Mainak Ghosh, M.Sc.

Doctoral Student and Junior Research Fellow

Innovation and Entrepreneurship Research

+49 89 24246-574
mainak.ghosh(at)ip.mpg.de

Personal Website:

https://ghoshmainak.github.io

Areas of Interest:

Machine Learning, Natural Language Processing, Sentiment Analysis

Academic Résumé

Since 03/2020
Junior Research Fellow and Doctoral Candidate, Max Planck Institute for Innovation and Competition (Innovation and Entrepreneurship Research)

10/2017 - 11/2019
Master of Science (M.Sc.) in Data Engineering & Analytics, Technical University of Munich (TUM); Master Thesis: “Multilingual Opinion Mining on Social Media Comments Using Unsupervised Neural Clustering Methods”

11/2017 - 02/2018
Student Research Assistant, Max Planck Institute for Social Law and Social Policy, Munich

05/2013 - 07/2013
Research Intern, Indian Statistical Institute, Kolkata, India

06/2012 - 07/2012
Summer Intern, Globsyn Business School, Kolkata, India

2010 - 2014
Bachelor of Engineering (B.E.) in Computer Science & Technology, Indian Institute of Engineering Science & Technology, Shibpur, India

Work Experience

03/2018 - 03/2020
Working Student, IDS GmbH – Analysis and Reporting Services (IDS), Munich

08/2014 - 09/2017
Software Engineer, Acclaris Business Solutions Pvt Ltd, Kolkata, India

Academic Prizes and Honors

2013
Cognizant Certified Student (CCS), IT Foundation Skills

2009
Award, Mathematical Competence Test, Association for Improvement of Mathematics Teaching (AIMT), Kolkata, India

2008
Certificate of Merit in Physical Science & Mechanics

Publications

Further Publications, Press Articles, Interviews

Hagerer, Gerhard; Moeed, Abdul; Dugar, Sumit; Gupta, Sarthak; Ghosh, Mainak; Danner, Hannah; Mitevski, Oliver; Nawroth, Andreas; Groh, Georg (2020). An Evaluation of Progressive Neural Networks for Transfer Learning in Natural Language Processing, in: Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), Marseille, 1376-1381.

  • A major challenge in modern neural networks is the utilization of previous knowledge for new tasks in an effective manner, otherwise known as transfer learning. Fine-tuning, the most widely used method for achieving this, suffers from catastrophic forgetting. The problem is often exacerbated in natural language processing (NLP). In this work, we assess progressive neural networks (PNNs) as an alternative to fine-tuning. The evaluation is based on common NLP tasks such as sequence labeling and text classification. By gauging PNNs across a range of architectures, datasets, and tasks, we observe improvements over the baselines throughout all experiments.
  • Conference Paper
  • Conference Volume
  • Event: 12th Language Resources and Evaluation Conference, Marseille, 2020-05-11

Presentations

10.09.2020
Knowledge Mining, Digitalization, Machine Learning
Research Seminar
Location: online (Munich)