Machine Learning & Data Science

ML

Data has become central to our daily lives and there is growing demand for professionals with data analysis skills.  Computing and data play an ever-growing role in all areas of human knowledge.  Applications of Machine Learning and Data Science are now pervasive in a wide variety of businesses looking to use data effectively, as well as in government agencies, academia and health care. These applications have led to an increased need to fundamentally understand the underlying mechanisms of statistical learning on these datasets, develop new and more powerful software and hardware tools for maximizing information extraction, and new strategies for application of these processes across a wide variety of application domains.

Our faculty are developing across the spectrum of deep theoretical and algorithmic foundations for data analytics and machine learning, and catered applications of these techniques to solve important human, institutional, and societal challenges. Our research spans a wide range of topics, that include:

·       Theoretical foundations of Data Science. Modeling, estimation, prediction, classification and learning of distributions and processes. Practical algorithms and fundamental limits of learning from data.

·       Digital signal processing, single and multi-channel time series analysis, compressed sensing and sparse signal recovery, wireless communications

·       Applications of data science to basic neuroscience, clinical measurements of behavior and physiology, surgical robotics and robot manipulation, autonomous vehicles and robot agents, integrated-circuit design and performance analyses, portfolio selection, DNA/RNA classification, application to studying of evolution, image and video processing and analysis, super-resolution imaging,

·       Information acquisition and interactive data analytics, network science, network representations from data, social networks, network structure and organization

·       Digital signal processing, real-time and low power data mining/analytics, software and/or hardware-based acceleration of data analytics, GPU and FPGA acceleration, model assurance, privacy-preserving data-analytics

·       Modeling of stochastic dynamical systems, time-series and stochastic-sampling algorithms.

Faculty

Adjunct Faculty