Machine Learning

blogs

Scaling graph representation learning algorithms in an implementation agnostic fashion

--Prof. Srinivasan Parthasarathy-- A graph, which consists of nodes and edges, is a pictorial representation of the relationship between various objects. Analysis of graphs can help in inferring social, biological, road, finance and other networks as well. Prof. Srinivasan Parthasarathy who is Professor at Dept. of Computer Science and Engineering and Department of Biomedical Informatics at the Ohio State University and RBCDSAI Distinguished Fellow gave a talk on his research in the area of graph analysis through a talk titled “Scaling graph representation learning algorithms in an implementation agnostic fashion” which was organized as a part of Sixth RBCDSAI Latent View Colloquium on 23rd March 2022.

• Apr 18, 2022

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Accurate and Interpretable AI models: Towards Deployable AI

A self-driving car bumps into a lamppost. A doctor prescribes the wrong treatment to a patient based on an AI-based diagnostic tool. A missile is misfired by an AI-based defense system. An unfair decision provided by a banking chatbot leads to loss of customers. The above examples are probably sufficient to explain why there is a need to tread carefully while deploying AI-based solutions in the real world. While wrong predictions made by recommendation systems in domains like retail might be inexpensive, such predictions in domains like healthcare, self-driving vehicles, banking or defense can cause hefty monetary losses or even loss of lives.

By Abhishek Ghose , By Balaraman Ravindran

• Mar 28, 2022

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Interpretable AI

--Prof. Cynthia Rudin-- For quite a long time, researchers and the public were thrilled by the wonders of machine learning. However, over the period of time, the community realized that the machine learning models aren’t a magic wand and they are as best as the data provided to them during the training and development stage. As the world started making decisions based on AI, there were soon conflicts between human and machine intelligence and therefore the need for explainability of black-box models become apparent.

• Oct 28, 2021

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Machine Learning for Robot Locomotion: Grounded Learning and Adaptive Parameter Learning

--Dr.Peter Stone-- Robot locomotion continues to remain a challenging problem in spite of the advances in the field. To gain more insights on the topic, a talk on “Machine Learning for Robot Locomotion: Grounded Learning and Adaptive Parameter Learning” was organized as a part of the fourth latent colloquium on 30th September 2021. The talk was delivered by Prof. Peter Stone, who is the David Bruton, Jr. Centennial Professor and Associate Chair of Computer Science, as well as Director of Texas Robotics, at the University of Texas at Austin.

• Sep 30, 2021

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events

Ninth RBCDSAI LatentView Colloquium by Prof. Leslie Valiant

January 5, 2024 09:00 AM to 10:00 AM

CS15, CSE Department, IIT Madras

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2nd RBCDSAI AI/ML Conclave on Manufacturing and Automotive Applications

August 29, 2019 09:00 AM

ICSR Auditorium, IIT Madras

conclave , machine learning , artificial intelligence

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Artificial Intelligence & Machine Learning Conclave

October 23, 2018 09:30 AM

ICSR Auditorium, IIT Madras

conclave , machine learning , artificial intelligence

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ACM India Summer School 2018

June 4, 2018 09:00 AM

Goa University, Goa

summer school , machine learning

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news

NetGenes - a web database for computationally predicted essential genes

bioRxiv

Researchers at RBCDSAI developed a machine learning model which identified more than 3.5 million …

essential genes , network , machine learning

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Data, AI and babies

The Hindu BusinessLine

Featuring IBSE’s work on developing a model to predict the possibility of preterm births to …

Machine Learning , Artificial Intelligence , Preterm birth

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Data Science for Cricket

ACM Datascience

Data Science, ML, AI are the flavors of the season all over the world and India is no exception. …

artificial intelligence , machine learning

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IIT Madras Alumni Association Organizes Hackathon To Solve Traffic Problem In Indian City

ndtv

The scenario for the Hackathon involved an Indian Smart City’s Traffic Police Department which …

machine learning , contest

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people

Balaraman Ravindran

Head, Robert Bosch Centre for Data Science and AI, Professor, Mindtree Faculty Fellow

Specialization:

Machine learning, Reinforcement learning, Social Network Analysis, Data and text mining

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Arun RajKumar

Assistant Professor

Specialization:

Machine Learning, Rank Aggregation, Statistical Learning

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Harish Guruprasad

Assistant Professor

Specialization:

Machine Learning, Learning Theory and Optimisation

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Ashish Tendulkar

Machine Learning Architect, Google Research

Specialization:

Natural Language Processing, Machine Learning, Computational Biology

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Dipra Bhagat

MS Scholar

Specialization:

Machine Learning, Bioinformatics

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Omkar Tanaji Tupe

Project Associate

Specialization:

Machine Learning, Explainable AI

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Argha Boksi

MS Scholar

Specialization:

Machine Learning, Deep Learning, Reinforcement Learning

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Prithaj Banerjee

MS Scholar

Specialization:

Machine Learning, Deep Learning

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Ritwiz Kamal

PhD scholar

Specialization:

Machine Learning, Bioinformatics

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Venkatesh K

Junior Research Fellow

Specialization:

Bioinformatics, Machine Learning

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Srinivas Sridharan

Project Associate

Specialization:

Machine Learning

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Pranshu Malviya

MS Scholar

Specialization:

Time series, Machine Learning

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Brintha Vijayakumar Padmavathy

PhD Scholar

Specialization:

Probabilistic Graphical Models, Computational biology, Machine learning

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P Krishna Kumar

PhD Scholar

Specialization:

Machine Learning, Theory of deep learning

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Rajan Kumar Soni

MS Scholar

Specialization:

Machine Learning, Explainability of neural networks, Deep learning

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Abdul Bakey Mir

Ph.D Scholar

Specialization:

Machine Learning, Rank Aggregation

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Sriram R

PhD Scholar

Specialization:

Data Analytics, Machine Learning

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Saurabh Desai

Project Associate

Specialization:

Artificial Intelligence, Computer Vision, Machine Learning, Interpretability and Deep Learning

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Lakshmi Narayan Pandey

MS Scholar

Specialization:

Machine Learning, Deep Learning

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Abhijeet Vjas

Dual degree

Specialization:

Machine Learning

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Anusha Kumar

PhD Scholar

Specialization:

Data Analytics, Machine Learning

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Sourav Kumar Mohanty

Project Associate

Specialization:

Machine Learning, Deep Learning, Representation Learning

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Harry Antony

Project Associate

Specialization:

Machine Learning, Loop Learning, Cognitive Science

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Amrit Seshadri Diggavi

Post Baccalaureate Fellow

Specialization:

Machine learning, AI

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Anshuk Uppal

Post Baccalaureate Fellow

Specialization:

Ranking, Machine Learning, Bayesian DL, RL

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Yadav Mahesh Lorik

Research Scholar

Specialization:

Machine Learning, Deep Learning

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Sudha S

MS (Research)

Specialization:

Multi-Armed Bandits, Machine Learning, Intelligent Tutoring Systems

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Joseph Hosanna Raj Isaac

Specialization:

Virtual Reality, Machine Learning, Machine Vision, Natural Language Processing

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Aravind Easwar

MS Scholar

Specialization:

Causal Discovery, Machine Learning, Deep Learning

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projects

Sridharakumar Narasimhan

Data Driven Monitoring of Water Distribution Networks

internet of things machine learning

Raghunathan Rengaswamy

Domain agnostic methods for integration of prior knowledge in learning algorithms

machine learning data science

publications

Jan-Michael Y. Carrillo, Vijith Parambil, Tarak K. Patra, Zhan Chen, Thomas P. Russell, Subramanian K. R. S. Sankaranarayanan, Bobby G. Sumpter, Rohit Batra,

Accelerated Sequence Design of Star Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning

The Journal of Physical Chemistry B 2024 128 (17), 4220-4230

https://doi.org/10.1021/acs.jpcb.3c08110

Deepak Maurya, Balaraman Ravindran,

Hypergraph partitioning using tensor eigenvalue decomposition

To appear in the proceedings of the Public Library of Science (PLOS ONE)

https://doi.org/10.1371/journal.pone.0288457

Tarun Kumar, Ramanathan Sethuraman, Sanga Mitra, Balaraman Ravindran, Manikandan Narayanan,

MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue communication

PLOS Computational Biology

https://doi.org/10.1371/journal.pcbi.1011022

Balaraman Ravindran, Sunita Sarawagi, Aditi Jain,

AI and Data Science Centers in Top Indian Academic Institutions

Communications of the ACM

https://doi.org/10.1145/3556634

Sudarsun Santhiappan, Jeshuren Chelladurai, B Ravindran,

TOMBoost: a topic modeling based boosting approach for learning with class imbalance

International Journal of Data Science and Analytics

https://doi.org/10.1007/s41060-022-00363-8

Arnhav Datar, Arun Rajkumar, John Augustine,

Byzantine Spectral Ranking

Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022)

Jatin Chauhan, Aravindan Raghuveer Jay, Nandy Rishi Saket, Balaraman Ravindran,

Multi-Variate Time Series Forecasting on Variable Subsets

ACM SIGKDD 2022

Nandy J, Saket R, Jain P, Chauhan J, Ravindran B, Raghuveer A,

Domain-Agnostic Constrastive Representations for Learning from Label Proportions

31st ACM International Conference on Information and Knowledge Management (CIKM 2022).

Lokesh Rajulapati, Sivadurgaprasad Chinta, Bala Shyamala, Raghunathan Rengaswamy,

Integration of machine learning and first principles models

AIChE Journal

[https://doi.org/10.1002/aic.17715]

Rishi Saket, Aravindan Raghuveer, Balaraman Ravindran,

On Combining Bags to Better Learn from Label Proportions

The 25th International Conference on Artificial Intelligence and Statistics - AISTATS 2022

https://research.google/pubs/pub51096/

Depen Morwani, Harish G.Ramaswamy,

Inductive Bias of Gradient Descent for Weight Normalized Smooth Homogeneous Neural Nets

33rd International Conference on Algorithmic Learning Theory (ALT) -2022

https://proceedings.mlr.press/v167/morwani22a.html

Daksh Anand, Vaibhav Gupta, Praveen Paruchuri, B Ravindran,

An Enhanced Advising Model in Teacher-Student framework using State Categorization

Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 6653-6660

https://ojs.aaai.org/index.php/AAAI/article/view/16823

Anasua Mitra, Priyesh Vijayan, Ranbir Sanasam, Diganta Goswami, Srinivasan Parthasarathy, Balaraman Ravindran,

Revisiting Link Prediction on Heterogeneous Graphs with A Multi-view Perspective

IEEE International Conference on Data Mining (ICDM 2022)

Deep Sahni, Satya Jayadev Pappu, Nirav Bhatt,

Aided Selection of Sampling Methods for Imbalanced Data Classification

In 8th ACM IKDD CODS and 26th COMAD.

https://doi.org/10.1145/3430984.3431029

Shiv Kumar Tavker, Harish Guruprasad, Harikrishna Narasimhan,

Consistent plug-in classifiers for complex objectives and constraints

Advances in Neural Information Processing Systems, 33

https://proceedings.neurips.cc//paper/2020/file/eab1bceaa6c5823d7ed86cfc7a8bd824-Paper.pdf

Depen Morwani, Harish G. Ramaswamy,

Inductive Bias of Gradient Descent for Exponentially Weight Normalized Smooth Homogeneous Neural Nets

arXiv:2010.12909

https://arxiv.org/abs/2010.12909

Tarun Kumar, Sankaran Vaidyanathan, Harini Ananthapadmanabhan, Srinivasan Parthasarathy, B Ravindran,

Hypergraph Clustering by Iteratively Reweighted Modularity Maximization

Applied Network Science

https://doi.org/10.1007/s41109-020-00300-3

Tarun Kumar, K Darwin, Srinivasan Parthasarathy, B Ravindran,

HPRA: Hyperedge Prediction using Resource Allocation

In 12th ACM Conference on Web Science

https://doi.org/10.1145/3394231.3397903

W.Liebal, N T.Phan, Malvika Sudhakar, Karthik Raman, Lars M. Blank,

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

Metabolites

https://doi.org/10.3390/metabo10060243

Sanjay Ganapathy, Swagath Venkataramani, Giridhur Sriraman, Balaraman Ravindran, Anand Raghunathan,

Dyvedeep: Dynamic variable effort deep neural networks

ACM Transactions on Embedded Computing Systems

https://doi.org/10.1145/3372882

Nandan Sudarsanam, Ramya Chandran, Daniel D Frey,

Conducting non-adaptive experiments in a live setting: a Bayesian approach to determining optimal sample size

Journal of Mechanical Design

https://doi.org/10.1115/1.4045603

Harish G. Ramaswamy, Saurabh Desai,

Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization

The IEEE Winter Conference on Applications of Computer Vision

https://doi.org/10.1109/WACV45572.2020.9093360

Nitesh Methani, Pritha Ganguly, Mitesh M Khapra, Pratyush Kumar,

PlotQA: Reasoning over Scientific Plots

In the IEEE Winter Conference on Applications of Computer Vision

https://doi.org/10.1109/WACV45572.2020.9093523

Ramya Vijayram, Nikhita Damaraju, Ashley Xavier, Koundinya Desiraju, Ramachandran Thiruvengadam, Raghunathan Rengasamy, Himanshu Sinha, Shinjini Bhatnagar, others,