Bio

Education

• International Student（2026 - ）：UNSW Sydney · School of Computer Science and Engineering
• Graduate（2025 - ）：Indian Institute of Technology Madras · Department of Computer Science & Engineering
• Summer Research Fellow（2024）：Indian Institute of Technology Madras · Department of Computer Science & Engineering · Prof. Akanksha Agrawal
• Undergraduate（2021 - 2025）：National Institute of Technology · Bachelor of Technology · Computer Science & Engineering

Research Interests

• Algorithms and their analysis: Graph algorithms, randomized algorithms, approximation algorithms, fixed-parameter algorithms, streaming algorithms
• Complexity theory: Boolean circuit complexity, structural complexity theory, reductions and completeness, complexity of isomorphism, coding theory
• Parallel and high-performance computing: High-level programming frameworks, graph analytics, spatial and graph clustering, dynamic algorithms

Technical Skills

• Languages：C/C++, Python, Java, Dart
• Data Science：NumPy, Pandas, Matplotlib, Scipy, OpenCV-Py, Sklearn, SQL
• Development：Django, JS, HTML, CSS, React, Node, Flutter
• Software：Git, Wireshark, CUDA Toolkit, NVIDIA HPC SDK

Blogs

• [02.05.2026] Efficient Multi-Word Compare-and-Swap: Teaching CAS to Juggle
• [30.06.2025] Baking with Algorithms: A Tasty Guide to Computational Complexity (And Why Some Problems Are Impossible Soufflés)
• [06.10.2024] Slicing Through Complexity: Baker’s Approximation Scheme for k-Outerplanar Graphs
• [26.08.2024] Exploring Parameterized Algorithms: A Path to Efficient Problem Solving in Computational Complexity

Publications

1. Computational Finance

   Computational Methods for Optimal Hedging in Markov Modulated Markets

   Albin James Maliakal

   Computational Finance

   Project Page Working Paper

Teaching & Sharing

1. Urban Computing

   Intelligent Traffic Monitoring and Control

   This presentation introduced an intelligent traffic monitoring system combining IoT architecture (V2X, RSUs, edge-cloud layers) with Q-Learning for dynamic signal optimization. It examined real-time data flows, modular design, and the synergy of distributed sensing with reinforcement learning.

   May 28, 2025

   PDF Website Offline (20+ people, 30 minutes)


2. Computational Finance

   Design and Comparative Analysis of Advanced Quantitative Trading Strategies

   This presentation examined advanced quantitative trading strategies in Python, focusing on momentum and mean reversion models applied to stock data. It covered data collection, preprocessing, technical indicators, and analyzed performance metrics and algorithmic trading outcomes.

   April 22, 2025

   PDF Website Offline (30+ people, 30 minutes)


3. Theory of Computation

   A Brief Introduction to Computational Complexity

   This presentation on Computational Complexity examined the resources required for solving computational problems, focusing on time and space complexity. It categorized problems into classes like P, NP, and PSPACE, enhancing understanding of algorithm efficiency.

   November 12, 2024

   PDF Website Offline (20+ people, 30 minutes)



Projects

1. Image Segmentation

   ChromaCut

   ChromaCut is a cutting-edge toolkit for high-precision image segmentation, integrating advanced superpixel over-segmentation and graph-based techniques like the Boykov-Kolmogorov (Min-cut/Max-flow) algorithm. It processes images by first dividing them into superpixels for detailed analysis, then applies graph-based methods for accurate segmentation. Using CIDE2000 for color evaluation and foreground extraction, it enhances segmentation precision across diverse image types.

   PDF Website


2. Image Segmentation

   NeuroCluster

   NeuroCluster is an advanced image segmentation toolkit leveraging the Improved Intuitionistic Fuzzy C-Means (IIFCM) algorithm, specifically tailored for magnetic resonance (MR) image analysis. This toolkit enables precise segmentation of MR images into distinct regions, enhancing the ability to identify and analyze various brain structures based on pixel intensity and color values.

   PDF Website Open Source


3. Speech Recognition

   FinSpeech

   This project aims to enhance speech recognition capabilities within banking dialogues by employing a Convolutional, Recurrent, and Dense Neural Network (CRDNN) alongside Language Model (LM) support. Utilizing the HarperValleyBank (HVB) corpus, the project strives to develop an ASR system that can accurately transcribe spoken banking dialogues, improving customer service experiences and operational efficiencies.

   PDF Website Open Source


4. Image Denoising

   AutoDigit

   This project investigates the use of autoencoders for denoising images to improve handwritten digit recognition accuracy using the MNIST dataset. By introducing noise to the dataset and employing an autoencoder for image reconstruction, the study aims to enhance digit classification performance, demonstrating the practical applications and effectiveness of autoencoders in deep learning.

   PDF Website Open Source



Contact

Address：
Kottayam, Kerala
Email：
albin@cse.iitm.ac.in
albin.maliakal@student.unsw.edu.au
albinjames2002@gmail.com
Tel：
(+91) 9495401332
Personal public account：
albinjm