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Prof. Venkateswara Rao Kagita

Assistant Professor Gr-I

Department of Computer Science and Engineering

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Information

Room No: Cluster 1 - B +91-870-2464279 venkat.kagita@nitw.ac.in Bio Sketch

    Key Notes

    • Journal(s): 14
    • Conference(s): 12
    • PhD: Current - 7

    26

    PUBLICATIONS

    7

    DOCTORAL STUDENTS

    6

    PROJECTS

    Research Areas

    Artificial intelligence
    Recommender Systems, Machine Learning, Deep Learning
    EDUCATION QUALIFICATION
    Degree Institute Year
    Doctor of Philosophy University of Hyderabad, Hyderabad. 2019
    Master of Technology University of Hyderabad, Hyderabad. 2013
    Bachelor of Technology Gudlavalleru Engg College,JNTU 2009
    COURSES HANDLED
    Course L-T-P Credit Degree Level
    Deep Learning(CS401) 3-0-0 3 UG
    Artificial Intelligence(CS315) 3-0-0 3 UG
    Models in Deep Learning(CS16038) 3-0-0 3 PG
    Machine Learning(CS353) 3-0-0 3 UG
    Data Structures and Algorithms(CS1106) 3-0-2 4 UG
    Computational Mathematics Practice(CS5204) 1-1-2 3 PG
    Mathematics for Computer Science(CS5102) 1-1-0 2 PG
    Deep Learning(CS6326) 3-0-0 3 PG
    Computational Thinking(CS5108) 0-1-2 2 PG
    Fundamentals of Data Structures(CS395) 3-0-0 3 UG
    Principles of Data Warehousing and Data Mining(CS5354) 3-0-0 3 PG
    Artificial Intelligence(CS5311) 3-0-0 3 PG
    Data Science Fundamentals(CS5104) 3-0-0 3 PG
    RESEARCH IDs
    ORCID
    ORC ID
    Google Scholar
    Google Scholar ID
    PUBLICATIONS
    Journal(s)
    Cross-domain recommender systems via multimodal domain adaptation, By Adamya Shyam, Ramya Kamani, Venkateswara Rao Kagita, Vikas Kumar, ELSEVIER, Computers and Electrical Engineering, vol.Volume 123, Part D, pp.110300, 2025
    UniRecSys: A unified framework for personalized, group, package, and package-to-group recommendations, By Adamya Shyam, Vikas Kumar, Venkateswara Rao Kagita, Arun K. Pujari, ELSEVIER, Knowledge-Based Systems, vol.289, pp.111552, 2024
    High-Performance Computing for Static Security Assessment of Large Power Systems, By Venkateswara Rao Kagita, Ram Krishan, Sanjaya Kumar Panda, TAYLOR & FRANCIS LTD , 2-4 PARK SQUARE, MILTON PARK, ABINGDON, England, OXON, OX14 4RN, Connection Science, vol., pp., 2023
    Data augmentation for recommender system: A semi-supervised approach using maximum margin matrix factorization, By Shaikh Shamal, Venkateswara Rao Kagita, Vikas Kumar, and Arun K. Pujari. , ELSEVIER, Expert Systems with Applications, vol.Volume 238, Part B, pp.121967, 2023
    Inductive Conformal Recommender Systems, accepted , By Venkateswara Rao Kagita, Vikas Kumar, Arun K Pujari, and Vineet Padmanabhan, WORLD SCIENTIFIC PUBL CO PTE LTD , 5 TOH TUCK LINK, SINGAPORE, SINGAPORE, 596224, Knowledge based Systems, vol., pp., 2022
    Efficient Computation of Top-K Skyline Objects in Data Set With Uncertain Preferences, By Nitesh Sukhwani, Venkateswara Rao Kagita, Vikas Kumar, Sanjaya Kumar Panda, IGI GLOBAL , 701 E CHOCOLATE AVE, STE 200, HERSHEY, USA, PA, 17033-1240, Data Warehousing and Mining, vol., pp., 2021
    Skyline Recommendation with Uncertain Preferences, By Venkateswara Rao Kagita, Arun K Pujari, Vineet Padmanabhan, and Vikas Kumar, ELSEVIER , RADARWEG 29, AMSTERDAM, Netherlands, 1043 NX, Pattern Recognition Letters, vol., pp., 2019
    roup Preserving Label Embedding for Multi-label Classification, By Vikas Kumar, Arun K Pujari, Venkateswara Rao Kagita, and Vineet Padmanabhan, ELSEVIER SCI LTD , 125 London Wall, London, England, EC2Y 5AS, Pattern Recognition, vol., pp., 2019
    Multi-label Classification Using Hierarchical Embedding, By Vikas Kumar, Arun K Pujari, Vineet Padmanabhan, Sandeep Kumar Sahu, and Venkateswara Rao Kagita, PERGAMON-ELSEVIER SCIENCE LTD , THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND, OX5 1GB, Expert Systems with Applications, vol., pp., 2018
    Collaborative Filtering Using Multiple Binary MMMFs, By Vikas Kumar, Arun K Pujari, Sandeep Kumar Sahu, Venkateswara Rao Kagita, and Vineet Padmanabhan, ELSEVIER SCIENCE INC , STE 800, 230 PARK AVE, NEW YORK, USA, NY, 10169, Information Sciences, vol., pp., 2017
    Proximal Maximum Margin Matrix Factorization for Collaborative Filtering, By Vikas Kumar, Arun K Pujari, Sandeep Kumar Sahu, Venkateswara Rao Kagita, and Vineet Padmanabhan, ELSEVIER , RADARWEG 29, AMSTERDAM, Netherlands, 1043 NX, Pattern Recognition Letters, vol., pp., 2017
    Conformal Recommender System, By Venkateswara Rao Kagita, Arun K Pujari, Vineet Padmanabhan, Sandeep Kumar Sahu, and Vikas Kumar, ELSEVIER SCIENCE INC , STE 800, 230 PARK AVE, NEW YORK, USA, NY, 10169, Information Sciences, vol., pp., 2017
    Bounds on Skyline Probability for Databases with Uncertain Preferences, By Venkateswara Rao Kagita, Arun K Pujari, Vineet Padmanabhan, ELSEVIER SCIENCE INC , STE 800, 230 PARK AVE, NEW YORK, USA, NY, 10169, Journal of Approximate Reasoning, vol., pp., 2017
    Virtual user approach for group recommender systems using precedence relations, By Venkateswara Rao Kagita, Arun K. Pujari, Vineet Padmanabhan, ELSEVIER SCIENCE INC , STE 800, 230 PARK AVE, NEW YORK, USA, NY, 10169, Information Sciences, vol., pp., 2015
    Conference(s)
    gCDR: A Group Aided Cross-Domain Recommendation Framework By Adamya Shyam, Kavita Kanwar, Vikas Kumar, Venkateswara Rao Kagita, International Conference on Big Data Analytics, 2024
    On Robustness of Finetuned Transformer-based NLP Models By Pavan Kalyan Reddy Neerudu, Subba Reddy Oota, Mounika Marreddy, Venkateswara Rao Kagita, Manish Gupta, EMNLP 2023, 2023
    Committee Selection using Attribute Approvals By Venkateswara Rao Kagita, Arun K Pujari, Vineet Padmanabhan, Haris Aziz, and Vikas Kumar, AAMAS, 2021
    Justified Group Recommender Systems By Venkateswara Rao Kagita, Arun K Pujari and Vineet Padmanabhan, PACIE, 2018
    Threshold-based Direct Computation of Skyline Objects for Database with Uncertain Preferences By Venkateswara Rao Kagita, Arun K Pujari, Vineet Padmanabhan, Vikas Kumar, and Sandeep Kumar Sahu, PRICAI, 2016
    GP-SVM: Tree Structured Multiclass SVM with Greedy Partitioning By Sandeep Kumar Sahu, Arun K Pujari, Venkateswara Rao Kagita, Vikas Kumar, and Vineet Padmanabhan, ICIT, 2015
    A Novel Social-Choice Strategy for Group Modeling in Recommender Systems By Venkateswara Rao Kagita, Krishna Charan Meka, Vineet Padmanabhan, ICIT, 2015
    Bi-directional Search for Skyline Probability By Arun K. Pujari, Venkateswara Rao Kagita, Anubhuti Garg, Vineet Padmanabhan, CALDAM, 2015
    Recommender system algorithms: A comparative analysis based on monotonicity By T.V.R.Himabindu, Vineet Padmanabhan, Venkateswara Rao Kagita, Arun K. Pujari, ICAPR, 2015
    reedy Partitioning based Tree Structured Multiclass SVM for Odia OCR By Sandeep Kumar Sahu, Arun K Pujari, Vikas Kumar, Venkateswara Rao Kagita, and Vineet Padmanabhan, NCVPRIPG, 2015
    Collaborative filtering by PSO-based MMMF By Sowmini Devi, Venkateswara Rao Kagita, Arun K. Pujari, Vineet Padmanabhan, SMC, 2014
    Precedence Mining in Group Recommender Systems By Venkateswara Rao Kagita, Vineet Padmanabhan, Arun K. Pujari, PReMI, 2013
    PROJECT / CONSULTANCY
    Development of biologically informed multi-omics deep learning framework for clinical diagnosis of cancer subtypes for precision therapy
    Role: Co-Principal Investigator
    Type: Research
    Sponsor: TIH-AI4ICPS
    Project Cost (INR): 1903088
    Date of Commencement: 15-01-2024  
    Duration: 26 Months
    Status: Ongoing
    RecExplain: A Transparent Recommender Systems through Explainable Latent Factor Model
    Role: Co-Principal Investigator
    Type: Research
    Sponsor: IoE-DU
    Project Cost (INR): 400000
    Date of Commencement: 01-09-2023   Date of Completion: 31-08-2024
    Duration: 11 Months
    Status: Completed
    Participatory budgeting with multi-dimensional constraints
    Role: Principal Investigator
    Type: Research
    Sponsor: SERB-SIRE
    Project Cost (INR): 1722538
    Date of Commencement: 03-11-2023   Date of Completion: 05-05-2024
    Duration: 6 Months
    Status: Completed
    Dynamic and Static Security Analysis of Large Power System using HPC
    Role: Co-Principal Investigator
    Type: Research
    Sponsor: DST-NSM
    Project Cost (INR): 4800000
    Date of Commencement: 15-03-2021   Date of Completion: 31-03-2024
    Duration: 36 Months
    Status: Completed
    Deep multi-source data fusion network for recommender system
    Role: Co-Principal Investigator
    Type: Research
    Sponsor: IoE-DU
    Project Cost (INR): 300000
    Date of Commencement: 01-09-2022   Date of Completion: 31-08-2023
    Duration: 11 Months
    Status: Completed
    Unified Framework for Personal, Group, and Package Recommendations
    Role: Principal Investigator
    Type: Research
    Sponsor: RSM
    Project Cost (INR): 500000
    Date of Commencement: 01-04-2020   Date of Completion: 31-03-2021
    Duration: 11 Months
    Status: Completed
    RESEARCH FELLOWS / PhD STUDENTS
    Current PhD Students
    Busireddy Nagarjuna Reddy
    Area of Research: Machine Learning/Recommender Systems
    Daweed Omer Ahmed
    Area of Research: Machine LEarning/Deep Learning
    Lohitha Mallireddy
    Area of Research: Machine Learning/Deep Learning
    Pasupunooti Anusha
    Area of Research: Machine Learning/ Deep Learning
    PRASAD EMMADI
    Area of Research: Machine Learning/Recommendation Systems
    SHAILENDRA KUMAR
    Area of Research: RECOMMENDER SYSTEMS
    Tadaboina Ramya
    Area of Research: Machine Learning/Deep Learning
    CONFERENCE / WORKSHOP / SYMPOSIUM / SHORT TERM COURSE / FACULTY DEVELOPMENT PROGRAMME
    AWARDS AND ACCOLADES
    2023
    DST-SERB-SIRE Fellowship
    2022
    IEEE Senior Fellow
    ADDITIONAL RESPONSIBILITIES
    •  CCPD CSE Coordinator (Continuing from July, 2024)
    •  Faculty advisor for 2023 batch CSE (A) (Continuing from June, 2023)
    •  Erp coordinator (September, 2021 - August, 2024)
    •  Faculty advisor for 2019 batch CSE (B) (January, 2020 - June, 2023)
     Last updated on May 20, 2025