Rabeya Akter

Hello, I am Rabeya Akter.

I am currently pursuing M.Sc. in the Department of Robotics and Mechatronics Engineering, University of Dhaka.

Previously, I have worked at Therap BD Ltd. as an Associate Software Engineer (QA), where I led quality assurance efforts for several machine learning systems. I also interned at Pathao Limited, where I worked on data science projects.

I hold a B.Sc. in Robotics and Mechatronics Engineering from the University of Dhaka. During my undergraduate studies, I worked as a research assistant with Dr. Sejuti Rahman, where I developed a continuous sign language translation system that achieved competitive performance.

Connect with me!

Research

I'm interested in natural language processing, computer vision, machine learning, multimodal learning, human-robot interaction and robotics.

Completed

T3Time: Tri-Modal Time Series Forecasting via Adaptive Multi-Head Alignment and Residual Fusion

Abstract: Multivariate time series forecasting (MTSF) seeks to model temporal dynamics among variables to predict future trends. Transformer-based models and large language models (LLMs) have shown promise due to their ability to capture long-range dependencies and patterns. However, current methods often rely on rigid inductive biases, ignore intervariable interactions, or apply static fusion strategies that limit adaptability across forecast horizons. These limitations create bottlenecks in capturing nuanced, horizon-specific relationships in time-series data. To solve this problem, we propose T3Time, a novel trimodal framework consisting of time, spectral, and prompt branches, where the dedicated frequency encoding branch captures the periodic structures along with a gating mechanism that learns prioritization between temporal and spectral features based on the prediction horizon. We also proposed a mechanism which adaptively aggregates multiple cross-modal alignment heads by dynamically weighting the importance of each head based on the features. Extensive experiments on benchmark datasets demonstrate that our model consistently outperforms state-of-the-art baselines, achieving an average reduction of 3.37% in MSE and 2.08% in MAE. Furthermore, it shows strong generalization in few-shot learning settings: with 5% training data, we see a reduction in MSE and MAE by 4.13% and 1.91%, respectively; and with 10% data, by 3.70% and 1.98% on average. Code is available at: https://github.com/monaf-chowdhury/T3Time

SignFormer-GCN : Continuous Sign Language Translation Using Spatio-Temporal Graph Convolutional Networks

Abstract: Sign language is a complex visual language system that uses hand gestures, facial expressions, and body movements to convey meaning. It is the primary means of communication for millions of deaf and hard-of-hearing individuals worldwide. Tracking physical actions, such as hand movements and arm orientation, alongside expressive actions, including facial expressions, mouth movements, eye movements, eyebrow gestures, head movements, and body postures, using only RGB features can be limiting due to discrepancies in backgrounds and signers across different datasets. Despite this limitation, most Sign Language Translation (SLT) research relies solely on RGB features. We used keypoint features, and RGB features to capture better the pose and configuration of body parts involved in sign language actions and complement the RGB features. Similarly, most works on SLT research have used transformers, which are good at capturing broader, high-level context and focusing on the most relevant video frames. Still, the inherent graph structure associated with sign language is neglected and fails to capture low-level details. To solve this, we used a joint encoding technique using a transformer and STGCN architecture to capture the context of sign language expressions and spatial and temporal dependencies on skeleton graphs. Our method, SignFormer-GCN, achieves competitive performance in RWTH-PHOENIX-2014T, How2Sign, and BornilDB v1.0 datasets experimentally, showcasing its effectiveness in enhancing translation accuracy through different sign languages. The code is available at the following link: https://github.com/rabeya-akter/SignLanguageTranslation.

Classical machine learning approach for human activity recognition using location data

Abstract: The Sussex-Huawei Locomotion-Transportation (SHL) recognition Challenge 2021 was a competition to classify 8 different activities and modes of locomotion performed by three individual users. There were four different modalities of data (Location, GPS, WiFi, and Cells) which were recorded from the phones of the users in their hip position. The train set came from user-1 and the validation set and test set were from user-2 and user-3. Our team ’GPU Kaj Kore Na’ used only location modality to give our predictions in test set of this year’s competition as location data was giving more accurate predictions and the rest of the modalities were too noisy as well as not contributing much to increase the accuracy. In our method, we used statistical feature set for feature extraction and Random Forest classifier to give prediction. We got validation accuracy of 78.138% and a weighted F1 score of 78.28% on the SHL Validation Set 2021.

Bornil : An open-source sign language data crowdsourcing platform for AI enabled dialect-agnostic communication

Abstract: The absence of annotated sign language datasets has hindered the development of sign language recognition and translation technologies. In this paper, we introduce Bornil; a crowdsource-friendly, multilingual sign language data collection, annotation, and validation platform. Bornil allows users to record sign language gestures and lets annotators per form sentence and gloss-level annotation. It also allows validators to make sure of the quality of both the recorded videos and the annotations through manual validation to develop high-quality datasets for deep learning based Automatic Sign Language Recognition. To demonstrate the system’s efficacy; we collected the largest sign language dataset for Bangladeshi Sign Language dialect, perform deep learning based Sign Language Recognition modeling, and report the benchmark performance. The Bornil platform, BornilDB v1.0 Dataset, and the codebases are available on https://bornil.bengali.ai.

Ongoing

Solving CAPTURe: Advancing Spatial Reasoning and Amodal Counting in Vision-Language Models

We are working on the CAPTURe benchmark(https://arxiv.org/pdf/2504.15485?), which evaluates the ability of vision-language models (VLMs) to perform amodal counting—inferring the number of occluded objects based on visible patterns. This task reflects critical spatial reasoning skills that current VLMs largely lack. Our goal is to develop a system that improves counting accuracy under occlusion by enhancing pattern recognition, integrating visual and textual cues, and exploring hybrid approaches such as inpainting pipelines, coordinate extraction, or reasoning modules.

Projects

Included here are projects completed as part of coursework, as well as independent personal endeavors.

Course Projects

Automated Stock Trading Using Approximate Q Learning

Developed an automated stock trading system using approximate Q-learning to optimize buy, sell, and hold decisions for three Bangladeshi stocks, achieving an 11% return on investment and demonstrating the effectiveness of reinforcement learning in financial decision-making.

A Comprehensive Comparative Analysis of Emotional Support Delivery by NAO Robots and Humans Across Varied Emotional States

Conducted a comparative analysis of emotional support delivery by NAO robot and humans across 3 emotional states, developing a system and questionnaire to quantify emotional impact, comfort levels, communication clarity, and overall satisfaction, with the goal of assessing the efficacy of robotic versus human emotional support and identifying areas for improvement.

Personal Projects

Project HeatAlert: Predicting Heatwaves in Dhaka

This project aims to develop a machine learning-based system to forecast heatwave events in Dhaka city using historical weather data. By analyzing patterns in temperature, humidity, and other meteorological indicators, the model provides early warnings to help mitigate the impact of extreme heat on public health and urban infrastructure.

Education

Below is a concise summary of my academic background to date.

■ Bachelor of Science in Robotics & Mechatronics Engineering [2019–2024]
Department of Robotics & Mechatronics Engineering,
University of Dhaka,
Dhaka, Bangladesh
- CGPA: 3.75/4.00 (Final Year: 3.88/4.00)

-Final Year Project: Continuous Bangla Sign Language Translation: Mitigating the Expense of Gloss Annotation with the Assistance of Graph [Presentation]

- Notable Courses: Artificial Intelligence, Introduction to Machine Learning, Digital Image Processing and Robot Vision, Digital Signal Processing, Introduction to Robotics, Advanced Robotics, Intelligent Systems and Robotics, Human Robot Interaction, Object Oriented Programming, Fundamentals of Programming, Fundamentals of Computing, Microcontroller and Programmable Logic Controller, Digital Logic Circuit and Microprocessor, Control System Design, Linear Algebra, Differential and Integral Calculus, Multivariate and Vector Calculus, Differential Equations and Coordinate Geometry, Mathematical Analysis for Engineers, Statistics for Engineers, Fundamentals of Electrical and Electronics Engineering, Fundamentals of Mechanical Engineering, Fundamentals of Mechatronics Engineering, Advanced Mechatronics Engineering, Industrial Management

■ Higher Secondary School Certificate (HSC) [2016–2018]
Holy Cross College,
Dhaka, Bangladesh
- GPA: 5.00/5.00

■ Secondary School Certificate (SSC) [2006–2016]
Jatrabari Ideal High School,
Dhaka, Bangladesh
- GPA: 5.00/5.00

Work Experience

I am committed to pursuing a research-driven career, whether in academia or industry. Below are some highlights of my professional experiences so far.

■ Research Assistant [June 2025 – Present]
Department of Robotics and Mechatronics Engineering, University of Dhaka
Supervisor: Dr. Sejuti Rahman
◦ Conducting research on personality-based human-robot interaction by integrating real-time emotional detection to personalize Pepper robot.

■ Associate Software Engineer, QA [Apr 2024 – May 2025]
Therap (BD) Ltd.
Team: Machine Learning
◦ Conducted research on Amazon Alexa devices and sound detection systems; performed literature review on action recognition datasets and contributed to the design of data collection for action recognition.
◦ Lead quality assurance efforts for the video redaction system to develop and implement comprehensive test plans and cases to validate machine learning algorithms used in video processing.
◦ Collaborated with cross-functional teams to identify and resolve complex bugs, significantly improving system reliability.

■ AIM Intern [Jan 2024 – Mar 2024]
Pathao Limited
Team: Data Science, Analytics & Insights
◦ Developed 5 key performance indicators (KPIs) and metrics for brand health measurement of Pathao Courier, aligning with company-wide strategic goals.
◦ Designed and implemented comprehensive questionnaires using data-driven methodologies to assess brand health metrics, improving response quality and depth of insights.

■ Undergraduate Research Assistant [Jan 2023 – Jan 2024]
Department of Robotics and Mechatronics Engineering, University of Dhaka
Supervisor: Dr. Sejuti Rahman
◦ Designed and implemented a novel architecture for BdSL translation, establishing the first benchmark in this low-resource language domain.
◦ Authored a manuscript detailing innovative methodology and findings, published in PLOS One (Q1 Journal).

Awards and Scholarships

Over the years, I’ve been honored with a few recognitions—made possible by the invaluable support of mentors and peers who have greatly influenced my growth.

■ Research Grant

• Special Innovation Fund, Bangladesh Ministry of Science and Technology (MoST), Fiscal Year 2023–2024
  Project ID: SRG-232431

■ Academic Scholarship

• Talent Pool Scholarship, Higher Secondary Certificate Examination 2018
  Dhaka Education Board

■ Hackathons

• Honourable Mention, NASA Earth Observatory (EO) Dashboard Hackathon 2021
  Organized by NASA, ESA, and JAXA
• 11th Place, Sussex-Huawei Locomotion Challenge 2021
  Organized by University of Sussex and Huawei
• Second Runner-up, Ada Lovelace Datathon 2021
  Organized by Bangladesh Open Source Network (BdOSN)

■ Others

• 10th Place, 3rd Women’s Mathematics Olympiad 2019
  Organized by Women’s Mathematics Olympiad Committee & A F Mujibur Rahman Foundation
• 2nd Place, Pi Day Celebration 2019
  Organized by Department of Mathematics, University of Dhaka

Leadership

I dedicate part of my time to serving communities. Here are a few selected contributions.

■ Student Activity Secretary [Jan 2023 – Dec 2023]
IEEE, University of Dhaka Student Branch
◦ Organized several workshops, interactive sessions, and industry expert experience-sharing sessions.

■Mentor [Jul 2019 – Dec 2020]
Bangladesh Robot Olympiad (BdRO)
◦ Conducted robotics workshops in multiple high schools and developed engaging quizzes for BdRO.

■ Mentor [Jun 2024 – Sep 2024]
Bangladesh Artificial Intelligence Olympiad (BdAIO)
◦ Designed and delivered lessons on classification algorithms and evaluation metrics for a group of 10 high school students, and created quizzes to assess their understanding.

■ Speaker: Dui Prishthar Bisheshoggo [Dec 2023]
IEEE Women In Engineering
◦ Delivered a talk on personal experiences in hackathons and datathons, encouraging increased participation of women in tech competitions.