Siddalingappa, Rashmi

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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, Hussain, Showket, S, Deepa, Dheerendra, Pradeep, Gornale, Shivanand, L, Muralidhara B. and Kothandan, Gugan (2026) Precision Medicine Gene Network Analyser: part I—cancer driver gene identification through network topology and ensemble machine learning. Genomics & Informatics, 24 (12).
Siddalingappa, Rashmi ORCID: https://orcid.org/0000-0001-9786-8436, S, Deepa, Savitha, Margaret, P, Kalpana, Stella Mary I, Priya, Gornale, Shivanand ORCID: https://orcid.org/0000-0001-5373-4049, B A, Lakshmi, Li, Kefeng and Wen Goh, Khang (2026) Adaptive Phoneme State Learning Architecture for Enhanced Speech Recognition Using Backpropagation Neural Network and Hidden Markov Model. F1000Research, 15. p. 338.
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.

My Most Viewed

#	Item title
1	A Hybrid Ensemble of Denoising Autoencoders and Deep Learning Models for Fetal Image Analysis	158	90
2	A Comparative Analysis of L1, L2, and L1L2 Regularization Techniques in Neural Networks for Image Classification	135	217
3	Survey on Handwritten Signature Biometric Data Analysis for Assessment of Neurological Disorder using Machine Learning Techniques	117	107
4	FEDGE: FEDerated learning at the EDGE on space platforms using deep neural network architectures	87	32
5	Deep Learning Techniques for a Comprehensive Analysis of Fetal Biometric Parameters Across Trimesters	79	36
6	Bi-directional long short term memory using recurrent neural network for biological entity recognition	74	36
7	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	69	26
8	Anomaly Detection on Medical Images using Autoencoder and Convolutional Neural Network	67	30
9	A Novel ML Approach for Computing Missing Sift, Provean, and Mutassessor Scores in Tp53 Mutation Pathogenicity Prediction	63	20
10	Gender Classification Based on Online Signature Features using Machine Learning Techniques	60	17