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.
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 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 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 .
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 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 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.
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 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 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.
Alvaro Köhn-Luque is an Associate Professor at the Oslo Center for Biostatistics and Epidemiology, University of Oslo, and Group Leader at the Department of Medical Genetics, Oslo University Hospital. His work bridges mathematical modeling with clinical applications, particularly in cancer research. His academic background includes a PhD in Mathematical and Computational Biology from Complutense University of Madrid (2012), preceded by multiple Master's degrees in Mathematics and Physics from Spanish universities. Dr. Köhn-Luque's research focuses on mathematical oncology , developing computational models to understand cancer dynamics and improve treatment strategies. His work spans multiscale modeling of tumor growth, personalized cancer medicine through computer simulations, and biomarker discovery using machine learning approaches. He has made significant contributions to modeling breast cancer progression and treatment response, particularly in the context of endocrine therapy and CDK4/6 inhibition. His recent publications demonstrate a strong trend toward integrating mechanistic learning approaches that combine mathematical models with machine learning techniques. This hybrid methodology allows for more accurate prediction of treatment outcomes while maintaining biological interpretability. His work frequently involves collaboration with clinical researchers to ensure models are grounded in real patient data and have direct translational potential. Computational modeling of tumor heterogeneity and drug response Development of methods for phenotypic deconvolution in cancer cell populations Integration of multi-omics data for personalized treatment prediction Application of birth-death processes to model tumor evolution Creation of user-friendly computational tools for biomedical researchers Dr. Köhn-Luque has supervised multiple PhD students including Even M Myklebust, Salim Ghannoum, and Xiaoran Lai, and has secured funding for projects including RESCUE, BigInsight, and Integreat. His research demonstrates a consistent trajectory from theoretical mathematical biology toward increasingly clinically relevant applications in personalized cancer medicine.
Jungeun (Jenny) Won is an Assistant Professor of Research in the Department of Biomedical Engineering at the School of Engineering and Applied Sciences, University at Buffalo. Her research focuses on optical imaging , biomedical device development , medical image analysis , and artificial intelligence in OCT . She leads the Translational Biophotonics Laboratory , where she develops advanced OCT techniques for medical applications such as diabetic retinopathy , otitis media , and biofilm analysis . Contact: 215J Bonner Hall, Buffalo NY 14260, jungeunw@buffalo.edu Related Links: CV PDF , Google Scholar , Lab Website Her recent work involves high-resolution OCT for longitudinal studies on retinal degeneration, VISTA OCTA for blood flow analysis, and 3D motion correction algorithms to enhance image quality. She also explores multimodal imaging combining OCT with Raman spectroscopy for bacterial differentiation and microplasma-based therapies for ear infections.
Professor Kelly Lyons is a Professor at the Faculty of Information at the University of Toronto, cross-appointed to the Department of Computer Science. Her research focuses on service science, knowledge mobilization, social media, and data-driven innovation. Prior to academia, she held roles at IBM Toronto Lab's Centre for Advanced Studies. She has secured extensive funding from NSERC, IBM, and industry partnerships, and has advised numerous graduate students. Her work bridges interdisciplinary collaboration, emphasizing AI governance, digital economy impacts, and fostering Women in Technology initiatives. Research interests include the application of social platforms in service systems, data science for knowledge translation, and the societal implications of AI. Key projects involve analyzing gender dynamics in user reviews, open-source software structures, and pandemic-driven innovation trends. Her grants span data science, healthcare analytics, and smart city technologies. Publications span empirical studies on GitHub collaboration, AI governance frameworks, and biomedical knowledge systems. She received the Best Paper Award for 'The Effect of Collaborative Games on Group Work' (2015). Her teaching emphasizes service systems design and project management, with a focus on practical, interdisciplinary learning. Active in scholarly service, she chairs the Consortium for Software Engineering Research and serves on ACM-W's Executive Council. Collaborations include cross-institutional projects with University College London and UCL on AI governance frameworks. Her work extends to promoting STEM education and diversity in tech.
Associate Professor Joshua San Miguel leads research in computer architecture and systems at the University of Wisconsin-Madison, with an affiliate role in Computer Sciences. His work focuses on energy-efficient computing for IoT devices, microarchitecture innovations, and networks-on-chip. He holds a PhD (2017) and BASc (2012) from the University of Toronto. Education: PhD in Electrical & Computer Engineering, University of Toronto (2017) BASc in Engineering Science (ECE), University of Toronto (2012) Research Interests: Approximate computing for energy harvesting systems Branch prediction and value prediction in processors Cache architectures and networks-on-chip for many-core processors Intermittent computing resilience His recent work emphasizes value-level parallelism (Carat/uSystolic), RTL simulation acceleration (TaroRTL), and personalized neural network inference (CAP’NN). His research has been recognized with the NSF CAREER Award (2021) and multiple IEEE Micro Top Picks. Grants & Advising: Active in supervising advanced independent studies and master’s/dissertation research. Extensive grant funding includes the NSF CAREER Award and the Grainger Faculty Scholarship. Labs & Teams: Leads research groups focused on approximate computing and energy-efficient architectures within the Electrical & Computer Engineering department.
Prof. Wim Desmet is a full professor at the Faculty of Engineering Science and head of the Department of Mechanical Engineering at KU Leuven . His research focuses on advanced modeling techniques for mechanical systems, including: noise and vibration control in automotive and industrial systems computational acoustics and interval field uncertainty modeling metamaterials for broadband vibroacoustic performance AI-driven diagnostic systems in renewable energy and manufacturing Current research projects address challenges in electric vehicle drivetrains, wind turbine monitoring, and multi-physical digital twin development. He actively contributes to academic governance as: Managing Director of KU Leuven Head of Subdivision HIST Chair of multiple executive committees Member of 15+ academic and administrative councils
