The Machine Learning Department invites applications for summer and fall 2024 internships. We have research projects covering many areas of machine learning. The internship will involve research and development of novel machine learning algorithms with applications in image/video understanding, natural language processing, data analytics, bioinformatics, and healthcare. Our internships normally result in high-quality publications. Minimum duration of the internships are usually 3 months, and the exact dates are flexible.
The Machine Learning Department has been at the forefront of research in such areas as deep learning, support vector machines, and semantic analysis for almost two decades. The research in our department has been published in premier venues and has won numerous awards, including the 2010 IEEE Neural Networks Pioneer Award, the 2012 IEEE Frank Rosenblatt Award, the 2012 Benjamin Franklin Medal, the 2013 NEC C&C Prize, ICML 2018 Test of Time Award, and NeurIPS 2018 Test of Time Award.
We conduct research on various aspects of machine intelligence and reasoning, from the exploration of new algorithms to applications in computer vision, semantic comprehension, and bioinformatics. Our researchers have extensive expertise in theoretical and application aspects of machine learning. Ongoing projects focus on multimodal reasoning, text understanding, deep generative models, representation learning, few-shot/zero-shot learning, interpretable models, physics informed machine learning, human-collaborative AI agents, and reasoning with combinatorial optimization, among others. Publishing is an integral part of our activities as a means for calibrating the quality of the research and to ensure staying at the forefront of technology. Application projects emphasize technologies that solve real world problems, and many of our research results have been and will be transferred into industry products.
Currently our department is tackling challenges in imparting abstract reasoning capabilities to machine learning and facilitating effective human-machine collaboration, and how these enable new applications in sustainable environment, smart manufacturing, safe cities, natural language processing, and personalized healthcare.
• Currently enrolled in PhD/MSc program in computer science, statistics, electrical engineering, computational biology, or equivalent.
• Research experience in machine learning.
• Strong computational background.
• Strong programming and scripting skills.
• Strong communication and collaboration skills.