Columbia Entrepreneurship

Data, Ethics and Decision-Making Lecture Series: Dr. Jeanette M. Wing, Avanessians Director of the Data Science Institute
2017-09-20 16:30 - 18:00 UTC
OPEN TO ALL
ABSTRACT:
The Data, Ethics, and Decision-making Speaker Series hosted by the Institute for Social and Economic Research and Policy (ISERP) presents Dr. Jeannette M. Wing, Avanessians Director of the Data Science Institute and Professor of Computer Science in at Columbia University, on “Using Data for Good: What does it mean?“. Professor Wing will be laying out her vision for the Institute, which will focus on her desire to think seriously about the definition and practice of using data for good.

Data Science Institute Colloquium Series Event: What Can Deep Learning Learn from Linear Regression
2017-09-22 10:00 - 11:00
OPEN TO ALL
ABSTRACT
When training large-scale deep neural networks for pattern recognition, hundreds of hours on clusters of GPUs are required to achieve state-of-the-art performance. Improved optimization algorithms could potentially enable faster industrial prototyping and make training contemporary models more accessible.
In this talk, I will attempt to distill the key difficulties in optimizing large, deep neural networks for pattern recognition. In particular, I will emphasize that many of the popularized notions of what make these problems “hard” are not true impediments at all. I will show that it is not only easy to globally optimize neural networks, but that such global optimization remains easy when fitting completely random data.
I will argue instead that the source of difficulty in deep learning is a lack of understanding of generalization—namely understanding behavior on new and unseen data. By appealing to standard concepts from linear regression, I will describe why certain popular theories of generalization fail to explain the success of large neural nets. I will close with some possible approaches to patching this theory and guiding the engineering of deep learning models with enormous capacity.

Biopharmaceutical Product Development & Data Science Symposium (Event)
2017-10-10 08:00 - 17:00
OPEN TO ALL
SAVE THE DATE
Tuesday, October 10, 2017, 8am–5pm
New Jersey Institute of Technology, NJIT Campus Center Atrium
150 Bleeker Street, Newark, NJ 07103
KEYNOTE SPEAKERS:
Dr. Jacqueline Law
Global Head & VP, Real World Data Science
Roche/Genentech
Dr. Shahram Ebadollahi
Chief Technology Officer
IBM Watson Health
The symposium will highlight two areas. Morning sessions will address the application of data analytics and big data in drug discovery, preclinical and clinical development. The afternoon session will examine how data analytics and big data can impact process development and manufacturing of biopharmaceutical products.
REGISTER HERE.

Data for Good
2018-02-16 12:00 - 13:30
OPEN TO ALL

Friday, February 16, 2018
12:00PM-1:30PM
CEPSR 750

In this talk, we discuss a systematic evaluation of the impact of financial regulations concerning the collateralization of derivative trades on systemic risk – a topic that has been vigorously discussed since the financial crisis in 2007/08. Experts often disagree on the efficacy of these regulations. Compounding this problem banks regard their trade data required for a full analysis as proprietary. We adapt a simulation technology combining advances in graph theory to randomly generate entire financial systems sampled from realistic distributions with a novel open source risk engine to compute risks in financial systems under different regulations. This allows us to consistently evaluate, predict and optimize the impact of financial regulations on all levels – from a single trade to systemic risk – before it is implemented. The resulting data set is accessible to contemporary data science techniques like data mining, anomaly detection and visualization. We find that collateralization reduces the costs of resolving a financial system in crisis, yet it does not change the distribution of those costs and can have adverse effects on individual participants in extreme situations.
Remote participants can register for online streaming in advance at:
https://columbiauniversity.zoom.us/meeting/register/4e9e74be7f16abb08c34be5db4a05ad8
Jointly sponsored by the Data Science Institute and the Institute for Social and Economic Research and Policy

Data for Good
2018-02-20 17:30 - 19:30
LOCATION
Bloomberg Center, Cornell Tech
2 West Loop Road
New York, NY 10044
February 20, 2018
Reception: 5:30pm-6:30pm
Talk: 6:30pm-7:30pm
Every field has data. We use data to discover new knowledge, to interpret the world, to make decisions, and even to predict the future. The recent convergence of big data, cloud computing, and novel machine learning algorithms and statistical methods is causing an explosive interest in data science and its applicability to all fields. This convergence has already enabled the automation of some tasks that better human performance. The novel capabilities we derive from data science will drive our cars, treat disease, and keep us safe. At the same time, such capabilities risk leading to biased, inappropriate, or unintended action. The design of data science solutions requires both excellence in the fundamentals of the field and expertise to develop applications which meet human challenges without creating even greater risk.
The Data Science Institute at Columbia University promotes “Data for Good”: using data to address societal challenges and bringing humanistic perspectives as—not after—new science and technology is invented. Started in 2012, the Institute is now a university-level institute representing over 250 affiliated faculty from 12 different schools across campus. Data science literally touches every corner of the university.
In this talk, Jeannette Wing, Director of the Data Science Institute, will present the vision on how the Institute plans to address some of the key challenges and opportunities of data science, highlighting educational and research activities, as well as future initiatives that may directly impact the data science community at Columbia, New York City, and beyond.

Data-Driven Discovery and Decision Making - A Paradigm Shift for Large-Scale Experimental Science
2018-02-27 11:00 - 12:15
Tuesday, February 27, 2018
11:00AM-12:15PM
CEPSR 414, DAVIS AUDITORIUM

“Data-Driven Discovery and Decision Making – A Paradigm Shift for Large-Scale Experimental Science”
New instrument technologies are enabling a new generation of in-situ and in-operando experiments, with extremely fine spatial and temporal resolution, that allows researchers to observe as physics, chemistry and biology are happening. These new methodologies go hand in hand with an exponential growth in data volumes and rates – petabyte scale data collections and terabyte/sec. At the same time, scientists are pushing for a paradigm shift. As they can now observe processes in intricate details, they want to analyze, interpret and control those processes. Given the multitude of voluminous, heterogenous data streams involved in every single experiment, novel real-time, data-driven analysis and decision support approaches are needed to realize their vision. This talk will discuss state-of-the-art streaming analysis for experimental facilities, its challenges and early successes. It will present work currently carried out at Brookhaven National Laboratory and identify areas for collaboration.
Kerstin Kleese van Dam is the director of the Computational Science Initiative (CSI) at the Department of Energy’s Brookhaven National Laboratory (BNL), in Long Island, NY. With 100 petabytes, BNL hosts the second largest scientific data archive in the US and the fourth largest in the world, it processes annually in access of 400 petabytes of scientific results.
CSI conducts leading edge computer science and applied mathematics research to address the associated analysis challenges, specific focus areas are machine learning, visual analytics and programming models.

Before she joined BNL Kerstin was associated division director at Pacific Northwest National Laboratory, Director of Computing at University College London Medical School and Data Management Group Lead at the Science and Technology Facilities Council in the UK.

Hosted by Prof. Steven Nowick (Dept. of Computer Science)

Data Science Day (Event)
2018-03-28 09:00 - 17:00
Open to all
Regiser here
Celebrating 5 years of Data Science at Columbia University
Join us 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

C++: An Invisible Foundation
2018-09-13 16:00 - 18:00
OPEN TO ALL

REGISTER HERE

Thursday, September 13th, 2018
4:00 p.m. – 5:00 p.m. Lecture
5:00 p.m. – 6:00 p.m. Reception
Brown Institute | 2nd Floor, Pulitzer Hall (2950 Broadway)

C++ is one of the key foundations of our software. It is invisible to most people because they use it only indirectly. It’s in your computer and your phone. It’s in the machines that manufacture, your computer, and your phone. It’s in most cars, including all the self-driving ones. It’s on Mars, and in deep sea-robots. It’s what runs your Java virtual machine and your Python AI/ML scripts.

In this Distinguished Lecture, Dr. Stroustrup will briefly explain what technical aspects of C++ makes it so useful. He will focus on design principles, but also touch upon resource management and what it takes to be efficient in various contexts. Finally, he will comment on the challenges facing the C++ community.

About Bjarne Stroustrup

Dr. Bjarne Stroustrup is the designer and original implementer of C++ as well as the author of The C++ Programming Language (Fourth Edition), A Tour of C++, Programming: Principles and Practice using C++ (Second Edition), and many popular and academic publications. Dr. Stroustrup is a Managing Director in the technology division of Morgan Stanley in New York City as well as a visiting professor in Columbia University’s Department of Computer Science. He is a member of the U.S. National Academy of Engineering, and an IEEE, ACM, and CHM fellow. He received the 2018 Charles Stark Draper Prize, the IEEE Computer Society’s 2018 Computer Pioneer Award, and the 2017 IET Faraday Medal. His research interests include distributed systems, design, programming techniques, software development tools, and programming languages. He is actively involved in the ISO standardization of C++. He holds a master’s degree in mathematics from Aarhus University, where he is an honorary professor, and a Ph.D. in computer science from the University of Cambridge, where he is an honorary fellow of Churchill College.

The Distinguished Lectures in Computational Innovation series highlights programmers, data scientists, and other practitioners from the private sector who lead cutting-edge technology initiatives such as Python, C++, and the Open Source Initiative. The events include a presentation, question & answer session, and post-event networking reception.

All Columbia University students, faculty, postdocs, and administrators are welcome to register for and attend these events.

Data for Good (or Scary AI and Other Dangers of Big Data)
2018-09-14 13:00 - 14:30
OPEN TO ALL
RSVP HERE

Friday, September 14, 2018
Location: Pupin 428
1:00PM – 2:30PM

Abstract: In our data-rich world, we in the technology community have a responsibility to ensure that we use data for good. In this talk, under the acronym FATES, I will focus on these aspects of the responsible use of data: fairness, accountability, transparency, ethics, safety and security. I will give examples of how data-hungry AI-based systems can lead to harmful decisions and even fatal errors. But I will also give examples of new techniques we can use to reduce some of these bad effects. Above all, we need to work with ethicists, social scientists, and humanists to build systems with FATES in mind as we design our technology not after we deploy it.

Bio: Biography: Jeannette M. Wing is Avanessians Director of the Data Science Institute and Professor of Computer Science at Columbia University. From 2013 to 2017, she was a Corporate Vice President of Microsoft Research. She is Consulting Professor of Computer Science at Carnegie Mellon where she twice served as the Head of the Computer Science Department and had been on the faculty since 1985. From 2007-2010 she was the Assistant Director of the Computer and Information Science and Engineering Directorate at the National Science Foundation. She received her S.B., S.M., and Ph.D. degrees in Computer Science, all from the Massachusetts Institute of Technology.
Professor Wing’s general research interests are in the areas of trustworthy computing, specification and verification, concurrent and distributed systems, programming languages, and software engineering. Her current interests are in the foundations of security and privacy, with a new focus on trustworthy AI. She was or is on the editorial board of twelve journals, including the Journal of the ACM and Communications of the ACM.
She is currently a member of: the National Library of Medicine Blue Ribbon Panel, the Science, Engineering, and Technology Advisory Committee for the American Academy for Arts and Sciences; the Board of Trustees for the Institute of Pure and Applied Mathematics; the Advisory Board for the Association for Women in Mathematics; and the Alibaba DAMO Technical Advisory Board. She has been chair and/or a member of many other academic, government, and industry advisory boards. She received the CRA Distinguished Service Award in 2011 and the ACM Distinguished Service Award in 2014. She is a Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE).

Cross-device User Clustering at Adobe
2018-09-26 17:00 - 18:30
OPEN TO ALL

REGISTER HERE

Wednesday, September 26, 2018
5:00PM – 6:30PM
Schapiro CEPSR, Davis Auditorium (412)

ABSTRACT: As people now engage with digital properties using a myriad of devices such as laptops, smart phones, tablets, connected TVs and gaming consoles, the traditional cookie-based or device-level views of online user interaction are too narrow. Even when using a single device, a person may be assigned multiple IDs due to cookie churn or the use of different browsers. Marketers are looking through a fragmented lens and are spending their marketing dollars without understanding more than a fractional part of consumer interactions.

This talk will give an overview of how Adobe is tackling this problem using graph processing techniques, going over our journey from the initial research ideas to a fully productized product that works at scale running in the cloud while respecting user privacy. Focus will be on our open-source technology stack, including Apache Spark, GraphX, OpenTSDB, … along with a description of our algorithms and the challenges we went through to run them at very large scale on Amazon Web Services.

Bio: Charles is a data engineer/scientist at Adobe, where he has been involved on many projects revolving around big data, infrastructure and data science. Passionate about data, Charles is constantly on the lookout for the newest technologies and applications related to data science at scale. After graduating with a Master’s degree in Computer Science and Artificial Intelligence from EPITA, France, in 2010, Charles has since focused his career on the online advertising industry, working at an ad network, a Demand-Side Platform, and currently Adobe’s Data Management Platform.

Data Science Institute | Annual Town Hall | 2018
2018-09-28 10:00 - 11:30
OPEN TO ALL

REGISTER HERE

Friday, September 28, 2018
10:00AM – 11:30AM
** NOTE NEW LOCATION: Maison Francaise | East Gallery | Buelle Hall**

The Data Science Institute of Columbia University’s mission is to advance the state-of-the-art in data science; to transform all fields, professions, and sectors through the application of data science; and to ensure the responsible use of data for the benefit of society.

The Institute invites the Columbia Community to join us at our annual Town Hall to hear about recent highlights and to discover existing and emerging opportunities for engagement with the Institute.

Data Science Institute-Industry-Innovation Seminar: GE
2018-10-04 17:00 - 18:30
OPEN TO ALL

REGISTER HERE

Dr. Paul Ardis, Research Mission Leader at GE Global Research | “Going Further: Changing the Airline Industry Beyond the Aircraft”

Thursday, October 4, 2018
5:00PM – 6:30PM
Schapiro CEPSR, Davis Auditorium (412)

Abstract: Analytics are opening up new possibilities in the aviation sector as we think beyond the airplane during flight. Putting together GE’s expertise in AI for manufacturing and service with airline passenger analytics and intelligent supply chains, we are working towards a future where efficiency and agility is realized to maximize efficiency and minimize disruption.

Bio: Dr. Ardis has been leading AI research in the aviation sector at GE Global Research since 2013, having previously served as a professor at Rochester Institute of Technology and as a research contractor to the Air Force Research Laboratory.

Foundations for Research Computing Distinguished Lecture Series: Dr. Lorena Barba, Associate Professor, Department of Mechanical and Aerospace Engineering, George Washington University
2018-10-11 16:00 - 18:00
OPEN TO ALL

REGISTER HERE

Thursday, October 11, 2018
4:00 p.m. – 5:00 p.m. Lecture
5:00 p.m. – 6:00 p.m. Reception
Brown Institute | 2nd Floor, Pulitzer Hall (2950 Broadway)

Lorena A. Barba is an associate professor of mechanical and aerospace engineering at the George Washington University in Washington, DC. She holds a PhD in aeronautics from the California Institute of Technology and BSc/PEng degrees in mechanical engineering from Universidad Técnica Federico Santa María, Chile. Her research includes computational fluid dynamics, high-performance computing, computational biophysics, and animal flight.

An international leader in computational science and engineering, she is also a long-standing advocate of open source software for science and education, and she is well known for her courses and open educational resources. She was a recipient of the 2016 Leamer-Rosenthal Award for Open Social Sciences, and in 2017, was nominated and received an honorable mention in the Open Education Awards for Excellence of the Open Education Consortium.

Prof. Barba received the NSF Faculty Early CAREER award (2012), was named CUDA Fellow by NVIDIA Corp. (2012), is an awardee of the UK Engineering and Physical Sciences Research Council (EPSRC) First Grant program (2007), is an Amelia Earhart Fellow of the Zonta Foundation (1999) and a leader in computational science and engineering internationally. She is a member of the Board of Directors for the NumFOCUS non-profit, and a member of the editorial board for IEEE/AIP Computing in Science and Engineering, The Journal of Open Source Software, and The ReScience Journal.

Truth-Seeking in the Age of Disinformation
2018-10-12 13:00 - 14:30
Friday, October 12, 2018
1:00PM – 2:30PM
Pupin Hall, Room 428

“Truth-Seeking in the Age of Disinformation”

Abstract: Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. In this talk, we will present several related problems. First, in the context of investigative journalism, we will address the problem of automatically identifying which claims in a political debate are most worthy and should be prioritized for fact-checking. We will then present a general-purpose deep learning framework for fully automatic fact checking using external sources, which taps the potential of the entire Web as a knowledge source to confirm or reject a claim. We will further extend this framework to the context of community question answering, where the goal is to decide whether an answer to a factual question is factually true or false. We will also cover some related problems such as stance detection, trust-worthiness estimation for Web sites, political ideology, bias and propaganda detection, as well as finding seminar users and opinion manipulation trolls in news community forums.

Bio: Dr. Preslav Nakov is a Senior Scientist at the Qatar Computing Research Institute, HBKU. His research interests include computational linguistics and natural language processing, machine translation, question answering, fact-checking, sentiment analysis, lexical semantics, Web as a corpus, and biomedical text processing.

At QCRI, he leads a project whose aim is to develop a news aggregation application to limit the effect of fake news, propaganda and media bias by helping users step out of their bubble and achieve a healthy news diet. He is also a co-PI of an MIT-QCRI collaboration project on Arabic Speech and Language Processing for Cross-Language Informatio n Search and Fact Verification, and he was a co-PI of another MIT-QCRI collaboration project on Speech and Language Processing for Arabic (2013-2016).

Preslav Nakov received his PhD degree in Computer Science from the University of California at Berkeley (supported by a Fulbright grant and a UC Berkeley fellowship), and a MSc degree from the Sofia University. He was a Research Fellow at the National University of Singapore, a honorary lecturer in the Sofia University, and a research staff in the Bulgarian Academy of Sciences.

Foundations for Research Computing Distinguished Lecture Series: Dr. Eric Xing, Professor, Department of Machine Learning, Carnegie Mellon University Founder, Chief Executive Officer, and Chief Scientist, Petuum, Inc.
2018-11-08 16:00 - 18:00
OPEN TO ALL

REGISTER HERE

Thursday, November 8, 2018

4:00 p.m. – 5:00 p.m. Lecture
5:00 p.m. – 6:00 p.m. Reception
Brown Institute | 2nd Floor, Pulitzer Hall (2950 Broadway)

Dr. Eric P. Xing is Founder, CEO and Chief Scientist at Petuum Inc. He is a Professor in the School of Computer Science at Carnegie Mellon University. He is also the Associate Department Head for Research of the Machine Learning Department and the Founding Director of the Center for Machine Learning and Health at CMU. For his distinguished contributions in AI/ML, he was elected a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI).

Dr. Xing is a thought and innovation leader in Machine Learning and Artificial Intelligence. His principal research interests are in the development of machine learning and statistical methodology and large-scale computational system and architectures, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic complex systems. His pioneering research has created numerous AI/ML foundational techniques, such as the Parameter Server, distance metric learning, distributed network inference, dynamic networks, dynamic nonparametric Bayesian models, spectral graphical models, and variational inference. He has authored or co-authored over 300 publications, while receiving multiple Best Paper Awards.

Dr. Xing is a board member of the International Machine Learning Society, program chair and general chair of the International Conference of Machine Learning (ICML), and a former member of the U.S. Department of Defense Advanced Research Projects Agency (DARPA) Information Science and Technology (ISAT) Advisory group. He is the recipient of numerous awards including: The National Science Foundation (NSF) Career Award; Alfred P. Sloan Research Fellowship in Computer Science; United States Air Force Office of Scientific Research Young Investigator Award; and the IBM Open Collaborative Research Faculty Award.

Data Science Institute-Industry-Innovation Seminar: Johnson & Johnson
2018-11-14 10:30 - 12:00
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.
