CV

Link to my Full CV - here

Education

• Ph.D in Computer Science, University of California, Irvine, 2026 (expected)
• Integrated M.Sc. (B.S.+ M.S.) in Mathematics, National Institute of Technology, Rourkela, 2016

Work experience

• Winter, 2023 - Present: Graduate Research Assistant
  • ETAD Lab, UCI

  • Duties includes: My research focuses on advancing fairness and interpretability in Machine Learning, particularly through fair representation learning. I have expertise in working with deep neural networks, neuro-symbolic AI frameworks such as the Kolmogorov-Arnold Network (KAN) and Large Language Models (LLMs). In my recent works, I have developed algorithms to mitigate algorithmic bias through fair learning techniques and provided convergence guarantees. Additionally, I leverage generative models (such as WGANs), and adversarial models (such as ROAD) as the state-of-the-art baseline models to further back our proposed models. My work includes developing fairness-aware learning frameworks that are distributionally robust, and capable of addressing the fairness–accuracy trade-off to achieve equitable outcomes without compromising predictive reliability. Currently, my research focuses on developing Fair Multimodal learning framework robust to input perturbations, with performance guarantees to substantiate our claims. I have worked with real-world college admissions datasets obtained through NDAs with universities as a part of my experimental work. Simultaneously, I am also involved with a project, dealing with Capstone project, where we are working on integrating multimodal data, text and tabular (academic records, behavioral indicators, demographics), to predict and prevent dropout risk. My technical expertise spans Python (NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn), PyTorch, and CUDA (/MPS) for GPU-accelerated computations, enabling scalable and effective implementation of deep learning frameworks.

  • Supervisor: Prof. Sergio Gago-Masague
• Fall 2022 - Fall, 2023: Graduate Research Assistant
  • Dept. of Computer Science, UCI
  • Duties included: We worked on deep symbolic regression techniques using reinforcement learning algorithm which uses the policy gradient concept to produce mathematical expression(s) in the form of a syntax tree which is generated stochastically. The work focuses on improving the reward function through optimization technique by simultaneously improving the learning algorithm through noisy data inputs.
  • Supervisor: Prof. Eric Mjolness
• Fall 2019 - Present: Teaching Assistant
  • Dept. of Computer Science, UCI
  • Courses: Capstone Project (CS 180, CS 297P), System Design (ICS 53), Boolean Algebra and Discrete Mathematics (ICS 6B).

Skills

• Programming Language: C, C++, Python (Pandas, Scikit-learn, Matplotlib, Seaborn), SQL
• Deep Learning packages: PyTorch, TensorFlow
• Software: MATLAB, Anaconda, Jupyter Notebook, Mathematica, Visual Studio, PyCharm, GitHub