Columbia Entrepreneurship

Data Science Institute-Industry-Innovation Seminar: Johnson & Johnson
2018-11-14 10:30 - 12:00 UTC
OPEN TO ALL

REGISTER HERE

Amel Lageat, Senior Director, Consumer Business: “People centric approach to optimize Data Science, Commercial impact and Leadership”

Wednesday, November 14, 2018
10:30AM – 12:00PM
Schapiro CEPSR, Davis Auditorium (412)

Abstract: In a world of Infobesity, analysts, engineers, professionals, executive leaders, and people now have access to more data and analytics opportunities that we can ever make sense of. However, a genuine people centric approach can provide the sharpest guidance in designing relevant strategies and solutions: it makes data, models, and analytics more meaningful and purposeful, and also leads to marketing and commercial impact in global organizations. In this talk, I will use various business examples to discuss this premise, including negative targeting (voluntarily deciding to not advertise to a person), the UX of marketing mix modeling (who is the real user?), and servant leadership to data experts (what does it look like?).

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Bio: Amel Lageat is heading up Global Analytics for the Johnson &Johnson Consumer Division. She has spent the last 20 years leading various commercial organizations within J&J (Marketing, Market research and analytics), in France, the UK and the US. Her recipe for impact is a fruitful combination of data, curiosity and empathy.

Data Science Institute Poster Session: Smart Cities
2018-11-14 18:00 - 20:00
Mudd Hall, 500 W. 120 St., New York, NY 10027
Room/Area: 407 (Data Science Institute)

The Data Science Institute invites you to poster session that will highlight some of the recent and current research done by our centers. Open to the public an larger Columbia community. Light refreshments provided.

Event Contact Information:
Data Science Institute
212-854-5660
datascience@columbia.edu

Data Science Institute Poster Session: Foundations of Data Science
2018-11-19 18:00 - 20:00
OPEN TO ALL

RSVP HERE

Mudd Hall, 500 W. 120 St., New York, NY 10027
Room/Area: 407 (Data Science Institute)

The Data Science Institute invites you to poster session that will highlight some of the recent and current research done by our centers. Open to the public an larger Columbia community. Light refreshments provided.

Event Contact Information:
Data Science Institute
212-854-5660
datascience@columbia.edu

Data Science Institute Poster Session: Financial & Business Analytics
2018-11-26 18:00 - 20:00
OPEN TO ALL

REGISTER HERE

Mudd Hall, 500 W. 120 St., New York, NY 10027
Room/Area: 407 (Data Science Institute)

The Data Science Institute invites you to poster session that will highlight some of the recent and current research done by our centers. Open to the public an larger Columbia community. Light refreshments provided.

Event Contact Information:
Data Science Institute
212-854-5660
datascience@columbia.edu

Data Science Institute Poster Session: Computing Systems
2018-11-28 18:00 - 20:00

Learning Engines for Healthcare: Using Machine Learning to Transform Clinical Practice and Discovery
2019-01-29 13:00 - 14:00
OPEN TO ALL COLUMBIANS

Lecture in Precision Medicine: Mihaela van der Schaar

“Learning Engines for Healthcare: Using Machine Learning to Transform Clinical Practice and Discovery”

Prof. Mihaela van der Schaar, John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine, University of Cambridge

January 29, 2019

1:00PM – 2:00PM
William Black Building, Alumni Auditorium,
650 W. 168th Street, New York, NY 10032

Professor van der Schaar is John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Turing Faculty Fellow at The Alan Turing Institute in London, where she leads the effort on data science and machine learning for personalized medicine. Prior to this, she was a Chancellor’s Professor at UCLA and MAN Professor of Quantitative Finance at University of Oxford. She is an IEEE Fellow (2009). She has received the Oon Prize on Preventative Medicine from the University of Cambridge (2018). She has also been the recipient of an NSF Career Award, 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award. She holds 33 granted USA patents. Her current research focus is on data science and machine learning for medicine and education.

The overarching goal of her research is to develop cutting-edge machine learning, AI and operations research theory, methods, algorithms and systems to understand the basis of health and disease; develop methodology to catalyze clinical research; support clinical decisions through individualized medicine; inform clinical pathways, better utilize resources & reduce costs; and inform public health.

To do this, she is creating what she call Learning Engines for Healthcare (LEH’s). An LEH is an integrated ecosystem that uses machine learning, AI and operations research to provide clinical insights and healthcare intelligence to all the stakeholders (patients, clinicians, hospitals, administrators). In contrast to an Electronic Health Record, which provides a static, passive, isolated display of information, an LEH provides dynamic, active, holistic & individualized display of information including alerts.

DSI Health Analytics Center
2019-02-01 15:30 - 17:30
OPEN TO ALL

The Data Science Institute invites you to poster session that will highlight some of the recent and current research done by our centers. Open to the public and larger Columbia community. Light refreshments provided.

Friday, February 1, 2019
3:30 PM – 5:30 PM

Mudd Hall, 500 W. 120 St., New York, NY 10027
Room/Area: 407 (Data Science Institute)

Computational Social Science Poster Session
2019-02-06 18:00 - 20:00
The Data Science Institute invites you to poster session that will highlight some of the recent and current research done by our centers. Open to the public and larger Columbia community. Light refreshments provided.

Wednesday, February 6, 2019
6:00 PM – 8:00 PM

Mudd Hall, 500 W. 120 St., New York, NY 10027
Room/Area: 407 (Data Science Institute)

Event Contact Information:
Data Science Institute
212-854-5660
datascience@columbia.edu

FDT Center for Intelligent Asset Management - FinTech Lecture Series
2019-02-08 15:30 - 17:00
OPEN TO ALL COLUMBIANS

FDT Center for Intelligent Asset Management – FinTech Lecture Series

“Fair Algorithms for Machine Learning”

Michael Kearns

Friday, February 8, 2019

Davis Auditorium
3:30 pm lecture followed by 4:30 pm reception

Michael Kearns is a professor in the Computer and Information Science Department at the University of Pennsylvania, where he holds the National Center Chair. He is also Head of Research in Morgan Stanley’s AI Center of Excellence. At Penn, Kearns has secondary appointments in the Department of Economics, and in the departments of Statistics and Operations, Information, and Decisions in the Wharton School. He is the founding director of the Warren Center for Network and Data Sciences and the founder and former director of Penn Engineering’s Networked and Social Systems Engineering Program. His research interests includes topics in machine learning, artificial intelligence, algorithmic game theory and quantitative trading and finance. Kearns is an elected Fellow of the American Academy of Arts and Science, the Association for Computing Machinery, and the Association for the Advancement of Artificial Intelligence. He has consulted widely in the technology and finance industries, and on a variety of related regulatory and legal matters.

Data Science Institute-Industry-Innovation Seminar: Amazon
2019-03-06 18:00 - 19:30
OPEN TO COLUMBIANS

Byron Cook, Professor of Computer Science at University College London (UCL) and Director of Automated Reasoning at Amazon Web Services

Wednesday, March 6, 20108
6:00PM – 7:30PM
Schapiro CEPSR, Davis Auditorium (412)

Abstract: This talk will discuss the development and use of formal verification tools within Amazon Web Services (AWS) to increase the security assurance of its cloud infrastructure and to help customers secure themselves. I’ll also discuss some remaining challenges that could inspire future research in the community.

Bio: Byron Cook is Professor of Computer Science at University College London (UCL) and Director of Automated Reasoning at Amazon Web Services. Byron’s interests include computer/network security, program analysis/verification, programming languages, theorem proving, logic, hardware design, operating systems, and biological systems. Byron is the founder and leader of Amazon’s Automated Reasoning Group (ARG).e at University College London (UCL) and Director of Automated Reasoning at Amazon Web Services

Data Science Day
2019-04-03
OPEN TO ALL
REGISTER HERE
Wednesday, April 3, 2019
9AM–5PM
Announcing the Keynote Speaker:
Brad Smith
President of Microsoft
Join the Columbia Data Science Institute for demos and lightning talks by Columbia researchers presenting their latest work in data science. The event provides a forum for innovators in academia, industry and government to connect.
Roone Arledge Auditorium
Lerner Hall | Columbia University
2920 Broadway, New York, NY 10027

Molecular Innovations for Live Cell Imaging (Presentation)
2019-05-09 16:00 - 18:00
Thursday, May 9, 2019
4:00PM – 6:00PM

Buell Hall

515 West 116th Street

New York, NY 10027

The fluorescent proteins revolutionized our ability to study protein function directly in the cell by enabling individual proteins to be selectively labeled through genetic encoding of a fluorescent tag. As researchers seek to make increasingly sophisticated dynamic measurements of protein function in the cell to unravel molecular mechanism, we designed a chemical tag to combine the advantages of genetic encoding with a modular organic fluorophore. With TMP-tag, the protein of interest is tagged with E. coli dihydrofolate reductase, which can subsequently be labeled with a cell permeable trimethoprim-fluorophore conjugate. Here we demonstrate that TMP-tag is a robust cellular reagent. We present recent results exploiting the modular nature of the chemical tag to generate TMP-tags for specific applications in single-molecule, super-resolution, and multi-color imaging. We look forward to innovations at the interface of chemical tag technology and spectroscopy for biological imaging.
Virginia W. Cornish graduated summa cum laude from Columbia University with a B.A. in Biochemistry in 1991, where she did undergraduate research with Professor Ronald Breslow. She earned her Ph.D. in Chemistry with Professor Peter Schultz at the University of California at Berkeley and then was a Postdoctoral Fellow in the Biology Department at M.I.T. under the guidance of Professor Robert Sauer. Virginia joined the faculty of the Chemistry Department at Columbia in 1999, where she carries out research at the interface of chemistry and biology, and was promoted to Associate Professor with tenure in 2004 and then Professor in 2007. Her laboratory brings together modern methods in synthetic chemistry and DNA technology to expand the synthetic capabilities of living cells. Her research has resulted in 59 research publications and several patents and currently is supported by multiple grants from the NIH and NSF. Virginia has been recognized for her research by awards including an NSF Career Award (2000), a Sloan Foundation Fellowship (2003), the Protein Society Irving Sigal Young Investigator Award (2009), and the American Chemical Society Pfizer Award in Enzyme Chemistry (2009). In addition to her research and teaching, Virginia enjoys spending time with her husband and their three children.

Please contact marley.bauce@columbia.edu with any event related questions.

Machine Learning in Finance Workshop
2019-05-17 08:15 - 18:00
OPEN TO ALL

Purchase Tickets Here

Friday, May 17, 2019
8:15 AM – 6:00 PM
Columbia University – Lerner Hall
2920 Broadway, New York, NY 10027
The Data Science Institute (DSI) at Columbia University and Bloomberg are pleased to announce a workshop on “Machine Learning in Finance”. The workshop will be held at Columbia University under the auspices of the Financial and Business Analytics Center, one of the constituent centers in the DSI, and the Center for Financial Engineering.
Boxed lunches will be provided. There will also be regular coffee breaks throughout the day and a wine and cheese reception after the event.

The Center for Financial Engineering and the Financial Analytics Center at Columbia University

Organizer of Machine Learning in Finance Workshop 2019

The Center for Financial Engineering is an interdisciplinary research center established in 2007 to encourage research on financial engineering and mathematical modeling in finance. It aims at fostering research collaboration among Columbia faculty, graduate students and affiliates, facilitating their contacts with financial institutions and corporations and enhancing the visibility of Columbia as a center for research and innovation in financial engineering.
The Center’s research activities deal with the pricing and hedging of derivative securities, the statistical modeling of financial markets, computational methods in finance, risk management, asset allocation, and portfolio optimization.
The Financial Analytics Center is one of the key centers at the Data Science Institute at Columbia University. It brings together expertise in finance theory, machine learning, statistics, signal processing, operations, and natural language processing, and supports collaborations with an appropriately trained student body as well as with the financial industry.

Deep Learning to Solve Challenging Problems with the SVP of Google AI
2019-08-27 16:00 - 17:30
OPEN TO ALL

DEEP LEARNING TO SOLVE CHALLENGING PROBLEMS
Speaker: Jeff Dean, SVP Google AI / Lead, Google Brain Project
Date: Tuesday, August 27, 2019
Time: 4:00pm – 5:30pm
Location: Mudd 451 – Computer Science Building
Light refreshments will be served
For the past eight years, Google Research teams have conducted research on difficult problems in artificial intelligence, on building large-scale computer systems for machine learning research, and, in collaboration with many teams at Google, on applying our research and systems to many Google products. As part of our work in this space, we have built and open-sourced the TensorFlow system (tensorflow.org), a widely popular system designed to easily express machine learning ideas and to quickly train, evaluate, and deploy machine learning systems.
We have also collaborated closely with Google’s platforms team to design and deploy new computational hardware called Tensor Processing Units, specialized for accelerating machine learning computations. In this talk, I’ll highlight some of our recent research accomplishments, and will relate them to the National Academy of Engineering’s Grand Engineering Challenges for the 21st Century, including the use of machine learning for healthcare, robotics, language understanding and engineering the tools of scientific discovery. I’ll also cover how machine learning is transforming many aspects of our computing hardware and software systems.
This talk describes joint work with many people at Google.

Data Science Institute-Industry-Innovation Seminar: Goldman Sachs
2019-10-01 11:00 - 12:30
Free, open to the public, and does not require registration.

Tuesday, October 1, 2019

11:00 AM – 12:30 PM

Schapiro CEPSR – Davis Auditorium

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

Speaker #1: Mayur Thakur, Head of the Data Analytics Group, Global Compliance Division
Biography: Mayur Thakur is head of the Data Analytics Group in the Global Compliance Division. He joined Goldman Sachs as a managing director in 2014. Prior to joining the firm, Mayur worked at Google, where he designed search algorithms for more than seven years. Previously, he was an assistant professor of computer science at the University of Missouri. Mayur earned a PhD in Computer Science from the University of Rochester in 2004 and a BTech in Computer Science and Engineering from the Indian Institute of Technology, Delhi, in 1999.

Speaker #2: Suruchi Deodhar, Vice President, Goldman Sachs
Biography: Suruchi is a Vice President at Goldman Sachs, leading the Communication Risk Engineering team in the Global Compliance Division. She joined the firm in 2015 after completing her PhD in Computer Science from Virginia Tech. Her PhD research focused on developing large scale data integration techniques and big-data systems for evaluation and forecasting of epidemics.

NYC DS3 Seminar - How Can Machine-Learning Methods Help to Make Scientific Inferences?
2019-10-09 17:30 - 18:30
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

AI and Entrepreneurship Talk: Parag Agrawal, Parallel.ai CEO and Cofounder
2026-02-05 11:00 - 12:00
The Columbia Engineering AI Entrepreneurship Series is a bi-weekly speaker series that brings students and faculty at Columbia together with founders, VCs, technologists, and business leaders to learn about the process of transitioning lessons from research and the classroom into products and value.

This week join to hear from:

Parag Agrawal, Parallel.ai CEO and Cofounder
11AM February 05, 2026 Davis Auditorium
Parag Agrawal is the founder of Parallel Web Systems, a company unlocking the web for AI agents. Previously, he spent 11 years at Twitter, where he joined as an engineer before serving as CTO, and then CEO. Parag has a PhD from Stanford University in Computer Science and a Bachelor’s degree in Computer Science and Engineering from IIT, Bombay.

February 27 Lecture Series in AI: Ali Farhadi, CEO of AI2
2026-02-27 10:30 - 12:00
Columbia Engineering Lecture Series in AI
Friday, February 27: Ali Farhadi, CEO of Allen Institute for Artificial Intelligence (AI2) & Professor, University of Washington

10:30-11:00am Check-in
11:00-12:00pm Lecture

Advance registration is required for both Columbia affiliates and non-affiliates
REGISTER HERE
ABOUT THE LECTURE
“What is the Language Model of a Crow?”
ABOUT THE SPEAKER
Ali Farhadi is the CEO of the Allen Institute for AI (Ai2) and a Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Ali has been a professor at the UW Allen School since 2012. He first joined Ai2 in 2015 to lead the institute’s computer vision research team with a focus on visual common-sense reasoning and the role of actions and interactions in visual understanding. While at Ai2, he co-founded Xnor.ai, the first on-device deep learning startup that was acquired by Apple in 2020. Ali led Apple’s next-generation machine learning efforts until 2023 when he rejoined Ai2 as CEO in July. Ali’s research impact has been globally recognized with several best paper awards at CVPR, NeruIPS, AAAI, NSF Career Award, and the Sloan Fellowship, and he was named one of Forbes’s Top 5 AI Entrepreneurs in 2018.

Data Science and AI Exchange 2026
2026-04-14
Join the Data Science Institute on April 14th to explore how leaders across academia, industry, and government are shaping the future of Responsible AI.
Learn more.
