Find Academic Supervisors

• Biometrics
• Behavioral Biometrics
• Human-Computer Interaction (HCI)
• +8 more

Rubén Vera-Rodríguez is a researcher at the Biometrics and Data Pattern Analytics (BiDA) Lab, part of the Escuela Politécnica Superior at Universidad Autónoma de Madrid, Spain. His work focuses on biometrics, human-computer interaction, and privacy-enhancing technologies, with strong emphasis on mobile and wearable systems. Research Interests: His primary research areas include behavioral biometrics (keystroke dynamics, gait recognition), face recognition, synthetic data generation, and privacy-preserving methods in biometric systems. He explores the use of deep learning and transformer models for mobile authentication and digital phenotyping. The trends in his recent publications show increasing use of synthetic data, explainable AI, and transformer architectures in biometric systems. His work spans both technical innovation and privacy-aware design, contributing to benchmark datasets and open challenges. Scientific Contributions: Co-organizer of the SVC-onGoing and WAMWB workshops. Contributor to major biometric databases including BehavePassDB and ChildCIdb. Active in privacy-focused biometric research, as seen in surveys on sensor vulnerabilities. Advising and Collaborations: He collaborates extensively with researchers such as Ruben Tolosana, Aythami Morales, and Julian Fierrez. While no formal students are listed, he leads and contributes to team-based research projects and competitions. Labs and Teams: He is a core member of the Biometrics and Data Pattern Analytics (BiDA) Lab at UAM, a research group focused on advancing biometric technologies with attention to real-world usability and ethical considerations.

Woldaregay, Ashenafi ZebeneView profile

Researcher

• Digital Health
• mHealth
• Artificial Intelligence in Healthcare
• +7 more

Woldaregay, Ashenafi Zebene is a researcher at UiT The Arctic University of Norway, affiliated with the Faculty of Health Sciences and the Department of Clinical Medicine. His work bridges digital health, artificial intelligence, and clinical applications, particularly in diabetes management and infectious disease surveillance. He is a key contributor to the EDMON (Electronic Disease Surveillance and Monitoring Network) project, which leverages self-recorded health data from people with Type 1 diabetes for early outbreak detection. PhD in Digital Health / Health Informatics (2021) Master’s in Electronic Disease Surveillance (2016) His research focuses on applying machine learning, data science, and AI to solve pressing healthcare challenges. Key areas include blood glucose pattern analysis in diabetes, anomaly detection, mHealth adoption, wearable technology, and syndromic surveillance. He has developed models for infection detection, surgical risk prediction, and deidentification of clinical text. His work emphasizes real-world applicability, patient engagement, and data privacy. The most recent articles show a strong trend toward transformer models, synthetic data, and instruction-guided NLP in clinical contexts, alongside continued work in mHealth usability, caregiver support, and personalized health monitoring. His publications span journals in health informatics, medical informatics, and digital health, reflecting interdisciplinary collaboration. Scientific contributions include: Development of the EDMON system for real-time infection monitoring Creation of datasets for wearables and mHealth motivation factors Systematic reviews on reinforcement learning in diabetes and AI in healthcare security Innovation in cluster detection algorithms (K-CUSUM) for outbreak detection While direct information on grants and advising is not provided, his role as a doctoral candidate and co-author on numerous student-led studies suggests involvement in research mentoring. He collaborates extensively with researchers such as Gunnar Hartvigsen, Eirik Årsand, and Karl Øyvind Mikalsen, indicating membership in a large, active digital health research group. He is involved in a multidisciplinary research lab focused on digital health innovation, mHealth systems, and AI-driven clinical decision support. The team works on real-time monitoring, data privacy, and user-centered design of health technologies, with applications in chronic disease management and public health surveillance.

Zear IbrahimView profile

Lecturer

• Assistive Technologies
• Computer Vision
• Augmented Reality
• +4 more

Dr. Zear Ibrahim is an Associate Lecturer in the Department of Computer Science at Brunel University London within the College of Engineering, Design and Physical Sciences. He holds a PhD and BSc in Computer Science from Brunel University and extensive lecturing experience in programming fundamentals. His current roles include Module Lead for CS1603 (Introductory Programming), SAS Base Programmer Certification Training Lead, Academic Admission Tutor, and Designated Safeguard Officer. He also maintains the SwiftBot-Connect application for mobile robotics infrastructure. His educational background includes: PhD in Computer Science (Computer Vision & Biomedical Engineering) from Brunel University London BSc in Computer Science (Software Engineering) from Brunel University London SAS Certified Base Programmer for SAS 9 Dr. Ibrahim's research focuses on deploying open-sourced computer vision and augmented reality technologies through mobile devices to create non-invasive assistive healthcare solutions for older adults. His work spans Computer Mediated Reality Technologies (CMRT), mobile depth sensing, motion tracking, convolutional neural networks for object detection, biometric student engagement analysis, and natural language processing applications. He investigates the digitization of manual healthcare processes through emerging reality technologies. His publication record demonstrates a concentrated focus on healthcare technology integration, exemplified by his 2019 Journal of Biomedical Informatics article which established a conceptual framework for CMRT in healthcare intervention. Current research trajectories indicate expansion into mobile-based assistive technologies using open-source sensor systems and real-time biometric analysis. Dr. Ibrahim has supervised numerous undergraduate Group Projects, Final Year Projects, and MSc Dissertations while teaching across multiple programming and data visualization modules. His research was supported by an EPSRC Doctoral Training Partnership (grant 1819220) during his doctoral studies. He serves as SwiftBot Infrastructure Technology Lead, maintaining the SwiftBot-Connect application deployed on major app stores. He actively contributes to the HCI Research Group and leads the development of educational technology infrastructure within the department, including SAS certification training programs and safeguarding protocols for student welfare.

Attila ImreView profile

Professor

• thermodynamics
• energetics
• physical chemistry
• +9 more

Dr. Attila Imre is a Full-Time Professor in the Department of Power Machines and Systems at Budapest University of Technology and Economics (BME) . He also serves as a Part-Time Scientific Advisor at the MTA Energy Research Center . His research spans thermodynamics, energetics, physical chemistry, and ecology, with a focus on working fluids, phase transitions, and energy systems. Doctor of Science (DSc), Hungarian Academy of Sciences (2015) PhD in Physics, ELTE (1996) DrUniv in Solid-State Physics, ELTE (1994) MSc in Physics, ELTE Faculty of Physics (1990) Imre’s research integrates theoretical and applied studies in thermodynamic cycles, metastable fluids, and supercritical systems. He has pioneered work on working fluid selection, cycle efficiency, and cold energy utilization, particularly in organic Rankine cycles and power-to-methane technologies. His 15 most recent publications emphasize artificial intelligence in energy systems, economic analyses of sustainability, and thermodynamic modeling of extreme-state fluids. Key trends include AI-driven performance mapping, techno-economic evaluations of waste heat recovery, and ecological applications of fractal geometry. Scientific Awards : Humboldt Scholarship, Johannes Gutenberg University, Mainz, Germany (1999) Imre has held prestigious postdoctoral roles at the University of Tennessee and senior research positions in Hungarian institutions. His work bridges energy engineering, computational modeling, and interdisciplinary challenges in translation and cultural studies.

• Artificial Intelligence
• Multi-agent Systems
• Decision Support Systems
• +3 more

Luís Manuel da Silva Conceição is an Assistant Professor at the Polytechnic Institute of Porto's School of Engineering, affiliated with the Department of Computer Engineering. He concurrently serves as a Researcher at GECAD (Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development), an FCT-rated Excellent research unit. His academic credentials include a PhD in Computer Science (2023) from the University of Minho, a Master's (2017) and Bachelor's (2014) in Computer Engineering from ISEP. Conceição's research focuses on Artificial Intelligence with specialization in multi-agent systems, decision support systems, and natural language processing. His applied research spans Industry 4.0 and Healthcare domains, including AI solutions for vascular disease management and e-commerce innovation. He coordinates the international REMO project and has participated in Horizon Europe, H2020, ITEA, FCT, and P2020 projects as PI/co-PI. His recent publications demonstrate strong trends in health informatics (remote patient monitoring, federated learning, digital mental health) and decision support systems (argumentation dialogues, sentiment analysis, distributed consensus). Industrial applications feature prominently, especially in predictive maintenance and human-computer interaction for e-commerce. Awards: ITEA Award of Excellence Innovation (2024) for research impact in health technology Conceição actively supervises graduate researchers: currently mentoring 9 master's students , 4 undergraduate students , and co-supervising 2 PhD candidates . His industry collaborations involve applying AI to real-world challenges through joint R&D projects with technology centers. At GECAD, he contributes to intelligent systems research infrastructure and promotes ethical AI through public lectures and roundtables.

J.C. ScholtesView profile

Professor

• Natural Language Processing
• Information Retrieval
• Text Mining
• +5 more

J.C. Scholtes is an Extra-ordinary Professor of Text Mining at the Department of Knowledge Engineering, Faculty of Science and Engineering, University of Maastricht. He is also a Senior Research Fellow at the Dutch School for Information and Knowledge Systems (SIKS), a Board Member at IPRally, and a Venture Partner at ENDEIT Capital. M.Sc. in Computer Science from Delft University of Technology Ph.D. in Computational Linguistics from University of Amsterdam His research expertise spans Natural Language Processing, Machine Learning, and Artificial Intelligence, with applications in legal, medical, business, and regulatory domains. He focuses on text mining, machine translation, question-answering systems, and information extraction from unstructured data. Recent publications (2024–2025) emphasize context-aware machine translation, misinformation detection in recommendation systems, healthcare data analysis, and food science applications. Key themes include integrating deep learning architectures (e.g., Transformers), optimizing search and translation efficiency, and leveraging hybrid human-machine approaches. He has collaborated widely in industry and academia, notably deploying e-discovery software for institutions like the UN War Crimes Tribunals and FBI-ENRON. His career history includes leadership roles at ZyLAB (1987–2021) and prior service in the Royal Dutch Navy.

Jörn OstermannView profile

Professor

• Video Coding
• Computer Vision
• Machine Learning
• +9 more

Prof. Jörn Ostermann is a Full Professor and Head of the Institut für Informationsverarbeitung at Leibniz Universität Hannover since 2003, with prior roles at AT&T Bell Labs and AT&T Labs-Research. He served as Dean of the Faculty of Electrical Engineering and Computer Science (2011–2013) and member of the Senat (since 2020). His research spans video coding, computer vision, machine learning, 3D modeling, and computer-human interfaces , with applications in SAR imaging, predictive maintenance, children's speech analysis, and cochlear implants. Key projects include Next Generation Video Coding , Conditional Coding for Learned Compression , and GreenAutoML4FAS . Notable trends in his recent publications (2025–2023) include Neural network-based video compression Uncertainty estimation in speech recognition Zero-delay coding for cochlear implants Domain adaptation for aerial image segmentation 3D mesh compression standards Error concealment in VVC coding Scientific recognitions: AT&T Standards Recognition Award (1998) ISO Award (1998) IEEE Fellow (2005) Distinguished Lecturer, IEEE CAS Society (2002/2003) MPEG Convenor (2020–2023) He co-authored a graduate textbook on Video Communications , holds >30 patents, and has led >20 research projects. His work bridges academic research and industrial standardization, particularly in MPEG and IEEE committees.

Lusi LiView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Wireless Communications
• +5 more

Lusi Li is an Assistant Professor in the Department of Computer Science at Old Dominion University's Batten College of Engineering & Technology. She earned her Ph.D. from University of Rhode Island in 2021, with prior degrees from Zhongnan University of Economics and Law. Ph.D. in Electrical, Computer, and Biomedical Engineering (URI, 2021) B.S. and M.S. in Computer Science (Zhongnan University, 2014 & 2017) Her research focuses on Computer Vision and Machine Learning , developing scalable algorithms for robust representation learning from large-scale data with applications in industrial processes and wireless communications. Key areas include multi-view/multi-modal learning , transfer learning , few-shot/zero-shot learning , and imbalanced learning . Recent publications reveal expertise in semantic communication , 3D shape understanding , and dynamic spectrum management , with 2025 papers at MILCOM and ACM MM. Her work often integrates graph learning and domain adaptation techniques across wireless systems and industrial fault diagnosis. Awards include multiple federal grants (NSF, ICAR) and institutional honors (FP3, Cheng Fund). She actively mentors graduate students in research and serves on editorial boards for Information Fusion and Pattern Recognition . National Science Foundation (NSF) Grant for dynamic spectrum sharing ICAR grants for coastal community AI systems ODU's FP3 and Cheng Fund awards URI graduate fellowships and awards Her teaching portfolio includes undergraduate courses in theoretical computer science and graduate-level pattern recognition.

• Machine Learning
• Deep Learning
• Medical Informatics
• +3 more

Nasmin Jiwani is a Research Scholar with 56 publications, an h-index of 20, and 921 total citations. Her academic work spans interdisciplinary applications of machine learning and deep learning techniques across medical diagnostics, cybersecurity, and IoT systems. Her research focuses on Medical Informatics where she develops AI-driven diagnostic tools for liver disease prediction, diabetic retinopathy classification, and acute lymphoblastic leukemia detection. In Cybersecurity , she pioneers image-based malware classification using deep learning architectures. Her IoT Systems research includes supply chain optimization and smart security applications. Recent work shows increasing emphasis on healthcare technology integration, particularly in cancer diagnostics and autism spectrum disorder analysis. Collaboration patterns indicate strong partnerships with Ketan Gupta (30 co-authored papers), J. Logeshwaran, and Neda Afreen across IEEE conferences and journals. Her publication trajectory demonstrates consistent output with 10+ papers annually since 2022, showing particular strength in translating theoretical machine learning models to practical healthcare and security applications.

Nambi NallasamyView profile

Assistant Professor

• Machine Learning
• Computer Vision
• Ophthalmology
• +5 more

Nambi Nallasamy is an Assistant Professor at the University of Michigan, holding joint appointments in the Departments of Ophthalmology and Visual Sciences and Computational Medicine and Bioinformatics. His research focuses on the intersection of machine learning and ophthalmology, particularly in improving cataract surgery outcomes and analyzing surgical quality. His work applies methodologies such as computer vision, machine learning, and natural language processing to healthcare research, with a strong emphasis on clinical informatics and computer vision challenges in ophthalmology. He is actively involved in developing AI tools for surgical training and assessment, including systems like CatSkill and CatStep. Recent publications highlight his contributions to predictive modeling for intraocular lens (IOL) refraction, automated surgical phase classification, and understanding social risk factors in microbial keratitis. His research also extends to corneal transplantation techniques and the impact of steroid use on corneal opacity in veterinary ophthalmology. Nallasamy is a board-certified ophthalmologist and fellowship-trained cornea specialist, with clinical expertise in corneal and ocular surface diseases, including corneal transplantation (DSEK, DMEK, DALK, PKP), cataract and refractive surgery, and limbal stem cell deficiency treatments. He collaborates with multidisciplinary teams and researchers such as Shahzad Mian, Maria Woodward, and Binh Duong Giap, advancing AI applications in ophthalmology and surgical outcomes analysis. His work bridges computational methods with clinical practice to enhance diagnostic and surgical precision.

Juraj VladikaView profile

Researcher

• Natural Language Processing
• NLP Applications for Medicine and Healthcare
• Automated Fact-Checking
• +4 more

Juraj Vladika is a Research Associate at the Chair of Software Engineering for Business Information Systems (sebis) at the Technical University of Munich , affiliated with the School of Computation, Information and Technology and the Department of Computer Science . His research focuses on Natural Language Processing , with applications spanning Medicine and Healthcare , Automated Fact-Checking , Information Retrieval , Question Answering , and Legal Tech . Education: Bachelor's and Master's in Computer Science from University of Zagreb Exchange semesters at Technical University of Vienna and Pontificial University Comillas Research Projects: Juraj contributes to AI-driven medical knowledge systems (e.g., Aidvice for cancer care), scientific claim verification ( VeriSci ), and Legal Tech exploration ( NLawP ). Teaching: Lecture Advisor for Natural Language Processing and Software Engineering courses (2022–2025) Organizer for SEBA Lab Courses and Conversational AI Workshops Publications (2022–2025) emphasize LLM evaluation, medical fact-checking, legal NLP applications, and privacy-preserving AI. He also provides Guided Research opportunities in his expertise areas.

Vladimir GolkovView profile

Researcher

• Deep Learning
• High-Dimensional Data
• Geometric Data
• +6 more

Dr. Vladimir Golkov is a Postdoctoral Researcher at the Technical University of Munich (TUM) within the School of Computation, Information and Technology, specifically in Informatics 9 (Computer Vision Group). He works under the supervision of Prof. Dr. Daniel Cremers and maintains an active research profile in deep learning applications for medical imaging and biomedical data analysis. His primary research interests focus on deep learning since 2014, with specialization in high-dimensional and geometric data structures, data-processing goals beyond supervised learning (including clustering and anomaly detection), and applications in biomedicine and physics. Dr. Golkov has successfully mentored students who have gone on to pursue PhD studies at prestigious institutions including TUM, LMU, Mila, ETHZ, Cambridge, and Stanford. Analysis of his recent publications reveals a strong trend toward medical imaging applications, particularly in MRI technology, where he combines deep learning approaches with traditional physics-based methods. His work frequently bridges the gap between theoretical machine learning advances and practical medical applications, with significant contributions to diffusion MRI, sequence optimization, and 3D data processing. He has also expanded into emerging areas including large language models for medical applications and equivariant deep learning architectures. Dr. Golkov receives grant support from the Deutsche Telekom Foundation and maintains an active publication record with numerous contributions to leading conferences including ISMRM, MICCAI, and NeurIPS workshops. His research demonstrates a consistent trajectory of innovation at the intersection of computer vision, deep learning, and biomedical applications. He is actively involved in teaching and mentoring, with students from his projects advancing to top PhD programs worldwide. His office is located at Boltzmannstrasse 3, 85748 Garching, Germany (Office: 02.09.061), and he can be contacted at vladimir.golkov@tum.de.

Johannes ZinkView profile

Researcher

• Graph Drawing
• Computational Geometry
• Algorithmic Complexity
• +1 more

Dr. Johannes Zink is a Post-Doc researcher at the Chair of Efficient Algorithms (Prof. Kobourov) at Technical University of Munich (TUM), Campus Heilbronn since October 2024. Previously, he was a Post-Doc at University of Würzburg (2023-2024) and completed his PhD there under the supervision of Prof. Alexander Wolff. His research focuses on graph drawing, computational geometry, and algorithmic complexity problems. Zink's research interests span multiple areas within theoretical computer science, with particular emphasis on graph drawing algorithms, computational geometry problems, and complexity analysis. His work often involves finding efficient algorithms for geometric representations of graphs, analyzing the segment number of planar graphs, and developing techniques for polyline simplification. He has made significant contributions to understanding upward planar drawings, tangle complexity, and level planarity problems. His publication record shows a strong trend toward geometric graph representations, with many papers focusing on visualization techniques, algorithmic solutions for drawing problems, and complexity analyses. His research combines theoretical insights with practical applications, particularly in visualization systems and geometric data processing. Awards and Recognition Multiple 1st place wins at Graph Drawing Live Challenges (2018, 2020, 2023, 2024) PhD prize from Lower Franconian Memorial Year Foundation (2024) PhD prize from University of Würzburg Institute of Computer Science (2023) Deutschlandstipendium scholarship (2015-2017) Bachelor's thesis prize from University of Würzburg (2016) Zink has supervised numerous bachelor's and master's theses, demonstrating his commitment to mentoring the next generation of researchers. His teaching portfolio includes courses on computational geometry, advanced algorithms, and graph visualization. He has also served on program committees for major conferences including EuroCG 2025 and GD 2023, reflecting his standing in the academic community.

Michael A. GrassoView profile

Associate Clinical Professor

• Clinical Informatics
• Biomedical Informatics
• Consumer Health Informatics
• +8 more

Michael A. Grasso, MD, PhD, is a Clinical Associate Professor in the Department of Medicine and Emergency Medicine at the University of Maryland School of Medicine. He practices Emergency Medicine and serves as Director of the Clinical Informatics Group and Program Director for Clinical Informatics graduate programs at the University of Maryland Baltimore. His education includes: BS in Microbiology, University of Maryland (1983) MS in Computer Science, American University (1986) PhD in Computer Science, University of Maryland Baltimore County (1997) MD, George Washington University (2005) Internal Medicine Residency, University of Maryland (2008) Clinical Informatics Fellowship, University of Maryland (2013) Dr. Grasso's research focuses on knowledge representation and reasoning for large-scale clinical data systems. Key areas include: Big data analytics for chronic disease prediction (diabetes, rheumatoid arthritis, opioid misuse) Patient safety and quality improvement in emergency medicine Impact of online consumer health information on clinical decision-making His 13 recent publications (2011-2022) reveal strong trends in Veterans Health Administration data analysis, opioid prescribing patterns, and machine learning applications. Work emphasizes clinical decision support, predictive modeling for mortality/disease, and ICD-10 coding impacts in emergency settings. Scientific honors include: Fellow of the American College of Physicians (FACP) Fellow of the American Medical Informatics Association (FAMIA) Three medical/computing honor societies As Program Director for Clinical Informatics graduate programs, he mentors students in this emerging field. He has secured over $2,000,000 in grants from NIH, FDA, NIST, NASA, and DoD for: Knowledge representation with big clinical repositories Emergency department quality/safety initiatives Consumer health information impact studies He leads the University of Maryland Clinical Informatics Group, leveraging Veterans Health Administration data (35M patients) and University of Maryland Medical System's EPIC repository (14 hospitals) to develop semantic-enhanced machine learning for disease prediction and treatment efficacy.

Irina NikishinaView profile

Researcher

• Semantics
• Argument Mining
• Taxonomy Enrichment
• +3 more

Irina Nikishina is a postdoctoral researcher at the University of Hamburg's Department of Informatics, working in the Language Technology Group under Prof. Chris Biemann. As a researcher in computational linguistics and natural language processing, she contributes to projects like ACQuA-2.0, focusing on semantics, argument mining, taxonomies, and knowledge graphs. PhD in Computational and Data Science and Engineering (2022), Skolkovo Institute of Science and Technology Bachelor's and Master's degrees from National Research University Higher School of Economics (NRU HSE) Her research spans taxonomy enrichment, comparative question answering systems, and biomedical concept representation. She organizes shared tasks like RUSSE’2020 and RuArg-2022, and co-founded the RusNLP semantic search engine for Russian NLP conferences. She chairs the Network Analysis track at the International Conference on Analysis of Images, Social Networks and Texts (AIST) and has served as secretary for AIST 2020 and 2021. Recent publications focus on large language models' performance in lexical semantics, multilingual comparative argumentation systems, and knowledge graph integration for QA tasks. Her work includes developing tools like TaxFree for candidate-free taxonomy enrichment and exploring cross-modal approaches for taxonomic graph expansion.