keller's research centers on operating systems, distributed systems, and HPC with specialized expertise in Linux-based parallel programming tools and file I/O optimization. His work bridges theoretical system models with practical performance tuning, particularly in high-performance computing environments where syscall tracing and storage optimization are critical. Recent publications reveal consistent focus on system-level performance analysis, evolving from foundational file I/O profiling (2020) to advanced syscall tracing mechanisms (2022) and forward-looking access optimization frameworks (2025). These works demonstrate applied research in Linux ecosystems with direct relevance to HPC infrastructure. As Program Coordinator for the Applied Computer Science (Master) program and laboratory manager for two key facilities, he oversees academic development and hands-on technical training. His consultation hours (Tuesdays 1-2 PM by appointment) support student engagement in systems research. He actively contributes to national HPC infrastructure as a Member of the State User Committee (LNA) bwHPC, influencing regional high-performance computing strategies while maintaining his laboratory management responsibilities at Esslingen.

• Analysis
• Geometry
• Probability
• +3 more

Nicholas Marshall is an Assistant Professor in the Department of Mathematics at Oregon State University. His work bridges analysis, geometry, and probability with strong applications in data science. Current faculty: Oregon State University Postdoctoral training: Princeton University (NSF Fellowship) Doctoral training: Yale University Undergraduate education: Clarkson University His research focuses on: Interplay between geometric structures and probabilistic models Development of numerical methods for high-dimensional data Applications in cryo-electron microscopy and hyperdimensional computing Analysis of stochastic algorithms and convergence properties Recent publications indicate significant contributions to: Harmonic expansion techniques Equivariant function learning Momentum-accelerated optimization methods Binary hyperdimensional geometry NSF Postdoctoral Fellowship (Princeton) He actively mentors graduate and undergraduate students, including Wyatt Whiting, Peter Cowal, Heather Fogarty, and Seth Alderman. Teaching appointments include advanced courses in probability theory, numerical linear algebra, and mathematics of data science.

Alaa ShetaView profile

Professor

• Machine Learning
• Deep Learning
• Evolutionary Computation
• +9 more

Alaa Sheta is a tenured Professor of Computer Science at Southern Connecticut State University , New Haven, CT, USA. With over 180 refereed publications, three authored books, and extensive funded research, he is a globally recognized authority in machine learning, evolutionary computation, image processing, and robotics. Education B.E. Electronics & Communication Engineering, Cairo University, 1988 M.Sc. Electronics & Communication Engineering, Cairo University, 1994 Ph.D. Computer Science, George Mason University, USA, 1997 Research Interests Prof. Sheta’s research integrates machine learning , deep learning , and evolutionary algorithms to solve complex real-world problems. Core themes include image and signal processing for medical and industrial applications, autonomous robotics for navigation and inspection, big-data analytics for environmental and financial forecasting, and software reliability modeling using computational intelligence. His work frequently leverages meta-heuristic optimization techniques such as genetic algorithms, particle swarm optimization, and hybrid neuro-fuzzy systems. Publication Trends From 2015-2021, Prof. Sheta’s publications reveal a clear pivot toward deep learning and healthcare informatics , with multiple studies on obstructive sleep-apnea diagnosis using ECG and depth-sensor data, brain-tumor detection in MR images, and mobile-health applications. Earlier work emphasizes industrial process modeling , power-system optimization , and software effort estimation , reflecting sustained contributions across both theoretical algorithmic advances and high-impact interdisciplinary applications. Scientific Awards & Honors Best Poster Award, SGAI International Conference on Artificial Intelligence, Cambridge, UK, 2011 Senior Member, IEEE Vice-President, Arab Computer Society (2011) Associate Editor, International Journal of Advanced Computer Science and Applications (IJACSA) Associate Editor, International Journal of Computational Complexity and Intelligent Algorithms (IJCCIA) Advising & Grants Prof. Sheta has successfully supervised more than 30 master’s and Ph.D. students in the United States, United Kingdom, Jordan, and Syria. His research has been funded by the U.S. National Science Foundation , as well as agencies in Egypt, Saudi Arabia, and Jordan. He has also consulted for the Egyptian Ministry of Communication & IT (2002-2004) and UNDP Smart Schools project (2003). Labs, Workshops & Leadership He is the founder and chair of the Advanced Computation for Engineering Applications (ACEA) workshop series, held five times across Egypt, Jordan, and Saudi Arabia. He served as Program Chair of the Science and Information Conference 2013 in London and has held academic leadership roles such as Associate Dean (2008-2009) and Assistant Dean for Planning & Development (2006-2008) at Al-Balqa Applied University, Jordan.

Loutfouz ZamanView profile

Associate Professor

• User research in gaming
• Game analytics and data science
• Game evaluation and testing
• +2 more

Dr. Loutfouz Zaman is an Associate Professor in the Game Development and Interactive Media department at Ontario Tech University , part of the Faculty of Business and Information Technology . He holds a PhD in Computer Science with a focus on Human-Computer Interaction from York University. His research explores visual programming interfaces, game analytics, and extended reality technologies, with a focus on practical applications such as automated bug detection and user experience optimization. Dr. Zaman has collaborated on projects funded by MITACS, NSERC, and industry partners, addressing challenges in healthcare incident management, language learning gamification, and air traffic control simulation training. He teaches courses ranging from introductory game math to graduate-level topics in human-computer interaction and machine learning for game analytics. Education: Bachelor of Science in Computer Science (Software Systems) – Concordia University Master of Science in Computer Science (Human-Computer Interaction) – York University PhD in Computer Science (Human-Computer Interaction) – York University Research Interests: Dr. Zaman’s work spans user research in gaming, game evaluation methodologies, and emerging technologies like AR/VR. His team focuses on developing tools for visual game analytics and automated testing, with recent projects including mixed reality fitness gaming interfaces and deep learning-based bug detection systems. Grants & Collaborations: His industry partnerships include projects on healthcare incident management during pandemics, CRM gamification, and pet identification systems using computer vision. He actively seeks doctoral candidates to expand research in visual analytics, XR technologies, and automated bug detection. Labs & Affiliations: Dr. Zaman is affiliated with the Software and Informatics Research Centre (SIRC) and contributes to the university’s efforts in bridging academic research with real-world applications.

Mustafa ÖzuysalView profile

Assistant Professor

• Computer Vision
• Object Detection
• Object Tracking
• +5 more

Mustafa Özuysal is an Assistant Professor in the Department of Computer Engineering at the College of Engineering, Izmir Institute of Technology (İYTE), where he leads the Visual Intelligence Research Group. His research focuses on computer vision, including object detection, tracking-by-detection, and real-time scene text recognition, with applications on mobile devices. Research Interests: Large-scale object detection Object detection and tracking on mobile platforms Real-time scene text recognition Feature learning from image and video sequences Augmented reality and camera egomotion estimation His scholarly work includes influential publications in IEEE TPAMI, IET Computer Vision, and top-tier conferences such as CVPR and ECCV, particularly in local feature descriptors like BRIEF and keypoint recognition using random ferns. His research emphasizes efficient, real-time algorithms suitable for embedded and mobile systems. Scientific Contributions: Co-developer of the BRIEF descriptor, widely used in computer vision for fast binary feature matching. Contributor to tracking-by-detection frameworks and homography estimation methods robust to occlusions and viewpoint changes. Dr. Özuysal has taught a range of undergraduate and graduate courses, including Introduction to Image Understanding, Vision-Based Tracking and Modeling, Data Structures, and Mobile Application Development. He advises the Visual Intelligence Research Group and continues to advance research in scalable and efficient vision systems.

Jukka SuomelaView profile

Associate Professor

• Distributed Computing
• Distributed Algorithms
• Local algorithms
• +7 more

Jukka Suomela is an Associate Professor in the Department of Computer Science at Aalto University, Finland. His research focuses on the theoretical foundations of distributed and parallel computing, with a strong emphasis on locality and algorithmic complexity. Current roles: Associate Professor, Aalto University Research group: Distributed Algorithms Key conferences: PC Chair for DISC 2019, SIROCCO 2016, Local Chair for ALGO 2018 Steering Committee: Vice Chair, DISC Research Interests include distributed algorithms, local algorithms, parallel computing, graph algorithms, and computational complexity. His work explores fundamental limits of distributed systems and the role of locality in algorithm design. Notable Scientific Awards : FOCS 2019 Best Paper Award DISC 2012 & 2017 Best Paper Awards MSc Thesis Award 2006, University of Helsinki Best Junior Researcher 2008, University of Helsinki Nokia Scholarship 2008 Teaching includes Programming Parallel Computers (freely available), CS-E4510 Distributed Algorithms, and other courses in the Finnish Computer Science major/minor programs. His publications span distributed systems theory, graph algorithms, and interdisciplinary projects in linguistics and cultural analytics.

• Image Processing and Computer Vision
• Machine Learning
• Evolutionary Computation
• +1 more

Seyed Jalaleddin Mousavirad (Jalal) serves as a Postdoctoral researcher at Mid Sweden University in Sundsvall, Sweden, within the Department of Computer and Electrical Engineering (DET) and affiliated with the STC Research Centre. His research focuses on advancing AI-driven solutions for sustainable technologies and complex optimization problems. He earned his PhD in Computer Engineering specializing in Artificial Intelligence from the University of Kashan, Iran. Previous academic appointments include Assistant Professor at Hakim Sabzevari University (Iran), instructor roles at the University of Tehran (2018-2019) and Azad University (2019-2020), and a Research Fellow position at the University of Beira Interior (Portugal) where he contributed to the European GreenStamp project on sustainable Android applications. Dr. Mousavirad's research spans Image Processing and Computer Vision, Machine Learning, Evolutionary Computation, and Applied Artificial Intelligence, with significant contributions in pattern recognition, metaheuristic algorithms, and neural network optimization. His work demonstrates strong interdisciplinary applications in healthcare diagnostics, power systems, and medical imaging. Recent publications reveal a pronounced trend toward federated learning frameworks for privacy-preserving medical analysis, adversarial robustness in diffusion models, and hybrid optimization techniques for ECG classification and brain tumor detection. This reflects a strategic focus on translating AI innovations into practical healthcare and sustainability solutions. He actively contributes to the academic community as a guest editor for journals including Computational Intelligence and Neuroscience, Entropy, and Mathematical Biosciences and Engineering. His editorial leadership extends to organizing special sessions at IEEE CEC and EvoApplications conferences. Dr. Mousavirad maintains extensive peer-review commitments across 50+ prestigious venues including IEEE Transactions on Evolutionary Computation and IEEE Transactions on Cybernetics. His collaborative research includes international engagements at Xi'an Jiaotong-Liverpool University (China) and current work within Mid Sweden University's STC Research Centre on energy-aware computing and neural network optimization.

• Machine Learning
• Computer Vision
• Medical Imaging Analysis
• +5 more

Almabrok Essa is an Assistant Professor in the Department of Mathematics, Computer Science, and Data Science at John Carroll University. His research focuses on advanced machine learning techniques applied to medical imaging, computer vision, and signal processing. He holds a faculty position at Dolan Science Center E207 and can be reached via email at aessa@jcu.edu. His work bridges theoretical computer science with practical applications in healthcare, environmental monitoring, and security systems. Research interests include deep learning architectures for medical diagnostics (e.g., chest X-ray analysis and skin lesion segmentation), steganography for secure image transmission, and remote sensing technologies for geospatial analysis. He has pioneered methods like Multibit LSB matching for data hiding and developed frameworks like COVID-CLNet for pandemic response. His contributions span over 20 peer-reviewed articles from 2015 to 2025, showcasing innovations in face recognition algorithms, brain signal analysis, and environmental pattern recognition. Notable applied projects include real-time heart/respiration monitoring systems and automated machinery threat detection for infrastructure protection. His research demonstrates strong interdisciplinary potential, combining computational methods with domains like biomedical engineering, environmental science, and cybersecurity.

Richard Martin LusbyView profile

Associate Professor

• Operations Research
• Mathematical Optimization
• Logistics and Supply Chain Management
• +5 more

Richard Martin Lusby is an Associate Professor in the Department of Technology, Management and Economics at DTU Management, Technical University of Denmark. His academic work focuses on Operations Research with specialization in mathematical optimization applied to transportation and energy systems. His research spans railway scheduling, container logistics, maritime transport, and energy storage optimization. Department: Department of Technology, Management and Economics Section: Section for Operations Research, Division for Management Science Location: Building 358, Room 145A, Academy Road, 2800 Kgs. Lyngby, Denmark Contact: rmlu@dtu.dk, Tel: 45253084 ORCID: 0000-0002-8652-6219 Professor Lusby's research interests center around mathematical optimization methods applied to complex transportation and energy systems. His work spans railway scheduling problems, container logistics optimization, maritime transport operations, and energy storage scheduling. He develops advanced algorithms including Lagrangian Relaxation, Benders decomposition, and stochastic programming approaches to solve challenging real-world problems in these domains. His research has practical applications in improving efficiency and sustainability in transportation networks and energy systems. His recent publications demonstrate a strong focus on optimization techniques for transportation systems, particularly in railway operations and container logistics, as well as energy storage scheduling. The research shows a consistent pattern of developing sophisticated mathematical methods to address complex operational challenges in transportation networks, with increasing attention to sustainability considerations in maritime shipping and energy systems. Professor Lusby has been involved in significant research projects including the SWITCH project (Sustainable Network Design - Electrifying Container Shipping), which received funding from Den Danske Maritime Fond. This project explores how battery-powered ships could replace fossil fuels in global shipping through mathematical modeling and AI-driven optimization. He also supervises PhD students, including Georgios Vassos who completed a thesis on "Procurement Policy Analysis in Intermodal Container Logistics". His work has practical implications for improving efficiency in transportation networks, reducing environmental impact in maritime shipping, and optimizing energy systems operations. Through his research, Professor Lusby contributes to both theoretical advances in optimization methods and their practical applications in critical infrastructure sectors.

Amir H. BanihashemiView profile

Professor

• Digital Communications
• Wireless Communications
• Coding and Information Theory
• +5 more

Amir H. Banihashemi is a Professor of Systems and Computer Engineering at Carleton University, holding a P.Eng. designation. He earned his B.A.Sc. from Isfahan University of Technology, M.A.Sc. from Tehran University, and Ph.D. from the University of Waterloo. His academic career includes postdoctoral research at the University of Toronto and roles such as Director of the Broadband Communications and Wireless Systems (BCWS) Centre at Carleton until 2013. He has authored over 100 publications in top-tier journals and conferences, focusing on coding theory, digital communications, and signal processing. Notable awards include the 2017 IEEE Ottawa Section Outstanding Engineer Award and Carleton's Research Achievement Awards (2006, 2012). His research group actively explores LDPC codes, compressed sensing, and spectrum sensing in cognitive radios. Key contributions include error floor analysis, trapping set elimination, and efficient decoding algorithms. Education: B.A.Sc. Electrical Engineering, Isfahan University of Technology (GPA 19.4/20) M.A.Sc. Communication Engineering, Tehran University (GPA 18.74/20) Ph.D. Electrical and Computer Engineering, University of Waterloo Research Interests: Digital and Wireless Communications Coding and Information Theory Signal Processing Algorithms LDPC Codes Design and Analysis Recent Projects: Compressed Spectrum Sensing for Cognitive Radios (2021 FED Research Award) Error Floor Mitigation in LDPC Decoding Trapping Set Characterization Awards highlight his pioneering work in error correction coding and digital communications. His funded research includes NSERC Discovery Grants and industry partnerships. He has supervised numerous graduate students and advised postdoctoral fellows in coding theory and communications.

• Artificial Intelligence
• Bioinformatics
• Machine Learning
• +7 more

Enrico Blanzieri is an Associate Professor at the University of Trento's Department of Information Engineering and Computer Science. His research focuses on artificial intelligence, bioinformatics, quantum computing, machine learning, and computational biology. He actively contributes to interdisciplinary projects such as Vitis OneGenE for gene network analysis and quantum machine learning pipelines. His teaching includes courses like Algorithms for Bioinformatics, Quantum Machine Learning, and Data Mining. Blanzieri's work bridges theoretical foundations and practical applications, with notable contributions in quantum annealing-based algorithms, gene network expansion using distributed computing, and socially-competent artificial agents. His research integrates computational methods with biological systems, addressing challenges in precision agriculture and systems biology. His recent publications emphasize quantum machine learning advancements, causal role attribution in gene networks, and ethical AI frameworks for language models. Collaborative projects include the GENE@HOME volunteer computing initiative for bioinformatics tasks. Blanzieri also engages in educational activities, supervising courses that blend theoretical computer science with real-world applications.

Jingjing MengView profile

Researcher

• Multimedia
• Computer Vision
• Machine Learning
• +3 more

Jingjing Meng is a Senior Scientist affiliated with the Computer Science and Engineering Department at the University at Buffalo, SUNY, and Amazon. She holds a Ph.D. from Nanyang Technological University (NTU, Singapore), advised by Prof. Yap-Peng Tan, along with an M.S. from Vanderbilt University and a B.E. from Huazhong University of Science & Technology, China. Her research focuses on multimedia, large multimodal models, product recommendation/search, and computer vision applications. Notable contributions include work on surgical triplet recognition, 3D object representation, and video summarization. She has received the 2016 IEEE Transactions on Multimedia Best Paper Award. Service Roles: Technical Program Co-Chair (ICME 2024), Tutorial Co-Chair (ACM MM 2024), Area Chair (AAAI 2021-2025), and Associate Editor for IEEE TMM, Signal Processing: Image Communication, and others. Leadership: Member of IEEE IVMSP TC, VSPC TC, and MSA TC committees, and a Senior Member of IEEE. Teaching includes courses like Multimedia Systems (CSE 534), Computer Graphics (CSE 410/580), and Discrete Structures (CSE 191). Her work bridges theoretical advancements and practical applications in multimedia and AI.