Dr. Islam obtained his Ph.D. in computer science from University of Memphis in December 2020, and his advisor was Deepak Venugopal. Since then, he has been an IT Specialist at St. Jude Children’s Research Hospital and now is a visiting assistant professor at Trinity College. Dr. Islam did his internship at Adobe Research where he developed an explainer model to identify sensitive frames in videos. He developed numerous models for scalable inference and learning methods in Markov Logic Networks (MLNs) utilizing distributed computing frameworks. He also implemented neuro-symbolic models coupling between symbolic AI and deep neural networks. Currently he is collaborating with multiple research groups for developing image reconstruction, particle picking and denoiser models in computer vision domain. Prior to starting his Ph.D., Dr. Islam had been in the software industry for multiple years. He is highly interested in neuro-symbolic AI, distributed computing and computer vision applications.

• Mohammad Maminur Islam, Somdeb Sarkhel and Deepak Venugopal, Augmenting Deep Learning with Relational Knowledge from Markov Logic Networks, IEEE Big Data 2020.
• Mohammad Maminur Islam, Somdeb Sarkhel and Deepak Venugopal, On Lifted Inference using Neural Embeddings, AAAI 2019.
• Mohammad Maminur Islam, Somdeb Sarkhel and Deepak Venugopal, Learning Mixtures of MLNs, AAAI 2018.
• Mohammad Maminur Islam, Khan Mohammad Al Farabi, Somdeb Sarkhel and Deepak Venugopal, Scaling up Inference in MLNs with Spark, IEEE Big Data 2018.
• Mohammad Maminur Islam, Somdeb Sarkhel and Deepak Venugopal, Learning Embeddings for Approximate Lifted Inference in MLNs, NIPS 2018 (workshop paper).
• Md Manjurul Ahsan, Kishor Datta Gupta, Mohammad Maminur Islam, Sajib Sen, Md Rahman, Mohammad Shakhawat Hossain, Study of Different Deep Learning Approach with Explainable AI for Screening Patients with COVID-19 Symptoms: using CT Scan and Chest X-RAY image dataset.
• Mohammad Maminur Islam, Mohammad Khan Al Farabi and Deepak Venugopal, Adaptive Blocked Gibbs Sampling for Inference in Probabilistic Graphical Models, IJCNN 2017.