Anoop Dobhal has over 5 years of experience in machine learning and big data. He received his M.Tech from IIT Bombay in 2014 and B.Tech from DCRUST, Murthal in 2008. He currently works as an Associate at Cognizant Technology Solutions where he has applied machine learning techniques like random forests and decision trees to tasks like anomaly detection, policy renewal prediction, and risk analysis of anonymized data sets. He has also worked as a visiting lecturer and published a paper on privacy risk metrics. His areas of expertise include programming languages like Python and R as well as big data technologies like Hadoop, Hive and Spark.

1. ANOOP DOBHAL Email : anoop.dcrust@gmail.com
Contact : +91-9003194690
DOB : October 21, 1986
EDUCATIONAL QUALIFICATION
Examination Board/University Institute Year % / CPI
Post Graduation (M.Tech.)
(PG Specialization : CSE) IIT Bombay IIT Bombay 2014 6.81
Graduation (B.Tech.)
(UG Specialization : CSE) MDU, Rohtak DCRUST, Murthal 2008 67.50%
Intermediate (12th) CBSE GMSSS-19 Chandigarh 2004 65.40%
Matriculation (10th) CBSE HMSSS-10 Panchkula 2002 76.80%
Professional Experience
• July,2014-Present: Associate at Cognizant Technology Solution Private Limited
◦ Risk Analysis of Anonymized Data Sets using Hadoop and Machine Learning
∗ Finding associated risk in terms of sensitive information leak by defining various
risk matrices parameters such as k-anonymity, l-diversity, t-closeness, informa-
tion gain and information loss
∗ Checking the initial anonymized data sets correction level against these risk ma-
trices parameters using CART (Classification and Regression Tree)
∗ Developed MapReduce programs and Pig scripts to parse the data
∗ Used Mapreduce, Pig and Sqoop for calculating various risk values Anonymized
Data Sets
∗ Managed and reviewed Hadoop log files
◦ Network Intrusion Detection using Machine Learning
∗ Given extracted features from TCP data, classifying it into different classes of at-
tack or normal
∗ Performed feature reduction using Correlation Matrix and applied Principle Com-
ponent Analysis to reduce the dimension of features groups (high pair wise cor-
relation)
∗ Used Random Forest and Decision Tree with GMM(Gaussian Mixture Model) for
classification, results are same from both approach with minute differences
∗ Final result is evaluated based on Confusion Matrix of normal(positive) VS at-
tack(negative)
◦ Insurance Policy Renewal Model using Machine Learning
∗ Given various features data, finding whether customer will renew the insurance
policy

2. ∗ Performed sentiment analysis by collecting data from various sources like Face-
book, Twitter
∗ Built model for finding renewal tendency of policies by customer using Neural
Networks
• Feb,2009-May,2011: Worked as Visiting Lecturer at Shree Ram Institute of Technical
Education, Panchkula
◦ Conducted Labs and lectures for C/C++, Java, Data Structure, SQL
M.Tech. Projects
• Adaptive Testing for Classification of Learners and Quiz Questions using Clicker
(Guide: Prof. Deepak B. Phatak)
◦ Given a set of questions, recommending questions to users according to their past
responses
◦ Approach based on Rasch Model
◦ Implemented Collaborative filtering Recommendation System for the adaptive model
• Automatic Music Chords Generation
(Guide: Guide: Prof. G Sivakumar)
◦ Given a music piece as input with several measures, predicted the chords sequence
for melodies
◦ Approach based on Hidden Markov Model
◦ Computed the most likely chord progression using Viterbi Algorithm in Statistic and
Machine Learning Toolbox in MATLAB
• Legal Contract Document Classification
(Guide: Guide: Prof. G Sivakumar)
◦ Given both document structure as well as local document features, classifying the
documents
◦ Vector of TF-IDF weighted indicator features are prepared for both title and body text
separately
◦ Concatenated vector is used as a feature vector for classification using linear kernel
based SVM
• Designed block encryption function
(Guide: Prof. Virendra R Sule)
◦ Designed block encryption function for 32-bit block for enciphering instead of the
original 64-bit enciphering function
◦ Carried out the avalanche tests on the cipher
◦ Generated Stream rank using CTR mode and plot the result of stream rank test
Publications
• Privacy Risk Metrics for Internal and External Threat Analysis- An Enterprise Perspec-
tive
(The 2015 international conference on security and management (SAM’15))
◦ Identity and attribute disclosure of enterprise data is hazardous
◦ Developed a solution to handle multiple sensitive attributes and provide risk profiles
for them based on both insider and external threat sources.

3. Positions of Responsibility
• Teaching Assistantship, IIT Bombay
◦ Computer Programming and Utilization (Prof. Abhiram Ranade, Autumn 2012-13)
Worked as a Lab instructor assisting students to cope up with course pace and was
involved in grading them
◦ Aakash Project (Prof. Deepak B. Phatak, Autumn 2013-14)
∗ Mentor-ship for the project "Optimizing Moodle LMS for Improving User Re-
sponse Time"
∗ Responsible for mentoring interns, assisting students to cope up with course pace
in lab assignments
Courses Taken
• Machine Learning, Introduction to Data Science, Advanced Computer Architecture, In-
troduction to Cryptography and Number Theory, Applied Linear Algebra, Artificial In-
telligence, Implementation Techniques for relational database Systems, Software Archi-
tecture, Decision Analysis and Game Theory
Areas of Expertise
• Programming Languages: C, C++, Java, R
• Scripting Languages: Python, Shell
• Web Technologies: JavaScript, HTML, CSS, JSP, WML, xHTML, XML
• Big Data Ecosystems: Hadoop, Hive, Hbase, Pig, Sqoop, Mahout
• Operating Systems: Linux, Windows
• Database: MySQL, PostgreSQL, Oracle
• Frameworks: Spring, Hibernate, Web Frameworks (Struts, Spring MVC, JAX-WS)
• Development Tools: LATEX, Eclipse, IntelliJ Idea, Net-beans