Find Academic Supervisors

Gokila DoraiView profile

Assistant Professor

• Digital Forensics
• Mobile Data Analysis
• Internet of Things (IoT)
• +5 more

Dr. Gokila Dorai is an Assistant Professor at Augusta University's School of Computer and Cyber Sciences , specializing in Digital Forensics with applications to Mobile and IoT Devices . She holds a Ph.D. in Computer Science and has developed targeted data extraction models for evidence retrieval using Machine Learning and Computer Vision . Education: Ph.D. (2019), MS (2016) from Florida State University, BE (2007) from Anna University Her research focuses on AI-based digital forensics , user data privacy , and security solutions for wearable and IoT devices. She has pioneered frameworks for privacy-preserving data access and blockchain-based forensic provenance . Recent publications demonstrate her expertise in mobile forensics (Amazon/Etsy apps), blockchain frameworks (ForensiBlock), IOT security (FLARE system), and privacy policy analysis using NLP techniques. NSA Cybersecurity Research Innovation Grant ($494,999.51) NSF Research Grant ($569,500) with socio-technical framework AURI Emerging Scientist Award Golden Pen Award Dr. Dorai actively mentors students through programs like Scholarship for Service (SFS) , CURS Research , and Phi Kappa Phi conferences. She leads the Digital Forensics & AI Research Lab (DFAIR Lab) in Augusta University's Georgia Cyber Center, fostering collaborations between academia, government, and industry.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +6 more

Xue Yang is a Professor in the Department of Computer Science and Engineering at Shanghai Jiao Tong University's School of Electronic Information and Electrical Engineering. With an extensive publication record spanning from 2023 to 2026, Dr. Yang has established herself as a prominent researcher in multiple interdisciplinary fields at the intersection of computer science and practical applications. Dr. Yang's research interests span a wide spectrum of cutting-edge computational fields including Artificial Intelligence , Machine Learning , Quantum Computing , Image Processing , and Healthcare Applications of AI . Her work demonstrates a remarkable ability to bridge theoretical computer science with practical applications across diverse domains such as biomedical informatics, oceanography, and e-commerce. The interdisciplinary nature of her research is evident in publications that combine computer vision with medical applications, quantum computing with image processing, and machine learning with environmental monitoring. An analysis of Dr. Yang's recent publications reveals a strong focus on developing novel algorithms and computational frameworks that address complex real-world problems. Her work shows particular strength in generative models , feature learning architectures , and data-driven optimization approaches. The consistent publication output across multiple high-impact venues demonstrates sustained research productivity and influence in her fields of expertise. Exponential stability analysis for nonlinear systems Advanced machine learning architectures for biomedical applications Quantum-classical hybrid computing models Blockchain applications for network security Computer vision techniques for specialized imaging scenarios

• Quantum Key Distribution
• Neuroscience
• Machine Learning
• +3 more

Dr. Yijun Wang is a researcher affiliated with the Chinese Academy of Sciences, State Key Laboratory on Integrated Optoelectronics, and Institute of Semiconductors in Beijing, China. His work spans multiple disciplines including quantum key distribution, brain-computer interfaces, and machine learning applications in cybersecurity and biomedical imaging. Key Research Areas: Quantum cryptography, Neural signal processing, Transformer-based models, Network security, and Digital health systems. Recent publications highlight advancements in EEG authentication systems 2026 , radar-based activity recognition 2025 , and quantum communication security 2025 . His work demonstrates interdisciplinary expertise bridging theoretical algorithms and practical engineering solutions. Scientific contributions include: Developing attention mechanisms for image restoration 2025 Creating reliability-enhanced BCIs via graph-driven fusion 2025 Designing semi-supervised solvers for CAPTCHA breaking 2021 Dr. Wang's collaborations appear across journals like Pattern Recognition , IEEE Transactions , and conferences including AAAI 2025 , ICLR 2025 , and EMBC 2023 . Current projects integrate LLMs with digital phenotyping for hypertension management 2025 and explore self-taught reasoning mechanisms 2025 .

Balajee VamananView profile

Associate Professor

• Computer Networks
• Datacenter Networks
• Low-Latency Networks
• +7 more

Balajee Vamanan is an Associate Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC), where he has held a faculty position since October 2015. His office is located in CDRLC 4458 and SEO 1310, with contact details including email bvamanan@uic.edu and phone (312) 996-9442. He teaches core systems and networking courses such as CS 361 (Systems Programming), CS 450 (Introduction to Networking), and CS 594 (Advanced Computer Networking), emphasizing hands-on programming and critical problem-solving in modern networked environments. Education: Bachelor's degree from Birla Institute of Science & Technology (BITS), Pilani, India. Ph.D. from Purdue University (August 2015). Research Interests: His work centers on computer networks , with primary focus on datacenters , low-latency networks , software-defined networking (SDN) , router architecture , and cellular networks . Secondary interests include computer systems research, memory system architecture, and in-memory caches. He bridges theoretical networking concepts with real-world implementation, leveraging industry experience from NVIDIA and Google to address performance bottlenecks in network hardware and protocols. Publication Trends: Recent work (2020-2025) demonstrates concentrated innovation in datacenter transport protocols (e.g., MTP for in-network computing), programmable switch optimizations (TCAM utilization, memory management), and congestion control for RDMA environments. His research increasingly intersects distributed machine learning training with networking challenges, while maintaining strong contributions to cellular background traffic management and topology design for incast workloads. Scientific Awards: No scientific awards, fellowships, or medals are documented in the provided materials. Advising and Grants: He advises multiple Ph.D. students (Seyri, Vardekar, Saxena) and undergraduate researchers. Past students have secured roles at Google, Cisco, Microsoft, and VMware. His lab receives sustained funding from the National Science Foundation (NSF) through grants including BIGDATA: RDMA-Based Datacenter Networks for Big Data Applications, FMitF: Injecting Formal Methods into Internet Standardization, and CNS Core: Network-wide Policy Enforcement in Programmable Networks. Labs and Collaborations: Leads a research group within UIC's BITS Lab, a cross-disciplinary hub for networking, systems, and security. Maintains active industry partnerships with AT&T Research, Microsoft, and NVIDIA, alongside academic collaborations with UT Austin, University of Utah, Purdue University, and Indian Institute of Science's Parimal Parag group.

• Fuzzy Sets
• Intuitionistic Fuzzy Sets
• Data Analysis
• +6 more

Alžbeta Michalíková serves as Associate Professor at Matej Bel University's Faculty of Arts, Department of Computer Science. Her academic profile spans mathematical foundations and applied computational research with significant contributions to fuzzy logic systems. Her research focuses on Fuzzy Sets and Intuitionistic Fuzzy Sets with applications in Computer Vision, Biology, and Medicine. She investigates mathematical properties of these structures and explores integrations with Neural Networks and Evolutionary Algorithms. Recent work emphasizes smart city applications, air quality analysis, and digital education tools. Her publication trends show evolution from theoretical fuzzy mathematics (2005-2015) to applied AI systems (2020-present), particularly in environmental monitoring and transportation systems. Key themes include uncertainty modeling, image classification, and real-time data processing for urban applications. Erasmus+ KA2 projects on air pollution technologies (2022-2024) KEGA projects for digital educational tools (2020-2025) VEGA research on fuzzy models (2011-2017) APVV projects on scientific outreach She teaches Mathematics for Computer Scientists, Numerical Methods, and specialized Fuzzy Sets courses. Her technical infrastructure includes ORCID (0000-0002-0568-9279), SCOPUS (24448471500), and ResearchGate profiles. Current work focuses on digital identification keys for mycology education and AI-driven smart city analytics.

• Energy transition
• Energy efficiency and management
• Smart Building technologies
• +8 more

Jean-Philippe Bacher is a Full Professor at the University of Applied Sciences and Arts Western Switzerland (HES-SO), specifically affiliated with the Fribourg School of Engineering and Architecture. He serves as a key researcher at the Institute for Applied Research in Energy Systems, where he leads multiple significant research initiatives focused on energy transition and building performance optimization. His work bridges academic research with practical applications in the energy sector, particularly in the context of Switzerland's Energy Strategy 2050. Professor Bacher's research interests span the critical domains of energy transition, energy efficiency and management, Smart Building technologies, and building performance analysis. His expertise extends to photovoltaic systems, energy storage solutions, Building Information Modeling (BIM), and the intersection of Internet of Things (IoT) technologies with building management. He has developed a particular focus on understanding human behavior in built environments and its impact on energy consumption patterns. His work often addresses the complex challenges of district energy systems and innovative approaches to building decarbonization strategies, with numerous publications demonstrating his leadership in these fields. An analysis of his recent publications reveals a strong trajectory toward integrated solutions for energy transition in the built environment. His work increasingly focuses on the convergence of multiple technologies - particularly the combination of BIM, IoT, and virtual reality for facility management, as well as the integration of photovoltaics with hydrogen-based storage systems for seasonal energy balancing. His research demonstrates a methodological shift toward data-driven approaches using machine learning techniques to optimize energy systems at multiple scales, from individual buildings to district-level infrastructure. The consistent theme across his publications is the practical application of research to address Switzerland's and Europe's energy transition challenges, with particular attention to grid stability issues arising from decentralized renewable energy production. EXPOSITION project (2024-2025): Experimentation Platform for Smart Districts focusing on grid stability issues ProRen project (2019-2021): Interventions on existing building stock for large-scale energy optimization Multi-Confort project (2011-2020): Comprehensive approach to workplace comfort considering physiological, psychological, and functional components CityPulse project (2017-2020): Sensor platform for urban quality of life monitoring in Fribourg's BlueFactory district Professor Bacher leads a substantial research team within HES-SO, collaborating with numerous colleagues across multiple disciplines. His work demonstrates strong industry engagement through practical applications of research findings and technology transfer initiatives. He serves as a bridge between academic research and practical implementation in the energy sector, particularly in the context of Switzerland's Energy Strategy 2050 and the challenges of integrating renewable energy sources into existing infrastructure.

• Digital Holography
• Image Processing
• Deep Learning
• +5 more

Bruno Cornelis is a postdoctoral researcher at Vrije Universiteit Brussel's Electronics and Informatics school, with a focus on biomedical signal processing, digital holography, and deep learning applications. His work spans both fundamental and applied research projects. Education : Master's thesis on Quantization-based Watermarking (2007) and Digital Holographic Image Processing (2007) Research Interests include: Deep Learning for Image Restoration and Biomedical Applications Sparse Signal Representations in Holography and Anomaly Detection Photoplethysmography (PPG) Signal Analysis Art Classification via Mathematical Signal Models Recent Publication Trends show a shift towards real-time traffic data disaggregation, PPG-based health monitoring, and deep learning architectures for image reconstruction. His work often bridges theoretical signal processing with practical biomedical/transportation applications. Honors : BARCO/FWO Master Thesis Award (2007) Collaborations involve EU-funded initiatives like GEAR and Tech4Health, with cross-disciplinary teams in Belgium and China. He has presented at IEEE conferences on transportation systems (ITSC 2022) and medical imaging (DATE 2025).

Sanda-Maria AvramView profile

Lecturer

• Authorship Attribution
• Artificial Intelligence
• Natural Language Processing
• +7 more

Dr. Sanda-Maria Avram is a faculty member at Babeș-Bolyai University in Cluj-Napoca, Romania, where she teaches in the Faculty of Mathematics and Computer Science. She currently instructs courses in Operating Systems for bachelor's students and oversees the elaboration of Bachelor Theses. Her teaching approach emphasizes practical application and student engagement in both undergraduate and master's programs, having recently completed the sixth iteration of her Adaptive Web Design course with 81 master's students across six sections. Dr. Avram's research spans multiple domains with a current focus on authorship attribution through artificial intelligence, natural language processing, and stylometry. Her work aims to achieve and surpass AI-based results through mathematical approaches. Previously, her research encompassed knowledge discovery, data visualization, web mining, e-learning systems, and networking. She has developed several educational platforms including SINE, PULSE, and PULSE Extended for student evaluation in laboratory settings, addressing the specific needs of Romanian universities where laboratory sessions are mandatory and guided by a single tutor. Her publication record reveals a strong trend toward applying computational methods to solve practical problems in education and networking. Recent work focuses on linguistic analysis and authorship attribution using advanced AI techniques, while earlier publications address educational analytics and network routing protocols. She has demonstrated that integrated analytics using authentication data provide more reliable insights for e-learning environments than standard tools like Google Analytics. Dr. Avram leads a student research group focused on mathematical approaches to authorship attribution, encouraging students with strong mathematical skills to collaborate. She actively maintains academic profiles on Google Scholar, ResearchGate, and Academia.edu (under the names SandaDragos and SandaMariaAvram), where she has established a network of academic collaborators and shares her research findings with the broader scholarly community.

• Machine Learning
• Embedded Systems
• Neural Architecture Search
• +4 more

Paolo Gastaldo serves as Associate Professor in the Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN) at the University of Genoa's School of Engineering. His academic appointment focuses on electronic engineering (IINF-01/A classification) with teaching responsibilities spanning undergraduate and graduate programs including Electronic Engineering and Information Technologies. His research centers on machine learning for embedded systems, specializing in neural architecture search techniques optimized for resource-constrained environments. Key investigation areas include hardware-aware AI deployment on microcontrollers, energy-efficient traffic classification, and affordance segmentation for wearable robotics. His work bridges theoretical machine learning with practical hardware constraints, targeting applications in IoT security, edge computing, and smart wearable systems. Analysis of his recent publications reveals a strong trend toward Pareto-optimal solutions balancing computational efficiency and accuracy in neural network design. His research demonstrates particular expertise in adapting deep learning models for microcontroller deployment while maintaining cybersecurity functionality. The publications consistently address the challenge of implementing complex AI tasks within strict memory and power limitations of embedded devices. Gastaldo maintains active research supervision with regular publication output, primarily through the University of Genoa's institutional repository (IRIS). His work shows strong interdisciplinary connections between computer science, electrical engineering, and robotics applications.

Diego Borro YagüezView profile

Professor

• Computer Graphics
• Virtual Reality
• Augmented Reality
• +6 more

Diego Borro is a Full Professor (Catedrático) in Computer Science and Artificial Intelligence at TECNUN, Technological Campus of the University of Navarra , where he has been part of the faculty since 2004. He is a leading researcher at CEIT since 2003, focusing on Robotics, Virtual/Augmented Reality, Computer Vision, and Artificial Intelligence. His academic credentials include a PhD in Computer Science (2003) and an MS in Computer Science (2000) from the University of Navarra and University of Basque Country respectively. His research spans from 3D tracking and haptics to industry 4.0 applications and medical robotics, with over 34 journal papers and 65 conference articles. He has supervised 14 doctoral theses and participated in 55+ research projects. His leadership roles include heading CEIT's Simulation Unit (2012-2016) and Vision and Robotics (V&R) research line (2016-2022), currently serving as main researcher at Intelligent Systems for Industry 4.0 group (SS4I4). Accredited as Full Professor (Catedrático) by ANECA (3 sexenios) Member of IEEE, ACM, and Eurographics societies Key projects: STEPbySTEP exoskeleton benchmark, WARM AR maintenance systems, and inner ear drug delivery research

József SÜTŐView profile

Associate Professor

• Machine Learning
• Digital Signal Processing
• Microcontroller Applications
• +3 more

Dr. József SÜTŐ is an Associate Professor at the University of Debrecen's Faculty of Informatics, Department of IT Systems and Networks. His research focuses on machine learning, digital signal processing, and microcontroller applications for agricultural and biomedical systems. University of Debrecen, Faculty of Informatics Department of IT Systems and Networks Research areas combine computer science with environmental and health monitoring, including: Deep learning for insect pest management Edge computing in embedded systems Human activity recognition algorithms Computer vision for traffic and plant analysis Recent publications emphasize real-time AI deployment on low-power hardware, such as Raspberry Pi and microcontrollers, with applications in agricultural monitoring and wearable devices. He also investigates comparative model efficiency between shallow and deep learning architectures.

Vasil HnatyshinView profile

Professor

• Computer Networks
• Network Security
• Data Science
• +3 more

Vasil Hnatyshin is a Full Professor and Department Head of the Department of Computer Science within the College of Science & Mathematics at Rowan University. He has established himself as a prominent figure in computer science education and research, with a focus on network technologies and data science applications. His educational background includes: Ph.D. in Computer and Information Sciences from the University of Delaware M.S. in Computer and Information Sciences from the University of Delaware B.S. in Computer Science from Widener University Hnatyshin's research expertise spans multiple critical areas of computer science, with particular emphasis on Internet and Computer Networks, Computer and Network Security, and Data Science. His work bridges theoretical concepts with practical applications, especially in network simulation and security protocols. He has developed innovative approaches to network modeling and has contributed significantly to the understanding of routing protocols in mobile ad hoc networks. His recent research has expanded into machine learning applications, particularly in metabolomics and clustering algorithms, demonstrating his ability to apply computational methods to interdisciplinary problems. An analysis of his publication record reveals a consistent research trajectory focused on computer networking fundamentals that has evolved to incorporate data science and machine learning techniques. His early work centered on network protocols, bandwidth distribution, and quality of service mechanisms, while more recent publications demonstrate an expansion into data clustering algorithms, machine learning applications, and wireless communication systems. This evolution reflects the broader shifts in computer science research toward data-intensive approaches while maintaining his foundational expertise in network technologies. Dr. Hnatyshin has secured significant research funding through multiple collaborative projects with Bristol-Myers Squibb (BMS), serving as Principal Investigator on several initiatives including the Rowan-BMS Collaboration projects spanning from 2014 to 2022. These projects focused on areas such as PCO project development, automated image classification frameworks, EDM software improvements, and automated data analysis. Hnatyshin is an active member of professional organizations including the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE), demonstrating his engagement with the broader computer science community. His work has been cited numerous times, with his OPNET User Guide book being particularly influential in network simulation education and practice.

Andrew SungView profile

Professor

• Machine Learning
• Deep Learning
• Cybersecurity
• +7 more

Andrew Sung is a Professor at the University of Southern Mississippi (USM) in Hattiesburg, affiliated with the Chain Technology Center (TEC). His distinguished academic career spans over four decades, focusing on the critical intersection of machine learning and cybersecurity, with significant contributions to multimedia forensics and digital security. His educational background includes: PhD from Stony Brook University (1984) MS from University of Texas System Office (1980) BS from National Taiwan Normal University (1976) Professor Sung's research centers on applying machine learning to security challenges, with groundbreaking work in deepfake detection, malware analysis, and steganalysis. His publications demonstrate expertise in developing privacy-preserving techniques like machine unlearning, creating robust deepfake detection frameworks, and innovating Android security solutions. He has pioneered methods in moving target defense for software-defined networking and advanced forensic techniques for identifying digital forgeries in images and audio, addressing evolving threats in AI-generated content and network infrastructure. Analysis of his 15 most recent publications (2019-2024) reveals a dominant research trajectory toward combating AI-generated threats. His work increasingly focuses on deepfake detection through ensemble learning and dataset distillation, machine unlearning for privacy compliance, and sophisticated malware analysis techniques. This trend highlights his strategic pivot toward securing digital ecosystems against next-generation AI threats while maintaining strong foundations in traditional cybersecurity domains like network defense and steganalysis. No scientific awards were mentioned in available sources. Information regarding students advised or research grants was not provided in the available documentation. Professor Sung conducts his research at the Chain Technology Center (TEC) at USM, where he leads investigations into cutting-edge security challenges at the forefront of artificial intelligence and digital forensics.

• Transport Engineering
• Public Transit Analysis
• Smart Card Data Applications
• +3 more

Dr. Mahmoud Mesbah is a Senior Research Fellow at the School of Civil Engineering , University of Queensland , specializing in transport engineering and urban mobility . His work leverages smart card data , GIS , and machine learning to analyze passenger behavior , trip purpose inference , and transit network optimization . Research Themes : Public transit modeling, traffic violation analysis, travel time reliability, and data-driven urban mobility solutions. Email : mahmoud.mesbah@uq.edu.au

Álvaro Herrero CosioView profile

Professor

• Artificial Intelligence
• Machine Learning
• Multi-Agent Systems
• +6 more

Álvaro Herrero Cosio is a Professor at the Department of Digitalization within the Higher Polytechnic School of the University of Burgos, Spain. His academic career spans since 2005, with expertise in applied Artificial Intelligence (AI) across industrial systems, cybersecurity, environmental sciences, e-health, and business management. Research Interests: He focuses on soft computing, machine learning, multi-agent systems, and hybrid AI models. His work addresses real-world problems like cybersecurity threat detection, environmental monitoring, healthcare diagnostics, and telecom project success prediction. Recent Publications: His 2024-2023 work includes advancements in web traffic attack detection, neonate cry analysis, UAV human detection, Ecuadorian tourism modeling, and solar energy forecasting. These studies demonstrate cross-disciplinary AI applications. Scientific Roles: He serves as Senior Member of IEEE and chairs the IEEE Systems, Man & Cybernetics Society - Spanish Chapter. He contributes to editorial boards of Soft Computing and Progress in Artificial Intelligence . Educational Initiatives: He promotes STEM careers via programs like FIRST LEGO League Burgos and World Robot Olympiad Burgos, alongside talent development initiatives.