Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• Smart Environments
• +3 more

Mario Muñoz Organero serves as a Full Professor in the Telematics Engineering Department at University Carlos III of Madrid, concurrently directing the University Master's Degree in Connected Industry. His academic profile centers on leveraging computational intelligence to address complex challenges across healthcare, urban systems, and educational frameworks through rigorous interdisciplinary collaboration. His research spans Artificial Intelligence, Machine Learning, and Smart Environments with significant applications in Healthcare Informatics (pancreatic cancer registries, diabetes management), Transportation Systems (traffic flow optimization, commuter stress analysis), and Educational Technology (generative AI for learning, micro-credentials). He pioneers context-integrated neural network architectures for sensor data interpretation in real-world environments, emphasizing practical deployment in smart cities and clinical settings. Analysis of his 2023-2025 publications reveals a distinctive interdisciplinary trajectory where machine learning methodologies bridge traditionally separate domains. Recurrent and graph neural networks form the technical backbone for projects ranging from pandemic-influenced traffic modeling to blockchain-enabled agricultural trading in Colombia, while explainable AI frameworks increasingly inform educational applications. This cross-pollination of techniques demonstrates exceptional adaptability in addressing domain-specific constraints across medical, transportation, and pedagogical contexts.

• Computer Networking
• Quality of Experience (QoE)
• Video Streaming
• +3 more

Bjørn Jonny Villa serves as Associate Professor at the Norwegian University of Science and Technology (NTNU), specializing in computer networking with emphasis on adaptive video streaming and Quality of Experience (QoE) optimization. His research addresses critical challenges in home networks, public WiFi security, and bandwidth management, as evidenced by publications spanning 2010-2014 in top venues including IEEE, Springer, and international journals. Villa earned his PhD from NTNU in 2014 with the dissertation "Enhancing Quality Aspects of Adaptive Video Streaming in Home Networks," establishing foundational work for his subsequent research. His academic journey reflects deep specialization in network performance optimization for multimedia delivery systems. Core research interests include adaptive HTTP video streaming, QoE measurement and optimization, network security vulnerabilities (particularly in public WiFi), and active probing techniques for bandwidth estimation. Villa employs experimental user studies and traffic analysis to develop practical solutions for improving streaming fairness and network resource allocation, with significant contributions to understanding how burst durations and traffic shaping impact user-perceived quality. Analysis of Villa's publication timeline reveals consistent focus on video streaming challenges: early work (2010-2011) established monitoring frameworks and home gateway optimization, mid-period research (2012-2013) advanced fairness algorithms and traffic shaping, while his 2014 output expanded into security implications of public networks. His collaborative approach is evident through recurring partnerships with Poul Einar Heegaard and Anders Instefjord across multiple publications. No scientific awards are documented in the available records. Similarly, no information regarding research grants or student supervision appears in the provided materials. Villa actively engages with the research community through conference presentations including Forskningsdagene (2013) and NIK conferences, and contributes to public discourse through media appearances in Aftenposten and Inside Telecom. His work operates within NTNU's telecommunications research ecosystem, focusing on practical implementations of network optimization techniques for real-world video delivery systems.

• Telecommunications Engineering
• Radio Wave Propagation
• Wireless Communication Networks
• +5 more

Nikolay Dimitrov Tashkov is a Professor at Technical University of Gabrovo, Bulgaria, with extensive experience in telecommunications engineering and radio wave technology. He maintains offices in cabinets 2206 and 2213 and teaches Radio Wave Engineering at the bachelor's level. His research spans multiple domains of telecommunications with a particular focus on wireless networks, signal propagation, and broadcast systems. Professor Tashkov's research interests center around telecommunications engineering with specialization in radio wave propagation, wireless communication networks, and digital signal processing. His work examines various modulation techniques including QPSK, 16QAM, and 64QAM, and investigates signal behavior in different environments from urban settings to indoor spaces. He has made significant contributions to understanding radio frequency planning, monitoring systems for broadcast technologies, and the design of wireless networks for both municipal and office environments. His publication record shows a consistent focus on practical applications of telecommunications theory, with recent work analyzing DVB-T systems, IPTV streaming, DAB+ monitoring, and Wi-Fi coverage patterns. The research demonstrates a progression from fundamental radio wave theory to increasingly complex modern communication systems, with particular attention to quality of service metrics and real-world implementation challenges. Professor Tashkov has successfully supervised four PhD students to completion, with research topics including digital modulation format optimization, telecommunication network monitoring and control, radio frequency planning, and digital modulation schemes in wireless communications. His research has been supported by seven completed projects, including both internal university projects and external collaborations like the BG051 project supporting scientific personnel growth in engineering sciences. His laboratory work appears focused on practical telecommunications systems, with publications covering RF monitoring equipment, signal analysis tools, and network design methodologies. The research infrastructure likely includes facilities for testing wireless networks, broadcast systems, and signal processing equipment, though specific laboratory names are not mentioned in the available documentation.

Chuan HongView profile

Assistant Professor

• Biostatistics
• Bioinformatics
• Machine Learning in Healthcare
• +7 more

Dr. Chuan Hong is an Assistant Professor of Biostatistics & Bioinformatics at Duke University School of Medicine and a member of the Duke Clinical Research Institute (DCRI) and Biostatistics, Epidemiology, and Research Design (BERD) Methods Core. With a PhD in Biostatistics from the University of Texas Health Science Center at Houston, she completed postdoctoral training in Biostatistics at Harvard T.H. Chan School of Public Health (2016-2018) and Biomedical Informatics at Harvard Medical School (2018-2019). PhD, Biostatistics - University of Texas Health Sciences Center, Houston (2016) Postdoctoral Training - Harvard T.H. Chan School of Public Health (2016-2018) Postdoctoral Training - Harvard Medical School (2018-2019) Her research focuses on integrating machine learning with clinical data science to address critical healthcare challenges. Key areas include algorithmic fairness, cardiovascular risk prediction for women with adverse pregnancy outcomes, equitable pulse oximetry, and privacy-preserving federated learning techniques for multi-site EHR analysis. She leads NIH-funded projects on stroke risk prediction and Long COVID phenotyping, while exploring generative AI applications in clinical workflows. Recent publications highlight her methodological innovations in federated learning (FairFML, FedIMPUTE), domain-specific pretraining for medical imaging, and AI evaluation frameworks for healthcare communication. Her work bridges statistical rigor with practical clinical informatics solutions, particularly in debiasing clinical algorithms and optimizing health data infrastructure. NIH Grant: Overcoming inequities in Pulse oximetry Through clinical InformatiCs (2025-2029) Brigham & Women's Hospital Grant: Machine Learning for CVD Risk Prediction in Women (2023-2027) American Heart Association Grant: Debiasing Clinical Care Algorithms (2024-2025) Harvard/VA Subcontract: MAVERIC Project (2024-2025) NIH Grant: Stroke Risk Prediction with Machine Learning (2020-2025)

• Optimization
• Operations Research
• Renewable Energy Systems
• +3 more

António Ismael Freitas Vaz serves as Associate Professor with Habilitation at the School of Engineering, University of Minho, and holds a Senior Researcher position at the ALGORITMI Research Centre. As a core member of the SEOR (Systems Engineering and Operations Research) R&D Group, he directs research in mathematical optimization methodologies with applications spanning energy systems, additive manufacturing, and biomedical engineering. His institutional profile includes verified metrics: h-index 17, 1,505 citations, and 41 publications including 34 in Q1/Q2 journals. His research program centers on developing advanced optimization frameworks including multi-objective, derivative-free, and semi-infinite programming techniques. Key application domains feature renewable energy integration (demand-response co-optimization, cost-effectiveness analysis), 5-axis 3D printing (curved layer path planning, build orientation optimization), and medical diagnostics (automated tumor detection in wireless capsule endoscopy). Methodological innovations focus on particle swarm optimization, DC programming, and gradient descent complexity for complex constrained problems. Analysis of his 15 most recent publications (2018-2022) reveals three dominant research thrusts: energy systems optimization (33% of output), additive manufacturing (33%), and medical imaging (13%), with foundational optimization theory comprising the remainder. This distribution demonstrates strategic application of core methodologies to high-impact engineering challenges, particularly in sustainable energy transition and advanced manufacturing. The consistent publication in top-tier venues like Renewable and Sustainable Energy Reviews and Applied Energy underscores disciplinary influence. Scientific Awards: No specific awards, fellowships, or medals were documented in the provided profile information. Regarding academic advising, the source material contains no listings of PhD or Master's students supervised. The funding section explicitly indicates zero recorded projects ("Fundings (0)"), suggesting either institutional management of grants outside individual reporting or incomplete profile documentation. His h-index and publication volume imply significant research leadership despite absent grant details. As an integral contributor to the SEOR R&D Group at ALGORITMI, Vaz participates in a cross-disciplinary research ecosystem focused on operational research applications. The group maintains industry partnerships in energy, manufacturing, and healthcare sectors, facilitating translation of optimization algorithms into practical solutions for industrial partners and public administration through the Centre's thematic lines.

Kristian Bernhard NilsenView profile

Adjunct Professor

• Neurophysiology
• Electrophysiology
• Neuropathic Pain
• +5 more

Kristian Bernhard Nilsen serves as an Adjunct Professor in the Department of Neurology at the University of Oslo's Faculty of Medicine, with his primary affiliation at Oslo University Hospital (OUS), Rikshospitalet. His clinical and research activities focus on advanced neurophysiological diagnostics and neurological disorders. His primary research interests span Neurophysiology , Electrophysiology , and Neuropathic Pain , with significant contributions in Sleep Disorders , Autonomic Nervous System function, and Diabetic Neuropathy . His work integrates clinical neurology with advanced diagnostic methodologies, particularly in EEG analysis, nerve conduction studies, and motor unit estimation techniques. Recent publications demonstrate a strong emphasis on standardization of neurodiagnostic protocols, pain mechanisms in neurological conditions, and occupational health impacts on neurological function. Analysis of his 15 most recent publications reveals a consistent focus on methodological rigor in neurodiagnostics, with particular attention to ALS quantification techniques, diabetic neuropathy diagnostics, and EEG criteria validation. His work frequently addresses clinical translation challenges, especially in standardizing nerve conduction studies and developing wearable diagnostic technologies. A significant portion examines occupational health factors like shift work impacts on neurological function and pain perception. No scientific awards or specific grant information is publicly documented in the provided materials. His research appears integrated within larger institutional frameworks at Oslo University Hospital and the University of Oslo's Faculty of Medicine. While no specific laboratory or research team is detailed in the provided text, his publication patterns suggest active collaboration within neurophysiology research groups focused on diagnostic standardization and neuropathic pain mechanisms. His work on the MScanFit method and DICOM waveform encoding indicates leadership in technical standardization initiatives for clinical neurophysiology.

Ahmed DohaView profile

Associate Professor

• Business Model Innovation
• Artificial Intelligence
• Generative AI
• +5 more

Dr. Ahmed Doha is an Associate Professor at the Sprott School of Business, Carleton University, specializing in Supply Chain Management. He holds a PhD in Operations Management and Information Systems from York University, Canada, and MSc/BSc degrees in Electrical and Computer Engineering from Queen’s University and Mansoura University, respectively. Education: PhD (York), MSc (Queen’s), BSc (Mansoura) Current sabbatical status Contact: 5036 Nicol Hall, Carleton University, Ottawa, ON K1S 5B6 His research focuses on applying emerging technologies like artificial intelligence (AI) and the Internet of Things (IoT) for business model innovation and value creation. Key areas include: Generative AI and Large Language Models (LLMs) in organizational and personal AI assistants E-commerce, recommendation systems, and crowdsourcing applications Semantic ontologies and knowledge bases integration Combating corruption through AI-enabled business models Research methodology follows full-cycle design science principles, from problem identification to field experimentation. Funded by SSHRC, NSERC, CANARIE, and industry partners. Scientific awards: NSERC Alexander Graham Bell Canada Graduate Scholarship Teaching: PhD-level AI research methods, applied AI, technology management, and supply chain fundamentals

Alex MouapiView profile

Lecturer

• Energy Harvesting
• Wireless Sensor Networks
• RF Circuit Design
• +3 more

Dr. Alex Mouapi is a Lecturer in the Department of Electrical Engineering at Polytechnique Montréal, specializing in energy harvesting systems for wireless communications and IoT. His research bridges theoretical frameworks with industrial applications, particularly in autonomous sensor networks. Education Ph.D. in Engineering, Université du Québec à Chicoutimi (UQAC)/Université du Québec en Abitibi-Témiscamingue (UQAT) M.Sc. in Engineering, Université du Québec en Abitibi-Témiscamingue (UQAT) Master in Physics (Electronics), University of Dschang, Cameroon B.Sc. in Physics, University of Dschang, Cameroon Diploma in Technical Education (Electrotechnics), University of Douala, Cameroon Research Focus Dr. Mouapi's research centers on sustainable energy solutions for wireless devices. Key areas include: Energy Harvesting Systems: RF and piezoelectric microgenerators for self-powered sensors Circuit Design: Optimization of voltage doublers, rectifiers, and power management circuits Industrial IoT: Applications in mining, manufacturing, and infrastructure monitoring Predictive Algorithms: Energy forecasting and allocation for network efficiency Publication Trends His 33 publications demonstrate consistent focus on energy autonomy for industrial sensors. Recent works (2020-2022) emphasize RF harvesting circuit optimization and IoT integration, with theoretical contributions in efficiency modeling and practical validations through industrial case studies. Laboratory & Collaborations While no dedicated lab is specified, his research involves simulations and prototyping for energy systems. Collaborations include industrial partners in mining and manufacturing sectors.

• Medical Imaging
• Educational Technology
• Affective Computing
• +5 more

JIA Xibin serves as a full Professor and doctoral/master's thesis supervisor at Beijing University of Technology's Faculty of Information Technology and Dublin International College. She holds editorial responsibilities for the TIIS journal and maintains active memberships in the China Computer Federation (CCF) and China Society of Image and Graphics (CSIG), including specialized committees for Machine Vision and Big Video Data. Her educational foundation spans a B.S. in Wireless Technology from Chongqing University (1991), M.S. in Measuring and Testing Technology from North University of China (1996), and Ph.D. in Computer Application Technology from Beijing University of Technology (2007). International experience includes visiting scholar positions at University of California Riverside (2015) and Flinders University (2009). Research focuses on intelligent medical imaging for liver disease diagnosis, affective computing in educational contexts, and cognitive behavior modeling through multimodal fusion techniques. Her methodology integrates representation learning with transfer and few-shot learning paradigms to address data scarcity in medical applications. Current publications demonstrate consistent focus on domain adaptation and medical image analysis , with significant contributions to multimodal MRI interpretation for non-alcoholic fatty liver disease and hepatocellular carcinoma. Her work bridges theoretical machine learning with clinical applications through deep neural network architectures. Active research leadership includes principal investigator roles for: National Natural Science Foundation grant on non-invasive liver disease assessment (2019-2022) Beijing Natural Science Foundation project on campus safety risk prediction (2020-2022) These projects emphasize big data analytics for healthcare and educational safety systems, reflecting her dual expertise in technical innovation and practical implementation.

Peter BollenView profile

Professor

• Laboratory Animal Science
• 3R Principles (Replacement, Reduction, Refinement)
• Animal-Based Research
• +3 more

Peter Bollen is Professor and head of the Department of Experimental Medicine at the University of Copenhagen and Rigshospitalet, serving as the central animal facility responsible for animal-based research, surgical skills training, laboratory animal science education, and veterinary support. He holds board positions at the National Committee for Laboratory Animals and Alternatives and the Danish 3R Center, and acts as an ad-hoc consultant for AAALAC International. His research centers on advancing laboratory animal science through rigorous application of 3R principles, with specific focus on pain behavior assessment in rodents, anesthesia protocol optimization, surgical technique refinement, and development of alternative methodologies. This work directly addresses critical gaps in animal welfare while enhancing scientific validity of preclinical studies. Recent publications (2022-2025) reveal a multidisciplinary trajectory spanning biomedical engineering innovations like wireless capsule endoscopy and implanted transmitters, alongside foundational studies in rodent anesthesia and pain modeling. Key trends include cross-pollination between veterinary medicine, engineering, and behavioral neuroscience to develop more humane and precise experimental approaches. As department head, Bollen oversees extensive educational programs including surgical skills courses and laboratory animal science certification, while managing institutional resources for animal procurement, husbandry, and veterinary care across multiple research projects. The Department of Experimental Medicine operates as an integrated hub supporting researchers through comprehensive animal facility services, with dedicated teams focused on veterinary medicine, surgical support, and 3R implementation across the University of Copenhagen and Rigshospitalet research ecosystems.

• Ecoacoustics
• Biodiversity Monitoring
• Machine Learning Applications
• +5 more

Sarab Sethi is a Lecturer in the Department of Life Sciences at Imperial College London, where he develops computational approaches for biodiversity monitoring through ecoacoustics. His work integrates machine learning, sensor technology, and ecological fieldwork to address conservation challenges, particularly in tropical ecosystems. His research focuses on advancing soundscape ecology through innovative methodologies including random forest analysis of avian migration patterns, development of low-power acoustic monitoring systems, and standardization of acoustic indices. Key contributions involve demonstrating how soundscapes predict species occurrence in tropical forests and creating open-source tools like the SAFE Acoustics network for real-time ecosystem monitoring. Recent publications reveal a strong trend toward scalable biodiversity assessment solutions, with increasing emphasis on robotics-assisted surveys, virtual acoustics validation, and robust machine learning models that function effectively under noisy field conditions. His work consistently bridges theoretical ecology with practical conservation applications. Dr. Sethi actively contributes to scientific discourse through peer review for high-impact journals including Nature , Nature Reviews Biodiversity , and Proceedings of the National Academy of Sciences , demonstrating significant scholarly engagement within the ecological research community. He leads the development of critical infrastructure for ecoacoustic research, including the Acoustic Index User's Guide and validation frameworks for ecoacoustic metrics, which enable standardized biodiversity assessment across global ecosystems. His fieldwork in Malaysian Borneo provides foundational data for tropical conservation applications.

• Random Number Generation
• Signal Synchronization
• Telecommunications
• +6 more

Professor Mieczysław Jessa is a distinguished faculty member at Poznań University of Technology, serving in the Faculty of Information Technology and Telecommunications within the Institute of Multimedia Telecommunications. With a scientific discipline focused 100% on Information and Communication Technology, he has established himself as a leading researcher in random number generation, signal synchronization systems, and spectrum sensing techniques. His research interests center around hardware-based random number generators for cryptographic applications, precise synchronization of oscillators using GNSS signals, and advanced spectrum sensing methodologies. Professor Jessa's work bridges theoretical foundations with practical implementations, particularly in telecommunications security and precision timing systems. His recent publications demonstrate continued innovation in applying machine learning to spectrum sensing and developing low-complexity random number generation solutions for FPGAs. Professor Jessa maintains an active publication record with multiple articles in 2024-2025, reflecting his ongoing research contributions. His work spans both theoretical aspects of stochastic processes and practical implementations in measurement systems and secure communications. As an academic supervisor, Professor Jessa has guided doctoral research in areas including FPGA-based random sequence generation and spectrum sensing techniques, with documented supervision of three doctoral students through 2019. His technical contributions include the development of synchronization systems like the DST-16 clock distributors and SP-4000 measurement systems, demonstrating the practical application of his research in real-world telecommunications infrastructure.

Łukasz KulasView profile

Associate Professor

• Antenna Engineering
• Microwave Engineering
• Wireless Communications
• +5 more

Dr. Łukasz Kulas serves as an Associate Professor at the Department of Microwave and Antenna Engineering within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His research spans antenna design, wireless communications, and RFID systems with emphasis on millimeter wave applications and chipless RFID technology. His primary research interests include millimeter wave antenna arrays, wireless connectivity testing methodologies, and AI integration in communication networks. Key contributions involve LTCC-based Van Atta array design, low-cost Bluetooth testbed development, and radar cross section enhancement using 3D printed lenses for RFID applications. Recent publications reveal a strong trend toward applying artificial intelligence to industrial wireless systems (InSecTT project) and avionics intra-communications, while advancing fundamental antenna technologies for harsh environments. The work demonstrates consistent focus on practical implementations of wireless solutions across multiple frequency bands. No scientific awards were documented in the source material. No information regarding student supervision or research grants was provided in the available text. Dr. Kulas participates in collaborative research projects related to wireless communications infrastructure, though specific laboratory facilities or team structures were not detailed in the source documentation.

• Constitutional Law
• Administrative Law
• Police Law
• +3 more

Magali Christine Böger serves as a Professor in the Department of Law at the University of Police and Public Administration North Rhine-Westphalia (HSPV NRW), specializing in Constitutional Law and Police Law. Her academic work focuses on the intersection of state intervention powers, digital surveillance, and fundamental rights protections within German administrative and constitutional frameworks. Her research interests center on Intervention Law (Eingriffsrecht) , exploring critical tensions between public safety imperatives and constitutional rights. Key areas include digital data collection by service providers, vehicle telemetry surveillance, domestic violence interventions, and judicial oversight mechanisms for police procedures. Her scholarship consistently addresses how technological advancements challenge traditional legal safeguards, particularly regarding privacy rights and state authority in the digital age. Constitutional implications of police surveillance technologies Legal frameworks for domestic violence protection orders Digital privacy in telecommunications and mobility contexts Judicial review requirements for bodily examinations Constitutional balancing tests in security legislation Professor Böger's publications demonstrate consistent engagement with practical legal challenges faced by law enforcement agencies while maintaining rigorous constitutional analysis. Her collaborative work with colleagues like Klein and Gellenbeck appears in specialized German legal publications addressing contemporary police law dilemmas.

Alfred HößView profile

Professor

• Autonomous Driving
• Automotive Electronics
• Electric Mobility
• +6 more

Prof. Dr. Alfred Höß is a Professor of Electrical Engineering at the Amberg-Weiden University of Applied Sciences, where he has served since 1995. He chairs the examination committee for multiple engineering programs including Electrical and Information Technology, Software Systems Technology, and Industrial IT. His academic leadership extends through over a decade of service on the university senate and various planning committees. Dr. Höß completed his electrical engineering studies at Friedrich-Alexander-Universität Erlangen-Nürnberg (1983-1987), earning his diploma with distinction in 1988. He earned his PhD from Ruhr-Universität Bochum in 1991 with distinction, followed by industry experience at Siemens AG in both medical and automotive divisions before joining academia. Friedrich-Alexander-Universität Erlangen-Nürnberg: Electrical Engineering (1983-1987) Ruhr-Universität Bochum: PhD in High-Frequency Technology (1988-1991) His research spans cutting-edge automotive technologies with particular focus on autonomous driving systems, electric mobility solutions, and wireless communication architectures. Dr. Höß leads multiple EU-funded research projects including Archimedes, AI4CSM, AUTBUS, and Powerized, with emphasis on practical implementations for real-world transportation challenges. His work integrates artificial intelligence with edge computing to solve complex problems in vehicle communication, battery management, and autonomous navigation systems. His publication portfolio demonstrates strong emphasis on practical applications of machine learning in automotive contexts, particularly in range prediction for electric vehicles, federated learning for battery management, and communication systems for autonomous vehicles operating in challenging environments. His research shows consistent progression from fundamental electrical engineering principles to advanced AI integration in transportation systems. Dr. Höß has received notable academic recognition including the Diplompreis Elektrotechnik in 1988 and the Gebrüder-Eickhoff-Preis in 1992 for his doctoral work. Diplompreis Elektrotechnik (1988) Gebrüder-Eickhoff-Preis (1992) He actively mentors numerous graduate students across multiple research projects, supervising master's theses and research assistantships. His laboratory for electrical measurement technology serves as the foundation for hands-on student research. Dr. Höß secures substantial research funding through EU projects and industry collaborations, focusing on practical implementations of advanced automotive technologies. His administrative leadership includes chairing examination committees for multiple engineering programs, demonstrating his commitment to academic excellence and curriculum development. Dr. Höß directs the Electrical Measurement Technology Laboratory at Amberg-Weiden UAS, which serves as the primary research facility for his automotive electronics work. His research teams collaborate across multiple EU-funded projects including ADACORSA for drone communications, PRYSTINE for programmable automotive intelligence systems, and AUTBUS for rural autonomous transportation solutions. These interdisciplinary teams combine expertise in electrical engineering, computer science, and applied mathematics to tackle complex challenges in modern mobility systems.