The Machine And Language Learning (MALL) Lab at the Indian Institute of Science (IISc), Bangalore is a group of researchers, engineers, and students from the Department of Computational & Data Sciences (CDS) and the Department of Computer Science and Automation (CSA). The group is led by Partha Talukdar.

News and Events

  • Feb 2017: Our research is featured in Economic Times. Read the article here .
  • Dec 2016: Sharmistha wins the best poster award at the Grace Hopper Conference India 2016
  • Dec 2016: Partha receives an IBM Faculty Award
  • Dec 2016: Thanks to Google for a Focused Research Award
  • Nov 2016: Prakhar talks about his recent experience at HCOMP
  • Oct 2016: Prakhar wins Best Poster Award at IBM I-CARE 2016. Congratulations Prakhar!
  • Oct 2016: Madhav receives student scholarship from EMNLP and travel support from Google to participate in EMNLP, 2016, Thanks EMNLP and Google!
  • Oct 2016: Prakhar receives travel scholarship from Microsoft Research to participate in HCOMP, 2016, Thanks Microsoft!
  • Aug 2016: Ashutosh and Naganand join the lab as PhD students, welcome!
  • Jul 2016: Prakhar's paper on Quality Control in Collaborative Crowdsourcing is accepted at HCOMP-16
  • Jul 2016: Madhav and Uday's paper on Relation Schema Induction is accepted at EMNLP 2016.
  • Jun 2016: Sharmistha writes about her trip to Carnegie Mellon University (CMU) and NAACL 2016 conference
  • May 2016: Danish talks about his recent ICWSM experience
  • Apr 2016: Prakhar wins Best Poster Award at Joint Research Students Symposium of Electrical Division EECS-2016
  • Jan 2016: MALL Lab secures 10th position (out of 170 teams worldwide) in the Allen AI Science Challenge. Try out our demo.
  • August 2015: Sharmistha, Madhav, and Chandrahas join the lab as PhD students. Welcome!


Our research is motivated by the following thesis: background world knowledge is key to intelligent decision making. While we humans routinely use such background knowledge (e.g., rose-hasColor-red, Bangalore-isACityIn-India, etc.) in making decisions in our daily lives, intelligent machines (e.g., systems using Machine Learning) unfortunatly don't have access to such knowledge. Our research is focused on bridging this knowledge bottleneck and in making broad-coverage world knowledge available to machines (and humans) at the right granularity and at the right time.

We view unstructured Web data (e.g., Webpages, tweets, blogs, etc) as one rich source of such knowledge. One of our primary research goals is to extract, orgazine, and make readily available the knowledge trapped inside such unstructured text data on a large scale. To achieve these goals, our research spans the areas of Machine Learning and Natural Language Processing.

In addition to Web-scale unstructured text corpus, we are also interested in understanding how knowledge is organized and processed in the human brain. We are of the opinion that text corpus and brain imaging data (e.g., fMRI, MEG, etc.) offer complementary views of the same latent phenomenon, and we would like to benefit by reconciling these two modalities.

MALL Lab actively collaborates with the Read the Web (NELL) and the Brain Research Group at CMU. Additional collaborators include Polo Chau (Georgia Tech), Christos Faloutsos (CMU), and Nikos Sidiropoulos (UMinnesota).


Our research is generously supported by

We are also supported by Amazon's AWS Cloud Credits for Research program and hardware grants by Nvidia Corporation .