Sparse Coding Lab

What We Do

Developing an AI system that will observe and learn from the world around it

Stock photo; colleagues collaborating while coding at a computer

The Sparse (SPiking And Recurrent SoftwarE) Coding Lab at Drexel’s College of Computing & Informatics explores AI frameworks that mimic how the mammalian brain senses and understands the world. The researchers’ goal is to develop an AI system that will learn much like an infant learns, by observing the world and advancing from those observations. The model should learn the structure of the world and existing associations and accurately make predictions. Research applications include generative AI, cryptography and distributed systems.

Research Faculty & PhD Students

View a complete list of researchers on the Sparse Coding Lab's website.

Recent Publications

  • Azizpour, Aref & Nguyen, Tai & Shrestha, Manil & Xu, Kaidi & Kim, Edward & Stamm, Matthew. (2024). “E3: Ensemble of Expert Embedders for Adapting Synthetic Image Detectors to New Generators Using Limited Data”, 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Link to full paper
  • Kim, Edward & Daniali, Maryam & Rego, Jocelyn & Kenyon, Garrett. (2024). “The Selectivity and Competition of the Mind’s Eye in Visual Perception.” In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5720-5724. IEEE, 2024. 10.1109/ICASSP48485.2024.10448046. Link to full paper
  • Kim, Edward. (2024). "Nevermind: Instruction Override and Moderation in Large Language Models." arXiv preprint arXiv:2402.03303. Link to full paper
  • Shakibajahromi, Bahareh & Kim, Edward & Breen, David. (2024). “RIMeshGNN: A Rotation-Invariant Graph Neural Network for Mesh Classification.” In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3138-3148. 10.1109/WACV57701.2024.00312. Link to full paper
  • Daniali, Maryam & Kim, Edward. (2023). “Perception Over Time: Temporal Dynamics for Robust Image Understanding.” 5656-5665. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 10.1109/CVPRW59228.2023.00599. Link to full paper
  • Daniali, Maryam & Galer, Peter & Lewis-Smith, David & Parthasarathy, Shridhar & Kim, Edward & Salvucci, Dario & Miller, Jeffrey & Haag, Scott & Helbig, Ingo. (2023). “Enriching representation learning using 53 million patient notes through human phenotype ontology embedding.” Artificial Intelligence in Medicine. 139. 102523. 10.1016/j.artmed.2023.102523. Link to full paper
  • Hannan, Darryl & Nesbit, Steven & Wen, Ximing & Smith, Glen & Zhang, Qiao & Goffi, Alberto & Chan, Vincent & Morris, Michael & Hunninghake, John & Villalobos, Nicholas & Kim, Edward & Weber, Rosina & MacLellan, Christopher. (2023). “MobilePTX: Sparse Coding for Pneumothorax Detection Given Limited Training Examples.” Proceedings of the AAAI Conference on Artificial Intelligence. 37. 15675-15681. 10.1609/aaai.v37i13.26859. Link to full paper
  • Kim, Edward & Robinson, Lucy & Isozaki, Isamu & Robertson, Noreen & Cairns, Charles & Tripathi, Satvik & Seyfert-Margolis, Vicki. (2023). “A Coronavirus Cohort Case Study - Dataset Trends using Machine Learning Methods.” In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 4213-4219. 10.1109/BIBM58861.2023.10385496. Link to full paper

View a complete list of publications on the SPARSE Coding Lab’s website.

Recent Grant Awards

  • Neuro-inspired Oversight for Safe and Trustworthy Large Language Models" National AI Research Resource Pilot, NSF. PI, 5/2024.
  • “Defending the Real: Protecting Against Emerging Generative AI Cybersecurity Threats”, Co-PI, $50,000, Drexel Area of Excellence, AEO, 8/1/2023.
  • “Spartacus-X: Sparse Coding and Extraction of Ultrasound Knowledge for Explainable POCUS AI.” Defense Advanced Research Projects Agency, DARPA. Co-PI, $1,000,000, 5/1/2021.
  • “CAREER: Sparse Associative Deep Learning using Neural Mimicry in Multimodal Machine Learning.” National Science Foundation NSF CAREER Award. PI, $494,464, 6/1/2019.

Visit the Sparse Coding Lab website for more information