AI in Multimedia - Image and Video Processing Lab (AIM-IVPL))

Welcome to the AI in Multimedia – Image and Video Processing Lab!

Here at Northwestern University’s IVPL, we conduct cutting-edge research to address the current challenges and applications of image and video processing. Our lab primarily focuses on machine learning and other data science techniques to solve these problems, and much of our research has been published in top journals and conferences. Our team of postdoctoral researchers, doctoral students, as well as masters and undergraduate students, are led by Prof. Aggelos Katsaggelos in the Department of Electrical and Computer Engineering at Northwestern University’s McCormick School of Engineering (and by courtesy in Computer Science and Radiology). Prof. Katsaggelos is also Co-Director, Center for Scientific Studies in the Arts (NU-ACCESS), Deputy Director of Computation, Center for Computational Imaging and Signal Analytics in Medicine, Core Faculty Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA), and Faculty in Master of Science in Artificial Intelligence (MSAI), Center for Physical Genomics and Engineering, Master of Science in Robotics, and Center for Deep Learning.

Machine Learning Refined

Providing a unique approach to machine learning, this text contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts necessary to conduct research, build products, tinker, and play. By prioritizing geometric intuition, algorithmic thinking, and practical real world applications in disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology, this text provides readers with both a lucid understanding of foundational material as well as the practical tools needed to solve real-world problems. With in-depth Python and MATLAB/OCTAVE-based computational exercises and a complete treatment of cutting edge numerical optimization techniques, this is an essential resource for students and an ideal reference for researchers and practitioners working in machine learning, computer science, electrical engineering, signal processing, and numerical optimization.

2nd edition IS OUT! View on: – Amazon – Cambridge

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.

Fundamentals of Machine Learning and Deep Learning in Medicine

This book provides an accessible introduction to the foundations of machine learning and deep learning in medicine for medical students, researchers, and professionals who are not necessarily initiated in advanced mathematics but yearn for a better understanding of this disruptive technology and its impact on medicine. Once an esoteric subject known to few outside of computer science and engineering departments, today artificial intelligence (AI) is a widely popular technology used by scholars from all across the academic universe. In particular, recent years have seen a great deal of interest in the AI subfields of machine learning and deep learning from researchers in medicine and life sciences, evidenced by the rapid growth in the number of articles published on the topic in peer-reviewed medical journals over the last decade. The demand for high-quality educational resources in this area has never been greater than it is today, and will only continue to grow at a rapid pace.

Coursera: Fundamentals of Digital Image and Video Processing

In this class you will learn the basic principles and tools used to process images and videos, and how to apply them in solving practical problems of commercial and scientific interests (Almost one million students enrolled). View on: – Coursera

The Gravity Spy project enlists citizen scientists to help physicists and astronomers working on the Laser Interferometer Gravitational-wave Observatory (LIGO).With help from volunteers, the Gravity Spy team hopes to increase public engagement with science and to provide training to both citizens and their machine learning algorithms.Gravity Spy research paper “Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO” wins 2023 Spanish Society of Statistics and Operations Research – BBVA Foundation award for “Best applied contribution in Statistics“. Become a volunteer today! View on: – Zooniverse

Northwestern’s IDEAS program is transforming graduate STEM education by prioritizing integrated research and learning, professional skill development, and preparation for data-intensive careers throughout the academic and professional sector. Our students are driven to ponder the questions no one has yet thought to ask, provide appropriate research and analysis, and communicate the response for a variety of audiences and clientele. View on: – CIERA

CuBISM is a five-year interdisciplinary research and educational program at the intersection of science and art, focusing on art as a catalyst for the development of innovative and holistic approaches to change over time in complex materials systems. Through computationally based imaging of structure in the materials used to create works of art, the central challenge is to tie the visual appearance of painted surfaces to the underlying molecular and microscopic material structures, and to use this correlation to investigate material properties. View on: – CuBISM