Speakers

Thursday May 2nd, 2019

Barbara Rae-Venter, J.D., Ph.D - Keynote

Barbara Rae-Venter, J.D., Ph.D., is a retired intellectual property attorney who specialized in the patenting of biotechnology inventions. She earned a J.D. from the University of Texas at Austin Law School and a B.A. double major in Psychology and Biochemistry (Special Projects) and a Ph.D. in Biology (Biochemistry) at the University of California at San Diego. She is licensed to practice before the US Patent and Trademark Ofﬁce and the State Bar of California (inactive). Barbara is a Search Angel with DNAAdoption.org helping adoptees ﬁnd their birth relatives and also helps teach the online autosomal DNA (atDNA) classes that DNAAdoption.org offers to help adoptees use their atDNA to ﬁnd birth relatives.

Barbara’s identiﬁcation of the mother of Lisa Jensen, abducted in infancy and then abandoned by her abductor as a 5 year, led to solving the murder of the Allenstown Four in Allenstown, NH. https://www.forensicmag.com/article/2017/02/tale-abandoned-girls-dna-led-notorious-cold-case. Barbara subsequently determined the true identify of Lisa’s abductor, a man of many aliases, as Terry Peder Rasmussen. More recently, using the same technique as she used to identify Lisa and Rasmussen, Barbara identiﬁed a suspect for the notorious Golden State Killer as Joseph James DeAngelo. The technique Barbara used in these three cases is now being used to solve many previously “unsolvable” cases.

For her work, Barbara was recognized by the journal Nature “Ten people who mattered this year,” in 2018. https://www.nature.com/immersive/d41586-018-07683-5/index.html

Barbara continues to work with law enforcement assisting with identiﬁcation of suspects in violent crimes and the identiﬁcation of unidentiﬁed victims of violent crime. An example of a recent case on which Barbara assisted is https://www.forensicmag.com/news/2018/11/genealogy-cracks-another-cold-case-homicide-carlsbad. Additional press release: https://www.charlotteobserver.com/news/state/north-carolina/article225508170.html

Barbara also helps to train law enforcement professionals in the use of atDNA. Barbara can be reached at genealogyconsult@gmail.com.

Steven H. Kleinstein
Associate Professor of Pathology
Interdepartmental Program in Computational Biology and Bioinformatics
Yale School of Medicine, New Haven, CT, USA

Dr. Steven Kleinstein is a computational immunologist with a combination of “big data” analysis and immunology domain expertise. He is currently an Associate Professor (tenured) in the Department of Pathology and Department of Immunobiology at the Yale School of Medicine. He is a member of the Human and Translational Immunology program, the Yale Center for Medical Informatics and the Interdepartmental Program for Computational Biology and Bioinformatics. Dr. Kleinstein received a B.A.S. in Computer Science from the University of Pennsylvania and a Ph.D. in Computer Science from Princeton University. Prior to joining Yale, Dr. Kleinstein worked as a Computational Scientist at Physiome Sciences, Inc. (Princeton, NJ) and as a research scientist at Princeton University, where he ran the Program in Integrative Information, Computer and Application Sciences (PICASso).

Dr. Kleinstein’s research interests include both developing new computational methods and applying these methods to study human immune responses. Somatic hypermutation (SHM) and B cell affinity maturation, the core of adaptive immunity, have been a particular focus of his work, with a major emphasis on large-scale B cell receptor repertoire analysis (AIRR-Seq). His lab makes available the widely-used Immcantation framework (http://immcantation.org), which provides a start-to-finish analytical ecosystem for AIRR-Seq analysis. Another significant research emphasis involves methods development for other high-throughput immune profiling data types, such as transcriptomics, with several applications to infection and vaccination responses (e.g., influenza). His lab has been involved in many collaborations with experimental and clinical groups, and several NIH consortia including: (1) the Modeling Immunity for Biodefense (MIB) program, (2) the Big Data to Knowledge (BD2K) initiative, and (3) the Human Immunology Project Consortium (HIPC).

Caitlin Rivers, PhD, MPH
Senior Scholar, Assistant Professor
Expertise: Epidemiology; outbreak science; disease modeling
Center for Health Security
John Hopkins Bloomberg School of Public Health

Dr. Rivers is a Senior Scholar at the Johns Hopkins Center for Health Security and an Assistant Professor in the Department of Environmental Health and Engineering at the Johns Hopkins Bloomberg School of Public Health. Her research focuses on improving outbreak preparedness and response through the use of modeling and forecasting, data standards and data sharing, and public health policy.

Prior to joining the Center, Dr. Rivers worked for 2 years as a civilian epidemiologist at the Army Public Health Center, where she served concurrently as Branch Chief in the Disease Epidemiology Division, Chief Epidemiologist of the Health of the Force surveillance report product line, and Program Manager of the Acute Respiratory Disease Program. In those roles, Dr. Rivers worked with a team tasked with monitoring the health of army soldiers and their families. During that time Dr. Rivers also participated in a National Science and Technology Council interagency working group aimed at bringing pandemic prediction and forecasting in capabilities into the federal government.

Helio Costa, PhD
Instructor, Departments of Pathology and Biomedical Data Science
Founding Director, Stanford Clinical Data Science Fellowship (2018 - Present)
Assistant Lab Director, Molecular Genetic Pathology Laboratory (2017 - Present)
Member, Maternal & Child Health Research Institute (MCHRI)
Stanford Medical School

Helio Costa, PhD, is a geneticist with expertise in genomics, molecular biology, molecular oncology, and bioinformatics. He is currently an Instructor within the Departments of Pathology and Biomedical Data Science at Stanford Medical School. Dr. Costa's research utilizes next-generation sequencing to develop new clinical genome and transcriptome profiling methods with the end goal of translating these tools to clinical diagnostic tests for implementation at Stanford Health Care. His research group is also interested in developing data science and machine learning methods to model and predict clinical outcomes and aid in clinical decision support. He is the founding director of the Stanford Clinical Data Science Fellowship where post-doctoral fellows engage in interdisciplinary clinical research and embed in health care workflows learning, building and deploying real-world health data solutions in the Stanford Health Care system. Additionally, he is an Attending Geneticist, and Assistant Lab Director of the Molecular Genetic Pathology Laboratory for Stanford Health Care. Dr. Costa received his BS in Genetics from University of California, Davis, his PhD in Genetics from Stanford University School of Medicine, and his ABMGG Clinical Molecular Genetics and Genomics fellowship training from Stanford University School of Medicine.

David Kotz, PhD
Professor of Computer Science
Dartmouth College
https://www.cs.dartmouth.edu/~dfk/

David Kotz is the Champion International Professor in the Department of Computer Science. He previously served as Interim Provost, as Associate Dean of the Faculty for the Sciences, as the Executive Director of the Institute for Security Technology Studies, and on the US Healthcare IT Policy Committee. His research interests include security and privacy, pervasive computing for healthcare, and wireless networks. He has published over 200 refereed papers, obtained over $67m in grant funding, and mentored nearly 100 research students. He is a Fellow of the IEEE, a Distinguished Member of the ACM, a 2008 Fulbright Fellow to India, and an elected member of Phi Beta Kappa. After receiving his A.B. in Computer Science and Physics from Dartmouth in 1986, he completed his Ph.D in Computer Science from Duke University in 1991 and returned to Dartmouth to join the faculty.

Alfredo Tirado-Ramos, PhD
Associate Professor of Biomedical Data Science
Associate Professor of Epidemiology
Director, Biomedical Data Science Research Software Laboratory
Director, Biomedical Informatics at SYNERGY Clinical and Translational Science Institute
Scientific Director of Biomedical Informatics, Dartmouth-Hitchcock Health Care System
Dartmouth Geisel School of Medicine
https://bmds.dartmouth.edu/faculty/alfredo-tirado-ramos-phd

Alfredo is Associate Professor of Biomedical Data Science and of Epidemiology at the Geisel School of Medicine at Dartmouth College, where he leads a group of researchers and informatics experts in creating and implementing informatics methods and techniques for the development of data-centric informatics systems, reaching from prevention to treatment and rehabilitation. He was recruited in May 2018 as Director of Translational Informatics at Geisel and Scientific Director for Biomedical Informatics at Dartmouth-Hitchcock Health System, with a mandate to strengthen informatics-based innovation among Dartmouth’s translational researchers. His role in both D-H and Geisel has already improved collaborations for Informatics-based projects between Dartmouth College and D-H. On the other hand, his research interests include novel methods to produce standardized processes and tools which can be leveraged when developing reliable computable phenotypes that are not sensitive to the specific enterprise Electronic Health Record vendors or institutions, but can be leveraged by Electronic Health Record systems to become more usable and relevant to researchers; a pragmatic leveraging of such new resources will advance the design of studies and clinical trials with 21st century technology, and bring our clinicians and other stakeholders onboard; this ability to identify cohorts of people with particular health conditions, across healthcare organizations, by using common definitions has proven to have an intrinsic value for clinical quality measurement, health improvement, and research. Furthermore, he also studies computational simulation of complex systems, such as infectious disease networks. Sexual contact networks are a subset of agent-based infectious disease models that are ideal for modeling sexually transmitted diseases; his work on this subject focuses on models for HIV transmission that handles randomized partner selection, disease progression, basic agent behavior, heterosexual and homosexual networks, testing, treatment, and a non-static population that ages and dies, allowing for long running simulations.

Carly Bobak, BA, MSc
PhD Candidate in Quantitative Biomedical Sciences (QBS)

BWF Mentors:
Jane Hill, PhD (http://engineering.dartmouth.edu/hill-lab/)
Dr. James O’Malley, PhD (https://bmds.dartmouth.edu/faculty/james-omalley-phd)

BWF Project:
Development of a multi-cohort, multi-omics approach for identifying diagnostic tuberculosis biomarkers

A multi-cohort approach allows for the commonalities between many important subgroups of a Tuberculosis (TB) susceptible population (such as children and those with HIV confection) to be studied on a larger scale in order to identify common biomarkers indicative of active TB which are more clinically relevant. I am aiming to integrate many previous transcriptomic studies to predict potential metabolic biomarkers exhaled on breath through the use of pathway and network analysis.

Carly A. Bobak is a current PhD. Candidate in the Quantitative Biomedical Sciences program at Dartmouth College and has an MSc. in Applied Mathematics from the University of Guelph. She is co-mentored by Dr. Jane E Hill (Professor of Engineering) and Dr. A. James O’Malley (Professor of Biostatistics). Carly emphasizes and advocates for the importance of interdisciplinary collaboration to increase the quality of research. She is currently investigating improved computational methods for the discovery of diagnostic biomarkers for Tuberculosis. Other researchers included in this effort have expertise in analytical chemistry, biostatistics, microbiology, computer science, engineering, and infectious disease clinicians. Carly is a current fellow in of the Institutional Program Unifying Population and Laboratory Based Sciences award from the Burroughs Wellcome Fund and is also a Quantitative Biomedical Sciences fellow at Dartmouth College.

Meghan Muse, BA
PhD Candidate in Quantitative Biomedical Sciences (QBS)

BWF Mentors:
Brock Christensen, PhD (http://www.christensen-lab.com/)
Diane Gilbert-Diamond, ScD
(https://geiselmed.dartmouth.edu/faculty/facultydb/)

BWF Project:
Investigating epigenetic alterations in the context of acute weight change

In my work as a second year Burroughs-Wellcome fellow, I am studying how acute weight change in the context of gestational weight gain during pregnancy as well as weight loss following bariatric surgery is associated with alterations to DNA methylation across various tissue types.

After receiving my undergraduate degree in biology from Dartmouth College, I returned to Dartmouth to pursue a graduate degree in the Quantitative Biomedical Sciences program. Through my co-mentorship with Dr. Brock Christensen and Dr. Diane Gilbert-Diamond, my research has focused on investigating associations between alterations in DNA methylation and acute weight change as a means of better understanding how BMI is associated with disease risk at the epigenetic level. I am currently a third year PhD candidate in the QBS program, a second year fellow on the Burroughs-Wellcome/Dartmouth Big Data in the Life Sciences Training Program, and a QBS teaching fellow in the Department of Epidemiology.

Monica Elisa Espinoza, BS
PhD Candidate in Quantitative Biomedical Sciences (QBS)

BWF Mentors:
Anne Hoen, PhD (https://www.hoenlab.org/)
Michael Whitfield, PhD (https://geiselmed.dartmouth.edu/whitfield/)

BWF Project:
Metagenomic Characterization of the Gastrointestinal Microbiome of Patients with Systemic Sclerosis (SSc)

My doctoral work focuses on elucidating the potential environmental triggers of Systemic Sclerosis (Ssc) that contribute to disease pathogenesis and progression. I am working to relate the immune response of patients with this disease as deduced through RNA sequencing of tissue biopsies to the microbial community metagenome found on these tissues through statistical models and multi-omic data integrating network analyses.

I am a native of Southern California, where I additionally completed my BS in Biological Sciences with Excellence in Research from the University of California, Irvine. I matriculated to Dartmouth College after my undergraduate degree to pursue tripartite training in Epidemiology, Biostatistics, and Bioinformatics in the QBS program.