We are a small, active and growing group of Researchers at the Centre of Excellence in Artificial Intelligence at the Mahindra University, Hyderabad, India. We are located at the Supercomputer Laboratory with advanced NVidia GPU-based supercomputer DGX-1 and other top-of-the-line CPU-based computing servers. The B.Tech program in Artificial Intelligence offered by the École Centrale College of Engineering in Mahindra University is also academically and technically supported by us.
The Centre came into effect on Aug 1, 2018. Further information on our Centre is provided below.

Our Research Focus Areas
Investigations
- Exploration of Evolutionary Optimization for training in Deep Learning, and concomitant impact on different aspects of DL functionality developed under the back-propagation paradigm
- Investigating Transpose CNNs with fractional strides in novel architectures designed for complex process monitoring and control in real time using limited sensor data
- Novel Deep Learning Architectures for Incremental Learning
- Optimal Synthesis of Associative Memories
- Quantum Inspired Neural Network training algorithm, and Particle Swarm Optimization Algorithm
- Cognitive Modelling, through Conceptual Spaces and Social Computing, for generation and comprehension of Metaphorical mappings
- Design of scalable fuzzy based clustering algorithm for big data using High Performance Computing
- Learning in absence of sufficient training data: Deep Transfer and Adversarial Learning.
Applications
- Advance prediction of adverse digressions in continuous time systems, using Extreme Learning Machines, LSTMs and Koopman Operator
- Game Theory based Expert Systems for complex industrial reactors
- Applications of Multi-Objective Evolutionary Algorithms in diverse sectors
- Applications of Associative Memories for text/audio/video storage and retrieval
- Detection and Prevention against Cyberbullying on Social Media platforms
- Modelling of Cultural and Social contexts for identification of inappropriate texts
- Classification of disease datasets, and medical images for identifying adverse regions
- Prediction of agricultural crop yields using ANNs
- Enhancement of Agricultural Crop Yields by developing seeds resistant to drought, heat and other adverse conditions
- Fuzzy based federated learning model for Internet of Things
- Analysis of Musical features(Acoustic) and properties, Music Information Retrieval and Recommendation systems
- Structural Health Monitoring of different classes of structures, incorporating evaluation of crack detection and propagation from images and video footages, vibration analysis
- Learning robust deep models for industry/healthcare related applications inspired from multimedia and IoT
- Zero-Shot Learning: Identifying samples from those classes that were not observed during training (in the domain of Computer Vision)
People
(in alphabetical order)
Arya Kumar Bhattacharya
https://www.mahindraecolecentrale.edu.in/faculty/arya-kumar-bhattacharya
Neha Bharill
https://www.mahindraecolecentrale.edu.in/faculty/neha-bharill
Om Prakash Patel
https://www.mahindraecolecentrale.edu.in/faculty/om-prakash-patel
Prafulla Kalapatapu
https://www.mahindraecolecentrale.edu.in/faculty/prafulla-kalapatapu
Rama Murthy Garimella
https://www.mahindraecolecentrale.edu.in/faculty/rama-murthy
Raghu Kishore Neelisetti
https://www.mahindraecolecentrale.edu.in/faculty/raghu-kisore-neelisetti
Sanatan Sukhija
https://www.mahindraecolecentrale.edu.in/faculty/sanatan-sukhija
Sunny Rai
https://www.mahindraecolecentrale.edu.in/faculty/sunny-rai
Projects:
External Projects:
Title: Missile Launch Improvement using Evolutionary and Multi-Objective Evolutionary Algorithms
Amount: INR 9.2 Lakhs
Organization: Defence Research and Development Organization
Duration: 3 Years (2016 – 2019).
Title: Development of Real-time, Adaptive, Intelligent Mechanisms for monitoring and control of complex industrial processes within Industrial IoT Frameworks
Amount: 23 Lakhs
Organization: Cyber-Physical Systems Division, Department of Science & Technology, GoI
Duration: 3 Years (2018 – 2021).
Title: Development of Warehouse Resource Allocation System
Amount: 5.2 Lakhs
Organization: GROUND INC., Japan
Duration: 0.5 Years (2020).
Internal Projects:
Title: Design of a Novel Machine Learning Algorithms using High-Performance Computing for Next Generation Sequence Analysis of Soybean Genomes
Amount: 1.5 Lakhs
Organization: École Centrale College of Engineering, Mahindra University, Hyderabad
Duration: 2 Years (2019 – 2021).
Title: Novel Deep Learning Architectures
Amount: 0.5 Lakhs
Organization: École Centrale College of Engineering, Mahindra University, Hyderabad.
Duration: 2 Years (2019 – 2021).
Publications:
International Journals
- P. Jha, A. Tiwari, N. Bharill, M. Ratnaparkhe, M. Mounika and N. Nagendra, “A Novel Scalable Kernelized Fuzzy Clustering Algorithm Based on In-Memory Computation for Handling Big Data”, IEEE Transactions on Emerging Topics in Computational Intelligence, DOI: 10.1109/TETCI.2020.3016302, 2020.
- O.P. Patel, N. Bharill, A. Tiwari, V. Patel, O. Gupta, J. Cao, J. Li, M. Prasad, “Advanced Quantum Based Neural Network Classifier and its Application for Objectionable Web Content Filtering”, IEEE Access, vol. 7, pp. 98069-98082, 2019
- S. Rai and S. Chakraverty (2020), A Survey on Computational Metaphor Processing. ACM Computing Surveys (CSUR), 53(2), 2020, pp. 1-37. DOI: https://doi.org/10.1145/3373265
- S. Rai, A. Jain, and P. Pandey, (2019). Inclusion of Wikipedia, a language specific knowledge resource to generate and update a synset in WordNet. International Journal of Technology, Policy and Management, 19(4), 405-419, 2019, DOI: 10.1504/IJTPM.2019.104062.
International Conferences
- P. Jha, A. Tiwari, N. Bharill, M. Ratnaparkhe, N. Nagendra, and M. Mounika, “Fuzzy Based Kernelized Clustering Algorithms for Handling Big Data Using Apache Spark, Congress on Intelligent Systems CIS, Sep 05-06, 2020, World Conference in Virtual Format (Accepted).
- S. Thapa, D.K. Jain, P. Singh, N. Bharill, A. Gupta, M. Prasad, “Data-Driven Approach based on Feature Selection Technique for Early Diagnosis of Alzheimer Disease”, Proc. of 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow (UK), 19-24 July, 2020.
- B. Siva Raju, A. Sowmya and G. Rama Murthy,” Emotion Detection using Visual Information with Deep Autoencoders,” Proceedings of 2018 IEEE Symposium Series on Computational Intelligence ( SSCI 2018 ).
- B. Siva Raju, A. Sowmya and G. Rama Murthy” Emotion Detection using visual information with Deep Auto- Encoders,” IEEE, 5th International Conference for Convergence in Technology, 2019.
- K. I. Basha and G. Rama Murthy, “Emotion Classification: Novel Deep Learning Architectures,” Proceedings of IEEE International Conference on Advanced Computing & Communication Systems (ICACCS), 15th-16th March 2019.
- G. Rama Murthy, M. Dileep and R. Anil, “Novel Ceiling Neuron Model and its Applications,” IEEE International Joint Conference on Neural Networks ( IJCNN ), 2019, July 2019, Budapest, Hungary.
- G. Rama Murthy, Vamshi Krishna Reddy, Devaki and Divya, “Optimal Synthesis of Hopfield Associative Memory,” Proceedings of ICMLDS 2019, December 2019, ACM Digital Library.
- G. Rama Murthy, Vidya Sree, Jyothi, Mahalakshmi and Manasa Jagannadham,” Deep Neural Networks: Incremental Learning,” Proceedings of Intellisys 2020, Springer publishers.
- K. Imthiyaz and G. Rama Murthy, “Novel Deep Learning Architectures: Feature Extractor and Radial Basis Function Neural Network”, IEEE International Conference on Computational Performance Evaluation ( ComPE), 2020.
- G. Rama Murthy Aman Singh, GC Jyothi Prasanna, Manasa Jagannadan, Maha Lakshmi Bairaju, Vidya Sree Vankam,” 1-D/2-D/3-D Hopfield Associative Memories”, Proceedings of International conference on Artificial Intelligence & Machine Learning (ICAIML), 2020.
- J. H. Go, T. Jan, M. Mohanty, O. P. Patel, et al., “Visualization Approach for Malware Classification with ResNeXt”, IEEE World Congress on Computational Intelligence (WCCI), Glasgow (UK), July 2020
- S. Kommireddy, P.R. Pandey and K.N. Raghu, “Detection of Heart Arrhythmia Using Hybrid Neural Networks”, IEEE TENCON, Nov 16-19, 2020, Osaka, Japan.
- U.M. Gollapudi, M. Perla, S. Hitesh, R. Kumaran, S. Rai and A. Das, “Understanding User Vulnerability Towards Radicalization on Twitter” 5th International Conference on Computational Social Science IC2S 2 July 17-20, 2019, University of Amsterdam, Netherlands.
- S. Rai, A. Garg and S. Chakravarty, “Understanding the role of visual features in Emoji Similarity”, International Conference on Intelligent Information Technologies, pp. 89-97, Springer, Singapore, 2018. DOI: https://doi.org/10.1007/978-981-13-3582-2_7.
- R. Arulkumaran, S. Rajesh, A.K. Bhattacharya and V.S.K. Narahari, “Real Time Predictions Of Adverse Digressions In Critical and Noisy Industrial Processes Using LSTMs”, IEEE-HYDCON, Sep 2020, Hyderabad
- K. Sahil and A. K. Bhattacharya, "Accurate Replication of Simulations of Governing Equations of Processes in Industry 4.0 Environments with ANNs for Enhanced Monitoring and Control," 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 2019, pp. 1873-1880.
- R. R. Annapureddy, A. K. Bhattacharya and N. R. M, "Adaptive Critic Design for Extreme Learning Machines applied to noisy and drifting industrial processes," 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, 2018, pp. 327-334.
- R. Ravinithesh, A.K. Bhattacharya and G. Rishita, “Advance Predictions of critical digressions in a noisy industrial process- performance of Extreme Learning Machines versus Artificial Neural Networks”, IFAC-PapersOnLine, Volume 51, Issue 1, 2018, Pages 98-105, DOI: https://doi.org/10.1016/j.ifacol.2018.05.017 also at https://www.sciencedirect.com/science/article/pii/S2405896318301770.
- P. Thangeda, A.K. Bhattacharya, G. Rajeswari and Ashok Kumar, “Synthesis of Optimal Trajectories in Aerial Engagements using Differential Evolution”, IFAC-PapersOnLine, Volume 51, Issue 1, 2018, Pages 90-97, DOI: https://doi.org/10.1016/j.ifacol.2018.05.016 or https://www.sciencedirect.com/science/article/pii/S2405896318301769.
- S.R. Gautam, M. Jahnavi, P. Thangeda and A.K. Bhattacharya, “Synthesis of optimal trajectories in tactical aerial engagements using Multi-Objective Evolutionary Algorithms”, Advances in Multidisciplinary Analysis and Optimization, Lecture Notes in Mechanical Engineering, Springer Singapore, 2021 (Accepted).
Book Chapters
- S. Rai, S. Chakravarty and D.K. Tayal, “Metaphors in Business Applications: Modelling Subjectivity Through Emotions for Metaphor Comprehension”, in Pinarbasi, F., & Taskiran, M. (Ed.), Natural Language Processing for Global and Local Business (pp. 134-153), 2021. IGI Global. http://doi:10.4018/978-1-7998-4240-8.ch006
- H. Narayan and A.K. Bhattacharya, “Accurate, real-time replication of governing equations of physical systems with Transpose CNNs for Industry 4.0 and Digital Twins”, in Machine Learning in Industry, eds. J. P. Davim and S. Datta, ISSN: 2365-0532, Springer Switzerland, 2021 (Accepted).
Patents:
- Arya K. Bhattacharya, “Digital Twins using Convolutional Neural Networks to emulate detailed conditions within represented physical processes”, Indian Patent Applied; Application number: 202141007423 (2021).
Computing Facilities in Supercomputer Laboratory
The Supercomputer Lab of Mahindra University is created out of the baseline requirements for supporting high intensity computations for Artificial Intelligence research and applications, that incorporate Machine learning, Deep learning and Data Science.
The core composition of this lab is the DGX-1 supercomputer platform, at whose kernel is a dual-core CPU server with 20 processors and 8 Tesla V100 GPU cards made up of 40,960 Nvidia CUDA cores, all connected through NVLink which minimizes internal communication overheads. On this kernel is built the platform that is a complex stack of components and software including AI Deep Learning frameworks, libraries and drivers.
This software stack is supported by DGX-1 cloud management services which continually provide updates and additional inputs. The software stack is composed of the most popular deep learning frameworks, as well as Nvidia DIGITS deep learning training application, third-party accelerated solutions, the Nvidia Deep learning SDK, Docker and drivers.
Deep Learning tasks, particularly training of complex Artificial Neural Network (ANN) architectures with many layers, hundreds of thousands of parameters, and tens of thousands of data samples, take enormous computing times if the task is performed serially on a single-thread. Instead, when launched on parallel threads, the training data can be split into multiple subsets and each launched on one thread so that the ANN training can be speeded up, ideally, by a factor equal to the number of threads. The GPU-based architecture of DGX-1 works on this principle to speed up AI applications.
The DGX-1 heart of the Mahindra University Supercomputer Lab is reinforced with a number of top-of-the-line CPU Servers that can be seamlessly scaled up in numbers. A fast Infiniband-based network connectivity is in the process of installation to connect these servers so that CPU-based parallelism can also be attained for simulations not attuned to GPU processing.
A Dassault-Systemes based 3D-Experience package is also installed in this lab. This package is an overlay on a suite of software that includes CATIA, DELPHI and other related modules, and facilitates Augmented Reality – Virtual Reality based immersive experience. A set of 30 Workstations coupled with a Server are installed and linked to the other platforms of this laboratory for facilitation of the complete 3D-Experience package. This will enable the attainment of a 3D-Experience Centre of Excellence at this lab.
Thus from the above matter it will be clear that the Supercomputer Laboratory, that is home to our Centre of Excellence in Artificial Intelligence – also supports 3D-Experience and other facilities for high-intensity computing in a spectrum of domains like Mechanical, Aerospace, Civil, Electrical, Communications and Natural Sciences. At the core of this is of course, massive computing power, enabled by the DGX-1 Supercomputer, multiple powerful Servers, and a set of 30 Workstations, all interconnected through an extremely fast data communication network. What is not so apparent, however, is that these co-located facilities can themselves be the trigger for a new and unique experience in an Indian Research Institution, namely, the coupling of AI with the trans-domain world of engineering in multiple new and creative ways of which visionaries can see possibilities that have yet to be manifested in the mundane world.
Contact: deanresearch@mahindrauniversity.edu.in