![]() Cross-disciplinary Biological Data Analysis Tasks of interest often include object detection or classification, parameter and model estimation, and signal reconstruction from limited, noisy measurements, as well as various means for signal compression. Research in this area involves characterizing and learning the structural and statistical properties of the signals and the sensors that acquire them, and applying fundamental theory from statistical inference and estimation theory. Sometimes people are used to process signals, through crowdsourcing. Statistical signal processing methods provide a principled and systematic framework for developing high-performance algorithms and understanding their fundamental limits of performance. Modern applications require development and implementation of highly accurate and sophisticated methods for such purposes. ![]() In addition to such "natural" signals, a variety of other “man-made” signals (such as flows in computer networks, radar or communication waveforms) also contain information of great interest. Examples include multimedia (speech, music, images, video), geophysical and biomedical signals. Statistical Signal ProcessingĪ great variety of algorithms have been developed to process and analyze a wide range of signals of interest. How can we guarantee that the quality of predictions and decisions made by these algorithms continues to improve, while being able to store, summarize, and manipulate data at scale? How can we control overfitting when the same dataset is reused by multiple interconnected learning algorithms? How can we balance conflicting demands of predictive performance and individual or institutional privacy? Research efforts in the area of Data Science and Machine Learning focus on developing mathematical and algorithmic tools to address many of these problems. Moreover, social and physical scientists are embracing machine learning and data analytics to contend with the massive datasets that are quickly becoming the norm in their research. Further, data can be used not just to reason about the world but create ideas and artifacts that have never been imagined before. Salient examples include credit scoring, product recommendation systems, health care analytics, and financial forecasting emerging technologies, such as self-driving cars, also rely heavily on fast, reliable, and safe algorithmic decision-making. ![]() Increasingly, both routine and critical decisions and predictions are made by sophisticated algorithms on the basis of large volumes of data. Research Data Science and Machine Learning ![]()
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