Rashidul Islam

Staff Research Scientist

Team: Trustworthy AI

Research areas: Machine learning, AI fairness and ethics 

Rashidul Islam, Staff Research Scientist.

Dr. Rashidul Islam joined Visa Research as a Staff Research Scientist in August 2022. Rashidul received his Ph.D. in Information Systems at the University of Maryland, Baltimore County (UMBC) in 2022, and M.Sc. in Information Systems at UMBC in 2020. Before that, he also received M.Sc. in Electrical Engineering, and B.Sc. in Applied Physics, Electronics and Communication Engineering at University of Dhaka, Bangladesh. Prior to joining Visa Research, Rashidul was a Research Assistant at Foulds Research Group, UMBC with a focus on intersectional approach for fair machine learning .

As a member of the trustworthy AI team, his research interests are in the broader field of AI and ML, particularly in the area of AI Fairness and Ethics. It is now well understood that AI systems frequently behave unfairly and discriminatorily toward specific demographic groups when trained on data without the proper attention. Various AI-automated tasks may suffer negative societal effects as a result of this phenomenon. Rashidul’s research is focused on building socially responsible machine learning methods by modeling, measuring, and correcting unfairness or implicit bias. He has published several works in top conferences such as AAAI/ACM Conference on AI, Ethics and Society (AIES), The Web Conference (formerly known as WWW), ACM International Conference on Intelligent User Interfaces (IUI), AAAI Conference on Web and Social Media (ICWSM), SIAM International Conference on Data Mining (SDM), IEEE International Conference on Data Engineering (ICDE), North American Chapter of the Association for Computational Linguistics (NAACL), and others.

Publications

  1. C. Wang, K. Wang, A. Bian, R. Islam, K. Keya, J. Foulds and S. Pan, "Do Humans Prefer Debiased AI Algorithms? A Case Study in Career Recommendation", ACM International Conference on Intelligent User Interfaces (IUI), 2022.
  2. R. Islam, S. Pan, and J. R. Foulds, "Can We Obtain Fairness for Free?", AAAI/ACM Conference on Artificial Intelligence, Ethics and Society (AIES), 2021.
  3. R. Islam, K. Keya, Z. Zeng, S. Pan, and J. R. Foulds, "Debiasing Career Recommendations with Neural Fair Collaborative filtering", The Web Conference (formerly known as WWW), 2021.
  4. K. Keya, R. Islam, S. Pan, I. Stockwell and J. R. Foulds, "Equitable Allocation of Healthcare Resources with Fair Survival Models", SIAM International Conference on Data Mining (SDM), 2021.
  5. Z. Zeng, R. Islam, K. Keya, J. Foulds, Y. Song, and S. Pan, "Fair Representation Learning for Heterogeneous Information Networks", AAAI Conference on Web and Social Media (ICWSM), 2021.
  6. K. Keya, R. Islam, S. Pan, I. Stockwell and J. R. Foulds, "Equitable Allocation of Healthcare Resources with Fair Cox Models", AAAI Fall Symposium on AI in Government and Public Sector (AAAI FSS), 2020.
  7. J. R. Foulds, R. Islam, K. Keya, S. Pan, "Bayesian Modeling of Intersectional Fairness: The Variance of Bias", SIAM International Conference on Data Mining (SDM), 2020.
  8. J. R. Foulds, R. Islam, K. Keya, S. Pan, "An Intersectional Definition of Fairness", IEEE International Conference on Data Engineering (ICDE), 2020.
  9. J. R. Foulds, R. Islam, K. Keya, S. Pan, "Differential Fairness", NeurIPS Workshop on Machine Learning with Guarantees, 2019.
  10. R. Islam, K. Keya, S. Pan, and J. R. Foulds, "Mitigating Demographic Biases in Social Media-based Recommender Systems", ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) Social Impact Track (extended abstract), 2019.
  11. R. Islam and J. R. Foulds, "Scalable Collapsed Inference for High-Dimensional Topic Models", Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2019.
  12. M. Hosseini, R. Islam, L. Marni, T. Mohsenin, "MPT: Multiple Parallel Tempering for High-Throughput MCMC Samplers", IEEE International System-on-Chip Conference (SOCC), 2018
  13. R. Islam, W. D. Hairston, T. Oates, T. Mohsenin, "An EEG Artifact Detection and Removal Technique for Embedded Processors", IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2017.
  14. M. Hosseini, R. Islam, A. Kulkarni, T. Mohsenin, "A Scalable FPGA-based Accelerator for High-Throughput MCMC Algorithms", IEEE Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2017.