Khan Mohammad Al Farabi

I am currently serving as a Postdoctoral Research Associate at the School of Computing, University of Georgia, where I am engaged in significant research activities. I obtained my Ph.D. in Computer Science from the University of Memphis, with my research interests focusing on Explainable Artificial Intelligence, Machine Learning, Natural Language Processing, and Deep Learning. As a prominent researcher in artificial intelligence, I have advanced Markov Logic Networks (MLNs) to enhance machine learning explanations, thereby improving model interpretability, accuracy, and scalability. My innovations in predicting spam texts with relational explanations, alongside my optimization of MLNs, highlight my valuable contributions to the Explainable Artificial Intelligence field and my essential role within the research community.

My current research endeavors aim to develop a responsible, equitable, and explainable artificial intelligence (XAI) inference model. This model is designed not only to perform inference but also to provide human-interpretable explanations, thereby enhancing digital forensic analysis and supporting law enforcement in criminal investigations. Additionally, I am engaged in bioinformatics research focused on disease detection through the inference of protein-to-protein interactions, utilizing machine learning models and protein language models. Furthermore, my research involves detecting anomalies in Internet of Things (IoT) devices by leveraging Explainable Artificial Intelligence. To achieve these objectives, I draw upon my extensive expertise in explainable artificial intelligence, machine learning, data mining, relational statistical inference models—including Markov logic networks—and natural language processing.

I possess a substantial background in student engagement, demonstrating a strong commitment to fostering a classroom environment that addresses diverse student needs while promoting critical thinking and reasoning skills. I served as a Postdoctoral Fellow at the School of Computer and Cyber Sciences at Augusta University from January 2022 to November 2023, where I mentored Ph.D. and master's students. My teaching experience is extensive; I instructed a range of computer science courses as an Assistant Professor at American International University-Bangladesh (AIUB) from May 2008 to July 2014. Furthermore, during my Ph.D. studies, I worked as a Graduate Assistant at The University of Memphis, where I was involved in mentoring undergraduate students and evaluating assignments. My diverse experiences underscore my commitment to education and my dedication to making a positive impact within the academic community.

Currently, I am engaged as a Postdoctoral Research Associate at the University of Georgia, USA. My expertise lies in Explainable Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, NLP, and Large Language Models. Specifically, I am focused on developing an inference model for predicting protein-protein relationships using Explainable Artificial Intelligence models. Furthermore, in my recent work I developed a transfer learning-based inference model using Explainable Boosting Machine to detect the unseen anomalies of the IoT devices, and my model is also applicable to generic datasets (non-IoT data) efficiently. In future, I am planning to explore Agentic AI to develop the automated tool to perform tasks like inference, search similarity more efficiently without fine tuning and human involvement.

• 1. Khan Md. Al Farabi, Somdeb Sarkhel, Sanorita Dey and Deepak Venugopal, Interpretable Explanations for Probabilistic Inference in Markov Logic, IEEE Big Data, 2021. (https://ieeexplore.ieee.org/document/9671572)
• 2. Khan Md. Al Farabi,Somdeb Sarkhel,Sanorita Dey, and Deepak Venugopal, Fine grained Explanation using Markov Logic,ECML/PKDD 2019.
• 3. Khan Al Farabi, Somdeb Sarkhel and Deepak Venugopal, “Efficient Weight Learning in High-Dimensional Untied MLNs”, AISTATS 2018.
• 4. Mohammad Maminur Islam,Khan Mohammad Al Farabi,Somdeb Sarkhel, and Deepak Venugopal,”Scaling up Inference in MLNs with Spark, IEEE Big Data 2018.
• 5.Mohammad Maminur Islam, Khan Mohammad Al Farabi and Venugopal Deepak,”Adaptive Blocked Gibbs Sampling for Inference in Probabilistic Graphical Models,”IJCNN 2017,May 14-19, 2017 Anchorage, Alaska,USA.
• 6.Kabir Md. Humayun, Khan Md. Al-Farabi, “A Probabilistic Scheme for Reducing the Packet Loss in Mobile IPv6,” Special Issue Papers, Journal of Networks, vol. 7, no 12, pp. 1912-1924, December, 2012.
• 7. Al-Farabi, K. M., Kabir, M. H.,” Reducing Packet Loss in Mobile IPv6,” Computer and Information Technology (ICCIT), 2011 14th International Conference, December 2011, pp. 38-43.
• 8. ”SecFob: A Remote Keyless Entry Security Solution”, 2023 IEEE International Smart Cities Conference (ISC2),Braxton Bolt, Hoda Maleki, Gagan Agrawal, Jeffrey D Morris, Khan Farabi.
• 9. Evaluating and Extending Techniques for Fine-Grained Text-Topic Prediction for Digital Forensic Data,Khan Mohammad Al Farabi, Gagan Agrawal, Gokila Dorai, Rajon Bardhan, Hoda Maleki, Thomas E. Kadri,15th EAI International Conference on Digital Forensics & Cyber Crime,2024.

• I would like to explore my research in the following areas of Computer Science in the next ten years and I have a plan to write proposals to have funding for research:
• Artificial Intelligence
• Machine Learning
• Data Mining
• Data Visualization
• Computer Vision
• Computer Graphics & Vision
• Deep Learning