Find Academic Supervisors

Jesús Requena CarriónView profile

Professor

• Data Science
• Machine Learning
• Computer Modelling
• +6 more

Jesús Requena Carrión is the Executive Vice Dean and Director of Queen Mary School Hainan (QMSH), and a Professor of Data Science and Engineering at Queen Mary University of London's School of Physical and Chemical Sciences. His research focuses on statistical data analysis, machine learning applications in healthcare and robotics, computer modeling of complex systems, and ethical approaches to data science. He advocates for rigorous and ethical use of digital technology and authored the open-access book Principles of Machine Learning: A Deployment-First Perspective . His work spans interdisciplinary areas, including healthcare technology, serious games for emotion and decision-making studies, and sensor systems for infrastructure maintenance. He leads the Data Science and Engineering Research Group, emphasizing practical deployment and societal impact. Recent research trends include machine learning-driven solutions for medical diagnostics, autonomous systems, and educational tools. Publications reflect innovation in robotics trajectory planning, neurophysiological risk assessment, and stress recognition via wearable sensors. He emphasizes collaboration in global education through QMSH and contributes to neuromorphic sensor networks and bio-inspired engineering applications. No scientific awards are explicitly listed, but his work impacts diverse fields from biomedical engineering to urban planning.

• Computer Algebra
• Sparse Interpolation
• Symbolic-Numeric Computation
• +4 more

Wen-shin Lee is a Lecturer at the University of Stirling's Division of Computing Science and Mathematics, specializing in computational mathematics and signal processing. Her research focuses on exponential analysis, sparse interpolation, and symbolic-numeric computation. She holds a PhD from North Carolina State University and has held positions at institutions like the University of Antwerp and INRIA. Current affiliations include the Computational Mathematics and Optimisation Research Group (COMMON). Education: Bachelor’s in Mathematics, National Taiwan University PhD in Computational Mathematics, North Carolina State University Research Interests: Her work bridges computer algebra and signal processing, emphasizing applications like antenna positioning, radar imaging, and texture decomposition. Recent trends include sub-sampled exponential analysis, validated algorithms, and high-resolution signal reconstruction from sparse data. Labs/Groups: Active in the COMMON group at the University of Stirling and collaborates on the EXPOWER project (Exponential Analysis Empowering Innovation).

• Indoor Positioning
• Wireless Localization
• Machine Learning Applications
• +3 more

Lucie Klus is a Postdoctoral Researcher in Electrical Engineering actively advancing indoor positioning systems through innovative wireless signal processing and machine learning techniques. Her work bridges theoretical algorithms with practical applications for indoor localization, focusing on fingerprinting methodologies and real-world dataset optimization. Core research spans Indoor Positioning (100% fingerprint relevance), Wearable Device integration (46%), Radio Map analysis (42%), and K-means clustering (35%) Recent breakthroughs include dynamic localization using Intersection over Union metrics and multidimensional compression of positioning datasets via EWOk framework Key publications demonstrate strong synergy between Wi-Fi fingerprinting, deep learning interpretation, and quality-of-service optimization in constrained environments Her 18 research outputs (2019-2024) reveal consistent focus on solving data sparsity challenges through autoencoders, extreme learning machines, and novel radio map compression techniques, with increasing emphasis on multi-device compatibility and measurement density.

• Machine Learning
• Federated Learning
• Artificial Intelligence
• +12 more

Jie Ding is an Associate Professor in the Department of Statistics at the University of Minnesota, College of Science and Engineering. His research bridges statistical theory and artificial intelligence, with a focus on federated learning, continual learning, and AI applications in biomedicine and telecommunications. Research Interests: Jie Ding's work centers on developing robust and scalable machine learning systems. His interests include federated and collaborative learning frameworks, statistical modeling under data scarcity, and AI-driven solutions for biomedical engineering and 5G network optimization. He investigates foundational aspects of model selection, information fusion, and adversarial robustness. The recent publications highlight a strong trend in distributed and secure AI, particularly in federated learning with dynamic resource allocation, backdoor attack analysis, and AI applications in cardiac organoid maturation. His work integrates statistical rigor with real-world deployment challenges across heterogeneous systems. Scientific Awards: No awards explicitly listed in the provided text. Advising and Grants: Dr. Ding serves as Principal Investigator (PI) on multiple major research grants, including an NSF CAREER award on continual learning and projects funded by the U.S. Department of Defense and NIH. These projects involve collaborative research with Harvard University and focus on AI-driven flexible electronics, information fusion, and 5G measurement modeling. While specific student names are not listed, his leadership of large-scale research initiatives implies active graduate student mentoring. Labs and Teams: Dr. Ding leads a research group focused on AI and statistical learning, collaborating with interdisciplinary teams in engineering, computer science, and biomedical research. His projects involve partnerships with Harvard University and the National Institutes of Health, indicating a strong network in both academic and applied research domains.

• Speech Intelligibility
• Phonetics and Phonology
• Otorhinolaryngology
• +13 more

Virginie Woisard is an Associate Professor and Hospital Practitioner affiliated with Toulouse - Jean Jaurès University and Toulouse University Hospital. She is a key member of the NeuroPsychoLinguistics Laboratory (LNPL) and holds clinical and research roles in the ENT Department at Larrey Hospital and the Oncorehabilitation Unit at the Toulouse University Cancer Institute. She also directs the University Training Center for Speech Therapy in Toulouse. Her research interests lie at the intersection of speech science, otorhinolaryngology, and oncology rehabilitation. She focuses on speech intelligibility (acoustic, prosodic, and phonetic dimensions), aerodigestive functions (physiology, pathophysiology, and assessment), and cancer care (patient pathways, sequelae, disability, and quality of life), particularly for upper aerodigestive tract cancers. Her methodological approaches include experimental psycholinguistics, automatic speech processing, functional assessments, and clinical trials. Her recent publications (2022–2024) demonstrate a strong trend in developing and validating objective, automated tools for assessing speech and swallowing disorders in cancer patients. These include the Carcinologic Speech Severity Index, pseudo-word intelligibility tests, holistic communication scores, and psychometric guidelines. Her work bridges clinical practice with advanced linguistic and acoustic analysis, particularly in French-speaking populations. Scientific Awards and Recognition: President of the French Society for Swallowing and Dysphagia (2021–2024) Member of the European Society of Swallowing Disorders Research Group (since 2021) Member of the European Union of Phoniatrists’ Dysphagia Committee Editorial board member for multiple journals: Revue de laryngologie Otologie Rhinologie , Folia Phoniatrica et Logopedia , Annals of Otolaryngology , Case Reports in Otolaryngology , and European Archives of Otorhinolaryngology Virginie Woisard actively leads and coordinates major research projects such as PhLES-NID (ANR-funded) and VULCANO (Institut National du Cancer) , and participates in multi-center trials like Voice4PD-MSA and PETAL . She has supervised numerous university diplomas (DU/DIU) in phonology, voice, speech, swallowing, and neurolaryngology since 1998, indicating a long-standing commitment to training and academic mentorship. Her work emphasizes therapeutic education, rehabilitation, and improving patient quality of life. She leads the Parolothèque scientific interest group and is deeply involved in interdisciplinary research connecting linguistics, medicine, and technology. Her lab, the NeuroPsychoLinguistics Laboratory (LNPL), serves as a hub for clinical and experimental research in speech and language disorders.

Peter FarkašView profile

Professor

• Communication Theory
• Coding Theory
• Information Transfer
• +4 more

Prof. Ing. Peter Farkaš, DrSc is a distinguished academic at the Institute of Applied Informatics within the Faculty of Informatics at Pan-European University . He also teaches at the Faculty of Electrical Engineering and Information Technology (FEI STU) and has held visiting positions at the Faculty of Computer and Information Systems and Mathematics at Kingston University, UK. His career spans academia and industry, including roles at Tesla VRÚSE, Australian Telecommunications Research Centre, Siemens, and Sandbridge Technologies. Research Focus : Communication Theory, Coding Theory, and applications in information transfer, storage, and wireless systems. Key Contributions : Development of novel error-correcting codes (e.g., 5G LDPC codes, Reed-Solomon extensions), CRC polynomial design for E-DCH protocol, and multidimensional complementary codes. Publication Trends : Recent works emphasize coding theory advancements for 5G/LTE networks, multidimensional orthogonal codes for interference mitigation, and interdisciplinary applications of AI in democracy. His research bridges theoretical innovation with practical implementation in telecommunications. Scientific Awards : 2003: SIEMENS VIP recognition 2004: Werner von Siemens Excellence Award (with PhD student Matúš Turcsány) Advising & Grants : Supervised PhD students including Matúš Turcsány and collaborated with industry experts. Led EC-funded projects (NEXWAY, CRUISE, WINNER Phase II) and managed grants from VEGA, APVV, British Council, Royal Society, and DAAD. Labs & Collaborations : Active in the Institute of Applied Informatics at Pan-European University and contributed to projects with international institutions like Kingston University and SIEMENS PSE Slovakia.

Branko SarićView profile

Assistant Professor

• Mathematical Analysis
• Applied Mathematics
• Integration Theory
• +3 more

Dr. Branko Sarić is an Assistant Professor at the Department of Mathematics, Faculty of Technical Sciences Čačak, University of Kragujevac, Serbia. His academic career spans multiple institutions, including Belgrade University, Novi Sad University, and Kragujevac University. He specializes in mathematical analysis with applications to integration theory, numerical methods, and real analysis. Education: Diplom-Ingenieur in Automatic Control (Belgrade University, 1984), Master's in Mathematical Sciences (Novi Sad University, 2009), PhD in Mathematical Sciences (Kragujevac University, 2017). His research focuses on advanced integration techniques, including Kurzweil-Henstock integrals and H1 integrals, singularities in multivector fields, Fourier series of discontinuous functions, and mathematical solutions to physics problems like perihelion precession. His work bridges pure mathematical analysis with applied computational methods. He has published 5 SCI-indexed articles and accumulated 35 citations (Google Scholar). His teaching includes Applied Mathematics, Selected Chapters of Mathematics, and Numerical Methods for IT and Electrical Engineering programs. He has been affiliated with Faculty of Technical Sciences Čačak since 2004.

Bartosz JabłońskiView profile

Researcher

• Image processing
• Signal filtering
• Motion capture
• +1 more

Bartosz Jabłoński is affiliated with the Faculty of Information and Communication Technology at Wrocław University of Science and Technology, specifically the Department of Control Systems and Mechatronics . His research focuses on image processing, signal filtering, motion capture, and partial differential equations. Key research trends across his publications include: Development of quaternion-based algorithms for motion processing and clustering Application of multiscale analysis to improve motion capture systems Implementation of anisotropic filtering techniques for rotational trajectory smoothing Integration of agile methodologies in technical education Use of collaborative tools for Parkinson disease diagnosis His work spans both theoretical algorithm development and practical applications in biomedical engineering and educational reform.

• Control systems
• System identification
• Pattern recognition
• +3 more

Professor Ewaryst Rafajłowicz is affiliated with the Faculty of Information and Communication Technology at Wrocław University of Science and Technology, serving as Head of the Department of Control Systems and Mechatronics. His research spans control systems, pattern recognition, image processing, and spatio-temporal dynamics, with recent work focusing on iterative learning control, signal classification, and vibration analysis in industrial systems. Control systems System identification Pattern recognition Image processing Statistical process control Mechatronic control Recent publications highlight his expertise in functional data analysis, Bernstein polynomial applications, and shape-preserving descriptors for classification tasks. He has contributed to both theoretical advancements in control algorithms and practical implementations in industrial settings, particularly in vibration damping and mechatronic systems.

Michał PiórekView profile

Researcher

• Dynamical systems
• Machine learning
• Geospatial data analysis
• +1 more

Michał Piórek is a researcher in the Department of Computer Engineering at the Faculty of Information and Communication Technology , Wrocław University of Science and Technology. His research focuses on dynamical systems , machine learning , and geospatial data analysis , with applications in urban planning, biomechanics, and nonlinear time series modeling. His work spans multiple domains including: Development of machine learning surrogate models for urban noise prediction Quaternion-based clustering frameworks PCA modifications for geospatial time series Chaos analysis in nonlinear systems Recent publications highlight interdisciplinary applications of his research in environmental science and human motion analysis. Contact: michal.piorek@pwr.edu.pl

David Jaime Cocovi SolbergView profile

Researcher

• Environmental Chemistry
• Analytical Chemistry
• Chromatographic Analysis
• +7 more

David Jaime Cocovi Solberg is a postdoctoral researcher at the University of Natural Resources and Life Sciences, Vienna (BOKU), focusing on instrumentation design for exposome assessment. He is also a visiting professor at Gdańsk University of Technology (GUT) and maintains academic ties with the University of the Balearic Islands (UIB), where he completed his education and contributed to the FITRACE research group. His interdisciplinary work bridges environmental chemistry, analytical instrumentation, and data automation. Education: Bachelor in Chemistry, University of the Balearic Islands (UIB), 2010 Master in Chemical Science and Technology, UIB, 2011 PhD in Chemical Science and Technology, UIB, 2017 (cum laude) Predoctoral stays at DTU (Denmark), BOKU (Austria), and University of Valencia (Spain) His research focuses on environmental chemistry, chromatographic analysis, automation, and instrumentation technology. He specializes in the orthogonalization of separations, data processing using Python, and the development of miniaturized, field-deployable analytical systems. His work supports exposome research through innovative instrumentation and software tools. The 15 most recent publications highlight a strong trend in developing automated, miniaturized analytical systems for environmental monitoring, particularly for emerging contaminants, PFAS, and endocrine disruptors. His research integrates separation science, microfluidics, sensor networks, and machine learning, with a consistent emphasis on open-source software (e.g., CocoSoft) and real-time, on-site detection. The keywords span environmental chemistry, analytical instrumentation, and data science, reflecting his interdisciplinary approach. Scientific Awards: Recipient of 3 research awards (specific names not provided) Holds intellectual property for the software 'CocoSoft' He has delivered over 300 hours of teaching in graduate and undergraduate programs, particularly in Instrumental Methods in Environmental Chemistry and Soil Chemistry and Control at UIB. He has served as a reviewer for leading journals including Science of the Total Environment , Talanta , Micromachines , and Sensors . He has not disclosed specific grant funding, but his research output and software development suggest active project involvement. His professional service includes membership in SEQA, SECyTA, QUIMIBAL, SETAC, and the judicial expert panel of the Balearic Islands High Court. David Cocovi Solberg is actively involved in the Flow Injection and Trace Analysis (FI-TRACE) Consolidated R+D+I Group at UIB as a member, contributing to trace analysis and flow-based systems research. His work combines laboratory innovation with practical applications in environmental monitoring, supported by a strong publication record and software development.

• Machine Learning
• Compressed Sensing
• Privacy-Preserving Techniques
• +4 more

Jia Liang is a researcher at Henan Polytechnic University's School of Electrical Engineering and Automation, with a focus on Machine Learning , Compressed Sensing , and Privacy-Preserving Techniques . His work bridges Computer Science and Signal Processing , particularly in Radar Imaging and Medical Image Analysis . Key Collaborations: Di Xiao, Ying Luo, Qun Zhang, Hui Huang Technical Expertise: Federated Learning, SAR Imaging, Compressive Sensing, Adversarial Learning His research emphasizes secure data processing in IoT and cloud environments, with recent innovations in cross-disciplinary applications like biosignal analysis for cysticercosis diagnosis . Publications span top venues including IEEE Transactions on Aerospace Systems and Remote Sensing . Notable trends include privacy-preserving machine learning for federated systems and 3D radar imaging of rotating targets, alongside medical imaging solutions for chest radiographs and optical coherence tomography .

Georg PölzlbauerView profile

Researcher

• Machine Learning
• Data Visualization
• Artificial Intelligence
• +3 more

Georg Pölzlbauer is a researcher affiliated with the Department of Information and Software Engineering at Vienna University of Technology (TU Wien). His work focuses on machine learning, data visualization, and artificial intelligence, particularly leveraging self-organizing maps (SOM) for exploratory data analysis. He holds a Dipl.-Ing. (Diploma in Engineering) and a Dr.techn. (Doctor of Technical Sciences). His research emphasizes advanced visualization techniques for SOMs, including vector fields and graph-based methods, applied to diverse domains like petroleum data and political datasets. He has contributed to supervised learning algorithms inspired by self-organization, such as Decision Manifolds. Publications span algorithm design, cluster analysis, and pattern recognition, with applications in music feature extraction and industry-specific data visualization. No scientific awards are explicitly mentioned, but his work reflects sustained engagement with computational and visual data analysis. While no advising or grant information is provided, his affiliation with TU Wien’s research unit suggests active participation in collaborative academic projects.

• Machine Intelligence
• Signal Processing
• Machine Learning
• +6 more

Danilo P. Mandic is a Professor of Machine Intelligence at Imperial College London, Department of Electrical and Electronic Engineering. His research focuses on machine learning, signal processing, and biomedical engineering, with notable contributions to graph data analytics, tensor networks, and wearable sensors. Mandic has supervised numerous students, including those who have won prestigious awards such as the Ivor Tupper Prize and Sir Bruce White Prize. Education: PhD in Nonlinear Adaptive Signal Processing (Imperial College London, 1999). Previously taught at the Universities of East Anglia and Banja Luka. Research Interests: Machine Intelligence, Signal Processing (including EEG/ECG analysis), Wearable Health Technologies, Financial Signal Processing, and Tensor Networks. He has pioneered work on graph signal processing and in-ear biosensors. Key Awards: 2019 Dennis Gabor Award (INNS), 2023 IEEE Engineering in Medicine Prize Paper Award, 2018 IEEE Signal Processing Magazine Best Paper Award. Grants & Labs: Leads projects in neurotechnology, sensor signal processing, and smart grid analysis. Collaborates with industry and international institutions like RIKEN Brain Science Institute. Edited books on complex-valued adaptive filters and recurrent neural networks. Teaching: Courses on signal processing and machine learning. Notable for integrating wearable sensors into curricula. Awarded Imperial College's Excellence in Research Supervision (2014).

ERHAN İNCEView profile

Professor

• Mobile Communications
• Channel Coding
• Multi-carrier Techniques (OFDM, OFDMA)
• +7 more

Prof. Erhan İnce is a faculty member in the Department of Electrical and Electronic Engineering at Eastern Mediterranean University (EMU), where he has served since 1998. He holds a PhD in Communications from the University of Bradford (1997) and MS/BS degrees in Electrical and Electronic Engineering from the University of Bucknell (1992, 1990). His research focuses on mobile communications, channel coding, OFDM/OFDMA, statistical signal processing, and facial recognition. He has supervised numerous PhD and MS students, including Ramin Bakhshi, Syed Amjad Ali, and Waseem Qassab Bash. Key projects include 'Monitoring KKTCELL's Base Stations' (2018-2019) and 'Background Subtraction and Lane Occupancy Analysis' (2011). He received an award at the 1998 Symposium on Communication Systems & Digital Signal Processing. His work spans peer-reviewed journals (IEEE Access, Digital Signal Processing) and books on video surveillance. He actively contributes to IEEE and other academic communities.