Dr. Stephanie Hicks is an Associate Professor in Biomedical Engineering and Biostatistics at Johns Hopkins University, with affiliations in multiple centers including the Malone Center for Engineering in Healthcare and the Center for Computational Biology. Her research focuses on developing computational methods and open-source software for analyzing single-cell and spatial transcriptomics data to enhance understanding of human health and disease. She holds a PhD from Rice University and completed postdoctoral training at Dana-Farber Cancer Institute and Harvard. Education: B.S. Mathematics (LSU), M.A./Ph.D. Statistics (Rice University), Postdoc in Biostatistics/Data Science (Dana-Farber/Harvard). Research interests span scalable computational methods, machine learning, and biomedical data science. Notable contributions include tools like SpotSweeper for spatial transcriptomics quality control and the spatialLIBD package for spatial data visualization. Awards include Fellow of the American Statistical Association, COPSS Emerging Leader Award, and Myrto Lefkopoulou Lectureship. She co-hosts the Corresponding Author podcast and actively promotes open science through initiatives like R-Ladies Baltimore. Labs/Teams: Leads the Hicks Lab, collaborates across departments at Johns Hopkins, and engages in interdisciplinary projects in spatial genomics and computational biology.
Andrea Cini is a postdoc researcher affiliated with the Graph Machine Learning Group and the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) at the University of Lugano (USI). He also holds a position as a SNSF postdoc fellow at the University of Oxford under Prof. Michael Bronstein, focusing on machine learning for time series forecasting and graph processing. His research integrates graph deep learning methodologies with spatiotemporal dynamics, emphasizing applications in healthcare, energy systems, and intelligent systems. Education: PhD in Computer Science and Engineering (USI, 2020), supervised by Prof. Cesare Alippi MSc and BSc in Computer Science and Engineering (Politecnico di Milano) Visiting researcher at Imperial College London (Prof. Danilo Mandic) Research interests span graph neural networks , time series forecasting , and spatiotemporal data processing . His work has introduced influential methods such as the Torch Spatiotemporal library, and has been recognized with a best paper award. Recent publications emphasize applications in relational conformal prediction, hierarchical forecasting, and energy grid optimization. Awards include the Best Paper Award for contributions to graph-based forecasting methodologies. Current projects are funded by the Swiss National Science Foundation, exploring graph-based reinforcement learning and spatiotemporal modeling at the University of Oxford. Collaborations include affiliations with the Northernmost Graph Machine Learning group at UiT the Arctic University of Norway. His research bridges theoretical advancements and industrial applications in fields like healthcare dynamics prediction and smart grid optimization.
Conrad Zapanta is a Teaching Professor in the Department of Biomedical Engineering and Associate Dean of Undergraduate Studies at Carnegie Mellon University's College of Engineering. With a PhD in Bioengineering from Penn State University (1997) and BS in Mechanical Engineering with Biomedical Option from CMU (1991), his work bridges cardiovascular medical device development and biomedical engineering education reform. Education: Ph.D., Bioengineering, Pennsylvania State University (1997) BS, Mechanical Engineering (Biomedical Option), Carnegie Mellon University (1991) His research on cardiovascular medical devices spans prosthetic heart valves , circulatory support systems , and computational fluid dynamics . In education, he pioneered competency-based training frameworks and redesigned biomedical engineering curricula as Associate Department Head for Education (2009-2022). His Bioengineered Organs Initiative affiliation drives interdisciplinary innovation. Recent publications highlight his dual focus: 2025 studies examine pharmacogenomic implementation and pancreatitis pain genetics, while 2024 work includes electrospun vascular scaffolds , FDA CFD validation , and bioartificial liver trials . His 2023 articles address online instructor training and critical care severity metrics . Awards: ASEE Theo Pilkington Award (2016) Fellow, Biomedical Engineering Society Fellow, American Institute for Medical and Biological Engineering Zapanta leads undergraduate education strategy at CMU, evaluates biomedical engineering programs for ABET, and contributes to NIH panels. His lab work includes 3D-bioprinted vascular constructs and thrombosis modeling , while media features highlight innovations like his associate deanship and AIMBE fellowship .
Michael William Sjoding, MD, MSc, is an Associate Professor of Internal Medicine at the University of Michigan Medical School. He holds Center Memberships at the Institute for Healthcare Policy and Innovation, Center for Computational Medicine and Bioinformatics, Precision Health Initiative, and e-Health and Artificial Intelligence Initiative. His research focuses on leveraging machine learning and artificial intelligence to address critical care challenges, particularly in acute respiratory distress syndrome (ARDS), sepsis, and healthcare equity. He investigates interventions to improve clinical decision-making, ventilator management practices, and reducing racial disparities in medical device accuracy (e.g., pulse oximetry). Recent work includes developing AI-driven diagnostic models for ARDS, analyzing practice variations in critical care, and understanding the impact of anti-anaerobic antibiotics on patient outcomes. Dr. Sjoding is actively involved in translational research, bridging computational methods with clinical practice to enhance patient care. Education: MD and Master of Science degrees from University of Michigan. Research interests include: 1) AI applications in critical care (e.g., chest X-ray analysis, predictive modeling), 2) Pulmonary and critical care medicine (ARDS pathophysiology, mechanical ventilation strategies), 3) Health equity (racial disparities in medical technology accuracy, algorithmic bias in healthcare AI), and 4) Clinical epidemiology (antibiotic stewardship, postoperative complications). His studies often involve large-scale data analysis and multicenter collaborations to ensure generalizability. Recent publications highlight advancements in ARDS detection algorithms, delayed extubation practices, and racial disparities in medical testing patterns affecting AI training data. He collaborates with interdisciplinary teams to address urgent clinical questions through computational approaches.
Georgios Manis is an Associate Professor in the Department of Computer Science and Engineering at the School of Engineering, University of Ioannina, Greece. He holds a PhD from the National Technical University of Athens and has been a faculty member at the University of Ioannina since 2002, progressing from Lecturer to Associate Professor in 2018. He has also served as temporary teaching staff at the University of Patras, University of Crete, and University of Ioannina in the late 1990s and early 2000s. Education: B.Sc. in Computer Engineering (Diploma), National Technical University of Athens (NTUA), 1987–1992 MSc in Advanced Methods in Computer Science (Distributed and Parallel Systems), Queen Mary, University of London, 1992–1993 PhD in Computer Engineering, NTUA, School of Electrical and Computer Engineering, 1993–1997 His research interests lie at the intersection of Biomedical Engineering and Computing Systems , with a strong emphasis on Biomedical Signal Processing , Entropy Analysis , and Machine Learning . He has pioneered work in Bubble Entropy —a parameter-free entropy measure—and developed fast algorithms for entropy computation. His work also extends to compiler design and parallel computing, particularly in the automatic parallelization of recursive functions and loops. The trends in his recent publications reflect a dual focus: (1) biomedical applications involving entropy, heart rate analysis, and disease diagnosis using machine learning (especially Random Forests and SVMs), and (2) high-performance computing, including parallelization techniques and compiler optimizations for multi-core and SVP architectures. His research is highly interdisciplinary, combining signal processing, algorithm design, and clinical applications. Scientific Leadership and Recognition: Guest Editor, Special Issue on “Entropy in Biomedical Engineering”, Entropy (MDPI) Member of the IPAN Laboratory, University of Ioannina Active contributor to IEEE, Elsevier, and MDPI journals He has supervised several graduate students and is involved in funded research projects such as Palimpsest and Homore , focusing on smart systems for cultural interaction and elderly monitoring. His advising contributions are evident in co-authored papers with students like Evanthia Tripoliti and Aristeidis Mastoras. He teaches both undergraduate and postgraduate courses, including Compilers I/II and Biomedical Data Analysis . Laboratories and Teams: He is a member of the IPAN lab at the University of Ioannina, which supports interdisciplinary research in informatics and biomedical applications. His collaborative network includes researchers from Greece and abroad, particularly in the fields of biomedical signal analysis and entropy-based methods.
Andrew Farmery is a Professor of Anaesthetics at the University of Oxford , where he serves as Head of the Nuffield Division of Anaesthetics . He is also a Fellow & Tutor in Medicine at Wadham College , Oxford, overseeing undergraduate medical education. Research Interests : His work bridges biomedical engineering and respiratory physiology , focusing on developing intravascular sensors and analytical techniques to monitor respiratory and cardiovascular dysfunction in critically ill patients. Key projects include the Inspired Sinewave Technique (IST) , which quantifies lung heterogeneity , cardiac output , and ‘baby lung’ volume in ARDS and COPD models. He emphasizes translating these innovations into medical devices for improved patient care, supported by grants from the Wellcome Trust , EPSRC , and NIHR . Publications : His recent articles highlight the IST as a non-invasive tool for assessing lung injury , ventilation heterogeneity , and hemodynamic changes in animal models . Studies span ARDS , COPD , and PPE sustainability , reflecting interdisciplinary applications. Methodological works include computational simulations and CT validation for physiological modeling. Labs & Teams : He leads the Respiratory Physiology and Biomedical Engineering Group and is affiliated with the Ventilator Weaning and Extubation in Neurocritical Care Network , integrating clinical and engineering expertise.
Dr. Alexander Ferworn is an Adjunct Professor in the Faculty of Computing and Software at McMaster University, specializing in computer science applications for public safety and emergency response systems. His work bridges robotics, human-computer interaction, and blockchain technology to address challenges in urban search and rescue, disaster relief, and medical data processing. His research focuses on integrating drones, haptic navigation systems, and virtual reality simulators to enhance emergency responder capabilities. Key contributions include frameworks for IED neutralization training ( Universal Simulation Platform ), blockchain-based aid delivery systems, and haptic feedback mechanisms for hazardous environments. He has extensively collaborated on projects involving canine-assisted technology and 3D disaster scene reconstruction. Dr. Ferworn’s scholarly activity spans 25 years with 126 publications in venues like IEEE International Conference on Safety, Security, and Rescue Robotics, IEEE GEM Conference, and Simulation & Gaming . His work has been referenced in patents and adopted by institutions like the Health Information Systems Research Centre . While specific teaching details remain unmentioned, his research portfolio demonstrates sustained engagement with disaster response systems since 1999.
Eigil Samset is a Professor II in the Department of Informatics at the University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences. He is a key member of the Digital Signal Processing and Image Analysis (DSB) research group and contributes to the Strategic Research Initiative MEDIMA (Multimodal Medical Imaging and Image Analysis). His work is also associated with the INIUS (Intelligent Image-Guided Surgery) project, reflecting his strong focus on translational research in medical imaging. His research interests span artificial intelligence, deep learning, medical image analysis, echocardiography, and image-guided surgery . He specializes in developing advanced computational methods for cardiac ultrasound, including automated segmentation, motion tracking, and 3D/4D reconstruction. His work bridges computer science and clinical cardiology, aiming to improve diagnostic accuracy and procedural guidance. The recent publications show a consistent trend in applying deep learning to automate measurements in echocardiography, such as left ventricular strain, outflow tract diameter, and chamber segmentation. There is also a strong emphasis on biomechanical modeling, image fusion (e.g., ultrasound with fluoroscopy or CT), and real-time visualization for surgical applications. His work frequently appears in high-impact journals in medical imaging and biomedical engineering. He has advised or collaborated closely with numerous researchers and students, as evidenced by his frequent senior authorship on publications involving PhD candidates and postdocs. His research is supported by institutional affiliations and collaborative projects rather than individual grant mentions in the provided text. He is actively contributing to the field, with recent work in 2024, and maintains an academic email at the University of Oslo. There is no indication of retirement or emeritus status.
Anis Yazidi is an Associate Professor at the Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, where he leads research in the Research Group for Digital Signal Processing and Image Analysis. His academic profile demonstrates significant contributions to artificial intelligence, machine learning, and signal processing with over 50 publications between 2021-2025 in high-impact venues including IEEE Transactions, Frontiers journals, and AAAI proceedings. Professor Yazidi's research spans multiple interconnected domains with particular emphasis on Tsetlin Machines, deep learning for medical applications, and signal processing theory. His work bridges theoretical foundations with practical implementations, developing novel frameworks like DREAMS for EEG analysis with model card reporting and interpretable methods for ECG classification. His contributions to learning automata theory, particularly in convergence analysis of Tsetlin-based algorithms, represent significant theoretical advances. The research portfolio also extends to cybersecurity applications of AI for IoT protection, agricultural technology for plant disease detection, and renewable energy modeling for wind-speed statistics. Yazidi's publication record reveals a strong interdisciplinary approach, with collaborations across computer science, neuroscience, medical diagnostics, and engineering disciplines. His recent work shows increasing focus on trustworthy AI systems, ethical considerations in medical applications, and specialized neural architectures tailored for specific data modalities including EEG, ECG, and eye-tracking data. The research demonstrates both theoretical rigor in algorithm development and practical implementation for real-world problems. Professor Yazidi maintains an active collaboration network with researchers across the Department of Informatics at UiO, particularly with Pedro Lind, Hugo Lewi Hammer, and Paal Engelstad, while also engaging in international collaborations. His work contributes significantly to both the theoretical foundations of learning systems and their practical implementation in diverse application domains from healthcare to renewable energy.
Dr. Daniel Akrawi is a Conjoint Associate Lecturer in Cardiology at the University of New South Wales (UNSW), School of Clinical Medicine. He is a practicing Cardiologist currently pursuing a PhD in angiography-based Fractional Flow Reserve (CAAS-vFFR) and has contributed to research in Preventative Cardiology, Advanced Cardiac Imaging, and Cardio-Oncology. His research spans a diverse range of topics, including: Preventative Cardiology Advanced Cardiac Imaging techniques Cardio-Oncology Fractional Flow Reserve (FFR) for coronary stenosis assessment Disordered eating psychopathology linked to spirituality/religion The trends in his publications reflect a dual focus on clinical cardiology advancements and interdisciplinary research at the intersection of mental health and cardiovascular medicine. Notable areas include computational angiography, coronary stenosis severity measurement, and the psychosocial dimensions of eating disorders. No scientific awards or student advising information were explicitly mentioned in the provided texts.
Francesco Longo is an Assistant Professor at the Department of Mechanical, Energy and Management Engineering , University of Calabria, Italy. He serves as Director and Scientific Responsible of the Modeling & Simulation Center – Laboratory of Enterprise Solutions (MSC-LES), the sole Italian member of the McLeod Modeling & Simulation Network (MS&Net). His academic career spans teaching 'Design of Production Systems' and 'Industrial Plant Laboratory' since 2005, with visiting researcher roles at Rutgers University and University of Genoa. Education: PhD in Mechanical Engineering (2006), Summa Cum Laude Degree in Mechanical Engineering (2002) Research Focus: Modeling & Simulation applied to production systems, supply chain security, digital twins, and human-robot collaboration His recent publications (2024-2025) demonstrate interdisciplinary work across digital twin technology (for food industry, human-robot workspaces), human-centered design (smart operators, wearable AR solutions), and sustainability (ESG impacts, energy efficiency). He has served as Editorial Board Member for Simulation journals and General Chair at multiple European Modeling & Simulation Symposia. Scientific Recognition: MISS Certificate of Appreciation, ICAMES 2008 First Runner Up Technical Expertise: HLA/RTI Standards, 3D Real-Time Simulation, Industry 4.0/5.0 Tools As MSc course tutor since 2002 and conference organizer (22nd EMSS, MAS 2010), he bridges academic research with industrial applications through partnerships with Rutgers University , Kennedy Space Center/NASA , and University of Ottawa . His lab develops simulation tools for maritime security and ergonomic workstation design.
Professor John A Rogers is a leading academic in materials science and biomedical engineering, currently holding the Louis Simpson and Kimberly Querrey Professor position at Northwestern University . He is also the founding Director of the Querrey-Simpson Institute of Bioelectronics , with joint appointments in Biomedical Engineering, Mechanical Engineering, Electrical Engineering, Chemistry, and Neurological Surgery. His research spans bio-integrated electronics, flexible devices, and nanofabrication technologies. Education : BA/BS in Chemistry and Physics (University of Texas, 1989); SM in Physics and Chemistry (MIT, 1992); PhD in Physical Chemistry (MIT, 1995). Rogers’ work focuses on Soft, skin-like electronics for vital signs monitoring, Bioresorbable devices for cardiac and neural applications, Injectable optoelectronics in neuroscience, and 3D microsystems for biomedical research. His team pioneers stretchable silicon , transient electronics , and bio-inspired fabrication methods. Recent research trends include millimeter-scale pacemakers , wireless skin-interfaced systems , and closed-loop bio-optoelectronics . These innovations leverage flexible substrates , nanoscale thermocapillary flows , and soft lithography for unprecedented biocompatibility and functionality. Scientific Awards : Sigma Xi William Procter Prize (2023), IEEE Biomedical Engineering Award (2023), James Prize (2022), Guggenheim Fellowship (2021), MacArthur Fellowship (2009), and multiple academy fellowships. Rogers leads a multidisciplinary team and has co-authored over 1000 peer-reviewed papers, with more than 100 patented technologies commercialized through startups. His lab’s 3D electronic pericardium and skin-integrated microfluidics exemplify his commitment to translating fundamental science into clinical solutions.
Dr. Anne Bonnin serves as a Beamline Scientist at the Paul Scherrer Institute (PSI) in Switzerland, where she has been instrumental in X-ray imaging research since joining the X-ray Tomography Group in 2014 and assuming her current role at the TOMCAT Beamline in 2016. Affiliated with PSI's Center for Photon Science and Laboratory for Macromolecules and Bioimaging, she operates at the forefront of synchrotron-based imaging techniques. Her academic foundation includes a PhD from INSA de Lyon focused on material properties for explosive detection, followed by postdoctoral work at the European Synchrotron Radiation Facility (ESRF) in X-ray diffraction and phase contrast tomography, and an NSF Research Fellowship for paleontology research at Harvard University and ESRF. Specializing in X-ray imaging (micro/nano-tomography, phase-retrieval) and powder diffraction, Dr. Bonnin leads the bioimaging program at TOMCAT with particular emphasis on the international Heart Imaging Project. Her research develops novel methodologies for materials characterization across diverse domains including cardiac microstructure analysis, paleontology, and neurodegenerative disease modeling, with significant contributions to understanding material behavior at microscopic scales. Her recent publications (2019-2021) demonstrate strong interdisciplinary impact, advancing X-ray imaging applications in energy storage (battery materials), biomedical research (cardiac/auditory systems), and materials engineering (aerogels). A defining trend is the integration of machine learning for image analysis, alongside methodological innovations like non-rigid image stitching and Fourier ptychography. These works reflect extensive international collaboration and address critical challenges in healthcare, energy, and fundamental material science. Dr. Bonnin leads the Heart Imaging Project to quantify cardiac microstructure using contrast-agent-free X-ray phase-contrast imaging, while actively contributing to the SLS2.0 upgrade project preparing TOMCAT for multiscale, multimodal, and dynamic tomographic capabilities. Her collaborative framework spans global researchers in materials science, paleontology, and biomedical engineering. As manager of the TOMCAT nanoscope—a full-field imaging setup achieving 150 nm 3D resolution—she enables cutting-edge research in absorption and phase-contrast imaging. Her team within the X-Ray Tomography Group drives the bioimaging program forward, particularly through the Heart Imaging Project's dynamic cardiac studies using modified Langendorff setups.
Steven Ballet is a researcher at the Department of Chemistry, Vrije Universiteit Brussel (VUB), with a focus on peptide chemistry and medicinal applications. His work bridges chemical synthesis with biomedical translation, particularly in pain management and drug delivery systems. Research areas include: Peptide and peptidomimetic design GPCR-targeted ligands for opioid reduction Injectable hydrogels for controlled molecule release Nanobody-based molecular chaperones Recent publications highlight collaborations with bioreactor engineering teams and structural biology groups, advancing therapies for neurodegenerative and cardiac conditions. Projects like [ORACLE] and NAIROBI demonstrate translational impact. Scientific awards include: 2017 - 1st place ie-net-price for Bioengineers 2018 - 35EPS Bursary 2014 & 2017 - Chemistry excellence awards (KVCV) He supervises PhD students in molecular pharmacology and leads fundamental/applied projects involving bioreactors, NMR technology, and supramolecular assemblies.
Associate Professor Emily Wong is a computational genomics researcher at the University of New South Wales (UNSW) and the Victor Chang Cardiac Research Institute. She holds a PhD and MSc in Bioinformatics and Computational Genomics from the University of Sydney and a BSc from UNSW. Her work focuses on integrating big data with in vivo experiments to decipher genetic and molecular mechanisms underlying cell diversity and disease regulation. Education: PhD (Bioinformatics & Computational Genomics), University of Sydney MSc (Bioinformatics & Computational Genomics), University of Sydney BSc, UNSW Research Focus: Regulatory evolution across mammalian tissues Enhancer conservation and function in development Epigenomic reprogramming in disease Iron homeostasis in aging and cancer Cis-regulatory syntax in transcriptional precision Her recent publications highlight cross-disciplinary approaches to understanding enhancer emergence, aging-related stem cell dynamics, and epigenetic therapy in breast cancer. She has received prestigious fellowships including an EMBO Postdoctoral Fellowship and an Australian Research Council Discovery Early Career Fellowship.
