Introductory presentation of the Biomedical Learning and Student - PowerPoint PPT Presentation

Jul 20, 2023 •192 likes •447 views

Introductory presentation of the Biomedical Learning and Student Training BLaST program What is BLaST? Student-focused program to Enhance integration of research and teaching in biomedicine in Alaska Enhance integration of rural

Introductory presentation of the Biomedical Learning and Student Training BLaST program
What is BLaST? • Student-focused program to – Enhance integration of research and teaching in biomedicine in Alaska – Enhance integration of rural locations into the biomedical research training environment – Increase diversity (with a focus on rural students) of undergraduate students interested in biomedical research – Ultimately, increase diversity of the biomedical workforce
Who is BLaST? • BLaST is a TEAM, • You are BLaST if you are interested in student mentoring in biomedical sciences
Who is BLaST? • Leadership team: • Administrative and Student Training Cores – Barbara Taylor • Research Enrichment Core – Karsten Hueffer • Institutional Development Core – Arleigh Reynolds
Who is BLaST? Administrative team: • BLaST Program Administrator • BLaST Program Coordinator • BLaST Fiscal Technician
Who is BLaST? • Teaching/Research team: – BLaST Scholars – Graduate Mentoring Assistants – Laboratory Research and Teaching Technicians – Biology faculty at partner campuses Ilisagvik College in Barrow; UAS in Juneau, Sitka and Ketchikan; UAF Fairbanks and CRCD campuses in Bethel, Dillingham, Interior-Aleutians, Kotzebue, and Nome
Where is BLaST • BLaST administrative staff will be housed in the Department of Veterinary Medicine • But all of Alaska is in the BLaST zone.... Ilisagvik; UAF (Bristol Bay, Chukchi, Community and Technical College, Fairbanks, Interior-Aleutians, Kuskowim, Northwest); UAS (Juneau, Ketchikan, Sitka) UAA in future years?
What are BLaST opportunities for me • All team members: – Mentoring support – Travel to workshops and meetings – Curriculum development – Scholarships and fellowships – Comprehensive advising – Research support – Integration of research and teaching – Building of research infrastructure equipment and renovation
What are BLaST opportunities for me • Undergraduate students – Scholarships – Research opportunities – Mentoring and advising – Integrated curriculum through Connections courses
What are BLaST opportunities for me • Graduate students – Mentoring fellowships – Training in mentoring – Travel
What are BLaST opportunities for me • Faculty – Training in mentoring – Pilot project research support – Travel – Faculty development (ACE initiative) – Equipment
What are BLaST opportunities for me • Rural campuses – Faculty development – Scholarships and fellowships – Renovation and equipment – Boot camps and workshops on site – Travel – Pilot project research support
Leadership Philosophy • Teamwork • Efficiency • Transparency • Adherence to Best Practices • Having a BLaST!
Central Themes • Active Experiential Learning • Tiered Mentoring • Comprehensive Advising • Integration of Teaching and Research
Central Themes • Diversification of the NIH-funded workforce • Education pipeline – with multiple entry and exit points • One Health – Subsistence Health
buckle up, come along for the ride, it will be a BLaST

Recommend

TextMed: A Multi-Agent System with Reinforcement Learning Agents for Biomedical Text Mining

TextMed: A Multi-Agent System with Reinforcement Learning Agents for Biomedical Text Mining Michael Camara Janyl Jumadinova Oliver Bonham-Carter September 9, 2015 Big Data Biomedical Research PubMed: U.S. National Library of Medicine

346 views • 31 slides

Biomedical data sharing to enable Learning Health Systems Jonathan C. Silverstein, MD, MS, FACS,

Biomedical data sharing to enable Learning Health Systems Jonathan C. Silverstein, MD, MS, FACS, FACMI Chief Research Informatics Officer, Health Sciences and Institute of Precision Medicine Visiting Professor, Department of Biomedical

1.13k views • 45 slides

BIOMIMICRY: LEARNING FROM NATURE 15-20 min introductory

BIOMIMICRY: LEARNING FROM NATURE 15-20 min introductory slideshow | Presenter Notes Lets begin with a simple question: What is nature? Most

105 views • 7 slides

Deep Learning For Medical Knowledge Extraction From * Unstructured Biomedical Text Andrew Beam,

Deep Learning For Medical Knowledge Extraction From * Unstructured Biomedical Text Andrew Beam, PhD Postdoctoral Fellow Department of Biomedical Informatics Harvard Medical School 05/10/2017 *work in progress @AndrewLBeam AI &

528 views • 21 slides

Introductory Applied Machine Learning The primary aim of the course is to provide the student with

Introductory Applied Machine Learning The primary aim of the course is to provide the student with a set of practical tools that can be applied to solve real-world problems in machine learning. Nigel Goddard School of Informatics Machine

716 views • 8 slides

Learning the Species of Biomedical Named Entities from Annotated Corpora Xinglong Wang and Claire

Outline Background and Motivation Tagging Species to Biomedical Named Entities Conclusions and Future Work Learning the Species of Biomedical Named Entities from Annotated Corpora Xinglong Wang and Claire Grover LREC 29 May 2008 Xinglong Wang

742 views • 23 slides

Objectives Understand the breadth of biomedical informatics Know the biomedical

10/25/2019 Trends and Future Directions in Biomedical Informatics Office of the Vice Chancellor for Research Research & Innovation Month Objectives Understand the breadth of biomedical informatics Know the biomedical

295 views • 8 slides

Opening the course Introductory session Facilitators Guide Learning objectives 1. To convey

in cooperation with the Opening the course Introductory session Facilitators Guide Learning objectives 1. To convey knowledge about international human rights standards relevant to the work of the participants 2. To encourage the

662 views • 7 slides

Introductory Course for Commercial Dog Breeders Part 3: Maintaining Your License Learning

Introductory Course for Commercial Dog Breeders Part 3: Maintaining Your License Learning Objectives 1. Describe annual renewal process 2. Describe how to notify APHIS AC of changes in ownership or location 3. Explain the compliance

305 views • 30 slides

Math 140 Introductory Statistics The science of learning from data in the presence of

Statistics Math 140 Introductory Statistics The science of learning from data in the presence of variability. Professor Silvia Fernndez Chapter 1 Based on the book Statistics in Action by A. Watkins, R. Scheaffer, and G. Cobb. Our first

863 views • 8 slides

Introductory Course for Commercial Dog Breeders Part 12: Brokers and Auctions Learning

Introductory Course for Commercial Dog Breeders Part 12: Brokers and Auctions Learning Objectives By the end of this unit you should be able to: 1. Explain the type of identification needed for animals being sold to a broker or sold at an

481 views • 14 slides

Introductory Course for Commercial Dog Breeders Part 2: The Licensing Process Learning

Introductory Course for Commercial Dog Breeders Part 2: The Licensing Process Learning Objectives By the end of this unit you should be able to: 1. License Application Process 2. Time limits for prelicense inspections 3. Prelicense inspection

530 views • 38 slides

Machine Learning and Deep Contemplation of Data Joel Saltz Department of Biomedical Informatics

Machine Learning and Deep Contemplation of Data Joel Saltz Department of Biomedical Informatics Stony Brook University CCDSC October 5, 2016 From BDEC: Domain: Spatio-temporal Sensor Integration, Analysis, Classification

540 views • 53 slides

1 Precision medicine: what does it mean? Cardiovascular disease: knowing who to treat

Biomedical Imaging and Genetic (BIG) Data Analytics in Dementia and Oncology Deep Learning for Precision Medicine Wiro Niessen Biomedical Imaging Group Rotterdam Departments of Radiology & Medical Informatics Erasmus MC Imaging

826 views • 10 slides

Introductory Notes on Machine Translation and Deep Learning February 20, 2017 Jindich

NPFL116 Compendium of Neural Machine Translation Introductory Notes on Machine Translation and Deep Learning February 20, 2017 Jindich Libovick, Jindich Helcl Charles Univeristy in Prague Faculty of Mathematics and Physics Institute of

532 views • 18 slides

Introductory Course for Commercial Dog Breeders Topic 5: Exercise Learning Objectives 1.

Introductory Course for Commercial Dog Breeders Topic 5: Exercise Learning Objectives 1. Describe the components of a written exercise plan. 2. Describe exercise requirements for dogs. 3. Describe a few ways in which you can provide

370 views • 26 slides

Recent Trends in 3D Computer Vision and Deep Learning Introductory meeting Winter Semester

Recent Trends in 3D Computer Vision and Deep Learning Introductory meeting Winter Semester 2019/20 PD Dr. Habil. Federico Tombari Fabian Manhardt, Johanna Wald Goals You are going to learn: about relevant recent trends in the field

532 views • 13 slides

Introductory overview Introductory Overview Introduce the team Agenda and aims and

Spotlight oncompleting the count An overview of methods for ensuring quality and completeness of your census estimates Introductory overview Introductory Overview Introduce the team Agenda and aims and objectives of the day

1.1k views • 77 slides

Deep Learning Retinal Vessel Segmentation From a Single Annotated Example Praneeth Sadda 1 , John

Deep Learning Retinal Vessel Segmentation From a Single Annotated Example Praneeth Sadda 1 , John A. Onofrey 1 , Xenophon Papademetris 1,2 1 Department of Radiology and Biomedical Imaging, Yale University 2 Department of Biomedical Engineering,

507 views • 16 slides

Scheduling Plans Introductory Concepts CSCI [4|6] 730 Embellish on the introductory

Scheduling Plans Introductory Concepts CSCI [4|6] 730 Embellish on the introductory concepts Case studies Operating Systems Look at real time scheduling. Practical system have some theory, and lots of tweaking CPU Scheduling

579 views • 15 slides

Introductory Statistics Refresher Dr. Julia L. Sharp Short Course on Introductory Statistics

Introductory Statistics Refresher Dr. Julia L. Sharp Short Course on Introductory Statistics Part III Sharp (Clemson University) ASA 1 / 26 Hypothesis Testing As an example, suppose that I claim that I am excellent free throw shooter,

559 views • 26 slides

Sparse stochastic processes and biomedical image reconstruction Michael Unser Biomedical Imaging

Sparse stochastic processes and biomedical image reconstruction Michael Unser Biomedical Imaging Group EPFL, Lausanne, Switzerland Joint work with P. Tafti, Q. Sun, A. Amini, M. Guerquin-Kern, E. Bostan, etc. Tutorial presentation, FRUMAN,

424 views • 30 slides

11/02/2016 UKUROVA UNIVERSITY Outline DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING

11/02/2016 UKUROVA UNIVERSITY Outline DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING Meta Learning on Small Biomedical Informatics Background Big Data/ Small Data Biomedical Datasets Machine Learning Algorithms How to

333 views • 4 slides

Manchester Biomedical Research Centre Professor Ian Bruce, BRC Director Manchester Biomedical

Manchester Biomedical Research Centre Manchester Biomedical Research Centre Professor Ian Bruce, BRC Director Manchester Biomedical Research Centre Delivering P4 Medicine Greater Manchester A Devolved Health and Social Care System 2nd

345 views • 18 slides