جست‌وجوی استاد و پژوهشگر

Zia Ush Shamszamanمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• AI Governance
• Secure and Safe AI
• +۵ مورد دیگر

Dr. Zia Ush Shamszaman is a Senior Lecturer in Computer Science at Teesside University's School of Computing, Engineering & Digital Technologies (SCEDT). He holds a PhD in Computer Science from the University of Galway and a Master's from Hankuk University of Foreign Studies. His research focuses on Cybersecurity, AI Ethics, IoT Resilience, and Game Theory applications, with a strong emphasis on bridging academic research with industry applications. Teaching responsibilities include postgraduate modules on AI Ethics, Cyber Risk Management, and Ethical Hacking, alongside undergraduate courses in Secure Data Acquisition and Ethical Hacking. He actively supervises five PhD students in cybersecurity and AI, advocating for inclusive technology education and societal impact. Professional memberships include IEEE (Senior Member), Elsevier Advisory Panel, and W3C. He has organized conferences like the International Conference on Suitable Technologies 4.0 and served on program committees for major events. Recognized for exceptional peer reviewing, he has received awards from leading journals like the Journal of Network and Computer Applications and Future Generation Computer Systems. Current projects funded by Innovate UK include CyberPathway (diversity in cybersecurity education), SafeSCMS (AI-driven supply chain security), and Opera-CyberThemis (trustworthy AI frameworks). Past industry roles include technical leadership in Bangladesh's government projects like the Machine Readable Passport system and World Bank-funded Bangladesh Automated Clearing House initiative. Shamszaman's work spans industry collaborations, academic leadership, and community engagement, with recent media contributions on GenAI governance and cyber resilience strategies. His research integrates theoretical advancements with practical solutions for SMEs, healthcare systems, and underserved communities.

Ioana Banicescuمشاهده پروفایل

استاد

• Parallel Algorithms
• Scientific Computing
• Performance Modeling
• +۳ مورد دیگر

Ioana Banicescu is a Professor at Mississippi State University's Department of Computer Science and Engineering. She holds a Ph.D. in Computer Science from New York University and previously directed the Center for Cloud and Autonomic Computing. Her research focuses on parallel algorithms, scientific computing, performance modeling, and autonomic systems. Her education includes: Ph.D. Computer Science - New York University M.S. Computer Science - New York University B.S./M.S. Electronics and Telecommunications - Polytechnic University of Bucharest Banicescu's research explores fundamental challenges in high-performance computing, including dynamic load balancing, performance optimization, and autonomic resource management. Her recent work emphasizes explainable AI for cybersecurity systems and robust resource allocation models for heterogeneous computing environments. Her publication record demonstrates consistent focus on performance optimization in parallel systems, with recent expansion into cybersecurity applications using machine learning approaches. She maintains collaborations with national laboratories and international research forums. Banicescu has received multiple National Science Foundation awards for research excellence and serves on numerous program committees for IEEE/ACM conferences. She contributes to the academic community through editorial roles for Cluster Computing and the International Journal on Computational Science and Engineering.

Natalie Haynesمشاهده پروفایل

استاد

• Hospitality Revenue Management
• Hotel Pricing Strategy
• Big Data Applications in Tourism
• +۴ مورد دیگر

Natalie Haynes serves as Division Head for Global Business, Hospitality and Tourism Management within Sheffield Business School at Sheffield Hallam University, leading academic programs in international business, hospitality management, tourism management, and aviation. Her leadership spans recruitment, employability, and international partnerships since assuming the role in 2024. Her research expertise focuses on Hospitality Revenue Management , Hotel Pricing Strategy , and Big Data Applications in Tourism , with significant contributions to understanding how data-driven systems impact pricing decisions. She examines revenue management across diverse sectors including hotels, campsites, and visitor attractions, while also exploring gender dynamics in the event industry. Analysis of her 15 most recent publications reveals a strong emphasis on post-pandemic revenue recovery , temporal pricing models , and ethical considerations in hospitality operations . Her work bridges theoretical economic principles with practical revenue management applications, increasingly incorporating big data analytics while questioning over-reliance on automated systems. Senior Fellow of the Higher Education Authority Editorial Board Member, Journal of Tourism Futures Natalie actively supervises DBA and PhD students in innovation and smart services while serving as an external examiner for other institutions. Her industry background in hotel sales, marketing, and revenue management with major brands provides practical grounding for her academic work, particularly evident in her collaborative white paper on hospitality management education with the Council of Hospitality Management Education.

• Digital Transformation
• Educational Technology
• Curriculum Analytics
• +۱ مورد دیگر

Venky Shankararaman is Professor of Information Systems (Education) and Vice Provost (Education) at Singapore Management University. A PhD graduate from University of Strathclyde (1992), he holds the SMU Fortitude Fellow in Computing Education position and serves as President of Asia-Pacific Association for International Education. Research expertise spans digital transformation, educational analytics, and enterprise architecture. Primary focus areas include AI governance frameworks, curriculum analytics, and microservices design patterns. Publications emphasize practical applications in government AI systems (ACQAR framework), educational technology (ChatGPT mentoring), and enterprise architecture (microservices decision frameworks). Recent work explores explainable AI for public sector and project-based learning models. Major Awards: Public Administration Medal (Silver) - Singapore Government (2023) Distinguished Education Award - SMU (2023) AIS Best Conference Paper Award (2020) Public Administration Medal (Bronze) - Singapore Government (2017) SAP Outstanding Academic Award (2014) Advises PhD students Kevin TAUKOOR Heman and Ng Kok Leong. Leads university-wide education initiatives including the Data Access and Use Committee and Teaching Bank project. Pioneered SMU's competency-based curriculum analytics tools and digital transformation frameworks for banking. Serves on ACM Education Advisory Committee and developed national skills frameworks for Singapore's IT workforce.

Liangxiu Hanمشاهده پروفایل

استاد

• Big Data Analytics
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Professor Liangxiu Han is Professor of Computational Science at Manchester Metropolitan University's Department of Computing and Mathematics, where she also serves as co-Director of the Centre for Advanced Computational Science and Deputy Director of the ManMet Crime and Well-Being Big Data Centre. Her research focuses on developing novel approaches to big data analytics, machine learning, and AI, with applications in precision agriculture, healthcare, smart cities, and cybersecurity. Professor Han's research integrates fundamental and applied work in large-scale data processing, intelligent management of distributed systems, and mathematical modeling. Her current activities aim to provide intelligent ICT-based solutions for societal challenges in food security, healthcare, and sustainable urban development. Her recent publications demonstrate leadership in applying AI to medical challenges like Alzheimer's drug discovery and glaucoma diagnosis. The research combines Bayesian deep learning, computational chemistry, and neuropharmacology to develop innovative therapeutic approaches and diagnostic tools.

Vahid Rafeمشاهده پروفایل

مدرس

• Search-Based Software Engineering
• Blockchain Technology
• Formal Verification
• +۳ مورد دیگر

Dr. Vahid Rafe is Lecturer and co-program lead for Computer Science at Goldsmiths, University of London. His research focuses on search-based software engineering and blockchain technology, with additional expertise in formal verification and model transformation. Research integrates artificial intelligence with software engineering practices including automated testing techniques, bug localization, and formal verification. Recent work applies machine learning to software quality assurance, combinatorial testing optimization, and blockchain security analysis. Publications demonstrate consistent innovation in AI-enhanced software engineering methods, particularly hybrid algorithms for test generation and deep learning approaches for software maintenance. Recent work addresses security vulnerabilities in cryptographic systems.

Iacopo Colonnelliمشاهده پروفایل

استادیار

• Scientific Workflow Systems
• High-Performance Computing
• Cloud Computing
• +۶ مورد دیگر

Iacopo Colonnelli is an Assistant Professor (RTDA) at the University of Turin's Computer Science Department and a member of the Alpha Research Group (Parallel Computing) and CINI HPC Key Technologies and Tools lab. He has established himself as a leading researcher in scientific workflow systems, bridging cloud computing and high-performance computing environments. His research focuses on workflow modeling and management in heterogeneous distributed architectures, with significant contributions through open-source tools including StreamFlow (a container-native workflow management system), Jupyter Workflow, and CAPIO (middleware for I/O streaming). These tools enable hybrid workflows execution on cloud-HPC infrastructures and have gained recognition through multiple European projects and initiatives. Colonnelli's publication record shows consistent output in top venues related to parallel and distributed computing, with recent work focusing on federated learning, confidential computing, and workflow standardization. His most recent publications (2024-2026) demonstrate strong engagement with cutting-edge challenges in cross-facility federated learning, workflow representation languages, and performance evaluation of confidential computing for bioinformatics applications. Scientific Awards: ITADATA 2023 Best PhD Thesis Award for 'Workflow models for heterogeneous distributed systems' StreamFlow selected as one of the three exploitable results of the EUPEX European project (2023) StreamFlow selected as an exploring technology by the EC Innovation Radar initiative (2023) 'Distributed Workflows With Jupyter' selected as Fall 2022 Editor's Choice by Future Generation Computer Systems journal HPC-Europa3 grant recipient for Barcelona Supercomputing Center visit (2019) HiPEAC grant recipient for ACACES 2019 Summer School (2019) As an active contributor to research infrastructure, Colonnelli serves as a member of the CWL Technical Team and founder of the CWL4HPC Working Group. He has organized multiple international conferences including PDP 2025 as program chair and CWLCon 2024. His service extends to numerous program committees for major HPC conferences and journals, demonstrating his standing in the international research community. Colonnelli leads significant contributions to multiple European research projects including Space Center of Excellence, European Pilot, EUPEX, TEXTAROSSA, ACROSS, DeepHealth, and HPC4AI, focusing on advancing HPC and AI technologies for scientific applications.

Scott D Bartonمشاهده پروفایل

دانشیار

• Robotics Engineering
• Music Perception and Cognition
• Audio Production
• +۳ مورد دیگر

Scott D Barton is an Associate Professor in the Department of Humanities & Arts at Worcester Polytechnic Institute (WPI), with affiliations in Robotics Engineering, Psychology, and Interactive Media & Game Development. His work bridges music, robotics, and cognition, focusing on robotic musical instruments, human-robot interaction, and perceptual studies. He holds a BA from Colgate University (1998), an MMus from Brooklyn College (2006), and a PhD from the University of Virginia (2012). Research interests include designing automated musical instruments, exploring how cognitive processes shape musical perception, and applying AI to music creation. Key projects involve the Music, Perception and Robotics Lab , where he develops systems like Cyther (a self-tuning robotic zither) and Parthenope (a robotic siren). His work has been showcased in venues like the International Conference on New Interfaces for Musical Expression. Barton emphasizes interdisciplinary collaboration, teaching students to merge technical skills with creative expression. Media coverage includes interviews in The New York Times and The Telegram & Gazette , discussing topics like urban sound phenomena and robot-human concerts. His performances blend algorithmic composition with live robotics, exemplifying WPI's focus on innovative engineering and art. Grants and collaborations are central to his work, though specific funding details are not provided. Advising focuses on guiding students in creating novel musical technologies and understanding perceptual frameworks. Upcoming events include performances at the International Computer Music Conference and workshops on computer music technology.

Qing Caoمشاهده پروفایل

دانشیار

• AI for Cyber-Physical Systems
• Networking and Distributed Systems
• Cyber Security
• +۵ مورد دیگر

Qing 'Charles' Cao is an Associate Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, part of the Tickle College of Engineering. His research focuses on AI-driven Cyber-Physical Systems, Networking, and Cyber Security, with applications in IoT, Edge Computing, and Smart Agriculture. He holds a PhD from the University of Illinois at Urbana-Champaign, an MS from the University of Virginia, and a BS from Fudan University, China. Education: PhD in Computer Science, University of Illinois at Urbana-Champaign, 2008 MS in Computer Science, University of Virginia, 2005 BS in Computer Science, Fudan University, Shanghai, China, 2002 Research Interests: His work emphasizes interdisciplinary solutions at the intersection of AI, networking, and security. Key areas include: - Blockchain-based security mechanisms for edge devices - LLM applications in public policy and climate decision-making - IoT-driven precision agriculture and livestock health monitoring - Probabilistic error reasoning and fault tolerance in distributed systems Grants & Awards: No awards explicitly listed, but his research has been supported by grants in areas like smart agriculture and edge computing infrastructure. Advising & Labs: While specific advisees are not listed, his research group focuses on collaborative projects involving students in cybersecurity, IoT, and AI systems. He has contributed to lab initiatives related to environmental computing and distributed sensor networks. Professional Activities: Active in conferences like AAAI, IEEE IPCCC, and ACM/IEEE Edge Computing Symposiums, presenting on topics ranging from LLM-assisted governance to secure edge architectures.

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.

Arash Mohammadiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Medical Imaging
• Cybersecurity
• +۴ مورد دیگر

Arash Mohammadi is an Associate Professor at the Concordia Institute for Information Systems Engineering (Concordia University), specializing in artificial intelligence, medical imaging, and cybersecurity. His research focuses on advancing AI-driven solutions in healthcare diagnostics, autonomous systems, federated learning, and edge computing. He supervises graduate programs in Information Systems Security, Electrical and Computer Engineering, and related disciplines. Key research interests include medical image classification using deep learning, reliability of autonomous driving systems, and secure communication in smart grids. His work integrates transformer architectures, Bayesian methods, and graph neural networks to address challenges in healthcare, robotics, and network optimization. Notable contributions include frameworks for lung nodule malignancy prediction, ECG-based emotion recognition, and secure federated learning in resource-constrained environments. Dr. Mohammadi has published extensively in top-tier venues such as IEEE conferences and journals, with a focus on practical applications of AI in healthcare and engineering systems. He actively engages in interdisciplinary projects involving biomedical signal processing, human-computer interaction, and resilience engineering for cyber-physical systems.

Marta Ariasمشاهده پروفایل

دانشیار

• Machine Learning
• Data Mining
• Design and Analysis of Algorithms
• +۳ مورد دیگر

Marta Arias is an Associate Professor in the Computer Science Department at Universitat Politècnica de Catalunya (UPC), Barcelona, and a member of the LARCA research group. She has held academic positions since 2007, including roles at Columbia University and the University of Edinburgh. Her research focuses on Machine Learning, Data Mining, Algorithm Design, and Logic in Computer Science. She teaches advanced courses in Machine Learning, Information Retrieval, and Complex Networks at undergraduate and master's levels, including the Erasmus Mundus program in Data Mining and Knowledge Management. Educations: PhD in Computer Science from Tufts University (2000-2004), Postgraduate Studies at University of Edinburgh (1999-2000), B.Sc. in Computer Science from UPC (1992-1997). Professional experience includes software engineering and research roles in New York and Barcelona. Research highlights include developing algorithms for real-time ranking systems, Twitter-based financial forecasting, and causal network analysis. Notable contributions include the 'Best Paper Award' at AIPESW@ECAI 2020 and a 3rd place award in football performance research. Her work spans theoretical foundations (e.g., Horn clause learning) and applied domains like cybersecurity, healthcare analytics, and energy systems. Teaching responsibilities include labs and lectures on Machine Learning, Information Retrieval, and Programming, with active involvement in curriculum design and pedagogical innovation. Ongoing projects include enterprise risk analysis, synthetic data generation, and network modeling.

Tze Meng Lowمشاهده پروفایل

استاد پژوهشی

• Parallel and High Performance Computing
• Formal Methods
• Graph Algorithms
• +۳ مورد دیگر

Tze Meng Low is an Associate Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. His research focuses on high-performance algorithms, formal methods, and hardware-software co-design, with an emphasis on achieving performance portability across architectures. He holds a Ph.D. and M.S. in Computer Science and dual B.A./B.S. degrees in Economics and Computer Science from the University of Texas at Austin. His research interests span parallel computing, graph algorithms, machine learning, and cyber-physical systems. He has contributed to projects like the DARPA BRASS initiative, collaborating on adaptive software systems for resource-challenged environments. His work also involves developing tools such as SMaLL and qLD, which address challenges in machine learning library instantiation and genomic analysis. Low has received the Dean’s Early Career Fellowship (2022) and led the $2.7M DARPA-funded BRASS project (2016–2020), supported by SpiralGen, Inc. and academic collaborators. His contributions include code generation frameworks like SPIRAL and advancements in linear algebra-based graph algorithms, emphasizing analytical models and automated code optimization. His research bridges theoretical formal methods with practical implementations, aiming to enhance software reliability and scalability in emerging domains. Collaborations include work on fault-tolerant coded computing and high-assurance systems for cyber-physical applications.

Hamza M. Zaferمشاهده پروفایل

دانشیار

• Red Sea World Studies
• Pre-Modern Knowledge Networks
• Ethiopic Literature
• +۴ مورد دیگر

Hamza M. Zafer is an Associate Professor of Arabian and African languages at the University of Washington, holding a Senior Lecturer position in the Program in African Studies. His research focuses on the languages and literatures of the Red Sea world from the 6th to 16th centuries, emphasizing pre-modern knowledge networks across Ethiopic, Arabic, Coptic, Sabaic, and Syriac traditions. His monograph, *Ecumenical Community*, examines early Muslim communitarianism in the post-Red Sea war context. Dr. Zafer earned his Ph.D. from Cornell University. His research interests span cross-cultural textual exchanges, linguistic anthropology, and the historiography of the Horn of Africa and Arabian Peninsula. His work bridges historical linguistics with socio-cultural analysis, highlighting trans-regional intellectual exchanges. His recent publications (2003–2009) include machine learning applications in bioinformatics, such as tumor classification and gene function prediction, reflecting an interdisciplinary approach. Earlier work (1997–1999) explored AI-driven crossword puzzle generation, showcasing computational creativity. No academic awards or grants are explicitly mentioned in the provided text. He advises no listed students, though his mentorship role in language studies may imply graduate supervision not detailed here. No lab affiliations or future projects are described in the available data.

Dominik Drexlerمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Machine Learning
• Classical Planning
• +۳ مورد دیگر

Dominik Drexler is a Research Fellow at Linköping University's Department of Computer and Information Science (IDA), part of the Artificial Intelligence and Integrated Computer Systems (AIICS) division. He completed his PhD at IDA in 2025, focusing on integrating learning and artificial intelligence planning to develop intelligent agents that exploit subgoal structures. His work bridges System 1 (learning-based) and System 2 (reasoning-based) approaches in AI, emphasizing hierarchical planning and policy reuse. He is a recipient of the 2024 Lawson Scholarship for excellence in international collaboration and the 2023 Optimal Planning Track Award at the International Planning Competition. **Education**: Bachelor's and Master's in Computer Science (specializing in AI) from the University of Freiburg, Germany. PhD in Computer Science at Linköping University (2025). **Research Focus**: Combining learning and classical planning to create agents that learn hierarchical policies and subgoal structures. Development of expressive policy languages and sketch-based methods for decomposing planning problems. Applications in autonomous systems and generalizable planning algorithms. **Contributions**: Open-source tools like DLPlan , Vanir , and Odin for state feature extraction and optimal planning. Active in international collaborations through the Wallenberg Autonomous Systems Program (WASP). **Teaching & Supervision**: Transitioning to expanded teaching and student supervision roles while continuing research. **Affiliations**: Member of the AIICS division and the Faculty of Science and Engineering at LiU.