Dr. Manajit Chakraborty
Lecturer in Computer Science
Of the multiple factors that led to me joining the Dyson Institute, the strongest reason was my innate yearning to be part of an alternative higher education system that not only imparts the requisite knowledge but also provides a conducive environment for real-world application. I believe this is profoundly missing in the traditional setup of current engineering institutions. On my visit to the Institute, I was overwhelmed by the uniqueness of the model which treated students as partners. The Institute takes pride in what they do and the care that goes into the support and development of undergraduates. As many of my dearest colleagues have pointed out, I wish I had the privilege to be an undergraduate at such an institution during my time.
There are very few companies that stress innovation as much as Dyson does. And I strongly believe that the culture the Dyson Institute imbibes in their students, not only makes them successful engineers but excellent and resolute human beings keen to change the world for the better. As an academic and researcher, I wanted to be part of this journey and enhance the Institute’s reputation as a world-class research and educational institution.
My love for Computer Science was piqued when I first studied Information Retrieval during my Master’s and ever since I have been enthralled by the myriad possibilities that Data Science holds in our day-to-day lives while also nurturing one’s academic pursuits.
Qualifications
- PhD (2017-): Faculty of Informatics, Universita della Svizzera italiana, Lugano, Switzerland
- Research scholar (2014-2017): IIT(BHU), Varanasi, India
- MEng (MTech) (2012-2014): IIT(ISM) Dhanbad, India
- BEng (BTech) (2008-2012): WBUT, Kolkata, India
- Big Data Analytics
- Systems Engineering Requirements and Modelling
Previously taught:
- Applied Programming
Research
My research centres on the interplay between Data Science, Information Retrieval, Text Mining, and Time-Series Forecasting, where I explore innovative methods to solve complex problems in these domains. Collaborating internationally across Switzerland, India, the UK, the US, and Australia, I have contributed several impactful publications on topics ranging from spam detection to multi-turn answer retrieval to analysing patent citation networks. I have served as a reviewer for key international journals and conferences while also contributing to the organisation of academic events. Since joining the Dyson Institute, I have also ventured into pedagogical research specifically in Engineering Education.
Interests: Data Science, Machine Learning, Information Retrieval, Time Series Forecasting, Text Analytics and NLP, Engineering Education
Publications
- F. Hafezi, M. Chakraborty and M. Ikhlaq, "From Students to Engineers: An Integrated Model for Educating the Whole Engineer," 2025 IEEE Global Engineering Education Conference (EDUCON), London, United Kingdom, 2025, pp. 1-5, doi: 10.1109/EDUCON62633.2025.11016510.
- M. Chakraborty, M. Byshkin, and F. Crestani, "Patent citation network analysis: A perspective from descriptive statistics and ERGMs," PLOS One, vol. 15, no. 12, pp. e0241797, Dec. 2020. DOI: 10.1371/journal.pone.0241797.
- M. Aliannejadi, M. Chakraborty, E. A. Ríssola, and F. Crestani, "Harnessing evolution of multi-turn conversations for effective answer retrieval," in Proceedings of the 2020 Conference on Human Information Interaction and Retrieval, New York, NY, USA, Mar. 2020, pp. 33–42. DOI: 10.1145/3343413.3377968.
- M. Chakraborty, S. Pal, R. Pramanik, and C. R. Chowdary, "Recent developments in social spam detection and combating techniques: A survey," Information Processing & Management, vol. 52, no. 6, pp. 1053–1073, Nov. 2016. DOI: 10.1016/j.ipm.2016.04.009.
- A. Agarwal, M. Chakraborty, and C. R. Chowdary, "Does order matter? Effect of order in group recommendation," Expert Systems with Applications, vol. 82, pp. 115–127, May 2017. DOI: 10.1016/j.eswa.2017.03.069.
- M. Chakraborty and F. Crestani, "Old is Not Always Gold: Early Identification of Milestone Patents Employing Network Flow Metrics," in Proceedings of the Swiss Text Analytics Conference, Winterthur, Switzerland, Jun. 2021, pp. 1–6 (Online), Vol. 2957.
