Dr Sham Puthiya Parambath

  • Research Associate (School of Computing Science)

Biography

Office: G132

 

I, Shameem Ahamed Puthiya Parambath, am a member of the Knowledge & Data Engineering Systems (KDES)  under the research group Information, Data and Analysis (IDA) in the School of Computing Science, University of Glasgow, working with Dr Christos Anagnostopoulos and Prof. Roderick Murray-Smith.

I hail from the small, picturesque coastal town of Thalassery in the state of Kerala, India, where I have spent the majority of my life. My home is situated next to the splendid Government Brennen College in Dharmadom, near a river. I cherish the memories of the two remarkable years I spent at Brennen College.

I completed PhD in Machine Learning from the University of Technology Compiegne (Sorbonne University Association) France. Throughout this academic journey, I had the privilege of being mentored by Dr. Nicolas Usunier and Dr. Yves Grandvalet. During my Masters at Umea University, I had the opportunity to collaborate with Dr. Sihem Amer-Yahia.

Prior to joining the University of Glasgow, I served as a Postdoctoral Researcher at the Qatar Computing Research Institute, Doha, Qatar. In my current role, I focus on enhancing sequential algorithms for various applications such as dynamic pricing, federated learning, and edge computing. At QCRI,I specialized in Recommender Systems, Knowledge Graphs, and Anomaly Detection. Notably, my research on group recommendation and cold-start recommendation received recognition through publications in AAAI and ECML conferences. Furthermore, my work on quantifying bias in knowledge graphs was published in UAI. During my doctoral studies, my research revolved around multi-objective learning algorithms for multi-class/multi-label classification and personalized recommendations, with publications in prestigious venues. such as NeurIPS and RecSys.

Research interests

 

  • Learning with feedback
  • Reinforcement Learning
  • Mutli-Armed Bandits
  • Recommender Systems
  • Federated Learning

 

Google Scholar: [link]

Publications

List by: Type | Date

Jump to: 2024 | 2022 | 2021 | 2020 | 2018 | 2017 | 2016 | 2014
Number of items: 14.

2024

Puthiya Parambath, S. A. , Anagnostopoulos, C. and Murray-Smith, R. (2024) Sequential query prediction based on multi-armed bandits with ensemble of transformer experts and immediate feedback. Data Mining and Knowledge Discovery, 38(6), pp. 3758-3782. (doi: 10.1007/s10618-024-01057-4)

Li, W., Anagnostopoulos, C. , Puthiya Parambath, S. and Bryson, K. (2024) LIFE: Leader-driven Hierarchical & Inclusive Federated Learning. In: 2024 IEEE International Conference on Big Data (IEEE BigData 2024), Washington D.C., USA, 15-18 Dec 2024, (Accepted for Publication)

Puthiya Parambath, S. A. , Al-Fahad, S. A. M., Anagnostopoulos, C. and Kolomvatsos, K. (2024) Sequential Block Elimination for Dynamic Pricing. In: The 2nd International Workshop on Data Mining in Finance (DMF 2024) at the IEEE International Conference on Data Mining, Abu Dhabi, United Arab Emirates, 09-12 Dec 2024, (Accepted for Publication)

Aladwani, T., Anagnostopoulos, C. , Puthiya Parambath, S. and Deligianni, F. (2024) CL-FML: Cluster-based & Label-aware Federated Meta-Learning for On-Demand Classification Tasks. In: 11th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2024), San Diego, CA, United States, 6-10 October 2024, (Accepted for Publication)

Aladwani, T., Parambath, S. , Anagnostopoulos, C. and Deligianni, F. (2024) The Price of Labelling: A Two-Phase Federated Self-Learning Approach. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2024), Vilnius, Lithuania, 9-13 September 2024, (Accepted for Publication)

Long, Q. , Anagnostopoulos, C. , Puthiya, S. and Bi, D. (2024) FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization. In: IEEE ICDM 2023, Shanghai, China, 1-4 December 2023, pp. 1187-1192. ISBN 9798350307887 (doi: 10.1109/ICDM58522.2023.00146)

2022

Puthiya Parambath, S. A. , Liu, S., Anagnostopoulos, C. , Murray-Smith, R. and Ounis, I. (2022) Parameter Tuning of Reranking-based Diversification Algorithms using Total Curvature Analysis. In: 8th ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR 2022), Madrid, Spain, 11-12 July 2022, ISBN 9781450394123 (doi: 10.1145/3539813.3545135)

2021

Puthiya Parambath, S. , Anagnostopoulos, C. , Murray-Smith, R. , MacAvaney, S. and Zervas, E. (2021) Max-Utility Based Arm Selection Strategy for Sequential Query Recommendations. In: 13th Asian Conference on Machine Learning (ACML 2021), 17-19 Nov 2021, pp. 564-579.

2020

Puthiya Parambath, S. A. and Chawla, S. (2020) Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations. Data Mining and Knowledge Discovery, 34(5), pp. 1560-1588. (doi: 10.1007/s10618-020-00708-6)

Mohamed, A., Parambath, S. A. , Kaoudi, Z. and Aboulnaga, A. (2020) Popularity Agnostic Evaluation of Knowledge Graph Embeddings. In: Thirty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI 2020), 3-6 Aug 2020, pp. 1059-1068.

2018

Puthiya Parambath, S. A. , Vijayakumar, N. and Chawla, S. (2018) SAGA: A Submodular Greedy Algorithm for Group Recommendation. In: Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA, USA, 2-7 Feb 2018, pp. 3900-3908.

2017

Sarkar, S., Chawla, S., Ahmad, S. , Srivastava, J., Hammady, H., Filali, F., Znaidi, W. and Borge-Holthoefer, J. (2017) Effective urban structure inference from traffic flow dynamics. IEEE Transactions on Big Data, 3(2), pp. 181-193. (doi: 10.1109/TBDATA.2016.2641003)

2016

Puthiya Parambath, S. A. , Usunier, N. and Grandvalet, Y. (2016) A Coverage-Based Approach to Recommendation Diversity On Similarity Graph. In: Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16), Boston, MA, USA, 15-19 Sept 2016, pp. 15-22. ISBN 9781450340359 (doi: 10.1145/2959100.2959149)

2014

Puthiya Parambath, S. , Usunier, N. and Grandvalet, Y. (2014) Optimizing F-measures by Cost-sensitive Classification. In: Advances in Neural Information Processing Systems 27 (NIPS 2014), Montreal, Canada, 8-13 Dec 2014, pp. 2123-2131.

This list was generated on Thu Nov 21 01:49:25 2024 GMT.
Jump to: Articles | Conference Proceedings
Number of items: 14.

Articles

Puthiya Parambath, S. A. , Anagnostopoulos, C. and Murray-Smith, R. (2024) Sequential query prediction based on multi-armed bandits with ensemble of transformer experts and immediate feedback. Data Mining and Knowledge Discovery, 38(6), pp. 3758-3782. (doi: 10.1007/s10618-024-01057-4)

Puthiya Parambath, S. A. and Chawla, S. (2020) Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations. Data Mining and Knowledge Discovery, 34(5), pp. 1560-1588. (doi: 10.1007/s10618-020-00708-6)

Sarkar, S., Chawla, S., Ahmad, S. , Srivastava, J., Hammady, H., Filali, F., Znaidi, W. and Borge-Holthoefer, J. (2017) Effective urban structure inference from traffic flow dynamics. IEEE Transactions on Big Data, 3(2), pp. 181-193. (doi: 10.1109/TBDATA.2016.2641003)

Conference Proceedings

Li, W., Anagnostopoulos, C. , Puthiya Parambath, S. and Bryson, K. (2024) LIFE: Leader-driven Hierarchical & Inclusive Federated Learning. In: 2024 IEEE International Conference on Big Data (IEEE BigData 2024), Washington D.C., USA, 15-18 Dec 2024, (Accepted for Publication)

Puthiya Parambath, S. A. , Al-Fahad, S. A. M., Anagnostopoulos, C. and Kolomvatsos, K. (2024) Sequential Block Elimination for Dynamic Pricing. In: The 2nd International Workshop on Data Mining in Finance (DMF 2024) at the IEEE International Conference on Data Mining, Abu Dhabi, United Arab Emirates, 09-12 Dec 2024, (Accepted for Publication)

Aladwani, T., Anagnostopoulos, C. , Puthiya Parambath, S. and Deligianni, F. (2024) CL-FML: Cluster-based & Label-aware Federated Meta-Learning for On-Demand Classification Tasks. In: 11th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2024), San Diego, CA, United States, 6-10 October 2024, (Accepted for Publication)

Aladwani, T., Parambath, S. , Anagnostopoulos, C. and Deligianni, F. (2024) The Price of Labelling: A Two-Phase Federated Self-Learning Approach. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2024), Vilnius, Lithuania, 9-13 September 2024, (Accepted for Publication)

Long, Q. , Anagnostopoulos, C. , Puthiya, S. and Bi, D. (2024) FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization. In: IEEE ICDM 2023, Shanghai, China, 1-4 December 2023, pp. 1187-1192. ISBN 9798350307887 (doi: 10.1109/ICDM58522.2023.00146)

Puthiya Parambath, S. A. , Liu, S., Anagnostopoulos, C. , Murray-Smith, R. and Ounis, I. (2022) Parameter Tuning of Reranking-based Diversification Algorithms using Total Curvature Analysis. In: 8th ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR 2022), Madrid, Spain, 11-12 July 2022, ISBN 9781450394123 (doi: 10.1145/3539813.3545135)

Puthiya Parambath, S. , Anagnostopoulos, C. , Murray-Smith, R. , MacAvaney, S. and Zervas, E. (2021) Max-Utility Based Arm Selection Strategy for Sequential Query Recommendations. In: 13th Asian Conference on Machine Learning (ACML 2021), 17-19 Nov 2021, pp. 564-579.

Mohamed, A., Parambath, S. A. , Kaoudi, Z. and Aboulnaga, A. (2020) Popularity Agnostic Evaluation of Knowledge Graph Embeddings. In: Thirty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI 2020), 3-6 Aug 2020, pp. 1059-1068.

Puthiya Parambath, S. A. , Vijayakumar, N. and Chawla, S. (2018) SAGA: A Submodular Greedy Algorithm for Group Recommendation. In: Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA, USA, 2-7 Feb 2018, pp. 3900-3908.

Puthiya Parambath, S. A. , Usunier, N. and Grandvalet, Y. (2016) A Coverage-Based Approach to Recommendation Diversity On Similarity Graph. In: Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16), Boston, MA, USA, 15-19 Sept 2016, pp. 15-22. ISBN 9781450340359 (doi: 10.1145/2959100.2959149)

Puthiya Parambath, S. , Usunier, N. and Grandvalet, Y. (2014) Optimizing F-measures by Cost-sensitive Classification. In: Advances in Neural Information Processing Systems 27 (NIPS 2014), Montreal, Canada, 8-13 Dec 2014, pp. 2123-2131.

This list was generated on Thu Nov 21 01:49:25 2024 GMT.

Supervision

Teaching

Database Theory & Applications

Additional information

Reviewer for AISTATS 2025, 2023, 2022; KDD 2024, 2025; AAAI 2024, 2021, 2020, 2019; CIKM 2020; SDM 2020