Laboratory of Statistical Artificial Intelligence and Machine Learning

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About the lab

Our laboratory works on fundamental artificial intelligence and machine learning problems, in particular generalisability (through transfer learning, meta-learning, zero-shot learning, and few-shot learning), generative models, and explainability. Our research is inspired from applications in computer vision, remote sensing, ubiquitous computing and ICT4D. The above image is a word cloud of the publications from the lab (courtesy: wordle)

Prospective PhD and MS Research Students

The lab is looking for students technically strong in Mathematics and Computer Science interested in the data science, machine learning, deep learning and allied areas. Prospective students must 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 science and machine learning techniques. Please reach out 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.

Recent News

  • CK has moved to the Department of Data Science at IIT Palakkad

  • Evaluating saliency based explanation methods, accepted to ICML workshop on theoretic foundation, criticism, and application trend of explainable AI, Congratulations Sam, Vidhya and Namrata.

  • Machine Learning Research Talk Series: Speakers include Dr. Hemanth Venkateswara (ASU), Dr. Gautam Kunapuli (Verisk Analytics), Dr. Chandrasekhar Lakshminarayan (IIT Pkd), Dr. Harshad Khadilkar (TCS Research). Dr. Amit Dhurandhar (IBM Research), Prof. Vivek Srikumar (Univ. Utah), Dr. Maneesh Singh (Verisk Analytics), Dr. Ganesh Ghalme (Technion), Dr. Adway Mitra (IIT Kgp), Prof. Akshat Kumar (SMU), Prof. Balaraman Ravindran (IIT M).

  • Characterizing GAN convergence through proximal duality gap, accepted to ICML 2021, Congratulations Sahil and Aroof.

  • Pho(SC)Net: an approach towards zero-shot word image recognition in historical documents, accepted to ICDAR 2021, Congratulations Anuj, and Thanks to our collaborator Dr. Chanda.

  • 2021(20) DST-Humboldt Foundation supported Indo-German Frontiers of Engineering Symposium, Chaired Session - Machine Learning and Big Data Analytics.

  • On duality-gap as a measure for monitoring GAN training, accepted to IJCNN 2021, Congratulations Sahil, Aroof, and Vineet.

  • PACE: posthoc architecture-agnostic concept extractor for explaining CNNs, accepted to IJCNN 2021, Congratulations Vidhya, and Uday.

  • MAIRE - a model agnostic interpretable rule extraction procedure for explaining classifiers, accepted to CD-MAKE 2021, Congratulations to Rajat, Nikhil, and Vidhya and Thanks to our collaborator Dr. Shweta Jain.

  • CK is serving as a PC memberreviewer for AAAI, ICML, NeurIPS, ICCV, KDD, and CVPR 2021. He is an expert reviewer for ICML 2021./

  • Due to ease of maintenance all new material related to teaching will be posted on the respective Google Classroom pages.

  • Congratulations Nikhil on receiving the Institute Silver Medal 2020!