Amrita Saha  Amrita Saha photo         

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Software Engineer, Research
India Research Laboratory, Bangalore, India
  +91dash9739dash484740

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Research Interest

Amrita Saha is a Research Engineer at IBM Research since 2012, where she has worked on various research problems at the intersection of language and vision, developing machine learning and deep representation learning techniques for learning to answer questions or converse or even debate over a mix of structured or unstructured multi-modal data. Her experience at IBM started with working on a futuristic Grand Challenge research on building an artificial Debater, to an industry-impacting research on bringing cognitive computing technologies to the world of fashion and more recently, to leading a broader research agenda on building interactive multimodal AI capabilities in collaboration with top-tier universities. She obtained her Masters degree in Computer Science from Indian Institute of Technology Bombay, India in 2012, prior to joining IBM Research. Previous to her Masters degree, she had worked on coding theory for Wireless Sensor Network Security during her Bachelor Course in Information Technology. Over the years she has published in various reputed conferences and journals like TACL, ACL, AAAI, SDM, NeurIPS, COLING etc, as well as organized workshop or served as PC member in some of them. Additionally she has also filed over 22 US patents during her time at IBM research. (Full Details in Resume)

Academic Research Collaborations at IBM [2017-Onwards]

IBM Cognitive Horizons: Complex Question Answering on Knowledge Bases (In collaboration with Prof. Soumen Chakrabarty, Indian Institute of Technology Bombay, India) [2018]

I led and mentored a small team of IBM researchers to drive this collaborative work on two research problems 1) Joint learning of continuous representations of Knowledge Bases from large scale KB and corpus 2) Learning different structured representations of KB using hyper-rectangular/subspace embeddings 3) Answering complex questions over large scale Knowledge Bases by learning to induce multi-lined programs, when distance-supervised only from the answer.

IBM Open Science Collaboration: Deep Learning for Human Computer Interaction Challenges on Open Datasets (In collaboration with Prof. Mitesh M. Khapra, Indian Institute of Technology Madras, India) [2017]

I led a small team of IBM researchers and mentored students on proposing different NLP problems on multimodal conversation, complex question answering on large scale Knowledge Bases and reading comprehension with complex language understanding and common sense reasoning. Other than this, as part of building towards some of these complex systems, I worked on different paradigms of common representation learning between multimodal and multilingual data. The collaboration led to 4 papers at top conferences, and one under submission, within a span of one year.

 

IBM Debating Technology: A Grand Challenge [2013-2016]

At IBM Research, I have been an integral part of the core team at IBM, which is developing a Computational Argumentation Framework for machines to argue and debate with humans over any open-ended topic of controversy. As a leading person on the module of Stance or Pro-Con analysis from the India Research Lab, who has been working since the inception of the grand challenge, I owned the module of Topic-Based Stance (Pro/Con) Classification of arguments in open-domain debates. More specifically I led the work on

  • Identification of free-form Topics in short claim sentences and long evidence passages and identifying the semantic relation between these open-domain topics
  • Modeling a Bayesian Non-Parametric solution for learning a knowledge graph of semantic relations (consistent or contrastive) between open domain concepts appearing in corpus like Wikipedia in a semi-supervised or unsupervised setting

 

IBM Visual Linguist: A Picture is worth a Thousand Words [2014 - 2015]

This was a far-reaching research project proposed by a very small (3-member) team who worked on a stretch to shape this project on understand open domain images and generating a natural language caption crisply describing it. As an integral part of that team, I owned the following modules

  • Led the work on a probabilistic graphical model based inference framework for a taxonomy-grounded aggregation of scores from multiple different classifiers pre-trained with different label sets
  • Implemented various Deep Learning modules for Image Understanding, Multimodal Representation Learning, Language Models and Corpus-Co-occurrence based action/attribute prediction in image
  • Built a Visual Search application for e-commerce (especially fashion) using the above image-understanding system (which was shortlisted in the top-9 out of over 60 submissions for the IBM Cognitive Hackathon, 2015 and later culminated in the project IBM Cognitive Fashion)

 

IBM Cognitive Fashion [2015 - 2016]

This project is aimed at bringing a plethora of cognitive computing technologies (machine learning, Deep Learning, image and natural language understanding and generation etc.) to the fashion world and leveraging the vast amounts of (structured and unstructured) fashion data available there. Again working in a very small team of 3 members, I have proposed and owned several modules on multimodal question-answering/dialogue-systems/recommender system/representation-learning/cross-domain retrieval, as well as

  • Co-organized a workshop “Machine Learning Meets Fashion” at the international conference of Knowledge Discovery and Data Mining 2016
  • Modeled a Deep Learning architecture on Fashion2Vec: Learning Joint multi-modal representations for cross-modal search in e-commerce in absence of catalogs
  • Modeled Multimodal dialogue systems that are enriched by structured sources like knowledge bases and catalogue data and unstructured sources like free-form description of products
  • Built interactive demos in various applications like multi-modal dialogue systems, cross-modal retrieval and visual search for different clients in the fashion/jewelry/e-commerce domain

 

Positions of Organizational Responsibility at IBM Research

  • Co-organized a first-of-a-kind workshop on Machine Learning meets Fashion, at Knowledge Discovery and Data Mining (KDD) conference, 2016
  • Lead organizer of the first workshop on Linguistics Meets Image and Video Retrieval at International Conference of Computer Vision (ICCV), 2019
  • Served as PC-member and in organizing and reviewing committee for workshops in several internationally acclaimed conferences like KDD and VLDB.
  • Organized the regular department meetings at IBM Research Lab for the Cognitive Technology and Services Team, for over two years
  • Handled miscellaneous responsibilities like hiring researchers and research software engineers and mentoring under-graduate students from various universities interning at IBM Research Lab over the last few years and training annotators to collect relevant data for various text/image analytics applications