Computational Sciences supports computational approaches to complex problems; develops software applications and platforms that facilitate access, visualization and sharing of data and algorithms and support JAX's research data and service products. In addition to our staff listing below, we have a full list of Computational Sciences Interns available.
My project focuses on combining spatial transcriptomics and imaging data to predict phenotypes in cancer and other diseases. In my previous research I developed TISMorph software (in Matlab) to add new cell shape features which common software were not able to capture. Then I used these features to distinguished between cancer and normal cells with single cell resolution and predicted the function of novel kinases. I also mathematically modeled nuclear Beta-catenin's switch like response to WNT stimulation. During my last four years I have worked closely with biologists, developed a pipeline for them in R, and mentored them to analyze, automatically label plate-based assays and visualize their data.
"As you navigate through the rest of your life, be open to collaboration. Other people and other people's ideas are often better than your own. Find a group of people who challenge and inspire you, spend a lot of time with them, and it will change your life." ~ Amy Poehler
Bri started with JAX, as an IT Project Manager, in April 2016, where she was involved in many key projects, including the launch of CKB’s public site ahead of the ASCO conference in 2016, aiding MCGI during their establishment, and the implementation of the separate clinical infrastructure at JAX Genomic Medicine in Farmington. Bri most recently supported the IT infrastructure team with multiple cloud based collaboration initiatives aimed to improve the means by which our staff collaborate; not only with each other, but external partners and researchers around the globe. These initiatives involved enabling the secure use of Google Genomics Cloud Platform, implementing Webex for video and audio collaboration, migrating TIS to Webex from Adobe connect for their educational Webinars, and bringing JAX a secure, cloud based storage solution that can be accessed from any device anywhere in the world with Box.
Bri has been a project manager since 2011, working in various fields including insurance, pharmaceuticals, and manufacturing, and holds a BS in Physician Assistant Studies, Public Health, and Related Clinical Sciences.
She is looking forward to bringing her skills to the Computational Sciences department.
Well-organized and proficient at multitasking and prioritizing. Hardworking, dedicated professional with extensive experience in Office Administration/HR Management. A strong work ethic with a commitment to integrity and excellence. A team player fostering trust and building strong relationships. Ability to work in any organization. I have provided administrative support for High Level Senior Directors. Expert in submitting Concur requests. Prescreens all candidates via phone and face-to-face interviews. Onboarding of new and exit interviews with departing employees. Equipment and software procurement. Video/room conference scheduling across multiple locations. Monthly reporting including transaction research. Expense submission and reimbursement. Partner with Human Resources.
I have a B.S. in Applied Mathematics from NYC College of Technology. I started my time at Jax in 2014 as a Software Quality Assurance Intern, joined Computational Sciences as an Associate Scientific Software Engineer in 2016 and became a Scientific Software Engineer in 2018. I'm a "full-stack" engineer who works on a variety of problems including genomics algorithm optimization in C/C++, RESTful API design and implementation using Flask, software reliability engineering, cloud computing, and project and systems design. I'm currently pursuing an M.S. in Computer Science as a member of the inaugural class at The Roux Institute at Northeastern.
I received my M.S. in Computer Science and Engineering and B.S. in Ecology and Evolutionary Biology from the University of Connecticut researching biological databases and ontologies. For over ten years I have been leading the development of web applications for multiple Fortune 500 companies in a number of industries including environmental safety, facility maintenance, and media.
I am excited to be bringing my research and industry experience to The Jackson Laboratory community and contributing to the development of robust, innovative software designed to help us solve some of the most complex problems facing the biomedical community today.
I study data-driven and modeling-related complexities that emerge from biomedical domains, and I am interested in advancing translational science principles and developing networks/systems inference approaches.
I hold a doctorate in Statistical Sciences from the University of Padua, Italy (1995), and was a graduate scholar at London School of Economics (1991-92), Northwestern University (1992) and UC Berkeley Statistics (1992-93). I then joined as a postdoc Stanford University (Computer Science PDP-AI Lab 1994-98), followed by a NATO-CNR Fellowship at the Niels Bohr Institute (Un. of Copenhagen) and the Danish Technical University in 1999 (neurocomputing and artificial learning), and an ERCIM fellowship at CWI - Center for Mathematics and Computer Science, in Amsterdam (2001-02) (stochastics).
After a research appointment at the Mathematical Sciences Research Institute UC Berkeley (2003) and a senior scientist appointment at Boston University (2004, Biomed. Eng. Dept.), I held a position at the biotech Serono in Evry (2005, Head of Methods) before joining the CRS4 Bioinformatics (Polaris Science & Tech Park, Pula-Italy) as the Head of the Quantitative Systems Biology Group (2006-11). Afterwards, while engaged (2012-15) in founding the Laboratory of Integrative Systems Medicine with the Institute of Clinical Physiology, National Research Council of Italy (CNR), and coordinating the CNR's Big Data in Health activities (2016-17), I joined the former Center for Computational Science (now IDSC) at the University of Miami as the Head of Computational Biology & Bioinformatics (2011-22).
I held faculty appointments in Shanghai (the Chinese Academy of Science, 2011), in Rio de Janeiro (the Fiocruz Foundation (2008-10), and I was appointed visiting scientist at the International Centre for Theoretical Physics in Trieste, Italy (2003), and at the Institut des Hautes Études Scientifiques (IHES) in Paris, France (2010). I am serving on a few Journals' Editorial Board, like Briefings in Bioinformatics, Journal of Translational Medicine, Frontiers in Medicine, in Public Health, in Big Data etc.
I am conducting research on methodological themes such as Data Science, Machine Learning, Radiomics, Systems Biology & Medicine. Cancer, brain disorders and other complex diseases are current application domains.
My professional background includes experiences in various industries and in diverse capacities which have giving me the opportunity to understand end-to-end intricacies of software development life cycles and software platforms. During my career, I have had the opportunity to plan, architect, develop, and lead software projects and teams with the ultimate goal of providing solutions that meet and exceed stakeholder’s expectations. My 15 years of work experience includes serving in various arenas, such as Supply Chain, E-Commerce Software Platforms, and Insurance industries
I recently joined JAX, and I am absolutely thrilled with the opportunity to contribute to the goal of research for tomorrow’s cures and personalized treatments. The world of genome research
and bioinformatics is absolutely fascinating, so I am enthusiastic to apply my skills and to broaden my knowledge as a dive deeper into this great domain and inspiring cause.
I hold a Masters in Bioinformatics minor in Health Informatics. My dissertation revolved around literature based discovery utilized to mine existing drug interaction evidences in the clinical trial Phase I and II studies and predicting novel drug interaction signals with mentions of adverse related events. Few interesting projects during my masters includes mining the highthroughput data to understand changes in gene expression, methylation and variant profiles in cancers such as Glioblastoma Multiforme and lung cancer using the computational methods and systems biology approaches. My research interests are to apply computational and machine learning approaches to understand the molecular mechanisms and biological complexities in tumors leading to better prediction of treatments.
My research has focused on blood transcriptomics and its application in patient-based research. My primary role at the Jackson Laboratory is to support the development of systems immunology and immunoinformatic research capacity – including through the implementation of training programs and the support of projects and investigators conducting immunology research at Jax.
I hold a Ph.D. degree in biophysics from Rice University with a focus in gene expression network analysis and neural network analysis. I developed models to study how biological structure spontaneously arises in an evolving system. After Ph.D. I worked as a data engineer at Two Sigma Investments,I built tools to automatically acquire, process and visualize financial data and maintained database used for trading and research.
I'm a computational scientist working at the intersection of genomics, machine learning and nonlinear dynamics in advancing data-driven scientific discovery. Specifically, I'm developing a novel based approach whereby RNA velocity is used to further dissect a purely expression-based clustering using ideas of attractors and basins in dynamical systems. In another direction, I'm also working on developing an integrated mouse phenotyping platform that is an end-to-end solution to data collection, automated behavior quantification using machine learning and downstream genetic analysis for the laboratory mouse.
I earned my PhD in physics focusing on the mathematical modeling of dynamical systems (deterministic and stochastic differential equations) on graphs (complex network topologies) to study the emergence of rich spatio-temporal patterns in various complex systems spanning from physics to ecology. After PhD, I did my first postdoc in the theoretical ecology group at the Institute for chemistry and biology of marine ecosystems in Germany, where I worked on developing mathematical models of biodiversity in multi-species ecosystems competing for limited resources and developed expertise in applying machine learning methods to learn species-species trait relationships. Subsequently, I took a second postdoc at NC state university where I worked on the design and implementation of physics aware neural networks that can learn to correctly predict the dynamics of a system while respecting the underlying conservation laws. I also worked a bit on design of latent models that would enhance the interpretability and explainability of models in scientific machine learning.
At JAX, I'm excited to work on predicting cell fate dynamics by constructing a transcriptome-wide continuous vector field from RNA velocity.
After obtaining my B.S. in Electrical Engineering and Computer Sciences at the University of Maine in May 2021, I began working as an Associate Scientific Software Engineer on the Computational Sciences team. My time thus far at JAX has been spent developing the BioConnect project. I work as a research and development engineer, seeking to find creative solutions to complex datasets and problems. My experience includes UI design, data modeling, web app design & development, and data visualization. My back-end experience is varying due to my Electrical Engineering background, but I'm working towards becoming a full-stack engineer.
I'm looking forward to expanding my knowledge at JAX and being a part of the Cube Initiative.
I use quantitative approaches to unravel the genetic factors that underlie complex disease susceptibility. I develop and apply computational and statistical methods to integrated large scale ‘omics data to provide further insights into the genetic pathways involved in the risk of disease.
I received my PhD in genetic epidemiology from the School of Medicine, University of Queensland, Australia, researching the genetic and environmental influences of obesity in Australian twin families. I completed post-doctoral positions at the University of Hong Kong working on founder mutations in Hirschsprung’s Disease in Han Chinese, and at the Singapore Eye Research Institute where I investigated the genetic variants linked to age-related macular degeneration and the role of endophenotypes in glaucoma in the three major ethnic groups in Singapore – Malay, Indian and Chinese. As a researcher at the Massachusetts General Hospital, I used exome chip and targeted sequencing data from related and unrelated individuals to identify novel genetic variants responsible for Type 2 Diabetes through their association with related quantitative phenotypes.
At the New York Genome Center, I was a senior bioinformatics scientist in genetic epidemiology and statistical genetics responsible for ensuring the integrity as well as the analysis and interpretation of pedigree-based data. I also provided lead support to several large ongoing projects which used sequencing and chip data to more precisely localize and characterize genes underlying previously identified variants in diverse ethnicities and races including African Americans, Puerto Ricans and Europeans. Before joining JAX, I was a population informaticist and team lead at Sema4, building out the molecular ancestry calculation pipeline for the expanded carrier test using new genotype technologies.
After receiving my degree in Museum Studies in 2010, I moved to St. Louis, MO to work at the Saint Louis Science Center as an exhibits and program evaluator. In 2018, I participated in a museum conference that changed my career trajectory by introducing me to human computer interaction concepts. I later found a bootcamp program for user experience design which then connected me to my first job as a UX designer. I joined CS in August 2022 and primarily design for BioConnect, but I also enjoy working collaboratively to increase usability across all CS applications.
“Quality is not an act, it is a habit.”— Aristotle. I am passionate about testing and validating software systems that help solve problems and work with my team to ensure that a quality product is delivered .
I hold Bachelors in Electronics Engineering and I have over 18 years of experience in Software Testing and validation. I have been involved with testing real time systems and complex integrated systems. I have led testing teams in various industrial domains such as E-Commerce, Oil and Gas, Nursing education and storage and network controller testing. This has helped me develop an insight to looking at applications from design as well overall system perspective. I love to work with my team at The Jackson Laboratory to develop and deliver quality software which helps scientific community in their research efforts to make a difference in biomedical world!
I have a strong background in the physical and computational sciences, large-scale computational techniques, and software design, allowing me to model biological systems in multidisciplinary projects.
In my post-doctoral research at Michigan State University, I have developed novel statistical and mathematical approaches in open-source packages PyIOmica, DigitalCellSorter, and DECNEO for time-series data analysis and discovery of the combinations of receptors from an extensive collection of single-cell RNA sequencing datasets. Currently, I am developing advanced computational approaches for quality control and downstream analysis of spatially resolved transcriptomics data. I am interested in algorithms exploring tumor growth, development dynamics, and gene expression regulation. I enjoy researching complex processes in the tumor microenvironment to help understand cancer biology questions through the analysis of Next Generation Sequencing data. I help automate and standardize data analysis workflows by creating Nextflow-based computational pipelines using state-of-the-art computational biology methods. My objective is to continue biostatistics analysis, develop bioinformatics algorithms and encapsulate these novel methods in reliable software for personal and HPC systems..
I worked as a programmer on medical software for 6 years, where I gained a strong background in software development. When a bug could negatively impact the health of a patient, it really impresses upon you the importance of attention to detail, testing, and writing bug-free, supportable, and well-documented code.
Areas of interest include machine learning, data analysis, image analysis, and data mining. Particularly interest in how to best apply standard/industry software engineering practices to scientific research software projects.
I'm interested in how to leverage open-source software to help advance scientific research. An active contributor to open-source projects, I contributed to the development of data analysis tools used in synchrotron experiments. As a member of the Computational Science team at JAX, I am working on full-stack applications using Angular, Python, Java and R.
My professional career started in the US Army Corps of Engineers. While stationed in Alaska, the Marshall Islands, the Republic of Korea and Iraq, I worked as a construction engineer, project manager, explosives and demolitions trainer, and logistics officer. After military service, I have been involved in both the worlds of startups and academia. I have led projects in a variety of fields including: hydrologic warning, field maintenance operations, crisis informatics, oceanographic event analysis, and software development. I have also been an assistant instructor in the Department of Spatial Information Science and Engineering at the University of Maine.
I hold a B.S. in mechanical engineering from the U.S. Military Academy at West Point, and a M.S. in environmental engineering from the the Missouri University of Science and Technology.
In my current role and throughout my research career, I have worked at the cutting edge of high throughput sequence data, bioinformatic analysis, and population genomics to answer research questions in evolutionary biology. In my present position I have a particular focus on the application of long-read sequencing technologies and on the transitioning of analysis methodologies from single, linear reference genomes to genome graphs.
I am a software engineer working to make a difference where biology and computer science meet. I excel in full-stack web applications, bioinformatics, and delivering stable products. I aim to bridge the gap between translational genomics and user experience.
I earned my Ph.D at the University of North Carolina at Chapel Hill in the toxicogenomics lab of Ivan Rusyn, where I studied the effects of genetic diversity on gene expression. I then worked at the Jackson Laboratory in Gary Churchill's group, where I developed and applied analytical methods for genetically diverse mice. I developed the first tools for haplotype reconstruction and genetic mapping in Diversity Outbred (J:DO) mice and have been involved in many projects that use J:DO mice. I currently work on tools and educational materials to help investigators use genetically diverse mice in their research.
My interest is to develop principled methods to solve biological problems using quantitative methods from bioinformatics, machine learning, data science, and mathematics. In our group, we apply these computational methods to build predictive models to support cancer precision medicine and to understand the molecular mechanisms leading to diseases.
Since 1998 I have been developing scientific software, for instance computational fluids dynamics on desktop and the web and data acquisition and analysis at the UK's largest science project (Diamond Light Source). At JAX I have been working with Chesler Lab to create a graph database which maps genetic information between humans and mice. Recently I have been working moving image analysis software to the cloud. Scientific software development is where my heart is and what makes the most interest for me at work.
I help to develop web applications, workflows and databases in domains such as iPSC cell line & CRISPR informatics, cancer tumor sample variants and genomics, murine phenotyping and genotyping, colony management/tracking, and interoperability with bioinformatics APIs, ontologies, and other resources.
Originally from the mid-Atlantic region I worked in clinical trials data management and GDB/OMIM at Johns Hopkins, worked overseas in west Africa and Jamaica for several years, then joined Jax in 2000 as a founding developer of the Mouse Phenome Database, later diversifying to other Computational Sciences projects. I'm active in several community organizations and music groups in Bar Harbor.
I have a background in research and model development in glaucoma, diabetes, and COVID-19. My current interests include using single cell and spatial transcriptomic technologies to investigate disease mechanisms and improving translation of animal research using genetically diverse models and cross-species analyses.
I hold a Ph.D. degree in Biostatistics from Tulane University. My research mainly focuses on developing and applying computational and statistical approaches to integrate and interpret large-scale biological “omics” data, with the goal of identifying disease mechanisms, biomarkers and treatment targets..
I hold a Ph.D. in Molecular Biology from the University Texas at Dallas and postdoctoral training in functional genomics and transcriptomics from The Scripps Research Institute, Jupiter, Florida. I transitioned from a research scientist to the field of research project/program management and at JAX, I am part of a robust team of faculty, software engineers, computational scientists, bioinformaticians, and statistical analysts at the Computational Sciences (CS) department whose mission is to provide the informatics resources and services to JAX’s faculty to conduct research of the highest scientific merit. As a Research Project Manager, I facilitate agile project management approaches to plan, facilitate, execute, and deliver software applications, tools, and platform to solve complex problems in genetics and genomics.
My research has focused on immune-genetics and immune-regulation at a resolution of both population and patient-driven questions. I used high dimensional biological data from WGS and functional immune profiling and applied multi-omics approaches by integrating data from RNA-Seq, PhIP-Seq and microbiome data. I am highly interested in building & deploying statistical models to understand biological patterns.
Studies the complex biological processes using machine learning and physics-based dynamical (linear/nonlinear) models using data-driven analysis. Develops computational methods, tools, pipelines, and interactive web apps to construct, model, simulate, and visualize the gene regulatory networks in various biological processes using literature-based evidence as well as RNA-Seq (bulk/single-cell), ATAC Seq, and CHiP Seq data. Employs deep learning on video-based assays and genomics data from behavioral studies to connect behavior and genetics.
My primary focus at JAX is on developing data management systems and tools for the exploration and visualization of complex biological data and its interpretation. As part of a multidisciplinary team at JAX, I frequently collaborate with both experimental and computational scientists on data analysis and visualization projects.
In my role as a Scientific Software Engineer, I am leading the JAX Synteny Browser, a novel tool for interactive visualization of regions of conserved synteny between two genomes based on their biological properties such as function and phenotypes.
Another hallmark initiative that I am closely involved in is JAX’s Patient–Derived Xenografts (PDX) program, a platform for data management, visualizations, analysis, reporting of cancer models studies. I lead the implementation of interactive visualizations for cancer treatment response studies (SOC) and develop automated software to run robust pipelines for the analysis of genomic variations in cancer models.
I am also a key member of the Mouse Phenome Database (MPD) project, an integrated platform to explore physiology and behavior through genetics and genomics, for which I create highly interactive data visualization tools using the latest cutting edge and open source technologies.
In addition, I also teach several data science and programming courses/workshops at JAX on coding skills in R, Python and SQL.
My doctoral work focused on vertebrate cell biology, but relied on genomic sequence analysis to identify a more tractable homolog of an ostensibly vertebrate-specific gene in C. elegans. I subsequently worked in the pharmaceutical industry on identifying potential drug targets, comparative genomics, RNA sequence assembly, and bulk annotation of sequence data. Later, as a researcher at USEPA, I identified transcriptomic signatures of exposure to various toxicant classes, developed predictive models for chemical risk assesment and worked on computational tools for contaminant screening using time-of-flight mass spectrometry. Since joining JAX, I have been leading small teams of computational scientists and software engineers, as well as developing pipelines for long-read sequence analysis and splice-variant analysis. I have also taught short courses on various aspects of applied statistics, machine learning, software development and bioinformatics.
I have a strong background in computational biology/bioinformatics with specific expertise in single-cell sequencing technology. I have analyzed transcriptomes as well as genomes of thousands of single cells from a range of cell types, including cancer, induced pluripotent stem cells, neuronal cells, blood cells, ovarian cells for various studies identifying gene signatures and/or mutations specific to particular cell types. Recently, we studied single cell transcriptomic profiles of glioblastoma (bran cancer) and identified S100A4 as an immunotherapy target. I have also been involved in the development of a computational approach to support the design and analysis of single cell RNA-seq experiments.
I hold a M.S. in Computer Science with a background and hands on experience in High Performance Computing, Analysis Pipeline Development, Genome Assembly & Annotation, Microbiome Data Analysis, Long-Read Sequencing (PacBio & Oxford Nanopore). Previously I worked in The Genome Institute (Washingtion University School of Medicine in St. Louis), The Weinstock Lab and Microbial Genomic Services at JAX. I am now a member of Computational Sciences and working on Pipeline developments & Microbiome related projects.
My overall goal is to apply and develop computational tools that analyze biological data to better understand the mechanisms of complex diseases, with a particular focus on AI and machine learning methods. As the Associate Director of Machine Learning & Imaging, Computational Sciences, The Jackson Laboratory, I strive for deep understanding of the most recent machine learning theories and development and identifying their applications in biomedicine, as well as developing new methods for improved analysis. I participated in the MODEL-AD project during the past five years which aims to develop and evaluate various mouse models for human late onset Alzheimer’s disease (AD). When I applied deep learning methods to extract MRI image features and linked them to genetics and metabolites for an AD study of the MODEL-AD project, I realized existing tools are insufficient to enable us to know which mouse strains best reflect the AD mechanisms in human. Hence, my group recently developed a graph neural network-based cross-species gene expression data analysis framework that aims to maximize the similarity between mouse disease models and human disease data. The longer-term goal of my group is to develop an expandable AI framework for cross-species analysis of multi-omics data which not only include genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiome data, but also images, including cell images, MRI, fMRI, DTI, CT and multiplexed tissue images.
I have published more than 45 peer-reviewed papers in various journals, including first or co-first author publications in Nature Genetics, American Journal of Human Genetics, Machine Learning, Journal of Computer and System Sciences, and IEEE Transactions on Information Theory.
Camille Liedtka has a background in medical Anthropology and Behavioral Analysis and is pursuing a master’s degree in Project Management at Northeastern University. She is currently working at Jackson Labs as an Associate Research Project Manager with the Computational Sciences team where she leverages agile methodologies and workflows to allow for projects to match the pace of innovation. Previously she worked at Wayfair as a customer service trainer where she wrote, developed and facilitated curriculum while leading international project teams focused on knowledge services & distribution.
I joined the Jackson Laboratory having previously worked on a diverse set of organisms. My Ph.D. focused on population and conservation genetics of a submersed aquatic plant species, which was targeted for restoration efforts. Prior to joining Jax, I was a member of the Department of Entomology at the Smithsonian Institution. In that role, I was responsible for the generation and analysis of target enrichment data used in phylogenomics. At Jax I have been involved in research projects associated with the Jax PDX program, PDX Network, PIVOT consortium, and Cube initiative.
I am a mathematician with a wide range of interests in systems biology. My current research focuses on machine learning tools to automatically identify biologically meaningful phenotypes from high-dimensional signals (e.g., molecular signatures, images, or time series) and gene prioritization for gene mapping experiments. I am currently a core leader for image analysis in the JAX Senescence Network Mouse Tissue Mapping Center and the Shock Center for Excellence in Aging Research. I am also interested in genetic network analyses and causal modeling for complex biological data.
My B.A. from the University of Vermont is in Pure Mathematics. I joined Jax Computational Sciences in 2020 as a Research Assistant and became a Research Data Analyst in 2022. My primary area of expertise is in image analysis using deep neural networks in Python and Julia with a focus on histological images. I am currently pursuing an M.S. in Data Science from the Roux Institute at Northeastern.
Broad experience working with animal model based LIMS systems, including UI design, system architecture and integration, data modeling, and development. When I started at Jax in 2003, I worked for Dr. Carol Bult in the NMF on the JaxTrack system supporting colony management and phenotyping. I subsequently joined Computational Sciences, where I worked on the JCMS and JaxLIMS systems. I spent a few years away from Jax working on a commercial SaaS LIMS system, primarily responsible for system architecture and DevOps. This gave me the opportunity to work with a large number of academic and commercial laboratories doing animal model research, and allowed me to participate in Microsoft engineering workgroups and case studies. I am excited to be back as a software engineer in Computational Sciences and look forward to working on the Cube initiative.
I joined the Jackson Laboratory in 2020 as a Computational Scientist. I am focusing on the statistical analysis of single cell omics and the development of methods for data normalization, variance stabilization, differential expression and image processing. Prior to the Jackson Laboratory, I was a Senior Research Fellow at the Cardiovascular Research Institute of the National University of Singapore. Before that, I was employed in RIKEN Yokohama and in the Bioinformatics Institute of Singapore as a senior researcher. I received my Ph.D. in Statistics from the University of Bristol, UK in 2008.
I received my PhD in Integrated Biomedical Science, concentration in cancer biology, from Ohio State University and my MS in Biomedical & Health Informatics from University of Washington. During my PhD, I focused on mouse genetics and genomics from the perspective of a bench scientist; my dissertation work involved investigating mutation types and numbers in preneoplastic cells and tissues from Fhit knockout mice. I completed a postdoctoral fellowship and MS degree simultaneously at the University of Washington where I investigated differential gene expression in the parasite Leishmania donovani.
I recently joined Jackson after many years of diverse commercial software development, including consumer, professional and medical software and firmware. Most recently I worked on a team that developed image processing algorithms for detection of physical features in coronary OCT (optical coherent tomography) scans. Prior to that I developed the image processing chain for a disposable endoscope used for direct visualization of pancreatic ducts. For this product I also designed the control feedback algorithm for automatic illumination control. I also worked on the image processing chain for an optical particle analysis system. While the system had its origin in marine biology research, it's also been used in pharmaceutical and industrial applications. Prior to working with the scientific and medical communities I worked for many years in commercial mapping. In addition to developing consumer and professional mapping applications, I also developed the software for a multi-sensor 360-degree panoramic camera. The purpose of this camera was to collect mapping data from a moving terrestrial platform to complement data collected from the aerial platforms. I look forward to applying these experiences to the diverse problems at Jackson.
I hold a PhD in Life Sciences from University of Tennessee/Oak Ridge National Laboratory Genome Science and Technology Program, with a focus in statistical and quantitative trait genetics of behavioral traits in genetic reference populations and integrative functional genomics of behavior across species and experimental platforms. During my Ph.D. program I investigated the increased precision and resolution in QTL mapping using mouse reference populations including the expanded BXD recombinant inbred (RI) strain panel and the Collaborative Cross (CC) reference population The BXD RI study highlighted the increase in statistical power obtained in using the expanded BXD RI strain panel. The CC study highlighted the increased allelic variation and QTL mapping precision achieved. Upon completion of my Ph.D., I joined the Computational Sciences - Statistics and Analysis group at The Jackson Laboratory in 2012 as a biostatistician, with responsibilities including QTL analysis, expression QTL analysis, gene expression analysis, statistical modeling of diverse biological datasets, and statistical consulting. In addition to my contributions towards the field of quantitative trait genetics and statistical genetics, I have undertaken several other data analysis tasks involving differential gene expression analysis using next generation sequencing technologies, leading to the generation of new or validation of existing hypothesis.
Over the past ten years, I specialized myself in big data analysis (i.e., Machine-Learning) applied to Genomics, Semantics and Next Generation Sequencing (NGS) data linked to single-cell and cancer research. I obtained my PhD in Computational biology and Evolutionary Genomics at Ecole Centrale de Lyon (Lyon University), France. My previous research at the Cancer Center of the University of Hawaii and the Center for Epigenomics (UCSD) elaborated advanced analytical methods and workflows for single-cell, multi-omics, and survival datasets. My current work involves working with single-cell ATAC-Seq and RNA-Seq datasets and developing new statistical methods for inferring epigenomics regulation.
Over 15 years of multi-faceted, cross-functional experience in managing and analyzing data generated by diverse high-throughput genomic technologies. Hands on experience and leadership in genomics, big data management, analysis pipelines, interpretation and application development in academic, government and commercial settings.
As a quantitative geneticist I am motivated to understand the translation of genetic variation into complex phenotypes, and the rapidly developing capacity in our field to interrogate the genetic and transcriptomic content of cells has allowed us to generate great insight into this process. In my work at The Jackson Laboratory I am focused on basic and translational cancer research using patient-derived xenograft models, as well as the development of computational methods and curated data resources required for integrative research.
My perspective on biology celebrates what diversity can teach us. Within the mouse species, I study genetically diverse populations such as the BXD, Collaborative Cross, and Diversity Outbred. With Elissa Chesler and colleagues in the CSNA, my work has reiterated that who a mouse is – their genetic makeup and its sex – matters greatly to how a mouse behaves and how their brain responds. I am particularly interested in comparative and cross-species techniques, which give us perspective on the conserved systems that underpin similar behaviors spanning the animal kingdom.
Though I now work in Computational Science, my training was as a mouse behavioral neuroscientist who studies genetics and genomics. My work convinced me that biologists must do two things to understand complex systems like the brain: 1) collaborate with each other, and 2) use high-throughput techniques like next-generation sequencing and high-performance computing. I am delighted to collaborate with multiple investigators at JAX, where I apply the skills I attained in computational work to find the new biology of disease that will lead us to better treatments.
I received my MS in mathematics and wrote my thesis on graph based image segmentation. I like to work on mathematical machine learning algorithms and optimization problems. I plan to develop and implement state of the art algorithms to solve high impact genomics problems.
Variation in observable traits, such as disease susceptibility, is pervasive in the natural world. Recent advances in sequencing and computation are providing us with an unprecedented view of patterns of genetic variation within species. A major outstanding challenge is to identify and characterize the specific genetic variants affecting complex traits, and the mechanisms through which they do so.
I am a biologist and data scientist who uses single cell functional genomics to understand the genetic basis of complex traits. I utilize genetically diverse model organism populations together with the methodology of statistical and quantitative genetics to reveal novel mechanistic insights into the biology of complex traits. My background and interests include cancer, cardiovascular disease, diabetes, and obesity/metabolic disorders.
My project focuses on the analysis of spatial single-cell proteomics data (e.g. Imaging Mass Cytometry, CODEX) and spatial transcriptomics data (e.g. Visium Spatial Gene Expression by 10x). Previously, in collaboration with biologists from Leiden University Medical Center I have developed interactive pipelines for the analysis of single-cell high-resolution images (ImaCyTe, SpaCeCo). My research interests also include the analysis of single-cell tissue images with Graph Convolutional Networks.
Recent advances in DNA sequencing technology led to the generation of vast amounts of sequencing data and provided an unprecedented opportunity to understand the complexity of the genome. However, this massive flood of sequencing data also created challenges in mining patterns of interest from this data. Moreover, the diversity of datasets [RNA-SEQ, ChIP-Seq, Exome, Whole-Genome, ATAC-Seq, HiC, etc.] generated from various technologies also demand more integrated evaluation across multiple sequencing platforms and data types. My dissertation focused on deriving meaningful patterns from these datasets generated from both model and non-model species, studying the variations in non-coding RNA (ncRNA) secondary structure, and developing novel methods for ncRNA detection in the genome using patterns of chromatin modifications. In my role as an associate computational scientist (2014-2016), computational scientist (2016-2019), and then Senior computational scientist (2019-2021) at The Jackson Laboratory (JAX), I worked and led various research projects in collaboration with the faculty. I currently serve as an Associate Director of the Genome Informatics group in the Jackson Laboratory, Department of Computational Sciences and lead a large team of Computational scientists and Bioinformatics Analysts. My group at JAX primarily focused on building systems/algorithms to effectively analyze next-generation sequencing data generated from various sequencing technologies from humans and mice. We have developed particular expertise in the analysis of patient-derived xenograft (PDX) cancer samples as a part of the NCI PDXNet and Pivot program. To sum up, my team focuses on research and building effective systems to help facilitate the research.
I joined The Jackson Laboratory's Computational Sciences Department to become the Deployment Lead in the Lab’s involvement in the NCI Cancer Biomedical Informatics Grid® (caBIG®) initiative and stayed on as a member of the Stats and Analysis group. My background in protein and genomic databases and conceptual representations of life sciences research has enabled me to contribute to the clinical and the PDX programs. I continue to gain experience in analysis of next generation sequencing, calling on my history of bench research in cancer, pharmacology, and transcriptional regulation. I have experienced the roller coaster ride of small start up companies and welcome the opportunity to be part of the JAX community. On any given day, I may spend time wrangling data for PIs or collaborators, launching a DNA-seq analysis, tracking down data for a potential PDX customer and checking out a new analysis tool or online database.
My initial contributions to science were the result of a Master of Science education program (University of Illinois - Champaign-Urbana) endeavor to identify quantitative trait loci affecting swine meat characteristics. Shortly after my leave of the University, I joined The Jackson Laboratory in 2007. The numerous collaborations that took place at the Laboratory have resulted in publications pertaining to a variety of conditions including: alopecia areata, asthma, cancer (of the brain, lung and skin), diabetes, chronic kidney disease, eye disease, and reproductive disorder. The general aim of these studies is to provide mouse model information as a method to improve human health. My role in these studies has been as a biostatistician consultant.
Ms. Sundberg has been involved with computer applications for 45 years. She developed herd health management software in 1976, a database for managing pesticide chemicals for the state of Indiana in the mid 1970s, and worked on various other projects for the Administrative Data Processing Center at Purdue University. During the past thirty years she has worked on a project for managing mouse breeding colonies (JAX Colony Management System, JCMS) and a relational database for medical records management (The Mouse Disease Information System, MoDIS). MoDIS was developed in 1987 to manage histopathological data from The Jackson Laboratory massive mouse production colony as well as research data. Over the years this evolved to integrate the Mouse Anatomy Ontology (MA) and Mouse Pathology Ontology (MPATH), to eventually provide an integrated tool for storage of basic research discoveries, linking gross and photomicrographs to case materials, and getting this information into publicly accessible databases such as Pathbase (http://www.Pathbase.net), and Mouse Genome Informatics (http://www.informatics.jax.org). Currently she is providing software quality assurance for these Computational Science projects: PDX (patient-derived xenograft) platform, GeneWeaver, BioConnect, Mouse Phenotype Database (MPD), Study Intake Platform (SIP), Diversity Informatics Platform (GeDI), POET, and PhenoPackets. .
I am 30+ year technology and research management professional with experience in leading complex programs and initiatives in the life sciences. My knowledge of strategic program leadership, research project management, information technologies, molecular biology and genetics combine with outstanding communication, analysis and collaboration skills to enable transformative team data science.
I am primarily trained as a computational scientist. During my Ph.D., I worked on the developing probabilistic machine learning methodologies for various computational problems in engineering systems (uncertainty quantification, stochastic partial differential equations, dynamical systems, inverse problems etc.). As a postdoc, I worked on the problems pertaining to the interpretability of sequence-function relationships learned by DNN models from high-throughput sequencing functional assay datasets. As a computational scientist at JAX, I work on integration of cross-species and multi-omics data.
I am a full-stack principal level scientific software developer with a passion for software development that enables scientific research. I have over two decades of experience in a variety of industries, with a focus in biotechnology.
My role at Jackson Laboratory is one of technical leadership on multi-developer projects, using agile techniques, striving to provide meaningful/usable systems in a timely manner.
Emerging single-cell transcriptomic and highly-multiplexed imaging methodologies are advancing our basic understanding of tumor heterogeneity and its impact on patient outcome and therapeutic response. My previous work involved predicting disease progression and drug response using bulk (principally, expression) data. My interests lie in leveraging these new single-cell modalities to improve our ability to extract insight from the wealth of existing (and clinically annotated) bulk data. For example, we have recently completed a deconvolution DREAM challenge comparing methods that infer immune sub-populations from bulk expression data. Several participant methods used single-cell RNA-seq to identify markers that could subsequently be used to detect the corresponding population in bulk data.
My research focuses on revealing the genetic underpinnings of traits that vary continuously in populations, including those sensitive to environmental inputs. During my doctoral training, I used a series of crosses to characterize the relative contributions of genetics and aging to variation in fertility among hybrid male mice. My postdoctoral work focused on identifying novel genetic modifiers of toxicant susceptibility using a genome-wide association study of developmental delay in C. elegans. Throughout my career, I have sought to improve our understanding of human health and disease using genetically diverse model organism populations. At JAX, I support ongoing projects using quantitative genetics in the Diversity Outbred population by developing and maintaining computational workflows for QTL mapping, haplotype reconstruction, and genomics broadly.
North Carolina State University
2014 - 2019
University of Delaware
2010 - 2014
Adv: Dr. Erik Andersen
2019 - 2022
Graduate Research Assistant
North Carolina State University
Adv: Dr. David Aylor
2014 - 2019
Undergraduate Research Assistant
University of Delaware
Adv. Dr. Shawn Polson & Dr. K. Eric Wommack
2011 - 2014
My research is focused on the mediation roles of molecular between exposures and health outcomes. I developed PMDDA data analysis workflow to perform exhaustive metabolomics annotations. I also proposed the concept of 'reactomics' to retrieving general chemical relationships among molecular in biological samples. Meanwhile, I developed 'gatekeeper' model to screen the molecular sensitive to multiple exposures. With background from both wet lab and dry lab, I am trying to improve regular statistical or machine learning model with biochemistry knowledge. I am also interested in reproducible research and built a R based data analysis image (xcmsrocker) for reproducible metabolomics data analysis.
I have a diverse expertise in analysis of Multi-omics datasets, Immunology, Microbiome, Developmental Biology, RNA Biology and Cloud Computing. During my Ph.D. I studied the downstream targets of Hox genes in developing hindbrain using mouse, zebrafish and chick models. My postdoc was focused on developing Bioinformatics pipelines for the analysis of microbiome data and various immunological studies. At Jax I’ve been working with Adam Williams on identification of lncRNAs in RNA-seq and other omics datasets; with Olga Anczukow on developing pipelines for splicing analysis; and with Karolina Palucka on studies in Immunology and Covid-19 research.
My interest mainly lies in developing and applying statistical, system computational approaches and machine learning algorithms to discover complex disease mechanisms, biomarkers and treatment targets by integrating multiple “omics” data.
My research mainly focuses on genetic dissection of complex diseases using/developing the state-of-the-art multi- and inter-disciplinary approaches of genomic technologies, and statistical and bioinformatical methods. The approaches involve genome-wide association analyses, genome-wide transcriptome analyses, proteome-wide protein expression profiling, epigenetic profiling by various statistical methods, including e.g. data imputation, regularized regression, pathway analysis, fine mapping. I am interested in developing and applying statistical methods and bioinformatics tools for integrating large, complex multi-omics datasets (e.g. DNA-Seq, bulk RNA-Seq, sc/snRNA-Seq) in research, e.g. Alzheimer's disease and cancer.