Find Academic Supervisors

Mariano CabezasView profile

Researcher

• computer vision
• medical imaging
• magnetic resonance imaging (MRI)
• +5 more

Mariano Cabezas is a researcher in medical imaging and computer vision, currently affiliated with Macquarie University and as an affiliate at the University of Sydney . His work focuses on automating brain MRI analysis for pathologies like multiple sclerosis, Alzheimer's disease, and tumors, with additional contributions to UAV image analysis. PhD in Computer Science (2013), University of Girona MSc in Automation, Computation, and Systems (2010), University of Girona BSc in Computer Science (2009), University of Girona Research Interests : Specializes in magnetic resonance imaging , lesion detection , deep learning , and image processing , with applications in multiple sclerosis , hearing loss , and UAV-derived ecological data . His recent work includes federated learning frameworks for cross-site MS lesion segmentation and pseudo-labeling techniques for longitudinal brain volume estimation. Publication Trends : Over the past five years, his research has emphasized federated learning (4 articles), lesion segmentation (9 articles), and UAV image analysis (3 articles), with a strong focus on clinical validation and cross-institutional collaboration. Labs & Collaborations : Contributed to the NIC-VICOROB group at the University of Girona and maintains affiliations with the Research Institute of the Hospital Vall d'Hebron (VHIR) in Barcelona and Macquarie University in Sydney. Actively develops open-source tools hosted on GitHub.

Lili QiuView profile

Professor

• Internet Networking
• Wireless Networking
• Mobile Systems
• +6 more

Lili Qiu is a Professor in the Department of Computer Science at The University of Texas at Austin, where she has been a faculty member since January 2005. She is an active member of the Wireless Networking and Communications Group (WNCG) and has made significant contributions to the field of networking research. Dr. Qiu previously spent 2001-2004 as a researcher at Microsoft Research in Redmond, WA, before joining UT Austin. Dr. Qiu's research spans Internet and wireless networking with a current focus on wireless network management and content distribution in mobile networks. Her work extends into diverse applications including acoustic imaging, metasurface applications, healthcare sensing technologies, and AI systems. She has pioneered research in areas such as acoustic motion tracking, passive RFID sensing, and wireless network optimization. Her research demonstrates a consistent pattern of innovation that bridges theoretical networking concepts with practical real-world applications, particularly in mobile and wireless systems. Her extensive publication record shows a clear evolution from fundamental networking research to increasingly interdisciplinary work that combines wireless systems with healthcare applications, AI, and novel sensing technologies. Recent publications demonstrate growing integration of machine learning techniques with traditional networking problems, as well as expansion into healthcare applications like Parkinson's disease modeling and non-invasive glucose monitoring. ACM Fellow IEEE Fellow National Academy of Inventors (NAI) Fellow ACM Distinguished Scientist NSF CAREER award Google Faculty Research Award Best paper award at ACM MobiSys'18 Best paper award at IEEE ICNP'17 Dr. Qiu has supervised numerous students, including a PhD dissertation that won the SIGMOBILE best dissertation award in 2020. She has served in significant leadership roles including chair of ACM SIGMOBILE, General co-chair for ACM MobiCom 2025, and various conference chair positions for IEEE ICNP, ACM CoNEXT, and other major networking conferences. Her research has been supported by substantial grants from NSF, Google, and other organizations, enabling her to lead cross-disciplinary research teams. As a member of the Wireless Networking and Communications Group at UT Austin, Dr. Qiu leads research efforts that combine networking expertise with innovations in sensing technologies, metasurfaces, and AI systems. Her lab has produced numerous influential results in mobile networking, wireless sensing, and network management, with applications spanning healthcare, consumer electronics, and communication infrastructure.

Ralf PeetersView profile

Professor

• Signal and Image Processing
• Systems and Control Theory
• Mathematical Modelling
• +7 more

Ralf Peeters is a Full Professor in Mathematics of Knowledge Engineering at Maastricht University's Faculty of Science and Engineering , Department of Advanced Computing Sciences. He serves as Vice-Dean of Research and Director of the STEM Graduate School, while leading the university's team at the inter-university research school DISC and co-chairing the Mathematics Centre Maastricht. Education: PhD in Mathematics (Free University, Amsterdam, 1994) Technical Mathematics (Delft University of Technology, 1988) Research Interests span applied mathematics, systems and control theory, signal/image processing, artificial intelligence, and biomedical engineering applications. His work bridges mathematical techniques with real-world challenges in healthcare and industrial systems. Recent Publications highlight advancements in deep learning for cardiac signal reconstruction, tensor-based signal decomposition, and recurrence plot analysis. These works integrate machine learning with clinical diagnostics, particularly in electrocardiographic imaging and arrhythmia characterization. Key Collaborations: Mathematics Centre Maastricht Dutch Mathematics Platform Dutch Institute of Systems and Control Leadership Roles: Vice-Dean of Research (FSE), Director of STEM Graduate School, Head of DISC-affiliated team, and Co-Chair of Mathematics Centre Maastricht. He has supervised over 25 PhD projects, emphasizing applied research across health and industrial domains.

Arnab NandiView profile

Professor

• Database Systems
• Human-Computer Interaction
• Human-in-the-Loop Data Analytics
• +5 more

Arnab Nandi is a Professor of Computer Science and Engineering at The Ohio State University, with a courtesy appointment in Biomedical Informatics. He holds leadership roles including Steering Committee Member for the Human-in-the-Loop Data Analytics (HILDA) Workshop and has served as Workshops co-chair for SIGMOD 2025-26 and Demonstrations co-chair for SIGMOD 2024. His research focuses on bridging human interaction and data infrastructure, spanning database systems, human-in-the-loop data analytics, and next-generation query interfaces. Nandi's work emphasizes interactive data exploration through projects like DICE (Distributed Interactive Cube Exploration), GestureDB (Querying Beyond Keyboards), and Omni (Multimodal Data Exploration). His recent research explores integrating LLMs into database education, augmented reality interfaces for data analytics, and multimodal approaches to video querying. Nandi has received numerous honors including the NSF CAREER Award, Google Faculty Research Award, IEEE TCDE Early Career Award, and the University's Alumni Award for Distinguished Teaching. He was also named to Columbus Business First's '40 under 40' and became an ACM Distinguished Member in 2024. As an educator, he teaches courses including CSE 3241 (Introduction to Database Systems), CSE 5242 (Advanced Database Systems), and CSE 5251 (Introduction to Software Startups). His educational innovations include DBTutor, which integrates LLMs into database systems education. At Ohio State, Nandi co-founded the OHI/O Program, which fosters tech culture through hackathons, and The STEAM Factory, an interdisciplinary research collaboration network. Prior to academia, he was founder and CEO of Mobikit, a connected vehicles data analytics startup acquired by Azuga Inc. (a Bridgestone company). His research has been supported by the NSF and industry partnerships, with applications spanning precision agriculture (CropFusion), clinical data pipelines (ICARUS), and interactive visualization systems (Perceptvis).

Andrew C. ShinView profile

Assistant Professor

• Metabolic Health
• Neuroendocrinology
• Branched-Chain Amino Acid (BCAA) Metabolism
• +5 more

Andrew C. Shin is an Assistant Professor at Texas Tech University within the Department of Nutritional Sciences . His work bridges metabolic health , neuroendocrinology , and neurodegenerative diseases , with a focus on how the brain regulates glucose homeostasis , BCAA metabolism , and bariatric surgery mechanisms . Ph.D. in Neuroscience, Michigan State University (2008) Postdoctoral Fellow, Pennington Biomedical Research Center and Icahn School of Medicine at Mount Sinai Dr. Shin’s research explores the neural pathways involved in appetite regulation , nutrient partitioning , and metabolic resistance . His NIH-funded projects investigate insulin signaling in POMC neurons , BCAA dynamics , and nicotine’s metabolic effects . Recent work highlights the role of the autonomic nervous system in BCAA regulation and its implications for obesity and diabetes . His 15 most recent publications reflect a focus on AI applications in nutrition , BCAA-related pathologies , Alzheimer’s disease , and metabolic surgery outcomes . Key themes include neuroendocrine control , nutritional interventions , and environmental impacts on metabolism . Scientific Awards NIH K01 Award Dr. Shin directs the Mouse Metabolic Phenotyping Facility and collaborates on synbiotic trials for cognitive aging . His work spans basic science and translational research , addressing metabolic disorders and their neurological consequences .

• Terahertz Imaging
• Medical Physics
• Computational Oncology
• +2 more

Pouyan Rezapoor is a Doctoral Researcher affiliated with Aalto University's Department of Electronics and Nanoengineering. His research focuses on terahertz imaging systems, medical physics, and computational modeling for cancer treatment. Research areas: Terahertz imaging, MRI-guided radiotherapy, and electromagnetic wave propagation Key collaborations: Zachary Taylor Group, Dan Ruan, and international researchers in biomedical engineering Current work examines radiation toxicity prediction in prostate cancer patients and optical system design for THz imaging. Recent publications analyze telecentric lens systems, cross-polarization effects, and computational methods to address feature correlation in clinical data.

• Artificial intelligence
• Biomedical signal processing
• Human movement analysis
• +12 more

Marco Ghislieri is an Assistant Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino, Italy. He is a member of the Interdepartmental Center PolitoBIOMed Lab and teaches in the Biomedical Engineering program, including courses like Neuroengineering and Design of Programmable Biomedical Devices . His research spans Artificial Intelligence, Biomedical Signal Processing, Neuroscience, and Rehabilitation Engineering . PhD in Bioengineering and Medical-Surgical Sciences (2017-2021) at Politecnico di Torino Thesis: Muscle Synergy Assessment during Cyclic and Non-Cyclic Movements His research focuses on muscle synergy analysis in Parkinson’s Disease (PD) patients post- Deep Brain Stimulation (DBS) , AI-driven gait analysis for fall prevention, and wearable sensor applications for stress-cognitive decline monitoring. He leads the S-CoDe and OMNIA-PARK projects, and contributes to PRIN as a team member. Recent publications highlight advancements in machine learning for intraoperative DBS targeting , statistical gait analysis , and neurorehabilitation tools . He serves as Associate Editor for Scientific Reports and Applied Bionics and Biomechanics , and Guest Editor for Frontiers in Neural Circuits . Awards include the Carlo J. De Luca Award (2022) , GNB Doctoral Award (2022) , and the Best Poster Award at M. Grattarola Summer School (2022) . He supervises Fabrizio Sciscenti (PhD candidate) and collaborates on neuroengineering and biomedical device design courses. His work addresses Goal 3 (Good Health) and Goal 4 (Quality Education) of the UN SDGs.

Soumik Purk3ayasthaView profile

Assistant Professor

• Biostatistics
• Machine Learning
• Causal Inference
• +7 more

Soumik Purkayastha is an Assistant Professor in the Department of Biostatistics and Health Data Science at the University of Pittsburgh School of Public Health. He also serves as a Research Biostatistician at the Center for Healthcare Evaluation, Research, and Promotion (CHERP) within the Department of Veterans Affairs, focusing on improving healthcare outcomes for veterans. B.Sc. (Hons.), St. Xavier's College, Kolkata, 2014-17 M.Stat. (Biostatistics), Indian Statistical Institute, 2017-19 M.S. in Biostatistics, University of Michigan, 2019-21 Ph.D. in Biostatistics, University of Michigan, 2019-24 His research develops scalable statistical and machine learning methods for biomedical studies, emphasizing information-theoretic frameworks for association and causality without traditional causal inference assumptions. Applications include mediation analysis , instrumental variables , and spatiotemporal forecasting of infectious diseases like SARS-CoV-2. He integrates Bayesian and semi/non-parametric approaches with computational challenges in statistical modeling. His publications focus on asymmetric association methods, infectious disease compartmental models (e.g., SEIR-fansy), and data-driven pandemic resilience strategies. Key themes include causal discovery , collider detection , and patient-reported outcome correlation analysis in clinical studies. Prior to joining Pitt, he worked with the Abecasis Group and Diabetic Foot Consortium at the University of Michigan. He has developed open-source software tools like SEIRfansy , fastMI , and comet , contributing to epidemiological and statistical methodology.

Sara MagliacaneView profile

Assistant Professor

• Causality
• Causal Representation Learning
• Causal Discovery
• +4 more

Sara Magliacane is an Assistant Professor at the University of Amsterdam and a Research Scientist at the MIT-IBM Watson AI Lab . She leads research at the intersection of causality and machine learning , focusing on improving AI robustness, generalization, and safety through causal reasoning. Her work spans causal representation learning , causal discovery , and causality-inspired ML in domains like reinforcement learning and dynamical systems. PhD in Artificial Intelligence (2017), VU Amsterdam MSc in Computer Engineering (2011), Politecnico di Milano/Torino BSc in Computer Engineering (2008), Università degli Studi di Trieste Her research explores causal variable identification from high-dimensional data (e.g., images, sequences) and causal graph discovery for domain adaptation. Methods include CITRIS , FANS-RL , and SNAP , with applications in embodied AI and biomedical data. The group emphasizes theoretical guarantees and scalable algorithms for real-world systems. Recent work trends include temporal causal modeling , intervention-efficient learning , and nonstationary reinforcement learning . Publications cover topics like causal discovery in partially observed settings , causal graph pruning , and sample-efficient concept learning , often combining neurosymbolic approaches with deep learning. Scientific Awards : ELLIS Scholar Sara supervises PhD students across universities (UvA, University of Pisa) and collaborates with institutions like TU Delft , Harvard , and IBM Research . She co-organizes workshops at premier conferences (NeurIPS, ICML, AISTATS) and teaches causality courses at the University of Amsterdam and Harvard Data Science Initiative. Her lab, Amsterdam Machine Learning Lab (AMLab) , investigates causal structure in embodied agents , safe reinforcement learning , and hybrid dynamical system modeling . The group maintains active partnerships with institutions such as MIT-IBM Watson AI Lab , Qualcomm , and Adyen .

Hua XuView profile

Professor

• Clinical Natural Language Processing
• Biomedical Informatics
• Large Language Models for Biomedicine
• +3 more

Dr. Hua Xu is the Robert T. McCluskey Professor of Biomedical Informatics and Data Science at Yale School of Medicine. He serves as Vice Chair for Research and Development in the Department of Biomedical Informatics and Data Science and as Assistant Dean for Biomedical Informatics at Yale School of Medicine. Dr. Xu leads the Clinical NLP Lab and is Chair of the NLP working group at the Observational Health Data Sciences and Informatics (OHDSI) program. Dr. Xu received his PhD in Biomedical Informatics from Columbia University, an MS in Computer Science from New Jersey Institute of Technology, and a BS in Biochemistry from Nanjing University. Dr. Xu is a renowned researcher in clinical natural language processing (NLP), having developed novel algorithms for important clinical NLP tasks such as entity recognition and relation extraction. His work has been top-ranked in over a dozen international biomedical NLP challenges. He has developed CLAMP, a comprehensive clinical NLP toolkit that has been successfully commercialized and adopted by hundreds of healthcare organizations worldwide. His research focuses on applying NLP technologies to diverse clinical and translational studies to accelerate clinical evidence generation using electronic health records data. Recently, he has been utilizing NLP to harmonize metadata of biomedical digital objects to promote FAIR principles in biomedicine, and his lab is actively working on developing large language models (LLMs) for diverse biomedical applications. Dr. Xu's recent publications demonstrate a clear trend toward leveraging large language models for biomedical applications. His work spans from benchmarking LLMs for clinical NLP tasks to developing specialized architectures like BiomedRAG (retrieval augmented LLMs for biomedicine). His research addresses critical healthcare challenges including adverse event extraction, oncology clinical trial analysis, and EHR-based association studies, showing how NLP can bridge the gap between unstructured clinical text and actionable medical insights. Dr. Xu's lab has achieved top rankings in numerous NLP challenges, including multiple #1 positions in i2b2 Temporal information extraction, SemEval Disease-modifier extraction, BioCREATIVE Chemical-induced disease extraction, and other prestigious competitions. His contributions to clinical NLP have significantly advanced the field's ability to extract meaningful information from complex medical texts. As the leader of the Clinical NLP Lab at Yale, Dr. Xu oversees research that forms a complete ecosystem: developing novel NLP methods, building robust software tools, and applying these technologies to clinical and translational research. His lab's work closes the loop between methodological innovation and practical healthcare applications, ensuring that advances in NLP directly benefit patient care and medical research.

Petr JanataView profile

Professor

• Cognitive Neuroscience
• Music Psychology
• Auditory Perception
• +4 more

Petr Janata is a Professor in the Department of Psychology at the University of California, Davis, and a faculty member at the UC Davis Center for Mind and Brain. His research centers on cognitive neuroscience of music, investigating neural mechanisms underlying music-evoked autobiographical memories and the experience of "groove." He serves on the Board of the Society for Music Perception and Cognition and co-founded the UC Music Experience Research Community Initiative (UC MERCI). Janata's educational background includes: Ph.D. in Biology (Neuroscience) from the University of Oregon (1996) B.A. in Interdisciplinary Studies (Biology/Psychology) from Reed College (1990) His research employs behavioral experiments, fMRI, EEG, and computational modeling to explore how music engages memory, emotion, and sensorimotor systems. Key projects examine music-evoked remembering, the psychology of groove, auditory attention mechanisms, and timbre-emotion links. His work reveals how music activates domain-general brain networks for expectation, memory, and emotional processing. Recent publications (2018-2025) show increasing focus on cross-cultural emotional responses to music, neural correlates of nostalgia, mental replay mechanisms, and clinical applications of music cognition. His lab develops innovative paradigms like the Groove Enhancement Machine (GEM) to manipulate sensorimotor synchronization while measuring subjective enjoyment. Janata's scientific recognition includes: Guggenheim Fellowship (2010) Dual Fulbright Fellowships (1990-91, 2010-11) Music Has Power Award from the Institute of Music and Neurological Function (2010) He has delivered over 100 invited lectures globally and served as scientific advisor to Coro Health LLC before founding Meamer, Inc. in 2017 to connect people through memories and music. His translational work bridges basic cognitive neuroscience with real-world applications in health and technology. The Janata Lab at UC Davis integrates neuroimaging, behavioral testing, and computational modeling to advance understanding of music cognition. Current projects explore lifespan neural changes in music processing, adaptive virtual partners for synchronization studies, and sonification systems for physiological monitoring.

Michalis VazirgiannisView profile

Professor

• Machine Learning
• Deep Learning
• Graph Analysis
• +11 more

Michalis Vazirgiannis is a Professor at LIX, École Polytechnique in France, where he leads the Data Science and Mining group (DaSciM). He holds a degree in Physics and a PhD in Informatics from Athens University (Greece), and a Master's degree in AI from Heriot Watt University, Edinburgh (UK). His academic career spans multiple prestigious institutions including Fraunhofer and Max Planck MPI in Germany, INRIA/FUTURS in Paris, AUEB in Greece, Telecom-Paristech, ENS in France, Tsinghua and Jiaotong Shanghai in China, and Deusto University in Spain. Professor Vazirgiannis's research focuses on machine and deep learning methods for graph analysis, including community detection, graph clustering, node embeddings, and influence maximization. His work in text mining encompasses Graph of Words, word embeddings with applications to web advertising and marketing, event detection, and summarization. He has active collaborations with industrial partners in analytics and machine learning for large-scale data repositories across various application domains such as recommendations, meeting summarization, influence metrics for scientific and social networks, and predictive maintenance. His recent publications demonstrate a strong emphasis on Graph Neural Networks, multilingual NLP (particularly for French and Arabic), and applications of deep learning to diverse domains including social networks, legal text, and biomedical data. There's a clear trajectory toward developing more efficient, explainable, and specialized models that address real-world challenges in data analysis. ERCIM fellowship Marie Curie EU fellowship Tencent "Rhino-Bird International Academic Expert Award" (2017) Best Paper Award at IJCAI 2018 Best Paper Award at CIKM 2013 Professor Vazirgiannis has supervised 29 completed PhD theses and has attracted significant R&D funding from national and international sources, including research agencies and industrial partners such as Google, Airbus, Huawei, Deezer, BNP, and LVMH. He leads or has led several academic research chairs including DIGITEO (2013-15), ANR/HELAS (2020-25), and AXA (2015-2018). The DaSciM research group, which he leads at École Polytechnique, has extensive experience in real-world R&D projects involving large-scale data mining. The team maintains active collaborations with major industrial partners including AIRBUS, Google, BNP, Tencent, and Tradelab, working on cutting-edge machine learning projects. The group has co-organized major conferences such as ECML PKDD 2011 and ECML/PKDD 2017 and participates in the senior organization of AI and data mining events like AAAI and IJCAI.

Sean AndersonView profile

Senior Lecturer

• Bioinspired Robotics
• Adaptive and Optimal Control
• System Identification
• +3 more

Dr Sean Anderson is a Senior Lecturer at the Department of Automatic Control and Systems Engineering , University of Sheffield , with over 15 years of experience in interdisciplinary research spanning robotics, control systems, and computational biology. He earned his MEng and PhD from the University of Sheffield, focusing on control systems and chemical engineering. Education: MEng in Control Systems Engineering, University of Sheffield (2001) PhD in Chemical and Process Engineering, University of Sheffield (2005) Research Interests include: Bioinspired robotics Adaptive and optimal control in biological systems Nonlinear system identification Computational neuroscience Acoustic and visual sensor fusion for localization His recent publications highlight innovations in robotic localization in hazardous environments, interpretable deep learning for control systems, acoustic sensing technologies, and data-driven modeling of complex systems. Key projects involve autonomous navigation in pipe networks, turbulence modeling, and biomedical signal processing. Grants and Funding: He has secured major grants from EU H2020 (£4M), EU FP7 (£2.9M), and EPSRC (£5.7M), focusing on bioinspired control algorithms, robotic safety, and infrastructure assessment. Teaching: He leads the ACS61011 Deep Learning module, emphasizing practical applications in robotics and signal processing.

Tom WenseleersView profile

Professor

• Theoretical Biology
• Evolutionary Biology
• Behavioural Ecology
• +7 more

Tom Wenseleers is a Professor at KU Leuven's Department of Biology within the Faculty of Science, where he leads the Laboratory of Socioecology and Social Evolution. His research spans theoretical and experimental approaches to evolutionary biology, with particular focus on social insect systems. Research spans social insects (ants, bees, wasps), microbes, viruses, and human systems Primary model organisms: social insects studying major evolutionary transitions Current projects examine caste determination, chemical communication, and evolutionary conflicts His research integrates theoretical modeling with experimental, behavioral, and comparative studies. Recent work combines genomic techniques and high-throughput GC/MS analysis to decipher chemical communication systems. Current trends show increasing interdisciplinary work spanning virology (SARS-CoV-2 variants), microbial ecology (antibiotic resistance), and robotics (pollinator behavior monitoring). The research demonstrates consistent application of evolutionary theory to diverse biological systems while maintaining social insects as the core model. Wenseleers actively mentors PhD students and postdocs, with recent graduates including Kamiel Debeuckelaere and Viviana Di Pietro. His lab receives substantial funding through multiple concurrent research projects, including Promotor roles on grants examining caste development in bee societies and microbial metabolite screening. The laboratory maintains strong international collaborations across Europe and South America. The lab operates within the Ecology, Evolution and Biodiversity Conservation unit at KU Leuven, with physical location at Naamsestraat 59, box 2466, 3000 Leuven. The research group maintains active outreach programs including science workshops for schools and public engagement events focused on insect conservation.

Heidi OttevaereView profile

Professor

• Photonics
• Biophotonics
• Optical fiber sensors
• +7 more

Heidi Ottevaere is a Professor at the Faculty of Engineering of the Vrije Universiteit Brussel (VUB) since October 1, 2009. She serves as the head of the Instrumentation and Metrology platform at the Photonics Innovation Center and leads the 'biophotonics' research unit of the Brussels Photonics Team (B-PHOT), which is chaired by Prof. Hugo Thienpont. Her work focuses on the design, fabrication, and characterization of photonic components and systems for diverse applications in medical diagnostics, environmental monitoring, and industrial processes. Dr. Ottevaere earned her Electrotechnical Engineering degree with majors in Photonics from Vrije Universiteit Brussel in 1997 and completed her PhD in Applied Sciences at the same institution in 2003. Her doctoral research focused on 'Refractive microlenses and micro-optical structures for multi-parameter sensing: a touch of micro-photonics.' Professor Ottevaere's research spans multiple cutting-edge areas of photonics with particular emphasis on biophotonics, micro-optics, and optical metrology . Her work bridges fundamental science with practical applications, developing novel photonic components and systems that address real-world challenges. She has pioneered research in miniaturized optical systems for medical diagnostics, environmental monitoring, and industrial applications. Her current research focuses on advancing lab-on-a-chip technologies, microfluidic optical sensors, and novel optical fiber systems for biomedical applications. She has developed microminiaturized, integrated plastic detection units for absorbance and laser-induced fluorescence measurements in microfluidic channels, enabling portable, robust, and disposable diagnostic systems. Her recent publications demonstrate a strong trend toward integrated optical sensing systems with applications in medical diagnostics and environmental monitoring. There's a clear progression from fundamental optical component design to complete system integration, with increasing emphasis on artificial intelligence for data analysis and computational imaging techniques. Her work bridges photonics with biomedical engineering, materials science, and data science, reflecting the interdisciplinary nature of modern photonics research. Dr. Ottevaere has been recognized with several prestigious awards: Best Application award (2008) Educational award - Bronze (2019) MOC09 Contribution Award Winners (2009) As an educator and mentor, Professor Ottevaere has promoted 9 PhD students and supervised numerous master's theses. She has secured substantial research funding from diverse sources including the Fund for Scientific Research Flanders (FWO), the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT), and multiple European Framework Programs. Her current portfolio includes projects on miniaturized biosensors for drinking water screening, precision manufacturing, and photonics education initiatives in Uzbekistan. She has coordinated multiple strategic research and networking projects with regional, national, and international funding bodies. Professor Ottevaere leads the biophotonics research unit within the Brussels Photonics Team (B-PHOT), one of Europe's leading photonics research groups. Her team includes researchers working on optical metrology, micro-optics fabrication, and biophotonic applications. She collaborates extensively with industry partners including Melexis, Umicore, and Anteryon, as well as academic institutions across Europe through various EU-funded projects. She has been instrumental in developing the interuniversity engineering curriculum 'Master in Photonics' which received the EC Erasmus Mundus quality label in 2006, and continues to be the driving force behind photonics education at VUB.