Biostatistics and Bioinformatics Department Faculty
The faculty within the Biostatistics and Bioinformatics Department support research projects needing expertise in the areas of biostatistics, statistical genomics, bioinformatics, informatics and biomedical data management. The activities of the faculty primarily fall into three broad categories: long-term collaborative projects, short-term research projects, and research on new statistical and bioinformatics analytical methods. Research activities include consultation and collaboration with scientists across the basic, population and clinical sciences engaged in the planning, conduct, analysis, and interpretation of data and information. In addition to providing research collaboration, members of the department are active in various institutional initiatives surrounding data acquisition and management.
Brooke L. Fridley, Ph.D. – Chair/Senior Member
The Fridley lab works in the area of statistical genomics as it relates to cancer genomics and pharmacogenomics.
Research Themes: Biostatistics, Statistical Genomics, Transcriptomic Studies, Bayesian Methods
Anders Berglund, PhD – Assistant Member
Dr. Berglund major research interest is in using bioinformatics tools for cancer research through collaborations. Dr. Berglund has a special interest in how to best visualize and analyze methylation data. Much of Dr. Berglund’s work is about how to best utilize publically available data, such as TCGA.
Research Themes: Bioinformatics, epigenetic studies, transcriptomic studies
Ann Chen, PhD – Associate Member
Dr. Chen’s research is in the development of statistical methods and computational tools for personalized therapy.
Research Themes: Tumor heterogeneity, single cell sequencing analyses, integrative analysis of multi-omics data
Dung-Tsa Chen, PhD – Senior Member
Dr. Chen’s primary areas of expertise include clinical trial design, genomic data analysis, mixed effect models, survival data analysis, and biomarker analysis. He developed several statistical designs for clinical trial, such as a Bayesian pick-the-winner design in a randomized phase II clinical trial and power calculation for predictive biomarker studies with survival data as endpoint, as well as several genomic signatures with patents awarded, such as malignancy-risk gene signature in breast cancer.
Research Themes: Clinical trials, biomarker data analysis, Time-To-Event data analysis
Steven Eschrich, PhD – Senior Member
The Eschrich Lab uses Bioinformatics and Machine Learning methods to answer translational research questions within cancer research, with a focus on Lung Cancer and Radiation Oncology. Methodological work includes reproducible research pipelines, normalization techniques and machine learning models from molecular data.
Research Themes: Bioinformatics, Machine Learning, Systems Oncology
Jongphil Kim, PhD – Associate Member
Dr. Kim’s primary areas of expertise include multiple comparisons, time-to-event data analysis, design of clinical trials, imaging data analysis, and concordance analysis. He collaborates extensively with oncologists and scientists affiliated with Blood & Marrow Transplantation (BMT), Malignant Hematology, Cancer Imaging, Radiology, Basic Sciences, Breast cancer, Cancer Epidemiology, and Health outcomes & Behavior program.
Research Themes: Multiple Comparisons, Time-To-Event data analysis, Clinical Trials, Radiomics, Radiology, BMT, and Malignant Hematology
Youngchul Kim, Ph.D. – Assistant Member
Dr. Youngchul Kim is dedicated to a personalized cancer treatment by developing prediction models of responsiveness of cancer patients after anti-cancer drug therapy based on pharmacogenomics data of preclinical cancer models. He also studies associations of virus/bacteria with cancer progression and patients' treatment outcomes by analyzing human microbiome data such 16s rRNA sequencing data.
Research Themes: personalized cancer treatment, anti-cancer drug repurposing, Microbiome
J. Ross Mitchell, Ph.D. - Artificial Intelligence Officer & Senior Member
Dr. Mitchell’s goal is to leverage advanced technologies to improve outcomes for cancer patients. His research is focused on algorithms to improve the diagnosis, treatment and monitoring of this disease. He uses Artificial Intelligence, Machine Learning, Deep Learning, Medical Imaging and Radiomics to build horizontal technology platforms. These are then applied vertically across multiple cancer types through close collaborations with Moffitt scientists and clinicians.
Research Themes: Artificial Intelligence, Machine Learning, Deep Learning, Radiomics, Cloud Computing, High Performance Computing
Qianxing (Quincy) Mo, PhD – Associate Member
Dr. Mo’s research focuses on the development of new statistical methods and software for integrative analysis of multi-omics data typically generated from microarray and next generation sequencing experiments. His clinical research collaborations primarily focus on design and analysis of clinical trials and examination of clinical and treatment factors predictive of clinical outcomes. His collaborations in basic science and genomic studies mainly focus on statistical analysis and mining of the data generated by experiments using microarray or high-throughput sequencing technology.
Research Themes: Integrative analysis of multi-omics data, Biostatistics, Bioinformatics, Clinical Trials.
Michael J. Schell, PhD – Senior Member
Michael Schell research focuses on strategic issues in statistical practice and its broad dissemination, particularly in clinical trials and other late science findings.
Research Themes: Statistical Analysis, Clinical Trials, High-Throughput Sequencing
Steve Sutton, PhD – Assistant Member
Dr. Sutton collaborates primarily with investigators focusing on psychosocial components of cancer prevention and quality of life following cancer treatment. Projects include randomized controlled trials of interventions targeting smoking cessation and cancer screening, large survey studies investigating health disparities, laboratory studies of smoking behavior, and observational studies of post-treatment quality of life.
Research Themes: Multiple imputation, Multi-level modeling
Jamie K. Teer, Ph.D.- Associate Member
Dr. Teer’s research focuses on tumor genomics, bioinformatics methods for identifying genomic variation, and storage and visualization of large omics datasets.
Research Themes: Genomics, High-Throughput Sequencing, Visualization
Mingxiang Teng, PhD, Assistant Member
Dr. Teng’s research focuses on developing computational methods to address data challenges in cancer genomics studies, such as data quality, data bias, pre-processing and functional annotation of high-throughput sequencing data.
Research Themes: Data Cleaning, High-throughput Sequencing, Bioinformatics
Xuefeng Wang, PhD – Assistant Member
Dr. Wang’s lab develops efficient machine learning and statistical methods, such as kernel learning and ensemble learning, for analyzing big data in genomics, drug development and personalized medicine. Dr. Wang is currently involved in multiple collaborative research projects in cancer immunology and cancer epidemiology at Moffitt Cancer Center.
Research Themes: Statistical Genomics; Computational Biology; Machine Learning
Xiaoqing Yu, PhD – Assistant Member
Dr. Yu’s research focuses on Next-generation sequencing data analysis, tumor-microenvironment interaction and its association with immunotherapy response, and application of Bayesian methods in high-throughput data.
Research Themes: Cancer genomics, Cancer immunology, single cell sequencing analyses, Tumor microenvironment