Faculty Profile

About

Dr. Kalyan N is an Assistant Professor at the Computer Science and Engineering(Data Science) Department of BMS College of Engineering. Before his current position, he worked as a Assistant Professor at CMR University for 2 years and Guest Faculty Member at Bangalore University for one year. Kalyan obtained his Ph. D. in Computer Science and Applications on Privacy-Preserving Data mining from RV College of Engineering (VTU) - Belgaum. He also has a Master's in Technology with a specialization in Bioinformatics (2014) from PESIT and a Bachelor of Engineering in Computer Science (2012) from NMIT. Dr. Kalyan is an interdisciplinary researcher with areas of interest in Computer Science and Bioinformatics and has publications in SCOPUS/SCI-indexed journals.

Education

• Doctor of Philosophy - Computer Science and Engineering
  RV College of Engineering (VTU)
  Passed Year: 2022 | Program Type: FullTime
• Master of Technology - Bioinformatics
  PES Institute of Technology
  Passed Year: 2014 | Program Type: FullTime
• Bachelors of Engineering - Computer Science and Engineering
  Nitte Meenakshi Institute of Technology
  Passed Year: 2012 | Program Type: FullTime

Selected Publications

• Conference | Published On : 24-02-2026
  Pramath KP; Banupriya Mohan; Kalyan Nagaraj; Shambhavi BR
  Biomedical data such as ECG and EEG are typically infrequent, private, or class-unbalanced, thereby posing extremely difficult conditions to develop strong machine learning models. To alleviate these issues, this work proposes a new synthetic data generation and classification paradigm integrating physics-constrained Generative Adversarial Networks (GANs) with a deep hybrid Convolutional Neural Network (CNN) and Transformer framework. The physics-constrained GANs are tuned to generate physiologically viable and realistic biomedical signals and can be applied for data augmentation in privacy-sensitive environments. The generated artificial data is used for training a strong model for classification that can provide disease profiling under different conditions. There is extensive validation on benchmark and semi-real datasets to assess the generalizability and performance of the proposed approach as a whole. Results show that the framework is good at classification, even with limited data, and highlights its applicability for real-world clinical use where data availability and confidentiality are of paramount importance.
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• Conference | Published On : 21-01-2026
  Priyanka Shivaramaiah, Roopesh Ramesh, Kalyan Nagraj
  Emerging applications in smart healthcare, intelligent transportation, autonomous robotics, and cybersecurity necessitate AI systems that extend beyond traditional automation to exhibit agentic capabilities-namely, autonomy, goal-directed behavior, and contextual awareness. This paper introduces a novel hybrid Agentic AI framework that combines Artificial Intelligence Markup Language (AIML) for symbolic reasoning with data-driven machine learning models for adaptive policy learning. The proposed architecture leverages AIML’s rulebased structure to enable semantic interpretation and highlevel decision logic, while ML modules provide real-time environmental perception and dynamic strategy adjustment. The system architecture supports continuous adaptation to changing operational contexts and alignment with long-term objectives. Experimental evaluations conducted across multiple domains, including autonomous navigation and intelligent virtual assistants, demonstrate superior performance in decision accuracy, adaptability, and contextual responsiveness compared to conventional AI baselines. The results underscore the effectiveness of hybrid symbolic-subsymbolic integration in building scalable, interpretable, and ethically grounded intelligent agents. Future work will focus on expanding the framework for multi-agent coordination and human-AI collaborative systems.
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• Conference | Published On : 19-12-2025
  Bommireddy Neha; G Sri Sai Meghana; Meet Jain; Kalyan Nagaraj
  Voice assistants have become essential tools for everyday tasks, yet their inability to provide domain-specific expertise, emotional awareness, and personalized interactions limits their effectiveness in specialized contexts such as mental health, finance, and education. This paper introduces Aurora, a next-generation AI voice assistant designed to address these gaps through a multi-personality architecture. Aurora integrates distinct personas such as a therapist, financial advisor, doctor and teacher-each with tailored knowledge domains, tones, and behaviors, enabling dynamic personality switching based on user's queries. The system employs advanced text-to-speech synthesis to adapt tone, pitch, and speaking style appropriate to each persona and emotional context, while a toxicity detection module ensures safe and ethical interactions by filtering inappropriate inputs. Built with technologies like Whisper for speech recognition, Eleven labs for expressive voice synthesis, and a session-based memory for contextual recall, Aurora delivers natural, human-like conversations. Preliminary evaluations highlight its potential to enhance user engagement and trust in specialized domains, paving the way for more intelligent, adaptive, and responsible voice technologies.
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• Conference | Published On : 19-12-2025
  Pavithra Ganta; Gourav Agarwal; Rajshekhar Jha; Kalyan Nagaraj
  Managing personal finances in today's fragmented fintech landscape presents significant challenges due to the absence of integrated, user-aware systems. This work proposes a unified, intelligent finance management framework that automates budgeting, credit recommendation, risk detection, and interactive advisory services. The system combines modular components-namely, a fraud detection engine using XGBoost, a personalized credit card ranker powered by learning-to-rank models, and a finance chatbot built on LLM with intent and context detection pipelines. Key innovations include categoryaware document embeddings, semantic user profiling, and rulebased behavior mapping for spending trends. Evaluation results demonstrate effective fraud classification, accurate credit matching, and scalable document-grounded advisory generation. The platform is designed for young professionals, freelancers, and digital users, and emphasizes scalability, explainability, and contextual intelligence across financial tasks.
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• Journal | Published On : 04-09-2025
  Amulyashree Sridhar, Kalyan Nagaraj, Shambhavi B R, Sindhu K
  The current research aims to discover applications of QML approaches in realizing liabilities within smart contracts. These contracts are essential commodities of the blockchain interface and are also decisive in developing decentralized products. But liabilities in smart contracts could result in unfamiliar system failures. Presently, static detection tools are utilized to discover accountabilities. However, they could result in instances of false narratives due to their dependency on predefined rules. In addition, these policies can often be superseded, failing to generalize on new contracts. The detection of liabilities with ML approaches, correspondingly, has certain limitations with contract size due to storage and performance issues. Nevertheless, employing QML approaches could be beneficial as they do not necessitate any preconceived rules. They often learn from data attributes during the training process and are employed as alternatives to ML approaches in terms of storage and performance. The present study employs four QML approaches, namely, QNN, QSVM, VQC, and QRF, for discovering susceptibilities. Experimentation revealed that the QNN model surpasses other approaches in detecting liabilities, with a performance accuracy of 82.43%. To further validate its feasibility and performance, the model was assessed on a several-partition test dataset, i.e., SolidiFI data, and the outcomes remained consistent. Additionally, the performance of the model was statistically validated using McNemar’s test.
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• Conference | Published On : 05-07-2025
  Kalyan Nagaraj, Amulyashree S
  The research work SleepPred: A machine learning tool to predict quality of sleep was presented at the International Conference on Data-Processing and Networking (ICDPN-2024) on 25th – 26th October 2024 organized by the Institute of Technology and Business (VŠTE) in , Czech Republic, Europe.
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• Journal | Published On : 05-05-2025
  Harisha S , Kalyan Nagaraj, Mohan Reddy R, Amulyashree Sridhar, S.R. Kiran Kumar, Y Surendranaik
  We report the synthesis of a heterocyclic azo dye, (E)-6-hydroxy-5-((5-mercapto-1,3,4-thiadiazol-2-yl)diazenyl)-1,3-dimethylpyrimidine-2,4(1H,3H)-dione (DSTDPT). The structure of DSTDPT was characterized using UV-Visible, FTIR, 1HNMR, and LCMS analyses. Its electrochemical properties were examined in a 0.2 M phosphate buffer (pH=7.2) with a carbon paste electrode (CPE). The electrodes sensitivity and selectivity toward paracetamol detection were enhanced by incorporating surfactants such as CTAB, SDS, and Triton X-100. The nonlinear optical metrics and electronic structure of DSTDPT were explored through density functional theory (DFT) computations using 6-31G (d, p)/B3LYP basis set. The enol form of DSTDPT demonstrated promising optical characteristics, including a first-order hyperpolarizability values approximately 19 times greater than that of urea. Molecular docking studies revealed favourable binding energies of DSTDPT towards B-cell lymphoma-extra-large (Bcl-xL) protein. To further investigate the stability and dynamics of the Bcl-xL protein and Bcl-xL-DSTDPT complex, molecular dynamics (MD) simulations were conducted. These studies confirmed that DSTDPT is a promising lead molecule with potent anti-cancer property. Finally, in vitro cytotoxic activity was evaluated against HCT-116 (human colon cancer) cell lines using MTT assay. The compound exhibited concentration-dependant cytotoxicity with IC50 values ranging from 22 µM - 36 µM, as compared to known cytotoxic agent cisplatin.
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• Patents | Published On : 02-05-2025
  Dr.Manjunath H, Dr. Kalyan Nagaraj, Seema Nagaraj, Padmanabha J, Vidya R, Usha M
  The design patent is Certified that the design of which a copy is annexed hereto has been registered as of the number and date given above in class 14-02 in respect of the application of such design to NETWORK INTRUSION DETECTION DEVICE in the name of 1.Dr.Manjunath H 2. Dr. Kalyan Nagaraj 3.Seema Nagaraj 4.Padmanabha J 5.Vidya R 6.Usha M.
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• Conference | Published On : 04-09-2024
  Kalyan Nagaraj, Prashanth H S, Amulyashree S
  The research work "A Novel ensemble model for prediction of occurrence of cancer" was presented on 5th Congress on Intelligent Systems international conference on 4th – 5th September 2024 organized by Christ University.
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• Journal | Published On : 09-08-2020
  Kalyan Nagaraj, Amulyashree Sridhar, Sharvani G S
  ENVISAGING PROMINENCE OF INDIAN TELECOM OPERATORS USING AN ENSEMBLE LINK BASED APPROACH
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• Journal | Published On : 29-11-2019
  Kalyan Nagaraj, Amulyashree Sridhar, Sharvani G S
  Title: The Eminence of Co-Expressed Ties in Schizophrenia Network Communities Journal: Data Publisher: MDPI
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• Journal | Published On : 31-08-2019
  Kalyan Nagaraj, Amulyashree Sridhar, Sharvani G S
  Title: Encrypting and preserving sensitive attributes in customer churn data using novel dragonfly based pseudonymizer approach Journal: Information Publisher: MDPI
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• Journal | Published On : 18-11-2018
  Kalyan Nagaraj, Amulyashree Sridhar, Sharvani G S
  Title: Detection of phishing websites using a novel twofold ensemble model Journal: Journal of Systems and Information Technology Publisher: Emerald Publishing Limited
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• Journal | Published On : 01-05-2018
  Kalyan Nagaraj, Amulyashree Sridhar, Sharvani G S
  Title: Emerging trend of big data analytics in bioinformatics: a literature review Journal: International Journal of Bioinformatics Research and Applications Publisher: Inderscience
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• Conference | Published On : 21-12-2017
  Kalyan Nagaraj, Amulyashree Sridhar, Sharvani G S
  Title: Identification of network communities and assessment of privacy using hybrid algorithm Conference: CSITSS
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• Journal | Published On : 02-12-2015
  Kalyan Nagaraj, Amulyashree Sridhar
  Title: A predictive system for detection of bankruptcy using machine learning techniques
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• Journal | Published On : 19-02-2015
  Kalyan Nagaraj, Amulyashree Sridhar
  Title: NeuroSVM: a graphical user interface for identification of liver patients Journal: IJCSIT
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Other Information

Dr. Kalyan has also served as a resource person and coordinator for various accreditations at the institute and department levels, as well as the AICTE Coordinator at Bangalore University. To top it all off, he has qualified for the UGC-NET, JRF, and the Karnataka-State Eligibility Test (K-SET) in Computer Science and Applications for the position of Assistant Professor. He has taken various courses of interest through MOOCs, including NPTEL.

* Nptel Certificate: Privacy and Security of Online Social Networks Certificate from IIID. [Elite]

* Nptel Certificate: DBMS – IIT Kharagpur. [ Elite]

* Nptel Certificate: Research Writing – IIT Kharagpur. [ Elite]

* Nptel Certificate: Business analytics and data mining Modeling using R – IIT Roorkee.

* Nptel Certificate: The Joy computing using Python – IIT Madras. [ Elite]

* Nptel Certificate: Discrete Mathematics – IIT Madras. [ Elite]

* Nptel Certificate: Programming in C++ - IIT Kharagpur.

* Nptel Certificate: NATE – NBA Accreditation Teaching and Learning - 2022

* Worked as a project trainee in Jubilant Biosys, Bangalore from Sept- 2013 to May 2014 for completing my M.Tech project.

* Participated in a 3-day ICBAI – Conference organized by ORSI Bangalore Chapter and Analytics Society of India at IISc, Bangalore.

* Participated in a two-day workshop on “Research Methodologies” jointly organized from CMRIT, Dept. of Telecommunication Engineering along with IEEE Communication Society and VTU E- learning Center, Mysuru, from 8th May – 9th May 2017.

* Participated in a three-day International Conference on “Computational Systems and Information Technology for Sustainable Solutions (CSITSS 2017) jointly organized from RVCE, Dept. of CSE and IEEE Computer Society, Bangalore Section, from 21st December- 23rd December 2017.

* Participated in one week Faculty Development Program on Internet of Things and Big Data Analytics organized by University Visvesvaraya College of Engineering, Bangalore between 28th February - 7th March 2019.

* Participated in cluster level training program on “Effective utilization of VTU Consortium e-Resources organized by Dept. of Library and Information Center, RVCE in collaboration with VTU, Belagavi on 21st August 2019.

* Delivered an online session on “Risk Management” for the online open course on Cognitive Security conducted by Dept. of Computer Science and Engineering, BMS Institute of Technology and Management, Bangalore from 16th June – 20th June 2020.

* Consultancy Project: "Legacy Gage Management Software Migration (Phase-I)", under ESYA ENGINEERING PVT LTD, Bengaluru, Dec. 2023. Grant Amount: Rs. 75,000.

* Consultancy Project: "Automated Report Generation and Notification Tool (Phase-I)", under BLOOM ELECTRICAL LIMITED, Newzealand, Jan. 2024. Grant Amount: Rs. 30,500.