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Francesca StradoliniView profile

Lecturer

• Bioelectronics
• IoT for Medical Monitoring
• Anesthesia Delivery Systems
• +3 more

Francesca Stradolini is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Electrical Engineering Doctoral School (EDEE). She holds a PhD in Electrical Engineering from EPFL (2018), with prior education in Bio Engineering and Neuroscience from the University of Genoa (2012, 2015). Her current work focuses on R&D engineering for medical devices, emphasizing system lifecycle management, human factors engineering, and compliance with CE/FDA regulations. As an invited guest, she contributes to usability engineering education and interdisciplinary research at EPFL. PhD: IoT Bio-Electronic Multi-Panel Device for On-line Monitoring of Anaesthesia Delivery (EPFL, 2018) MSc: Bio Engineering (University of Genoa, 2015) BSc: Bio Engineering (University of Genoa, 2012) Her research spans bioelectronics, IoT-enabled medical monitoring, and electrochemical sensor development for clinical applications. Key areas include anesthesia delivery systems, wearable diagnostics, implantable devices, and human factors engineering. Her work integrates fluidic systems, noble metal nanostructures, and open-source hardware (e.g., Raspberry Pi) to address challenges in traceability, fouling mitigation, and pharmacokinetic modeling. Scientific recognition includes the Ville de Lausanne Award (2019) , EPFL Outstanding PhD Thesis Distinction (2018) , and a Best Paper Award at MOBIHEALTH (2016) . She has published extensively in journals like IEEE TBIOCAS, Sensors and Actuators B, and IEEE Sensors Journal, with collaborative studies on propofol monitoring, carbon nanoallotrope drug detection, and implantable sensor validation. Her technical expertise includes managing medical device development lifecycles, designing IoT architectures for intensive care, and optimizing electrochemical sensors for stability in undiluted biological fluids. She has contributed to conferences such as IEEE BioCAS, ISCAS, and MOBIHEALTH, focusing on translational studies in personalized medicine and 3D-printed medical systems.

Alfred O. Hero, IIIView profile

Professor

• Data Science
• Statistical Machine Learning
• Distributed Optimization
• +7 more

Alfred O. Hero, III is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor. He is currently on leave from the University of Michigan as a Program Director in the CISE Directorate at the National Science Foundation. His primary appointment is in the Department of Electrical Engineering and Computer Science (EECS), with secondary appointments in the Department of Biomedical Engineering and the Department of Statistics. He is also affiliated with the UM Center for Computational Medicine and Bioinformatics (CCMB), the UM Graduate Program in Applied and Interdisciplinary Mathematics (AIM), the UM Applied Physics Program, and the Michigan Institute for Data Science (MIDAS), which he co-founded from 2015-2018. Hero's research focuses on data science, developing theory and algorithms for multimodality data collection, fusion, analysis and visualization that use statistical machine learning and distributed optimization. His work has applications in wearable technologies for personalized health and predictive medicine, spatio-temporal networks in biology, climate, and social discourse, anomaly detection, and data analysis for international security. His recent research interests include high dimensional spatio-temporal data analysis, multimodal data integration, statistical signal processing, and machine learning, with particular emphasis on predictive mathematical models for biological and physical sciences, social networks, network security and forensics, and personalized health and disease. His recent publications demonstrate a strong focus on high-dimensional statistical methods, contrastive learning, neural network optimization, change detection in temporal graphs, and applications in microbiome analysis and epidemic modeling. The research spans theoretical foundations in information theory and statistical learning while addressing practical applications across multiple domains. Scientific Awards: IEEE Signal Processing Society Best Paper Award (1998) Best Original Paper Award from Journal of Flow Cytometry (2008) Best Magazine Paper Award from IEEE Signal Processing Society (2010) SPIE Best Student Paper Award (2011) IEEE ICASSP Best Student Paper Award (2011) IEEE Signal Processing Society Technical Achievement Award (2014) IEEE Signal Processing Society Society Award (2015) IEEE Fourier Award (2020) University of Michigan Distinguished Faculty Achievement Award (2011) Hero has advised over 60 PhD students and 30 postdocs in areas including modeling, computation, and inference for large scale time varying data in the biosciences. He has received significant research funding from the Department of Energy, Army Research Office, Air Force Office of Scientific Research, and National Science Foundation. He has held leadership positions including President of the IEEE Signal Processing Society (2006-2007), Director of Division IX (Signals and Applications) on the IEEE Board of Directors (2009-2011), and Chair of the Committee on Applied and Theoretical Statistics of the US National Academies (2018-2020).

• Machine Learning
• Data Mining
• Neural Networks
• +3 more

Professor Irena Koprinska is a faculty member at the School of Computer Science, University of Sydney, specializing in Machine Learning, Data Mining, and Neural Networks. Her research focuses on practical applications in education, health, and energy sectors. She has received multiple awards, including the Dean’s Award for Outstanding Teaching (2017, 2008) and Best Paper Awards at CHI 2019 and other conferences. Koprinska has supervised 11 PhD and over 60 Honours students, many of whom have won prestigious scholarships like the Google Fellowship. Education: PhD and MSc in Computer Science, MEd in Higher Education. Research Interests: Develops algorithms for pattern extraction and predictive modeling in healthcare (e.g., sleep apnea prediction), education (student behavior analysis), and energy (solar power forecasting). Her work bridges algorithmic innovation with multidisciplinary collaboration. Publications: Over 100 articles in top journals/conferences, emphasizing applications of machine learning in health, energy, and education. Recent works include deep learning for sleep apnea and ensemble methods for solar forecasting. Awards: Highlighted awards include the Dean’s Teaching Awards, Best Paper recognitions, and the Thompson Research Fellowship (2018). Advising & Grants: Currently supervises Hanxue Yao. Previously led initiatives like the Data Science for Social Good workshop at ECML PKDD. Served as Associate Head for Research Education and Sub-Dean for Teaching & Learning. Labs/Teams: Leads the Computer Human Adapted Interaction Research Group, focusing on human-centric technology solutions.

John M. RuterView profile

Professor

• Ornamental Plant Breeding
• Nursery Crop Production
• Plant Genetics
• +4 more

Dr. John M. Ruter is the Allan M. Armitage Professor and Director of the UGA Trial Gardens within the College of Agricultural & Environmental Sciences at the University of Georgia. His work integrates plant breeding, genetics, and sustainable nursery crop production, focusing on ornamental plants for southeastern landscapes. Education: B.S. in Ornamental Horticulture, California Polytechnic State University (1984) M.S. in Ornamental Horticulture and Landscape Design, University of Tennessee (1986) Ph.D. in Horticultural Science, University of Florida (1989) Research interests include developing climate-resilient ornamental plants (e.g., Camellia oleifera , hibiscus, crape myrtle), mutation breeding via gamma irradiation, and optimizing nursery production systems. Recent projects focus on polyploid conifer evaluation and tea oil crop development for the southeastern U.S. Recent publications highlight advancements in induced mutagenesis (EMS, gamma irradiation), disease resistance screening (Cercospora leaf spot), and genetic characterization of native and ornamental species. Key subfields include ornamental breeding , genomic analysis , and sustainable production practices . As Director of the UGA Trial Gardens, he leads plant evaluation programs that inform regional landscaping decisions and cultivar development.

Bingzhang ChenView profile

Senior Lecturer

• Biological oceanography
• Theoretical Ecology
• Ecosystem modeling
• +9 more

Bingzhang Chen is a Senior Lecturer in the Department of Mathematics and Statistics at the University of Strathclyde, Faculty of Science. He previously held positions as a Chancellor’s Fellow at the same institution, a researcher at the Japan Agency of Marine-Earth Science and Technology (JAMSTEC), and was affiliated with Xiamen University and Mount Allison University. His academic journey began with a PhD from the Hong Kong University of Science and Technology. Education: PhD in Trophic interactions within the microbial food web, Hong Kong University of Science and Technology (Awarded 2009) His primary research interests lie at the intersection of biological oceanography and theoretical ecology, with a strong focus on ecosystem modeling. He investigates how biodiversity, particularly of phytoplankton, influences marine ecosystem functioning such as primary production and the biological carbon pump. A central theme in his work is understanding the differential temperature sensitivity between autotrophs and heterotrophs, a question that bridges statistical analysis, metabolic theory, and Earth system science. His recent publications highlight a consistent trend in developing and applying individual-based models (e.g., PIBM 1.0), analyzing large datasets on plankton thermal responses, and studying the impacts of climate change and anthropogenic activities (like nutrient input) on marine microbial communities across diverse regions from the South China Sea to the North Pacific and Scottish coastal waters. His work often combines modeling with observational data to address fundamental ecological questions. Scientific Awards: David Cushing Prize (2015) from the Journal of Plankton Research New Century Excellent Talent (2012) from the Ministry of Education of China Dr. Chen is actively involved in research supervision, currently guiding five PhD students. He has been the Principal Investigator on multiple research projects funded by organizations such as the Leverhulme Trust, FILAMO, and the National Science Foundation. His expertise in programming (R, Fortran, MATLAB) underpins his methodological approach. He also contributes to the scientific community as an Associate Editor for the prestigious journal Limnology and Oceanography . His work is associated with efforts to understand and model invasive species dynamics, such as the spread of Sargassum muticum in Scottish waters, and he is involved with external advisory groups like the MASTS Marine Artificial Intelligence Forum.

• Bioinformatics
• Genomics
• Cancer Genomics
• +7 more

Dr. Andre Kahles is a Lecturer in the Department of Computer Science at ETH Zürich, specializing in biomedical informatics. His research focuses on computational methods for analyzing large-scale genomic and transcriptomic data, with applications in cancer genomics, metagenomics, and precision medicine. He has contributed to the development of tools such as SplAdder for alternative splicing analysis, MetaGraph for petascale genomic data exploration, and SECEDO for subclone detection in cancer genomes. His work bridges algorithmic innovation with biological insights, addressing challenges in single-cell analysis, genome graph alignment, and multi-omics integration. Key research themes include: Developing scalable algorithms for processing nanopore sequencing and metagenomic data Characterizing somatic mutations and non-coding drivers in cancer genomes Advancing genome graph-based alignment and annotation methods Integrating multi-omics data for clinical decision-making and tumor profiling His publications span topics like RNA-seq analysis, chromothripsis in cancers, and global urban microbiome tracking through the MetaSUB consortium. Kahles has collaborated on landmark projects including the Pan-Cancer Analysis of Whole Genomes (PCAWG) and the Tumor Profiler Study.

• Hematopathology
• Molecular Diagnostics
• Flow Cytometry
• +7 more

Alexa Siddon, MD, is an Assistant Professor in the Department of Laboratory Medicine at Yale School of Medicine, with secondary appointments in Pathology. She serves as Hematopathology Fellowship Director, Director of the Molecular Diagnostics Laboratory, Director of Flow Cytometry, and Associate Director of the Hematology Laboratory. She is also affiliated with the Yale Cancer Center, Clinical Flow Cytometry Laboratory, and the Hematopathology Laboratory at the Veteran Affairs Connecticut Healthcare System. Dr. Siddon's research focuses on hematopathology, particularly myeloid malignancies, acute leukemias, and post-transplant monitoring. Her work integrates molecular diagnostics, flow cytometry, and genomic analysis to improve patient diagnosis and outcomes. She has co-authored over 10 peer-reviewed articles in recent years, frequently collaborating with leading hematologists and pathologists such as Christopher Tormey, Henry Rinder, and Rory Shallis. Her publications reveal a strong trend in translational research, emphasizing the clinical implications of genetic mutations (e.g., TP53, NPM1, IDH1/2), chimerism testing post-transplant, and immunophenotypic characterization of hematologic malignancies. The research spans disciplines including oncology, molecular biology, immunology, and clinical pathology, with a clear focus on precision diagnostics and prognostic modeling. Scientific Awards: ASCP Distinguished Educator Award Paul J. Strandjord Young Investigator Award Donald H. Buchholz Research Prize Dr. Siddon is actively involved in medical education, directing fellowship programs and mentoring trainees. She has no reported grants listed in the text, but her leadership in diagnostic laboratories and active publication record suggest ongoing research funding. She is not part-time, not retired, and remains a current, active faculty member. She is affiliated with multiple research entities including the Cancer Immunology Center, Center for Biomedical Data Science, and the Yale Cancer Center, contributing to interdisciplinary teams focused on hematologic malignancies and diagnostic innovation.

• Intensive Care Medicine
• Critical Care
• Sepsis
• +9 more

Stefan Kluge is a Professor at the Department of Intensive Care within the University Medical Center Hamburg-Eppendorf . With over 150 publications in 2025-2024 alone, his work focuses on critical care medicine, sepsis, ARDS, and cardiogenic shock. His recent research explores ARDS impact on liver cirrhosis outcomes Hyperoxia effects in traumatic brain injury Biomarkers for cardiogenic shock prognosis Novel retrograde limb perfusion techniques during ECPR Dual-energy CT for body composition analysis He contributes extensively to German national guidelines for nosocomial pneumonia and participates in multi-center critical care trials. Stefan Kluge's collaborations span neuro-intensive care , cardiovascular research , and infection/immunity domains. His work integrates machine learning applications and medical informatics to improve ICU decision-making.

Kelsey SwingleView profile

Assistant Professor

• Biomaterials Science
• Immune Engineering
• Reproductive Biology
• +6 more

Kelsey Swingle is an Assistant Professor of Bioengineering at Rice University, where she leads the Swingle Lab at the intersection of biomaterials science, immune engineering, and reproductive biology. Her research focuses on engineering therapeutic and vaccine technologies with translational potential. Ph.D. in Bioengineering from the University of Pennsylvania B.S.E. in Biomedical Engineering from Case Western Reserve University Dr. Swingle’s research explores the design of lipid nanoparticles (LNPs) and nucleic acid therapeutics for women’s health applications, including pre-eclampsia, preterm birth, and gynecologic cancers. Her work integrates bioengineering principles with immune modulation strategies to develop targeted therapies. The trends in her publications highlight advancements in LNP elasticity optimization for placental mRNA delivery, targeted systemic RNA delivery to the brain, and in utero gene editing applications. Her lab prioritizes interdisciplinary approaches to overcome biological barriers in women’s health. 2025 Solomon R. Pollack Award for Excellence in Graduate Bioengineering Research 2024 Muriel Joan Drew Hege Award for Women in Cellular Immunotherapy Research 2024 Penn Engineering Outstanding Teaching Award 2023 Gordon Research Conference Travel Award 2022 Society for Biomaterials STAR Award 2020 NSF Graduate Research Fellowship The Swingle Lab collaborates with the Texas Medical Center to develop precision nanomedicines. Her team employs in vitro, ex vivo, and in vivo models to study biomaterial interactions with female-specific tissues, emphasizing translational research and inclusive scientific communication.

Stéphane Marchand-MailletView profile

Associate Professor

• Machine Learning
• Data Mining
• Information Retrieval
• +5 more

Stéphane Marchand-Maillet is an Associate Professor at the University of Geneva's Faculty of Science, Department of Computer Science, leading the VIPER research group since 2000. His work focuses on high-dimensional data analysis, modeling, and indexing, with applications in Medicine (flow cytometry, imaging, patient records) and Digital Humanities. He co-directs a SNF-funded project on semantic multilingual editions of Geneva Council registers (1545-1550) and collaborates with HUG (Geneva University Hospitals). Education : PhD in Applied Mathematics and Operational Research from Imperial College London (1997) Postdoctoral stay at EURECOM Institute (France) Research Interests : Analysis of high-dimensional data spaces, medical data applications, multimodal information management, and digital humanities. His work intersects machine learning, data mining, and information retrieval. Leadership & Collaborations : Vice-President of the Foundation Board of Idiap Research Institute (Martigny) Member of the Steering Committee Collaboration with Fondation de l'Encyclopédie de Genève and SNF-funded projects

Franz Lennard RicklefsView profile

Professor

• Neurosurgery
• Neuro-oncology
• Molecular Neurobiology
• +5 more

Prof. Dr. med. Franz Lennard Ricklefs is a Senior Physician and Head of the Working Group at the Department of Neurosurgery, University of Hamburg Faculty of Medicine. He is a Medical Specialist in Neurosurgery with cross-disciplinary expertise in neuro-oncology, molecular pathology, and extracellular vesicle research. Affiliations: University Medical Center Hamburg-Eppendorf (UKE), European Liquid Biopsy Society (ELBS), International Consortium on Meningiomas (ICOM) Research Interests: His work focuses on neurosurgical oncology, particularly glioblastoma and meningioma pathobiology. He investigates DNA methylation patterns, extracellular vesicle biomarkers, and liquid biopsy implementation in clinical neuro-oncology. Additional interests include surgical outcomes for epilepsy and aneurysm management. Article Trends: Over the last decade, Dr. Ricklefs has published extensively on: Extracellular vesicle applications as liquid biopsy markers DNA methylation subclasses for glioblastoma and meningioma Multicenter surgical outcome benchmarking Immune evasion mechanisms in neuro-oncology Technological innovations in neurosurgical visualization Molecular characterization of rare CNS tumors Professional Contributions: He co-authored the MISEV2023 guidelines for extracellular vesicle studies and participates in international consensus reviews for meningioma classification. His collaborations span institutions across Europe and North America.

• Microbiology
• Immunology
• Medicinal Chemistry
• +4 more

Yan Liu is a Senior Lecturer in Glycosciences at Imperial College's Department of Metabolism, Digestion and Reproduction (Faculty of Medicine). She leads the Wellcome Trust-funded Carbohydrate Microarray Facility and co-leads the GlycoTWINNG Network. Her research focuses on glycan-mediated interactions in host-microbiota interfaces, with applications in infectious diseases and reproductive health. She holds a B.Sc. from Peking University and a Ph.D. from the University of Bristol, followed by postdoctoral work at Imperial College under Professor Ten Feizi FRS. Her academic journey includes roles as a Microarray Project Leader and co-investigator in the March of Dimes European Preterm Birth Research Centre. She is PI of a £1.34M Wellcome Trust grant maintaining Imperial's Carbohydrate Microarray Facility, advancing glycan array technologies. Research interests span microbiology, immunology, and biomolecular chemistry, with a focus on glycobiology. Key contributions include defining glycan interactions in pathogen-host systems (e.g., Candida albicans, adenoviruses), developing glycan probes, and analyzing vaginal microbiota impacts on preterm birth. Grants and collaborations include EPSRC Translational funding and the UK 'FluTrialMAP' consortium. Labs affiliations include the Glycosciences Laboratory and Imperial's Institute of Infection.

Oliver SchillingView profile

Assistant Professor

• Hydrogeology
• Biogeochemistry
• Environmental Tracer Techniques
• +3 more

Prof. Dr. Oliver Schilling is an Assistant Professor of Hydrogeology at the University of Basel and affiliated with Eawag , the Swiss Federal Institute of Aquatic Science and Technology. He leads research on surface water-groundwater interactions using integrated surface-subsurface hydrological models (ISSHM) and novel tracer techniques including dissolved atmospheric noble gases, environmental DNA, and radioactive tracers. Research Focus : Surface-subsurface hydrology, ecohydrology, groundwater-surface water interactions, tracer hydrogeology, climate change adaptation in water systems Key Projects : Integrated Hydrological Modeling for Operational Forecasting, Sustainable Nitrogen Fertilization, Slow Water Initiatives, Cryosphere-Groundwater Connectivity in Alpine Regions Scientific Contributions include developing HGS-PDAF (modular data assimilation framework), advancing microbial transport algorithms in HydroGeoSphere, and pioneering online flow cytometry for microbial analysis in high-turbidity environments. His work addresses drinking water production via bank filtration along alluvial rivers, critical for 30% of Swiss and 50% of European drinking water supply. Scientific Leadership : Editor, Hydrogeology Journal (since 2025) Associate Editor, Frontiers in Water (since 2021) Coordinator, Swiss Water-Earth Systems (WES) PhD School (2020–2022)

David LatulippeView profile

Professor

• Membrane Science
• Bioprocessing
• DNA Purification
• +4 more

David Latulippe is a Professor in the Department of Chemical Engineering at McMaster University. He joined McMaster in 2012 after postdoctoral work at Cornell University and a PhD at Penn State University, focusing on membrane filtration for DNA purification. His industrial experience includes roles at ZENON Environmental (now GE Water) in hollow-fiber membrane design for water treatment. Research interests include Membrane science and technology Bioprocessing of therapeutic viruses Microscale systems for biological applications Environmental engineering solutions for water treatment Current projects involve collaborations with industry partners like Ceapro and Aevitas, and the development of a biomanufacturing automation lab with Sartorius. Recent publications highlight advancements in Nanofiltration and microfiltration for viral vectors Conductive membranes for electrochemical applications Microfluidic systems for DNA analysis Environmental monitoring of biocides and microplastics Scientific recognition includes the Young Membrane Scientist Award (2014). Teaching activities focus on Fluid Mechanics (CHEMENG 2O04) and Industrial Separation Processes (CHEMENG 4M03).

Bärbel Finkenstädt RandView profile

Tutor

• Statistical Methodology
• Machine Learning
• Bayesian Inference
• +6 more

Bärbel Finkenstädt Rand is a Senior Tutor at the Warwick Medical School , University of Warwick, with extensive research contributions at the intersection of statistics, machine learning, and biomedical sciences. Her work focuses on developing advanced methodologies for analyzing temporal and spatio-temporal data, particularly in circadian rhythms and disease dynamics. Research Themes : Bayesian inference, Hidden Markov Models, circadian rhythm stability, transcriptional bursting, and wearable sensor data analysis. Collaborations : Chronotherapy Group at Warwick, Université Paris-Saclay, and interdisciplinary teams across medicine, genetics, and computational biology. Publications reveal a strong emphasis on circadian health monitoring, gene expression dynamics, and epidemic modeling using stochastic frameworks. Her recent work prioritizes personalized medicine applications through telemonitored biomarkers and IoT platforms . Methodological Innovations include spline-based HMMs, distributed delay systems, and harmonic modeling for nonstationary time series. Applications span oncology, sleep medicine, and population ecology.