SOET KRMU Faculty Ms. Pallavi Pandey

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Ms. Pallavi Pandey

Assistant Professor -Engineering & Technology

Interest Area(s)

  • Machine Learning
  • Deep Learning
  • Data mining
  • Signal processing 
  • Image processing
  • Analysis and Design of algorithm
 

Ms. Pallavi Pandey is currently associated with School of Engineering & Technology, K. R. Mangalam University, Gurugram. She is currently pursuing Ph.D from  Indira Gandhi Delhi Technical University for women, Kashmere Gate, New Delhi and did M.Tech in Computer Science and Engineering, from Jamia Hamdard, Delhi. Ms. Pallavi Pandey possesses nearly 10 years of teaching experience in BCA, MCA and B.Tech. Her area of interest includes Data Mining, Machine Learning, Signal and Image processing and Deep Learning. She has published several research papers in various international journals and conferences.

  • Degree [Ph.D.] (Submitted) in Computer Science and Engineering. Pursuing from Indira Gandhi Delhi Technical University for Women, New Delhi, India
  • Degree [M.Tech.] in Computer Science and Engineering, Jamia Hamdard New Delhi, India in the year 2014.
  • UGC NET in Computer Science and Applications (2012)
  • Joined K.R. Mangalam University on 1st Feb, 2022.
  • Senior Research Fellow (SRF), Dept of Computer Science and Engineering, Indira Gandhi Delhi Technical for Women, Kashmere Gate, New Delhi - 110006.Oct 2019 – 31 July 2022.
  • Junior Research Fellow (JRF), Dept of Computer Science and Engineering, Indira Gandhi Delhi Technical for Women, Kashmere Gate, New Delhi - 110006. Oct 2017 - Sept 2019.
  • Teaching experience of five years at KS Saket PG College, Ayodhya, U.P. and Narula Institute of Technology, Kolkata, WB. 2005 to 2010.
  • Electroencephalography signal analysis using Data mining techniques
  • Writer identification with handwriting images
 
  • Attended a summer training workshop on “Regression and Deep Learning” from 11-12 June 2018 at South Asian University, New Delhi.
  • Attended a Faculty Development program on “Signal & Image processing: Issues Challenges and Techniques” from 1st to 12th January 2018 organised by Dept. of Electronics and communication engineering, Deenbandhu Chhotu Ram university of Science and Technology, Murthal, Sonepat, Haryana.
  • Attended a GIAN (Global initiative for academic networks) course on “Biomedical signal acquisition, processing and analysis” organized by MHRD, Govt. of India and IIT Roorkee, held at IIT Roorkee on 19th to 23rd December 2017.
  • Attended a Brain Modes 2017 international conference held at NBRC from 11th to 14th December 2017 organized by National Brain Research Centre, Manesar, Gurgaon, Haryana, India.
  • Attended workshop on “Deep learning and Parallel architectures” at MNIT Jaipur, organized by NVIDIA and MNIT Jaipur on 26th and 27th of Oct. 2017.
  • Attended FDP on “Latest trends and research in machine Learning” at Bharati Vidyapeeth’s Institute of Management & Research New Delhi, A-4, Paschim Vihar, Delhi on 12 to 18 December 2016.
  • “Research methodology and Tools” At Indira Gandhi Delhi Technical University for Women, Delhi on Nov. 6, 2015.
  • Presented a paper entitled “Air pollution prediction using extreme learning machine: A case study on Delhi, India” at of First International Conference on Smart System, Innovations and Computing, Manipal University, Jaipur.
  • National workshop on virtual reality-visual story telling in architecture from 25 to 29 May 2018 organized by Indira Gandhi Delhi Technical University for Women, Delhi.
  • Worked as a Coordinator for one day workshop on cloud computing held on 21 August 2017 organized by organized by Indira Gandhi Delhi Technical University for Women, Delhi in collaboration with APTRON solutions private limited.
  • “Mobile architecture and programming by using J2ME (Asha OS), Nokia X (Android), Python & Linux (Raspberry pi)” organized by Indira Gandhi Delhi Technical University for Women, Delhi in collaboration with Nokia University Relations from 16-6-2014 to 25-7-2014.
  • Got Premier research award on 25 December 2022 by the university IGDTUW, Kashmere Gate, Delhi for the publication: Pandey, P., & Seeja, K. R. (2020). Subject independent emotion recognition system for people with facial deformity: an EEG based approach. Journal of Ambient Intelligence and Humanized Computing, 1-10.
  • Worked on a project for U.P. Jal Nigam at Infogen software solutions, Lucknow, U.P. 
  • Successfully completed course and got certificate on DEEP LEARNING through NPTEL by Prof Mitesh K Khapra, IIT Chennai on OCT 2018. 
  • Successfully completed 4 weeks online certification course in “Pattern Discovery in    Data Mining” authorized by University of Illinois at Urbana-Champaign and offered through Coursera in April 2015. 
  • Got 4th rank in IBM Tech. Quiz at the annual fest Integration-2010 at Indian statistical institute, Kolkata. 
 
  • Pandey, P., & Seeja, K. R. (2020). Subject independent emotion recognition system for people with facial deformity: an EEG based approach. Journal of Ambient Intelligence and Humanized Computing, 1-10. (SCI Indexed, Publisher: Springer, IF: 7.103) 
  • Pandey, P., & Seeja, K. R. (2019). Subject independent emotion recognition from EEG using VMD and deep learning. Journal of King Saud University-Computer and Information Sciences. (SCI Indexed, Publisher: Elsevier, IF: 13) 
  • Pandey, Pallavi, and K. R. Seeja. (2018) "Forensic Writer Identification with Projection Profile Representation of Graphemes. " Proceedings of First International Conference on Smart System, Innovations and Computing. Springer, Singapore. (Scopus Indexed) 
  • Pandey, P., & Seeja, K. R. (2019). Subject-Independent Emotion Detection from EEG Signals using Deep Neural Network. In International Conference on Innovative Computing and Communications, (pp. 41-46). Springer, Singapore. (Scopus Indexed)
  • Pandey, P., & Seeja, K. R. (2019). Emotional state recognition with eeg signals using subject independent approach. In Data Science and Big Data Analytics (pp. 117-124). Springer, Singapore. (Scopus Indexed)
  • Pandey, P., & Seeja, K. R. (2022). A One-Dimensional CNN Model for Subject Independent Emotion Recognition Using EEG Signals. In International Conference on Innovative Computing and Communications (pp. 509-515). Springer, Singapore.