Craig H. Meyer is a Professor in Biomedical Engineering and Radiology & Medical Imaging at the University of Virginia. He holds a Ph.D. from Stanford University and leads the Rapid MRI Research Group, focusing on developing advanced MRI techniques for cardiovascular disease, neural disorders, and pediatrics. His work integrates physics, signal processing, and machine learning to improve MRI acquisition and processing speed. Education: Ph.D. in Biomedical Engineering, Stanford University. Research Interests: Medical and Molecular Imaging, Signal and Image Processing, Biomedical Data Sciences, Biomechanics, and Cardiovascular Engineering. His innovations include fast spiral imaging, conjugate phase reconstruction, and machine learning-enhanced MRI denoising. Awards: Notably includes the Dean’s Award for Excellence in Team Science (2014), Fellowships from NAI (2021), AIMBE (2015), and ISMRM (2013). He also authored two landmark MRI papers recognized as pivotal in the field. Teaching: Courses include BME 6310 (Computation and Modeling in Biomedical Engineering) and BME 8782 (Magnetic Resonance Imaging). He emphasizes translational research, with applications in clinical MRI advancements and collaborative interdisciplinary projects. Labs/Groups: Rapid MRI Research Group focuses on cutting-edge MRI technologies, including real-time cardiac imaging and artifact reduction through deep learning.
Dr. Hongye Zhang serves as a Lecturer in Superconducting and Cryogenic Electric Machines at the School of Engineering, University of Edinburgh, while maintaining a Visiting Research Fellow position at the University of Manchester. He actively contributes to the European Society for Applied Superconductivity (ESAS) as a Board Member and chairs the international HTS 2026 workshop. His educational foundation includes: BSc and MSc in Electrical Engineering from Xi’an Jiaotong University (2015, 2018) Diplôme d’ingénieur (MEng) from École Centrale de Lyon (2018) PhD in Applied Superconductivity from the University of Edinburgh (2021) Dr. Zhang’s research centers on decarbonizing transport through superconducting/cryogenic electric machines for hydrogen-powered aircraft, integrating artificial intelligence with superconductor technology and cryogenic techniques. His work targets net zero emissions by developing high-power-density propulsion systems that leverage hydrogen energy and advanced numerical modeling of superconductors. Analysis of his 2022-2025 publications reveals dominant themes in superconducting machine design for wind energy and electric aviation, with significant contributions to loss mitigation, flux pump technology, and trapped field magnet applications. His research bridges fundamental superconductor characterization with practical system integration for renewable energy. Recognized with the 2021 IEEE Council on Superconductivity Graduate Study Fellowship, his professional engagements include: Early Career Editorial Board Member for Elsevier’s Superconductivity journal Technical Editor for IEEE Transactions on Applied Superconductivity Program Committee Member for SMT 2023 He leads critical research within the £54-million H2GEAR project developing hydrogen-electric aircraft propulsion, while teaching Power Engineering 2 and Electrical Machines courses. His advisory roles span doctoral supervision and industry collaboration through Energy Systems research institute. Based at the University of Edinburgh’s Faraday Building, Dr. Zhang directs a research group focused on hydrogen energy applications and superconducting machine testing, with strong ties to the H2GEAR consortium and ESAS working groups.
Adam Khalifa is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Florida. His research focuses on low-power analog/RF/Mixed-mode ASIC design, miniaturization of biomedical devices, wireless powering solutions, and neural stimulation/recording techniques in animal models. He holds a PhD from Johns Hopkins University and degrees from The Hong Kong University of Science and Technology. His work emphasizes implant packaging, electrode microfabrication, and coil design for medical applications. Key research areas include developing energy-efficient wireless systems for implanted devices, such as magnetoelectric antennas and galvanic body-coupled powering. He has pioneered advancements in miniaturized implantable devices, including the 'Microbead' stimulator. His NIH T32 Fellowship (2019) and Ferdinand H. Fellowship (2018) reflect his impactful contributions. Publications highlight innovations in wireless power transfer, metamaterials for biomedical implants, and injectable microdevice fabrication. His work spans from circuit-level modeling to in vivo validation, emphasizing both technical and biological integration challenges. Collaborative efforts address challenges like implant migration tracking via MRI and energy harvesting for battery-free systems.
Dr. Andrea Lecchini Visintini is an Associate Professor at the School of Electronics and Computer Science , University of Southampton. He specializes in systems modelling and control with applications in aerospace engineering and biomedical domains, utilizing Monte Carlo methods for stochastic optimization. Cyber-Physical Systems Research Group Institute for Life Sciences Research Focus: His work bridges computational methods with practical applications in: Neurovascular coupling and brain tissue pulsation analysis Advanced control strategies for aerospace systems Stochastic optimization in machine learning and fault detection Medical imaging and diagnostic protocol development Publication Trends: Recent work emphasizes interdisciplinary approaches combining computational neuroscience with engineering, focusing on brain hemodynamics, MIMO system control, and data augmentation techniques for imbalanced datasets. Supervision: Currently supervising PhD student Xuankun Cai in Computer Science.
Rui Li is an Associate Professor in the Ph.D. program at Rochester Institute of Technology's Golisano College of Computing and Information Sciences. She directs the Lab for Use-inspired Computational Intelligence (LUCI), focusing on AI applications in computational biology and medical imaging. Education includes: B.Sc. in Computer Science, Harbin Institute of Technology M.Sc. in Computer Science, Tianjin University of Technology Ph.D. in Computing and Information Sciences, RIT Research integrates statistical machine learning with computational biology, medical image analysis, and human visual attention modeling. Current projects include deep learning for histopathology, multimodal medical image registration, and gene network inference. Publications demonstrate consistent focus on medical AI applications, with recent advances in unsupervised image registration, interactive segmentation, and multimodal fusion techniques. Key trends include self-supervised learning, uncertainty-aware models, and human-AI collaboration frameworks. Awards include the NSF CAREER Award for developing adaptive machine intelligence systems. Advises multiple PhD students on projects spanning deep learning architectures, biomedical image analysis, and biological network modeling. Leads several NSF-funded projects including human-centered image understanding systems and gene-protein network inference tools. Directs LUCI lab investigating machine learning for healthcare applications and teaches graduate courses in Statistical Machine Learning and Deep Learning.
Dr. José del R. Millán is a Professor and holds the Linda Steen Norris & Lee Norris Endowed Chair in Neuroengineering at The University of Texas at Austin's Chandra Family Department of Electrical and Computer Engineering. He also serves as a Professor in Dell Medical School's Department of Neurology, a courtesy Professor in Biomedical Engineering, and is affiliated with the Mulva Clinic for the Neurosciences, Institute for Neuroscience, Texas Robotics, and the UT CARE Initiative. His work focuses on brain-machine interfaces (BMI), neuroprosthetics, and translating BMI technologies for individuals with motor/cognitive disabilities and able-bodied users. Education: PhD in Computer Science (1992, Technical University of Catalonia). Previous roles include Defitech Foundation Chair in Brain-Machine Interface at EPFL (Switzerland) and visiting scholar positions at Berkeley, Stanford, and the International Computer Science Institute. Research Interests: Neuroengineering, BMI applications in healthcare and assistive robotics, statistical machine learning for neural signals, and neurorehabilitation. Key contributions include EEG-based BMI systems, closed-loop neurostimulation, and wearable neurotechnology. Awards: IEEE Fellow (2017), Norbert Wiener Award (2011), and Fellow of the International Academy of Medical and Biological Engineering (2020). Grants & Labs: Co-director of UT CARE, leader in clinical neuroprosthetics and neurorobotics. Active in developing BMI-driven wheelchairs, VR integration for BCI, and EEG-based speech prosthetics. Research outputs emphasize translational neurotechnology, with projects funded by industry and governmental agencies. Labs/Teams: Clinical Neuroprosthetics & Brain Interaction Lab, Texas Robotics, Wireless Networking and Communications Group (WNCG).
Pekka Marttinen is a tenured Associate Professor of Machine Learning at Aalto University, Department of Computer Science, and leads the Machine Learning for Health (Aalto-ML4H) group within the Helsinki Institute for Information Technology HIIT. Education: M.Sc. in Applied Mathematics, University of Helsinki (2004) Ph.D. in Statistics, University of Helsinki (2008) Title of Docent in Information and Computer Science, Aalto University (2015) Research Focus: His methodological work spans large language models, reinforcement learning, deep learning, probabilistic machine learning, and causal inference . These techniques are applied to critical domains of healthcare, bioinformatics, statistical genetics, epidemiology, and personalized medicine . The group develops novel algorithms, theoretical guarantees, and open-source software that enable data-driven discovery and decision-making in medicine and biology. Publication Trends: Across 2022-2024 the lab has concentrated on (i) rigorous causal reasoning over temporal clinical data, (ii) principled uncertainty quantification in LLMs, (iii) representation learning for neural network comparison, and (iv) translational projects that turn raw EHRs into actionable clinical insights. Earlier work integrated high-dimensional genomics with metabolomics and mapped evolutionary forces in bacterial pathogens. Scientific Awards & Recognition: While no specific awards are listed, his sustained publication record in top-tier venues (NeurIPS, ICML, AISTATS, Nature Genetics, PLOS CB) and his role as responsible professor of the Machine-Learning, Data-Science and AI major signify significant peer recognition. Advising & Grants: Prof. Marttinen currently mentors 8 PhD students as primary supervisor and an additional 7 PhD students as co-supervisor. He has already graduated 8 PhDs since 2014. The group is supported through competitive funding including the Finnish Center for Artificial Intelligence (FCAI) doctoral program. Labs & Teams: He directs the Machine Learning for Health (Aalto-ML4H) research group, comprising postdocs Hans Moen, Ti John, Alexander Nikitin, Negar Safinianaini, Linli Zhang, Zhiyuan Li, and the above-mentioned PhD cohort.
Daniel Razansky is a Full Professor at the Department of Information Technology and Electrical Engineering, ETH Zurich, leading the Professorship for Biomedical Imaging. His research spans engineering, physics, biology, and medicine, focusing on developing advanced in vivo imaging tools like optoacoustic tomography and ultrasound neuromodulation. His recent work emphasizes multi-scale functional and molecular imaging , with applications in neuroscience , Alzheimer’s disease , and stroke diagnostics . Collaborations include National Tsing Hua University and the EU Horizon consortium SWEEPICS. Current projects target hybrid imaging systems (e.g., MRI-MSOT) and image-guided neuromodulation. Scientific awards include the IPPA James Smith Prize for his contributions. His lab has secured significant grants, including a $2.5M NIH award and SNSF funding. He mentors PhD students like Quanyu Zhou and Eva Remlova, who have received accolades for their research. The Razansky Lab at ETH Zurich’s Preclinical Imaging Center explores medical microrobotics , dynamic fluid flow imaging , and neuroimaging techniques , aiming to bridge engineering with clinical applications.
Una-May O'Reilly is a Principal Research Scientist at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), leading the ALFA group. She holds a PhD in Computer Science from Carleton University (1995), with prior roles including a postdoctoral appointment at MIT's Artificial Intelligence Laboratory. Her research focuses on cybersecurity, adversarial AI, software security, and disinformation dynamics, applying evolutionary algorithms and machine learning to address arms races in cyber defense and societal challenges like climate change communication. Education: B.Sc., University of Calgary M.C.S., Carleton University Ph.D., Carleton University (1995) Research Interests: Adversarial machine learning for secure systems Coevolutionary algorithms in cybersecurity and healthcare Program comprehension via neuroscience and AI Climate disinformation mitigation on social media Large language model applications in code synthesis and threat hunting Key Projects: Adversarial Cyber Security : Modeling cyber attack-defense arms races GIGABEATS : AI-driven medical sensor data analysis for critical care MOOC Learner Project : Data science for online education insights Awards: EvoStar Award (2013) for contributions to evolutionary computation Fellow of ACM Sig-EVO Leadership & Service: Co-founder and Vice-Chair of ACM Sig-EVO Former Chair of GECCO (2005), major evolutionary computation conference Editorial roles in Evolutionary Computation and Genetic Programming and Evolvable Machines Labs & Groups: Leads the AnyScale Learning for All (ALFA) group at CSAIL, focusing on scalable AI for cybersecurity, healthcare, and education.
Emily D. Gottfried, PhD, is an Associate Professor at the Medical University of South Carolina (MUSC) in the Department of Psychiatry and Behavioral Sciences, College of Medicine. She serves as Director of the MUSC Sexual Behaviors Clinic and Lab (SBCL) and CPSPD Student Education & Research, conducting forensic evaluations, physiological sexual arousal assessments, and mentoring students. She is licensed in South Carolina and Georgia, and a National Register Health Service Psychologist. Bachelor’s in Psychology, San Diego State University Master’s in Psychology, Teachers College, Columbia University PhD in Clinical Psychology, Florida State University Her research focuses on sex offender risk assessment , malingering detection (PPG/VPP technology), female offender psychology , and forensic evaluation validity . Recent work includes dimensional personality models and QAnon-related threat analysis . She has published extensively on psychopathy , penile/vaginal plethysmography , and mental health court outcomes . Notable publication trends include Forensic validity of symptom detection tools Psychopathy in female offenders Technology's role in sexual behavior assessment Telehealth adaptation during pandemics MMPI-2-RF applications in correctional settings Psychological factors in civil commitment She leads ongoing funded studies on female sexual arousal and police officer behavioral outcomes , operating at the intersection of forensic science and clinical psychology .
Dr. Merry Mani is an Associate Professor in Radiology and Imaging Sciences and Biomedical Engineering, specializing in biomedical imaging and signal processing. Her work focuses on advancing MRI-based imaging technologies to study neurological disorders such as Alzheimer's, Autism, and Epilepsy. She holds a Ph.D. in Electrical and Computer Engineering from the University of Rochester (2014) and completed a postdoctoral fellowship at the University of Iowa School of Medicine (2018). Her research combines biophysical modeling with machine learning to explore brain microstructures. Key achievements include the NNARSAD Young Investigator Grant and NIH-funded projects like 'Fast Multi-dimensional Diffusion MRI with Sparse Sampling'. Her lab develops cutting-edge reconstruction methods like qModeL and MUSSELS, prioritizing high spatio-temporal resolution imaging. Major contributions span diffusion MRI acquisition, model-based deep learning, and clinical applications in neurodegenerative diseases. Notable grants include NIH R01EB031169 for Alzheimer’s neurodegeneration studies and projects on rTMS for depression. Her work bridges imaging innovation with clinical impact, aiming to improve diagnosis and treatment through advanced imaging biomarkers.
Dr. Craig S. Levin is a Professor of Radiology at Stanford University's Molecular Imaging Program at Stanford (Nuclear Medicine), with courtesy appointments in Physics, Electrical Engineering, and Bioengineering. He also holds memberships in Bio-X, the Cardiovascular Institute, the Wu Tsai Human Performance Alliance, and the Stanford Cancer Institute. Dr. Levin received his B.S. Summa Cum Laude in Physics and Mathematics from UCLA in 1985, followed by M.S., M.Phil., and Ph.D. degrees in Physics from Yale University in 1987 and 1993. His educational achievements were recognized with multiple honors including Phi Beta Kappa, Sigma Pi Sigma, and various departmental awards at UCLA. Dr. Levin's research focuses on the development of novel instrumentation and software algorithms for molecular imaging. His work spans medical physics, biomedical engineering, and instrumentation development with specific emphasis on positron emission tomography (PET), gamma camera technology, and multimodal imaging systems. His laboratory explores new concepts in radiation detection, image reconstruction algorithms, and the application of these technologies to cancer, heart disease, and neurological disorders. A notable aspect of his research involves pushing the physical limits of sensitivity and spatial, spectral, and/or temporal resolutions in imaging systems. His recent publications demonstrate a strong focus on enhancing PET technology, particularly time-of-flight capabilities, with significant work on improving coincidence timing resolution, developing MR-compatible PET systems, and applying deep learning techniques to image reconstruction and normalization. His research shows a clear trajectory toward higher resolution imaging with improved quantitative accuracy for both clinical and preclinical applications. Dr. Levin's scientific achievements have been recognized with numerous awards: American Institute for Medical and Biological Engineering's College of Fellows Academy of Radiology Research Distinguished Investigator Recognition Award National Research Service Award from NIH (1993-5) Pilot Research Award from the Society of Nuclear Medicine (1996) Multiple honors from UCLA including Phi Beta Kappa and Sigma Pi Sigma Full Tuition and Research Fellowship and Bates Graduate Fellowship from Yale University As an educator and mentor, Dr. Levin directs the NIH-NCI funded T32 Stanford Molecular Imaging Scholars postdoctoral training program and serves as a Doctoral Dissertation Advisor for students in Bioengineering and Biophysics. He currently advises five postdoctoral scholars and three doctoral candidates. His laboratory, the Molecular Imaging Instrumentation Laboratory, comprises approximately 20 members who work on developing new imaging technologies and translating them into clinical applications. Dr. Levin has secured substantial NIH funding as Principal Investigator along with grants from other government agencies, industry partners, and private institutions to support his research program. Dr. Levin's Molecular Imaging Instrumentation Laboratory is at the forefront of developing new imaging technologies that bridge physics, engineering, and medicine. The lab focuses on creating instrumentation for in vivo imaging of cellular and molecular signatures of disease, with particular emphasis on pushing the physical limits of imaging performance. Their work spans computer modeling, sensor development, electronics design, data acquisition systems, and advanced image processing algorithms. The lab maintains strong industry partnerships to translate their innovations into products used for patient care worldwide.
Prof. Dr. Mike Martin is a leading researcher at the University of Zurich's Center for Gerontology . His work spans cognitive aging, social development in old age, and life-span developmental psychology, with a focus on ecological validity in aging research. Professor, University of Zurich Director, Zurich Longitudinal Study on Cognitive Ageing Co-editor, Journals of Gerontology series Key research areas include: Healthy aging and quality of life Cognitive-emotional interactions in aging Dyadic adaptation in dementia caregiving Mobile sensing of aging-related behaviors Participatory research methodologies Language use as a biomarker of aging His recent publications analyze: GPS mobility and cognitive function Machine learning in reminiscence detection Prospective memory trajectories Emotion regulation in couples Digital interventions for cognitive health Neuroimaging correlates of aging
Reinhard Heckel is a Tenured Associate Professor (equivalent to Professor) of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM), and Adjunct Faculty in Electrical and Computer Engineering at Rice University. He was previously an Assistant Professor at Rice (2017–2019), a postdoc in the Berkeley Artificial Intelligence Research (BAIR) Lab at UC Berkeley, and a researcher at IBM Research Zurich. Education: PhD, 2014 – ETH Zurich Visiting PhD student – Department of Statistics, Stanford University Research Interests: His work centers on machine learning and information processing with three major thrusts: (1) developing algorithms and theoretical foundations for deep learning, especially for accelerated magnetic resonance imaging ; (2) establishing rigorous mathematical and empirical underpinnings for modern machine-learning systems; and (3) leveraging DNA as a digital information-storage medium , including error-correction coding and system design for DNA-based storage. Across more than 100 peer-reviewed papers since 2017, Heckel’s research exhibits a strong interdisciplinary blend of computational imaging , machine-learning theory , and molecular data storage . Recent 2024–2025 publications show intensive focus on robust MRI reconstruction using diffusion priors, evaluation of bias in large web-text corpora, and state-of-the-art error-correcting codes for DNA storage channels. A forthcoming book, Deep Learning for Computational Imaging (Oxford University Press), consolidates his contributions to the field. Outreach & Media: Keynote and panel talks at DLD, TUM, and major ML conferences Op-eds in Frankfurter Allgemeine on ChatGPT and DNA storage Science features on Netflix, BBC, and German television (Galileo, “Gut zu Wissen”) Research Environment: At TUM he leads a group investigating theoretical and applied aspects of deep learning, compressed sensing, and coding for DNA storage. Open-source repositories on GitHub (e.g., dna_data_storage , supplement_deep_decoder ) provide code and data supplements accompanying his publications.
John R Anderson is the Richard King Mellon University Professor of Psychology and Computer Science at Carnegie Mellon University (CMU), affiliated with the Department of Psychology within the Dietrich College of Humanities and Social Sciences. His research focuses on understanding higher-level cognition, particularly mathematical problem-solving, through the development of the ACT-R cognitive architecture—a computational framework simulating human cognitive processes. This architecture integrates behavioral, neural, and educational data to model learning and decision-making. Anderson’s work bridges cognitive science, neuroscience, and educational technology. He investigates how brain imaging (e.g., fMRI, EEG) can reveal the temporal dynamics of cognitive processes and improve instructional methods. His research emphasizes analyzing brain activity time courses to uncover underlying mechanisms of problem-solving and skill acquisition. Key Research Themes: Cognitive architectures, neural correlates of learning, computational models of memory, and intelligent tutoring systems. Notable Contributions: Development of the ACT-R architecture, integration of neuroimaging with cognitive modeling, and studies on skill transfer and learning strategies. Anderson’s publications include seminal books like Cognitive Psychology and Its Implications and How Can the Human Mind Occur in the Physical Universe? His work has advanced understanding of associative memory, strategic decision-making, and the application of cognitive models in educational technology. His lab, the ACT-R Research Group, collaborates across disciplines to model complex cognitive tasks and their neural foundations. Current projects analyze real-time brain activity to refine educational interventions and improve human-machine interaction.
