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Tamara Ilieva-TsvetkovaView profile

Assistant Professor

• Geodetic measurements
• Spatial databases
• CAD and GIS systems
• +5 more

Tamara Ilieva-Tsvetkova is a Senior Assistant Professor at the Department of Geodesy and Geoinformatics within the Faculty of Geodesy at the University of Architecture, Civil Engineering and Geodesy (UACEG) in Sofia, Bulgaria. She also serves as a Technical Associate at the Center for Quality and Accreditation and maintains her office in Cabinet 411P, with reception hours on Mondays from 11:30-12:30 during the winter semester of the 2025-26 academic year. Her educational background includes: Master's degree in Geodesy from UACEG, Sofia (2007) Master's degree in Marketing with specialization in "Advertising Management" from UNWE, Sofia (2015) Doctorate in "General, Higher and Applied Geodesy" from UACEG, Sofia (2019) Tamara Ilieva-Tsvetkova's research focuses on geospatial technologies and methodologies, with particular expertise in geodetic measurements, spatial databases, CAD and GIS systems development, engineering project management, and coordinate systems. She is proficient in English (excellent level) and Russian (basic level), and possesses extensive technical skills in AutoCAD, ArcGIS, QGIS, PostgreSQL with PostGIS, and web mapping libraries like Leaflet and OpenLayers. Her recent publications (2021-2024) show a strong trend toward web-based GIS applications, indoor positioning systems, climate change monitoring using geospatial data, and the integration of artificial intelligence with geospatial technologies. Her work spans practical applications in urban planning, transport infrastructure risk assessment, real estate management, and environmental monitoring, with a particular focus on Bulgarian territories. She is an active member of professional organizations: Chamber of Engineers in Investment Design Chamber of Engineers in Geodesy European Geosciences Union (EGU) Tamara Ilieva-Tsvetkova has participated in numerous research and construction projects: Upgrading information systems for AGKK (2013-2014) Information platform for environmental research and modeling at UACEG (2018-2019) Center of Excellence "Heritage BG" (2018-2023) Geodetic surveying for the South Stream gas pipeline Cadastral mapping for multiple Bulgarian villages and regions Geodetic survey for Sofia Metro expansion Geodetic survey for Sofia Airport infrastructure Her technical expertise spans multiple domains including geodetic measurement processing, GIS system development, spatial database management, and programming for geospatial applications using C#, SQL, JavaScript, and various GIS APIs.

Matthias H. HennigView profile

Professor

• Computational Neuroscience
• Large-Scale Neural Recordings
• Neural Population Activity Modeling
• +4 more

Matthias H. Hennig is a Professor of Computational Neuroscience at the University of Edinburgh, School of Informatics, Institute for Adaptive and Neural Computation. He leads the Hennig Lab, which focuses on the intersection of neuroscience and computational methods. His research spans three main areas: Development of tools for analysis of large-scale neural recordings, including the SpikeInterface project and Herding Spikes software Modeling neural population activity using data-driven approaches like Targeted Neural Dynamical Modeling Studying retinal development and function, particularly spontaneous network activity patterns like retinal waves The lab has produced significant methodological contributions to neural data analysis, with software tools used internationally. Recent publications (2019-2022) show consistent output in high-impact journals across computational neuroscience, neural data analysis methods, and biological studies of neural circuits. The research demonstrates a strong focus on developing reproducible analysis pipelines for next-generation electrophysiology data. Professor Hennig currently supervises six PhD students across multiple CDT programs and has an impressive track record of student placements in both academic and industry positions. His international collaborations span institutions in Europe, North America, and India, reflecting the global impact of his work.

• High Performance Computing
• Distributed Systems
• Deep Learning
• +4 more

Muhammad Mustafa Rafique is an Associate Professor in the Department of Computer Science at Rochester Institute of Technology's Golisano College of Computing and Information Sciences. His research specializes in optimizing large-scale computing systems, with focus areas including: High-performance computing (HPC) resource management Distributed deep learning acceleration Fault-tolerant cloud architectures GPU-accelerated checkpointing systems Serverless computing frameworks Dr. Rafique's work demonstrates consistent innovation in improving computational efficiency for containerized HPC workflows, multi-GPU scheduling, memory optimization, and distributed training pipelines. His publications frequently appear in premier IEEE/ACM conferences, reflecting contributions to systems performance engineering. While no awards or student advisees are mentioned in available materials, his research collaborations include co-authors from institutions worldwide, indicating active engagement in the high-performance computing research community. Current work explores emerging memory technologies like CXL and advanced containerization techniques for next-generation datacenters.

Ahmed Ezzeldin KhaledView profile

Associate Professor

• Internet of Things
• Smart Healthcare Systems
• Location-aware Services
• +6 more

Ahmed Ezzeldin Khaled is an Associate Professor in the Department of Computer Science at Northeastern Illinois University (NEIU) in Chicago, USA, and a Visiting Associate Professor in the Masters Program in Computer Science at The University of Chicago since March 2024. He joined NEIU in August 2018 after completing his Ph.D. in Computer Engineering from the University of Florida. His educational background includes: Ph.D. in Computer Engineering from University of Florida (2018) M.S. in Computer Engineering from Cairo University (2013) B.S. from Cairo University Dr. Khaled's research focuses on designing systems and applications for distributed systems with the Internet of Things (IoT) as a primary use case. His work spans three main areas: smart healthcare systems and e-Health applications, location-aware services for smart spaces, and utilizing data management techniques for descriptive and predictive analytics. He has developed the Atlas IoT Framework, which includes IoT Device Description Language (IoT-DDL), Atlas Thing Architecture, and Inter-Thing Relationship Framework. His research involves software and hardware platforms from different vendors with various operating environments, focusing on creating interoperable systems for smart spaces. Analysis of Dr. Khaled's recent publications shows a strong trend toward practical implementations of IoT frameworks, particularly in healthcare applications. His work bridges theoretical concepts with real-world deployments, emphasizing context-aware systems, data pipelines, and location-based services. The research demonstrates expertise in creating frameworks that can work across different hardware platforms and communication protocols while addressing specific challenges in healthcare monitoring and smart space development. Dr. Khaled actively mentors students across multiple research domains including IoT applications, healthcare systems, and cloud architecture. His advising approach emphasizes connecting research with teaching, believing that "through the teaching process, we gain a deeper perspective of the subjects we teach and a better view on how the various concepts are linked into a 'bigger picture'." He has developed courses related to operating systems, computer networks, parallel computing, database systems, and the Internet of Things, bringing his research expertise directly into the classroom. Dr. Khaled leads the IoT & Systems Lab at NEIU, which focuses on developing practical IoT solutions for real-world applications. Current active projects include the Atlas IoT Framework for smart spaces, IoMT and Digital Healthcare systems, IoT Emulator (E-IoT) for application development and testing, and Location Based Services for smart tourism. The lab works with various hardware and software platforms to develop and test IoT applications before deployment in real environments, with a particular emphasis on healthcare applications and location-aware services.

Gary TanView profile

Professor

• computer security
• formal methods
• programming languages
• +3 more

Gang (Gary) Tan is a Professor in the Computer Science and Engineering Department at Pennsylvania State University, co-directing the Institute for Networking and Security Research (INSR). His research bridges computer security, formal methods, and programming languages to develop practical solutions for software vulnerabilities and AI fairness. Education: B.E. in Computer Science from Tsinghua University Ph.D. in Computer Science from Princeton University Dr. Tan specializes in applying compiler techniques and formal verification to security challenges, with seminal work on cache side-channel attacks and fairness in machine learning. His Security of Software (SOS) Group develops frameworks that integrate theoretical guarantees into real-world systems, emphasizing measurable security outcomes and ethical AI. Recent projects focus on quantifying bias in neural networks and mitigating speculative execution vulnerabilities. Analysis of his 2021-2025 publications reveals a strategic pivot toward AI security, where he pioneers methods for fairness testing (e.g., information-theoretic debugging) and repair (e.g., NeuFair). Concurrently, his security work evolves from foundational side-channel research (SpecSafe, 2021) toward hardware-software co-design solutions, demonstrating consistent innovation across theoretical and applied domains. Scientific Awards: James F. Will Career Development Professorship NSF CAREER Award Google Research Award (two instances) Distinguished Reviewer Award at 2018 IEEE Symposium on Security and Privacy Outstanding Research Award at Penn State Ruth and Joel Spira Excellence in Teaching Award Best Paper Award at PLDI 2024 Dr. Tan leads the SOS Group with funding from NSF (including CAREER), DARPA (ISAT study group membership), and industry partners like Google. His grants support interdisciplinary projects spanning secure compilation, fairness engineering, and hardware security, while his teaching excellence award reflects commitment to pedagogy in core systems courses. He co-directs Penn State's Institute for Networking and Security Research (INSR), fostering collaboration between systems, security, and AI researchers. The SOS Group maintains active partnerships with industry security teams and contributes to open-source tools for vulnerability detection, with recent work expanding into fairness certification for machine learning pipelines.

Hongyu ZhangView profile

Professor

• Intelligent software engineering
• Software analytics
• Data-driven software engineering
• +4 more

Hongyu Zhang is a Professor and Dean of the School of Big Data and Software Engineering at Chongqing University, China, and an Honorary Professor at The University of Newcastle, Australia. Previously, he served as a Lead Researcher at Microsoft Research Asia and an Associate Professor at Tsinghua University, China. He received his PhD from the National University of Singapore in 2003. His academic journey spans prestigious institutions, combining industry research experience with academic leadership. Dr. Zhang's research interests focus on intelligent software engineering, software analytics, data-driven software engineering, software fault management, testing and debugging, and software maintenance and reuse. His work centers on improving software quality and productivity by mining and analyzing vast amounts of software data. Over the years, he has developed innovative methods that apply data mining, machine learning (including deep learning), and information retrieval techniques to extract knowledge from software data and solve complex software engineering problems. His research spans three major areas: intelligent programming (code search, code summarization, code generation), intelligent quality prediction (defect prediction, cloud failure prediction, performance prediction), and intelligent fault detection and diagnosis (log-based fault detection, crash-based fault localization, bug report analytics). His recent publications demonstrate a clear trend toward integrating large language models and deep learning techniques with traditional software engineering practices. The research spans intelligent programming assistance, code security, UI automation, distributed systems optimization, and performance analysis. His work increasingly focuses on practical applications of AI in software engineering, with emphasis on real-world impact in industrial settings, particularly in microservices, cloud systems, and large-scale software development environments. 8 ACM Distinguished Paper Awards Best Paper Award: How Long Will it Take to Mitigate this Incident for Online Service Systems? David Lorge Parnis Fellowship Senior Member of IEEE Distinguished Member of ACM Distinguished Member of CCF Fellow of Engineers Australia (FIEAust) Recognized in The Australian's Top Researchers special edition as leading researcher in Software Systems World's Top 2% Scientists (career-long) Dr. Zhang has successfully advised numerous PhD and Master's students who have gone on to prominent positions at leading technology companies and academic institutions worldwide. His research has been supported by significant grants including Australian Research Council Discovery Projects (as Lead CI) and multiple National Science Foundation of China projects. His work has made tangible impacts in industry, most notably through the Microsoft Developer Assistant project which received over 450K downloads in 2016. He leads research groups focused on intelligent software engineering and software analytics, with strong collaborations between Chongqing University, The University of Newcastle, and Microsoft Research. His teams develop practical tools for code intelligence, log analysis, and fault diagnosis that are deployed in real-world online service systems.