NYC DS3 Seminar - How Can Machine-Learning Methods Help to Make Scientific Inferences?
2019-10-09 17:30 - 18:30 UTC
Free, open to the public, and does not require registration.

Wednesday, October 9, 2019

5:00 PM – 5:30 PM: Welcome
5:30 PM – 6:30 PM: Talk

Schapiro CEPSR – Davis Auditorium

530 West 120th Street, 4th Floor
New York, NY 10027

Talk Abstract: Machine learning in the form of standard supervised classification algorithms is not all that useful or productive in the natural sciences, because the (effective) goals of these algorithms aren’t very similar to the goals of scientific inquiry. However, the ML community has delivered great ideas and methods for building, fitting, and validating extremely flexible models. I argue that if we want to exploit the good things about ML but achieve truly scientific goals, we need to do two things: We need to augment or modify the (currently trivial) causal structure of the ML methods to represent our very strong domain-specific beliefs about how the data are generated. And we need to be careful to use ML methods only in the parts of our problems for which we don’t care about the latent structure or parameters (that is, use them to model nuisances, not use them to do everything). I give examples from stellar astrophysics where adding ML components into larger causal models has created new scientific capabilities.

Speaker: Professor David W. Hogg, NYU|MPIA|Flatiron

Speaker Biography: After a Ph.D. in Physics from Caltech and a few years at the Institute for Advanced Study, Hogg came to New York University in 2001 and was granted tenure there in 2007. His work at NYU has ranged from fundamental cosmological measurements to stellar dynamics to planet search and characterization. His work includes a significant engineering component. He spends a part of each year at the Max Planck Institute for Astronomy in Heidelberg, Germany, where he is a visiting member of the research staff, and a part of each week at the Flatiron Institute of the Simons Foundation, where he is a group leader.

FinTech Lecture Series - Where on Earth is AI Headed?
2019-10-25 14:00 - 15:30
Free, open to the public, and does not require registration.

Where on Earth is AI Headed?

Friday, October 25, 2019

2:00 PM – Lecture

3:00 PM – Reception

Schapiro CEPSR – Davis Auditorium

530 West 120th Street, 4th Floor
New York, NY 10027

Speaker: Tom M. Mitchell – Founders University Professor, School of Computer Science

Speaker Biography: Tom M. Mitchell is the Founders University Professor in the School of Computer Science at Carnegie Mellon University, where he created the world’s first academic Machine Learning Department. Mitchell’s research explores machine learning theory, algorithms and applications, as well as the impact of AI on society. He has testified to the U.S. Congress several times on AI impacts on society, and he co-chaired the 2017 U.S. National Academy study on “Information Technology, Automation, and the U.S. Workforce.” Mitchell advises a variety of young and old companies internationally on their AI product and business strategies, and his research has been featured in popular press from the New York Times, to CCTV (China’s national television network), to CBS’s 60 Minutes. He is a member of the U.S. National Academy of Engineering, a member of the American Academy of Arts and Sciences, and a Fellow and Past President of the Association for the Advancement of Artificial Intelligence (AAAI).

Data Science Institute Data For Good Series: Professor Sunil K. Agrawal
2019-11-01 12:00 - 13:30
Free, open to the public, and does not require registration.

Friday, November 1, 2019

12:00 PM – 1:30 PM

Northwest Corner Building – Room 1406
550 West 120th Street, 14th Floor
New York, NY 10027

**REGISTRATION IS REQUIRED: PLEASE CLICK HERE TO REGISTER.**

Robotics to Characterize, Retrain, and Restore Human Movements

Neural disorders and old age limit the ability of humans to perform activities of daily living. Robotics can be used to probe the human neuromuscular system and create new pathways to characterize, relearn, or restore functional movements. Dr. Agrawal’s group at Columbia University Robotics and Rehabilitation (ROAR) Laboratory has designed innovative technologies and robots for this purpose. These technologies have been tested on subjects in a variety of studies to understand the human cognitive and neuro-muscular response. Human experiments have targeted patients with stroke, cerebral palsy, Parkinson’s disease, ALS, Vestibular disorders, elderly subjects and others. The talk will provide an overview of some of these technologies and scientific studies performed with them.
Speaker: Sunil K. Agrawal, Ph.D., Professor, Department of Mechanical Engineering and Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY 10027, USA. (Email: Sunil.Agrawal@columbia.edu, Web: https://roar.me.columbia.edu )
Speaker Biography: Sunil K. Agrawal received a Ph.D. degree in Mechanical Engineering from Stanford University in 1990. He is currently a Professor and Director of Robotics and Rehabilitation (ROAR) Laboratory at Columbia University, located both in engineering and medical campuses of Columbia University. Dr. Agrawal has published more than 500 journal and conference papers, three books, and 14 U.S. patents. He is a Fellow of the ASME and AIMBE. His honors include a NSF Presidential Faculty Fellowship from the White House in 1994, a Bessel Prize from Germany in 2003, and a Humboldt US Senior Scientist Award in 2007. He is a recipient of 2016 Machine Design Award from ASME for “seminal contributions to design of robotic exoskeletons for gait training of stroke patients” and 2016 Mechanisms and Robotics Award from the ASME for “cumulative contributions and being an international leading figure in mechanical design and robotics”. He is a recipient of several Best Paper awards in ASME and IEEE sponsored robotics conferences. He has also held international visiting positions that include Technical University of Stuttgart, Hanyang University in Korea, University of Ulster in Northern Ireland, Biorobotics Institute of SSSA in Pisa, Peking University in China. He has successfully directed 28 PhD student theses and currently supervises the research of 10 PhD students at ROAR laboratory. He is the founding Editor-in-Chief of the journal “Wearable Technologies” from Cambridge University Press.
Website: https://me.columbia.edu
Google Scholar Page:
https://scholar.google.com/citations?hl=en&user=zavGyr4AAAAJ&view_op=list_works&sortby=pubdate

Data Science Day 2020
2020-03-13 08:00 - 17:00
Open to the Public
RSVP

The in-person conference will now be held on Monday, September 14, 2020 at Columbia University’s Alfred Lerner Hall (2920 Broadway, New York, NY 10027).
If you are able to join us in September, you can now re-register for September’s Data Science Day via the new Eventbrite page here.

Online Sneak Peek for Data Science Day 2020
2020-03-31
Open to the public.
The in-person conference will now be held on Monday, September 14, 2020 at Columbia University’s Alfred Lerner Hall (2920 Broadway, New York, NY 10027).
If you have purchased a ticket for March 31, you will receive a full refund within the immediate future.
If you are able to join us in September, you can now re-register for September’s Data Science Day via the new Eventbrite page here.
Details for the online sneak peek are TBA

Data Science Day 2020 | Ethics & Privacy: Terms of Usage
2020-09-14 10:00 - 11:30
RSVP here

As part of the Data Science Institute’s flagship annual event, this virtual session will bring together thought leaders who are driving discussions on ethics and privacy in data science and engineering.
Ethics and Privacy: Terms of Usage will feature lightning talks and presentations from leading Columbia University researchers. Eric Schmidt, former Google Chief Executive Officer and Executive Chairman and Co-Founder of Schmidt Futures, will join for a discussion with the Institute’s Avanessians Director Jeannette M. Wing.

Machine Learning in Science and Engineering (MLSE)
2020-12-14 08:00 - 2020-12-15 17:00
Register here.
MLSE will highlight the latest research in artificial intelligence and machine learning that are advancing science and engineering fields at large. This conference will demonstrate how data-driven approaches may help solve emerging challenges, and will showcase new research from a diverse range of disciplines. Representatives from academia, government, and industry will join virtually to explore the future of science and engineering across 11 dedicated tracks.

Data Science Day 2021
2021-04-21 10:00 - 13:00
Open to the public
Register here

Data Science Day provides a forum for innovators in academia, industry, and government to connect. We hope you will join us in April for the keynote address, an interactive virtual poster session, and lightning talks by Columbia University researchers on their latest work in data science. Learn more at datascienceday.net.
The Keynote will be delivered by Pat Bajari, a Chief Economist at Amazon and Vice President of Amazon’s Core AI team. His team of software engineers and scientists in machine learning, statistics, operations research, and econometrics has helped to build scalable systems for supply chain, transportation, pricing, automated marketing, robotics, forecasting, human resources, and more.

Columbia-IBM Center for Blockchain and Data Transparency Showcase 2021
2021-06-16 10:30 - 14:00
The Columbia-IBM Center for Blockchain and Data Transparency brings together cross-disciplinary teams to advance innovation in blockchain and data transparency and closely associated topics for the good of society that will drive new science and technology, develop thought leadership, and influence policy.
Join the showcase to explore new topics, learn about upcoming initiatives, and celebrate the Center’s recent accomplishments. Each session will be led by senior representatives from Columbia University and IBM. Tickets are free.
RSVP

Data Science Day 2022
2022-04-06 08:00 - 17:00
Data Science Day provides a forum for innovators in academia, industry, and government to connect. The April 6, 2022 event will feature lightning talks from leading Columbia University faculty members; a poster session on the latest data science innovation; and keynotes from senior industry and government representatives.
Register here
