Professor Provost is known for his work on evaluating machine learning algorithms and AI systems, for his work on applying ROC[clarification needed] analysis to AI systems, for his work on social network data analysis, for his work on combining humans and machine learning, and for his work on machine learning for targeted marketing, online advertising, and activity monitoring.
The 2017 European Research Paper of the Year (AIS & CIONET).[3]
The best paper in the journal Information Systems Research in 2015.[4]
The 2009 INFORMS Design Science award for social network-based marketing,[5]
IBM Faculty Awards for outstanding research in data mining and machine learning,
A President’s Award from NYNEX Science and Technology
Best Paper Awards from the ACM SIGKDD conference in 1997, 2008, and 2012,[6][7] and
Awards in SIGKDD’s annual KDDCUP data mining competition.[8]
Professor Provost was on the founding teams for five startups, including Dstillery, Integral Ad Science (IAS), Everyscreen Media, Predicube, and Detectica.
Professor Provost is coauthor (with Tom Fawcett) of the book, Data Science for Business, which often tops Amazon's best-seller lists in data mining and data modeling.[9]
Professor Provost was a Scientific Advisor for the ISI Foundation (which awards the Lagrange Prize), served as Editor-in-Chief of the journal Machine Learning for 6+ years. He is a member of the editorial boards of the Journal of Machine Learning Research (JMLR) and the journal Data Mining and Knowledge Discovery (DMKD/DAMI). He was elected as a founding board member of the International Machine Learning Society.
↑ Provost, Foster; Fawcett, Tom (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking (1ed.). ISBN978-1449361327.
Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.
Waikato Environment for Knowledge Analysis (Weka) is a collection of machine learning and data analysis free software licensed under the GNU General Public License. It was developed at the University of Waikato, New Zealand and is the companion software to the book "Data Mining: Practical Machine Learning Tools and Techniques".
SIGKDD, representing the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining, hosts an influential annual conference.
Jiawei Han is a Chinese-American computer scientist and writer. He currently holds the position of Michael Aiken Chair Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. His research focuses on data mining, text mining, database systems, information networks, data mining from spatiotemporal data, Web data, and social/information network data.
Panagiotis G. Ipeirotis is a professor and George A. Kellner Faculty Fellow at the Department of Technology, Operations, and Statistics at Leonard N. Stern School of Business of New York University.
Usama M. Fayyad is an American-Jordanian data scientist and co-founder of KDD conferences and ACM SIGKDD association for Knowledge Discovery and Data Mining. He is a speaker on Business Analytics, Data Mining, Data Science, and Big Data. He recently left his role as the Chief Data Officer at Barclays Bank.
Rayid Ghani is a Distinguished Career Professor in the Machine Learning Department and the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. Previously, he was the director of the Center for Data Science and Public Policy, research associate professor in the department of computer science, and a senior fellow at the Harris School of Public Policy at the University of Chicago. He was also the co-founder of Edgeflip, an analytics startup that grew out of the Obama 2012 Campaign, focused on social media products for non-profits, advocacy groups, and charities. Recently, it was announced that he will be leaving the University of Chicago and joining Carnegie Mellon University's School of Computer Science and Heinz College of Information Systems and Public Policy.
David Bennett Madigan is an Irish–American statistician and academic. He is currently Provost and Senior Vice-President for Academic Affairs at Northeastern University. Previously he was Professor of Statistics at Columbia University. From 2013 to 2018 he was also the Executive Vice-President for Arts and Sciences and Dean of the Faculty of Arts and Sciences and from 2008 to 2013 he served as Chair of the Department of Statistics, both at Columbia University. He was Dean of Physical and Mathematical Sciences at Rutgers University (2005–2007), Director of the Institute of Biostatistics at Rutgers University (2003–2004), and Professor in the Department of Statistics at Rutgers University (2001–2007).
Gregory I. Piatetsky-Shapiro is a data scientist and the co-founder of the KDD conferences, and co-founder and past chair of the Association for Computing Machinery SIGKDD group for Knowledge Discovery, Data Mining and Data Science. He is the founder and president of KDnuggets, a discussion and learning website for Business Analytics, Data Mining and Data Science.
Jure Leskovec is a Slovenian computer scientist, entrepreneur and associate professor of Computer Science at Stanford University focusing on networks. He was the chief scientist at Pinterest.
Jianchang (JC) Mao is a Chinese-American computer scientist and Vice President, Google Assistant Engineering at Google. His research spans artificial intelligence, machine learning, computational advertising, data mining, and information retrieval. He was named a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2012 for his contributions to pattern recognition, search, content analysis, and computational advertising.
Arthur Zimek is a professor in data mining, data science and machine learning at the University of Southern Denmark in Odense, Denmark.
Geoffrey J. Gordon is a professor at the Machine Learning Department at Carnegie Mellon University in Pittsburgh and director of research at the Microsoft Montréal lab. He is known for his research in statistical relational learning and on anytime dynamic variants of the A* search algorithm. His research interests include multi-agent planning, reinforcement learning, decision-theoretic planning, statistical models of difficult data, computational learning theory, and game theory.
Gautam Das is a computer scientist in the field of databases research. He is an ACM Fellow and IEEE Fellow.
Cynthia Diane Rudin is an American computer scientist and statistician specializing in machine learning and known for her work in interpretable machine learning. She is the director of the Interpretable Machine Learning Lab at Duke University, where she is a professor of computer science, electrical and computer engineering, statistical science, and biostatistics and bioinformatics. In 2022, she won the Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (AAAI) for her work on the importance of transparency for AI systems in high-risk domains.
Hui Xiong is a data scientist. He is a distinguished professor at Rutgers University and a distinguished guest professor at the University of Science and Technology of China (USTC).
Jiliang Tang is a Chinese-born computer scientist and associate professor at Michigan State University in the Computer Science and Engineering Department, where he is the director of the Data Science and Engineering (DSE) Lab. His research expertise is in data mining and machine learning.
Tina Eliassi-Rad is an American computer scientist and the inaugural President Joseph E. Aoun Professor at Northeastern University. Her research is at the intersection of artificial intelligence, network science, and applied ethics. In 2023, she won the Lagrange Prize for her work on ethical approaches to artificial intelligence.
Himabindu "Hima" Lakkaraju is an Indian-American computer scientist who works on machine learning, artificial intelligence, algorithmic bias, and AI accountability. She is currently an Assistant Professor at the Harvard Business School and is also affiliated with the Department of Computer Science at Harvard University. Lakkaraju is known for her work on explainable machine learning. More broadly, her research focuses on developing machine learning models and algorithms that are interpretable, transparent, fair, and reliable. She also investigates the practical and ethical implications of deploying machine learning models in domains involving high-stakes decisions such as healthcare, criminal justice, business, and education. Lakkaraju was named as one of the world's top Innovators Under 35 by both Vanity Fair and the MIT Technology Review.
Nitesh V. Chawla is a computer scientist and data scientist currently serving as the Frank M. Freimann Professor of Computer Science and Engineering at the University of Notre Dame. He is the Founding Director of the Lucy Family Institute for Data & Society. Chawla's research expertise lies in machine learning, data science, and network science. He is also the co-founder of Aunalytics, a data science software and cloud computing company. Chawla is a fellow of the Association for Computing Machinery (ACM) and a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). He has received multiple awards, including the 1st Source Bank Commercialization Award in 2017 and the IBM Big Data Award in 2013. One of Chawla's most recognized publications, with a citation count of over 24,000, is the research paper titled "SMOTE: Synthetic Minority Over-sampling Technique." Chawla's research has garnered a citation count of over 56,000 and an H-index of 78.
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