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Make Data Work
Oct 15–17, 2014 • New York, NY

Hardcore Data Science

Ben Lorica (O'Reilly), Ted Dunning (MapR, now part of HPE), Tim Kraska (Brown University), Alice Zheng (Amazon), Anna Gilbert (University of Michigan), Jon Kleinberg (Cornell University), Kira Radinsky (eBay | Technion), Rob Fergus (New York University and Facebook), Ben Recht (University of California, Berkeley), Brian Whitman (Spotify), Hanna Wallach (Microsoft Research NYC & University of Massachusetts Amherst), Dafna Shahaf (The Hebrew University of Jerusalem)
9:00am–5:00pm Wednesday, 10/15/2014
Hardcore Data Science
Location: 1 E14/1 E15
Average rating: ****.
(4.27, 15 ratings)

Data science is a hot topic, but much of it is simply Business Intelligence in a new mantle. In this track, we push the envelope of data science, exploring emerging topics and new areas of study made possible by vast troves of raw data and cutting-edge architectures for analyzing and exploring information. We’ll cover topics such as data management, machine learning, natural language processing, crowdsourcing, and algorithm design.

Who should attend: Data scientists, data engineers, statisticians, data modellers, and analysts with a strong understanding of data science fundamentals, will find themselves at home in this tutorial, as will CTOs, Chief Scientists, and academic researchers.

Photo of Ben Lorica

Ben Lorica


Ben Lorica is the chief data scientist at O’Reilly. Ben has applied business intelligence, data mining, machine learning, and statistical analysis in a variety of settings, including direct marketing, consumer and market research, targeted advertising, text mining, and financial engineering. His background includes stints with an investment management company, internet startups, and financial services.

Photo of Ted Dunning

Ted Dunning

MapR, now part of HPE

Ted Dunning is the chief technology officer at MapR, an HPE company. He’s also a board member for the Apache Software Foundation, a PMC member, and committer on a number of projects. Ted has years of experience with machine learning and other big data solutions across a range of sectors. He’s contributed to clustering, classification, and matrix decomposition algorithms in Mahout and to the new Mahout Math library and designed the t-digest algorithm used in several open source projects and by a variety of companies. Previously, Ted was chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems and built fraud-detection systems for ID Analytics (LifeLock). Ted has coauthored a number of books on big data topics, including several published by O’Reilly related to machine learning, and has 24 issued patents to date plus a dozen pending. He holds a PhD in computing science from the University of Sheffield. When he’s not doing data science, he plays guitar and mandolin. He also bought the beer at the first Hadoop user group meeting.

Photo of Tim Kraska

Tim Kraska

Brown University

Tim Kraska is an Assistant Professor in the Computer Science department at Brown University. Currently, his research focuses on Big Data management for machine-learning and hybrid human/machine database systems. Before joining Brown, Tim Kraska spent 2 years as a PostDoc in the AMPLab at UC Berkeley after receiving his PhD from ETH Zurich, where he worked on transaction management and stream processing. He was awarded a Swiss National Science Foundation Prospective Researcher Fellowship (2010), a DAAD Scholarship (2006), a University of Sydney Master of Information Technology Scholarship for outstanding achievement (2005), the University of Sydney Siemens Prize (2005), a VLDB best demo award (2011) and an ICDE best paper award (2013).

Photo of Alice Zheng

Alice Zheng


Alice Zheng is a senior manager of applied science on the machine learning optimization team on Amazon’s advertising platform. She specializes in research and development of machine learning methods, tools, and applications. She’s the author of Feature Engineering for Machine Learning. Previously, Alice has worked at GraphLab, Dato, and Turi, where she led the machine learning toolkits team and spearheaded user outreach; and was a researcher in the Machine Learning Group at Microsoft Research – Redmond. Alice holds PhD and BA degrees in computer science and a BA in mathematics, all from UC Berkeley.

Photo of Anna Gilbert

Anna Gilbert

University of Michigan

Anna Gilbert received an S.B. degree from the University of Chicago and a Ph.D. from Princeton University, both in mathematics. In 1997, she was a postdoctoral fellow at Yale University and AT&T Labs-Research. From 1998 to
2004, she was a member of technical staff at AT&T Labs-Research in Florham Park, NJ. Since then she has been with the Department of Mathematics at the University of Michigan, where she is now a Professor. She has received
several awards, including a Sloan Research Fellowship (2006), an NSF CAREER award (2006), the National Academy of Sciences Award for Initiatives in Research (2008), the Association of Computing Machinery (ACM) Douglas Engelbart Best Paper award (2008), the EURASIP Signal Processing Best Paper award (2010), a National Academy of Sciences Kavli Fellow (2012), and the SIAM Ralph E. Kleinman Prize (2013).

Her research interests include analysis, probability, networking, and algorithms. She is especially interested in randomized algorithms with applications to harmonic analysis, signal and image processing, networking, and massive datasets.

Photo of Jon Kleinberg

Jon Kleinberg

Cornell University

Jon Kleinberg is the Tisch University Professor of Computer Science and Information Science at Cornell, where his research focuses on the social and information networks that underpin the Web and other on-line media. He is the author of the books “Algorithm Design” (with Eva Tardos) and “Networks, Crowds, and Markets” (with David Easley). He is the recipient of awards including a MacArthur Fellowship, the Nevanlinna Prize, the Harvey Prize, the ACM SIGKDD Innovation Award, and the ACM-Infosys Foundation Award in the Computing Sciences, and he is a member of the National Academy of Engineering and the National Academy of Sciences.

Photo of Kira Radinsky

Kira Radinsky

eBay | Technion

Kira Radinsky is the chief scientist and director of data science at eBay, where she is building the next-generation predictive data mining, deep learning, and natural language processing solutions that will transform ecommerce. She also serves as a visiting professor at the Technion, Israel’s leading science and technology institute, where she focuses on the application of predictive data mining in medicine. Kira cofounded SalesPredict (acquired by eBay in 2016), a leader in the field of predictive marketing—the company’s solutions that leveraged large-scale data mining to predict sales conversions. One of the up-and-coming voices in the data science community, Kira is pioneering the field of web dynamics and temporal information retrieval. She gained international recognition for her work at Microsoft Research, where she developed predictive algorithms that recognized the early-warning signs of globally impactful events, including political riots and disease epidemics. She was named one of MIT Technology Review’s 35 young innovators under 35 for 2013 and one of Forbes’s 30 under 30 rising stars in enterprise technology for 2015; in 2016, she was recognized as woman of the year by Globes. Kira is a frequent presenter at global tech events, including TEDx and the World Wide Web Conference, and she has published in Harvard Business Review.

Rob Fergus

New York University and Facebook

Rob Fergus is an Associate Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University. He is also a Research Scientist at Facebook, working in their AI Research Group. He received a Masters in Electrical Engineering with Prof. Pietro Perona at Caltech, before completing a PhD with Prof. Andrew Zisserman at the University of Oxford in 2005. Before coming to NYU, he spent two years as a post-doc in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT, working with Prof. William Freeman. He has received several awards including a CVPR best paper prize, a Sloan Fellowship & NSF Career award and the IEEE Longuet-Higgins prize.

Photo of Ben Recht

Ben Recht

University of California, Berkeley

Ben Recht is an associate professor in the Department of Electrical Engineering and Computer Sciences and the Department of Statistics at the University of California, Berkeley. Ben’s research focuses on scalable computational tools for large-scale data analysis, statistical signal processing, and machine learning. He explores the intersections of convex optimization, mathematical statistics, and randomized algorithms. He is particularly interested in simplifying the analysis and manipulation of noisy and incomplete data by exploiting domain-specific knowledge and prior information about structure. Ben is the recipient of an NSF Career Award, an Alfred P. Sloan Research Fellowship, and the 2012 SIAM/MOS Lagrange Prize in Continuous Optimization. He is currently on the Editorial Boards of Mathematical Programming and the Journal for Machine Learning Research.

Photo of Brian Whitman

Brian Whitman


Brian is recognized as a leading scientist in the area of music and text retrieval and natural language processing.

He received his doctorate from MIT’s Media Lab in 2005 in Barry Vercoe’s Machine Listening group and a masters degree in computer science from Columbia University’s Natural Language Processing Group. Brian’s research focuses on the cultural analysis of music through large scale data mining and machine learning.
Brian recorded and performed as Blitter until he co-founded The Echo Nest, a Spotify subsidiary, and he currently works on large scale automated music synthesis.

Hanna Wallach

Microsoft Research NYC & University of Massachusetts Amherst

Hanna Wallach is a researcher at Microsoft Research in New York City and an assistant professor at the University of Massachusetts Amherst’s School of Computer Science, where she is one of five core faculty members involved in UMass’s recently formed Computational Social Science Initiative. Hanna develops new machine learning methods for analyzing the structure, content, and dynamics of complex social processes, such as the US political system, the US patent system, and software development communities. Her research contributes to machine learning, Bayesian statistics, and, in collaboration with social scientists, to the nascent field of computational social science. Her work on infinite belief networks won the best paper award at AISTATS 2010. Hanna has organized several workshops on Bayesian latent variable modeling and computational social science. She also co-founded the annual Women in Machine Learning Workshop. Hanna holds a B.A. in Computer Science from the University of Cambridge, an M.S. in Cognitive Science and Machine Learning from the University of Edinburgh, and a Ph.D. in Physics from the University of Cambridge.

Photo of Dafna Shahaf

Dafna Shahaf

The Hebrew University of Jerusalem

Dafna Shahaf is an Assistant Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem.
Her research is about making sense of massive amounts of data. She designs algorithms that help people understand the underlying
structure of complex topics, connect the dots between pieces of information, and turn data into insight. Her
work has received multiple awards, including the IJCAI Early Career Award Best Research Paper at KDD’10. She received her PhD from Carnegie Mellon University. Prior to joining the Hebrew University, she was a postdoctoral fellow at Microsoft Research and Stanford University.