Research

Abhishek Bichhawat

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Abhishek’s research is primarily focused on building secure systems by “design” while providing formal security guarantees about real systems, with an emphasis on creating solid foundations for practical solutions. Abhishek currently works on verification of security properties of cryptographic protocols used in real-world applications and the extraction of verified reference implementations of these protocols. He also works on securing the flow of secret and confidential data through applications and preventing security leaks due to bugs in the program code. Abhishek is also interested in exploring aspects of inferring security policies from code or logs that may help debug and enforce the policies in different applications.

Recent Research Papers:

• Permissive runtime information flow control in the presence of exceptions
  
• Automating Audit with Policy Inference
  
• Gradual Security Types and Gradual Guarantees
  
• Contextual and Granular Policy Enforcement in Database-backed Applications
  
• First-order Gradual Information Flow Types with Gradual Guarantees
  

Anirban Dasgupta

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Anirban Dasgupta is currently the N. Rama Rao Chair Professor of Computer Science & Engineering at IIT Gandhinagar. Prior to being at IIT Gandhinagar, he was a Senior Scientist at Yahoo Labs Sunnyvale. Anirban works on algorithmic problems for massive data sets, large scale machine learning, analysis of large social networks, and randomized algorithms in general. He is also interested in problems related to quantitative social sciences.

Anirban did his undergraduate studies at IIT Kharagpur and doctoral studies at Cornell University. He has received the Google Faculty Research Award (2015), the Cisco University Award (2016), and the ICDT Best Newcomer Award (2016), and the Google India AI/ML Award (2020).

Recent Research Papers:

• Analyzing Topic Transitions in Text-Based Social Cascades Using Dual-Network Hawkes Process
  
• Streaming Coresets for Symmetric Tensor Factorization
  
• On Coresets for Regularized Regression
  
• On Additive Approximate Submodularity
  
• Efficient Hierarchical Clustering for Classification and Anomaly Detection
  

Bireswar Das

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Bireswar's research interests are in Algorithms for Algebraic and Combinatorial Problems and Computational Complexity Theory.

Recent Research Papers:

• Nearly Linear Time Isomorphism Algorithms for Some Nonabelian Group Classes
  
• Compact Data Structures for Dedekind Groups and Finite Rings.
  
• Space efficient representations of finite groups
  
• Polynomial-time algorithm for isomorphism of graphs with clique-width at most three
  
• Space Efficient Representations of Finite Groups
  

Balagopal Komarath

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Balagopal’s research interests are in the areas of computational complexity theory and algorithms. His research is mainly about analyzing the power and limitations of natural models of computation which include models that capture a wide variety of algorithms for interesting computational problems and models that are inspired by real-world constraints.

Recent Research Papers:

• On the complexity of detecting hazards
  
• Karchmer-Wigderson Games for Hazard-free Computation
  
• Graph Homomorphism Polynomials: Algorithms and Complexity
  
• On the Complexity of Hazard-free Circuits
  
• Comparator Circuits over Finite Bounded Posets
  

Krishna P Miyapuram

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Krishna's research integrates the cognitive processes of learning and decision making mechanisms in humans. Ongoing projects include behavioral and neural correlates of statistical learning, perceptual and value-based decision making. A wide range of experimental and analytic techniques such as psychophysics, eye tracking, brain imaging methodologies (EEG, fMRI) and computational modelling (Reinforcement learning, Bayesian approaches) are used. With machine learning approaches applied to structural MRI images, He is working on early detection of Alzheimer’s’ disease.

Recent Research Papers:

• Classifying Oscillatory Brain Activity Associated With Indian Rasas Using Network Metrics
  
• Risk prediction of prodromal Alzheimer’s disease using deep learning of MRI scans
  
• A Novel Approach Towards Early Detection of Alzheimer’s Disease Using Deep Learning on Magnetic Resonance Images
  
• Brain Connectivity Based Classification of Meditation Expertise
  
• Movie Identification from Electroencephalography Response Using Convolutional Neural Network
  

Manoj Gupta

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Manoj's works span various aspects of Graph Algorithms. We have a reasonably good understanding of efficiently finding properties of a graph like a radius, shortest paths, minimum spanning tree in a static graph. But there are other algorithmic models in which it is unclear how to find or maintain these properties. Some of these models are Dynamic Graphs, Fault-Tolerant Graphs, Graphs in the streaming setting, etc. Manoj works on graph problems in these models. Apart from this, he also likes to explore fundamental data structure problems.

Recent Research Papers:

• Deep Learning for Brain Tumor Segmentation using Magnetic Resonance Images
  
• Artificial Neural Networks to Solve the Singular Model with Neumann-Robin, Dirichlet and Neumann Boundary Conditions
  
• Computational Intelligent Paradigms to Solve the Nonlinear SIR System for Spreading Infection and Treatment Using Levenberg–Marquardt Backpropagation
  
• A Stochastic Intelligent Computing with Neuro-Evolution Heuristics for Nonlinear SITR System of Novel COVID-19 Dynamics
  
• Future 5G Network Based Smart Hospitals: Hybrid Detection Technique for Latency Improvement
  

Mayank Singh

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Mayank Singh’s interest lies in applying data mining, machine learning (ML), natural language processing (NLP), and network science techniques to different problems in scholarly information science. He also works on linguistic problems involving low-resource Indian Languages. He has published extensively in data mining/NLP conferences like SIGKDD, CIKM, ACL, NAACL, ECIR, etc. He has been the recipient of the GYTI award by SRISTI in 2017 and the early career research award, SERB India 2019.

Recent Research Papers:

• Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections
  
• Reverse Dictionary Using an Improved CBoW Model
  
• ACL-SQL: Generating SQL Queries from Natural Language
  
• Review Based Recommendations with Human-like Reasons
  
• Genealogical Tree Construction of Research Paper
  

Neeldhara Misra

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Neeldhara's research interests include the interplay of structural graph theory and graph algorithms; tools and techniques in parameterized complexity; computational perspectives on combinatorial games; voting mechanisms, with a special focus on strategic behaviour; fair allocations of resources. She is also keen on understanding means of bringing computational thinking to school education, and using visual tools and narratives for exposition in general. She offers a course on introductory competitive programming on the NPTEL platform.

Recent Research Papers:

• A parameterized perspective on protecting elections
  
• Fair Division Is Hard Even for Amicable Agents
  
• Equitable Division of a Path
  
• Special Issue “New Frontiers in Parameterized Complexity and Algorithms”: Foreward by the Guest Editors
  
• Color spanning objects: Algorithms and hardness results
  

Nipun Batra

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Nipun’s work sits at the intersection of machine learning and ubiquitous computing, for critical domains such as energy consumption, healthcare, and air quality. He summarises his work as scalable sensing, where looks at different perspectives such as:

1. Lowering sensing cost by retrofitting everyday objects
2. Active learning
3. Transfer learning

Recent Research Papers:

• Vartalaap: What Drives #AirQuality Discussions: Politics, Pollution or Pseudo-science?
  
• Generative Fashion for Indian Clothing
  
• PlantDoc: A Dataset for Visual Plant Disease Detection.
  
• Active Learning for Air Quality Station Location Recommendation
  
• Solar Energy Forecasting Using Machine Learning
  

Sameer G Kulkarni

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Sameer obtained his Master's (Computer Engineering) from University of Southern California, Los Angeles, USA and PhD (Computer Science) from University of Göttingen, Göttingen, Germany. He is currently serving as an Assistant Professor in Computer Science and Engineering at IIT Gandhinagar. His research interests include Network Function Virtualization, Software Defined Networking, Network Security and Data Reliability and Distributed Systems.

Sameer was the recepient of the Outstanding PhD Dissertation Award at IEEE Technical Committee on Scalable Computing 2019. He has also achieved the Outstanding Reviewer award at “Elsevier Computer Communications”, 2017. He is Seven-time recipient of QUALSTAR award at Qualcomm, San Diego for exemplary work in different aspects of Audio sub system from June 2010 to Feb 2015.

Recent Research Papers:

• Mu: An Efficient, Fair and Responsive Serverless Framework for Resource-Constrained Edge Clouds
  
• Analyzing Open-Source Serverless Platforms: Characteristics and Performance
  
• Assessing Container Network Interface Plugins: Functionality, Performance, and Scalability
  
• NFVnice: Dynamic Backpressure and Scheduling for NFV Service Chains
  
• REINFORCE: Achieving Efficient Failure Resiliency for Network Function Virtualization-Based Services
  

Shanmuganathan Raman

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Shanmuganathan Raman (Shanmuga) obtained his MTech (Biomedical Engineering) and PhD (Electrical Engineering) degrees from IIT Bombay. He is currently serving as an Associate Professor in Electrical Engineering jointly with Computer Science and Engineering at IIT Gandhinagar. His research interests include Computer Vision, Deep Learning, and Computer Graphics.

Shanmuga was a recipient of the Microsoft Research India PhD Fellowship for the year 2007. He obtained an Excellence in PhD Thesis Work Award at IIT Bombay in 2012. He has published more than 80 peer-reviewed articles in journals and conferences of repute in the areas of Computer Vision and Signal Processing including ICCV, CVPR, ECCV, WACV, ICASSP, ICIP, ICVGIP, IEEE Transactions on Signal Processing, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Signal Processing Letters, and Pattern Recognition. He is the recipient of several sponsored research projects as PI including SERB Core Research Grant, SERB IMPRINT-2, ISRO RESPOND, and SERB MATRICS. He was the course coordinator for 3D Digitization for Cultural Heritage at IIT Gandhinagar which inaugurated the GIAN program across India. He has also received the Jibaben Patel Chair in AI (2020) and Excellence in Research Award (2021) at IIT Gandhinagar.

Shanmuga is an avid reader and has served as a member of the Senate Library Committee. He is an enthusiastic instructor who has been teaching a wide spectrum of courses in Electrical Engineering and Computer Science and Engineering. He works towards making even the most complex concepts accessible to every student in the classroom.

Recent Research Papers:

• DeepObjStyle: Deep Object-based Photo Style Transfer
  
• ShuffleBlock: Shuffle to Regularize Deep Convolutional Neural Networks
  
• DeepCFL: Deep Contextual Features Learning from a Single Image
  
• APEX-Net: Automatic Plot Extractor Network
  
• LS-HDIB: A Large Scale Handwritten Document Image Binarization Dataset
  

Shouvick Mondal

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The domain of Shouvick's research is software engineering and testing. Research activities focus on the improvement of existing and the development of new scalable and performant software analysis methodologies to ensure construction of high-quality and trustworthy software systems. Shouvick has published and presented in conferences and journals such as ICSME, ICST, JSS, and TSE. He regularly serves as a Program Committee member in the Artifact Evaluation track of conferences such as ISSTA, and PPoPP. Shouvick is also a journal reviewer for JSS, and IST.

Recent Research Papers:

• ColosseumColosseum: Regression Test Prioritization by Delta Displacement in Test Coverage
  
• Soundy Automated Parallelization of Test Execution
  
• Mahtab: Phase-wise acceleration of regression testing for C
  
• Finding Shortest Triangular Path and its Family inside a Digital Object
  
• Consensus prioritization of regression test-cases supported by parallel execution windows
  

Yogesh Kumar Meena

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Yogesh K Meena is an Assistant Professor at the Indian Institute of Technology Gandhinagar, India, with a Ph.D. in computer science from Ulster University, Londonderry, U.K. He received his Integrated Post-Graduate (B.Tech. and M.Tech.) degree in information technology from the Indian Institute of Information Technology and Management, Gwalior, India.

Prior to being at IIT Gandhinagar, he was an AI Software Development Engineer and Senior AI Software Development Engineer at the University of Essex, UK, where he worked on an Innovate UK – UKRI-funded project in collaboration with Mersea Homes Ltd. Before that, he was a Postdoctoral Research Officer in the FIT Lab at Swansea University, UK, where he worked on an EPSRC-funded project in collaboration with Google, DST Innovations Ltd, and IIT Bombay. Prior to that, he was a Postdoctoral Research Assistant at the School of Computing, Engineering, and Intelligent Systems at Ulster University, UK, and a Visiting Research Fellow at the Center for Mechatronics and the Department of Humanities and Social Sciences at IIT Kanpur, India. His primary research interests include Human-Computer Interaction, Brain-Computer Interface, Eye Tracking, Motor Control and Rehabilitation, Artificial Intelligence & Machine Learning, Internet of Things, and User Experience and Usability.

Yogesh's work focuses into two main directions: (1) to deliver intelligent technologies and systems; and (2) to transform community health and care. In particular, I have contributed towards development of novel human-centred technologies conducive for use by the elderly, people with disabilities, children with dyslexia, emergent users, and non-tech savvy users.

Recent Research Papers:

• Detection of Dyslexic Children Using Machine Learning and Multimodal Hindi Language Eye-Gaze Assisted Learning System
  
• Light-In-Light-Out (Li-Lo) Displays: Harvesting and Manipulating Light to Provide Novel Forms of Communication.
  
• ProHealth eCoach: User-Centered Design and Development of an eCoach App to Promote Healthy Lifestyle with Personalized Activity Recommendations.
  
• PV-Pix: Slum Community Co-design of Self-Powered Deformable Smart Messaging Materials.
  
• PV-Tiles: Towards Closely-Coupled Photovoltaic and Digital Materials for Useful, Beautiful and Sustainable Interactive Surfaces.