Erwin Schoof is an Associate Professor at the Department of Biotechnology and Biomedicine , Technical University of Denmark. He leads the Cell Diversity Lab and focuses on advancing proteomics and mass spectrometry technologies. Expertise in single-cell proteomics , stem cell niches , and bioinformatics . Active in myelofibrosis and leukemia research , with applications in UN Sustainable Development Goals . Research Trends from 2025–2024 include: Machine learning-driven peptide sequencing (InstaNovo, InstaNexus). Single-cell resolution tools for mapping hematopoietic stem cells and tumor microenvironments . Biomarker discovery in chronic diseases and respiratory conditions . Supervision : Mentors multiple PhD students on single-cell proteomics , omics data analysis , and biotherapeutic production . Labs & Collaborations : Collaborates with international teams on plasma proteomics , 3D bioengineering , and advanced mass spectrometry workflows .
Dr. Bin Zhu is a Research Fellow in the School of Mechanical Engineering Sciences at the University of Surrey, affiliated with the Centre for Engineering Materials. He obtained his PhD from the same institution, focusing on multiscale residual stress evaluation and mechanical property characterization using microscopy and large-scale facilities. His research develops techniques for harsh environments to enhance material longevity by managing manufacturing-induced residual stress, with applications in nuclear fusion components. Education PhD, University of Surrey (Research focus: Multiscale residual stress evaluation and mechanical property characterization) Research Focus Dr. Zhu's research centers on three interconnected areas: 1) Multiscale residual stress evaluation using advanced techniques like plasma-focused ion beam and neutron diffraction; 2) In situ mechanical testing under extreme conditions; and 3) Computational modeling for predicting stress distributions and material behavior. His work primarily addresses nuclear fusion reactor challenges, particularly laser-welded Eurofer97 steel components, where residual stress critically impacts structural integrity. Publication Trends Dr. Zhu's recent publications (2021-2025) demonstrate three key themes: 1) Advanced residual stress analysis in nuclear materials using machine learning, neutron imaging, and synchrotron techniques; 2) High-temperature mechanical performance of welded joints for fusion reactors; and 3) Biomimetic material characterization, including bioinspired composites and biological light-diffraction mechanisms. His methodologies consistently integrate multiscale experimental approaches with computational modeling.
Xiaoning Qian is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, where he also serves on the Faculty Advisory Committee for the Texas A&M Institute of Data Science (TAMIDS) and the Executive Committee for the Texas A&M TRIPODS Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He holds a joint appointment in the Applied Math group within the Computational Science Initiative at Brookhaven National Laboratory (BNL). Previously, he was an Associate Professor (2018-2022) and Assistant Professor (2013-2018) at Texas A&M, and an Assistant Professor in the Department of Computer Science and Engineering at the University of South Florida (2009-2013). Dr. Qian received his B.S.E. and M.S.E. degrees from Shanghai Jiaotong University, China, and his M.Ph. and Ph.D. degrees in Electrical Engineering from Yale University. Dr. Qian's research focuses on developing mathematical models and computational algorithms in signal processing, machine learning, and Bayesian methods, particularly in learning, uncertainty quantification, and experimental design. His work spans multiple disciplines, with applications in life sciences and materials science. His research group, the Biomedical Imaging, Sensing, and Genomic Signal Processing Group, actively applies probabilistic models and optimization algorithms to solve complex problems in interdisciplinary domains. His research has evolved from foundational work in bioinformatics and biomedical image processing to more recent applications in materials science and broader AI for science initiatives. Dr. Qian has received numerous scientific awards and recognitions including: National Science Foundation (NSF) CAREER Award Segers Family Dean's Excellence Professorship II in the College of Engineering TEES (Texas A&M Engineering Experiment Station) Senior Faculty Fellow Montague-Center for Teaching Excellence Scholar J. T. Oden Faculty Fellow at the University of Texas, Austin Finalist of the 2023 INFORMS QSR Best Paper Faculty Impact Fellow from the Department of Electrical & Computer Engineering As an advisor , Dr. Qian has mentored numerous graduate students through their PhD and MS programs, with many of his alumni securing positions at prestigious institutions and companies including NIH/NCBI, Microsoft, Baidu Research Lab, and Qualcomm. His research has been supported by multiple grants, including an NSF CAREER award and collaborative research funding from the Information Integration and Informatics program. He is actively recruiting postdoc and graduate student research assistants for projects in machine learning and optimization methods with applications in bioinformatics and materials science. Dr. Qian is involved with several research initiatives including the Objective-Based Uncertainty Quantification (ObjectiveUQ) project, which provides a mathematical framework for integrating prior knowledge and data while enabling effective operational and experimental design under uncertainty. He also co-organizes the Bio-Seminar series for the Biomedical Imaging, Sensing & Genomic Signal Processing group at Texas A&M.
Asst. Prof. Zehra Evrim Kanat serves as an Assistant Professor in the Textile and Fashion Design Department at Canakkale Onsekiz Mart University's Faculty of Fine Arts since 2020, following research assistant roles at Tekirdağ Namik Kemal University (2014-2020) and Ege University (2009-2014). Her expertise bridges academic research and industry applications in textile engineering. Her educational foundation includes: Doctorate in Textile Engineering from Ege University (2008-2013) Postgraduate in Textile Engineering from Ege University (2005-2007) Undergraduate in Textile Engineering from Ege University (1997-2002) Dr. Kanat's research centers on textile engineering and design, with specialized focus on thermal and moisture management properties of fabrics. She investigates yarn technology, cloth construction, and smart textile applications to enhance comfort in sportswear and technical garments. Her methodological approach integrates artificial neural networks and statistical modeling for predictive analysis of fabric behavior under varying humidity conditions, contributing significantly to thermophysiological comfort research. Publication trends reveal consistent advancement in thermal resistance modeling of knitted and woven fabrics, with recent expansion into smart textiles for sports applications and biomimetic design principles. Her work demonstrates strong interdisciplinary connections between textile science, materials engineering, and functional apparel design. Her research leadership includes: EU-supported project on Textile Insights and Future Aspects (2023-2024) Study on Hatay Silk's fiber properties versus Chinese-Japanese hybrid silk (2022-2023) TUBITAK-funded expert system for woven fabric defect diagnosis (2017-2018) Multiple projects on moisture management properties of technical textiles Dr. Kanat actively contributes to academic discourse through peer review for leading textile journals and participates in interdisciplinary art exhibitions that explore cultural and historical textile narratives.
Dr. Tim Conrad is a researcher at the Zuse Institute Berlin in the Visual and data-centric computing department under the Mathematics of Complex Systems division. He leads projects at the intersection of computational biology, AI, and medical data analysis. Projects: Geometric Learning for Single-Cell RNA Velocity Modeling, MODAL MedLab, Sparse Compressed Sensing in -Omics Data, BIFOLD (Big Data and Machine Learning) Research Networks: Affiliated with MATH+ and MODAL Research Campus His research focuses on applying machine learning , network optimization , and sparse data analysis to biological and medical challenges including disease modeling, microbiome dynamics, and ECG classification. Recent work explores hybrid PDE-ODE epidemic models and federated learning in healthcare. 2023-2025 publications highlight trends in AI for biological networks , temporal community detection , and medical signal processing . He co-authored studies on SARS-CoV-2 simulations, proteomics feature selection, and multi-label ECG analysis. His 2004 doctoral thesis at Monash University laid foundations for later work in metabolic pathway analysis. Awarded as a Zuse Fellow , he contributes to open science initiatives like FAIR data sharing . Collaborations span institutions including Freie Universität Berlin and Charité in medical informatics and clinical applications.
Prof. Julia Herzen holds the Associate Professorship of Physics in Biomedical Imaging at the Department of Physics , TUM School of Natural Sciences , Technical University of Munich . Her research focuses on advancing X-ray imaging techniques using synchrotron radiation and laboratory sources, with applications in medical diagnostics and tissue analysis. Position: Associate Professor Department: Physics School: TUM School of Natural Sciences University: Technical University of Munich Contact: julia.herzen@tum.de Her core research interests include: Quantitative multi-modal X-ray imaging (spectral & phase-contrast) 3D virtual histology of human tissue Breast cancer detection improvement Lung disease imaging (emphysema, pneumonia) X-ray phase-contrast tomography Dark-field imaging material decomposition Recent publications demonstrate expertise in dark-field imaging for lung pathology , phase-contrast CT for organoid visualization , and spectral X-ray applications in multi-material differentiation . Her team explores clinical translation of X-ray techniques for non-invasive diagnostics . She supervises PhD students and teaches Biomedical Engineering courses, including: Quantitative X-Ray Imaging (3 VI) Image Processing in Physics (2 VO) Biostatistics (2 VO) Advanced Lab Courses in X-ray Micro-CT
Michael E. McHenry is a Professor of Materials Science and Engineering at Carnegie Mellon University's College of Engineering. He holds appointments with multiple research centers including the Data Storage Systems Center, Engineering Research Accelerator, Materials Research Science and Engineering Center, and Wilton E. Scott Institute for Energy Innovation. Dr. McHenry received his BS in Metallurgical Engineering and Materials Science from Case Western Reserve University in 1980, his PhD in Materials Science and Engineering from MIT in 1988, and completed a postdoctoral fellowship at Los Alamos National Laboratory. His research focuses on soft magnetic nano-composites for power and energy applications, with particular expertise in metal amorphous nanocomposites (MANCs) for high-efficiency electric motors and power systems. His work spans advanced materials processing, magnetic properties under various conditions, and rare earth materials criticality. His research portfolio demonstrates a clear progression toward practical applications of magnetic materials, particularly in high-power density, high-efficiency motors that can operate at high rotational speeds with minimal energy loss. His publications reveal a strong focus on translating fundamental materials science into engineering solutions for energy conversion, with significant emphasis on rare earth-free alternatives and high-frequency applications. IEEE Distinguished Lecturer (2013) TMS Awardee for Research Excellence (2014) Subject of TMS Symposium in Honor of M. E. McHenry (2016) NATO Series Lecturer on Rare Earth Criticality (2016/17) Dr. McHenry has co-founded CorePower Magnetics Inc. with Paul Ohodnicki and Samuel Kernion, commercializing soft magnetic technologies with applications in grid modernization and electric vehicles. His extensive publication record and leadership in major research initiatives including a MURI on high-temperature magnetic materials and an ARPA-E program demonstrate significant impact in both academic and industrial contexts. He has served in various leadership roles for Magnetism and Magnetic Materials and Intermag Conferences, and continues to advise on rare earth scarcity issues for organizations like NATO.
Yuzhe Yang is an Assistant Professor of Computational Medicine and Computer Science at UCLA, with a visiting research scientist role at Google Health. He holds a PhD in Computer Science from MIT (2024), advised by Dina Katabi, and a B.S. with honors from Peking University. Research Focus: Machine learning for healthcare, medical AI fairness, and AI-driven biomedical discovery Key Contributions: Ten Notable Advances (Nature Medicine) and Ten Crucial Advances (The Lancet Neurology) His lab develops Trustworthy Learning Algorithms and Generalist Health Models that integrate Multimodal Data for personalized health coaching. Notable projects include AI-based Parkinson's Disease Biomarkers via nocturnal breathing and Foundation Models for Equitable Medicine . Recent publications at ICLR 2025 (wearable foundation models), Nature Medicine 2024 (medical AI fairness), and Science Advances 2025 (vision-language medical bias) highlight his interdisciplinary work. He serves on ML4H workshops and reviews for top conferences like NeurIPS and ICML. Awards include Forbes 30 Under 30 , Takeda Fellowship , and Baidu PhD Fellowship . Advising opportunities: Recruiting PhD students (CS/CompMed) and postdocs in AI for health. Lab: Health Intelligence Lab (HAIL)
Sebastian Seung is a Professor at Princeton University , affiliated with both the Department of Computer Science and the Princeton Neuroscience Institute . His career spans Harvard University (Ph.D., 1990), Bell Laboratories, and Massachusetts Institute of Technology before joining Princeton in 2014. An External Member of the Max Planck Society and 2008 Ho-Am Prize recipient, Seung merges machine learning with neuroscience . Research Focus : Pioneering connectomics , Seung developed technologies for reconstructing neural circuits from high-resolution brain images, including FlyWire for collaborative brain mapping. His work explores brain function, development, and plasticity , drawing parallels between fly visual systems and convolutional networks . Awards & Affiliations : 2008 Ho-Am Prize in Engineering External Member, Max Planck Society Technical Contributions : Led breakthroughs in 3D connected component labeling and high-throughput EM imaging for mammalian brains, partnering with NIH’s BRAIN Initiative to scale connectomics to whole mouse brains. Seung’s team has shifted from EM analysis to interpreting connectomes , focusing on neural circuit function and biological mechanisms in flies and mice. His lab alumni network spans institutions, advancing AI and neuroscience globally.
James C. Gee is a Professor of Radiologic Science in Radiology at the University of Pennsylvania's Perelman School of Medicine. He serves as Director of the Penn Image Computing and Science Laboratory and Co-Director of the Translational Biomedical Imaging Center , with affiliations in Bioengineering and Applied Mathematics graduate groups. His research focuses on biomedical image analysis, specialization in segmentation, registration, and morphometry applied to neurodegenerative diseases and multi-organ systems. Education : B.S. in Computer Science/Electrical Engineering (University of Washington, 1987), Ph.D. in Computer and Information Science (University of Pennsylvania, 1996) Research : Quantitative medical imaging methods, brain connectomics, neurodegeneration mapping, and translational imaging technologies Publications : 15+ recent works on AI-driven image analysis for Alzheimer's disease, cardiac amyloidosis, and radiomics applications Leadership : Directs MSE-DS Online Degree Program, co-chairs Radiology DCOAP Committee, and founded RISE (Radiology Initiative to Support Inclusive Excellence) His laboratory develops advanced computational tools like ITK-SNAP for biomedical imaging, with applications in both in vivo clinical imaging and ex vivo histology . The work spans cross-disciplinary collaborations in computer science, neuroscience, and clinical medicine.
Qi Long is a Professor at the University of Pennsylvania, holding joint appointments in the Department of Biostatistics, Epidemiology and Informatics (Perelman School of Medicine), Department of Computer and Information Science (School of Engineering and Applied Science), and Department of Statistics and Data Science (The Wharton School). He serves as Founding Director of the Center for Cancer Data Science, Associate Director of the Penn Institute for Biomedical Informatics, and Associate Director for Quantitative Data Science at the Abramson Cancer Center. His research bridges statistical and machine learning (ML/AI) method development with biomedical applications, focusing on precision medicine and population health. Education : Ph.D. (2005) and M.S. (2003) in Biostatistics from University of Michigan; B.S. (1998) in Computer Science from University of Science and Technology of China. Research interests include: Robust statistical and ML/AI methods for big health data (-omics, EHRs, imaging, mHealth) Multimodal data integration and subgroup heterogeneity analysis Missing data, causal inference, Bayesian methods, and clinical trials Data privacy, algorithmic fairness, and responsible AI in healthcare Foundation models and agentic AI for biomedicine His publications focus on privacy-preserving AI, fairness-aware ML, and integrative models for multi-omics and EHRs. Recent work explores LLMs and watermark detection in hybrid human-AI settings. Scientific Awards : Elected fellow: AAAS, ASA, IMS, ISI, AMIA He leads large NIH- and ARPA-H-funded initiatives, directing statistical coordinating centers for national clinical trials. His lab trains numerous PhD/Master’s students and postdocs, many of whom hold prestigious academic or industry positions.
Justin P. Haldar is a Professor in the Ming Hsieh Department of Electrical and Computer Engineering at the University of Southern California (USC), with a joint appointment in the Department of Biomedical Engineering. He co-directs the Biomedical Imaging Group and serves as Director of the Signal and Image Processing Institute. His affiliations include the Dornsife Cognitive Neuroscience Imaging Center, the Brain and Creativity Institute, and the Dynamic Imaging Science Center. Education : B.S. and M.S. in Electrical Engineering (2004, 2005), Ph.D. in Electrical and Computer Engineering (2011) from the University of Illinois at Urbana-Champaign. His research focuses on computational imaging, inverse problems, and magnetic resonance imaging (MRI), with an emphasis on constrained image reconstruction, parameter estimation, and novel data acquisition strategies. His work combines physical modeling, high-dimensional signal structures, and fast computational algorithms to address MRI's limitations in speed, noise, and cost. Recent publications analyze challenges like the 'hidden noise' problem in MR reconstruction (2025) and innovations in dynamic imaging. His research has enabled faster MRI exams and next-generation imaging techniques by exploiting dimensionality's 'blessings' while mitigating its 'curses.' Scientific awards : NSF CAREER Award (2014) IEEE ISBI Best Paper Award (2010) IEEE EMBC First-Place Student Paper Award Haldar's leadership roles include Chair of the IEEE Signal Processing Society's Technical Committee on Computational Imaging and editorial positions at IEEE Transactions on Computational Imaging and Magnetic Resonance in Medicine . He actively mentors students and develops novel MRI approaches at USC's Michelson Center for Convergent Bioscience.
Chris Fuller, Ph.D., is the Samuel Langley Distinguished Professor of Engineering at the College of Engineering , Virginia Tech. He leads the Vibrations and Acoustics Laboratory (VAL) , focusing on active/passive noise control systems, metamaterials, and their application to aerospace, medical devices, and industrial machinery. Education: Ph.D. (1979) and B.E. (1974) from the University of Adelaide, Australia. Research Interests: Structural acoustics, adaptive materials, machine learning in noise prediction, and biomedical acoustics (e.g., neonatal incubators). Awards: ASME Rayleigh Award (2017), NASA Team Achievement Award (1996), and Fellow of the Acoustical Society of America. Recent Publications: Highlight advancements in drone noise reduction using neural networks, metamaterials for HVAC systems, and poro-elastic materials for low-frequency noise control.
Professor Ali Yapar is a faculty member at Istanbul Technical University in the Electronics and Communication Engineering department. His research focuses on Electromagnetics , Microwave Engineering , and Antenna Technologies , with a particular emphasis on inverse scattering problems and microwave imaging for biomedical applications. He has supervised numerous graduate students and led projects related to breast cancer treatment and rough surface imaging. PhD in Electronics and Communication Engineering from Istanbul Technical University (1997) MSc in Electronics and Communication Engineering (1995) His recent publications analyze advanced techniques for microwave hyperthermia systems, reverse time migration methods, and Newton-based solutions for electromagnetic inverse scattering. Key projects include TUBITAK-funded initiatives on microwave tomography and brain stroke imaging. He serves as a project investigator and executive for electromagnetic research programs. Research areas span Electromagnetic Wave Propagation , Green's Function Applications , and Dielectric Material Analysis . Collaborations include IEEE members and international researchers in computational electromagnetics.
Libo Chen is an Assistant Professor at Uppsala University's Department of Electrical Engineering; Solid State Electronics. His work focuses on neuromorphic tactile systems, bioinspired e-skin, and self-powered transducers. Research Interests : Neuromorphic engineering for tactile feedback Stretchable and self-healing electronics Energy harvesting for bioinspired systems Triboelectric transducers and sensors Surface chemistry of mesoporous materials Publication Trends : Over the past five years, Chen has published in interdisciplinary areas spanning Materials Science , Neuroengineering , and Chemical Physics , with a focus on tactile systems, self-healing materials, and hybrid energy applications. Labs & Teams : He is affiliated with Uppsala University's Ångström Laboratory, a hub for advanced materials and electronics research.
