جست‌وجوی استاد و پژوهشگر

Maria Braoudakiمشاهده پروفایل

دانشیار

• Pediatric Cancer Genomics
• Genetics
• Epigenetics
• +۴ مورد دیگر

Dr. Maria Braoudaki is a Reader in Molecular Medicine at the University of Hertfordshire, UK. She holds a PhD in Molecular Microbiology from Aston University (2004) and an MSc in Molecular Medicine (with distinction) from the National and Kapodistrian University of Athens. Her research focuses on pediatric cancer genomics, epigenetics, and proteomics, with emphasis on microRNA profiling and high-throughput omics strategies. She has authored over 40 peer-reviewed publications and received prestigious awards, including the L’OREAL-UNESCO Award for Women in Science (2016) and recognition from the Cretan Scientists Association (2022). Research Projects: Cancer Management using AI (Co-Investigator, 2022–present) Apafix fixation for tissue analysis (Principal Investigator, 2022) Research Interests: MicroRNA-driven therapeutic strategies in pediatric and adult cancers Epigenetic regulation in oncogenesis Omics-based approaches for cancer diagnosis and prognosis Key Contributions: Pioneered work on microRNA signatures in medulloblastoma and glioblastoma Developed novel biomarker strategies for lung and colorectal cancers Explored extracellular vesicles as therapeutic delivery systems Grants & Collaborations: Lead investigator in Apafix fixation studies Co-Investigator in AI-driven cancer recurrence prediction (CaRPrAI, 2021–2022)

• Artificial Intelligence in Radiology
• Machine Learning
• Health Informatics
• +۲ مورد دیگر

Dr. Axel W. E. Wismüller is a Professor of Radiology and Biomedical Engineering at the University of Rochester Medical Center . As Director of the AI Radiology Laboratory , he uniquely combines clinical practice in cardiothoracic/body imaging with pioneering research in Artificial Intelligence (AI) for medical imaging . Holding MD, MSc (Physics), and PhD (Electrical Engineering) degrees from German institutions, he maintains dual US and German medical licenses and board certifications. Ludwig-Maximilians-Universitaet München (1990-1996) Technical University of Munich (MD 1992, PhD 2006) Ludwig Maximilians University Munich (Residency 1997-2005) His research focuses on machine learning and network connectivity analysis in neuroimaging, particularly for HIV-associated neurocognitive disorders and multiple sclerosis . He invented the XOM algorithm (patented in US/EU) and developed AI systems for chest radiograph analysis , breast cancer imaging , and osteoporosis diagnostics . His work on large-scale Granger causality has transformed brain network analysis methodology. Scientific awards include SPIE Senior Membership (2016), NIH/NIDA R01 funding (2012-2015), and NYSTAR public-private partnerships . Supervising over 30 PhD/MSc students across engineering, computer science, and medicine , he bridges clinical and computational disciplines through active roles in ACR, RSNA, and SPIE .

• Big data
• Data science
• Data analytics
• +۱۲ مورد دیگر

Paolo Garza is an Associate Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where he also serves as Coordinator of the College of Computer, Film and Mechatronics Engineering. He is a member of the DBDM research group and the SmartData@PoliTO laboratory, and actively contributes to academic governance through roles in teaching coordination and PhD program committees. Education: Bachelor’s in Computer Engineering, Polytechnic University of Turin (2001) PhD in Computer and Systems Engineering, Polytechnic University of Turin (2005) His research centers on data science, big data analytics, data mining, and machine learning , with applications in emergency management, real-time communications, Earth observation, and cybersecurity. He has led and participated in numerous national and commercial research projects, including AI4CTI and NODES (PNRR), and has collaborated with industry partners like Cisco Systems. His work bridges theoretical algorithm development and practical deployment in critical systems. The recent publications reflect a strong trend toward multimodal AI, crisis informatics, and intelligent networking . Articles span computer vision for environmental monitoring (e.g., burned area detection, canopy estimation), ML for real-time communication quality, multimodal document understanding, and crisis response systems. His team leverages deep learning, transformers, and vision-language models across diverse domains. Scientific Service: Associate Editor, Knowledge and Information Systems (2024–) Associate Editor, Expert Systems with Applications (2022–) General Co-Chair, IEEE AICT Conferences (2022, 2023) He mentors several PhD students and leads funded research initiatives focused on AI for sustainable industry and cyber threat intelligence. His teaching includes graduate courses on big data processing, distributed architectures, and data science lab methods. He has also directed commercial training programs and research contracts in machine learning and cybersecurity. Research Labs & Teams: DBDM - Database and Data Mining Group (DAUIN) SmartData@PoliTO - Big Data and Data Science Laboratory LAB 5 - Research Laboratory (DAUIN)

Francesco Picarielloمشاهده پروفایل

پژوهشگر

• Electrical and Electronic Measurements
• Data Acquisition Systems
• Compressive Sampling
• +۷ مورد دیگر

Francesco Picariello is a Researcher at the Department of Engineering (DING) at the University of Sannio, specializing in Electrical and Electronic Measurements (ING-INF/07). His academic profile shows extensive expertise in measurement systems, data acquisition, and signal processing with applications across multiple domains including biomedical engineering, structural health monitoring, and wireless communications. His research interests focus on the design, implementation, and metrological characterization of mobile measurement systems, wireless sensor networks, data acquisition systems based on compressed sensing, signal processing, and image processing. These research activities have been applied to intelligent transportation systems, aerial photogrammetry using UAVs, indoor navigation, motion tracking, power consumption analysis of processors, wideband data acquisition systems, and physiological monitoring applications. Dr. Picariello's work demonstrates significant trends in compressive sampling techniques, with numerous publications on ECG signal processing, RF emitter localization, and structural health monitoring systems. His research shows a clear progression toward IoT integration, wearable medical devices, and security applications for communication channels. His scientific collaborations include institutions such as the University of Naples 'Parthenope', Military University of Technology of Warsaw, National Institute of Standards and Technology (NIST), Keysight Laboratories, CERN Institute, and the University of Tokyo, reflecting the international recognition of his work. As an active researcher with publications spanning from 2016 to 2025, Dr. Picariello has made substantial contributions to measurement science, particularly in the development of novel techniques for data acquisition, signal processing, and system metrological characterization across multiple application domains.

• Machine Learning
• Data Mining
• Big Data
• +۴ مورد دیگر

Jesse Davis is a Professor at the Department of Computer Science , KU Leuven , actively contributing to the Machine Learning group and the Sports Analytics Lab . He is part of the Faculty of Engineering Science and the Leuven.AI Institute . Ph.D. in Computer Sciences from University of Wisconsin-Madison (2007) M.S. in Computer Sciences from University of Wisconsin-Madison (2005) B.A. in Computer Science from Williams College (2002) His research focuses on machine learning, data mining, big data analytics, and sports analytics, with significant work in: Transfer learning and Markov logic networks Anomaly detection and semi-supervised learning Medical NLP and biomechanical data analysis Soccer performance metrics and tactical analysis His recent work explores spatio-temporal data analysis in sports and explainable AI for medical applications, with collaborations spanning finance, healthcare, and semiconductor manufacturing. Notable scientific awards include: Best Paper Award (Applied Data Science Track) at KDD 2019 Best Technical Paper Award at Intelligence Analysis Workshop He advises numerous PhD and Master's students in areas like: Football analytics Tree ensemble compression Medical question-answering systems Biomechanical load prediction His lab develops tools such as: GSSL for Markov network structure learning TODTLER for transfer learning Alchemy system for Markov logic networks

Jussi Taipaleمشاهده پروفایل

استاد

• Gene regulation
• Transcription factors
• Systems biology
• +۴ مورد دیگر

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 Singhمشاهده پروفایل

استاد

• Accountability in AI Systems
• Responsible AI Development
• Data Governance and Stewardship
• +۷ مورد دیگر

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 Zhouمشاهده پروفایل

دانشیار

• Health Informatics
• Medical Image Analysis
• Machine Learning
• +۳ مورد دیگر

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
• +۵ مورد دیگر

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-Rotheمشاهده پروفایل

استاد

• Regenerative Medicine
• Osteoarthritis Research
• Osteo-Oncology
• +۷ مورد دیگر

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
• +۴ مورد دیگر

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 Adamsمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence in Radiology
• Quantitative Imaging Biomarkers
• Oncological Imaging
• +۵ مورد دیگر

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 Spencerمشاهده پروفایل

استادیار

• Childhood Development
• Childhood Education
• Language Development
• +۵ مورد دیگر

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
• +۷ مورد دیگر

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
• +۷ مورد دیگر

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.