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.
In data analysis, anomaly detection is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behavior. Such examples may arouse suspicions of being generated by a different mechanism, or appear inconsistent with the remainder of that set of data.
Daniela L. Rus is a roboticist and computer scientist, Director of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), and the Andrew and Erna Viterbi Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology. She is the author of the books Computing the Future and The Heart and the Chip.
Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions. Such high-dimensional spaces of data are often encountered in areas such as medicine, where DNA microarray technology can produce many measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions equals the size of the vocabulary.
Subra Suresh is an Indian-born American engineer, materials scientist, and academic leader. He is currently Professor at Large at Brown University and Vannevar Bush Professor of Engineering Emeritus at the Massachusetts Institute of Technology (MIT). He was Dean of the School of Engineering at MIT from 2007 to 2010 before being appointed as Director of the National Science Foundation (NSF) by Barack Obama, where he served from 2010 to 2013. He was the president of Carnegie Mellon University (CMU) from 2013 to 2017. Between 2018 and 2022, he was the fourth President of Singapore's Nanyang Technological University (NTU), where he was also the inaugural Distinguished University Professor.
Hans-Peter Kriegel is a German computer scientist and professor at the Ludwig Maximilian University of Munich and leading the Database Systems Group in the Department of Computer Science. He was previously professor at the University of Würzburg and the University of Bremen after habilitation at the Technical University of Dortmund and doctorate from Karlsruhe Institute of Technology.
Materials genome is an analogy to genomes in biology, but in a conceptual sense: the many important phases, defects, and processes that make up engineered materials are the "genome" of materials science. The Materials Genome Initiative (MGI) is a federal, multi-agency effort to design, manufacture, and deploy materials and materials-based technologies significantly faster and cheaper than ever before. The MGI partially references the Human Genome Project, but only conceptually, and is a broader and less targeted effort.
Radhika Nagpal is an Indian-American computer scientist and researcher in the fields of self-organising computer systems, biologically-inspired robotics, and biological multi-agent systems. She is the Augustine Professor in Engineering in the Departments of Mechanical and Aerospace Engineering and Computer Science at Princeton University. Formerly, she was the Fred Kavli Professor of Computer Science at Harvard University and the Harvard School of Engineering and Applied Sciences. In 2017, Nagpal co-founded a robotics company under the name of Root Robotics. This educational company works to create many different opportunities for those unable to code to learn how.
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.
Addie Wagenknecht is an American artist and researcher living in New York City and Liechtenstein. Her work deals primarily with pop culture, feminist theory, new media and open source software and hardware. She frequently works in collectives, which have included Nortd Labs, F.A.T. lab, and Deep Lab. She has received fellowships and residencies from Eyebeam, Mozilla, The Studio for Creative Inquiry at Carnegie Mellon University and CERN.
Hannu Tauno Tapani Toivonen is a Finnish computer scientist and professor at the University of Helsinki.
Zhou Zhihua is a Chinese computer scientist and Professor of Computer Science at Nanjing University. He is the Standing Deputy Director of the National Key Laboratory for Novel Software Technology, and Founding Director of the LAMDA Group. His research interests include artificial intelligence, machine learning and data mining.
Suresh Venkatasubramanian is an Indian computer scientist and professor at Brown University. In 2021, Prof. Venkatasubramanian was appointed to the White House Office of Science and Technology Policy, advising on matters relating to fairness and bias in tech systems. He was formerly a professor at the University of Utah. He is known for his contributions in computational geometry and differential privacy, and his work has been covered by news outlets such as Science Friday, NBC News, and Gizmodo. He also runs the Geomblog, which has received coverage from the New York Times, Hacker News, KDnuggets and other media outlets. He has served as associate editor of the International Journal of Computational Geometry and Applications and as the academic editor of PeerJ Computer Science, and on program committees for the IEEE International Conference on Data Mining, the SIAM Conference on Data Mining, NIPS, SIGKDD, SODA, and STACS.
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).
Wei Wang is a Chinese-born American computer scientist. She is the Leonard Kleinrock Chair Professor in Computer Science and Computational Medicine at University of California, Los Angeles and the director of the Scalable Analytics Institute (ScAi). Her research specializes in big data analytics and modeling, database systems, natural language processing, bioinformatics and computational biology, and computational medicine.
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.
Matthias Grossglauser is a Swiss communication engineer. He is a professor of computer science at EPFL and co-director of the Information and Network Dynamics Laboratory (INDY) at EPFL's School of Computer and Communication Sciences School of Basic Sciences.
S. ("Muthu") Muthukrishnan is a computer scientist of Indian origin, known for his work in streaming algorithms, auction design, and pattern matching. He is vice president of sponsored products, Amazon (company) Advertising.
Systems cause representational harm when they misrepresent a group of people in a negative manner. Representational harms include perpetuating harmful stereotypes about or minimizing the existence of a social group, such as a racial, ethnic, gender, or religious group. Machine learning algorithms often commit representational harm when they learn patterns from data that have algorithmic bias. While preventing representational harm in models is essential to prevent harmful biases, researchers often lack precise definitions of representational harm and conflate it with allocative harm, an unequal distribution of resources among social groups, which is more widely studied and easier to measure. However, recognition of representational harms is growing and preventing them has become an active research area. Researchers have recently developed methods to effectively quantify representational harm in algorithms, making progress on preventing this harm in the future.
