Upcoming Miniprojects - V semester

The following mini-projects are being offered to the 5th semester students. Participating students will have the opportunity to contribute to the formation of a focused research group and may continue their involvement through future projects, workshops, and collaborations. Each project may be undertaken individually or in teams, as specified. The list below includes project titles, required background, and a brief description of the objectives and expected deliverables.

1. UAV Configuration Selection Using Data-Driven Multi-Criteria Decision Making

• Team Size: 4 members.
• Background: AE, AS.

• This project aims to enhance your understanding of the current state-of-the-art in drone technologies. Students will explore various UAV configurations and apply data-driven, multi-criteria decision-making (MCDM) methods to identify optimal design choices. The outcome will support conceptual UAV design in both academic and industry settings.

• A basic understanding of UAV system components and performance metrics is expected. Familiarity with Python is desirable, and a willingness to learn it during the project is essential.

2. Implementation of Airfoil Panel Method with Laminar and Turbulent Boundary Layer Modeling

• Team Size: Up to 2 members
• Background: AE, AS.

• Students will get hands-on experience with coding a numerical method, interpreting aerodynamic results, and performing validation—a foundational step in computational aerodynamics. The students should learn and use Python language.

3. Compositional Tensor Algebra: Designing a DSL for Machine Learning Models

• Team Size: Up to 2 members
• Background: CS, CD, IS. Students should have basic programming experience and an interest in tensor mathematics.

• Students passionate about programming languages and open-source development are encouraged to take up this project. The objective is to build an embedded Domain-Specific Language (DSL)—preferably in Haskell—to express operations in tensor algebra and calculus, with applications in deep learning and numerical computing.

NOTE

The final project should be hosted on a public open-source package repository (e.g., GitHub, GitLab, Hackage). It must include well-structured documentation, an installation guide, and at least five beginner-friendly tutorials or usage examples to help new users get started.

Open Projects

I welcome self-motivated students from any branch, year, or level of experience to bring their own project ideas for discussion. If you’re passionate, curious and willing to learn, we can explore it together. Please read further to know my interests and expertise.

Research Focus

I’m broadly interested in using mathematics and programming to solve problems in science and engineering. My current focus is on the use of functional programming — especially Haskell — for building tools and frameworks in scientific computing.

Applied mathematics shows up in almost every aerospace sub-domain. Final selection of application area for projects will be made after a short 1:1 discussion to ensure alignment of interests. That said, I am still sharing application areas I am planning to explore:

• Design of satellite constellation missions with coverage planning
• Conceptual design of aero-vehicles for planetary or space exploration
• Scheduling problems for airline operations
• Multiphysics modeling (ex - coupled heat transfer and structures)
• Aircraft Design with special focus on electric-powered and hydrogen-powered aircrafts
• Machine-learning for modeling fluid flows and structurs
• Quantum computing for aerodynamics
• Flight mechanics of airships

Core Research Areas

• Functional Programming for Scientific Computing
  HPC integration, algorithmic reformulation, and mathematical modeling using Haskell.

• Numerical Analysis and Computational Methods
  Solvers and schemes for differential equations, simulations, and physical systems.

• Optimization Algorithms and Strategies
  Evaluation of Multi-disciplinary optimization architecture, high-fidelity design optimization using adjoint methods, optimal control with simultaneous design optimization problems.

Research Group Policies

Once you’re in the group, you’re part of an active, collaborative, and professional learning environment. We aim to grow together — as thinkers, researchers, and creators. Here’s how we work:

Research Training

We conduct regular training and discussions to develop strong research skills.

Collaborative Culture

Collaboration is encouraged across projects. Peer discussions and constructive feedback are part of the weekly routine, to troubleshoot commonly faced problems.

Weekly Check-ins / Sync-ups

Everyone is expected to give brief weekly updates. These help track progress, unblock issues, and keep ideas flowing.

Project Documentation

Maintain a shared research log or notebook (markdown or overleaf) with clear records of objectives, decisions, and results.

Version Control (Git)

All code must be managed through GitHub (or similar), with clean commit history and meaningful documentation.

Academic Integrity

Plagiarism, code copying, or misrepresenting contributions is strictly prohibited. Always cite sources and give credit where it’s due.

Respect Deadlines

We value each other’s time. Set realistic goals and communicate proactively if you’re unable to meet them.

Openness to Learning

You are not expected to know everything. Asking questions, making mistakes, and learning from them is a core part of research.

Mentorship & Peer Support

Senior students are encouraged to mentor juniors. We grow faster when we teach each other.

Code of Conduct

Respect diverse perspectives, communicate kindly, and maintain a safe and inclusive environment for all.

===> To apply, just send me a 3-line email expressing your interest and background.