Indian Institute of Technology Ropar


Laboratory of Statistical Artificial Intelligence and Machine Learning (LSAIML)


Our laboratory works on fundamental artificial intelligence and machine learning problems, in particular transfer learning and domain adaptation. Our research is inspired from applications in multimedia, ubiquitous computing and ICT4D. The above image is a word cloud of the publications from the lab (courtsey: wordle)

- RECENT UPDATES -

Date   Update
Oct 19 Akanksha successfully defends her MS Thesis. Congratulations Akanksha!
Aug 19 Thanks to Google and TensorFlow for the generous support for CS618 course on Artificial Neural Networks.
Jul 19 Thanks to Google for providing $2000 worth GCP credits!
May 19 Akanksha receives the best poster presentation award at IIT Ropar Research Conclave. Congratulations!
Apr 19 Microsoft, ACM-IARCS, and WiCV provide support to Akanksha for participating in CVPR 2019
Mar 19 Semantically Aligned Bias Reducing Zero Shot Learning - paper accepted to CVPR 2019. Congratulations Akanksha and Prateek!
Jan 19 Collaborative Research Support from DST for Additive Manufacturing & Machine Learning based Development of Indigenous Hydrogen Fuel Cell Stack, PI- Dhiraj Mahajan
Nov 18 Supervised Heterogeneous Feature Transfer via Random Forests - paper accepted to the Artificial Intelligence Journal. Congratulations Sanatan!
Sep 18 Grateful to DST for supporting CK's travel to ACM MM
Aug 18 Grateful to DST, Microsoft, and ACM for supporting Sanatan's travel to ECML
Fall 2018 CK will be teaching CSL503/603 - Machine Learning. Check the course website for the details
July 18 Deep Cross modal learning for Caricature Verification and Identification (CaVINet) - Full research paper accepted to ACM MM 2018. Congratulations Skand, Jatin, and Himanshu!
June 18 Web-Induced Heterogeneous Transfer Learning with Sample Selection - paper acccepted to ECML-PKDD 2018. Congratulations Sanatan!
Spring 2018 CK will be teaching CSL302 - Artificial Intelligence. Check the course website for details
Nov 17 Poverty prediction from satellite images through deep learning - paper accepted to IAAI 2018. Congratulations Shailesh and Tushar!
Nov 17 Garbage in images (GINI) dataset released.
Nov 17 Congratulations Sanatan for getting a paper accepted to ACM-CODS-COMADS 2018 and a student abstract to AAAI 2018
Fall 2017 CK will be teaching CSL603 - Machine Learning. Check the course webpage for details.
Apr 17 Congratulations Tushar Aggarwal! on being selected to particpate in the prestigious Heidelberg Laureate Forum
Mar 17 Grateful to NVIDIA for donating a TitanX Pascal card through the academic hardware grant request program.
Spring 2017 I will be teaching CSL302 - Artificial Intelligence. Check the course webpage for details.
Dec 16 Spot Garbage team wins the INAE Student Project of the year award for 2016. Congratulations Gaurav, Kaushal and Mohit!
Nov 16 Brain Segmentation paper accepted to ICVGIP 2016. Congratulations Apoorva and Gaurav!
Aug 16 Grateful to Microsoft for the unrestricted research grant.
Fall 2016 I will be teaching CSL603 - Machine Learning in Fall 2016. Check the course webpage for details.
June 16 Spot Garbage paper accepted to UbiComp 2016! Congratulations Gaurav, Kaushal and Mohit - an all undergraduate team!
June 16 Thanks ACM IARCS, IJCAI, and Microsoft for supporting Sanatan's travel to IJCAI 2016.
May 16 Grateful to NVIDIA for donating a TitanX card through the academic hardware grant request program.
Apr 16 Team KudaPehchano from IIT Ropar consisting of Gaurav, Kaushal, and Mohit are the 2016 Microsoft Imagine Cup National Champions in the World Citizenship category! Congratulations guys! and Good luck at the World Semifinals.
Apr 16 Sanatan's first paper accepted to IJCAI 2016!
May 16 DST-YSS project approved for conducting research in "Activity Learning in Smart Environments"
- Co-authored book on Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data was published by Wiley.

Prospective PhD and MS Research Students

The lab is looking for students technically strong in Mathematics and Computer Science interested in the data mining, machine learning, pervasive computing and related fields. Prospective students must also possess excellent programming and communication skills. Please contact if you are one of them. The lab is also interested in industry/health care related problems that can be solved using data mining and machine learning techniques. Please do contact if you have an interesting problem.

Summer Internship

Students technically strong in Mathematics and Computer Science with an aptitude for data mining, ubiquitous computing and related fields may apply through the INSA SRF program. The lab will not be taking students outside this program and will not be able to respond to your individual queries.