The Geisel School of Medicine Events Calendar
The Geisel School of Medicine Events - ( Subscribe )
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Biochemistry & Cell Biology Seminar Series
Tuesday, April 11, 2023
3:00pm - 4:00pm
In-person: Chilcott Auditorium, Vail Building, Geisel School of Medicine at Dartmouth
Online: Zoom
“All Residues Considered: Selectivity determinants in peptide-binding domains”
Jeanine Amacher, PhD
Associate Professor of Biochemistry
Western Washington University
Host: Nicholas Gill, PhD
If you would like to receive the link and password for this Zoom meeting, please email Jenni.Hinsley@dartmouth.edu.
Tuesday, April 11, 2023
3:00pm - 4:00pm
In-person: Chilcott Auditorium, Vail Building, Geisel School of Medicine at Dartmouth
Online: Zoom
“All Residues Considered: Selectivity determinants in peptide-binding domains”
Jeanine Amacher, PhD
Associate Professor of Biochemistry
Western Washington University
Host: Nicholas Gill, PhD
If you would like to receive the link and password for this Zoom meeting, please email Jenni.Hinsley@dartmouth.edu.
Graduate Program in Biochemistry & Cell Biology
Ph.D. Thesis Presentation
Tuesday April 11, 2023
10:00 AM ET
In-Person: Chilcott Auditorium
Online: Zoom
"Inhibition of CAL PDZ domain via non-canonical and allosteric interactions reveals new targeting sites and strategies"
Research Advisor: Dean Madden, PhD
If you would like to receive the link and password for this Zoom meeting, please email Jenni.Hinsley@dartmouth.edu.
Ph.D. Thesis Presentation
Tuesday April 11, 2023
10:00 AM ET
In-Person: Chilcott Auditorium
Online: Zoom
"Inhibition of CAL PDZ domain via non-canonical and allosteric interactions reveals new targeting sites and strategies"
Research Advisor: Dean Madden, PhD
If you would like to receive the link and password for this Zoom meeting, please email Jenni.Hinsley@dartmouth.edu.
Infant Ankyloglossia, What Do We Know?
Mary O’Connor, MD, MPH
Clinical Professor of Pediatrics, Geisel School of Medicine at Dartmouth
General Academic Pediatrics, Dartmouth Health Children’s
Professor of Pediatrics Emerita, University of Colorado School of Medicine
Mary O’Connor, MD, MPH
Clinical Professor of Pediatrics, Geisel School of Medicine at Dartmouth
General Academic Pediatrics, Dartmouth Health Children’s
Professor of Pediatrics Emerita, University of Colorado School of Medicine
Medicine Grand Rounds
Friday, April 14, 2023
8:00 a.m. - 9:00 a.m.
William N. Chambers Lecture
“Aging Among Homeless Populations: Causes, Consequences, Solutions”
Margot Kushel, MD
Professor of Medicine, University of California San Francisco
Director, UCSF Center for Vulnerable Populations
Director, UCSF Benioff Homelessness and Housing Initiative
You may attend virtually via the livestream link:
http://med.dartmouth-hitchcock.org/education/dept_medicine_grand_rounds_live.html
Friday, April 14, 2023
8:00 a.m. - 9:00 a.m.
William N. Chambers Lecture
“Aging Among Homeless Populations: Causes, Consequences, Solutions”
Margot Kushel, MD
Professor of Medicine, University of California San Francisco
Director, UCSF Center for Vulnerable Populations
Director, UCSF Benioff Homelessness and Housing Initiative
You may attend virtually via the livestream link:
http://med.dartmouth-hitchcock.org/education/dept_medicine_grand_rounds_live.html
The Department of Biomedical Data Science at Geisel invites you to attend a Grand Rounds presentation by Dr. Qi Long, Professor of Biostatistics, Epidemiology, and Informatics and of Computer and Information Science at the University of Pennsylvania on Thursday, April 13 from 12:00-1:00pm at DHMC, Auditorium H (or via Zoom).
Talk title: “Advancing Data Science for Intelligent and Equitable Health”
Host: Tor Tosteson, ScD
Location: In-person at DHMC, Auditorium H or via Zoom (no registration required)
Zoom meeting ID: 503 779 5102
Zoom passcode: 6501974
URL: https://dartmouth.zoom.us/j/5037795102
Phone (if needed for audio only, or to join by phone only): 669-900-6833
Presentation Summary
Rapid advances in technologies have enabled generation and collection of vast amounts of health data in research studies, from healthcare delivery, and from other real-world sources. While such data offer great promises in advancing intelligent and equitable health, they also present daunting analytical challenges. One notable example is the data from electronic health records (EHRs) that are recorded at irregular time intervals with varying frequencies and include structured data such as labs and vitals, codified data such as diagnosis and procedure codes, and unstructured data such as clinical notes and pathology reports. They are typically incomplete and fraught with other data errors and biases. What’s more, data gaps and errors in EHRs are often unequally distributed across patient groups: People with less access to care, often people of color or with lower socioeconomic status, tend to have more incomplete EHRs. In this talk, I will discuss these challenges and share my research group’s recent work on developing robust statistical and machine learning methods for addressing some of these challenges. Our research experience has demonstrated that a trans-disciplinary health data science approach that involves collaboration between statisticians, informaticians, computer scientists, and physician scientists can accelerate innovation in harnessing the transformative power of EHRs to tackle complex real-world problems and exert meaningful impact in medicine. To this end, I will also discuss some open questions that present opportunities for future research.
Biography
Qi Long, PhD is a professor in the Department of Biostatistics, Epidemiology, and Informatics and in the Department of Computer and Information Science at the University of Pennsylvania (UPenn). Dr. Long is the founding Director of the Center for Cancer Data Science, Associate Director of the Penn Institute for Biomedical Informatics, and Director of the Biostatistics and Bioinformatics Core in the Abramson Cancer Center at UPenn. He is an elected Fellow of AAAS and ASA, and elected member of ISI. Dr. Long’s research purposefully includes novel statistical research and impactful biomedical research. The thrust of his research is to develop statistical and machine learning methods for advancing equitable, intelligent health and medicine. He has developed robust methods for analysis of big health data (including, but not limited to, -omics, electronic health records, and imaging data), causal inference, analysis of incomplete data, and Bayesian modeling. Most recently, his research has also branched into trustworthy data science including data privacy and algorithmic fairness. His methods research has been supported by the NIH, the Patient-Centered Outcomes Research Institute (PCORI), and the NSF. He currently co-leads the Pre-medical Cancer Immunotherapy Network for Canine Trials (PRECINCT), which is part of NIH/NCI's Cancer Moonshot Initiative, and the Statistical and Data Coordinating Center for the Risk Underlying Rural Areas Longitudinal (RURAL) Cohort Study funded by NIH/NHLBI. The rich, yet complex data from these large-scale studies present exciting opportunities for methods research.
Talk title: “Advancing Data Science for Intelligent and Equitable Health”
Host: Tor Tosteson, ScD
Location: In-person at DHMC, Auditorium H or via Zoom (no registration required)
Zoom meeting ID: 503 779 5102
Zoom passcode: 6501974
URL: https://dartmouth.zoom.us/j/5037795102
Phone (if needed for audio only, or to join by phone only): 669-900-6833
Presentation Summary
Rapid advances in technologies have enabled generation and collection of vast amounts of health data in research studies, from healthcare delivery, and from other real-world sources. While such data offer great promises in advancing intelligent and equitable health, they also present daunting analytical challenges. One notable example is the data from electronic health records (EHRs) that are recorded at irregular time intervals with varying frequencies and include structured data such as labs and vitals, codified data such as diagnosis and procedure codes, and unstructured data such as clinical notes and pathology reports. They are typically incomplete and fraught with other data errors and biases. What’s more, data gaps and errors in EHRs are often unequally distributed across patient groups: People with less access to care, often people of color or with lower socioeconomic status, tend to have more incomplete EHRs. In this talk, I will discuss these challenges and share my research group’s recent work on developing robust statistical and machine learning methods for addressing some of these challenges. Our research experience has demonstrated that a trans-disciplinary health data science approach that involves collaboration between statisticians, informaticians, computer scientists, and physician scientists can accelerate innovation in harnessing the transformative power of EHRs to tackle complex real-world problems and exert meaningful impact in medicine. To this end, I will also discuss some open questions that present opportunities for future research.
Biography
Qi Long, PhD is a professor in the Department of Biostatistics, Epidemiology, and Informatics and in the Department of Computer and Information Science at the University of Pennsylvania (UPenn). Dr. Long is the founding Director of the Center for Cancer Data Science, Associate Director of the Penn Institute for Biomedical Informatics, and Director of the Biostatistics and Bioinformatics Core in the Abramson Cancer Center at UPenn. He is an elected Fellow of AAAS and ASA, and elected member of ISI. Dr. Long’s research purposefully includes novel statistical research and impactful biomedical research. The thrust of his research is to develop statistical and machine learning methods for advancing equitable, intelligent health and medicine. He has developed robust methods for analysis of big health data (including, but not limited to, -omics, electronic health records, and imaging data), causal inference, analysis of incomplete data, and Bayesian modeling. Most recently, his research has also branched into trustworthy data science including data privacy and algorithmic fairness. His methods research has been supported by the NIH, the Patient-Centered Outcomes Research Institute (PCORI), and the NSF. He currently co-leads the Pre-medical Cancer Immunotherapy Network for Canine Trials (PRECINCT), which is part of NIH/NCI's Cancer Moonshot Initiative, and the Statistical and Data Coordinating Center for the Risk Underlying Rural Areas Longitudinal (RURAL) Cohort Study funded by NIH/NHLBI. The rich, yet complex data from these large-scale studies present exciting opportunities for methods research.