Maneesha Rani Saha

  •  PhD Fall'25 Aspirant || Fresh Graduate @ Dept. of CSE, BUET.
  •  Adjunct Lecturer @ CSE departments of ULAB and BRAC University.
  •  Research Interest: ML System Security, Human Centered Security, Trustworthy AI and Edge Computing .
  •  Recipient of Dean's List Scholarship for academic excellence.
  •  Co-Author of 3 research papers.


About Me

πŸ‘‹ I'm Maneesha, a fresh graduate from Dept. of CSE, BUET. I'm interested to work in the fields of ML System Security, Human Centered Security, Trustworthy AI and Cloud and Edge Computing. My current career goal is to pursue a Ph.D. in my field of interest.

πŸ’Ό I'm currently working as an Adjunct Lecturer at CSE departments of ULAB and BRAC University.

πŸ“œ I've co-authored 3 research papers. Among these, one paper titled "An Empirical Study of Gendered Stereotypes in Emotional Attributes for Bangla in Multilingual Large Language Models" has been accepted in the 5th Workshop on Gender Bias in Natural Language Processing at ACL 2024!

πŸ“š My undergraduate thesis was on "Dependency, Deadline and Priority Aware Multi-Queue Based Dynamic IoT Task Scheduling in Heterogeneous Fog Environment" under the supervision of Dr. Rezwana Reaz. In this work, we proposed a task scheduling algorithm that considers the dependencies and priorities of delay-sensitive IoT tasks to optimize system performance and efficiency. Our work got accepted as a short paper in The 17th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2024)!.

πŸ† In my undergraduate life at buet, I've regularly participated and won multiple competitions including BWCSE Project Showcasing 2023 and BUET CSE FEST Hackathon (API and Cloud Category) 2022. I have also been involved in various leadership roles in IEEE Computer Society BUET Student Branch Chapter and BUET Security Club. My responsibilities included organizing events, managing teams, and coordinating with other clubs and organizations of other universities.

🎑 In my leisure time, I enjoy reading thriller novels, listening to music and watching tv-series and movies.

Book News and Updates

All News

My Resume

Book Work Experience

  • Department of Computer Science and Engineering, BRAC University
    Adjunct Lecturer
    October 2024 - Present

    Course Instructor of Programming Language - II (CSE 111) and Data Structures (CSE 220)                                                                                                                       


  • Department of Computer Science and Engineering, ULAB
    Adjunct Lecturer
    October 2024 - Present

    Course Instructor of Statistics and Probability (STA 2101), Structured Programming Laboratory (CSE 1202) and Digital Logic Design Laboratory (CSE 2102)


All Work Experience

Book Research & Publications

An Empirical Study of Gendered Stereotypes in Emotional Attributes for Bangla in Multilingual Large Language Models
An Empirical Study of Gendered Stereotypes in Emotional Attributes for Bangla in Multilingual Large Language Models

Jayanta Sadhu (BUET), Maneesha Rani Saha, Dr. Rifat Shahriyar (Professor, BUET)

Accepted at 5th Workshop on Gender Bias in Natural Language Processing at ACL 2024
April 2024 - July 2024

Abstract: The influence of Large Language Models (LLMs) is rapidly growing, automating more jobs over time. Assessing the fairness of LLMs is crucial due to their expanding impact. Studies reveal the reflection of societal norms and biases in LLMs, which creates a risk of propagating societal stereotypes in downstream tasks. Many studies on bias in LLMs focus on gender bias in various NLP applications. However, there's a gap in research on bias in emotional attributes, despite the close societal link between emotion and gender. This gap is even larger for low-resource languages like Bangla. Historically, women are associated with emotions like empathy, fear, and guilt, while men are linked to anger, bravado, and authority. This pattern reflects societal norms in Bangla-speaking regions. We offer the first thorough investigation of gendered emotion attribution in Bangla for both closed and open source LLMs in this work. Our aim is to elucidate the intricate societal relationship between gender and emotion specifically within the context of Bangla. We have been successful in showing the existence of gender bias in the context of emotions in Bangla through analytical methods and also show how emotion attribution changes on the basis of gendered role selection in LLMs. All of our resources including code and data are made publicly available to support future research on Bangla NLP.

tag LLM Emotion Attributes Bangla Gender Bias
Social Bias in Large Language Models For Bangla: An Empirical Study on Gender and Religious Bias
Social Bias in Large Language Models For Bangla: An Empirical Study on Gender and Religious Bias

Jayanta Sadhu (BUET), Maneesha Rani Saha, Dr. Rifat Shahriyar (Professor, BUET)

Currently under review at COLING 2025
May 2024 - July 2024

Abstract: The rapid growth of Large Language Models (LLMs) has put forward the study of biases as a crucial field. It is important to assess the influence of different types of biases embedded in LLMs to ensure fair use in sensitive fields. Although there have been extensive works on bias assessment in English, such efforts are rare and scarce for a major language like Bangla. In this work, we examine two types of social biases in LLM generated outputs for Bangla language. Our main contributions in this work are: (1) bias studies on two different social biases for Bangla (2) a curated dataset for bias measurement benchmarking (3) two different probing techniques for bias detection in the context of Bangla. This is the first work of such kind involving bias assessment of LLMs for Bangla to the best of our knowledge. All our code and resources are publicly available for the progress of bias related research in Bangla NLP.

tag LLM Bangla Gender Bias Bangla Religious Bias Ethics Fairness
Dependency, Deadline and Priority Aware Multi-Queue Dynamic Task Scheduling Using Heterogeneous Resources in Fog Environment
Dependency, Deadline and Priority Aware Multi-Queue Dynamic Task Scheduling Using Heterogeneous Resources in Fog Environment

Muhammad Ehsanul Kader (BUET)*, Maneesha Rani Saha*, Dr. Rezwana Reaz (Asistant Professor, BUET) (* equal contribution)

Undergraduate Thesis

Accepted at The 17th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2024)
June 2023 - June 2024

Abstract: Internet of Things (IoT) has emerged as a transformative paradigm and revolutionized our way of engaging with technology in everyday life. The limited computing resources and storage capacity of IoT devices have necessitated the integration of heterogeneous fog resources in IoT task execution. Moreover, some IoT applications such as healthcare services are highly delay-sensitive; hence need immediate processing and response. On the other hand, the best utilization of heterogeneous storage and processing capacity of fog resources plays a pivotal role in improving the response time of IoT applications. Thus, an adaptive task scheduling algorithm for executing IoT tasks in a fog environment is required which will consider the heterogeneity of available resources and various response time requirements of different IoT applications. In this paper, we propose a non-preemptive batch task scheduling algorithm with dynamic resource allocation. The proposed method addresses the challenges of scheduling tasks with varying priority levels, deadline constraints, and dependencies utilizing the concepts of Directed Acyclic Graphs (DAGs), priority queues, and dynamic queue switching. We introduce a novel numerical scoring mechanism that prioritizes the execution of tasks with higher urgency and critical dependencies. The major goals of the proposed technique are to reduce the overall average response time, makespan, and average deadline violation time; and increase the throughput and task completion rate. We have implemented our algorithm in a Java based proprietary simulator. Our simulation results demonstrate that the proposed algorithm significantly improves the average response time, makespan, throughput, task completion rate and deadline violation time compared to the existing priority based batch scheduling algorithms. Precisely, our algorithm reduces average response time by at least 50.2% and makespan by at least 8.18%, and increases throughput by at least 13% and task completion rate by at least 4.3% compared to Shortest Job First (SJF) and Multi Level Feedback Queue (MLFQ).

tag IoT Task Scheduling Directed Acyclic Graphs Multi Level Priority Queue Fog Computing Heterogeneous Systems Dynamic Resource Allocation
All Publications

education Education

  • B.Sc. in Computer Science and Engineering
    Bangladesh University of Engineering and Technology
    April 2019 - July 2024
    CGPA: 3.78/4.00 (3.86 in Final Year)
  • H.S.C. in Science
    Udayan Uchcha Madhyamik Bidyalaya
    June 2016 - July 2018
    GPA: 5.00/5.00

skill Technical Skills

  • Programming Languages
    C C++ Java Python HTML/CSS JavaScript SQL
  • Frameworks
    Spring Boot NodeJS Bootstrap JQuery ReactJS
  • DBMS
    Oracle PostgreSQL MySql
  • Security Tools
    Autopsy Wireshark NMap Gophish
  • Graphic Designing
    Adobe Illustrator Adobe XD Figma Canva
  • Miscellaneous
    LATEX Firebase Git
  • Soft Skills
    Team management Problem‑solving Public Speaking Creative Writing