**Advisor:** Wolfgang Gatterbauer, Carnegie Mellon Univ.

** Abstract: **
Multi-relational graphs are a convenient and intuitive representation of most real-world data and they can grow to the order of millions of nodes with billions of edges.
Being able to run inference algorithms, like

We are currently exploring approximate inference algorithms for higher-order potential MRFs where energy functions are defined over clusters (or groups) of nodes. Specifically, I am looking into the trade-off between

**Advisor:** Partha Talukdar, IISc

** Abstract: **
Multi-armed bandit (MAB) problem has been extensively studied for settings where reward distributions do not change over epochs.
The vanila stochastic-MAB problem is best described by the example of gambler who plays one of K arms at each epoch, defined by an unknown reward distribution. Rewards are observed only for the chosen arm and that too after it is played. The gambler's objective is to maximize the cummulative rewards by

In this work, we study the impact of dynamic (continuously changing) rewards in scenarios with faster budget depletion. I am currently looking into the most relaxed budgeted variant of

**Advisor:** Partha Talukdar, IISc

Our MALL lab participated in the Allen AI Science Challenge 2016 that aimed at building an automated answering system for 8th-grade multiple choice science questions. We stood **10th among 170** participating teams on global leaderboard. **Try our demo!**

Our model was a double-logistic ensemble over the below modules:

- M1: Information retrieval
- M2: Representation learning over question-answering trained DNNs
- M3: Edge retrieval from Knowledge Graphs constructed by OpenIE over large unstructured web data
- M4:
**Inference over constructed knowledge graphs for reasoning** - M5: Pointwise Mutual Information statistics
- M6: Textual Entailment

Description of the entire model can be found here.

**Advisor:** Partha Talukdar, IISc

** Abstract: **
Automatic construction of large knowledge graphs (KG) by mining web-scale text datasets has received considerable attention over the last few years, resulting in several KGs such as NELL, Google Knowledge Vault, etc. These KGs consist of thousands of predicate-relations (e.g., isPerson, isMayorOf ) and millions of their instances called beliefs (e.g., (Bill de Blasio, isMayorOf, New York City)). Estimating accuracy of such automatically constructed KGs, especially under limited budget, is a challenging problem due to their size and diversity. This important problem has not been addressed in previous research we fill this gap and propose KGEval in this work.

KGEval is an instance of a novel crowdsourcing paradigm where dependencies among tasks posted to the crowd workers are exploited. We demonstrate that the objective optimized by KGEval is in fact NP-Hard and submodular, and hence allowing for the application of greedy algorithms with approximation guarantees. We demonstrate KGEval's effectiveness through extensive comparisons against multiple competitive baselines on real-world datasets. We have made all the

**Advisor:** Vani M, NITK

** Abstract: **
AI agents (robots) can learn much faster if they abstract lower level fine details of their environment and combine several primitive actions into one large abstracted

In this work, we aim to identify a few bottleneck states within the state-space which should form termination states for Options framework. To identify

**Advisor:** Carolyn P RosÃ©, IPTSE'13, CMU

** Abstract: **
Intelligent Tutoring Systems (ITS) which are socially aware and more human-like in their behavior, have always been at the heart of automated learning tools. Collborative platforms, where students are engaged in active learning process, need to model online conversations which can capture real-world semantics and identify appropriate intervention point.
In this work, we study how academic-conversations can be modelled using state-transition diagrams and learn when/how an automated agent can intervene in collaborative learning conversations.
We propose a two-pass method, filter pass and trigger pass.
To identify deviations from central topic, the filter pass uses frequency of domain specific jargons and categorizes dialouges into attributes like Proposal, Question, Doubt, Comment, Clarification and Consensus/Agreement. Depending on the trend of Confusion versus Consensus in the conversation, the automated tutor steps in appropriately.
We analyze the group conversation on High school Biology class dataset, Undergrad Thermodynamics class dataset and Graduate Chemistry dataset to understand problems such as rushed conclusion, responsive by asking elaborations, to only ask questions etc.

- Machine Learning, by Prof.Chiranjib Bhattacharyya and Shivani Agrawal
- Foundations of Data Science, by Prof.Ravi Kannan and Ramesh Hariharan
- Computational Methods of Optimization, by Prof.Chiranjib Bhattacharyya
- Probability and Statistics, by Prof.Ambedkar Dukkipati
- Linear Algebra, by Prof.Vittal Rao

- Artificial Intelligence and Expert Systems
- Distributed Algorithms
- Design and Analysis of Algorithms
- Advanced Data Structure and Algorithms
- Data Structure and Algorithms

- Linear Algebra
- Discrete Mathematical Structures (Probability, Graph Theory, Combinatorics)
- Concrete Mathematics
- Number Theory and Cryptography
- Engineering Mathematics I (Real Analysis and Multivariate Calculus)
- Engineering Mathematics II

- Thesis project - I and II
- Theory of Computation
- Operating Systems (+lab)
- Database Systems (+lab)
- System Programming
- Computer Networks (+lab)
- Advanced Topics in Networks and Distributed Computing
- Computer Organisation and Architecture
- Microprocessors and Interfacing (+lab)
- Software Engineering / Object Technology
- Computer Graphics