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Posters and Coffee
Sameer Kumar · Tomasz Kornuta · Oleg Bakhteev · Hui Guan · Xiaomeng Dong · Minsik Cho · Sören Laue · Theodoros Vasiloudis · Andreea Anghel · Erik Wijmans · Zeyuan Shang · Oleksii Kuchaiev · Ji Lin · Susan Zhang · Ligeng Zhu · Beidi Chen · Vinu Joseph · Jialin Ding · Jonathan Raiman · Ahnjae Shin · Vithursan Thangarasa · Anush Sankaran · Akhil Mathur · Martino Dazzi · Markus Löning · Darryl Ho · Emanuel Zgraggen · Supun Nakandala · Tomasz Kornuta · Rita Kuznetsova

Fri Dec 13 10:00 AM -- 11:10 AM (PST) @

Author Information

Sameer Kumar (Google)
Tomasz Kornuta (IBM Research AI)
Oleg Bakhteev (Moscow Institute of Physics and Technology)
Hui Guan (North Carolina State University)
Xiaomeng Dong (GE Healthcare)
Minsik Cho (IBM)
Sören Laue (Friedrich Schiller University Jena / Data Assessment Solutions)
Theodoros Vasiloudis (Research Institutes of Sweden)

My research deals with large scale learning for decision trees and graphs, including the development of novel algorithms and contributions to data analytics frameworks. The projects that I have been involved in included developing algorithms that make use of streaming frameworks such as Apache Flink and Apache Samoa, and contributing to the design and implementa- tion of learning systems capable of handling massive datasets, such as the machine learning library for Apache Flink. A research area that I am currently exploring is online decision trees, and more specifically quantifying the uncertainty in the predictions of random forests, with the aim of bringing the techniques to online learning. I’ve also recently become involved in the development of XGBoost, focusing on improving its distributed gradient boosted tree implementation.

Andreea Anghel (IBM Research)
Erik Wijmans (Georgia Institute of Technology)
Zeyuan Shang (MIT)
Oleksii Kuchaiev (NVIDIA)
Ji Lin (MIT)
Susan Zhang (OpenAI)
Ligeng Zhu (MIT)
Beidi Chen (Rice University)

I'm a third year Ph.D. Student at Rice University and working with Dr. Anshumali Shrivastava. My research topic is hashing in large-scale learning. I work closely with Dr. Rebecca Steorts on Record Linkage. I had my undergrad in Berkeley and my Advisor was Randy Katz. My topic was data mining.

Vinu Joseph (University of Utah)
Jialin Ding (MIT)
Jonathan Raiman (Dali)
Ahnjae Shin (Seoul National University)

Team coordinator of AutoML System. Works on two projects. 1. Hyper-parameter Optimization System. Poster on System for ML workshop 2. Multi-tenant GPU resource manager for AutoML

Vithursan Thangarasa (University of Guelph/ Vector Institute)
Anush Sankaran (IBM Research)

Senior Research Scientist with IBM Research * I have a PhD with specialization in Unsupervised Feature Learning, with key skills in Deep Learning and Computer Vision. * Served as IBM Global Research Technical Co-Lead on shaping the agenda for "Machine Learning for Creativity", as part of the Global Technical Outreach program. Received Outstanding Technical Contribution from IBM Research (2017 - 2018). Also, was the lead organizer for the workshop on “Machine Learning meets Creativity” in SIGKDD 2017, along with Google Brain, Sony CSL, and UIUC. * I have almost 4 years of industry experience in leading teams for producing high quality research output as well as leading a product pipeline. Most recently, I technically lead a team for building and developing Neural Network Modeller into Watson Studio as a technical commercial product. * I have an active publication record with research papers in NeurIPS 2019, AAAI 2018, EMNLP 2018, WWW 2018, Pattern Recognition Journal, Information Fusion Journal etc.

Akhil Mathur (Nokia Bell Labs)
Martino Dazzi (IBM Research Zurich)
Markus Löning (University College London & The Alan Turing Institute)
Darryl Ho (MIT)
Emanuel Zgraggen (MIT)
Supun Nakandala (University of California, San Diego)
Tomasz Kornuta (IBM Research AI)
Rita Kuznetsova (IBM Research)

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