I am a highly skilled and accomplished researcher with a strong background in mathematics, applied statistics, and reliability and life testing. I hold a PhD in Reliability and Life Testing, and I have published numerous research papers in prestigious SCI journals. My passion for learning and research has been recognized through awards such as the Inspire Scholarship and Senior Research Fellowship. With a solid foundation in mathematics and applied statistics, I have been able to apply my expertise to cutting-edge areas of research, particularly in the field of Artificial Intelligence (AI) and machine learning. I am currently working as a Research Associate at the prestigious Indian Institute of Technology (IIT) Roorkee, in the Department of Computer Science, where I am contributing to the development of an AI-driven trading platform. My work involves applying machine learning algorithms and statistical techniques to analyze market trends, predict trading patterns, and optimize trading strategies. Throughout my academic and professional career, I have demonstrated a keen ability to leverage my mathematical and statistical expertise to solve complex problems and make meaningful contributions to the field of AI and machine learning. I am driven by a curiosity to explore new frontiers in machine learning and continually seek to expand my knowledge and skills in this exciting and rapidly evolving field. I am enthusiastic about collaborating with other researchers, exploring new methodologies, and contributing to the advancement of machine learning and AI. I am committed to pushing the boundaries of what is possible through my research and making a positive impact on the field. With my strong academic foundation, research experience, and passion for machine learning, I am poised to make significant contributions to the field and drive innovation in the intersection of mathematics, statistics, and artificial intelligence.

  • Birthday: 22 September
  • Phone: +91-6396769198
  • City: Roorkee, Uttarakhand
  • Email: shubham.cs@sric.iitr.ac.in


Reliablity and Life Testing

Bayesian Inference

Computer Vision

Natural Language Processing

Machine Learning



Image Processing


Ph.D. in Statistics

November 2018 - February 2023
Thesis Title:
  • Some Contributions to Estimation Procedures for Lifetime Models using Censored Data

M.Sc. in Applied Statistics and Informatics

July 2016 - May 2018
Relevant Coursework
  • Statistical Inference
  • Regression Analysis
  • Machine Learning

B.Sc. Honours in Mathematics

July 2012 - July 2015
Relevant Coursework
  • Linear Algebra
  • Real Analysis
  • Optimization Techniques
  • Experience
  • FDP'S

Indian Institute of Technology Roorkee

March 2023 - Present

Research Associate

  • Conducting research on various machine learning algorithms and statistical models to develop and optimize trading strategies.
  • Analyzing large datasets to identify patterns, trends, and opportunities for algorithmic trading.
  • Collaborating with other team members to implement and test trading algorithms, and monitoring their performance to continuously improve the platform's trading capabilities.

Ramjas College, University of Delhi

October 2021 - October 2022

Assistant Professor

  • Delivering lectures and conducting tutorials on statistical concepts, methods, and applications to undergraduate students.
  • Mentoring and supervising students on research projects and providing guidance on statistical analysis and interpretation of data.
  • Engaging in scholarly activities such as publishing research papers, attending conferences, and staying updated with the latest developments in the field of statistics.

1. E & ICT Academy, IIT Kanpur

Faculty Development Program on Python Programming – A Practical Approach for 2 weeks (80 Credits)

2. E & ICT Academy, IIT Kanpur

Faculty Development Program on Introduction to NoSQL Architecture with MongoDB for 1 week

  • Publications
  • Conferences
  1. Ajit Chaturvedi, Renu Garg & Shubham Saini (2021). Estimation and testing procedures for the reliability characteristics of Kumaraswamy-G distributions based on the progressively first failure censored samples, OPSEARCH 59, 494–517. https://doi.org/10.1007/s12597-021-00523-7 (Springer, IF:1.6)
  2. Shubham Saini, Ajit Chaturvedi & Renu Garg (2021) Estimation of stress–strength reliability for generalized Maxwell failure distribution under progressive first failure censoring, Journal of Statistical Computation and Simulation, 91:7, 1366-1393, https://doi.org/10.1080/00949655.2020.1856846 (Taylor & Francis, IF:1.225)
  3. Shubham Saini, Sachin Tomer & Renu Garg (2021) On the reliability estimation of multicomponent stress–strength model for Burr XII distribution using progressively first-failure censored samples, Journal of Statistical Computation and Simulation, 92:4, 667-704 https://doi.org/10.1080/00949655.2021.1970165 (Taylor & Francis, IF:1.225)
  4. Shubham Saini & Renu Garg (2022). Reliability inference for multicomponent stress–strength model from Kumaraswamy-G family of distributions based on progressively first failure censored samples, Computational Statistics, 37, 1795–1837 (2022). https://doi.org/10.1007/s00180-021-01180-6 (Springer, IF:1.405)
  5. Shubham Saini, Sachin Tomer & Renu Garg (2022) Inference of multicomponent stress-strength reliability following Topp-Leone distribution using progressively censored data, Journal of Applied Statistics, https://doi.org/10.1080/02664763.2022.2032621 (Taylor & Francis, IF:1.416)
  6. Shubham Saini & Renu Garg (2022) Non-Bayesian and Bayesian estimation of stress-strength reliability from Topp-Leone distribution under progressive first-failure censoring, International Journal of Modelling and Simulation, https://doi.org/10.1080/02286203.2022.2148878 (Taylor & Francis)
  7. Test Conferences


Twitter Analysis

Image recognition as Service


Languages and Databases

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My Address

Machine Intelligence Lab

Department of Computer Science, IIT Roorkee

Roorkee, Uttarakhand 247667

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