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Dr. G. Uday Kiran

Associate Professor & Program Coordinator
udaykiran.goru@bvrit.ac.in

Ph.D.: Data Mining, Jawaharlal Nehru Technical University, Hyderabad, 2022
PG: M.Tech (Neural Networks), Jawaharlal Nehru Technical University, Kakinada, 2010,
UG: B.Tech (CSE), Jawaharlal Nehru Technical University, Hyderabad, 2006.

Teaching Experience: 18 Years 05 Months
Research Experience: 06 Years 02 Months
Industry Experience:

Contact Number: +91 9885172564
BVRITN Employee ID: 381
JNTUH Registration ID: 90150404-115955
AICTE Registration ID: 1-2898618269

Dr. G. Uday Kiran received Ph.D. (Data Mining) from Jawaharlal Nehru Technological University Hyderabad M.Tech (Neural Networks) from Jawaharlal Nehru Technological University Kakinada. Currently, he is working as an Associate Professor in the Department of CSE (Artificial Intelligence and Machine Learning), B V Raju Institute of Technology, and has 14 Years of Teaching Experience.  

  1. Deep Learning
  2. Computer Vision and Image Processing
  3. Speech and Natural Language Processing
  4. Data Mining and Analytics
  5. Theory of Computation and Compilers
  1. Qualified in Faculty Eligibility Test (FET) in Dec 2010 conducted by JNTU Hyderabad.
  2. Completed Two Courses (Speech Signal Processing and Natural Language Processing) in IIIT Hyderabad (PGSSP Monsoon 2013).
  3. Recognized as NPTEL Star – Enthusiasts for July-December 2019. 
  1. L. K. Kumar, V. N. Thatha, P. Udayaraju, D. Siri, G. U. Kiran, and B. N. Jagadesh, “Analyzing public sentiment on the Amazon website: A GSK-based double path transformer network approach for sentiment analysis,” IEEE Access, vol. 12, pp. 28972–28987, 2024.

  2. A. Stateczny, G. Uday Kiran, G. Bindu, and K. Ravi Chythanya, “Spiral search grasshopper feature selection with VGG19–ResNet50 for remote sensing object detection,” Remote Sensing, vol. 14, no. 21, Art. no. 5398, 2022.

  3. V. Srilakshmi, G. U. Kiran, M. Mounika, A. Sravanthi, N. V. K. Sravya, and V. N. S. Akhil, “Evolving convolutional neural networks with meta-heuristics for transfer learning in computer vision,” Procedia Computer Science, vol. 230, pp. 658–668, 2023.

  4. G. U. Kiran and D. Vasumathi, “Predicting Parkinson’s disease using extreme learning measure and principal component analysis based mini SOM,” Annals of the Romanian Society for Cell Biology, vol. 25, no. 4, 2021.

  5. G. U. Kiran, “Disease detection using enhanced K-means clustering and Davies–Bouldin index in big data,” Journal of Green Engineering, vol. 10, no. 11, pp. 13089–13106, 2020.

  6. G. U. Kiran, V. Srilakshmi, R. Deepika, D. S. Saran, G. Sevanth, and R. Vamsi, “Dynamic ensemble learning with evolutionary programming and swarm intelligence for image classification,” Procedia Computer Science, vol. 230, pp. 669–678, 2023.

  7. H. V. Bhagat, G. U. Kiran, and M. Singh, “ImpClust: An algorithm to cluster chemical datasets for drug discovery,” International Journal of Intelligent Engineering and Systems, vol. 17, no. 1, 2024.

  8. G. U. Kiran, “Overlap clustering technique based on the improved hierarchical agglomerative clustering,” Journal of Green Engineering, vol. 10, no. 11, pp. 11594–11607, 2020.

  9. G. U. Kiran, “A hybrid clustering algorithm to reduce dimensions and optimal selection of K-centroids for healthcare datasets,” Journal of Applied Science and Computations, vol. 7, no. 10, pp. 110–119, 2020.

  10. V. Srilakshmi, G. Padmini, G. Sreenidhi, and B. V. Ramana, “Neural architecture search-driven optimization of deep learning models for drug response prediction,” Procedia Computer Science, vol. 252, pp. 172–181, 2025.

  11. V. Srilakshmi, B. Moulika, G. S. Mahitha, G. Laukya, and M. Ruthick, “Integrating NAS for human pose estimation,” Procedia Computer Science, vol. 252, pp. 182–191, 2025.

  1. B. Veerasekhar Reddy, V. N. Thatha, G. U. Kiran, and S. K. Shareef, “Diet recommendation system for human health using machine learning,” in Proc. 7th Int. Conf. on Computation System and Information Technology, 2023.

  2. B. Kusumasri, V. Srilakshmi, S. Satyavada, and G. U. Kiran, “Crop recommendation application using ensemble classifiers,” in Proc. IEEE Delhi Section Flagship Conf. (DELCON), pp. 1–7, 2023.

  3. R. Changala, H. N. Lakshmi, G. V. Krishna, H. B. Lakshmi, G. U. Kiran, and V. N. Thatha, “Sentiment analysis optimization using hybrid machine learning techniques,” in Proc. Parul Int. Conf. on Engineering and Technology (PICET), pp. 1–5, 2024.

  4. V. Srilakshmi, N. Ch, and G. U. Kiran, “Estimating 3D human pose using point-based pose estimation and single-stage method,” in Proc. 3rd Int. Conf. on Computing, Analytics and Networks (ICAN), 2022.

  5. G. U. Kiran, S. Gajula, A. Sravanthi, T. Veditha, and D. V. Reddy, “Inception V3 model-based approach for detecting defects on steel surfaces,” in Proc. 2nd Int. Conf. on Intelligent Data Communication Technologies, 2024.

  6. G. U. Kiran, R. Gandi, M. Lavanya, G. B. Desale, C. U. Rao, and B. V. Reddy, “Deep learning based abstractive text summarization: A survey,” in Proc. Parul Int. Conf. on Engineering and Technology (PICET), pp. 1–5, 2024.

  7. V. Srilakshmi, B. Priyanka, B. Dhanasai, K. Ashritha, and K. Smarandas, “Stack-based ensembles for robust neural image captioning,” in Proc. IEEE Delhi Section Flagship Conf. (DELCON), pp. 1–6, 2024.

  8. V. Srilakshmi, G. Padmini, B. Lavanya, A. Tejaswini, G. Vijay, and D. Ranveer, “One-shot stylization for transformative facial art using StyloMorph,” in Proc. IEEE Delhi Section Flagship Conf. (DELCON), pp. 1–8, 2024.

  9. G. U. Kiran, V. Srilakshmi, K. S. Harshith, K. Nikhita, L. V. S. Mokshith, and A. Sowmya, “Learning correspondences across domains for exemplar-based image translation,” in Proc. IEEE Delhi Section Flagship Conf. (DELCON), pp. 1–5, 2024.

  10. G. U. Kiran, V. Srilakshmi, M. Mounika, B. Lavanya, B. Priyanka, and G. Padmini, “Exploring the impact of neural architecture search on human pose detection techniques,” in Proc. IEEE Int. Conf. on Contemporary Computing, 2025.

S.NoCourseDurationMonth & YearGrade
1Python for Data Science4 WeeksApril, 2022Silver
2NBA Accreditation and Teaching – Learning in Engineering (NATE)12 WeeksDecember, 2021Silver
3Data Science for Engineers8 WeeksDecember, 2019Elite
4Accreditation and Outcome-based Learning8 WeeksDecember, 2019Silver
5Natural Language Processing12 WeeksDecember, 2019Pass
6Machine Learning for Engineering and Science Applications12 WeeksApril, 2019Pass
7Introduction to Automata, Languages and Computation12 WeeksApril, 2019Elite
8Programming, Data Structures and Algorithms using Python8 WeeksApril, 2019Pass
9Design and Analysis of Algorithms8 WeeksApril, 2019Elite
10Introduction to Machine Learning8 WeeksNovember, 2018Pass
11Programming, Data Structures and Algorithms using Python8 WeeksNovember, 2018Pass
  1. Half Week FDP for “Python for Data Science”.
  2. One and Half Week FDP for “NBA Accreditation and Teaching – Learning in Engineering (NATE)”.
  3. One Week FDP for “Data Science for Engineers”.
  4. One Week FDP for “Accreditation and Outcome-based Learning”.
  5. One and Half Week FDP for “Natural Language Processing”.
  6. One and Half Week FDP for “Machine Learning for Engineering and Science Applications”.
  7. One and Half Week FDP for “Introduction to Automata, Languages and Computation”.
  8. One Week FDP for “Introduction to Machine Learning”.
  9. Extensive Vision AI 4 Program, The School of AI.
  10. One Week FDP on “Amazon Web Services” from 22-08-2022 to 27-08-2022.
  11. Half-Week FDP on “Data Analysis Using Statistical Learning Techniques” from 21-03-2022 to 26-03-2022.

Industry Certifications:

  • Extensive Vision AI 4 Program

Coursera:

  1. Deep Learning Specialization offered by Deeplearning.ai
  2. Machine Learning Specialization offered by the University of Washington
  3. AWS Fundamentals Offered by AWS

EdX

  • Deep Learning with Python and PyTorch

IIT Bombay EdX Course:

  • LaTeX for Students, Engineers, and Scientists

Funding Proposal:

  • Member of a Funding Project “Virtual Assistant for Mobile Devices using Voice and Gesture Technologies” Technologies, Funded by ITRA, New Delhi.
  • Member – IQAC Institute Committee
  • Member – Board of Studies – CSE
  • CSE Department NBA Coordinator
  • CSE Department NAAC Coordinator
  • CSE Department Curriculum Coordinator