Computer Science and Engineering: Recent submissions

  • Target-augmented shared fusion based multimodal sarcasm explanation generation 
    Goel, Palaash; Akhtar, Md. Shad (Advisor) (IIIT-Delhi, 2024-11-28)
    Multimodal Sarcasm Explanation (MuSE) is a challenging natural language understanding task that deals with training machines to understand the semantic incongruence present in sarcastic social media posts comprising of an ...
  • Generative genomics 
    Verma, Shriya; Sethi, Tavpritesh (Advisor) (IIIT-Delhi, 2024-11-27)
    Generative genomics refers to the field of study focused on developing computational models and algorithms capable of generating synthetic genomic data. This report explores the application of transformer-based architectures ...
  • Generative genomics 
    Kamra, Samanyu; Sethi, Tavpritesh (Advisor) (IIIT-Delhi, 2024-11-27)
    Generative genomics generally refers to a study area that undertakes computational model devel opment and algorithms capable of developing synthetic genomic data. This report explores the application of transformer-based ...
  • Application of DL in NER in recipes 
    Singh, Ritisha; Bagler, Ganesh (Advisor) (IIIT-Delhi, 2024-12-01)
    Named Entity Recognition (NER) is a pivotal technique for extracting structured information from unstructured or semi-structured data. This project focuses on applying deep learning (DL) tech niques to NER for ingredient ...
  • Finance-LLMs: RAG multi-agent framework for financial analysis 
    Parmar, Akshat; Udandarao, Vikranth; Shah, Rajiv Ratn (Advisor) (IIIT-Delhi, 2025-05-05)
    In this work, we introduce FIND-MA (Financial Insight via a Network of Distributed Multi Agents), a Retrieval-Augmented Generation (RAG)-based multi-agent framework for funda mental company analysis aimed at enhancing ...
  • Spoofed speech detection 
    Kushwaha, Akash; Venaik, Utkarsh; Shah, Rajiv Ratn (Advisor) (IIIT-Delhi, 2024-11-26)
    With the increasing capabilities of artificial intelligence, the risk of impersonation and deepfakes has grown, particularly in audio, where ”spoofing attacks” involve altering or faking voices to deceive systems or ...
  • Acoustic detection and temporal localization of whale calls 
    Rajan, Bhargav Raman; Gupta, Anubha (Advisor); Gupta, Ananya Sen (Advisor) (IIIT-Delhi, 2025-07-27)
    Passive acoustic monitoring (PAM) enables large-scale, non-invasive observation of marine megafauna, but detecting and temporally localizing whale vocalizations in long, noisy recordings remains a challenge. This work ...
  • Leveraging temporal pupil dynamics and deep learning for interpretable ADHD diagnosis 
    Sharma, Swati; Shukla, Jainendra (Advisor); Ray, Sonia Baloni (Advisor); Chakrabarty, Mrinmoy (Advisor) (IIIT-Delhi, 2024-12-13)
    Attention-deficit/hyperactivity disorder (ADHD) is the most common behavioral condition and the second most common chronic illness in children. This neurodevelopmental disorder characterized by persistent patterns of ...
  • Fine-grained hardness of lattice problems 
    Ali, Farhan; Bera, Debajyoti (Advisor); Kumar, Rajendra (Advisor) (IIIT-Delhi, 2024-11-27)
    A lattice is a discrete set of points that follow a repeating arrangement of points and more formally defined as a discrete additive subgroup (like Zn) of a vector space (usually Rn). The most important computational ...
  • Multiomics data integration tools for biomarkers discovery 
    Rathore, Yatin; Sethi, Tavpritesh (Advisor) (IIIT-Delhi, 2024-12-13)
    Biomarkers are essential tools in modern medicine and research, enabling early disease detection, precise classification, and tailored therapeutic interventions. The integration of multi-omics data has transformed biomarker ...
  • Characterising noise in ontologies 
    Girdhar, Aditya; Goyal, Ananya; Mutharaju, Raghava (Advisor) (IIIT-Delhi, 2024-04-29)
    This thesis presents a comprehensive investigation into the nature and impact of noise on on tologies and the response of neuro-symbolic reasoners to noise. The project aims to develop a mechanism for introducing noise ...
  • Parameterized complexity of graph theoretic problems in fault-tolerant models 
    Longjam, Viviana; Majumdar, Diptapriyo (Advisor) (IIIT-Delhi, 2024-04-29)
    Fault tolerance is a crucial aspect of modern computing systems, ensuring that systems can continue to operate correctly even in the presence of faults or failures. In the case of graph theoretic problems, this translates ...
  • Parameterized complexity of graph problems with matroid constraints 
    Audrik; Majumdar, Diptapriyo (Advisor) (IIIT-Delhi, 2024-12-02)
    This report delves into the parameterized complexity of classical graph problems under matroid constraints. By exploring Independent Stable Set and various vertex-cut problems, we develop efficient algorithms leveraging ...
  • foodoscope expansion and brand development 
    Patel, Sanjeet Kumar; Bagler, Ganesh (Advisor) (IIIT-Delhi, 2025-07-18)
    The food industry is rapidly evolving, driven by increasing consumer demand for personalized, healthy, and sustainable dietary options. However, product development within the food sector continues to rely heavily on ...
  • CourseWise : course recommendation system 
    Dewan, Dhruv; Rajput, Dhruv; Srivastav, Sarthak; Grover, Anuj (Advisor) (IIIT-Delhi, 2025-07-18)
  • Android side channel attacks 
    Raza, Mohd Irfan; Chakravarty, Sambuddho (Advisor) (IIIT-Delhi, 2025-07-19)
    In the last decade, the smartphone boom made it accessible to everyone and everywhere. These days almost everyone has a smartphone and among them, most of the people spend most of their time on the smartphones. And smartphones ...
  • Accurate coresets 
    Ranjan, Sanskar; Shit, Supratim (Advisor) (IIIT-Delhi, 2024-12-04)
    This report shows my work where I introduced a unified framework for constructing accurate coresets in machine learning. Accurate coresets are weighted subsets of the original data that preserve the loss function of a ...
  • Counter speech ranking 
    Gupta, Karan; Akhtar, Md. Shad (Advisor) (IIIT-Delhi, 2024-11-27)
    This thesis presents the development of a novel ranking method for intent-conditioned counter speeches. The primary aim of this thesis is to analyze hate speech on online social platforms and identify the most effective ...
  • On recovering fair and accurate classifiers with imperfect distributions 
    Sharma, Mohit; Shah, Rajiv Ratn (Advisor); Deshpande, Amit (Advisor) (IIIT-Delhi, 2026-08)
    In the context of classification, the Bayes optimal classifier represents the best possible classification rule with respect to the 0-1 loss, for a given data distribution. In the setting of binary classification, it can ...
  • Automatic partitioning of parallel programs on heterogeneous multicore processors 
    Kansal, Rohak; Gupta, Samridh; Kumar, Vivek (Advisor) (IIIT-Delhi, 2024-11-27)
    With the advent of hybrid CPUs integrated with GPUs, achieving peak performance and energy efficiency necessitates the effective utilization of all available resources. This project explores workload partitioning strategies ...

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