Siddalingappa, Rashmi

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Biography
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Contact

profile_image:

Name:

Siddalingappa, Rashmi

Email:

r.siddalingappa@yorksj.ac.uk

Department:

Data and Computer Science

Organisation:

York St John University

Address:

6th Floor, Export Building, 1 Clove Cres, London E14 2BA

Country:

United Kingdom

Biography

Expertise:

Machine Learning, Artificial Intelligence, Natural Language Processing, Bioinformatics, Space Weather, Deep Neural Networks, Data Science, LLMs

Job title:

Lecturer in Department of Data and Computer Science

Biography:

I am Dr. Rashmi Siddalingappa. I work at the intersection of machine learning, natural language processing, biomedical informatics, and space sciences, and I am currently a Lecturer in Computer & Data Science at York St John University, UK (since August 2024). I earned my Ph.D. in Computer Science from Bangalore University in 2018, where my thesis focused on phonetic and semantic analysis for NLP. After my doctorate I was awarded a National Postdoctoral Fellowship from the Science & Engineering Research Board (DST, India) and conducted postdoctoral research at the Indian Institute of Science (IISc),the top research institute in India, where I received 35,00,000 INR. I then joined West Virginia University as a postdoctoral fellow (Sep 2022–Jul 2024), where I contributed to NASA’s Living With a Star and NSF’s ANSWERS projects. These projects were a part of 2 major awards LWA from NASA and ANSWERS from NSF. I have secured external funding and awards (including a Physics-funded research award and national fellowship funding), participated in multi-institutional grants, and helped prepare competitive proposals. My work spans precision medicine and cancer genomics, speech and phonetics for NLP, and predictive modeling in space weather. I have authored over 30 peer-reviewed journal papers and more than 24 conference papers, contributed book chapters, and have several manuscripts in-press or under review. In teaching and academic leadership I have been a Module Director, supervised undergraduate and postgraduate dissertations, and designed curriculum and labs for undergraduate and postgraduate courses. I bring strong technical skills—including Python, TensorFlow/Keras, scikit-learn, NLTK, PRAAT and experience with speech and genomic datasets—and I combine rigorous research with student mentorship and curriculum development. I am passionate about translating AI and interdisciplinary research into real-world impact and mentoring the next generation of computer scientists.

Qualifications:

BSc in Computer Science MSc in Computer Science PhD in COmputer Science (Natural Language Processing) Postdoc at IISc, India Postdoc at WVU, USA

My Latest

Siddalingappa, Rashmi ORCID: https://orcid.org/0000-0001-9786-8436, Hanumanthappa, M. and Reddy, M.V. (2018) Hidden Markov Model for speech recognition system—a pilot study and a Naive approach for speech-to-text model. In: Agrawal, S., Devi, A., Wason, R. and Bansal, P., (eds.) Speech and Language Processing for Human-Machine Communications. Advances in Intelligent Systems and Computing, 664 (664). Springer Nature, pp. 77-90
Hanumanthappa, M., Siddalingappa, Rashmi ORCID: https://orcid.org/0000-0001-9786-8436 and Reddy, Mallamma (2016) Text-to-Speech translation using Support Vector Machine, an approach to find a potential path for human-computer speech synthesizer. In: 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE
Hanumanthappa, M., Reddy, M.V. and Siddalingappa, Rashmi ORCID: https://orcid.org/0000-0001-9786-8436 (2015) Metrics for evaluating phonetics machine translation in Natural Language Processing through modified Edit Distance algorithm-A naïve approach. In: 2015 International Conference on Computer Communication and Informatics (ICCCI). IEEE
Aryan, Homayon ORCID: https://orcid.org/0000-0002-3096-5052, Bortnik, Jacob ORCID: https://orcid.org/0000-0001-8811-8836, Tobiska, W. Kent, Mehta, Piyush ORCID: https://orcid.org/0000-0002-5240-2322, Hogan, Benjamin, Siddalingappa, Rashmi ORCID: https://orcid.org/0000-0001-9786-8436 and Challa, Harshitha ORCID: https://orcid.org/0000-0002-5213-8592 (2026) Enhanced Radiation Exposure of Airline Crew and Passengers During the May 2024 Geomagnetic Storm. Journal of Geophysical Research: Space Physics, 131 (1). e2025JA034217.
Siddalingappa, Rashmi ORCID: https://orcid.org/0000-0001-9786-8436, S, Deepa, I, Priya Stella Mary, P, Kalpana and B A, Lakshmi (2025) FEDGE: FEDerated learning at the EDGE on space platforms using deep neural network architectures. International Journal of Information Technology.
Deepa, A., Siddalingappa, Rashmi ORCID: https://orcid.org/0000-0001-9786-8436, Kalpana, P., Loveline Zeema, J., Vinay, M., Jayapriya, J., Suganthi, J. and Priya Stella Mary, I. (2025) A Comparative Analysis of L1, L2, and L1L2 Regularization Techniques in Neural Networks for Image Classification. International Journal of Engineering Trends and Technology, 73 (10). pp. 107-116.
I, Priya Stella Mary ORCID: https://orcid.org/0009-0001-5878-1116, Siddalingappa, Rashmi ORCID: https://orcid.org/0000-0001-9786-8436, M, Vinay, S, Deepa, P, Margaret Savitha and M, Kannan (2025) Enhancing image compression through a novel Structural Fidelity Weighted Ensemble (SFWE) model. MethodsX, 15. p. 103695.
Siddalingappa, Rashmi ORCID: https://orcid.org/0000-0001-9786-8436, Gornale, Shivanand, Kumar, Sathish and Mane, Arjun (2022) Gender Classification Based on Online Signature Features using Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering (IJISAE), 10 (2). pp. 260-268.
Siddalingappa, Rashmi ORCID: https://orcid.org/0000-0001-9786-8436 and S, Deepa (2025) Enhancing Image Classification Performance through Hybrid Self-Supervised Learning Strategies. International Journal of Electronics and Communication Engineering, 12 (7). pp. 90-101.
Siddalingappa, Rashmi ORCID: https://orcid.org/0000-0001-9786-8436, P., Prakash, Gornale, Shivanand S. and Kumar, Satish (2024) A Knowledge Based Grade Prediction System using Machine Learning for Higher Education Institutions. Nanotechnology Perceptions, 20 (S14).

My Most Viewed

#	Item title
1	A Hybrid Ensemble of Denoising Autoencoders and Deep Learning Models for Fetal Image Analysis	106	61
2	Survey on Handwritten Signature Biometric Data Analysis for Assessment of Neurological Disorder using Machine Learning Techniques	64	52
3	A Comparative Analysis of L1, L2, and L1L2 Regularization Techniques in Neural Networks for Image Classification	59	121
4	The Role of the National Assessment and Accreditation Council in Ensuring Quality Education in the Indian Education System: An Analysis of Its Accreditation Standards and Grading Practices	40	18
5	Anomaly Detection on Medical Images using Autoencoder and Convolutional Neural Network	39	12
6	A Novel ML Approach for Computing Missing Sift, Provean, and Mutassessor Scores in Tp53 Mutation Pathogenicity Prediction	37	12
7	Reduced‐Order Probabilistic Emulation of Physics‐Based Ring Current Models: Application to RAM‐SCB Particle Flux	35	11
8	Anomaly Detection on Medical Images using Autoencoder and Convolutional Neural Network	34	24
9	Deep Learning Techniques for a Comprehensive Analysis of Fetal Biometric Parameters Across Trimesters	34	16
10	Gender Classification Based on Online Signature Features using Machine Learning Techniques	32	6