Welcome!
Our team’s research spans various theoretical and practical aspects of artificial learning (AI). We focus on designing and enhancing deep learning algorithms within complex and large-scale systems. In particular, we work on deep neural network efficiency and robustness, out-of-distribution detection, knowledge transfer, and continual learning methods. Our secondary mission is to explore applications and advances of novel AI/ML/CV approaches in real-time problems. The lab's research has been generously supported by the National Science Foundation (NSF), University of Maine, MSGC/NASA, UMaine Space Initiative, and Cisco.
Our project codes are available on our Github!
Summer Bootcamp: Introduction on Deep Learning, July 18th-19th, 2023, Roux Institute, Portland, Maine.
Prospective Students:
We are always looking for highly motivated students with an interest/background in Machine Learning, Data Science, and AI to join our group.
The focus of our research is on the following areas:
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Deep neural network robustness and out-of-distribution detection
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Multi-tasks learning and domain adaptation
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Reliable and safe navigation in complex environments
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Continual/Sequential learning models
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Human-in-the-loop supervision in artificial learning
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Adversarial learning and deep network robustness
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Graph Summarization and Subgraph Learning
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Online Feature Selection of Streaming Big Data
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High-Dimensional Network Structure Learning with Applications in Biology
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Quantifying and Analyzing the Interaction Content of Big Data Sets
Applicants with a background or interest in related research are welcome to apply. If you are interested to join our group please send your cover letter and CV to ssekeh@sdsu.edu.
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