I am Nayem, a 4th-year Ph.D. student in Luddy School of Informatics, Computing, and Engineering (Luddy SICE) at Indiana University, Bloomington. Additionally, I am completing my Ph.D. minor in Cognitive Science at Indiana University. I am working as a research assistant under the supervision of Professor Donald S. Williamson at the ASPIRE research group.

My current research interest lies in the field of speech enhancement in the context of deep learning architectures; in particular I am excited about enhancing speech in various noisy environments that can assist hearing aid and voice-assistive technologies. I am also working with the Public Health Department of Indiana University under the Protected Health Information (PHI) project on critical disease detection in a sensitive population of the community, which is a novel machine learning problem. During my undergrad studies, I worked on a handwritten character recognition system of Bangla/Bengali language, which is in the domain of image processing and artificial intelligence.

I have received my M.Sc. degree in Computer Science at Indiana University. Earlier, I completed my undergrad from the Department of Computer Science and Engineering (CSE) at Bangladesh University of Engineering and Technology (BUET), the top-ranked engineering university in Bangladesh. I am a Lecturer (on study leave) in the Department of Computer Science and Engineering (CSE) at United International University (UIU). I have also worked as a Software Engineer at REVE Systems on a multi-media service protocol design.

Recent News:

June 2020: Started internship at BOSE, MA.

May 2020: Presented paper virtually at IEEE ICASSP, 2020.

January 2020: Paper accepted at IEEE ICASSP, 2020.

December 2019: Completed M.Sc. degree in CS from Indiana University.

October 2019: Presented paper at IEEE MLSP, 2019.

September 2019: Student volunteer at INTERSPEECH, 2019.

July 2019: Paper accepted at IEEE MLSP, 2019.

Download my resume.

Find me at:

• LinkedIn
• Google Scholar

Selected Projects

Speech enhancement:

• Researched noise cancellation techniques to filter out wide-range of noises from human speech using noise masking approach by applying deep neural network models. (Matlab, Python, and DNN, RNN, LSTM, GAN)
• Analyzed effectiveness of stacked deep network structure on monaural speech separation with multiple objective targets.
• Investigated human emotion detection techniques in speech using deep neural models. (CNN, RNN)

Computer Vision:

• Recurrent Stacked Generative Adversarial Network (RSGAN) generates video clips based on a pre-condition like sentence description, action classes, or fMRI signals using a novel deep architecture.
• Image Matching to match images that are taken from different viewpoints of the same object; detect object like car from aerial snapshot; create a panoramic image stitching multiple images. (C++, CImg, OverFeat packages)

Machine learning:

• Formulated prediction models to detect gestational preeclampsia and gestational hyper-tension among nulliparous pregnant women. (Python, Random forest, DNN)
• Developed deep neural models that generate surrounding parts of a color image. (PixelRNN, PixelCNN)