Arjun Chandran Unni

About Me

With a background in energy and a focus on portfolio analysis during my PhD, I’ve shifted into the world of FinTech product development, driven by a passion for innovation. Specializing in quantitative portfolio analysis, risk modeling, and research, I’ve leveraged tools like MATLAB, Python, and Julia to work extensively on robo-advisors, structured products like fixed coupon notes, and various financial instruments. Despite this shift, I still hold a deep love for renewable energy, project financing, and energy source planning, and continue to explore these areas whenever possible.

Tech Stack

Microsoft Office

MATLAB

Python

Julia

SQL

html

Figma

CSS

FastAPI

Skills

Financial Modeling

Risk Analytics

Statistics

Sustainability

Financial Markets

Statistical Modeling

Data Analytics

Optimization Techniques

Renewable Energy

Asset Allocation

Quantitative Finance

Research

Product management

Fin-tech product development

Education

PhD in Energy

Asian Institute of Technology, 2016 - 2020

Master's in Power Electronics and Drives

VNIT Nagpur, 2011 - 2013

Bachelor's in Electrical Engineering

NIT Raipur, 2006 - 2011

Projects

Robo-advisor

Developed a comprehensive robo-advisor strategy and built quantitative models for asset allocation and risk analysis, using techniques like CVaR to optimize risk management across portfolios.

Wealth Management Product Development

As a product manager, I worked to make wealth management products more accessible, including developing robo-advisors, alternative investments, structured products, and smart cash offerings for diverse clients.

Energy Risk Analysis and Resource Planning

Led projects focused on energy modeling, policy studies, and resource planning, applying financial theories such as Markowitz mean-variance, Black-Litterman, and CVaR to optimize renewable energy investment.

Index Development Using Fuzzy Logic

Developed an innovative index combining economic and power parameters of energy sources to define risk and reward, utilizing fuzzy logic. This work was published in the high-impact journal Renewable Energy.

Controller Design for MagLev

Designed and compared three different controllers—PID, Fuzzy, and LQR—for a magnetic levitation system, presenting the findings at an IEEE conference with real-time simulation models.

Publications

PID, fuzzy and LQR controllers for magnetic levitation system

Cited by: 29 | Year: 2016

AC Unni, AS Junghare, V Mohan, W Ongsakul

International Conference on Cogeneration, Small Power Plants and …

Control of DC motor using genetic algorithm based PID controller

Cited by: 25 | Year: 2018

S Tiwari, A Bhatt, AC Unni, JG Singh, W Ongsakul

International Conference and Utility Exhibition on Green Energy for …

Fuzzy-based novel risk and reward definition applied for optimal generation-mix estimation

Cited by: 12 | Year: 2020

AC Unni, W Ongsakul

Renewable Energy 148, 665-673

Stochastic effects of renewable energy and loads on optimizing microgrid market benefits

Cited by: 7 | Year: 2015

V Mohan, JG Singh, W Ongsakul, AC Unni, N Sasidharan

Procedia Technology 21, 15-23

Optimizing generation mix using Markovitz mean-variance theory

Cited by: 4 | Year: 2018

AC Unni, W Ongsakul, S Tiwari

International Conference and Utility Exhibition on Green Energy for …

Fuzzy logic controller and LQR for magnetic levitation system

Cited by: 2 | Year: 2014

AC Unni, A Junghare

International Journal of Recent Technology and Engineering (IJRTE) 3 (1)

Fuzzy incorporated Black–Litterman model for renewable energy portfolio optimization

Cited by: 1 | Year: 2022

AC Unni, W Ongsakul, N Madhu M.

Electrical Engineering 10 (10.1007/s00202-022-01618-0)

Theories of Finance for Generation Portfolio Optimization