Course curriculum

  • 1

    On-Demand Sessions

    • Training and Operationalizing Interpretable Machine Learning Models by Francesca Lazzeri, PhD

    • Pipelining in Python with Snakemake with Biological Applications by Laura A. Seaman, PhD

    • Women Ignite Session

Instructors

Instructor Bio:

Machine Intelligence Scientist | Draper

Laura A.Seaman, PhD

Laura Seaman is a Senior Machine Intelligence Scientist at Draper where she applies machine learning and bioinformatics algorithms to a variety of applications. Dr. Seaman’s graduate work focused on using genetic data to identify alterations to the genomic structure in cancer. She is currently using data science for many applications including analysis of financial networks and identification of genetically engineered organisms. Dr. Seaman has a Bachelor of Science in Biological Engineering from the Massachusetts Institute of Technology, a Masters of Arts in Statistics from the University of Michigan, and a Doctor of Philosophy in Bioinformatics from the University of Michigan.

Senior Lead Machine Learning Scientist, Cloud Advocate at Microsoft

Francesca Lazzeri, PhD

Francesca Lazzeri, PhD is an experienced scientist and machine learning practitioner with over 12 years of both academic and industry experience. She is author of a number of publications, including technology journals, conferences, and books. She currently leads an international team of cloud advocates, developers and data scientists at Microsoft. Before joining Microsoft, she was a research fellow at Harvard University in the Technology and Operations Management Unit. Find her on Twitter: @frlazzeri and Medium: @francescalazzeri

CEO and Founder | Alectio

Jennifer Prendki

Jennifer is the founder and CEO of Alectio, the first startup focused on automated data curation and data collection optimization. She and her team are on a mission to help ML teams build models with less data. Prior to starting Alectio, Jennifer was the VP of Machine Learning at Figure Eight; she also led ML and Data Science at Atlassian and on the Search team at Walmart Labs. She is an accomplished speaker who enjoys addressing both technical and non technical audiences.

Instructors

Instructor Bio:

Partner - Data Science | RelationalAI

Beverly Wright

Dr. Beverly Wright has over 25 years leading and executing Data Science and Analytics through corporate, consulting, and academic experiences. Currently, she leads the Data Science Practice for RelationalAI and serves companies in Atlanta with analytics and data science solutions. Over her career, Dr. Beverly Wright has provided guidance to a variety of businesses and non-profit organizations to frame and solve critical issues using modeling and advanced analytics. In addition to her practitioner experience, Dr. Wright was a professor of graduate Analytics Practicum at Georgia Tech, Marketing Research and Analysis, Business Statistics, Graduate Research for Decision-Making, among other courses at multiple universities. She currently serves as Academic Director for UGA Executive Education, teaches Executive Education at Emory University, operates a nonprofit using data science for good called ATLytiCS, serves as host for TAG Data Talk, and regularly publishes and speaks at national conferences.

Postdoctorial Research Fellow | Center for Astrophysics, Harvard & Smithsonian

Rebecca Nevin

Rebecca Nevin is a postdoctoral fellow at the Harvard Smithsonian Center for Astrophysics where she investigates how galaxies evolve and change over cosmic time. She is extremely interested in applying machine learning to the overwhelmingly large datasets from the next generation of astronomy surveys and is looking to build collaborations with data scientists from different disciplines. When she's not puzzling over the challenges and intricacies of galaxies, she is passionate about science communication; she created a planetarium series called The Science of Sci Fi and is currently writing a book about asteroids.

Data Engineer of Advanced Data Analytics | Stanley Black & Decker

Tianyu Lan

Tianyu Lan is a Data Engineer at Advanced Data Analytics team, Stanley Black & Decker(SBD), a manufacturer of tools and solutions in construction, automotive, industrial, security and healthcare sectors. She earned her Master's degree in Computer Science at Emory University. She joined SBD as an intern in robotics team and then grew into a data engineer/scientist, responsible for leveraging Big Data technology to analyze large amounts of data and build insights for better decision making across multiple sales and operations planning initiatives at SBD.

Instructors:

Instructor Bio:

Executive Director of  South Big Data Innovation Hub/ Director of Strategic Partnerships for Georgia Institute of Technology, Institute for Data Engineering and Science (IDEaS)

Dr. Renata Rawlings-Goss

Dr. Renata Rawlings-Goss is a nationally recognized leader in Data Science & Big Data innovation. She is the Executive Director of the South Big Data Innovation Hub, a federally funded 16 state center connecting industry, academia, and government around data science innovation. She is also the Director of Strategic Partnerships for the Institute for Data Engineering and Science at the Georgia Institute of Technology, and the founder of Good with Data LLC, which runs The Data Career Academy for professional women. (bit.ly/The_Data_Career_Academy) As part of the White House Office of Science and Technology Policy, under President Obama, Dr. Rawlings-Goss founded the National Data Science Organizers Group and co-lead the writing team for the Federal Big Data Strategic Plan. She was also awarded as an inaugural Big Data Science and Technology Policy fellow through the AAAS and worked in the Office of the Director for the National Science Foundation – CISE-OAD. Dr. Rawlings-Goss is a biophysicist by training and through her roles, she has served as an executive strategist, researcher, career mentor, and policy advisor to Fortune 500 companies, individuals, as well as over 19 federal and state government agencies around data science education, Big Data, Digital Transformation, Public-Private Partnerships, Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning, Data Career Success, Professional Development and Data Innovation. Author of “Data Careers, Training, and Hiring”, her work has been recognized in the Washington Post, the Wall Street Journal, and as one of President Obama’s top 100 Impacts in Science and Technology.

Business Development Manager, AI/ML | Amazon Web Services

Charis Loveland

Charis Loveland works at Amazon Web Services as a business development manager on the cloud intelligence team specializing in artificial intelligence and machine learning (ML). She has a decade and a half of experience in product management, AI, data analytics, new product introduction, file and hardware storage, and software development. After launching a crowdsourcing platform for Azure ML at Microsoft, Charis founded Rue La La's data science team where she created and executed a roadmap and strategy to create personalized experiences and segment customers. Charis co-founded an ML startup and has taught several business courses at General Assembly as well as online courses about data science at MIT, Dartmouth, and Columbia. Charis serves as a coding instructor, mentor, and advocate for early STEM education and volunteers for several nonprofits that help promote greater diversity in technology.