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Chiang-Ching HuangView profile

Professor

• Biostatistics
• Genomics
• Bioinformatics
• +3 more

Chiang-Ching Huang is a Professor in the Department of Biostatistics at the University of Wisconsin-Milwaukee, College of Public Health . With a PhD in Biostatistics from the University of Michigan and a Master of Science in Statistics from the University of Iowa, he specializes in genomic data analysis, biomarker discovery, and precision medicine applications in cancer and cardiovascular diseases. PhD, Biostatistics, University of Michigan, Ann Arbor MS, Statistics, University of Iowa, Iowa City BS, Applied Mathematics, National Chiao-Tung University, Hsinchu, Taiwan His research focuses on integrating genomic-scale data with phenotypic information to predict disease susceptibility, treatment response, and survival outcomes in cancers like prostate cancer and paediatric brain tumors. He develops advanced analytical techniques for liquid biopsy applications, including cell-free DNA copy number variation analysis and digital PCR-based prognostic models. Recent publications highlight his work on epigenetic modifications in circulating DNA, machine learning approaches to genome-phenome mapping, and multi-gene risk scores for metastatic castration-resistant prostate cancer. His team also explores inflammatory pathways in autoimmune diseases like juvenile dermatomyositis through RNA-Seq and miRNA profiling.

• Medical Imaging
• Radiology
• Oncology
• +4 more

Tommaso Vincenzo Bartolotta is a Full Professor in the Department of Biomedicine, Neuroscience and Advanced Diagnostics at the University of Palermo's School of Medicine and Surgery. He specializes in advanced diagnostic imaging techniques, with office hours held at the Institute of Radiology within Palermo University Hospital. His research focuses on: Innovations in medical imaging (MRI, ultrasound, contrast-enhanced techniques) AI-driven diagnostics for oncology (breast, liver, prostate, brain tumors) Radiomics and quantitative imaging biomarker development Clinical applications of elastography and microvascular ultrasound Recent publications demonstrate a strong emphasis on artificial intelligence integration into radiological workflows, particularly for breast cancer characterization, liver disease assessment, and therapeutic response prediction. No scientific awards or student advising relationships were documented in the provided materials. Contact is available via institutional email.

• Quality Management
• Operations Management
• Statistical Process Control
• +3 more

Mohamad Nayebpour serves as Clinical Faculty in Management at the Marilyn Davies College of Business, University of Houston-Downtown, holding the rank of Visiting Assistant Professor since 2014. With over 35 years of combined industry and academic experience, he specializes in operations management, quality control, and statistical decision-making methodologies. His educational background includes: Ph.D. in Statistics from University of Louisiana at Lafayette (dissertation: Economic Design of On-line Quality Control for Attribute Characteristics) M.S. in Engineering Management from University of Southwestern Louisiana B.S. in Petroleum Engineering from Louisiana State University Research focuses intensely on Statistical Quality Control and Quality Management , extending into healthcare quality assessment, supply chain resilience, and machine learning applications for process monitoring. His work bridges theoretical statistics with practical industrial solutions, particularly in high-stakes sectors like healthcare and energy infrastructure where precision monitoring is critical. Publication trends reveal consistent evolution from traditional control chart methodologies (2003-2017) toward pandemic-responsive supply chain research and AI-integrated quality systems (2021-2025), demonstrating adaptability to emerging global challenges while maintaining statistical rigor. Professional recognition includes: Certified Quality Engineer credential from American Society for Quality (2015-2024) Academic service encompasses MGT 3332 course coordination, strategic planning committee membership, and extensive departmental curriculum development. Industry engagement spans continuous consulting for Revenew International since 2000, plus specialized quality control projects for DASH Spirits, Shell Services International, and healthcare institutions, translating academic research into operational improvements. Collaborative research networks include long-term partnerships with R. Noorossana on quality engineering projects and editorial contributions to AIMS International Journal of Management, reflecting commitment to advancing methodological standards in the field.

• Leukemia Research
• Acute Lymphoblastic Leukemia
• Epigenetics
• +7 more

Hesta McNeill is a researcher affiliated with Newcastle University , contributing to leukemia and cancer biology through molecular studies. Her work focuses on epigenetic regulators, fusion genes (e.g., RUNX1/ETO, MLL/AF4), and mechanisms of treatment resistance in hematological malignancies. Institutions: Newcastle University Roles: Researcher Research spans: Epigenetic control of leukemogenesis Transcriptional and chromatin dynamics in AML and ALL Development of hiPSC-based models for drug screening Role of angiopoietin1 in MLL-rearranged ALL Heat shock factor-1 interactions with p53 in DNA damage MDM2 inhibitors in prostate cancer therapy Publications highlight collaborations with Professors Vasily Grinev, Olaf Heidenreich, and others, emphasizing genome-wide approaches (RNAi, transcriptomics) and cancer stem cell dynamics. No specific awards, students, or part-time status are documented in the source data.

Jeremy M. GernandView profile

Associate Professor

• Environmental Health and Safety Risk Analysis
• Nanoparticle Exposure Risk Assessment
• Particulate Matter Analysis
• +7 more

Jeremy M. Gernand is an Associate Professor of Environmental Health and Safety Engineering at The Pennsylvania State University, affiliated with the John and Willie Leone Department of Energy and Mineral Engineering within the College of Earth and Mineral Sciences. With expertise spanning environmental health and safety risk analysis, nanoparticle exposure assessment, and engineering decision-making under uncertainty, Dr. Gernand contributes significantly to both academic research and practical applications in safety engineering. Dr. Gernand earned his B.S. in Mechanical Engineering from Texas A&M University (1998), followed by an M.S. in Mechanical Engineering from Rice University (2007), and completed his Ph.D. in Engineering and Public Policy at Carnegie Mellon University. His educational journey was complemented by eight years of professional experience as a Safety and Reliability Engineer in the aerospace and defense industries, where he worked on projects ranging from astronaut exercise equipment to aircraft radar systems. Dr. Gernand's research focuses on improving how engineers and policymakers understand and manage environmental health and safety risks associated with technology. His work spans three primary initiatives: characterizing real-world pollutant exposures and evaluating relevant policy; understanding the characteristics influencing nano- and ultrafine particle toxicity; and assessing the efficiency and efficacy of policies aimed at protecting worker and public health and safety. He employs quantitative risk analysis, probabilistic models, and data informatics, increasingly incorporating machine learning approaches to make the most of available data. Analysis of Dr. Gernand's recent publications reveals consistent themes across environmental health, safety engineering, and risk analysis. His work bridges fundamental research on nanoparticle toxicity with practical applications in occupational safety and policy evaluation. Notably, there's a growing emphasis on cognitive aspects of risk assessment, examining how engineers' biases affect their decision-making processes regarding risk. His research also shows increasing engagement with contemporary energy transition issues, particularly regarding hydrogen economy development and solar energy safety implications. Certified Safety Professional (CSP) Certified Reliability Engineer (CRE) Dr. Gernand actively mentors numerous students across various levels, from undergraduate researchers to postdoctoral scholars. His research group has secured significant funding from organizations including the Health Effects Institute, Penn State Institute for Energy and the Environment, and the National Institute for Occupational Safety and Health. Current projects include "Trends in Marcellus-Utica Shale Regional Air Quality due to Unconventional Oil and Gas Development" and "Investigating Indoor Air Pollutant Variability, Source Contributions, and Respiratory Health in Senegal." His teaching portfolio includes courses on Safety Engineering, Engineering Risk Analysis, and Machine Learning for Engineering Problems. Dr. Gernand leads a vibrant research laboratory focused on environmental health and safety risk analysis. His team employs both experimental and computational approaches to characterize exposures, assess risks, and evaluate policy effectiveness. Notably, his personal experience with vision impairment following a stroke in 2017 has informed his understanding of accessibility challenges and reinforced his commitment to inclusive research practices.

Bruce William TurnbullView profile

Professor

• Statistics
• Biostatistics
• Reliability Engineering
• +4 more

Bruce William Turnbull is a Professor at Cornell University in the School of Operations Research and Industrial Engineering, affiliated with the Departments of Statistical Science and Operations Research. His career spans roles at Stanford University (visiting faculty) and the University of Oxford (lecturer), but he has been a permanent faculty member at Cornell since 1976. Education : B.A. in Mathematics, Cambridge University (1967) M.S. in Operations Research, Cornell University (1970) Ph.D. in Statistics and Applied Probability, Cornell University (1971) M.A. in Mathematics, Oxford University (1973) Research Interests : Design and analysis of experiments for system reliability (engineering and biomedical applications) Statistical methods for clinical trials (group sequential analysis, adaptive designs) Spatial point process analysis (disease clustering, environmental exposure) Survival analysis with censored data Sequential analysis and statistical quality control Longitudinal biomarker modeling Publication Trends : Focus on clinical trials, reliability engineering, and spatial statistics Key subfields: survival analysis, adaptive trial design, changepoint detection, spatial clustering Collaborative work with Chris Jennison, L.C. Clark, and others in journals like Biometrika, JASA, and Statistics in Medicine Scientific Recognition : Recipient of the Snedecor Memorial Award (1979) Fellow of the American Statistical Association and Royal Statistical Society Editorial roles in journals such as Statistics in Medicine Advising & Collaborations : Advisor to students in biostatistics and statistics (not explicitly named) Consultant to organizations including Oak Ridge National Laboratory Member of Data and Safety Monitoring Committees for NIH and VA clinical trials Labs & Teams : Affiliated with the National Institute of Statistical Sciences (Board of Directors) Contributions to Cornell’s statistical research and clinical trial monitoring

• Radiation Oncology
• MRI-guided Radiotherapy
• Adaptive Radiation Therapy
• +8 more

Claus Belka is a Professor in the Department of Radiation Oncology at the Faculty of Medicine, Ludwig-Maximilians-Universität München. His work focuses on advancing MRI-guided radiotherapy, adaptive treatment strategies, and the integration of artificial intelligence in radiation oncology. Research Interests: His research spans radiation oncology, deep learning applications in medical imaging, MRI-guided and adaptive radiotherapy, proton therapy, and dosimetric optimization. He investigates real-time tumor tracking, auto-segmentation, motion prediction, and radiomics for prognostic modeling in cancers of the lung, head and neck, prostate, and liver. The recent publications reflect a strong trend toward leveraging deep learning for segmentation, motion prediction, synthetic imaging, and outcome modeling in MRI- and PET-guided radiotherapy. Key themes include uncertainty calibration, patient-specific modeling, low-field MR-Linac applications, and dose accumulation in adaptive settings. Scientific Awards: Advising and Grants: While specific students and funding sources are not listed in the provided text, his extensive publication record across multi-institutional and technical-clinical domains suggests active mentorship and leadership in major research initiatives, including clinical trials like the PUMA trial for pulmonary MR-guided radiotherapy. Labs and Teams: He is associated with a leading radiation oncology research group at LMU Munich, focusing on medical physics innovation, AI integration, and clinical translation of advanced radiotherapy techniques.

Ofer HarelView profile

Professor

• Missing Data Analysis
• Causal Inference
• Verification Bias
• +6 more

Ofer Harel is a Professor in the Department of Statistics at the University of Connecticut , where he also serves as Dean of the College of Liberal Arts and Sciences . His research focuses on advanced statistical methodologies for handling incomplete data, with applications spanning epidemiology, health research, clinical trials, and social sciences. Primary Affiliation: University of Connecticut Roles: Professor of Statistics, Dean of College of Liberal Arts and Sciences Research Focus: Missing data analysis, Bayesian methods, latent class modeling, and statistical applications in public health Research Interests span critical areas including: Missing Data Mechanisms: Developing and refining multiple imputation techniques Latent Class Analysis: Applying to health outcomes and behavioral studies Bayesian Modeling: For spatial and longitudinal health data Statistical Disclosure Control: Protecting sensitive demographic information Health Applications: Covering HIV prevention, cancer survivorship, and mental health Educational Statistics: Integrating computing tools into statistical pedagogy Publication Trends demonstrate expertise in: Advanced imputation frameworks for clinical and epidemiological research Latent class and multilevel modeling for diverse health outcomes Statistical ethics and data confidentiality in public health research Spatial and longitudinal analysis of disease patterns Methodological innovations in handling missing data across multiple disciplines Advising and Collaborative Work includes methodological contributions to: Non-inferiority trial designs with missing data challenges Bayesian spatial modeling applications in global health Improving statistical reporting and reproducibility

• Metabolomics
• Prostate Cancer
• Analytical Chemistry
• +4 more

Mohammed Zniber is affiliated with the Faculty of Natural Sciences and Technology at Turku University , focusing on interdisciplinary research for sustainable technologies. His research spans Metabolomics and H-NMR spectroscopy for disease biomarker discovery Bioinformatics tool development (e.g., Protomix package) Materials science for organic-metal complexes Recent publications highlight trends in prostate cancer diagnostics , urine profiling , and computational methods in metabolomics. Collaborations include work with researchers like T. Lamminen and T.-P. Huynh.

• Precision Medicine
• Molecular Oncology
• Translational Cancer Research
• +4 more

PD Dr. Marianna Kruithof-De Julio is a faculty member at the Bern Center for Precision Medicine (BCPM), affiliated with the University of Bern and Inselspital, the University Hospital of Bern. Her work bridges clinical medicine and molecular research, focusing on advancing personalized treatment strategies in oncology. Her research interests lie at the intersection of precision medicine and cancer biology, particularly in urological cancers such as prostate and bladder cancer. She investigates molecular mechanisms of tumor progression, genomic alterations, and potential biomarkers for diagnosis and therapy response. Her work emphasizes translational approaches to bring laboratory findings into clinical applications. The absence of listed publications in the provided text prevents a detailed analysis of publication trends. However, her affiliation with BCPM suggests a strong focus on genomics, data integration, and patient-centered research in oncology. No scientific awards listed in the provided text. While no formal advisees are listed, her role as a Privatdozent and research group leader implies mentorship of PhD and Master’s students within the Department of Biomedical Research and the Faculty of Medicine. Research funding likely comes through competitive grants supporting precision medicine initiatives at the national and institutional levels. She is an active member of the Bern Center for Precision Medicine (BCPM), a multidisciplinary research hub dedicated to developing tailored medical solutions through genomics, bioinformatics, and clinical data integration.

Katharina LückerathView profile

Professor

• Nuclear Medicine
• Theranostics
• Radioligand Therapy
• +13 more

Prof. Dr. Katharina Lückerath is a leading academic in nuclear medicine and serves as the Head of Preclinical Theranostics at the Clinic for Nuclear Medicine, University Hospital Essen, affiliated with the Faculty of Medicine at the University of Duisburg-Essen. Her research is centered on advancing theranostic approaches in oncology, integrating diagnostic imaging with targeted radionuclide therapies to improve cancer treatment outcomes. Her research interests span nuclear medicine, theranostics, radioligand therapy, molecular imaging, tumor biology, and resistance mechanisms in cancer . She investigates how tumor heterogeneity, metabolism, and microenvironmental factors influence treatment response, aiming to develop more effective and personalized therapies. Her work includes preclinical evaluation of PSMA- and FAPI-targeted agents, combination strategies, and the use of PET/CT and SPECT/CT for biomarker discovery. The recent publications reflect a strong focus on translational oncology , with studies on FAPI- and PSMA-based imaging and therapy, mechanisms of resistance, and novel pretargeting strategies. Her team utilizes advanced preclinical models and imaging technologies to validate new radioligands and optimize dosing and delivery. Collaborative work with international experts underscores the impact of her research in shaping clinical theranostic practices. Prof. Lückerath leads a multidisciplinary research group comprising PhD students, biologists, and radiopharmaceutical scientists, fostering innovation in experimental radiochemistry and preclinical imaging. Her team has access to state-of-the-art facilities including Molecubes PET/SPECT/CT systems and autoradiography tools, enabling high-throughput and high-resolution studies.

Alan HerschtalView profile

Research Fellow

• Biostatistics
• Clinical Trials Methodology
• Oncology
• +3 more

Alan Herschtal is a Senior Biostatistician and Research Fellow at Monash University's Clinical Trials Centre. He specializes in clinical trials methodology across Oncology and Cardiovascular disease. His key responsibilities include statistical design, analysis, reporting, and grant preparation. He holds leadership roles in two active research projects: 'Co-design and evaluation of a resource to improve patient-clinician communication in rural chronic disease settings' (2024–2027) and 'Flexible Modelling of Count Outcomes in Clinical Trials' (2024). His research focuses on Flexible parametric survival modelling and Modelling of zero-inflated data , with notable contributions to imaging advancements in prostate cancer (PSMA PET-CT) and lung cancer therapies (SABR vs. standard radiotherapy). His work aligns with UN Sustainable Development Goals related to health equity and innovation. Recent articles highlight advancements in trial design for eating disorders, pulmonary function post-radiotherapy, and cardiovascular risk screening. Projects emphasize improving rural healthcare communication and statistical methodologies for count data in clinical research. He collaborates internationally and leads grant applications, with a focus on translational research linking statistical innovation to clinical outcomes. His expertise bridges biostatistics, clinical oncology, and public health initiatives.

Matthew LloydView profile

Senior Lecturer

• Chemical Biology
• Enzymology
• Enzyme Kinetics
• +7 more

Matthew Lloyd is a Senior Lecturer in the Department of Life Sciences at the University of Bath, where he conducts research at the intersection of chemical biology, enzymology, and drug discovery. His work focuses on enzyme targets in cancer and infectious diseases, particularly alpha-methylacyl-CoA racemase (AMACR) in prostate cancer and tuberculosis. He collaborates with leading researchers including Professor K. Ravi Acharya, Professor Mike Threadgill, and Professor Tony James across disciplines to develop novel therapeutics. PhD in Clavulanic Acid Biosynthesis, University of Oxford (1989–1992) BSc in Biological Chemistry, University of Leicester (1986–1989) His research interests lie in chemical biology approaches to cancer, with a focus on enzyme mechanisms, inhibitor development, and high-throughput screening. He investigates AMACR’s role in fatty acid metabolism and ibuprofen activation, aiming to develop both therapeutic inhibitors and diagnostic assays. His work also extends to targeting tankyrases and developing delivery systems for potent anticancer drugs. The recent publications highlight a strong trend in enzymology and pharmacological methodology, particularly in enzyme kinetics, structural characterization of AMACR, and rigorous analysis of dose-response curves for accurate IC50/EC50 determination. His work bridges fundamental biochemistry with translational drug discovery. Fellow of the Royal Society of Biology (FRSB) (2018–2021) Editorial Board Member, The Biochemist, Biochemical Society (from 2017) Member, Biochemical Society (from 1990) Matthew Lloyd actively supervises doctoral students and leads or co-leads multiple research projects funded by Prostate Cancer UK and the World Cancer Research Fund. He has been Principal Investigator on projects such as 'AMACR Targeted Drugs for Treating Advanced Prostate Cancer' and 'Refinement of a Polymeric System Delivering CGIs to Prostate Tumours'. His collaborative network includes experts in chemistry, life sciences, and cancer biology, reflecting a multidisciplinary approach. He regularly presents at academic events, including the European Society of Applied Biocatalysis webinar and the Chris J. Schofield Symposium. His research is conducted within a collaborative team environment involving Dr. Tim Woodman, Professor Mike Threadgill, and Dr. Andy Thompson, focusing on enzyme-targeted therapies and drug delivery systems for cancer treatment.

Mika MurtojärviView profile

Academic

• Algorithms
• Programming
• Machine Learning
• +8 more

Mika Murtojärvi is a Senior Researcher and University Teacher in Software Engineering at the University of Turku. His work bridges research and education in computer science, with a focus on algorithms, machine learning, and software systems. He holds a PhD in Computer Science (2016) and has over a decade of teaching experience. Research Interests: His research spans algorithm development for applications in computational geometry, biomedical informatics, e-learning, and parallel computing. Key areas include survival prediction in cancer, EEG-based neurological classification, GPU-accelerated geographic algorithms, and platform-independent educational tools. His work often integrates real-world data with machine learning and optimization techniques. The recent publications highlight a strong trend in applying algorithmic and machine learning methods to healthcare and environmental challenges. Topics such as survival prediction in prostate cancer, EEG-based Parkinson’s classification, and GPU-optimized coastal algorithms reflect interdisciplinary research at the intersection of computer science, medicine, and geosciences. The use of R packages and e-learning systems also shows a commitment to practical, software-based solutions. Scientific Awards: No awards mentioned in the text. Advising and Grants: Mika supervises BSc theses and has taught various core computer science courses. While specific grants are not listed, his research in biomedical and environmental computing suggests involvement in funded projects. He has contributed to tools like ePCR, indicating software development for research dissemination. Labs and Teams: He collaborates with researchers such as Antti Airola, Tapio Pahikkala, and Jari Björne, suggesting active participation in research teams focused on machine learning and biomedical informatics. His work on GPU algorithms also indicates ties to high-performance computing groups.

• Medical Imaging
• Artificial Intelligence in Healthcare
• Radiomics
• +3 more

Valentina Giannini is an Assistant Professor at the Department of Oncology of the University of Turin , specializing in biomedical engineering and oncologic imaging. Her work focuses on applying artificial intelligence, radiomics, and deep learning to improve cancer diagnosis and treatment prediction. She teaches courses such as Bioengineering, Medical Imaging Systems, and Neurophysiological Techniques within the Scuola di Medicina . Her research emphasizes multi-center studies, AI-driven clinical decision support systems, and MRI/CT-based biomarkers for tumors like prostate and colorectal cancers. Giannini’s research has produced tools such as the METhodological RadiomICs Score (METRICS) for radiomics quality control and AI systems for automatic tumor segmentation. She collaborates across disciplines, integrating engineering principles with clinical oncology to enhance treatment outcomes. Her recent work highlights advancements in transparent AI systems, multi-vendor compatibility, and predictive analytics for therapy resistance. Key areas of expertise include: Radiomics and texture analysis in oncology Deep learning for medical image segmentation Prostate and colorectal cancer diagnostics AI ethics and algorithmic transparency Her contributions span 70+ peer-reviewed articles, emphasizing translational research bridging engineering and clinical practice. Giannini actively participates in EU-funded projects and collaborates internationally to drive innovation in cancer imaging technologies.