About

Nihal Singh is a talented Data Scientist with a demonstrated history of working in the computer software industry. He is currently pursuing an Integrated MSc in Applied Mathematics from Indian Institute of Technology, Roorkee and has an AIR 1 Amex'18. Nihal has a proactive, creative, innovative, and pragmatic approach to problem-solving, and an ability to think critically and independently. Nihal has experience in driving sales using different models and is skilled in Python, C++, Julia, Data Analysis, and Research. He has the ability to work with data owners and cross-functional teams in a commercial data science environment. Currently, Nihal is working as a Researcher at Indian Institute of Technology, Roorkee, where he is solving European options PDE using Deep Learning. He has solved 1-D Black Scholes Equation to price European options using Physics Informed Neural Networks (PINNs) and extended the idea to solve Non-Linear PDEs (and higher dimensions). In the past, Nihal has worked on various research projects at Indian Institute of Technology, Roorkee, including Mathematical Models for Information diffusion on Social Networks and Fluid Dynamics. He has transformed early-stage Epidemic models (SI, SIS, SIR) for the OSNs and simulated Explanatory models further categorized into Influence (Network structure, User attributes, content). He has also worked on the PageRank (Google) extrapolation for the OSNs formulating on the network graph. Nihal has experience in analyzing the S-SEIR model with adjusted probabilities on impact factors using Python. Nihal has a strong technical background and is proficient in various technologies such as Research Scientist, Deep Learning, Data Scientist, Python, Reinforcement Learning, Recommendation system, AI, ML, Neural Networks, Pytorch, test, SQL, Keras, Snowflake, Spark, Scikit, Big Data, Pandas, and Flask. He has relevant experience of 1.53 years and is currently working as a Data Scientist Intern at Yum! (PPO). Nihal is a dedicated professional with a passion for data science and a strong desire to learn and grow in the field.