Admin
Emam Hossain

Ph.D. Student

Causal Artificial Intelligence Lab (CAIL)

University of Maryland Baltimore County

Projects


Current Projects

iHARP: NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions (May 2022 - Present):
The iHARP project combines data science and polar research to advance physics-informed, data-driven discoveries, focusing on understanding the response of polar regions to climate change and its global implications. This interdisciplinary initiative unites experts from Earth science, geology, environmental science, oceanography, computer science, machine learning, and data science.

As part of this initiative, I have contributed to predictive modeling using machine learning and deep learning techniques. My work includes the development of time series classification models to analyze the evolution of supraglacial lakes over the Greenland Ice Sheet, offering insights into their dynamics and potential impact on sea-level rise.

In addition to predictive modeling, I am leveraging causal inference techniques to identify and understand the causal relationships among key climate variables, such as temperature, precipitation, and sea ice extent. By incorporating Causal Representation Learning (CRL) into traditional machine learning models, my research aims to uncover the causal structure of the system, enabling better predictions and a deeper understanding of the processes driving polar changes. This knowledge contributes to identifying factors with direct causal effects on sea ice melting and global sea level changes.


Previous Project

NSF Caching-as-a-Service (August 2021 - May 2022):
High-performance, scalable computing system designers have consistently used caching, but since it has been implemented in so many different ways, it may be challenging to standardize and scale in cloud systems. This project develops generalized Caching-as-a-Service (CaaS), elevating the usage of caching in cloud-scale storage systems. A wide range of applications that function in both private and public clouds are supported by the CaaS project. The CaaS project uses use cases from the Cloud, Big Data, and Deep Learning computing paradigms to demonstrate these advances.

Specifically, I worked on identifying different cache workload types (LRU-friendly, LFU-friendly, Scan, and Churn) directly from the trace request sequence. I used FIU’s home trace data from SNIA and developed several machine learning techniques that can successfully identify the abovementioned workload types.


About Me

I am currently pursuing my Ph.D. in the Information Systems Department at the University of Maryland Baltimore County (UMBC). I’m part of Prof. Md Osman Gani’s Causal Artificial Intelligence Lab (CAIL) at UMBC. My current research interests include machine learning, deep learning, causality, and sea ice forecasting. Specifically, now I’m working on the iHARP project funded by NSF, to conduct research about how machine learning and causal inference techniques can be applied together in sea ice forecasting. I also have a good interest in financial asset prediction and time series analysis.

Before that, I completed my Bachelor's and Master's degrees in Computer Science and Engineering from the University of Chittagong, Bangladesh. My Master’s thesis was on how machine learning can be incorporated with Belief Rule Based Expert Systems to predict stock price. The modified version of the thesis was later published in the Expert Systems with Applications journal (read here). Apart from that, my several other research papers have been published in international journals and conferences (see here).

I also worked as a lecturer in the Department of Computer Science and Engineering at Port City International University, Bangladesh for more than 3 and a half years. I conducted core computer science courses including but not limited to Artificial Intelligence, Pattern Recognition, Computer Graphics, Theory of Computation, and Object Oriented Programming. During that time, I also supervised the final year thesis of multiple undergraduate students.




Research Interests

My main interest is in machine learning, deep learning, and their applications. Recently, I’m also working on causal inference and how causality can be incorporated into machine learning models to better understand the causal relationship among the model parameters. Currently, I’m mainly focusing on applying machine learning and deep learning along with causal inference to better understand Greenland and Antarctic sea ice behavior and predict future Sea Ice Concentration (SIC) and Sea Ice Extent (SIE).


Publications


  1. Hossain, Emam, Hossain, M. S., Zander, P. O., & Andersson, K. (2022). Machine learning with Belief Rule-Based Expert Systems to predict stock price movements. Expert Systems with Applications, 117706.

  2. Dey, P., Hossain, Emam, Hossain, M. I., Chowdhury, M. A., Alam, M. S., Hossain, M. S., & Andersson, K. (2021). Comparative analysis of recurrent neural networks in stock price prediction for different frequency domains. Algorithms, 14(8), 251.

  3. Afroze, T., Akther, S., Chowdhury, M. A., Hossain, Emam, Hossain, M. S., & Andersson, K. (2021, July). Glaucoma detection using inception convolutional neural network v3. In International Conference on Applied Intelligence and Informatics (pp. 17-28). Springer, Cham.

  4. Hossain, Emam, Shariff, M. A. U., Hossain, M. S., & Andersson, K. (2021). A novel deep learning approach to predict air quality index. In Proceedings of International Conference on Trends in Computational and Cognitive Engineering (pp. 367-381). Springer, Singapore.

  5. Gupta, D., Hossain, Emam, Hossain, M. S., Hossain, M. S., & Andersson, K. (2020, December). An Interactive Computer System with Gesture-Based Mouse and Keyboard. In International Conference on Intelligent Computing & Optimization (pp. 894-906). Springer, Cham.

  6. Islam, M. S., & Hossain, Emam (2020). Foreign exchange currency rate prediction using a GRU-LSTM hybrid network. Soft Computing Letters, 100009.

  7. Islam, M., Hossain, Emam, Rahman, A., Hossain, M. S., & Andersson, K. (2020). A review on recent advancements in forex currency prediction. Algorithms, 13(8), 186.

  8. Gupta, D., Hossain, Emam, Hossain, M. S., Andersson, K., & Hossain, S. (2019). A digital personal assistant using Bangla voice command recognition and face detection. In 2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON) (pp. 116-121). IEEE.