8th Annual Machine Learning Symposium

8th Annual Machine Learning Symposium

Friday, March 28, 2014

The New York Academy of Sciences

Presented By

Presented by the Machine Learning Discussion Group at the New York Academy of Sciences


In our current digital age, a wealth of data is available at our fingertips. Often, the value of this 'Big Data' is not in the data itself, but the ability to learn from historical data in order to make predictions. Machine Learning, a branch of artificial intelligence, involves the development of mathematical algorithms that discover knowledge from specific data sets, and then "learn" from the data in an iterative fashion that allows predictions to be made. Today, Machine Learning has a wide range of applications, including natural language processing, search engine function, medical diagnosis, credit card fraud detection, and stock market analysis.

This symposium — part of an ongoing series presented by the Machine Learning Discussion Group at the New York Academy of Sciences — will feature Keynote Presentations from leading scientists in both applied and theoretical Machine Learning. Speakers include Rayid Ghani (University of Chicago), former Chief Data Scientist for the Obama for America 2012 re-election campaign; IBM specialist in automatic speech recognition, Brian Kingsbury; and Northwestern University's Jorge Nocedal, who shall discuss the role of machine learning in optimization.

2014 Spotlight Talk Awards

The New York Academy of Sciences congratulates the winners of the 2014 Spotlight Talk Awards, which recognized a series of the best oral research presentations delivered by early career investigators during the Symposium.

Generative Image Models for Visual Phenotype Modeling
Theofanis Karaletsos
Memorial Sloan-Kettering Cancer Center

Graph-Based Posterior Regularization for Semi-Supervised Structured Prediction
Jennifer Gillenwater, BS
University of Pennsylvania

Learning from Label Proportions: Algorithm, Theory, and Application
Felix X. Yu, MSc
Columbia University

Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch
Partha Pratim Talukdar, PhD
Carnegie Mellon University

Accelerated Parallel Optimization Methods for Large Scale Machine Learning
Haipeng Luo, PhD candidate
Princeton University

Learning Theory and Algorithms for Revenue Optimization in Second-Price Auctions with Reserve
Andrés Muñoz Medina
Courant Institute of Mathematical Sciences, New York University

Google is the proud sponsor of the Spotlight Talk awards.

Registration Pricing

Student / Postdoc Member$15
Nonmember (Academia)$65
Nonmember (Corporate)$85
Nonmember (Non-profit)$65
Nonmember (Student / Postdoc / Resident / Fellow)$45


* Presentation titles and times are subject to change.

March 28, 2014

9:00 AM

Registration, Continental Breakfast, and Poster Set-up

10:00 AM

Welcome Remarks
Brooke Grindlinger, PhD, The New York Academy of Sciences

10:10 AM

Keynote Address 1
Using Machine Learning Powers for Good
Rayid Ghani, MS, University of Chicago and Edgeflip

10:50 AM

Audience Q&A

Spotlight Talks: Session 1

A series of short, early career investigator presentations across a variety of topics at the frontier of Machine Learning science.

11:05 AM

Graph-Based Posterior Regularization for Semi-Supervised Structured Prediction
Jennifer Gillenwater, BS, University of Pennsylvania and University of Washington

11:10 AM

Learning from Label Proportions: Algorithm, Theory, and Applications
Felix X. Yu, MSc, Columbia University; Google Research; IBM Research

11:15 AM

Learning Theory and Algorithms for Revenue Optimization in Second-Price Auctions with Reserve
Andrés Muñoz Medina, MSc, Courant Institute of Mathematical Sciences, New York University; Google Research

11:20 AM

Generative Image Models for Visual Phenotype Modeling
Theofanis Karaletsos, PhD, Memorial Sloan-Kettering Cancer Center

11:25 AM

Scaling Graph-based Semi Supervised Learning to Large Number of Labels Using Count-Min Sketch
Partha Pratim Talukdar, PhD, Carnegie Mellon University

11:30 AM

Networking Break and Poster Session 1

12:20 PM

Keynote Address 2
Structured Classification Criteria for Deep Learning in Speech Recognition
Brian Kingsbury, PhD, IBM T.J. Watson Research Center, Yorktown Heights, New York, US

1:00 PM

Audience Q&A

1:15 PM

Networking Lunch

Spotlight Talks: Session 2

2:30 PM

Computational Identification of Genotype-Specific Effective Drug Combinations
Kaitlyn Gayvert, BA, Weill Cornell Medical College; Tri-Institutional Training Program in Computational Biology and Medicine

2:35 PM

Towards Minimax Online Learning with Unknown Time Horizon
Haipeng Luo, BS, Princeton University

2:40 PM

Accelerated Parallel Optimization Methods for Large Scale Machine Learning
Haipeng Luo, BS, Princeton University; AT&T Labs

2:45 PM

Nuclear Norm Minimization via Active Subspace Selection
Cho-Jui Hsieh, MS, University of Texas at Austin

Note Added in Press: Peder A. Olsen, PhD, IBM T.J. Watson Research Center, will be presenting this Spotlight Talk on behalf of co-author Cho-Jui Hsieh.

2:50 PM

Fast, Scalable Comment Moderation via Active Learning at The New York Times
Jason Capehart, BS, The New York Times

2:55 PM

Keynote Address 3
The Role of Optimization in Machine Learning
Jorge Nocedal, PhD, Northwestern University

3:35 PM

Audience Q&A

3:50 PM

Career Development Presentation

From Academia to Industry as a Machine Learning Scientist
Zachary Cohn, PhD, American Express

4:15 PM

Networking Break and Poster Session 2

4:50 PM

Award Presentation
Best Early Career Investigator Research Presentation

The Scientific Organizing Committee will announce the winner, selected from early career investigator oral and poster presentations made throughout the day

Google is the proud sponsor of the early career investigator Spotlight Talk awards.

4:55 PM

Closing Remarks

5:00 PM

Symposium Adjourns



Naoki Abe, PhD

IBM Research

Naoki Abe has been a Research Staff Member in the Data Analytics Research group since May 2001, and is engaged in research in Machine Learning, Data Mining, and their applications to problems in business analytics. Abe obtained his BS and MS in computer science from MIT in 1984 and a PhD in Computer and Information Sciences from the University of Pennsylvania in 1989. From 1984 to 1985, he worked as a researcher at IBM T.J. Watson Research Center. From 1989 to 1990, he was a postdoctoral researcher at U.C. Santa Cruz, where he conducted research in computational learning theory. During the 1990s, he was with NEC research laboratories in Japan, where he was engaged in research in machine learning and its applications to various areas, including data mining, e-commerce, natural language processing, and bioinformatics. During this period he was also involved with the MITI-sponsored Real World Computing project, and the MEXT-sponsored Discovery Science project. From 1998 to 2000, he was adjunct Associate Professor at the Tokyo Institute of Technology. Naoki has served as program committee members of ICML, ALT, and COLT, and is currently on the editorial board of the Journal of Machine Learning Research.

Corinna Cortes, PhD


Corinna Cortes is the Head of Google Research, NY, where she is working on a broad range of theoretical and applied large-scale machine learning problems. Prior to Google, Corinna spent more than ten years at AT&T Labs - Research, formerly AT&T Bell Labs, where she held a distinguished research position. Corinna's research work is well-known in particular for her contributions to the theoretical foundations of support vector machines (SVMs), for which she jointly with Vladimir Vapnik received the 2008 Paris Kanellakis Theory and Practice Award, and her work on data-mining in very large data sets for which she was awarded the AT&T Science and Technology Medal in the year 2000. Corinna received her MS degree in Physics from University of Copenhagen and joined AT&T Bell Labs as a researcher in 1989. She received her Ph.D. in computer science from the University of Rochester in 1993.
Corinna is also a competitive runner.

Brooke Grindlinger, PhD

The New York Academy of Sciences

Patrick Haffner, PhD

AT&T Labs-Research

Patrick Haffner is Lead Member of Technical Staff at AT&T Labs Research. In the 1990s, he pioneered Neural Networks for speech and image recognition, resulting in the first industrial deployment of Deep Learning by AT&T in 1996 (with Yann LeCun). He was also one of the lead inventor of the DjVu compression technology. Since 2000, his main focus has been large scale algorithms for speech, natural language and network applications.

Gunnar Rätsch, PhD

Memorial Sloan-Kettering Cancer Center

Data scientist Gunnar Rätsch develops and applies advanced data analysis and modeling techniques to data from genomics, high-throughput sequencing, clinical records and images.

He earned his Ph.D. at the German National Laboratory for Information Technology under supervision of Klaus-Robert Müller. His thesis is on iterative algorithms related to Boosting and Support Vector Machines. He was a postdoc with Bob Williamson and Bernhard Schölkopf. Gunnar Rätsch received the Max Planck Young and Independent Investigator award and was leading the group on Machine Learning in Genome Biology at theFriedrich Miescher Laboratory in Tübingen (2005-2011). In 2012 he joined Memorial Sloan-Kettering Cancer Center as Associate Faculty.

The Rätsch laboratory advances computational methods for the analysis of big data common in the biomedical sciences. The group utilizes, develops and integrates ideas from machine learning, operations research, sequence analysis, statistical genetics, text mining and computer vision with the aim to discover relationships in complex biomedical data.

John Langford, PhD

Microsoft Research

John Langford is a Doctor of Learning at Microsoft Research. His work includes research in machine learning, game theory, steganography, and Captchas. He was previously a Research Associate Professor at the Toyota Technological Institute in Chicago. He has worked in the past at IBM's Watson Research Center in Yorktown, NY, under the Goldstine Fellowship. He earned a PhD in computer science from Carnegie Mellon University in 2002 and a Physics/Computer Science double major from CalTech in 1997.

Mehryar Mohri, PhD

Courant Institute of Mathematical Sciences, New York University

Mehryar Mohri spent about 10 years at AT&T Bell Labs or AT&T Labs - Research (1995-2004), where, in the last four years, he served as the Head of the Speech Algorithms Department and as a Technology Leader, overseeing research projects in machine learning, text and speech processing, and the design of general algorithms. He joined the Courant Institute in the summer of 2004 as a Professor of Computer Science. In 2004, he was a Visiting Professor at Google Research for a full semester where he worked on several machine learning and algorithmic research projects. Since then, he continues to work at Google as a Research Consultant. His current topics of interest are machine learning, computational biology, and text and speech processing.

Robert Schapire, PhD

Princeton University

Robert Schapire received his ScB in math and computer science from Brown University in 1986, and his SM (1988) and PhD (1991) from MIT under the supervision of Ronald Rivest. After a short post-doc at Harvard, he joined the technical staff at AT&T Labs (formerly AT&T Bell Laboratories) in 1991. Since 2002, he has been on the faculty of Princeton University where he is currently the David M. Siegel '83 Professor in Computer Science. His awards include the 1991 ACM Doctoral Dissertation Award, the 2003 Gödel Prize, and the 2004 Kanelakkis Theory and Practice Award (both of the last two with Yoav Freund). He is a fellow of the AAAI, and a member of the National Academy of Engineering. His main research interest is in theoretical and applied machine learning.

Di Xu, PhD

American Express

Di Xu is vice president, Risk and Information Management at American Express. Di has been with American Express since 2001 in positions of increasing responsibility in decision science, including acquisition, underwriting and fraud and customer management modeling functions. He currently heads the global underwriting decision science team that supports new accounts underwriting and line management models for both US and International markets as well as acquisition and targeting models for the U.S. market. Di and his team are actively exploring cutting-edge machine learning research and its application in financial services. He earned a doctorate degree in Industrial Engineering and a Master of Science in Statistics, both from Rutgers University and Bachelor’s in Engineering in Control Theory from Shanghai JiaoTong University.

Keynote Speakers

Rayid Ghani, MS

Computation Institute & Harris School of Public Policy, University of Chicago

Rayid Ghani is a Research Director and Senior Fellow at the Computation Institute and the Harris School of Public Policy at the University of Chicago. He is also the co-founder of Edgeflip, an analytics and social media startup that is focused on helping non-profits, advocacy groups, and charities do better fundraising, volunteer recruiting, outreach and advocacy. Rayid is currently focused on using data, analytics (and other related buzzwords ) for social causes, both with the University of Chicago and Edgeflip. Rayid created and runs the Eric & Wendy Schmidt "Data Science for Social Good" Summer Fellowship which brings together aspiring data scientists to work on data science projects in partnership with governments and non-profits. At the university, Rayid directs the Center for Data Science and Public Policy and is also developing and teaching courses that bring together Data Science and Public Policy.

Before joining the University of Chicago and starting Edgeflip, Rayid was the Chief Scientist for the Obama 2012 Election Campaign focusing on analytics, data, and technology. Rayid did his graduate work in Machine Learning from Carnegie Mellon University. In his spare time, Rayid advises several startups and non-profits and speaks at, attends, and organizes academic and industry machine learning and data mining conferences.

Brian Kingsbury, PhD


Brian Kingsbury has been a research staff member at the IBM T. J. Watson Research Center since 1999. Currently he is co-PI and technical lead for LORELEI: an IBM-led consortium participating in the IARPA Babel program that includes six academic institutions as partners. He has contributed to IBM's entries in numerous competitive evaluations of speech technology, including Switchboard, SPINE, EARS, Spoken Term Detection, and GALE. Brian completed his PhD at the University of California, Berkeley, in 1998. He is an associate editor for IEEE Transactions on Audio, Speech, and Language Processing and a program chair for the 2014 International Conference on Learning Representations. From 2009 to 2011 he was a member of the Speech and Language Processing Technical Committee of the IEEE Signal Processing Society, and he served as a speech area chair for the 2010, 2011, and 2012 ICASSP conferences. His research interests include deep neural network acoustic modeling, large-vocabulary speech transcription, and keyword search.

Jorge Nocedal, PhD

Northwestern University

Jorge Nocedal is the Walter P. Murphy Professor of Industrial Engineering at Northwestern University. He obtained his B.S. in Physics from the National University of Mexico (UNAM) and a PhD. in Mathematical Sciences from Rice University. His research interests are in optimization algorithms and their application in machine learning and in areas involving differential equations. He is currently the Editor in Chief for the SIAM Journal on Optimization, is a SIAM Fellow, and was awarded the 2012 George B. Dantzig Prize.


Jason Capehart

The New York Times

Jason Capehart is a data scientist at The New York Times. His work focuses on the application of advances in statistics, machine learning, and technology to practical business problems. Over the course of his 7 year career he's created and deployed dozens of models in the finance industry, media industry, and for startups. Causal inference is his chief interest, but, even for a professional, he's worked on an unusually diverse set of fields including: social network analysis, natural language processing, hedging, speech analytics, and computer vision. Jason holds a Bachelor of Science from Carnegie Mellon University.

Zachary Cohn, PhD

American Express

Zachary Cohn is director of digital modeling for American Express Risk and Information Management. For the past five years, he has been working in digital acquisition and machine learning, on scalable techniques in response modeling and feature generation. He obtained a PhD in Mathematics from Stanford University in 2009.

Kaitlyn Gayvert

Weill Medical College of Cornell University

Kaitlyn Gayvert graduated from the State University of New York at Geneseo with a B.A. in Mathematics in 2012. She is currently a computational biology and medicine graduate student at Weill Cornell Medical in the laboratory of Dr. Olivier Elemento studying drug repositioning problems through the application of machine learning and statistical methods.

Jennifer Gillenwater

University of Pennsylvania

Jennifer Gillenwater is currently a computer science PhD student at the University of Pennsylvania. She earned her undergraduate degree from Rice University in Electrical Engineering in 2008. Her main area of interest is machine learning, with application to natural language processing. More specifically, she has worked on projects involving graphical models, semi-supervised learning, and submodular optimization. The natural language applications she has experience with include parsing, summarization, and web search. Most of her recent work has focused on two areas: posterior regularization and determinantal point processes. In addition to research, she has helped craft the machine learning course at the University of Pennsylvania and completed several internships at Google and Microsoft.

Cho-Jui Hsieh, MS

University of Texas at Austin

Cho-Jui Hsieh (UT Austin) is a Ph.D. student at University of Texas at Austin (UT-Austin). His research interests focus on large-scale machine learning and data mining. Cho-Jui obtained his B.S. degree in 2007 and M.S degree in 2009 from the computer Science Department of National Taiwan University (advisor: Chih-Jen Lin). Currently he is a member of big data group led by Inderjit Dhillon. He is the recipient of the IBM Phd fellowship in 2013-2015, the best research paper award in KDD 2010, and best paper award in ICDM 2012.

Theofanis Karaletsos, PhD

Memorial Sloan Kettering Cancer Center

Theofanis Karaletsos did his undergrad in computer science and computational biology at Universities in Munich, Germany. He worked towards his PhD at the Max Planck Institute for Intelligent Systems in Tübingen, jointly supervised by Karsten Borgwardt and John Winn (Microsoft Research Cambridge). His work revolves around developing and applying generative probabilistic models to a variety of problems with particular focus on vision and biological settings, such as unsupervised phenotype modeling. He recently moved to the group of Gunnar Raetsch at the Memorial Sloan Kettering Cancer Center in New York to work on the analysis of electronic medical records and pathology images in the context of cancer.

Partha Pratim Talukdar

Carnegie Mellon University

Partha Pratim Talukdar is a Postdoctoral Fellow in the Machine Learning Department at Carnegie Mellon University, working with Tom Mitchell on the NELL project. Partha received his PhD (2010) in CIS from the University of Pennsylvania, working under the supervision of Fernando Pereira, Zack Ives, and Mark Liberman. Partha is broadly interested in Machine Learning, Natural Language Processing, Data Integration, and Neurosemantics, with particular interest in large-scale learning and inference.

Felix X. Yu

Columbia University

Felix X. Yu is a fourth-year PhD student of Dept. of Electrical Engineering, Columbia University. His advisor is Prof. Shih-Fu Chang. Before coming to Columbia, Yu received his Bachelor's degree from Dept. of Electronic Engineering, Tsinghua University, China, in 2010. Yu's primary research interest is learning with weakly supervised data, and its applications in computer vision and multimedia. Yu's works have been recognized by prestigious academic awards including the IBM PhD Fellowship Award, Facebook PhD Fellowship Finalist Award, and ACM Multimedia Best Paper Award.

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