Rahul Sharma

Rahul is a dynamic computer science researcher with excellent communication and writing skills. Currently, He is working as an early-stage researcher and doctoral candidate at the software science department, information systems group, Tallinn university of technology, Estonia. He’s passionate about working on association rule mining, machine learning, algorithmic fairness and identification of the role of statistical paradoxes in AI, ML and big data based applications. His research interests broadly include:

Algorithimic fairness and biasness
Explainable and Trustoworthy AI
Role of statistical paradoxes(E.g., Simpson's Paradox) in AI, ML and Big Data
Identifying Causal and Confounding effects in Machine Learning
Machine Learning
Genralised Association Rule Mining

Rahul has over twelve years of experience in education, consulting, independent research. From 2014 to 2019, Rahul has worked as an assistant professor at Ajay Kumar Garg Engineering College, Ghaziabad, India. As an edupreneur. He has clearly recognized the basic needs of modern students in the institute and has taken many steps to strengthen research and innovation. In 2015, He started a Big Data Centre of Excellence to provide an ecosystem to expand research and impart state-of-the-art education on new technologies.

news

Aug 15, 2022	A paper titled “Detecting Simpson’s Paradox: A Machine Learning Perspective” has been Published in the 33rd International Conference on Database and Expert Systems Applications (Dexa-2022)
Feb 25, 2022	A paper titled “Towards Unification of Stastical Reasoning, OLAP and ARM” has been Published in the 27th International Conference on Database Systems for Advanced Applications(DASFAA-2022)
Jan 12, 2022	A paper titled “Why Not to Trust Big Data: Discussing Statistical Paradoxes” has been published in PMDB workshop under DASFAA 2022”

selected publications

DEXA-2022
Detecting Simpson’s Paradox: A Machine Learning Perspective

Sharma, Rahul, Garayev, Huseyn, Kaushik, Minakshi, Peious, Sijo Arakkal, Tiwari, Prayag, and Draheim, Dirk

In Database and Expert Systems Applications 2022

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The size of data collected around the world is growing exponentially, and it has become popular as big data. The volume and velocity of big data are facilitating the transition of machine learning (ML), deep learning (DL) and artificial intelligence (AI) from research laboratories to real life. There are numerous other claims made about Big Data. Can we, however, rely on data blindly? What happens when a dataset used to train ML models has a hidden statistical paradox? Data, like fossil fuels, is valuable, but it must be refined carefully for accurate outcomes. Statistical paradoxes are hard to observe in classical data cleaning and analysis techniques. Still, they are required to be investigated separately in training datasets. In this paper, we discuss the impact of Simpson’s paradox on categorical data and demonstrate its effects on AI and ML application scenarios. Next, we provide an algorithm to automatically identify the confounding variable and detect Simpson’s paradox within categorical datasets. The algorithm experiments on datasets from two real-world case studies. The outcome of the algorithm uncovers the existence of the paradox and indicates that Simpson’s paradox is severely harmful in automatic data analysis, especially in AI, ML and DL.
@inproceedings{dexa2022, author = {Sharma, Rahul and Garayev, Huseyn and Kaushik, Minakshi and Peious, Sijo Arakkal and Tiwari, Prayag and Draheim, Dirk}, editor = {Strauss, Christine and Cuzzocrea, Alfredo and Kotsis, Gabriele and Tjoa, A. Min and Khalil, Ismail}, title = {Detecting Simpson's Paradox: A Machine Learning Perspective}, booktitle = {Database and Expert Systems Applications}, year = {2022}, publisher = {Springer International Publishing}, address = {Cham}, pages = {323--335}, abbr = {DEXA-2022}, selected = {true}, bibtex_show = {true}, isbn = {978-3-031-12423-5} }
ADBIS-2022
Detecting Simpson’s Paradox: A Step Towards Fairness in Machine Learning

Sharma, Rahul, Kaushik, Minakshi, Peious, Sijo Arakkal, Bertl, Markus, Vidyarthi, Ankit, Kumar, Ashwani, and Draheim, Dirk

In New Trends in Database and Information Systems 2022

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In the last two decades, artificial intelligence (AI) and machine learning (ML) have grown tremendously. However, understanding and assessing the impacts of causality and statistical paradoxes are still some of the critical challenges in their domains. Currently, these terms are widely discussed within the context of explainable AI (XAI) and algorithmic fairness. However, they are still not in the mainstream AI and ML application development scenarios. In this paper, first, we discuss the impact of Simpson’s paradox on linear trends, i.e., on continuous values, and then we demonstrate its effects via three benchmark training datasets used in ML. Next, we provide an algorithm for detecting Simpson’s paradox. The algorithm has experimented with the three datasets and appears beneficial in detecting the cases of Simpson’s paradox in continuous values. In future, the algorithm can be utilized in designing a certain next-generation platform for fairness in ML.
@inproceedings{ADBIS 2022, author = {Sharma, Rahul and Kaushik, Minakshi and Peious, Sijo Arakkal and Bertl, Markus and Vidyarthi, Ankit and Kumar, Ashwani and Draheim, Dirk}, title = {Detecting Simpson's Paradox: A Step Towards Fairness in Machine Learning}, booktitle = {New Trends in Database and Information Systems}, year = {2022}, publisher = {Springer International Publishing}, address = {Cham}, pages = {67--76}, abbr = {ADBIS-2022}, selected = {true}, bibtex_show = {true}, isbn = {978-3-031-15743-1} }
DaWaK
Expected vs. Unexpected: Selecting Right Measures of Interestingness

Sharma, Rahul, Kaushik, Minakshi, Peious, Sijo Arakkal, Yahia, Sadok Ben, and Draheim, Dirk

In Big Data Analytics and Knowledge Discovery 2020

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Measuring interestingness in between data items is one of the key steps in association rule mining. To assess interestingness, after the introduction of the classical measures (support, confidence and lift), over 40 different measures have been published in the literature. Out of the large variety of proposed measures, it is very difficult to select the appropriate measures in a concrete decision support scenario. In this paper, based on the diversity of measures proposed to date, we conduct a preliminary study to identify the most typical and useful roles of the measures of interestingness. The research on selecting useful measures of interestingness according to their roles will not only help to decide on optimal measures of interestingness, but can also be a key factor in proposing new measures of interestingness in association rule mining.
@inproceedings{RahulP1, author = {Sharma, Rahul and Kaushik, Minakshi and Peious, Sijo Arakkal and Yahia, Sadok Ben and Draheim, Dirk}, editor = {Song, Min and Song, Il-Yeol and Kotsis, Gabriele and Tjoa, A. Min and Khalil, Ismail}, title = {Expected vs. Unexpected: Selecting Right Measures of Interestingness}, booktitle = {Big Data Analytics and Knowledge Discovery}, year = {2020}, publisher = {Springer International Publishing}, address = {Cham}, pages = {38--47}, isbn = {978-3-030-59065-9}, pdf = {dawak2020.pdf}, selected = {true}, bibtex_show = {true}, abbr = {DaWaK}, url = {https://dl.acm.org/doi/abs/10.1145/3474944.3474951}, html = {https://dl.acm.org/doi/abs/10.1145/3474944.3474951} }
DASFAA
Towards Unification of Statistical Reasoning, OLAP and Association Rule Mining: Semantics and Pragmatics

Sharma, Rahul, Kaushik, Minakshi, Peious, Sijo Arakkal, Shahin, Mahtab, Vidhyarthi, Ankit, and Draheim, Dirk

In Database Systems for Advanced Applications 2022

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Over the last decades, various decision support technologies have gained massive ground in practice and theory. Out of these technologies, statistical reasoning was used widely to elucidate insights from data. Later, we have seen the emergence of online analytical processing (OLAP) and association rule mining, which both come with specific rationales and objectives. Unfortunately, both OLAP and association rule mining have been introduced with their own specific formalizations and terminologies. This made and makes it always hard to reuse results from one domain in another. In particular, it is not always easy to see the potential of statistical results in OLAP and association rule mining application scenarios. This paper aims to bridge the artificial gaps between the three decision support techniques, i.e., statistical reasoning, OLAP, and association rule mining and contribute by elaborating the semantic correspondences between their foundations, i.e., probability theory, relational algebra, and the itemset apparatus. Based on the semantic correspondences, we provide that the unification of these techniques can serve as a foundation for designing next-generation multi-paradigm data mining tools.
@inproceedings{dasfaa2022, author = {Sharma, Rahul and Kaushik, Minakshi and Peious, Sijo Arakkal and Shahin, Mahtab and Vidhyarthi, Ankit and Draheim, Dirk}, title = {Towards Unification of Statistical Reasoning, OLAP and Association Rule Mining: Semantics and Pragmatics}, booktitle = {Database Systems for Advanced Applications}, year = {2022}, publisher = {Springer International Publishing}, address = {Cham}, selected = {true}, abbr = {DASFAA}, bibtex_show = {true} }
DASFAA
Why Not to Trust Big Data: Discussing Stastical Paradoxes

Sharma, Rahul, Kaushik, Minakshi, Peious, Sijo Arakkal, Shahin, Mahtab, Vidhyarthi, Ankit, Tiwari, Prayag, and Draheim, Dirk

In Database Systems for Advanced Applications 2022

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Big data is driving the growth of businesses, data is the money, big data is the fuel of the twenty-first century, and there are many other claims over Big Data. Can we, however, rely on big data blindly? What happens if the training data set of a machine learning module is incorrect and contains a statistical paradox? Data, like fossil fuels, is valuable, but it must be refined carefully for the best results. Statistical paradoxes are difficult to observe in datasets, but they are significant to analyse in every small or big dataset. In this paper, we discuss the role of statistical paradoxes on Big data. Mainly we discuss the impact of Berkson’s paradox and Simpson’s paradox on different types of data and demonstrate how they affect big data. We provide that statistical paradoxes are more common in a variety of data and they lead to wrong conclusions potentially with harmful consequences. Experiments on two real-world datasets and a case study indicate that statistical paradoxes are severely harmful to big data and automatic data analysis techniques.
@inproceedings{RhulA4, author = {Sharma, Rahul and Kaushik, Minakshi and Peious, Sijo Arakkal and Shahin, Mahtab and Vidhyarthi, Ankit and Tiwari, Prayag and Draheim, Dirk}, title = {Why Not to Trust Big Data: Discussing Stastical Paradoxes}, booktitle = {Database Systems for Advanced Applications}, year = {2022}, publisher = {Springer International Publishing}, address = {Cham}, selected = {true}, abbr = {DASFAA}, bibtex_show = {true} }