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Jussi TaipaleView profile

Professor

• Gene regulation
• Transcription factors
• Systems biology
• +4 more

Jussi Taipale is a Professor of Medical Systems Biology at Karolinska Institutet and holds professorships at University of Helsinki. His research focuses on transcription factor binding mechanisms , cancer genomics , and gene regulatory networks . The interdisciplinary Taipale Lab operates across three international locations: Wellcome Sanger Institute (UK), Karolinska Institutet (Sweden), and University of Helsinki (Finland), with over 20 members including senior scientists, postdoctoral fellows, and graduate students. Ph.D., University of Helsinki (1996) Postdoctoral training: University of Helsinki, Johns Hopkins University Research spans transcription factor cooperativity , epigenetic regulation , chromatin accessibility , and noncoding mutation analysis . Key methodologies include HT-SELEX , CUT&RUN , ATI assays , and CRISPR-based functional genomics . The lab has significantly advanced understanding of Myc-driven oncogenesis , TF-nucleosome interactions , and dinucleotide specificity mechanisms . Notable discoveries include chromatin context-dependent enhancers , water-mediated DNA recognition , and novel composite transcription factor motifs . The group maintains active collaborations across Europe and has trained numerous alumni now leading academic and industry positions worldwide.

Jatinder SinghView profile

Professor

• Accountability in AI Systems
• Responsible AI Development
• Data Governance and Stewardship
• +7 more

Jatinder Singh is a Professor at the RC Trust and Principal Research Associate (equivalent to Research Professor) at the Department of Computer Science & Technology, University of Cambridge. He is primarily affiliated with the University of Duisburg-Essen, Germany, where he leads the Compliant and Accountable Systems research group within the Law department. His work operates at the critical intersection of computer science, legal frameworks, and societal impact, focusing on practical implementations that align technology with regulatory requirements while addressing user and community concerns. Research interests center on accountability mechanisms for AI systems, responsible development practices, data governance, and privacy/security in emerging technologies. He examines governance, agency, trustworthiness, and transparency gaps in algorithmic systems through interdisciplinary socio-technical lenses. Current work addresses bias in LLMs, stakeholder participation frameworks, and human rights implications in domains like healthcare, maritime enforcement, and consumer IoT, emphasizing contextual awareness and real-world applicability. His 15 most recent publications (2025-2024) reveal dominant trends in AI transparency, fairness proxy development, and legal-compliance engineering. Key focus areas include stakeholder involvement in AI governance, bias mitigation in language models, data justice applications for vulnerable populations, and operationalizing human-centered AI in clinical settings. The work consistently bridges technical implementation with regulatory frameworks like the EU Cyber Resilience Act and GDPR. Scientific Awards: No awards or fellowships were mentioned in the provided text. Advising and Grants: The text does not specify PhD/Master's students or grant details. As leader of an active research group publishing high-impact work on EU regulations and human rights, he likely directs funded projects and mentors early-career researchers, though concrete evidence is absent in the source material. His position suggests involvement in interdisciplinary grant consortia addressing socio-technical challenges. Labs and Teams: Singh leads the Compliant and Accountable Systems research group at University of Duisburg-Essen, which collaborates across university-wide clusters including Artificial Intelligence and Society, Human-AI Interaction, Trustworthy Human Language Technologies, and Verification of Machine Learning. The group develops frameworks for legal compliance in AI, focusing on demonstrable accountability through tools for transparency, bias auditing, and stakeholder engagement in real-world deployments.

Weihua ZhouView profile

Associate Professor

• Health Informatics
• Medical Image Analysis
• Machine Learning
• +3 more

Weihua Zhou is a Tenured Associate Professor at Michigan Technological University in the College of Computing, with affiliations in Applied Computing, Biomedical Engineering, Computer Science, Electrical and Computer Engineering, and Mathematical Sciences. He holds a PhD from Southern Illinois University Carbondale, an MS and B.Eng. from Wuhan University, and completed postdoctoral research at Emory University. Academic Positions : Tenured Associate Professor (2025–present), Tenure-Track Assistant Professor (2019–2025) at Michigan Tech; Nina Bell Suggs Endowed Professorship (2015–2019) at the University of Southern Mississippi. Research Interests : Focuses on medical imaging and health informatics , particularly machine learning applications in cardiovascular diagnosis , osteoporosis risk stratification , and senile dementia early detection . Additional work includes radiomics for COVID-19 severity assessment , federated learning in medical segmentation , and deep learning for proximal femur strength prediction . Scientific Awards : USM Nina Bell Suggs Endowed Professorship, USM College of Arts and Sciences Scholarly Research Award, American Heart Association Research Leaders Academy (2017, 2018), USM Butch Oustalet Distinguished Professorship Research Award. Lab & Tools : Leads the Medical Imaging & Informatics Lab (MIILab-MTU) and contributes to the NSF/MRI GPU Cluster. Open-sourced tools include KD4COVID19 for radiomics analysis and ECGTools for ECG classification.

• Computer Vision
• Medical Image Analysis
• Pattern Recognition
• +5 more

Jaime S. Cardoso is a prominent researcher at the University of Porto and Institute for Systems and Computer Engineering, Technology and Science (INESC TEC) in Portugal. His extensive publication record spanning two decades demonstrates his leadership in computer vision, medical image analysis, and pattern recognition. His research primarily focuses on applying artificial intelligence to healthcare challenges, particularly in medical imaging and diagnostics. Cardoso's research interests center on explainable AI for medical applications, biometrics, and computer vision. His work bridges the gap between theoretical machine learning and practical medical solutions, with significant contributions to breast cancer diagnosis, medical image segmentation, and biometric security systems. He has developed innovative approaches to medical image analysis, including virtual staining techniques and privacy-preserving explanation methods for medical AI systems. His recent publications (2023-2025) reveal a strong emphasis on explainable AI in medical contexts, with multiple papers addressing how to make deep learning models more transparent and trustworthy for healthcare applications. He has also made significant contributions to face recognition technology, video anomaly detection, and specialized medical imaging techniques for breast cancer and neonatal EEG analysis. Among his scientific contributions are numerous collaborations with researchers across Portugal and internationally. His work has appeared in top-tier journals including IEEE Access, Medical Image Analysis, and Neurocomputing, reflecting the high impact of his research. Cardoso has supervised numerous students who have become established researchers in their own right, including Ricardo P. M. Cruz, Kelwin Fernandes, and Ana Filipa Sequeira. His research group appears to focus on the intersection of deep learning, medical imaging, and biometrics, with strong connections to clinical applications.

Rüdiger von Eisenhart-RotheView profile

Professor

• Regenerative Medicine
• Osteoarthritis Research
• Osteo-Oncology
• +7 more

Prof. Dr. Rüdiger von Eisenhart-Rothe is a full Professor and Chair of Orthopedics at the Technische Universität München (TUM), leading the Department of Orthopedics and Sports Orthopedics at the Klinikum rechts der Isar. His research focuses on regenerative medicine, osteo-oncology, endoprosthetics, and the application of machine learning in surgical planning. He completed his medical studies at LMU Munich and business administration at the University of Hagen, followed by a habilitation in orthopedics at Frankfurt’s Friedrichsheim Hospital. Notably, he received the Perthes Prize twice (2003, 2010) for contributions to shoulder and elbow surgery. His work integrates advanced imaging techniques, virtual planning, and biomaterial research to address challenges in joint replacement, infection control, and sarcoma management. Recent projects emphasize AI-driven diagnostic tools and personalized surgical approaches. Prof. von Eisenhart-Rothe collaborates with interdisciplinary teams to advance clinical outcomes in orthopedic surgery, particularly in knee and hip arthroplasty. Awarded the Perthes Prize twice, his contributions span academic leadership, clinical innovation, and translational research at TUM’s School of Medicine and Health. Current initiatives include optimizing prosthetic alignment via 3D modeling and evaluating synovial biomarkers for infection diagnosis.

• Network Biology
• Computational Systems Medicine
• Proteomics
• +4 more

Jörg Menche holds a full Professor position (Univ.-Prof.) at the University of Vienna, with dual appointments in the Faculty of Mathematics, Department of Mathematics, and Max Perutz Labs, Department of Structural and Computational Biology. His work bridges mathematical modeling and biomedical research through network-based approaches to complex disease mechanisms. Primary research interests include Network Biology, Computational Systems Medicine, and Immunometabolism, with specific focus on proteomic signatures in neuropathic pain, metabolic reprogramming in obesity, and autoimmunity networks. He develops innovative computational tools like VRNetzer for virtual reality network analysis and BioProfiling.jl for high-content imaging data integration, targeting intersections between metabolism, immunity, and environmental exposures. Recent publications (2019-2024) reveal a dominant trend in multi-omics network medicine, particularly in autoimmunity (AutoCore framework), cardiometabolic diseases, and rare genetic disorders. His work demonstrates exceptional translational impact, with multiple papers highlighted by news outlets and patents (e.g., 3 patents referencing 2021 research). While the text indicates involvement in one project (2016) and 13 activities, grant specifics and student mentoring details remain undisclosed. His collaborative network spans immunology, neuroscience, and toxicology, evidenced by co-authorships with clinical and computational teams. Menche operates within Max Perutz Labs, a premier molecular biology research center in Vienna. His team integrates computational biologists, cell biologists, and clinicians to develop network cartography methods for disease module identification, with strong emphasis on virtual reality interfaces and multi-omics validation.

Lisa AdamsView profile

Research Fellow

• Artificial Intelligence in Radiology
• Quantitative Imaging Biomarkers
• Oncological Imaging
• +5 more

Dr. Lisa Adams is a Research Fellow at the Technical University of Munich (TUM) and an Attending Radiologist at TUM University Hospital Rechts der Isar, affiliated with the Diagnostic and Interventional Radiology department. She holds an Albrecht Struppler Clinician Scientist Fellowship (2024) and leads research in the Quantitative Imaging Biomarkers for Predictive Healthcare focus group. Adams earned her MD from Charité - Universitätsmedizin Berlin (2016), completed board certification in radiology (2021), and conducted postdoctoral research at Stanford University (2022-2023). She transferred her Habilitation in Experimental Radiology to TUM in 2024. Her research integrates AI with radiology diagnostics, specializing in: Developing machine learning methods for early disease detection and risk assessment Advancing quantitative imaging biomarkers across organ systems Body composition analysis and biological age estimation Multimodal analysis for personalized healthcare Ethical implementation of AI in clinical practice Adams' recent publications demonstrate strong focus on AI applications in radiology, spanning large language models for clinical reporting, deep learning for medical image interpretation, nanoparticle tracking in regenerative medicine, and radiomics for cancer diagnosis. Her work consistently bridges technical innovation with clinical translation. Awards & Recognition: Walter-Friedrich-Prize, Deutsche Röntgengesellschaft (2023) Alavi Mandell Award (2021) Invest in the Youth Stipend, ECR Vienna (2017) She actively mentors doctoral candidates, having supervised seven PhD students to completion since 2020 with two more nearing submission. Adams secures substantial research funding from DFG, EU, Wilhelm Sander Foundation, Bayern Innovativ, and Berlin Institute of Health. She serves as Scientific Editor for European Radiology and Trainee Editorial Board Member for Radiology: Artificial Intelligence , while also holding leadership positions in the German Radiological Society.

Mercedes SpencerView profile

Assistant Professor

• Childhood Development
• Childhood Education
• Language Development
• +5 more

Mercedes Spencer is an Assistant Professor in the Department of Communication Sciences and Disorders at Northwestern University's School of Communication. Her research focuses on cognitive-behavioral correlates of reading development and identification of reading comprehension difficulties in children. PhD in Psychology, Florida State University Postdoctoral training, Department of Special Education at Vanderbilt University Research interests include executive function in literacy development, structural equation modeling of reading disorders, and early intervention strategies. Her work combines latent variable modeling and meta-analysis to examine developmental trajectories and cognitive profiles associated with reading comprehension challenges. Recent publications emphasize reading disorder prevalence, diagnostic classification frameworks, and cognitive correlates of literacy. She applies advanced statistical methods to address gaps in reading comprehension assessment and prevention. Awards 2021 Rebecca L. Sandak Young Investigator Award (Society for the Scientific Study of Reading) 2020 Early Career Award for Contributions to Research (International Dyslexia Association) Teaching responsibilities include statistics and experimental design courses (CSD 304 and CSD 404) for Communication Sciences and Disorders students. Affiliated with the Reading, Education, and Development Laboratory.

• Assistive Technologies
• Health Informatics
• Biomedical Engineering
• +7 more

Dr. Andreu Català Mallofré is a Full Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Systems, Automatic and Industrial Informatics Engineering. He serves as the principal investigator at the CETpD - Centre d'Estudis Tecnològics per a l'Atenció a la Dependència i la Vida Autònoma (Technical Research Center for Dependency Care and Autonomous Living) and leads the TOC - Tecnologia Orientada a la Comunitat (Community-Oriented Technology) research group. With an academic career spanning over 30 years, he specializes in assistive technologies, human-computer interaction, and biomedical data analysis. His research interests focus on Wearable health monitoring systems Parkinson's disease management solutions Frailty assessment algorithms Emotion-driven computing Community-embedded technology Gerontechnology applications These areas have produced 367 academic outputs including 59 indexed journal articles, 148 conference papers, and 66 competitive R&D projects. Recent publications demonstrate a clear trend toward multimodal health monitoring combining inertial sensors with biometric data for aging populations . Key application areas include freezing-of-gait detection in Parkinson's patients and distress estimation during assisted mobility. His 2024 work explores agricultural sustainability through data-driven fertilizer optimization. Scientific recognition includes UPC Social Council Award (2013) 20+ years as conference committee member Multiple competitive R&D grants 23391773000 Scopus ID Orcid 0000-0001-8775-1955 As academic advisor, he has mentored 3 PhD candidates including Daniel Manuel Rodriguez-Martin (Human Movement Analysis, 2020) Leonid Ivonin (Physiological Signal Processing, 2014) John Neal Abram Brown (Calm Computing, 2014) His teams operate within Vilanova i la Geltrú's School of Engineering, with strong industry and healthcare sector collaborations.

• Ophthalmology
• Glaucoma
• Open-Angle Glaucoma
• +7 more

Louis R. Pasquale, MD, is a Professor in the Department of Ophthalmology and also holds a joint appointment in the Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai. His clinical and research work is centered on glaucoma, particularly open-angle glaucoma and glaucoma surgery, with a strong emphasis on genetic and microvascular risk factors. Specialties: Ophthalmology, Glaucoma Clinical Focus: Glaucoma, Glaucoma Surgery, Open-Angle Glaucoma His research integrates advanced technologies such as capillaroscopy and artificial intelligence to understand disease mechanisms and improve diagnostics. Dr. Pasquale has authored over 288 publications, with recent work focusing on genetic associations with visual field defects, non-invasive microvascular imaging, and AI-driven classification of ocular hypertension subtypes. These studies reflect a trend toward precision medicine and data-driven ophthalmology. Dr. Pasquale is board-certified by the American Board of Ophthalmology and has received extensive training from leading institutions, including Johns Hopkins University and Temple University. He plays a key role in advancing the integration of AI into human health, particularly in eye care. MD: State University of NY at Stony Brook School of Medicine Internship: Internal Medicine, Albert Einstein College of Medicine-Yeshiva University Residency: Ophthalmology, Temple University Hospital-Episcopal Division Fellowship: Glaucoma, Wilmer Eye Institute-Johns Hopkins University He has not disclosed any scientific awards or industry conflicts in the provided text, but his extensive publication record and leadership in glaucoma research suggest national and international recognition. There is no mention of student advising or grant funding details, but his position and research output imply active mentorship and extramural support. Dr. Pasquale is a key figure in the development of AI applications for eye disease, working at the intersection of clinical ophthalmology and computational health sciences.

• Geometric Deep Learning
• Topological Deep Learning
• Topological Data Analysis
• +7 more

Bastian Alexander Grossenbacher is a Full Professor of Machine Learning at the University of Fribourg, where he leads the AIDOS Lab within the Department of Informatics, Faculty of Science and Medicine. He holds secondary appointments at the Institute of AI for Health and the Helmholtz Pioneer Campus of Helmholtz Munich. He is also a TUM Junior Fellow and a member of ELLIS and AI-LIFE. He co-directs the Applied Algebraic Topology Research Network (AATRN) and is involved in the Topology, Algebra, and Geometry in Data Science (TAG DS) initiative. His research lies at the intersection of geometry, topology, and machine learning, with a focus on Geometric Deep Learning, Topological Deep Learning, and Topological Data Analysis. He develops novel machine learning methods that use topological and geometric priors to improve model robustness and interpretability, particularly in biomedical applications. His work spans theoretical foundations and practical implementations in areas such as single-cell analysis, medical imaging, and network science. The 15 most recent publications (2023–2025) reveal a strong trend toward integrating algebraic and differential topology into deep learning architectures. Key themes include the use of Euler Characteristic and magnitude transforms, curvature-based analysis of graphs and data, simplicial and manifold-based neural representations, and the application of topological methods to biomedical data. His articles appear in top-tier venues such as Nature Communications, ICML, NeurIPS, ICLR, and IEEE conferences, reflecting both theoretical depth and high-impact applications. ERC Starting Grant Bastian Rieck is actively involved in mentoring and academic leadership. He supervises PhD students and postdoctoral researchers in the AIDOS Lab and is open to new collaborations, particularly with candidates from interdisciplinary backgrounds. He has secured significant research funding, including the ERC Starting Grant, and leads multiple collaborative research initiatives. He emphasizes open science, sharing code, data, and educational materials publicly. He leads the AIDOS Lab, which focuses on advancing machine learning through topological and geometric principles. He is also a co-director of the Applied Algebraic Topology Research Network (AATRN), which hosts a large online seminar series. Additionally, he maintains DONUT, a curated database of non-theoretical applications of topology, and is active in the ELLIS and AI-LIFE networks, fostering international collaboration in AI and health.

• Medical Imaging
• Public Health Outcomes
• Educational Assessment
• +3 more

Olasunkanmi Kehinde is an Assistant Professor in the Department of Health and Human Studies at Elizabeth City State University. Their research spans medical imaging applications in cardiology, pediatric health outcomes, and educational assessment methodologies. They hold an office in the STEM Complex Building, Room 326. Research focuses include cardiac MRI viability assessment, preterm infant mortality risk analysis, and innovative teaching strategies in STEM education. Their work integrates quantitative methods like item response theory and multilevel modeling with practical applications in healthcare and education systems. Recent publications (2021-2025) emphasize methodological advancements in educational measurement, cognitive assessment frameworks, and healthcare outcome analysis. No formal awards or grants are explicitly noted in the provided materials.

Pratik ShahView profile

Assistant Professor

• Artificial Intelligence in Medicine
• Computational Medicine
• Biomedical Imaging
• +13 more

Dr. Pratik Shah is an Assistant Professor in the Department of Pathology at the University of California, Irvine (UCI) School of Medicine. He leads a cutting-edge research group focused on translational artificial intelligence and biomedical technologies to advance diagnostic imaging, uncover biological mechanisms, and enable clinical deployment of digital therapeutics. Institution: University of California, Irvine School: School of Medicine Department: Department of Pathology Academic Rank: Assistant Professor Email: pratik.shah@uci.edu Dr. Shah's research lies at the intersection of computational medicine, artificial intelligence, and biomedical imaging. His work emphasizes developing interpretable and explainable AI systems that enhance model effectiveness, safety, and clinical utility. Key research areas include generative AI for nondestructive histopathology, deep learning for medical image analysis, AI-driven clinical decision support for infectious diseases, and multimodal imaging for biological insights. His lab is dedicated to creating equitable and responsible technologies to improve diagnosis and treatment of cancer, infectious diseases, and neurological disorders. The recent publications highlight a strong trend in applying deep learning to digital pathology, particularly in computational staining and tumor detection from non-stained images. There is a consistent focus on developing interpretable AI tools for medical image segmentation, with applications in cancer diagnostics, sepsis detection, and dental imaging. The research spans from foundational AI methods to clinical validation, showing a trajectory toward regulatory science and real-world deployment of AI in healthcare. MIT NEWS feature on computational staining research SPIE publication selected for Deep-Dive spotlight session SPIE publication selected for oral presentation IEEE BioInformatics and BioEngineering publication selected for oral presentation Dr. Shah has mentored a diverse group of students and postdoctoral associates, including PhD candidates in Mechanical Engineering at MIT and undergraduate researchers in Electrical Engineering and Computer Science. His trainees have contributed to high-impact publications in journals like JAMA Network and conferences like IEEE EMBC. He has fostered collaborations with institutions including Stanford, UCSF, and the FDA, indicating strong research outreach and interdisciplinary partnerships. His lab has received recognition through media features and conference selections, reflecting the significance and innovation of his work in AI for healthcare. Dr. Shah leads a multidisciplinary research team comprising postdoctoral associates, graduate students, research staff, and student researchers, primarily focused on machine learning and medicine. The lab has strong ties to MIT, where much of the prior research was conducted, and maintains collaborations with clinical and engineering experts across the US.

Mingjun ZhongView profile

Lecturer

• Machine Learning
• Computational Statistics
• Non-intrusive Load Monitoring
• +12 more

Dr. Mingjun Zhong is a Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen. His research focuses on machine learning and computational statistics with applications in healthcare, energy systems, and medical imaging. He is actively engaged in teaching and academic service, including editorial roles in prominent journals. Position: Lecturer Institution: University of Aberdeen School: School of Natural and Computing Sciences Email: mingjun.zhong@abdn.ac.uk Dr. Zhong's research interests center on probabilistic and statistical machine learning methodologies applied to real-world data. He works on healthcare data analysis, non-intrusive load monitoring (NILM), spectroscopy, EEG/fMRI, and energy disaggregation. His methodological expertise includes variational inference, Markov chain Monte Carlo, Bayesian matrix factorization, and deep learning. He has developed lightweight and efficient neural network models for applications in medical imaging and smart grids. The most recent publications reflect a strong trend in applying advanced machine learning techniques—particularly deep learning, self-supervised learning, and capsule networks—to diverse domains such as medical diagnostics, energy disaggregation, and clinical decision support. There is a clear emphasis on developing efficient, interpretable, and robust models for real-world deployment, often addressing challenges like class imbalance, domain adaptation, and data scarcity. Scientific recognition includes: Fellow of the Higher Education Academy (FHEA) Associate Editor, Neural Processing Letters Review Editor, Frontiers in Applied Mathematics and Statistics Regular reviewer for top-tier journals and conferences in AI and machine learning Grant reviewer for multiple funding bodies Dr. Zhong advises a number of students, as evidenced by co-authorships on numerous publications. His research is supported by academic collaborations and likely external grants, though specific funding details are not mentioned. He teaches courses in Robotics, Machine Learning, and Knowledge Representation and Reasoning, contributing significantly to the curriculum in computing sciences. He is involved in interdisciplinary research, particularly through projects like ARCHERY (Artificial intelligence to Revolutionise the patient Care pathway in Hip and knEe aRthroplastY), which integrates AI into orthopaedic care. His work bridges computer science, statistics, and domain-specific applications, demonstrating a strong commitment to impactful, application-driven research.

Feifei PanView profile

Professor

• Hydrology
• Environmental Modeling
• Remote Sensing
• +3 more

Feifei Pan is a Professor and Director of Graduate Studies in the Department of Geography at the University of North Texas. His research focuses on hydrological processes across multiple scales, integrating numerical models, remote sensing, and GIS techniques. Key areas include soil moisture dynamics, surface-groundwater interactions, water quality modeling, and vegetation-snow process visualization. Dr. Pan teaches courses such as Earth Science, Fluvial Geomorphology, Groundwater Hydrology, and Environmental Modeling. His work emphasizes solving complex environmental challenges through interdisciplinary approaches, including climate change adaptation and sustainable land-water management strategies. His research has addressed topics like the impact of land-atmosphere feedback on extreme weather events, optimization of rainwater harvesting systems, and the effects of climate warming on polar regions. He has published extensively in high-impact journals, focusing on hydrology, climate science, and geospatial technologies. Dr. Pan advises graduate and undergraduate students on research aligned with his expertise, fostering the next generation of environmental scientists through mentorship in fieldwork and computational modeling.