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Ph.D, Mathematics, University of Delhi

Master of Philosophy (M.Phil.), University of Delhi

Master of Science (M.Sc.), University of Delhi

Bachelor of Science (Honours) (B.Sc. Hons.), University of Delhi

Prof. (Dr.) Niteesh Sahni

Adjunct Faculty

Email niteesh.sahni@jgu.edu.in
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ORCID ID 0000-0002-1426-9882
Key Expertise Data Driven Mathematical Modelling comprising a rich interplay of Complex networks, Random matrix theory, and Applied Machine Learning, and Time series Analysis

Ph.D, Mathematics, University of Delhi

Master of Philosophy (M.Phil.), University of Delhi

Master of Science (M.Sc.), University of Delhi

Bachelor of Science (Honours) (B.Sc. Hons.), University of Delhi

Biography

Prof. (Dr.) Niteesh Sahni has a unique profile owing to his deep academic experience, interdisciplinary research portfolio, and proven contributions to cutting-edge curricular design. He is an Associate Professor at Shiv Nadar University and over the past 15 years at the Shiv Nadar University he has carved out a diverse teaching and research experience. Some highlights are detailed below:

Teaching Excellence

  • Dr. Sahni has taught and developed core and advanced AI & Data Science courses (e.g., Machine Learning through Python, Deep Learning, Bayesian Network Learning, Mathematical Framework of ML), which align with foundational and specialized components of GenAI, mathematical modelling, and applied ML.
  • His teaching spans both technical (Engineering, Computer Science) and non-technical domains, matching the learner diversity including non-tech learners, mid-career professionals, and learners with advanced tech background simultaneously.
  • Demonstrated success in online education (e.g., 14-week certification course on Mathematical Data Science for University of Delhi) through a unique pedagogy.
  • Dr. Sahni has delivered over a dozen workshops on Data Science, Python Computing, Mathematical Statistics, and Machine Learning for Faculty Development—positioning him as a mentor and capacity-builder.
  • He has developed and delivered advanced curriculum and certification courses that encompass academic rigor and market alignment. Notably, his leadership in organizing an intensive 6-day Topological Data Analysis (TDA) workshop for the HCL-ERS Next.AI Center of Excellence team underscores his industry engagement:
    • Covered TDA Mapper basics, applications to single-variable time series, and advanced filtration techniques for multivariate data
    • Demonstrated how to enhance ML models using topological features derived from Mapper
    • Focused on business-centric use cases: fraud detection, time series forecasting, and image analytics
    • The workshop received recognition in the HCL internal newsletter circulated to senior management

Research Impact

  • Dr. Sahni’s research in complex networks, financial modeling, and applied ML directly relates to modern technologies such as Big Data Analytics, Generative AI, Explainable AI to name a few. He has already guided two PhD students in this direction.
  • His ongoing research on sustainable development goals (SDGs) and emerging socio-economic modeling through machine learning and network theory bears the potential of innovations in policy making pertaining to a number of socially impactful domains. He is currently guiding one PhD student in this direction and is collaborating with leading scientists of the world.

Administrative Roles

  • Dr. Sahni has rich administrative experience and has held positions at the department and university levels. During his tenure as the Undergraduate Advisor of the department he contributed towards building new specializations,  certificate programs , and supported  industry-academia collaboration through applied research projects, internships, and industry relevant education modules.

Mentoring Capstone Projects in the MSc Data Science & AI Programme

Data Visualization - 2

Research on using Hyperbolic geometry to study stock markets has been covered by GS Mudur in IIT-Madras Shaastra Magazine in August 2025

Research award at Shiv Nadar University in 2013

Conducted numerous Capacity building workshops on Data Science & AI across a number of colleges of Delhi University

Developed and Delivered a 14 week certificate course on Data Science at Gargi College, University of Delhi in 2024

Served on a syllabus drafting committee of Department of Mathematics at University of Delhi

Reviewer for Plos-One Journal

Conducted a 6 day training workshop for HCL-ERS R&D team on Applications of Topological Data Analysis

Niteesh Sahni (with C. Sharma & I. Ansari), A novel portfolio construction strategy based on the core- periphery profile of stocks, Scientific Reports, Springer-Nature, 2025.

Niteesh Sahni (with P. Yadav & I. Ansari), Uncovering the hidden coreperiphery structure in hyperbolic networks, Physical Review E, 2025.

Niteesh Sahni (with P. Yadav & I. Ansari), Analyzing Communicability and Connectivity in the Indian Stock Market During Crises, Journal of Complex Networks, 2025.

Niteesh Sahni (with P. Yadav, and C. Sharma), Exploiting the geometry of heterogeneous networks: A case study of the Indian stock market, Soft Computing Journal, Springer-Nature, 2025.

Niteesh Sahni (with P. Yadav, and I. Ansari), Exploring the core–periphery and community structure in the financial networks through random matrix theory, Physica A: Statistical Mechanics and its Applications, 2025.

Apoorva Singh & Niteesh Sahni, Helson-Lowdenslager and de Branges type theorems in the setting of continuous rotationally symmetric norms, Banach Journal of Mathematical Analysis, Banach J. Math. Anal. 17(55), Springer, 2023.

Niteesh Sahni (with Apoorva Singh), Multiplication by finite Blaschke factors on a general class of Hardy spaces, Advances in Operator Theory 7(62), Springer, 2022.

Niteesh Sahni (with J. Anand, & S. Srivastava). On extension of Beurling–Helson– Lowdenslager theorem. Advances in Operator Theory 6(62), Springer, 2021.

Niteesh Sahni (with Charu Sharma), A mutual information based Rvine copula strategy to estimate VaR in high frequency stock market data, Plos One 16(6), 2021.

Niteesh Sahni (with A. Tiwari, S. Dixit, and S. Bordas), Machine learning approaches to identify and design low thermal conductivity,oxides for thermoelectric applications, Data Centric Engineering, Cambridge University Press, 2020.

Niteesh Sahni (with A. Kumar, and D. Singh), Invariance under finite Blaschke factors on BMOA, New York J. Math., 2017.

Niteesh Sahni (with A. Kumar, and D. Singh), Invariance under bounded analytic functions: generalizing shifts, New York J. Math., 2016.

Niteesh Sahni and Dinesh Singh, Lax-Halmos type theorems in Hp spaces, Houston J. of Math., 2015.

Niteesh Sahni and Dinesh Singh, Multiplication by Monomials on BMOA, Houston Journal of Math., 2014.

L.M. Saha and Niteesh Sahni, Chaotic Evolutions in a Modified Coupled Logistic Type Predator-Prey Model, Applied Mathematical Sciences, 2012.

L.M. Saha, Til Prasad Sharma, and Niteesh Sahni, Measuring Chaos in Some Discrete Nonlinear Systems, IJET, 2012.

Niteesh Sahni and Dinesh Singh, Invariant Subspaces of Certain Sub- Hilbert Spaces of H2, Proc. Japan Acad., 2011
Email niteesh.sahni@jgu.edu.in
ORCID ID 0000-0002-1426-9882
Key Expertise Data Driven Mathematical Modelling comprising a rich interplay of Complex networks, Random matrix theory, and Applied Machine Learning, and Time series Analysis
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