Dr. Neha Verma Malhotra is working as Sr. Assistant Professor in VIPS. She has completed her Ph.D. in Computer Science and Engineering from I.K. Gujral Punjab Technical University, India. She obtained her MCA from GGSIPU and M.Tech from USIT, GGSIPU in Information Technology. She obtained her B.Sc (Computer Science) from Delhi University. Dr. Neha started her career with AVL India Software Pvt. Ltd., Udyog Vihar, Gurgaon, as a software developer in embedded systems. Afterward, for her interest towards teaching, she joined VIPS, in the year 2008. She has attended several AICTE approved FDPs organized by reputed institutions like IIT Bombay, IIT Kharagpur, and Delhi University. She is working as an Associate Editor with various International Journals/ bodies like Elsevier, IGI Global, Bilingual Publishing Co- Singapore, BIQ- Malaysia, Universe Scientific Publication- Singapore, Bentham Science (Scopus). She has published research articles in conference proceedings and reputed SCI/ESCI international journals indexed in Scopus and Web of Science (Clarivate Analytics). She has four authored books on Data Structures and Algorithms to her credit published by Mercury Learning and Information in the USA and Canada and two patents. Dr. Neha is recently awarded as Women Researcher Award (2022)” by the International Scientist Awards on Engineering, Science and Medicine, YOUNG SCIENTIST AWARD-2019 by IFERP and awarded the best research paper award of the year 2019 by the Association of Indian Management Schools in the 31st AIMS Convention. She is a Life Time Member of SpARC Wing and IT Wing with Rajyoga Education & Research Foundation. She is a member of various reputed professional bodies like IAENG and CEGR. She has delivered guest lectures related to life skills in institutions under the University of Delhi. Her teaching and research interests include Data Mining, Business Intelligence, OOP with C++ and Java, Hadoop-MapReduce distributed programming, big data, cloud data mining, retail analytics

Cloud computing, Data Mining, Big data, Retail Analytics