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Ghulam Ahmed Ansari

Applied Research Engineer

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About Me

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I am currently working as an Applied Research Engineer at LinkedIn. My interests lie mainly in the areas of Deep Reinforcement Learning and its applications to other areas like Natural Language Processing (NLP) & Computer Vision(CV). Lately, I have worked primarily in empirical research, but I earnestly aim to build systems that can create impact. I am excited to explore the applications of machine learning that can make lives easier.

Experience

IBM Research Labs Bangalore

Research Engineer

  • Published papers in top conferences and journals like ACL, TACL and IJCAI within a span of a year
  • Also patented a bunch of ideas which are currently in the filing process

IBM Research Labs Bangalore

Internship

  • This internship had offered me an opportunity to experience important problems in Machine Learning
  • Hence, with a renewed perspective, I had shifted my career from VLSI to ML/DL
  • I worked on the necessary problem of touch-based authentication of users on mobile phones

Education

Indian Institute of Technology Madras, Chennai

Jul '12 - Jul '16

Bachelor of Technology in Electrical Engineering

Indian Institute of Technology Madras, Chennai

Jul '16 - Jul '17

Master of Technology in Electrical Engineering

Projects

IBM Research Labs, Bangalore    (Aug ’17 - Current)

Complex Program Induction for Querying Knowledge Bases in the Absence of Gold Programs  (TACL 2019)(ACL 2019)(Dec ’17 - Jan ’19)

  • Program Induction for Q&A is a paradigm of answering a question by instantiating Operator x Variable sequences
  • The program is executed over a KB and the system receives only the reward obtained as feedback
  • Our operators can encompass all kinds of questions in the CSQA dataset
  • On the CSQA dataset our CIPITR has a F1-score(%) of 58.92 which is +450% improvement than that of the state-of-the-art NSM (10.63)

Neural Program Induction for KBQA Without Gold Programs or Query Annotations  (IJCAI 2019)(May ’18 - Feb ’19)

  • KBQA on a more realistic setting wherein there is additional noise induced by absence of Query annotations and only final answer as supervision
  • The lack of gold Query annotations induces debilitating variance leading to catastrophic forgetting, especially when using RL methods
  • Proposed and realized a noise-resilient deep-RL Algorithm, SSRP that evades noise-induced instability through continual retrospection
  • On the WebQuestionsSP dataset, our SSRP has an overall F1-score(%) of 72.61 and it outperforms state-of- the-art NSM by +5.23%
  • On the noisy CQA-12K dataset we compared F1-Score(%) obtained by our SSRP extensively against SRP (CIPITR), A2C & NSM. On the entire test set, SSRP outperformed the closest baseline SRP by +24.3%

Realistic Online Symbolic Visual Question Answering  (Jan ’19 - Current)

  • The general notion of Symbolic Reasoning VQA methods is to answer a question by inducing neural programs over Operators×Concepts trajectories
  • Currently, SOTA works on VQA focus on offline learning from static datasets where offline learning involves only a fixed set of concepts
  • In a real life settings, unseen concepts can come up both in the referenced datasource(from where concepts are extracted) and questions from users
  • Building a lifelong learning model for VQA inspired from state machine literature
  • Proposed the idea/backbone for enabling continual learning in the given setting and implemented the entire symbolic reasoning pipeline in pytorch framework

IBM Research Labs, Bangalore    (Summer ’15)

Continuous authentication on mobile phones using touchscreen input  (May ’15 - Jun ’15)

  • Goal was to authenticate users based upon touch data
  • Computed 30 geometric feature representations of each stroke
  • Applied vector quantization methods on the stroke data to reduce the computational complexity and optimize data storage, eventually optimizing battery life
  • Achieved 17% Equal Error Rate on just single swipe classification on applying KNN based classifiers

IIT Madras, Chennai    (Jul ’12 - Jul '17)

Language Expansion in Text-Based Games  (Jun ’15 - Sep ’16)

  • Explored interaction based representation learning in the context of learning to play text-based games
  • Designed a Deep-RL based mechanism for expanding an agent’s vocabulary using the vocabulary of agents trained for multiple text-based games
  • Empirically established the utility of embeddings learned by our method by qualitatively analyzing the tSNE embeddings and the transfer learning performance

RL agent to play Flappy Bird game  (Dec ’13 - Jan ’14)

  • Implemented SARSA, SARSA(λ), Q-Learning and TD(λ) reinforcement learning algorithms
  • Obtained a best score of ≈24 which is much better than an average human’s score

Intelligent Ground Vehicle Competition  (May ’16)

  • Member of the image processing team, whose objective was to detect lanes on the ground and avoid obstacles
  • Engineered an algorithm for lane prediction given partial lane detection information and camera induced noise, for assisting in maintaining course

Publications

Complex Program Induction for Querying Knowledge Bases in the Absence of Gold Programs

Saha et al. 2019

TACL 2019, ACL 2019 Oral

Authors:


Neural Program Induction for KBQA Without Gold Programs or Query Annotations

Ansari et al. 2019

IJCAI 2019

Authors:


Language Expansion In Text-Based Games

Ansari et al. 2016

NIPS-DRL Workshop ’16

Authors:

Patents

Patents currently under the process of filing

IBM Research Labs, Bangalore

Hobbies

Skills

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