جست‌وجوی استاد و پژوهشگر

• Educational software design and development
• Collaborative learning
• Computer game development education
• +۷ مورد دیگر

Maksimenkova Olga Veniaminovna is an Associate Professor and Deputy Head of the Department of Software Engineering at the Faculty of Computer Science, National Research University Higher School of Economics (HSE University) in Moscow. With over 20 years of scientific and teaching experience, she has been working at HSE since 2009. Her academic journey includes positions as Senior Lecturer, Researcher at the International Laboratory for Intelligent Systems and Structural Analysis, and Deputy Director of the Center for Strategic Analytics and Big Data. Dr. Maksimenkova holds a Candidate of Technical Sciences degree (2018) in Information Systems and Processes, with a dissertation on improving educational information systems based on life cycle models of control and measuring materials. She graduated from Moscow State Institute of Electronics and Mathematics in 2006 with a specialty in Applied Mathematics and the qualification of Mathematician-Engineer. Her extensive professional development includes numerous advanced training programs in educational technologies, software engineering, and emotional intelligence. Her research focuses on educational software design, collaborative learning methodologies, game development education, and adaptive learning systems. She has made significant contributions to the field of educational technology through her work on data science education, virtual reality applications, and computer-supported collaborative learning environments. Dr. Maksimenkova's research bridges theoretical frameworks with practical applications in software engineering education. Analysis of her recent publications reveals a strong emphasis on game-based learning approaches, particularly using Unreal Engine for educational purposes. Her work demonstrates a consistent focus on practical applications of educational technology in software engineering contexts, with increasing attention to AI integration in educational settings. The publications show evolution from foundational work on educational software testing to more sophisticated applications involving game development, virtual reality, and adaptive learning systems. Honorary certificate of HSE University (March 2025) Gratitude from Higher School of Economics (December 2022) Multiple gratitudes from HSE University Vice-Rector (August 2021) Academic work bonus for six consecutive academic years (2013-2019) Inclusion in HSE University's high professional potential group (personnel reserve) Recognition in the Best Russian-language Scientific Works competition (2024) Dr. Maksimenkova actively supervises student course works and final qualification projects, and mentors graduate students. She has been instrumental in developing educational projects including the Summer School on Software Engineering and the GameDev Laboratory at ITHub College. Her grant activities include participation in the Microsoft Azure University Grant program (2016-2017) and various educational innovation projects at HSE University. She has coordinated teams for translating educational platforms and developed numerous educational materials for programming courses. As Academic Director of the Summer School on Software Engineering and Senior Analyst at the 'Vintorog' game development studio, Dr. Maksimenkova leads initiatives that bridge academic research with industry applications. She coordinates the OpenEdX-platform translation team and has been involved in developing educational games, including 'FCN Go!' released to celebrate the 10th anniversary of the Faculty of Computer Science. Her laboratory work focuses on practical applications of game development principles in educational contexts, creating tools that enhance the learning experience through interactive technologies.

Cornelius Alfred Jamesمشاهده پروفایل

استادیار بالینی

• Implementation of Emerging Technologies into Clinical Practice
• Integrating Artificial Intelligence and Machine Learning into Medical Education Curricula
• Evidence-Based Medicine and Clinical Reasoning
• +۱ مورد دیگر

Cornelius Alfred James, MD , is a Clinical Assistant Professor in the University of Michigan–Ann Arbor Medical School , with appointments in Internal Medicine, Pediatrics, and Learning Health Sciences. His work bridges clinical practice with artificial intelligence (AI) and machine learning (ML) to innovate medical education and improve patient care through digital health technologies. Education : BS (2005, Central Michigan University), MD (2011, Wayne State University School of Medicine), Residency (2015, Beaumont Health System), Chief Resident (2015). Dr. James specializes in integrating AI/ML into medical education , focusing on clinical decision-making , preventive care , and digital health . His research emphasizes evidence-based medicine and health equity , particularly in cardiovascular and geriatric care. Recent publications highlight his contributions to AI-driven curricula (e.g., DATA-MD, AIM-PC), digital health tools, and ethical AI adoption in clinical settings. He has also explored the role of spirituality in medical practice and trainee wellness . Scientific Awards : Health Sciences Teaching Academy (2017). Dr. James is actively involved in initiatives like the AI and Digital Health Innovation Center and the Caswell Diabetes Institute , advocating for technology-assisted patient relationships and systemic reforms in healthcare education.

• Deep Learning
• Federated Learning
• Data Analysis on Graphs
• +۹ مورد دیگر

Vinay Chakravarthi Gogineni is an Assistant Professor at The Maersk Mc-Kinney Moller Institute , University of Southern Denmark (SDU), specializing in SDU Applied AI and Data Science . His research integrates advanced AI techniques into healthcare, industrial IoT, and fusion energy systems, with a strong focus on federated learning, privacy-preserving AI, and ethical machine learning practices. Education: Ph.D. in Distributed Machine Learning, Indian Institute of Technology Kharagpur (Aug 2019) Research Interests: Dr. Gogineni's work spans Deep Learning , Federated Learning , and Graph Data Analysis . He develops personalized AI models for healthcare applications, including cervical cancer screening, colorectal cancer detection, and dementia prediction. His innovations in Machine Unlearning ensure compliance with GDPR and the EU AI Act, while his work on Physics-Informed Neural Networks enhances predictive accuracy in fusion energy systems. Key areas include self-supervised learning, continual learning, and fairness-aware AI. Scientific Awards: HC Ørsted Research Talent Award (2024, Denmark) ERCIM Alain Bensoussan Fellowship (2019) Best Paper Award , APSIPA ASC-2021, Tokyo Professional Roles: IEEE Senior Member and editorial board member of IEEE Sensors Journal . He teaches courses on AI for Healthcare Data and Calculus and Linear Algebra , fostering interdisciplinary education in applied AI.

• Data Mining
• Machine Learning
• Information Theory
• +۴ مورد دیگر

Manabu Kobayashi is a Professor at the Center for Data Science of Waseda University, with previous faculty roles at Shonan Institute of Technology (1998-2014). His research spans data mining, machine learning, and information theory, with a focus on applications in document classification, ECOC frameworks, and latent structural models. Current Affiliation: Waseda University (2018-present) Previous: Shonan Institute of Technology (2002-2014) Research Interests: Specializes in distance metric learning, latent class modeling, and fault diagnosis systems using probabilistic frameworks. Key areas include: ECOC (Error-Correcting Output Codes) optimization Collaborative filtering for recommendation systems Reconfigurable logic circuits using DG-CNTFETs Privacy-preserving distributed linear regression Scientific Awards: PC Conference Best Paper Award (2025) World CIST'18 Best Paper Award IEICE Achievement Award (2017) 74th Information Processing Society of Japan Conference Excellence Award (2012) Article Trends: Recent work focuses on latent structure analysis for relational data (2025), ECOC performance with noisy classifiers (2023), and flipped classroom effectiveness through log-based grouping (2022-2025). Earlier contributions include reconfigurable logic circuits (2013-2014) and probabilistic fault diagnosis (2011-2012).

John Breslinمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Social Semantics
• +۷ مورد دیگر

John Breslin is a Personal Professor in Electronic Engineering at the University of Galway, leading the TechInnovate/AgInnovate programs. His roles include Director of the Insight (Data Analytics) and VistaMilk (AgTech) research centers. He has authored 350+ publications and co-created the SIOC framework, widely used in web applications. His research spans Data Science, AI, Semantic Web, IoT, and Entrepreneurship. He has won multiple awards, including the Best Irish-Published Book and IIA Net Visionary Awards. His teaching spans 25 years, covering topics like Digital Control Systems and Innovation. He co-founded boards.ie, StreamGlider, and leads initiatives in Galway's innovation ecosystem. Research Interests: Data Science, AI, Social Semantics, IoT, Agricultural Technology, and Innovation. He is ranked top in Engineering and Computer Science citations at the University of Galway. His work contributes to UN SDGs related to Industry, Innovation, and Infrastructure. Education: Details not explicitly provided in text. Collaborations include Bosch, Cisco, IBM, and Harvard Medical School. Active in industry partnerships and startups, including the PorterShed Innovation District. Awards: Best Paper Awards, Entrepreneurship Recognition, and Academic Excellence. Supervised nearly 30,000 ECTS across modules, with a focus on graduate and postgraduate tech innovation programs.

Eric Postmaمشاهده پروفایل

استاد

• Artificial Intelligence
• Cognitive Science
• Pattern Recognition
• +۶ مورد دیگر

Eric Postma is a Full Professor of Artificial Intelligence at the Tilburg School of Humanities and Digital Sciences , specifically within the Department of Cognitive Science and Artificial Intelligence . He also holds a position at the Jheronimus Academy of Data Science in 's-Hertogenbosch, a collaboration between Tilburg University and Eindhoven University of Technology. Research Focus: Pattern recognition in humans and machines, with applications spanning art authentication, exoplanet detection, and medical AI (particularly glioma patient outcomes). Projects: Principal investigator in healthcare AI initiatives like predictive modeling for post-surgical cognitive function and co-investigator in multi-institutional projects like STEADFAST (swarm robotics for first responders) and MEGaNorm (brain dynamics modeling). Leadership: Member of SIGAI, IPN, Lorenz Center Computational Science Board, and advisory bodies like Kennisnet and CLAIRE. Research Trends: His recent publications emphasize interdisciplinary applications of AI, including dataset creation for pediatric body measurement analysis, vision transformers for art authentication, and fMRI data augmentation techniques. Key themes include ethical AI deployment, human bias vs. machine objectivity, and sustainability-focused AI through initiatives like the ILUSTRE project in Curaçao. Collaborations: Works with institutions across neuroscience (Rutten, Gehring), data science (Güven, Šafář), and engineering (Cuijpers). His research bridges theoretical AI with practical implementations in healthcare, cultural heritage, and environmental sustainability.

Muhammad Asadمشاهده پروفایل

مدرس

• Federated Learning
• Wireless Communication
• Blockchain Technologies
• +۷ مورد دیگر

Dr. Muhammad Asad is a Lecturer in Computer Science at the School of Engineering, Computing and Mathematics. With 13+ years of research experience, his work focuses on federated learning, wireless communication systems, and IoT network optimization. Primary Role: Lecturer in Computer Science Key Research Areas: Federated Learning, Wireless Communication, and IoT Security His research spans communication-efficient AI systems, blockchain-integrated learning frameworks, and energy optimization in sensor networks. Recent work includes security-enhanced federated learning protocols for vehicular networks and healthcare analytics. Publications (2018-2024) demonstrate consistent contributions to fields including: Privacy-preserving machine learning IoT-oriented network architectures Blockchain-based security systems Medical imaging diagnostics

Tingjian Geمشاهده پروفایل

استاد

• Data Management
• Uncertain Data
• Scientific Data
• +۶ مورد دیگر

Tingjian Ge is a Professor at the Miner School of Computer & Information Sciences , part of the Kennedy College of Sciences at the University of Massachusetts Lowell. He serves as the Graduate Coordinator for MS Programs and specializes in data management systems, focusing on probabilistic and uncertain data, scientific data, and data security and privacy. Education : B.E. (Tsinghua University), M.S. (University of California, Davis), Ph.D. (Brown University) His research spans data management, with emphasis on uncertain data processing, graph stream analysis, and blockchain applications. Recent work includes methods for real-time predictive event monitoring, fairness-aware analytics in dynamic networks, and security enhancements for blockchain systems. Key publication trends highlight contributions to resource optimization in graph streams, recommender systems, and cybersecurity. His expertise intersects database theory, privacy-preserving techniques, and networked data analysis.

Faycal Bouhafsمشاهده پروفایل

مدرس ارشد

• Programmable and software defined wireless networks
• Beyond 5G and 6G communication networks
• Security in Internet of Things
• +۴ مورد دیگر

Dr. Faycal Bouhafs is a Senior Lecturer at the School of Systems & Computing , UNSW Canberra . His research focuses on performance and reliability in wireless communication networks, with expertise in programmable networks, 5G/6G architectures, and IoT security. He leads initiatives in radio resource optimization and physical-layer security using software-defined approaches. Research Interests: Dr. Bouhafs investigates: Programmable wireless networks for dynamic resource allocation Beyond 5G/6G infrastructures supporting massive IoT deployments Security frameworks for cyber-physical systems and IoT ecosystems AI-driven optimization of radio access and network management Publication Focus: Recent works (2020-2025) demonstrate strong emphasis on: Software-defined wireless networking (SDWN) for spectrum sharing Physical-layer security via jamming and deep learning 6G resource optimization and IoT scalability Practical implementations using off-the-shelf equipment

Tughrul Arslanمشاهده پروفایل

استاد

• Integrated Electronic Systems
• Reconfigurable Hardware and Software
• Low Power Design
• +۶ مورد دیگر

Professor Tughrul Arslan holds the Chair of Integrated Electronic Systems at the School of Engineering, University of Edinburgh . He leads the Embedded Wireless and Wearable Sensor Systems (EWireless) Group and co-founded sensewhere Ltd. and Sofant Technologies . His research spans reconfigurable architectures, low-power wireless systems, and biomedical RF sensing. Academic Background: BEng and PhD in Electronics Professional Affiliations: Senior Member IEEE, Fellow IET, Chartered Engineer His work focuses on smart wearable devices , indoor positioning systems , and AI-driven healthcare monitoring . Recent publications emphasize microwave imaging for dementia detection , edge AI accelerators , and non-invasive tremor monitoring . Key contributions include patented technologies like the Reconfigurable Instruction Cell Architecture (RICA) . Award-winning academic, he has supervised over 40 PhD students and authored 400+ peer-reviewed papers. His external roles include Chief Technology Officer at sensewhere , driving commercialization of indoor navigation solutions.

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +۱۱ مورد دیگر

Fatima Boukari is an Associate Professor in Computer Science within the Division of Physics, Engineering, Mathematics and Computer Sciences at Delaware State University. Her research bridges artificial intelligence, deep learning, and mathematical modeling to develop robust solutions for biomedical engineering, cell biology, and agricultural technology challenges. Education: B.Sc. in Computer Science Engineering from University of Annaba, Algeria Dual M.Sc. degrees in Computer Systems Architectures and Parallel Computing from Algeria-Glasgow Ph.D. in Mathematics & Physics from Delaware State University Dr. Boukari's research centers on foundational Deep Learning architectures and mathematical modeling applied to biomedical diagnostics and cell dynamics analysis. Her work in reinforcement learning and transfer learning enhances decision-making for autonomous systems, while her cognitive modeling research decodes human perception using EEG data. She pioneers multi-modal distributed learning systems that maintain privacy across heterogeneous sensor networks, addressing critical gaps in military ISR applications. Her recent publications reveal a strong trajectory toward spectral data analysis for medical diagnostics and AI-driven cognitive modeling , with increasing emphasis on trustworthy AI solutions for healthcare and environmental sustainability. The consistent focus on cell segmentation/tracking algorithms demonstrates her commitment to advancing biomedical image analysis. Scientific Awards: No scientific awards, prizes, or fellowships listed in available information Dr. Boukari has mentored over 40 undergraduate and 2 graduate students from underrepresented STEM backgrounds. Her active research portfolio includes: NSF CISE grant for biomolecular detection using physics-informed machine learning Air Force RITA/UARC project on neuroscience computational modeling Air Force project building robust multi-modal distributed learning systems DE-CTR ACCEL project for COVID-19 respiratory disease diagnosis NSF grant for Delaware and Mid-Atlantic Data Science Corps Research scientist role in AI-CLIMATE National AI Research Institute She leads the Applied Interdisciplinary Data Science (AIDA) Laboratory and serves as Project Lead for the CAST E-IoT Center's four agricultural research thrusts. As Team Lead of the 1890 Working Group on Artificial Intelligence, she drives initiatives addressing climate change resilience and food security through responsible AI development.

Daniel Strüberمشاهده پروفایل

دانشیار

• Model-Driven Engineering
• AI Engineering
• Software Quality Assurance
• +۱۲ مورد دیگر

Daniel Strüber is an Associate Professor in Software Engineering at the joint Interaction Design and Software Engineering Division of Chalmers University of Technology and the University of Gothenburg, Sweden. He is also affiliated with the Department of Software Science at Radboud University Nijmegen, Netherlands. His research focuses on model-driven engineering, AI engineering, and variability management to enhance software quality, security, and collaborative development across domains like robotics and web systems. Education : Diplom (M.Sc. equivalent) in Computer Science from Philipps University Marburg (2011, with distinction), Ph.D. (summa cum laude) from Philipps University Marburg (2016). His research bridges empirical, formal, and engineering approaches to address developer challenges in ML-enabled systems. Key trends in his work include model-driven methods for ML deployment, variability management in robotics, and empirical studies of tool usability. He has co-edited a Journal of Systems and Software special issue on software product lines. Scientific Awards : Best Reviewer Awards (SoSyM 2025, GPCE 2024), Transformation Tool Contest Awards (2017, 2020), Best Paper Awards (VAMOS 2019, ICMT 2016), and multiple conference nominations. As a supervisor, he guides current Ph.D. students like Vladislav Indykov and Weixing Zhang, while his former advisees include Shayan Ahmadian and Samuel Idowu. Grants include a VR Open Call Grant (2022-2024) for SEMLA and a DFG Individual Fellowship (2019) for EUphORia. His teaching spans courses on Software Engineering for AI Systems and Software Product Lines , often integrated with real-world projects via GiPHouse.

Daniel Sonntagمشاهده پروفایل

استاد

• Intelligent User Interfaces
• Natural Language Processing
• Information Retrieval
• +۷ مورد دیگر

Prof. Dr.-Ing. Daniel Sonntag serves as Scientific Director of Interactive Machine Learning at the German Research Center for Artificial Intelligence (DFKI) and holds an Endowed Chair of Applied Artificial Intelligence at University of Oldenburg. He leads multiple significant research initiatives including No-IDLE (Interactive Deep Learning Enterprise), MASTER (Mixed reality ecosystem for teaching robotics), Ophthalmo-AI (intelligent eye care diagnostics), pAItient (AI applications in medicine), and XAINES (explainable AI with narratives). His research spans intelligent user interfaces, natural language processing, information retrieval, dialogue systems, common-sense modeling, and explainable machine learning methods for cognitive computing. His work particularly focuses on multimodal-multisensor interfaces for medical and health systems and Industry 4.0 applications. His research has had industrial impact, with SmartWeb's multimodal interaction design forming the technical infrastructure for Apple's Siri. His recent publications demonstrate strong trends in medical AI applications, particularly in eye tracking, radiology report generation with enhanced interpretability, and interactive federated learning frameworks. His work consistently bridges theoretical AI advances with practical medical and industrial applications. German High Tech Champion Award (2011) As a principal investigator, Sonntag leads national and European projects funded by the Federal Ministry of Education and Research, Federal Ministry for Economic Affairs and Energy, Federal Ministry of Health, and Horizon Europe. He has supervised numerous researchers including Michael Barz, Jan Zacharias, and Alexander Prange across multiple projects. His research group has successfully transitioned technologies to industry, with former researchers joining companies like Apple, Wacom, and Semvox. Sonntag's laboratory work centers on the Interactive Machine Learning department at DFKI, with strong connections to medical institutions and industry partners. His team develops cognitive assistance systems, mixed reality applications for smart factories, and AI-based diagnostic support tools, particularly in ophthalmology and radiology.

• Computer Security
• Privacy-enhancing Technologies
• Democracy-promoting Technologies
• +۳ مورد دیگر

Daniel Bosk is a Lecturer in the Department of Theoretical Computer Science at the School of Electrical Engineering and Computer Science, Royal Institute of Technology (KTH) since 2020. Previously, he served as a university lecturer in computer engineering at Mid Sweden University, Sundsvall from 2011. Dr. Bosk's research spans two interconnected domains: computer security with focus on democracy-promoting technologies, privacy-enhancing systems, anonymous credentials, and secure communication protocols; and computer science education with emphasis on e-learning methodologies, question-based learning approaches, and adaptations to remote teaching environments. His work on "pure question-based learning" represents a significant contribution to educational innovation in technical fields. Analysis of his publication record reveals a consistent trajectory examining both technical security solutions and their educational applications. His security research investigates practical implementations of privacy technologies in social contexts, while his educational research analyzes student behavior patterns, error management in programming, and effective remote teaching methodologies. This dual focus is reflected in his teaching of courses like "The Role of the Cybersecurity Engineer in Society" which bridges technical knowledge with social implications. Dr. Bosk teaches across a comprehensive range of computer science courses at KTH including Algorithms and Data Structures, Programming Technologies, Applied Cryptography, and Scientific Theory and Scientific Methodology in Cybersecurity, serving in multiple capacities as teacher, examiner, course coordinator, and course manager. His extensive experience with distance learning since 2011 has positioned him as a specialist in adapting computer science education to various delivery formats.

• Network Security
• Cybersecurity
• Privacy-Preserving Technologies
• +۳ مورد دیگر

Cihan Eryonucu is a Ph.D. student and researcher at the KTH Royal Institute of Technology , affiliated with the Networked Systems Security (NSS) Group and the WASP - Autonomous Systems and Software track . He is supervised by Professor Panos Papadimitratos . Research Focus: Security and privacy in participatory sensing systems, Sybil attack detection, and privacy-preserving protocols for location-based services. Teaching: Assistant roles in courses like Advanced Networked Systems Security (EP2510) and Building Networked Systems Security (EP2520) , and a teacher in Communication System Design (IK2200) . Expertise: His work addresses vulnerabilities in crowdsourced systems (e.g., Google Maps manipulation) and develops cryptographic methods for secure data aggregation. Publications: Recent works explore Sybil attack mitigation via presence verification, privacy-enhancing frameworks for urban sensing, and applications of homomorphic encryption to genomic and location data. Contact: Email eryonucu@kth.se | Room 4444, Kistagången 16, KTH Royal Institute of Technology.