Prof. Aruna Malapati  

Dept. of Computer Science and Information Systems

Birla Institute of Technology & Science, Pilani
Hyderabad Campus
Jawahar Nagar, Kapra Mandal
Dist.-Medchal-500 078
Telangana, India

Educational Details

  • Ph.D (Computer Engineering), Title - Protein Ligand Docking using Wavelet Analysis),  National Institute of Technology - Karnataka (formerly K.R.E.C) - Mar 2009, Supervisor (Prof K.C.Shet, NITK).
  • M.S (Software Systems), BITS-Pilani, 2000
  • B.E (Computer Science & Engineering),  Gulburga University, 1997

Professional Background

  • Associate Professor(CSIS), BITS-Pilani, Hyderabad Campus, August 2018 - tilldate
  • Assistant Professor(CSIS), BITS-Pilani, Hyderabad Campus, June 2010 - July 2018 tilldate
  • Associate Professor (Computer Science & Engineering), Bhoj Reddy Engineering College for Women, Hyderabad, Mar 2009 to May 2010
  • Associate Professor (Computer Science & Engineering), Aurora’s Scientific, Technological Research Academy (ASTRA) Hyderabad, July 06 to Dec 2008
  • Senior Lecturer (Computer Science & Engineering), NMAM Institute of Technology, Nitte, Oct 03 to June 2006
  • Senior Software Developer, Atmyside Ltd, London(U.K), Oct 2000 to Dec 2001
  • Lecturer(Computer Science & Engineering), Proudadevaraya Institute of Technology – HOSPET,Jan 2000 to Sep2000

Assignments Abroad: 

  • Visiting Researcher, Centrum Wiskunde & Informatica (CWI), Netherlands 8th June - 31st July 2015 (Funded  by BITS Pilani and Vrije Universiteit Amsterdam).
  • Senior Software Developer, Atmyside Ltd, London(U.K), Oct 2000 to Dec 2001



Positions held

  • Associate Dean Alumni Relations Sep 2021 - Tilldate
  • Faculty-In-Charge Centre for Innovation, Incubation and Entrepreneurship(CIIE) - Aug 2018 - Sep 2021

PhD Students

Past Student(s)

  • Dr.Avinash Kumar (2015PHXF0507H) defended Thesis titled "Aspect Based Sentiment Analysis of Social Media Content" on 24th May 2022
  • Dr.Sai Kiranmai Gorla (2012PHXF0531H) defended Thesis titled "Named Entity Recognition for Telugu Language" on 5th Oct 2021
  • Dr.Surender Singh Samanth (2013PHXF0200H) defended Thesis titled "Identification, Categorization and Summarization of Real-World Events from Twitter" in Dec 2020
  • Dr.Muthukumaran Kasinathan (2011PHXF0415H) - Co supervisor defended Thesis titled "Prediction and Probability Distribution of Defects in Software Systems" in 2018
  • Dr.Priyanka Purkayastha - Co supervisor defended Thesis titled "Classification, Structure prediction and Molecular dynamics study of novel protein targets involved in Neurodegenerative diseases " in 2016

Courses Teaching (I Semester 2022-2023)

  • CS F429 - Natural Language Processing
  • SS ZG537 - Information Retrieval

Courses Taught

  • Natural Language Processing
  • Information Retrieval
  • Compiler Construction
  • Data Mining
  • Data Warehousing
  • Computer Programming II
  • Computer Programming
  • Programming Languages and Compiler Construction
  • Data Structures and Algorithms(Tutorial)
  • Bioinformatics
  • Object Oriented Programming using Java
  • Object Oriented Analysis and Design
  • Software Engineering
  • Software Testing Methodologies
  • Operating Systems

Completed Projects

1.Project Title: Smart Traffic Analytics for Hyderabad city (13/7/2015 - 12/7/2017)
Amount: 10 Lakhs
Funding Agency: BITS   
In the proposed work we aim develop software application using Big Data  
  • to identify trends/patterns in traffic  
  • to calculate average speed in different locations and vehicles  
  • to monitor and manage Urban traffic
  • to suggest alternate routes based on the locations
  • to provide updates on traffic based on incidents, traffic jams etc.  
2. Credit Analytics: (In association with BITS Alumni)
The aim of this work is to evaluate housing loan data and analyze the defaults and find Co-relations between the loan approvals and real estate market fluctuations. The implementation of this work is on Hadoop.
3. Modelling Bayesian Networks for Performance and Capacity Management of Data centers.(In association with TRDDC Pune)
A data center consists of large numbers of computing, communication, and storage systems supporting wide range of applications and services. Consider, for instance, a banking application operated by a US-based investment bank. We observed that this data center hosts hundreds of DB2 applications on several logical partitions of mainframe boxes. The applications fire millions of queries every day and access a complex array of storage devices consisting of thousands of storage volumes and datasets. Such systems need automated solutions for performance and capacity management to better understand and control their operations.
For each component, an enterprise monitors many different metrics – including workload, latency, CPU, memory, IO and network utilizations, cache hit/miss rates, among others. Furthermore, each of these metrics is monitored at relatively fine time-scales (e.g., every few seconds). We propose to leverage Bayesian networks to analyze this data in order to perform following operations:
One of the biggest challenges in performing many performance and capacity operations is the construction of causal relationships between various system metrics. Consider an example system of a database tier with Oracle instances hosted on Windows/Linux machines where various metrics are monitoring at database instance, operating system, and system hardware. Deriving causal relationships across various system components/metrics  can prove very useful in gaining a better system understanding. It also opens up the opportunities for performance debugging, capacity planning, prediction, what-if analysis, etc.
Another very relevant application of Bayesian networks is in root-cause analysis of performance problems. In the event of performance problems at an application the root-causes behind performance problems can be diagnosed at application, compute, storage, network layers using belief propagation techniques.
Many infrastructure components are programmed to generate alerts based on certain definitions – e.g. an alert is generated when CPU utilization exceeds 90%. In today’s scenarios due lack of intelligent alert generation mechanisms or due to poor alert suppression mechanisms, large volumes of alerts are generated (~ 1million alerts per day). Analysis of these alerts becomes unmanageable. Bayesian networks can be used to identify dependencies across alerts. This information can then be used to suppress spurious alerts, generate signatures of correlated alerts, etc.

Research Interest

Areas of Interest

  • Natural Language Processing
  • Information Retrieval
  • Data Mining
  • Big Data
  • Bio Informatics


  • Received "Prof. Indira Parikh 50 Women in Education Leaders" citation at 6th World Education Congress on 23 Nov 2017.
  • Received "WOMEN IN EDUCATION" Award from The Dewang Mehta National Education Awards on 15th April 2017.
  • Received the Best Paper Award (Runner) in I-CARE 2013 (5th IBM Collaboration Academia Research exchange) for the paper titled “Comparative study on effectiveness of standard bug prediction approaches” 
  • Finalist for “INAE YOUNG ENGINEER OF THE YEAR – 2009”.
  • Conclave Invitee for “GOOGLE INDIA WOMEN IN ENGINEERING” award in February 2008.
  • Received “Best paper of the year 2008” in the computer Engineering section at Annual technical Session by institution of Engineers AP state center.
  • Received YOUNG ENGINEER OF THE YEAR 2007” award from Andhra Pradesh state govt and the Institution of Engineers AP state center.
  • Received BEST PAPER OF THE SESSION award for presenting a paper on “Data Mining Approaches for Insilco Drug Design” in the International Conference on systemics, Cybernetics and ICSCI 2005 organized by Pentagram Research Center Pvt Ltd in Jan-05.