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Collaborating Authors

Radiology: Artificial Intelligence

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Nooshin Abbasi is a post-doctoral research fellow at Brigham and Women's Hospital, Harvard Medical School, and a former research fellow at the Montreal Neurological Institute, McGill University. Her research interests include brain imaging, evidence-based imaging, and bioinformatics, with a focus on applying machine learning tools to large clinical and imaging datasets. Michael Dohopolski is a PGY5 radiation oncology resident. He has worked with Dr. Wang and Dr. Jiang at UT Southwestern on machine learning based clinical decision-making support tools with an emphasis on single prediction uncertainty estimation. She is in the Department of Neurosurgery, University of Pennsylvania, and Division of Neurosurgery, Children's Hospital of Philadelphia.


Fact Checking: Theory and Practice (KDD 2018 Tutorial)

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Was Da Vinci born in Florence? Does patient'Johnson' really have 300 heart-beats per minute? Checking the accuracy of facts is vital, for question answering, data cleaning, anomaly detection, fraud detection, and more. The emphasis is on the intuition behind each method, as well as on a practitioner's guide, highlighting the applicability of each method to each setting. A B.Sc. in computer science should suffice.


Announcing the First ODSC Europe 2020 Virtual Conference Speakers

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ODSC's first virtual conference is a wrap, and now we've started planning for our next one, the ODSC Europe 2020 Virtual Conference from September 17th to the 19th. We're thrilled to announce the first group of expert speakers to join. During the event, speakers will cover topics such as NLP machine learning quant finance deep learning data visualization data science for good image classification transfer learning recommendation systems and much, much more. Dr. Jiahong Zhong is the Head of Data Science at Zopa LTD, which facilitates peer-to-peer lending and is one of the United Kingdom's earliest fintech companies. Before joining Zopa, Zhong worked as a researcher on the Large Hadron Collider Project at CERN, focusing on statistics, distributed computing, and data analysis.


Learning with Support Vector Machines

Morgan & Claypool Publishers

Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees.


Knowledge Mining: A Cross-disciplinary Survey - Machine Intelligence Research

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Colored figures are available in the online version at https://link.springer.com/journal/11633 Yong Rui received the B. Sc. degree in electrical engineering from Southeast University, China in 1991, the M. Sc. degree in electrical engineering from Tsinghua University, China in 1994, and the Ph. He is currently the Chief Technology Officer and Senior Vice President of Lenovo Group, China. He is a Fellow of ACM, IEEE, IAPR, China SPIE, CCF and CAAI, and a Foreign Member of Academia Europaea. He holds 70 patents, and is the recipient of the prestigious 2018 ACM SIGMM Technical Achievement Award and 2016 IEEE Computer Society Edward J. McCluskey Technical Achievement Award.