Gautam Pal has more than 15 years of experience with leading global information technology companies and highly reputed UK universities. He worked in the King's College London, University of Liverpool, Accenture, Wells Fargo Bank USA and Hewlett Packard (HP) R&D. He has been a corporate consultant for Fortune 500 companies like Oracle, Mercedes Benz, Deloitte, Coforge and Waste Management. For over a decade, he managed global teams of big data and AI developers for large cooperates. He has extensive experience in leading, managing, and developing a technical team and creating relationships with clients. His professional experience includes Natural Language Processing, real-time big data analytics and cloud computing.

Gautam Pal undertook post-doctoral research in the King’s College London and University of Liverpool. He holds a PhD from University of Liverpool and a Master’s degree from Jadavpur University, Kolkata.

Operational intelligence, observability, multimodal analytics, distributed computing.

• Musi, E., O'Halloran, K. L., Carmi, E., Humann, M., Jin, M., Yates, S., & Pal, G. (2023). Mapping polylogical discourse to understand (dis) information negotiation: the case of the UK Events Research Programme. In The Routledge Handbook of Discourse and Disinformation (pp. 412-425). Routledge.
• Pal, G. (2022). An efficient system using implicit feedback and lifelong learning approach to improve recommendation. The Journal of Supercomputing, 78(14), 16394-16424. https://doi.org/10.1007/s11227-022-04484-6
• Pal, G., Atkinson, K., & Li, G. (2021). Real-time user clickstream behavior analysis based on apache storm streaming. Electronic Commerce Research, 1-31. https://doi.org//10.1007/s10660-021-09518-4
• O'Halloran, K. L., Pal, G., & Jin, M. (2021). Multimodal approach to analysing big social and news media data. Discourse, Context & Media, 40, 100467. https://doi.org/10.1016/j.dcm.2021.100467
• Pal, G., Hong, X., Wang, Z., Wu, H., Li, G., & Atkinson, K. (2019). Lifelong Machine Learning and root cause analysis for large-scale cancer patient data. Journal of Big Data, 6(1), 1-29. https://doi.org/10.1186/s40537-019-0261-9
• Pal, G., Atkinson, K., & Li, G. (2020, February). Managing heterogeneous data on a big data platform: a multi-criteria decision-making model for data-intensive science. In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 229-239). IEEE. https://doi.org/10.1109/BigComp48618.2020.00-69.
• Hong, X., Pal, G., Guan, S. U., Wong, P., Liu, D., Man, K. L., & Huang, X. (2019, June). Semi-unsupervised lifelong learning for sentiment classification: Less manual data annotation and more self-studying. In Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference (pp. 87-92). https://doi.org/10.1145/3341069.3342992.
• Pal, G., Li, G., & Atkinson, K. (2018, December). Big data ingestion and lifelong learning architecture. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 5420-5423). IEEE. https://doi.org/10.1109/BigData.2018.8621859.
• Pal, G., Li, G., & Atkinson, K. (2018). Multi-agent big-data lambda architecture model for e-commerce analytics. Data, 3(4), 58. https://doi.org/10.3390/data3040058
• Pal, G., Li, G., & Atkinson, K. (2018): Top-n Recommender System Using Big Data Item-to-Item Collaborative Filtering. In 2018 International Conference on Internet Studies, Takamatsu, Japan.
• Pal, G., Li, G., & Atkinson, K. (2018, August). Big data real time ingestion and machine learning. In 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP) (pp. 25-31). IEEE. https://doi.org/10.1109/DSMP.2018.8478598.
• Pal, G., Li, G., & Atkinson, K. (2018, October). Near real-time big data stream processing platform using cassandra. In 2018 4th International Conference for Convergence in Technology (I2CT) (pp. 1-7). IEEE. https://doi.org/10.1109/I2CT42659.2018.9058101.
• Pal, G., Li, G., & Atkinson, K. (2018, October). Big data real-time clickstream data ingestion paradigm for e-commerce analytics. In 2018 4th International Conference for Convergence in Technology (I2CT) (pp. 1-5). IEEE. https://doi.org/10.1109/I2CT42659.2018.9058112.
• Pal, G., Li, G., & Atkinson, K. (2017). Multi-agent Item to Item Contextual Big Data Recommender System. International Journal of Design, Analysis & Tools for Integrated Circuits & Systems, 6(1).
• Gautam Pal, Gangmin Li, Katie Atkinson (2017): A Multi-Agent Model for Big Data Analytics through Knowledge Graph. In: International Conference on Big Data Analytics and Business Intelligence (ICBDI), in Suzhou, China.
• Li, G., Chi, M., & Pal, G. (2017). Expert CF: Sparse Data Matrix Completion with Artificial Experts. International Journal of Design, Analysis & Tools for Integrated Circuits & Systems, 6(1).

• Operational Intelligence and observability, Simplilearn and Edureka (since 2013). 
• Real-time data analytics with Apache Spark, Coforge (2022)
• Analytics, visualization, observability and enterprise security with Splunk, Mercedes Benz India (2019-20)
• Big Data and NoSQL databases, Waste Management Inc (2019)

• Multimodal Analytics Bielefeld University, Germany (03-02-2020 to 21-06-2021, 18 months)
• Real Time Big Data Analytics. Accenture Technology Labs, Beijing (03-04-2017 to 31-12-2020, 36 months)

• Performer Awards (two) from Accenture (2015-16).
• Shared Success Awards from Wells Fargo Bank USA (2012).
• Achieving Excellence Award from Wells Fargo Bank USA (2011).

Member of the following committees 

• International Relations & Consulting 
• Media & Public Relations Committee 
• IT Advisory Committee
• Management Education Research Colloquium (conference committee). 

• Data Management and Big Data
• Management Information Systems
• Operational Intelligence and Observability
• ML Applications with Spark