May 9th - 11th, 2023

Research Frontiers

The Most Advanced Active Research, Summarized
Register Now & Save 60%

Rapid Pace of Advancement

Data Science is a broad field and advancing at a tremendous pace. Every few months new research, models, and advances are announced.  For data science practitioners it’s essential to keep abreast of the latest advances. However, given the demands on our time that can be a daunting task.

The Most Advanced Research, Summarized

The Data Science Research track is the first of its kind. Instead of having to parse the contents of countless papers or attend academic conferences, we bring the best to you. World-class academics, researchers, and professionals condense the latest research across focus areas and detail what’s important. This summary accelerates your insights on the latest research and serves as a foundation for more in-depth analysis.

Some of Our Past Research Frontiers Speakers

Professor, Computer ScienceNortheastern University

Tina Eliassi-Rad, PhD

Tina Eliassi-Rad is a Professor of Computer Science at Northeastern University. She is also a core faculty member at Northeastern University’s Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences at the University of Wisconsin-Madison. Her research is rooted in data mining and machine learning; and spans theory, algorithms, and applications of big data from networked representations of physical and social phenomena. She has over 100 peer-reviewed publications (including a few best paper and best paper runner-up awardees). Tina’s work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, and ethics in machine learning. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2017, Tina served as the program co-chair for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining  and as the program co-chair for the International Conference on Network Science . In 2020, she is serving as the program co-chair for the International Conference on Computational Social Science. Tina received an Outstanding Mentor Award from the Office of Science at the US Department of Energy in 2010; and became a Fellow of the ISI Foundation in Turin Italy in 2019. 

Just Machine Learning(Talk)

 

Research ScientistGoogle Brain

Luke Metz

Luke Metz is a research scientist at Google Brain working on meta-learning and learned optimizers. He’s interested in building general purpose, learned learning algorithms that not only perform well, but generalizes to new types of never before seen problems.

Learned Optimizers: Learning to Learn Optimization Algorithms(Talks)

VP of ML | Adjunct Professor | ChairArthurAI | Georgetown | CAMLIS

Keegan Hines, PhD

Keegan is VP of Machine Learning at ArthurAI and is also an Adjunct Assistant Professor at Georgetown University. Previously, he was the Director of Machine Learning Research at Capital One and has also held roles at cyberdefense firms. He is a Co-Founder of the Conference on Applied Learning for Information Security (CAMLIS) and holds a PhD in Neuroscience from the University of Texas.

Drift Detection in Structured and Unstructured Data(Talk)

Statistics Professor | Faculty ScientistUC Berkeley | Lawrence Berkeley National Laboratory

Michael Mahoney, PhD

Michael W. Mahoney is at the University of California at Berkeley in the Department of Statistics and at the International Computer Science Institute (ICSI). He is also an Amazon Scholar as well as a faculty scientist at the Lawrence Berkeley National Laboratory. He works on algorithmic and statistical aspects of modern large-scale data analysis. Much of his recent research has focused on large-scale machine learning, including randomized matrix algorithms and randomized numerical linear algebra, geometric network analysis tools for structure extraction in large informatics graphs, scalable implicit regularization methods, computational methods for neural network analysis, physics informed machine learning, and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis. He received his PhD from Yale University with a dissertation in computational statistical mechanics, and he has worked and taught at Yale University in the mathematics department, at Yahoo Research, and at Stanford University in the mathematics department. Among other things, he is on the national advisory committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI), he was on the National Research Council’s Committee on the Analysis of Massive Data, he co-organized the Simons Institute’s fall 2013 and 2018 programs on the foundations of data science, he ran the Park City Mathematics Institute’s 2016 PCMI Summer Session on The Mathematics of Data, and he runs the biennial MMDS Workshops on Algorithms for Modern Massive Data Sets. He is the Director of the NSF/TRIPODS-funded FODA (Foundations of Data Analysis) Institute at UC Berkeley. More information is available at https://www.stat.berkeley.edu/~mmahoney/.

WeightWatcher, an Open-Source Diagnostic Tool for Analyzing Deep Neural Nets(Talk)

Principal Data ScientistOracle

John Peach

A modern polymath, John holds advanced degrees in mechanical engineering, kinesiology and data science, with a focus on solving novel and ambiguous problems. As a senior applied data scientist at Amazon, John worked closely with engineering to create machine learning models to arbitrate chatbot skills, entity resolution, search, and personalization.

As a principal data scientist for Oracle Cloud Infrastructure, he is now defining tooling for data science at scale. John frequently gives talks on best practices and reproducible research. To that end, he has developed an approach to improve validation and reliability by using data unit tests and has pioneered Data Science Design Thinking. He also coordinates SoCal RUG, the largest R meetup group in Southern California.

Tired of Cleaning your Data? Have Confidence in Data with Feature Types(Workshop)

TensorFlow Developer EngineerGoogle

Robert Crowe

A data scientist and TensorFlow addict, Robert has a passion for helping developers quickly learn what they need to be productive. He’s used TensorFlow since the very early days and is excited about how it’s evolving quickly to become even better than it already is. Before moving to data science Robert led software engineering teams for both large and small companies, always focusing on clean, elegant solutions to well-defined needs.

From Experimentation to Products: The Production ML Journey(Talk)

 

Senior Research ScientistFacebook AI Research

Alex Peysakhovich, PhD

Alexander Peysakhovich is technically a behavioral economist, but he bristles a bit at being defined that narrowly. He’s a scientist in Facebook’s artificial intelligence research lab, as well as a prolific scholar, having posted five papers in 2016 alone. He has a Ph.D. from Harvard University, where he won a teaching award, and has published articles in the New York Times, Wired, and several prestigious academic journals. Despite these accomplishments, Peysakhovich says, “I’m most proud of the fact that I’ve managed to learn enough of lots of different fields so that I can work on problems that I’m interested in using those methods. I’ve co-authored with economists, game theorists, computer scientists, neuroscientists, psychologists, evolutionary biologists, and statisticians.

Peysakhovich’s interdisciplinary work is driven by his deep interest in understanding decision-making—both human and machine—and by his desire to figure out how artificial intelligence can improve our decision-making processes. He builds tools that help people make better choices, and machines that can turn data into, as he puts it, “not just correlations but actual causal relationships.”

Machine Learning for A/B Testing (Talk)

Professor | Founding Director | Co-founder and CTOStatistics and Data Science, MIT | IkigaiLabs

Devavrat Shah, PhD

Devavrat Shah is Andrew (1956) and Erna Viterbi Professor with the department of Electrical Engineering and Computer Science at Massachusetts Institute of Technology. He is the founding director of Statistics and Data Science at MIT. He is also a member of IDSS, LIDS, CSAIL and ORC at MIT. He co-founded Celect, Inc. (now part of Nike) in 2013 to help retailers decide what to put where by accurately predicting demand using omni-channel data. He is a co-founder and CTO of IkigaiLabs with the mission to build self-driving organizations by enabling data-driven operations with human-in-the-loop. His research focuses on statistical inference and stochastic networks. His contributions span a variety of areas including resource allocation in communications networks, inference and learning on graphical models, algorithms for social data processing including ranking, recommendations and crowdsourcing and more recently causal inference. He has made foundational contributions to the development of “gossip” protocols and “message-passing” algorithms for statistical inference which have been the building blocks of modern distributed data processing systems.His work spans a range of areas across electrical engineering, computer science and operations research. His work has received broad recognition, including prize paper awards in Machine Learning, Operations Research and Computer Science, and career prizes including 2010 Erlang prize from the INFORMS Applied Probability Society, awarded bi-annually to a young researcher who has made outstanding contributions to applied probability. He is a distinguished alumni of his alma mater IIT Bombay from where he graduated with the honor of President of India Gold Medal. His work has been covered in popular press including NY Times, Forbes, Wired and Reditt.

Automation for Data Professionals(Training)

ThaumaturgeHPCC Systems Solutions Lab

Dan S. Camper

Dan has been with LexisNexis Risk Solutions Group since 2014 and is an Enterprise Architect in the Solutions Lab Group. He has worked for Apple as well as Dun & Bradstreet, and he ran his own custom programming shop for a decade. He’s been writing software professionally for more than 40 years and has worked on a myriad of systems, using many different programming languages.

A New Indexing Technique for Quickly Fuzzy-Matching Entire Dataset Records(Talk)

M.S. Data Science at Harvard University, Z by HP Global Data Science Ambassador

Max Urbany

As Max progresses through his Master’s Program, he is particularly interested in intelligent digital accessibility design, along with the ethical analysis of existing predictive models. His passion for creating quality user-centered tools drives him to understand as much as he can about end users while leveraging what data can reveal.

Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

VP, Enterprise AI / Data Science Solutions at Future Tech

Dan Chaney

Dan Chaney is the VP, Enterprise AI / Data Science Solutions, for Future Tech Enterprise, Inc., an award-winning global IT solutions provider. He oversees all sales, marketing, and technical activities focused on Future Tech’s comprehensive range of AI and data science workstation solutions. Prior to joining Future Tech, Dan spent 20 years at Northrop Grumman, most recently serving as the company’s Enterprise Director of IT Solution Architecture & Engineering. Dan earned his bachelor’s and master’s degrees in communication and computer science from the University of Kentucky. Dan is a Certified Information Systems Security Professional (CISSP) and adjunct instructor for the University of Louisville’s cybersecurity workforce program sponsored by the National Centers of Academic Excellence in Cybersecurity.

Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

North America Business Development Manager, Z by HP

Kristin Hempstead

Kristin has been with HP for 11 years and is currently the North America business development manager for HP’s data science and artificial intelligence solutions focusing on federal, education, and public sector customers. She has an MBA from University in South Florida with a specialization in Finance and MIS and a BS in Agriculture from the University of Georgia.

Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)


More talks, hands-on workshop and training sessions
See all sessions

Who Should Attend

The Data Science Research track will prove invaluable to those of us looking to quickly understand in detail the topics that matter most in data science now

  • Experienced data scientists

  • Students and academics

  • Software engineers and architects

  • Business professionals interested in data science advancements

  • Experts from other domains looking to leverage data science

  • Beginners interested in the latest research

  • Researchers from academia and industry

  • Industry professionals

  • Technologists interested in new data science applications

  • Industry experts looking to assess the impact of data science

What Will You Learn

Data science is a broad and expanding field with many areas of study. Here are some of the main areas that our presenting researchers will be addressing:

  • Neural Networks

  • Machine Learning

  • Transfer Learning

  • Machine Vision

  • Natural Language Processing

  • Predictive Analytics

  • Pattern Recognition

  • Quantitative Finance

  • Speech Recognition

  • Time Series Analysis

  • Graph Theory

  • Network Analysis

  • Data Visualization

  • Anomaly Detection

Why Attend?

Hear from world-class researchers and academics about the top areas of active research

Take time out of your busy schedule to accelerate your knowledge of the latest advances in data science

Be the first amongst your peers to grasp changes that will affect the field in the next few years

Take advantage of and chose from another 80+ talks, tutorials, and workshops at ODSC West

Learn directly from top researchers what works and what doesn’t 

Connect and network with academics, research, and fellow professionals

Meet with peers and professionals looking to learn, connect, and collaborate

Get access to other focus area content including ML / DL, Data Visualization, Quant finance, and Open Data Science

ODSC East 2023 | May 9th - 11th

Register Now & Save 60%

ODSC Newsletter

Stay current with the latest news and updates in open source data science. In addition, we’ll inform you about our many upcoming Virtual and in person events in Boston, NYC, Sao Paulo, San Francisco, and London. And keep a lookout for special discount codes, only available to our newsletter subscribers!

Open Data Science

 

 

 

Open Data Science
One Broadway
Cambridge, MA 02142
info@odsc.com

Menu
Conferences
Extras
Copyright ODSC 2022. All Rights Reserved
Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Youtube
Consent to display content from Youtube
Vimeo
Consent to display content from Vimeo
Google Maps
Consent to display content from Google
Save