Algorithms & Theory
At the heart of all computer programs lie the mathematical foundations of computer science – theory and algorithms.
We delve into these theoretical concepts, understanding the fundamental abilities and limitations of the computational tools we work with, and use these insights to develop improved software and algorithms.
What We Do
Study the theory behind various algorithms, and explore different algorithm design paradigms.
Design and evaluate algorithms used in solving real-world problems.
Sub Areas
- Algorithmic Game Theory
- Combinatorial Algorithms
- Complexity Theory
- Constraint Satisfaction
- Cryptography
- Distributed Algorithms
- Fault Tolerance & Robustness
- Graph Theory & Algorithms
- Information Theory & Coding
- Learning Theory
- Logic
- Networking
- Optimisation
- Privacy & Security
- Quantum Information & Algorithms
- Transportation & Logistics Algorithms
Our Research Projects
Breaking the Box - On Security of Cryptographic Devices
This project focuses on advancing the security of cryptographic systems against active and passive adversaries, particularly in the context of tampering and side-channel attacks. By addressing foundational problems such as constructing non-malleable extractors, codes, and secret-sharing schemes, the research aims to develop robust cryptographic protocols that remain secure under a wide range of physical and mathematical attacks.
Computational Hardness of Lattice Problems and Implications
This project explores the computational hardness of lattice problems, aiming to develop optimal algorithms, establish near-tight complexity bounds, and investigate the limitations of proving lower bounds. It also seeks to introduce new fine-grained hardness assumptions with significant implications for cryptography and computational complexity, alongside extending and studying the scope of fine-grained hardness results.
A Hybrid Approach to Automatic Programming
The project introduces an innovative approach that combines traditional program analysis, neural machine translation, and human guidance to enhance accuracy and generalization in automated programming tasks, thereby making coding accessible to non-experts.
Safety and Reliability in Black-Box Optimization
This project seeks to enhance safety, reliability, and robustness in black-box optimization, exploring new function structures and addressing limitations. This includes extending decision-making frameworks to grey-box settings and multi-agent learning, utilizing a methodology blending theoretical analyses and algorithm development.
- Optimisation
Fault-tolerant Graph Structures: Efficient Constructions and Optimality
This project aims to enhance graph problem solutions in the presence of network failures by developing fault-tolerant constructions and optimized graph structures. Through novel approaches, it contributes to algorithmic understanding, graph theory, and real-life applications.
Rank Aggregation: Fairness and Computational Challenges
- Combinatorial Algorithms, Optimisation
Handling Massive Data under the Edit Metric: Clustering, Finding Median and Computational Hardness
- Combinatorial Algorithms, Optimisation
Computational Hardness Assumptions and the Foundations of Cryptography
This program seeks to broaden and diversify the foundations of cryptography by identifying new plausible computational hardness assumptions that can be used to construct cryptosystems. Our current approach is to study and construct "fine-grained" cryptographic primitives based on the conjectured hardness of various well-studied algorithmic problems.
- Complexity Theory, Cryptography
Our Research Groups
Deep Learning Lab
Our lab aims to contribute to the development of deep learning methods and the understanding of intelligence.
- Optimisation
Information Theory and Statistical Learning Group
Our group performs research at the intersection of information theory, machine learning, and high-dimensional statistics, with ongoing areas of interest including information-theoretic limits of learning, adaptive decision-making under uncertainty, scalable algorithms for large-scale inference and learning, and robustness considerations in machine learning.
- Information Theory & Coding, Learning Theory, Optimisation
STeAdS Virtual Group
Software Engineering and Technological Advancements for Society. A virtual group that uses Software engineering practices and Technological advancements (Cloud computing, Artificial Intelligence (EdgeAI, ML)) for the benefit of various aspects of society (healthcare, education, art & culture). Looking for students to collaborate on different projects. Look at ganeshniyer.github.io for details.
- Algorithmic Game Theory
