Conducting eXplainable Artificial Intelligence (XAI) Research with an Emphasis on Computer Vision and Object Detection [LinkedIn] [Scholar]
I am a machine learning researcher and engineer with over 5 years of experience implementing and training state-of-the-art machine learning models. My particular expertise is in computer vision, with an emphasis on eXplainable Artificial Intelligence (XAI).
My research endeavors are driven by a commitment to enhancing the trustworthiness and interpretability of AI models. It is my vision to facilitate the deployment of these models in real-world scenarios. By developing visually intuitive explanations of complex machine learning models, my goal is to make AI interpretable and trustworthy enough for use in practical applications. This includes creating inherently interpretable models, and using XAI as a tool to debug and enhance models through novel architecture and training schema.
- 2020-Present Henry M. Rowan College of Engineering, Rowan University Ph.D. in Electrical and Computer Engineering
- 2016-2020 Henry M. Rowan College of Engineering, Rowan University B.S. in Electrical and Computer Engineering
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[DOI] Transformers in time-series analysis: A tutorial.
Circuits, Systems, and Signal Processing 42, no. 12 (2023): 7433-7466.
Sabeen Ahmed, Ian E. Nielsen, Aakash Tripathi, Shamoon Siddiqui, Ravi P. Ramachandran, and Ghulam Rasool. -
[DOI] Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks
IEEE Signal Processing Magazine, vol. 39, no. 4, pp. 73-84, (2022).
Ian E. Nielsen, Dimah Dera, Ghulam Rasool, Ravi P Ramachandran, Nidhal Carla Bouaynaya. -
[DOI] EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust Models
IEEE Access, vol. 11, pp. 82556-82569, (2023).
Ian E. Nielsen, R. P. Ramachandran, N. Bouaynaya, H. M. Fathallah-Shaykh and G. Rasool -
[DOI] Targeted Background Removal Creates Interpretable Feature Visualizations
2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS), Tempe, AZ, USA, pp. 1050-1054, (2023).
Ian E. Nielsen, R. P. Ramachandran, N. Bouaynaya, H. M. Fathallah-Shaykh and G. Rasool