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GÖZDE ÖZSERT YİĞİTView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Data Science
• +5 more

Gözde Özsert Yigit is an Assistant Professor in the Department of Computer Engineering at Gaziantep University, Faculty of Engineering. Her research focuses on applying machine learning and AI to biomedical data analysis, environmental monitoring, and predictive modeling. PhD in Computer Engineering (2016-2022), Çukurova University MSc in Computer Engineering (2014-2016), Çukurova University BSc in Computer Engineering (2007-2012), Çukurova University Her work spans machine learning applications , including water quality monitoring , food image classification , and drug-target interaction prediction . Recent studies emphasize interpretable feature selection methods and handling imbalanced datasets. She has contributed to 5 journal articles and 12 conference proceedings between 2014-2025, covering topics like oxygen uptake prediction, admission decision models, and hybrid AI strategies for biomedical and environmental challenges. Currently supervises academic projects and has participated in national refereeing activities (e.g., Teknofest, 2020). Affiliated with YÖK Academic, ORCID, and Web of Science databases.

• Artificial Intelligence
• Machine Learning
• Probabilistic Inferencing
• +5 more

Barry Drake serves as an Adjunct Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he is also affiliated with the Faculty of Engineering and Information Technology and the Data Science Institute. Additionally, he holds the position of Co-Chief Scientist at the Digital Health Cooperative Research Centre and participates in government advisory committees. His career bridges academic research and industry application, with significant experience in translating research into practical technology solutions for business and government sectors. Drake earned his PhD in Computer Science from UNSW Australia (1998-2003) and a BSc with First Class Honors in Computer Science from UTS (1991-1996). His educational background is complemented by a Certificate of Proficiency for Radio Fitter/Mechanic Electronic Systems from the Royal Australian Navy. His research focuses on algorithms and software for practical smart systems, with particular expertise in applications of AI and machine learning, probabilistic inferencing and knowledge compilation, learning probabilistic models, and fast-efficient near-neighbor searching in ultra-high dimensional spaces. His work demonstrates a clear trajectory toward health informatics applications , especially in health systems and delivery of health services, where he has made significant contributions through projects like the Lumos statewide linkage programme. The analysis of his publication record reveals a consistent pattern of research bridging theoretical computer science with practical healthcare applications. His recent work shows increasing focus on health data integration, patient journey modeling, and privacy-preserving synthetic data generation for healthcare applications, while maintaining his foundational expertise in probabilistic models and efficient search algorithms. Inventor on 20 filed patents ORCID identifier: 0000-0003-0572-9936 Drake actively supervises Masters and PhD students according to his profile, and his funded research portfolio includes multiple grants from the Digital Health CRC and NSW Health. His industry experience, particularly his 13 years at Canon Information Systems Research Australia where he served as Senior Principal Engineer and lead researcher for machine learning, informs his approach to technology research methods from a commercial perspective. His current projects focus on the impact of integrated care in New South Wales, synthetic data generation, and patient journey modeling. As Co-Chief Scientist at the Digital Health CRC, Drake contributes to a major national initiative focused on digital health innovation. His work with the Lumos programme has created Australia's first statewide linked data asset across primary care and other health settings, providing unique insights about cross-setting healthcare utilization. This initiative represents a significant contribution to health data infrastructure in Australia.

Malte SchwarzkopfView profile

Associate Professor

• Distributed Systems
• Operating Systems
• Privacy-Preserving Systems
• +5 more

Malte Schwarzkopf is an Associate Professor of Computer Science at Brown University. He was previously a postdoc at MIT CSAIL (2016-2019) and earned his PhD at the University of Cambridge (2016). His research spans distributed systems, operating systems, and privacy-preserving systems, focusing on building high-performance, user-friendly systems through novel abstractions. Education: PhD, University of Cambridge, 2016 MA, University of Cambridge, 2013 BA, University of Cambridge, 2009 His work addresses critical challenges in datacenter scheduling, privacy compliance in web applications, and secure multi-party computation. Recent projects include Quicksand for resource allocation and Sesame for GDPR compliance by design. Scientific awards include the Google ML and Systems Junior Faculty Award (2025), NSF CAREER Award (2021), Amazon Research Award (2024), and multiple best paper/test-of-time awards at NSDI (2015), EuroSys (2013, 2023), and VLDB (2025). He has advised numerous students, including Justus Adam, Lillian Tsai, and Jon Gjengset, who are now at institutions like MIT, Google, and Stanford. His research is supported by NSF, Amazon, Google, Microsoft, and VMware.

Vince LyzinskiView profile

Associate Professor

• Statistical Network Inference
• Graph Matching
• Vertex Nomination
• +6 more

Vince Lyzinski is an Associate Professor in the Department of Mathematics at the University of Maryland, College Park, with additional affiliations in the Applied Mathematics, Statistics, and Scientific Computation (AMSC) program. His work spans statistical network inference, graph matching algorithms, and machine learning applications to complex networks. Ph.D. in Applied Mathematics and Statistics (2013), M.S.E. (2011), and M.A. (2007) from Johns Hopkins University B.S. in Mathematics from University of Notre Dame (2006) His research focuses on statistical network inference , particularly graph matching and vertex nomination , with applications to connectomics and adversarial activity analytics. Supported by DARPA MAA , AFOSR , and JHU HLTCOE , he develops algorithms like iGraphMatch for graph alignment and analysis. Recent publications (2025-2023) explore vertex label error impacts, network trimming for robustness, and stochastic blockmodel extensions. Key papers include "Asymptotically Perfect Seeded Graph Matching" and "ACRONYM: Augmented Degree-Corrected Network Models" . Scientific Awards : Best Paper Award at GTA3 2018 Workshop He has advised 13 Ph.D. students and 1 postdoc across UMD , JHU , and Texas A&M , including Keith Levin (now at UW-Madison) and Jesus Arroyo (now at Texas A&M). His DARPA-funded team includes researchers from UMass, UMD, BU, and JHU.

• Image Analysis
• Bioenergy
• Cartography
• +12 more

Paolo Zatelli is an Associate Professor at the University of Trento , Department of Civil, Environmental and Mechanical Engineering. His academic career spans over two decades, with research expertise in geomatics, photogrammetry, and open-source GIS applications. Department of Civil, Environmental and Mechanical Engineering (since 2001) Researcher in ICAR06 (Topography and Cartography) International collaborations in geospatial projects Education includes a degree in Environmental and Territory Engineering (1993, 110/110 with honors) and a PhD in Topographic and Geodetic Sciences (1998). He has taught courses in GIS, Digital Cartography, and GPS surveying. Research Interests focus on geospatial technologies and their applications. Key areas include digital photogrammetry , wavelet applications in geodesy , GPS survey planning , and open-source GIS development . His work extends to environmental monitoring, historical cartography analysis, and sustainable forest management systems. Publication Trends show sustained contributions to FOSS4G (Free and Open Source GIS) applications, with recent work covering topics like landscape metrics , EV charging infrastructure , beta diversity analysis , and historical map digitization using GRASS GIS and QGIS. Scientific Contributions include: Member, IAG Special Study Group 4.187 on Wavelets in Geodesy Organizing committee, IV Hotine-Marussi Symposium Key developer of GRASS GIS applications Contributor to GOCE satellite geodesy projects Teaching and Mentorship highlights his role in both academic and professional GIS training, with over 20 theses supervised. His Technical Contributions involve developing open-source tools for spatial database management, statistical analysis, and georeferencing workflows.

Song Jae-seungView profile

Professor

• Internet of Things (IoT)
• Machine-to-Machine Communications (M2M)
• Software Security
• +4 more

Professor Song Jae-seung is affiliated with Sejong University in the Department of Information Security , where he leads the Software Engineering and Security Lab . His academic rank is Professor , with a focus on Internet of Things (IoT) , Machine-to-Machine (M2M) Communications , and Software Security . Research spans IoT/6G networking , Air-gap security , Semantic smart cities , and AI-driven security frameworks Active in international standardization via oneM2M and TTA PG 308 Serves as journal editor and conference TPC member for IEEE events Developed cloud-based conformance testing and optical attack analysis techniques Recent publications address 6G IoT interworking , XR services , and air-gap defense , reflecting trends in smart city security and AI-enabled IoT . No students or awards are explicitly listed in the provided data.

Sang Il ParkView profile

Professor

• Computer Graphics
• Character Animation
• 3D Geometry Processing
• +4 more

Sang Il Park is a Professor in the Department of Software at Sejong University, South Korea, where he has been a faculty member since 2007 following postdoctoral work at Carnegie Mellon University's Robotics Institute (2005-2007) and a brief research position at Japan's Digital Human Research Center. Ph.D. in Computer Science, KAIST (2004) M.S. in Computer Science, KAIST (1999) B.S. in Computer Science, Yonsei University (1997) Professor Park's research centers on Computer Graphics , with deep expertise in Character Animation , 3D Geometry Processing , and Image Processing . His research fingerprint shows dominant activity in Motion Capture (100%), Transition Graphs (72%), and Motion Synthesis (72%). His work has evolved from foundational research in human motion capture to incorporating deep learning techniques for image restoration, demonstrating both consistency in core interests and adaptation to emerging technologies. His publication pattern reveals a sustained research program with significant outputs in 2008, 2010, 2014, 2018, and continuing through 2023. The research spans from character animation and motion synthesis to practical applications like document processing and weathering simulation, showing remarkable breadth within his specialized domain. h-index: 12 Total citations: 933 Presented pioneering research at ACM SIGGRAPH Referenced in 4 patents and 2 Wikipedia pages Professor Park leads the Computer Graphics Lab at Sejong University, where he mentors graduate students and conducts research in animation, modeling, rendering, and computational photography. His patent portfolio includes innovations in facial expression restoration and motion synchronization, demonstrating the practical impact of his research beyond academia.

• Computer Science
• Human-Computer Interaction
• Game Development
• +4 more

Luka Blašković, MSc in Informatics, is an Assistant Professor at the Faculty of Informatics in Pula, Croatia. He contributes to both undergraduate and graduate programs, teaching courses in Operating Systems, Programming in Scripting Languages, Business Process Management, Web Applications, and Distributed Systems. Department: Department of Informatics Research Focus: Computer Science, Game Development, Artificial Intelligence, User Experience Projects: Key participant in the EDIH Adria initiative (European Digital Innovation Hub) His research spans Human-Computer Interaction , Gaming Experience Evaluation , and Mobile Banking Applications . Recent work includes developing a conversational agent for personalized teaching materials (2024) and analyzing EDIH Adria TBI projects' impact (2025). Scientific publications include studies on Unity Engine game development (2023), vector databases (2022), and mobile banking quality (2022). While no explicit awards are listed, his active participation in international conferences like MIPRO underscores his academic engagement. Contact: luka.blaskovic@unipu.hr | Personal Website

Khalid MeksemView profile

Professor

• Plant Genetics
• Functional Genomics
• Disease Resistance
• +4 more

Khalid Meksem is a Professor in the Department of Plant, Soil, and Agricultural Systems at Southern Illinois University's College of Agricultural Sciences. He joined SIU as an Assistant Professor in 2000, advanced to Associate Professor in 2005, and was promoted to full Professor in 2009. His academic journey includes positions spanning over two decades at SIU where he has established himself as a leading researcher in plant genetics and genomics. Dr. Meksem's educational background demonstrates international training in plant sciences: Ph.D. (1995) from the University of Cologne at the Max-Planck Institute for Plant Genetics and Breeding M.S. (1990) from the University of Paris XI, France, in Plant Molecular and Cellular Biology and Plant Biotechnology B.S. (1989) from the University of Casablanca and the University of Fes, Morocco, in Biology and Genetics His research focuses on the molecular dissection of plant disease resistance mechanisms, particularly against nematodes and fungi, with significant work on soybean and potato genetics. Dr. Meksem's laboratory investigates both structural and functional genomics, exploring genetic diversity and crop improvement through mutation breeding and the generation of alternative alleles. His work bridges fundamental plant science with practical agricultural applications, particularly in developing disease-resistant crop varieties. Analysis of Dr. Meksem's publication history reveals a clear evolution from foundational genomic research to applied biotechnology. Early work focused on mapping disease resistance genes in soybean and potato, while more recent publications demonstrate a strategic shift toward developing transgenic solutions for agricultural challenges, particularly in modifying soybean oil composition and enhancing resistance to cyst nematodes. His research spans plant molecular biology, genomics, bioinformatics, and agricultural biotechnology, with consistent emphasis on practical applications for crop improvement. Dr. Meksem has received significant recognition for his research contributions: College of Agricultural Sciences Research Scholar award (2014) College of Agricultural Sciences Research Scholar award (2008) As an active member of the scientific community, Dr. Meksem serves as an editor and ad hoc reviewer for numerous international scientific journals and for national, federal, and international granting agencies. His laboratory has produced over 60 refereed manuscripts and 3 book chapters, in addition to editing 3 books. Dr. Meksem's research program has been supported through multiple patents, with recent work focusing on soybean genetic engineering for improved oil profiles and nematode resistance. His laboratory maintains active collaborations with other institutions, including the University of Missouri, as evidenced by joint patent applications.

• Computer Vision
• Deep Learning
• Image Processing
• +3 more

Chabbi Houda is a Full Professor at the Fribourg School of Engineering and Architecture, University of Applied Sciences and Arts Western Switzerland. She leads research at the iCoSys Institute for Complex Systems, focusing on AI-driven solutions for industrial and societal challenges. Her work bridges computer vision, deep learning, and practical applications from medical imaging to construction materials. Her research explores: Advanced computer vision for industrial inspection and monitoring Deep learning architectures for anomaly detection and predictive modeling Multimodal data fusion in surveillance systems AI applications in material science and healthcare She leads significant projects including: HIGH-ROAD (2025-2026): Hierarchical AI for road asset inventory using orthoimagery VideoCognition (2023-2025): Explainable AI for surveillance systems with Morphean SA SoNIT (2025-2028): Sonography nerve tracking with FHNW Concrete AI (2022-2025): Machine learning for recycled concrete optimization Her lab at iCoSys Institute collaborates with academic partners including University of Applied Sciences Northwestern Switzerland and industry leaders in medical tech, surveillance, and construction.

Charles PapermanView profile

Assistant Professor

• Circuit complexity
• Semigroup theory
• Finite models theory
• +3 more

Charles Paperman is a Lecturer (Assistant Professor) at the University of Lille with HDR (Habilitation à Diriger des Recherches) qualification. He is a member of the LINKS team at the CRIStAL laboratory (Centre de Recherche en Informatique, Signal et Automatique de Lille). His research focuses on theoretical computer science with particular emphasis on: Circuit complexity Semigroup theory Finite models theory Applications to databases Vectorized algorithms His research connects theoretical computer science with practical database applications, exploring how abstract mathematical structures can optimize data processing systems. Dr. Paperman teaches multiple courses including: Advanced Databases (Master 2 level) Algorithms and Programming Algorithm and Complexity Database Systems He has supervised several PhD students through their doctoral research.

Maria HalkidiView profile

Associate Professor

• Data Mining
• Machine Learning
• Recommender Systems
• +6 more

Maria Halkidi is an Associate Professor in the Department of Digital Systems at the University of Piraeus, Greece, where she conducts cutting-edge research in data mining and machine learning with over two decades of academic experience. Her educational background includes: Bachelor's degree in Informatics from the University of Piraeus (1997) Master's degree from Athens University of Economics and Business (1999) Ph.D. from Athens University of Economics and Business (2003) Dr. Halkidi's research focuses on fundamental and applied aspects of data mining, particularly recommender systems, graph data mining, cluster validity assessment, and distributed data mining. Her work bridges theoretical frameworks with practical implementations in sensor networks, social media, and real-time analytics, contributing significantly to algorithmic development in these domains. Analysis of her recent publications reveals a strong trajectory toward fairness and diversity optimization in recommender systems, alongside innovative approaches to graph clustering quality assessment and scalable sentiment analysis. Her research consistently addresses real-world challenges in dynamic data environments, with increasing emphasis on multi-stakeholder optimization and privacy-aware recommendation frameworks. Scientific awards: No specific awards or honors were documented in the provided materials. Dr. Halkidi has participated extensively in National and European-funded research projects, including a prestigious Marie-Curie fellowship at the University of California, Riverside. She serves on program committees for major international conferences in data mining and machine learning, demonstrating active community engagement. While specific graduate students aren't listed in the source materials, her professorial role indicates ongoing mentorship of Master's and Ph.D. candidates. She maintains active research affiliations with the Network oriented systems & services lab and the DataStories research group at the University of Piraeus, where she collaborates on interdisciplinary projects involving big data analytics, social network analysis, and distributed systems.

Mustafa ULAŞView profile

Assistant Professor

• Data Mining
• Big Data
• Machine Learning
• +7 more

Mustafa ULAŞ is an Assistant Professor in the Software Engineering Department at Fırat University, Turkey. He also serves as a University Advisor and Coordinator of the Digital Transformation and Software Office at Fırat University since October 2020. With academic roots entirely at Fırat University, he has established himself as a prominent researcher in data science and software engineering. Born in November 1981 in Elazığ, Turkey PhD in Electrical-Electronics Engineering (2011) Master's in Computer Engineering (2006) Bachelor's in Electrical-Electronics Engineering (2003) Dr. ULAŞ's research spans multiple domains of computer science and engineering with particular emphasis on practical applications. His work bridges theoretical computer science with real-world problems in healthcare, finance, and industrial systems. Recent publications reveal a strong focus on machine learning applications, especially in medical diagnostics and explainable AI, while maintaining his longstanding interest in VLF signal analysis for earthquake prediction. His publication record shows a clear evolution from foundational work in database systems and medical imaging to cutting-edge research in deep learning and explainable AI. The most recent articles (2024-2025) predominantly focus on healthcare applications of machine learning, particularly diabetes and cancer diagnosis, while maintaining parallel research streams in industrial applications, financial analytics, and drone network optimization. This multidisciplinary approach demonstrates his ability to adapt core computational techniques to diverse problem domains. Dr. ULAŞ has been actively involved in numerous research projects, including TÜBİTAK-funded initiatives such as the 'Enriched Virtual Laboratory' and 'A New Approach in Teacher Education: Effective Blended Learning.' His project portfolio spans infrastructure development, educational technology, and advanced research applications. As an educator, he teaches courses including C Programming and Algorithms, Internet-Based Programming, Server Operating Systems, and Web Project Management. His administrative roles include serving as University Advisor and Coordinator of the Digital Transformation and Software Office at Fırat University since 2020.

Martin AslettView profile

Researcher

• Bioinformatics
• Pathogen Genomics
• Parasite Genomics
• +3 more

Martin Aslett is a Bioinformatician working in the Pathogen Informatics team within the Parasites and Microbes group at the Wellcome Sanger Institute. He joined the Sanger Institute after working at the University of Cambridge Department of Pathology and the University of Edinburgh (based at EBI). His educational background includes: M.Sc. in Bioinformatics from the University of York B.Sc. Hons from the University of Leeds Aslett's research focuses on computational approaches to pathogen and parasite genomics, with expertise spanning: Genome sequencing and annotation of parasitic organisms Development of specialized bioinformatics databases and resources Comparative genomics across diverse pathogen species Evolutionary analysis of parasitic adaptations His publication record reveals a strong emphasis on neglected tropical diseases, particularly African trypanosomiasis, schistosomiasis, and leishmaniasis. He has made significant contributions to major international genome projects including the Glossina Genome Initiative and multiple Trypanosoma sequencing efforts. His work consistently bridges computational biology with practical applications for understanding parasite biology and disease mechanisms. Aslett has been instrumental in developing critical bioinformatics resources including GeneDB, TriTrypDB, and PomBase, which serve as essential tools for researchers worldwide studying pathogen genomics. His collaborative approach is evident through his extensive co-authorship on high-impact publications in journals such as Nature, Science, and Nucleic Acids Research.

• Particle Physics
• High Energy Physics
• ATLAS Experiment
• +5 more

Dr. Benedict Allbrooke is a Senior Research Fellow in Physics and Astronomy at the University of Sussex, within the School of Mathematical and Physical Sciences. He has been an active member of the ATLAS collaboration since 2015, contributing significantly to the experiment's trigger systems and physics analysis programs. His research focuses on experimental particle physics, with particular expertise in trigger system development, Higgs boson physics, and Standard Model validation. Dr. Allbrooke has played key roles in the ATLAS Hardware Track Trigger upgrade project and currently serves as the ATLAS Trigger Configuration and Database co-ordinator, managing critical infrastructure for data acquisition and analysis. Analysis of his recent publication record reveals a strong emphasis on Higgs boson properties, top quark physics, and searches for physics beyond the Standard Model. His work spans precision measurements of Standard Model processes and innovative searches for new phenomena, often requiring sophisticated trigger strategies to capture rare events. Dr. Allbrooke supervises PhD students and leads software development efforts for trigger configuration tools, providing opportunities for students to engage with cutting-edge high-energy physics research and international collaboration within the ATLAS experiment.