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Faculty Abhimanyu Bar

Abhimanyu Bar

Assistant Professor

Department of Computer Science and Engineering

Contact Details

abhimanyu.b@srmap.edu.in

Office Location

School of Engineering and Sciences Computer Science and Engineering Faculty Abhimanyu Bar

Faculty Information

Education

2023
PhD
University of Hyderabad, Telengana
India
2011
MCA
BPUT Rourkela, Odisha
India
2008
B.Sc
Utkal University, Odisha
India

Personal Website

Experience

  • Assistant Professor, School of Computer Science Engineering and Technology-Bennett University, Greater Noida, Delhi-NCR

Research Interest

  • My research focuses on classical rough set theory with a special emphasis on feature subset selection (reduct computation) and dimensionality reduction. We have developed optimal and near-optimal methods for reduct discovery, including approaches based on A* search and coarsest granularity criteria, aimed at simplifying decision systems while retaining predictive accuracy. Dr. Bar’s broader interests include rough sets, soft computing, data mining, and artificial intelligence, with applications in building reliable, efficient, and interpretable machine learning models. His work has contributed to the advancement of reduct computation techniques and their use in classification, knowledge representation, and ensemble model design
  • My current research focuses on developing explainable ensemble machine learning models using Classical Rough Set Theory. I investigate diversity-driven multiple reduct generation to construct ensemble frameworks where each base learner operates on distinct, information-preserving feature subsets. By leveraging the rule-induction capability and attribute reduction strength of Rough Sets, my work aims to enhance model transparency and interpretability without compromising predictive performance. I explore how reduct-based feature selection and discernibility concepts can provide clear, human-understandable explanations for ensemble decisions. Through this integration of Rough Set theory and Explainable AI, I seek to design robust, scalable, and interpretable ensemble systems for reliable decision-making in real-world applications.

Memberships

  • IEEE

Publications

Patents

Projects

Scholars

Interests

  • Artificial Intelligence
  • Data Science
  • Machine Learning
  • Soft Computing

Thought Leaderships

There are no Thought Leaderships associated with this faculty.

Top Achievements

Research Area

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Computer Science and Engineering is a fast-evolving discipline and this is an exciting time to become a Computer Scientist!

Computer Science and Engineering is a fast-evolving discipline and this is an exciting time to become a Computer Scientist!

Recent Updates

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Education
2008
B.Sc
Utkal University
India
2011
MCA
BPUT Rourkela
India
2023
PhD
University of Hyderabad
India
Experience
  • Assistant Professor, School of Computer Science Engineering and Technology-Bennett University, Greater Noida, Delhi-NCR
Research Interests
  • My research focuses on classical rough set theory with a special emphasis on feature subset selection (reduct computation) and dimensionality reduction. We have developed optimal and near-optimal methods for reduct discovery, including approaches based on A* search and coarsest granularity criteria, aimed at simplifying decision systems while retaining predictive accuracy. Dr. Bar’s broader interests include rough sets, soft computing, data mining, and artificial intelligence, with applications in building reliable, efficient, and interpretable machine learning models. His work has contributed to the advancement of reduct computation techniques and their use in classification, knowledge representation, and ensemble model design
  • My current research focuses on developing explainable ensemble machine learning models using Classical Rough Set Theory. I investigate diversity-driven multiple reduct generation to construct ensemble frameworks where each base learner operates on distinct, information-preserving feature subsets. By leveraging the rule-induction capability and attribute reduction strength of Rough Sets, my work aims to enhance model transparency and interpretability without compromising predictive performance. I explore how reduct-based feature selection and discernibility concepts can provide clear, human-understandable explanations for ensemble decisions. Through this integration of Rough Set theory and Explainable AI, I seek to design robust, scalable, and interpretable ensemble systems for reliable decision-making in real-world applications.
Awards & Fellowships
Memberships
  • IEEE
Publications
Contact Details

abhimanyu.b@srmap.edu.in

Scholars
Interests

  • Artificial Intelligence
  • Data Science
  • Machine Learning
  • Soft Computing

Education
2008
B.Sc
Utkal University
India
2011
MCA
BPUT Rourkela
India
2023
PhD
University of Hyderabad
India
Experience
  • Assistant Professor, School of Computer Science Engineering and Technology-Bennett University, Greater Noida, Delhi-NCR
Research Interests
  • My research focuses on classical rough set theory with a special emphasis on feature subset selection (reduct computation) and dimensionality reduction. We have developed optimal and near-optimal methods for reduct discovery, including approaches based on A* search and coarsest granularity criteria, aimed at simplifying decision systems while retaining predictive accuracy. Dr. Bar’s broader interests include rough sets, soft computing, data mining, and artificial intelligence, with applications in building reliable, efficient, and interpretable machine learning models. His work has contributed to the advancement of reduct computation techniques and their use in classification, knowledge representation, and ensemble model design
  • My current research focuses on developing explainable ensemble machine learning models using Classical Rough Set Theory. I investigate diversity-driven multiple reduct generation to construct ensemble frameworks where each base learner operates on distinct, information-preserving feature subsets. By leveraging the rule-induction capability and attribute reduction strength of Rough Sets, my work aims to enhance model transparency and interpretability without compromising predictive performance. I explore how reduct-based feature selection and discernibility concepts can provide clear, human-understandable explanations for ensemble decisions. Through this integration of Rough Set theory and Explainable AI, I seek to design robust, scalable, and interpretable ensemble systems for reliable decision-making in real-world applications.
Awards & Fellowships
Memberships
  • IEEE
Publications
Contact Details

abhimanyu.b@srmap.edu.in

Scholars