CCDS RA receives fully funded MS admission in the Concordia University

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Congratulations to Mohammad Raihanul Bashar Fahim! We’re happy to announce that he will be joining the Extended Reality and Interaction Technologies (EXiT) Lab at Concordia University, Canada for his Master’s in Computer Science starting in Fall 2023. He will be working in the field of Extended Reality, Human-Computer Interaction, and 3D user interface. Fahim had been one of our first RAs and an alumni of CSE IUB and has been working as a senior ML engineer since he left the lab.

Best of luck, Fahim!

CCDS RA receives fully funded PhD admission in the University of Houston

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Congratulations to Ovi Paul, one of our former RAs at CCDS and alumni of IUB, for embarking on his PhD journey. He will be doing his PhD in Geosensing System Engineering & Science program at the University of Houston! Ovi will also serve as a Graduate Research Assistant at the National Center for Airborne Laser Mapping. The center is based at the University of Houston and is operated in partnership with the University of California, Berkeley. At CCDS, he had been working on spatial data processing (land cover and land usage classification, urban buildup classification etc. from satellite data).

We wish him all the best.

CSE 425/525: Artificial Intelligence

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CSE424: Neural Network

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CEN/CSE 421: Machine Learning

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CSE417: Data mining and Data warehouse

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CSE317: Numerical Methods

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Accepted Paper: Radio Galaxy Classification at INNS DLIA 2023

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Our research paper, ‘Morphological Classification of Radio Galaxies using Semi-Supervised Group Equivariant CNNs,’ has been accepted for presentation at the esteemed INNS Deep Learning Innovations and Applications (INNS DLIA 2023) workshop, which is part of the International Joint Conference on Neural Networks (IJCNN 2023). The paper will also be published in the renowned Procedia Computer Science journal!

In this study, we tackled the challenge of limited labeled data in radio galaxy classification by employing a cutting-edge semi-supervised learning approach. By harnessing the power of Group Equivariant Convolutional Neural Networks (G-CNNs) as encoders, we achieved impressive results in classifying radio galaxies into the well-known Fanaroff-Riley Type I (FRI) and Type II (FRII) categories. [Link to Paper]

Explainable Hate Speech Detection: ICML 2023 Workshop Acceptance

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Recently, Md Fahim, RA of CCDS with co-authors from UToronto, IUT and Fordham University has a paper accepted in AI and HCI workshop of ICML 2023. The paper proposes an interpretability and explainability oriented model to detect hate speech utilizing the pre-trained large language models. It creates dynamic class specific conceptual subspaces from which class specific attention is obtained by projecting the contextual embedding onto those spaces. These attentions provide better explainability of the detection task.
Paper Link: HateXplain2.0: An Explainable Hate Speech Detection Framework Utilizing Subjective Projection from Contextual Knowledge Space to Disjoint Concept Space

CCDS RA Selected for the MIT Summer Geometry Initiative

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One of our RAs Munshi Sanowar Raihan has been selected for the MIT Summer Geometry Initiative (a six week research program) introducing undergrad and graduate students to the field of geometric processing. He is among the 32 fellows selected from all over the world. The program, organized by Prof Justine Solomon, PI, MIT’s geometry processing groups, provides students with the opportunity to collaborate with program staff that include graduate students, faculty and research scientists.