Data Mining

Dr. Shruti KOHLI

+91-981******* *****.******@*****.***

Senior Lecturer profile

A committed senior lecturer with over 12+ years of rich experience at leading academic

institutions of graduate/post graduate students from various social/cultural backgrounds.

Sound International on-site experience representing papers, tutorials in Analytics

Excellent track record fostering, motivating and encouraging student learning

Record in engaging students and developing their critical thinking, leading positive results.

Hands on rich experience for mentoring Doctoral and M. tech research students.

Leading Government, University Grant commission funded project in Mobile analytics

Birla Institute of Technology (Noida, New Delhi NCR, India) Oct 2006 – till date

Assistant Professor (Department of Computer Science)

Research

Published research papers in reputed journal of national/international level, which are

indexed in IEEE, Springer’s, Scopus, DBLP.

Received grant of 17,400/- USD from University Grant Commission for Research project

Smart use of web analytics, data mining technique to improving online Information Retrieval

Mentoring 7 research scholars (Ph.D./ M.Tech) in Information Retrieval, Web Analytics.

(3 students have completed M.Tech)

Web Analytic Consultant

Acted as ad-hoc Web Analytic consultant for Pinnacle Technologies (2009-2012) during Ph.D.

implementation phase.

A) Project Lead : Restructuring of the e-commerce portal ( www.slideworld.com)

B) Project Lead : Facilitate creation of useful reports for management, with mining of data using R

Presently acting as Web Analytic consultant for True Chip Solutions (http://www.truechip.net)

since March 2014

A) Project Lead : Consulting for improving Consumer engagement (TWEET Analysis) and Enhancing

business reporting, with mining of data using R

Teaching

Preparing and delivering regular lectures to undergraduate and postgraduate students.

Key subjects System Simulation & Modeling, Operational Research, Web technology,

E-commerce, Data Mining.

Regular visitor as Examination reviewer for M. Tech dissertation evaluation

Industrialize Mobile Incubator Cell workshops, which promotes digital transformation.

Administration

Undertaking of administrative work such as library purchase, student admission.

Acted as a Coordinator for BCA program in 2009

Member of various committee Institutional Academic,Technical Society, Cultural committee

Indian Airlines Limited, Computer Center ( New Delhi) April 2005 – Sept 2006

Computer Application Supervisor (Information Technology Department)

Lead team of engineers working on in-house software development projects, website restructuring,

automation of reports

Banarsidas Chandiwala Institute of Information Technology, ( New Delhi ) Aug 2003 – Mar 2005

Lecturer (Department of computer science and Mathematics)

Key Subjects undertaken : Computer Networks and Security, Operating System, Web Designing,

Contributed in developing learning material for Computer courses / practical activities.

Key member in Interview panel for research students.

Greater Noida Institute of Technology, (Noida New Delhi NCR) Aug 2002 – July 2003

Lecturer (Department of computer science and Mathematics)

Key Subjects undertaken : Foundation of IT, Operational Research, System Analysis Design, C, VB

V Customer Services India Pvt. Ltd. (New Delhi) July 2001 – July 2002

Quality Analyst

Contributed to set standards of quality and testing procedures, test strategy / test cases

Educational Background

Ph.D. – Technology (2012) Thesis titled ““Evolution of Analytical Quantified User Dependent

Models for improving user satisfaction in a search session of Search Engine.

M Phil - Operational Research (2004) MCA Master in Computer Application (2001)

M.Sc. - Operational Research (2001) B. Sc. - Computer Science (1999)

Professional Certification

Google Mobile App Analytics (2014) Google E-Com Analytics (2014) IELTS (2014) Score 7.5

Google Digital Analytics (2013) Search Engine Optimization (2008)

Professional Membership

Member of Informs IEEE ACM IAENG MIR Labs Society for Science and Education

Book/Chapter Publication

Course book : “Internet Programming” (2014) and Book on Web Technology (2012, 2013)

Chapter Publication for book on “Com Express - An anthology of collected essays on comm”(2012)

Chapter Publication for book titled “Fuzzy Expert Systems for Disease Diagnosis”,

Tutorial Presentation at International Level

Delivered tutorial session on Data Analysis with R to 20+ foreign students at UCC 2014, London

Papers presented at International Level

Modeling Anonymous Human Behavior on Social Media ICITST-2014, London Indexed in IEEE

Website Content Analysis Approach IEEE/WIC/ACM Conf in Macau, China - 2012, Indexed in IEEE

Intelligent User Behavior Rule Based System for Innovation Conf in Dubai, 2007 Indexed in IEEE

Dr. Shruti Kohli

******@********.**.** http://www.shrutikohli.com

RESEARCH STATEMENT

I am currently working as Assistant Professor Computer Science & Engineering at Birla Institute of Technology,

India. My research interests span across Information Retrieval, web/mobile analytics, areas of machine learning,

knowledge discovery and data mining. I have published numerous research papers and journal publications

during my research, some of them have been accepted in noteworthy forums, IEEE/ACM, Springer, complete

details are visible of my website. In addition to my role, am presently handling University Grant Commission,

government funded project in field of analytics and acting as adhoc consultant for www.truechip.net.

Background & Significance: With a strong analytical bent in mind I started working for my PhD thesis in 2007. I

spent the summers of 2007 at my campus working as a research intern with the Web and link analysis Group.

Web always fascinated me, with the popularity of the Web, a new discipline has emerged, called Web

Information Retrieval. It uses some concepts of traditional IR and introduces innovative concepts which will be

noteworthy for the Industry. Search engine, being a popular online tool of information retrieval (IR), has been a

center stage of research. The research objective was to develop “Analytically Quantified User Dependent

Models for improving satisfaction in a search session of search engine”. To achieve this objective, the

performances of existing search engines were evaluated from the user’s perspective. Google being a popular

search engine was exhaustively studied and used for conducting user-based analysis.

Gradually, it was identified that search system of any e-commerce platform could be studied and improved as per

user’s feedback. Research was conducted to develop adaptive search systems that could improve based on

user’s feedback (explicit as well as implicit). During the literature survey many complexities were identified that

impact information retrieval through Search Engine. It was identified that such issues could be resolved in two

ways a) By measuring relevancy of displayed results to the user query b) By measuring user satisfaction with the

search results. Our work could be broadly categorized under the later stream and is based on usability and user

satisfaction studies.

Firstly, my dissertation research identified sources of uncertainties that impact a search session. Methodologies

have been deployed to study impact with these uncertainties on success of a search session. We started process

of exploring “user modeling techniques” as one way of the potential domain for improving the effectiveness of

our adaptive search system that adapts to “user uncertainty”. User studies were conducted by deploying TAM

models (Technology Acceptance Model) on a search session. David’s TAM (a renowned model) was deployed to

assess the user acceptance and voluntary usage of a particular search application. The results largely validated

TAM, although the findings suggest that certain external variables, namely user based attributes such as length of

time since first use, and level of education, directly affect search usage behavior apart from their influence as

mediated through the perceived usefulness (PU) and perceived ease of use (PEOU) constructs.

Secondly, researchers have identified three hierarchical levels informational, navigational, and transactional

intent. During the study we defined and presented a comprehensive classification of user intent for Web

searching. To quantify results, we developed Query Analyzer tool to identify the type of query input by user. The

results were validated on search keyword logs extracted from a Web search engine (built locally) and also on

traffic logs of our university website.

Continuing the journey I developed, UDA (User Dependency Algorithm) to predict user’s intention (UI) of using a

search engine for future needs. Certainty theory and other data mining techniques were used to identify KPI for a

“User Satisfaction analysis for a search session”. The term ‘user dependency’ means the psychological satisfaction

of a user with the search result during a search session. It’s an indicative measure of user’ s trust in search engine,

hence increase likelihood of his return for future needs.

In a nutshell, UDA will effectively track the “user behavior” in a search session. It uses a fuzzy based approach to

determine the dependency and overall faith of user in the Search Engine. The proposed algorithm accepts ‘user

rating for the search session’ as input and provides a quantitative value of user dependency. See5 Classifier (An

Informal tutorial) was used to determine the rules for computing different levels of dependencies in the UDA. The

validity of algorithm and correctness of its result were judged according to survey conducted with a sample of

users at university. Results have been observed to be accurate and matched according to sampled user’s

satisfaction. To get industry preview, algorithm was implemented on www.slideworld.com and it revealed

positive results, which acted as a key input for remodeling of their portal structure, increasing user’s loyalty.

During the last stage, I concluded my journey with development of Rule Based System (RBS), a prototype system

which uses output of UDA and perform analysis on search engine logs to develop its database. This system gives a

qualitative output of user satisfaction with a search engine based on rules applied in its knowledge base.

Current Research (an overview…)

Augmentation of Trust Analysis on WEB (2015- Present) : In today’s ecosystem, information is streamlined on

Internet and Web2.0, but still the problem of Trust sustains at the user end as to what content provided by the

web should be trusted and up-to what extent ? Our research is an endeavor to provide a solution to this

persisting problem based on overall human behavior as HCI (Human-Computer Interaction) plays a vital role in

forming antecedents of TRUST and risk analysis.

Social Network Analysis and Opinion Mining (2014-present): The quantity of medical information in the social

media is growing at an exponential rate. Rigorous experiments have been done with R and tweet analysis to

mine the data available and with this our research domain has extended to big data. While researching it was

identified Social intelligence derived from health content has become key for competitive intelligence, reviews,

health related opinions and sentiments. Our work is focused on mining engagement of patients/medical

professionals harnessing public opinion to improve decision making.

Development of APP recommendation System (2013-present): Thousands of apps are uploaded and upgraded

to virtual world. Among these apps, some of them have high market response with respect to others. It’s

important to analyze, the best set of apps for the perfect set of crowd. We are developing model, analytic

hierarchy process (AHP), which will provide an intuitive model of a hierarchical structure capable of supporting

complex mobile apps comparisons & evaluation on the basis of various criteria.

Multi Criteria Decision making (MCDM) - 2012-2013: Hands on experience was done, on web data set using

WEKA and R. As next step concept of averaging operator for aggregation based regression was implemented on

web data. Conceptual Model has been developed to deploy Concept of Ordered Weighted Operators for

aggregation based regression on web data using MCDM.

Keyword Similarly Measure Tool’ (KSMT) – 2012 : We had developed KSMT, with an aim was to improve data

accuracy and overcome limitation of similar keywords being vastly separated in the google analytics report.

Methodology provides holistic view of data for similar keywords, by combining the metrics, bounce -rate, visits for

the similar keywords. Prototype of tool was presented in IEEE/ACM Web Intelligence conference in 2012.

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