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Dr. Pritpal Singh

Dr. Pritpal Singh

PhD (CSE), Tezpur (Central) University, India
PDF (NTUT, Taiwan)
Adjunct Research Professor (Jagiellonian University, Poland)

Assistant Professor

Department of Data Science and Analytics
Central University of Rajasthan, Ajmer, India

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About Myself

Pritpal Singh received the Ph.D. degree in computer science and engineering from Tezpur (Central) University, Tezpur, India, in February 2015. He received a Master Degree in Computer Applications from Dibrugarh University, Assam (India), in 2008. From July 2009 to June 2013, he was a Senior Research Fellow at the Department of Computer Science and Engineering, Tezpur (Central) University. In July 2013, he was appointed as a Lecturer at the School of Mathematics and Computer Applications, Thapar University, Punjab (India). From November 2015 to February 2019, he worked as an Assistant Professor at the Faculty of Computer Applications (FCA), CHARUSAT Campus, Anand, Gujarat (India).

He has been appointed as a Faculty with the School of Mathematics and Computer Applications, Thapar University, Patiala, India, in July 2013. He worked as a Postdoctoral Research Fellow with the Department of Electrical Engineering, National Taipei University of Technology, New Taipei, Taiwan, and as an Adjunct Professor (Research) with the Institute of Theoretical Physics, Jagiellonian University, Poland. He is currently an Assistant Professor with the Department of Data Science and Analytics, Central University of Rajasthan, Ajmer, India.

He has published numerous papers in refereed SCI journals, conference proceedings, book chapters, and books. His research articles can be found in IEEE Transactions on Systems, Man and Cybernetics: Systems, Information Sciences (Elsevier), Artificial Intelligence in Medicine (Elsevier), Computer Methods and Programs in Biomedicine (Elsevier), Knowledge-Based Systems (Elsevier), International Journal of Approximate Reasoning (Elsevier), Engineering Applications of Artificial Intelligence (Elsevier), Applied Soft Computing (Elsevier), Journal of Computational Science (Elsevier), Computers in Industry (Elsevier), Expert Systems With Applications (Elsevier), among others.

His research interests include ambiguous set theory, optimization algorithms (especially quantum-based optimization), time series forecasting, image analysis, fMRI data analysis, machine learning, and mathematical modeling and simulation.

Dr. Singh has been awarded a Postdoctoral Research Fellowship from the Ministry of Science and Technology, Taiwan, in March 2019. He also received the prestigious International Visiting Research Fellowship from the Foundation for Polish Science, Poland, in 2020.

Dr. Singh's name has been continuously listed among the world's top 2% of scientists in 2023 and 2024.

Education

Professional Positions

Academic Achievements

  1. Recipient of Visiting Research Adjunct Professor Fellowship from the Foundation for Polish Science, Poland for pursuing research in Institute of Theoretical Physics, Jagiellonian University (Nov. 16, 2020–March 31, 2022).
  2. Recipient of Best Paper Award for the article P. Singh, Y.-P. Huang, and T.-T. Lee. A Method for High-Resolution Satellite Image Compression using Type-1 and Type-2 Fuzzy Sets. Proc. of IEEE Int. Conf. on System and Engineering (ICSSE 2019), Dong Hoi City, Quang Binh, Vietnam, pp. 103–108, July 19–21, 2019.
  3. Recipient of financial assistance from the National Taipei University of Technology, Taiwan, for participating in IEEE ICSSE 2019–IEEE International Conference on System and Engineering, Vietnam (July 19–21, 2019).
  4. Recipient of financial assistance from the Taiwan Association of Systems Science and Engineering (TASSE), Taiwan, for participating in IEEE ICSSE 2019–IEEE International Conference on System and Engineering, Vietnam (July 19–21, 2019).
  5. Recipient of Postdoctoral Research Fellowship from the Ministry of Science and Technology (MOST), Taiwan for pursuing research in Department of Electrical Engineering, National Taipei University of Technology, under Grant No.: MOST 108-2811-E-027-500 (Mar. 01, 2019–Sep. 31, 2020).
  6. Recipient of financial assistance from the DST-SERB, Govt. of India, for participating in IEEE SMC 2018–IEEE International Conference on Systems, Man and Cybernetics, Japan (Oct. 07–10, 2018).
  7. Qualified Graduate Aptitude Test in Engineering, held on Feb. 10, 2010, conducted by Indian Institute of Technology (IIT), India.
  8. Recipient of Rajiv Gandhi National Fellowship Award in 2010 from University Grant Commission (UGC), India, for pursuing full-time Ph.D. in Computer Science and Engineering.

Books

Ambiguous Set Book
P. Singh. A Journey into Ambiguous Set Theory: Exploring Ambiguity. Publisher: Cambridge University Press, UK, ISBN: 1-0364-4111-3, Year: 2025
Biomedical Book
P. Singh. Biomedical Image Analysis: Special Applications in MRIs and CT Scans. Publisher: Springer International Publishing, ISBN: 978-981-99-9938-5, Year: 2024.
Time Series Book
P. Singh. Applications of Soft Computing in Time Series Forecasting: Simulation and Modeling Techniques. Publisher: Springer International Publishing, ISBN: 978-3-319-26292-5, Year: 2015.

Journals (SCI)

    Year: 2025

  1. P. Singh, The Fast Forward Quantum Optimization Algorithm: A study of convergence and novel unconstrained optimization. Computer Methods in Applied Mechanics and Engineering (Elsevier), 443, 118039, 2025. [IF = 6.9 (2025)]

    2. Year: 2024

  2. P. Singh, Quantum Wavefunction Optimization Algorithm: Application in Solving Traveling Salesman Problem. International Journal of Machine Learning and Cybernetics (Springer), DOI: https://doi.org/10.1007/s13042-024-02466-z, 2024. [IF = 3.1 (2023)]
  3. M. K. Muchahari, P. Singh and S. Das, Automated white matter lesions segmentation of MRIs for multiple sclerosis detection using fuzzy-entropy algorithm. International Journal of Fuzzy Systems (Springer), DOI: https://doi.org/10.1007/s40815-024-01878-x, 2024. [IF = 4.085 (2021)]
  4. P. Singh, Bhavna Saini, and Y.-P. Huang, AECA: An ambiguous-entropy clustering algorithm for the analysis of resting-state fMRISs of human brain and their functional connections. Modern Physics Letters B (World Scientific), XX (X), XX–XX, 2024. [IF = 1.9 (2022)]
  5. P. Singh and Y.-P. Huang, AKDC: Ambiguous Kernel Distance Clustering Algorithm for COVID-19 CT Scans Analysis. IEEE Transactions on Systems, Man and Cybernetics: Systems (IEEE), XX (X), XX–XX, 2024. [IF = 8.7 (2022)]
  6. P. Singh, From Ambiguous Sets to Single-Valued Ambiguous Complex Numbers: Applications in Mandelbrot Set Generation and Vector Directions. Modern Physics Letters B (World Scientific), XX (X), XX–XX, 2024. [IF= 1.9 (2022)]
  7. R. Nihalani, S. S. Chouhan, D. Mittal, J. Vadula, S. Thakur, S. Chakraborty, R. K. Patel, U. P. Singh, R. Ghosh, P. Singh, A. Saxena, Long Short-Term Memory (LSTM) model for Indian sign language recognition. Journal of Intelligent & Fuzzy Systems (IOS Press), 46 (4), 11185–11203, 2024. [IF= 2 (2023)]
  8. P. Singh and Y.-P. Huang, An ambiguous edge detection method for computed tomography scans of coronavirus disease 2019 cases. IEEE Transactions on Systems, Man and Cybernetics: Systems (IEEE), 54 (1), 352–364, 2024. [IF= 8.7 (2022)]

    9. Year: 2023

  9. P. Singh and Y.-P. Huang, A four-valued ambiguous logic: Application in designing ambiguous inference system for control systems. International Journal of Fuzzy Systems (Springer), DOI: 10.1007/s40815-023-01582-2, 2023. [IF= 4.085 (2021)]
  10. P. Singh and Y.-P. Huang, Membership functions, set-theoretic operations, distance measurement methods based on ambiguous set theory: A solution to a decision-making problem in selecting the appropriate colleges. International Journal of Fuzzy Systems (Springer), 25, 1311–1326, 2023. [IF= 4.085 (2021)]
  11. P. Singh and M. K. Muchahari, Solving multi-objective optimization problem of convolutional neural network using fast forward quantum optimization algorithm: Application in digital image classification. Advances in Engineering Software (Elsevier), 176, 103370, 2023. [IF = 4.255 (2021)]

    12. Year: 2022

  12. P. Singh, Marcin Wątorek, Anna Ceglarek, Magdalena Fąfrowicz, Koryna Lewandowska, Tadeusz Marek, Barbara Sikora-Wachowicz and Paweł Oświęcimka, Analysis of fMRI signals from working memory tasks and resting-state of brain: Neutrosophic-Entropy based clustering algorithm. International Journal of Neural Systems (World Scientific ), 32(4), 2250012, 2022. [IF= 6.325 (2020)]

    13. Year: 2021

  13. P. Singh and S. S. Bose, Ambiguous D-means fusion clustering algorithm based on ambiguous set theory: Special application in clustering of CT scan images of COVID-19. Knowledge-Based Systems (Elsevier), 231, 107432, 2021. [IF= 8.038 (2020)]
  14. P. Singh and S. S. Bose, A Quantum-Clustering Optimization Method for COVID-19 CT Scan Image Segmentation. Expert Systems With Applications (Elsevier), 185, 115637, 2021. [IF= 6.954 (2020)]
  15. P. Singh, FQTSFM: A fuzzy-quantum time series forecasting model. Information Sciences (Elsevier), 556, 57–79, 2021. [IF= 6.795 (2020)]
  16. P. Singh, A Type-2 Neutrosophic-Entropy-Fusion Based Multiple Thresholding Method for the Brain Tumor Tissue Structures Segmentation. Applied Soft Computing (Elsevier), 103, 107119, 2021. [IF= 6.725 (2020)]

    17. Year: 2020

  17. P. Singh and Y-P Huang. A Four-Way Decision-Making Approach using Interval-Valued Fuzzy Sets, Rough Set and Granular Computing: A New Approach in Data Classification and Decision-Making. Granular Computing (Springer), 5, 397–409, 2020. [IF= 5.5 (2022)]
  18. Y.-P. Huang, P. Singh, Wen-Lin Kuo and Hung-Chi Chu. A Type-2 Fuzzy Clustering and Quantum Optimization Approach for Crops Image Segmentation. International Journal of Fuzzy Systems (Springer), 23, 615–629, 2020. [IF= 4.406 (2019)]
  19. P. Singh. A neutrosophic-entropy based adaptive thresholding segmentation algorithm (NEATSA): A special application in MR images of Parkinson’s disease (PD). Artificial Intelligence in Medicine (Elsevier), 104, 101838, 2020. [IF= 3.574 (2018)]
  20. P. Singh, Y.-P. Huang and Shu-I Wu. An Intuitionistic Fuzzy Set Approach for Multi-Attribute Information Classification and Decision-Making. International Journal of Fuzzy Systems (Springer), 22(5), 1506–1520, 2020. [IF= 4.406 (2019)]
  21. Y.-P. Huang, P. Singh and Hung-Chou Kuo. A hybrid fuzzy clustering approach for the recognition and visualization of MRI images of Parkinson's disease. IEEE ACCESS (IEEE), 8(1), 25041–25051, 2020. [IF= 4.098 (2018)]
  22. P. Singh. A Novel Hybrid Time Series Forecasting Model Based on Neutrosophic-PSO Approach. International Journal of Machine Learning and Cybernetics (Springer), 11, 1643–1658, 2020. [IF= 3.844 (2018)]
  23. P. Singh. A neutrosophic-entropy based clustering algorithm (NEBCA) with HSV color system: Application in segmentation and visualization of Parkinson’s disease (PD) MR images. Computer Methods and Programs in Biomedicine (Elsevier), 189, 105317, 2020. [IF= 3.424 (2018)]

    24. Year: 2019

  24. P. Singh and Y.-P. Huang. A High-Order Neutrosophic-Neuro-Gradient Descent Algorithm Based Expert System for Time Series Forecasting. International Journal of Fuzzy System (Springer), 21(7), 2245–2262, 2019. [IF= 3.993 (2017)]
  25. G. Dhiman, P. Singh, H. Kaur, and R. Maini. DHIMAN: A Novel Algorithm for Economic Dispatch Problem based on Optimization Method using Monte-Carlo Simulation and Astrophysics Concepts. Modern Physics Letters A (World Scientific), 34(4), 1950032–1950046, 2019. [IF = 1.308 (2017)]

    26. Year: 2018

  26. P. Singh, S. Guo, R. Maini, H. Kaur, A. Kaur, H. Kaur, J. Singh and N. Singh. Intuitionistic Fuzzy-Logic-Entropy and Rough Set Theory-based Fuzzy Inference System for Data Classification. Applied Soft Computing (Elsevier), 71, 485–499, 2018. [IF= 3.907 (2017)]
  27. P. Singh, G. Dhiman, and A. Kaur. A Quantum Approach for Time Series Data Forecasting Based on Graph and Schrödinger Equations Methods. Modern Physics Letters A (World Scientific), 33(35), 1850208–1850231, 2018. [IF = 1.308 (2017)]
  28. P. Singh and G. Dhiman. Uncertainty Representation using Fuzzy-Entropy Approach: Special Application in Remotely Sensed High-Resolution Satellite Images (RSHRSIs). Applied Soft Computing (Elsevier), 72, 121–139, 2018. [IF = 3.541 (2018)]
  29. P. Singh, K. Rabadiya, and G. Dhiman. Four-Way Decision-Making System for the Indian Summer Monsoon Rainfall. Modern Physics Letters B (World Scientific), 32(25), 1850304–1850326, 2018. [IF = 0.687 (2017)]
  30. P. Singh and G. Dhiman. A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches. Journal of Computational Science (Elsevier), 27, 370–385, 2018. [IF = 1.748 (2018)]

    31. Year: 2017

  31. P. Singh. Indian Summer Monsoon Rainfall (ISMR) Forecasting using Time Series Data: A Fuzzy-Entropy-Neuro Based Expert System. Geoscience Frontiers (Elsevier), 9, 1243–1257, 2017. [IF = 4.256 (2017)]

    32. Year: 2016

  32. P. Singh. High-order fuzzy-neuro-entropy integration based expert system for time series forecasting. International Journal of Neural Computing and Applications (Springer), 28(12), 3851–3868, 2016. [IF = 1.569 (2014)]
  33. P. Singh. Rainfall and Financial Forecasting using Fuzzy Time Series and Neural Networks Based Model. International Journal of Machine Learning and Cybernetics (Springer), 9(3), 491–506, 2016. [IF = 1.11 (2015)]

    34. Year: 2015

  34. P. Singh. A brief review of modeling approaches based on fuzzy time series. International Journal of Machine Learning and Cybernetics (Springer), 8(2), 397–420, 2015. [IF = 1.11 (2015)]

    35. Year: 2014

  35. P. Singh and B. Borah. Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization. International Journal of Approximate Reasoning (Elsevier), 55, 812–833, 2014. [IF = 1.729 (2013)]

    36. Year: 2013

  36. P. Singh and B. Borah. High-order fuzzy-neuro expert system for daily temperature forecasting. Knowledge-Based Systems (Elsevier), 46, 12–21, 2013. [IF = 4.104 (2013)]
  37. P. Singh and B. Borah. An efficient time series forecasting model based on fuzzy time series. Engineering Applications of Artificial Intelligence (Elsevier), 26, 2443–2457, 2013. [IF = 1.625 (2013)]
  38. P. Singh and B. Borah. Indian summer monsoon rainfall prediction using artificial neural network. Stochastic Environmental Research and Risk Assessment (Springer), 27(7), 1585–1599, 2013. [IF = 1.961 (2013)]

    39. Year: 2012

  39. P. Singh and B. Borah. An effective neural network and fuzzy time series based hybridized model to handle forecasting problems of two factors. Knowledge and Information Systems (Springer), 38(3), 669–690, 2012. [IF = 2.225 (2012)]

Journals (Non-SCI)

    Year: 2025

  1. P. Singh, Data-Driven Ambiguous Cognitive Map for Complex Decision- Making in Supply Chain Management. Journal of Computational Mathematics and Data Science (Elsevier), 14, 100110, 2025.

    2. Year: 2023

  2. P. Singh, An investigation of ambiguous sets and their application to decision-making from partial order to lattice ambiguous sets. Decision Analytics Journal (Elsevier), 08, 100286, 2023.
  3. P. Singh, A general model of ambiguous sets to a single-valued ambiguous numberswith aggregation operators. Decision Analytics Journal (Elsevier), 08, 100260, 2023.
  4. P. Singh, Ambiguous set theory: A new approach to deal with unconsciousness and ambiguousness of human perception. Journal of Neutrosophic and Fuzzy Systems (American Scientific Publishing Group), 05(01), 52--58, 2023.

    5. Year: 2022

  5. P. Singh, Marcin Wątorek, Anna Ceglarek, Magdalena Fąfrowicz, and Paweł Oświęcimka, Analysis of fMRI Time Series: Neutrosophic-Entropy Based Clustering Algorithm. Journal of Advances in Information Technology, 13(3), 224--229, 2022.

International Conferences

    Year: 2024

  1. P. Singh. Ambiguous sets and various distance measurements. Proc. of 6th International Conference on Soft Computing and its Engineering Applications (icSoftComp2024), Bangkok, Thailand, pp. XX-XX, Dec. 10-12, 2024.

    2. Year: 2021

  2. P. Singh, M. Wątorek, A. Ceglarek, M. Fąfrowicz and P. Oświęcimka. Analysis of fMRI Time Series: Neutrosophic-Entropy based Clustering Algorithm. Proc. of 8th Intl. Conference on Soft Computing and Machine Intelligence (ISCMI 2021), Cairo, Egypt, pp. XX--XX, Nov. 26-27, 2021.

    3. Year: 2020

  3. P. Singh, Y.-P. Huang, W.-J. Chu and J.-H. Lee. A Fuzzy-Entropy and Image Fusion Based Multiple Thresholding Method for the Brain Tumor Segmentation. Proc. of IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC 2020), Toronto, Canada, pp. 2446-2451, Oct. 11-14, 2020.

    4. Year: 2019

  4. P. Singh, Y.-P. Huang and T.-T. Lee. A Novel Ambiguous Set Theory to Represent Uncertainty and its Application to Brain MR Image Segmentation. Proc. of IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC 2019), Bari, Italy, pp. 2460-2465, Oct. 06--09, 2019.
  5. P. Singh, Y.-P. Huang and T.-T. Lee. A Method for High-Resolution Satellite Image Compression using Type-1 and Type-2 Fuzzy Sets. Proc. of IEEE Int. Conf. on System and Engineering (ICSSE 2019), Dong Hoi City, Quang Binh, Vietnam, pp. 103--108, July 19-21, 2019.

    6. Year: 2018

  6. P. Singh and K. Rabadiya. High-Resolution Satellite Image Compression using Uncertain Color Space Based Method. Proc. of 24th Annual Int. Conf. on Advanced Computing and Communications (ADCOM 2018), International Institute of Information Technology (IIIT), Bangalore, pp. XX, Sep. 21-23, 2018.
  7. P. Singh and K. Rabadiya. Information Classification, Visualization and Decision-Making: A Neutrosophic Set Theory Based Approach. Proc. of 2018 IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC 2018), Miyazaki, Japan, pp. 409-414, Oct. 7-11, 2018.

    8. Year: 2017

  8. P. Singh and K. Rabadiya. Uncertain Information Classification: A Four-Way Decision Making Approach. Proc. of 9th Int. Conf. on Advances in Pattern Recognition (ICAPR), ISI, Bengluru, India, pp. 1-9, Dec. 27-30, 2017. [Acceptance Rate: 33%]
  9. P. Singh and G. Dhiman. A Fuzzy-LP Approach in Time Series Forecasting. Proc. of 7th Int. Conf. on Pattern Recognition and Machine Intelligence (PReMI 2017), ISI Kolkata, India, pp. 243-253, Dec. 5-8, 2017. [Acceptance Rate: 33%]

    10. Year: 2012

  10. P. Singh and B. Borah. Prediction of all India summer monsoon rainfall using an artificial neural network. Proc. of Opportunities and Challenges in Monsoon Prediction in a Changing Climate (OCHAMP' 2012), IITM, Pune, India, Feb. 21-25, 2012.

National Conferences

    Year: 2011

  1. P. Singh and B. Borah. An Efficient Method For Forecasting Using Fuzzy Time Series. NCTMI' 11, Tezpur University, Assam, India, pp. 67-75, 2011.
  2. P. Singh and B. Borah. A Multi-Purpose Forecasting Model Based On Fuzzy Time-Series. IDRBT Colloquium, IDRBT, Hyderabad, India, 2011.

Book Chapters

  1. P. Singh. Emerging Research on Applied Fuzzy Sets and Intuitionistic Fuzzy Matrices, chapter An Efficient Method for Forecasting using Fuzzy Time Series, pp. 287-304, IGI Global (USA), 2017.
  2. P. Singh. Computational Intelligence for Big Data Analysis, volume 19, chapter Big Data Time Series Forecasting Model: A Fuzzy-Neuro Hybridize Approach, pp. 55-71, Springer-Verlag/Heidelberg, 2015.
  3. P. Singh. Hybrid Soft Computing Approaches: Research and Applications, chapter Neuro-Fuzzy Hybridized Model for Seasonal Rainfall Forecasting: A Case Study in Stock Index Forecasting, pp. 361-385, Springer-Verlag/Heidelberg, 2015.
  4. S.K. Singh, M. Borah, and P. Singh. Introduction to Computer and Applied Mathematics, chapter Comparative study of Genetic algorithms, Modified-PSO, and Modified-Simulated Annealing on some Difficult Benchmark Test Functions, pp. 116-124, RM Research International Pvt. Ltd. (Singapore), 2023.

Countries Visited

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Japan
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Singapore
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Taiwan
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Vietnam
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Hong Kong
France Flag
France
Poland Flag
Poland
Dubai Flag
Dubai

Funding Research

DST-SERB Funding

Simulation and Modeling of Big Data Time Series

PI: P. Singh

Funding Agency: Department of Science and Technology (SERB), Govt. of India

Amount (in Lac): 23.78 (US $35,000 approx.)

Funding Date: Feb. 13, 2017

Status: Completed (Feb. 13, 2019)

Quantum Computation and Ambiguous Set Lab (QCASL)

Research Lab

Quantum Computation Research Lab is a pioneering research hub dedicated to advancing knowledge and applications in the cutting-edge field of quantum computation and quantum optimization.

Situated at the intersection of theoretical research and practical innovation, this lab is driven by the mission to explore new mathematical frameworks, computational techniques, and algorithms that address complex problems in uncertain environments and quantum domains.

Our lab conducts extensive research in the field of Quantum Wavefunction Optimization Algorithms, focusing on developing novel approaches to enhance the efficiency and accuracy of quantum computations.

Our work explores the application of optimization techniques to solve complex problems in quantum mechanics, particularly in the context of improving the precision of quantum wavefunction solutions.

Through the integration of metaheuristic algorithms such as Genetic Algorithms, Particle Swarm Optimization, and Simulated Annealing, our research investigates methods to minimize energy states and optimize parameters for quantum systems.

This research not only contributes to the theoretical advancement of quantum mechanics but also provides practical implications for optimizing quantum computing processes, enhancing computational power, and improving algorithmic efficiency in real-world applications.



Ambiguous Set Theory by Pritpal Singh

Ambiguous set theory, proposed by Pritpal Singh, is a new branch of mathematics and logic that deals with the representation and analysis of uncertainty and imprecision in data.

His work focuses on exploring the properties and applications of ambiguous sets, which extend traditional fuzzy sets by incorporating additional membership degrees to represent uncertainty more comprehensively.

This includes the development of novel mathematical models for dealing with membership degrees such as: “true”, “false”, “partially true”, and “partially false”, allowing for more nuanced classification of elements.

Singh's research also explores practical applications in data science, decision-making, and optimization.

Additionally, he has contributed significantly to the theoretical foundations of ambiguous set theory, offering insights into:

  • Operational semantics
  • Algebraic structures
  • Integration with linguistic, logic and interval analysis

His pioneering efforts aim to improve how real-world systems are modeled when certainty is unattainable, enhancing accuracy and reliability in decision-making processes.

Information and Announcement

Specialization

  • Soft computing
  • Ambiguous set theory
  • Optimization algorithms (especially quantum-based optimization)
  • Time series prediction
  • Bio-medical image analysis

Internships

  • Project internships are available for B.E./M.TEch./MCA/M.Sc. with specialization in Computer Science/Computer Science & Engineering/Data Science or related areas.

PhD Vacancy

  • PhD seats are available for B.E./M.TEch./MCA/M.Sc. with specialization in Computer Science/Computer Science & Engineering/Data Science or related areas.

Current PhD students

  • Shafiu Maitoro (School of Graduate Studies, Universiti Putra Malaysia)
  • Ajay Saini (Department of Data Science & Analytics, Central University of Rajasthan)