Abstract

The rapid evolution of semiconductor technologies, marked by the transition to FinFETs and increasingly complex CMOS nodes, has intensified challenges in circuit design. Process variations, aging effects, and stringent performance requirements demand innovative strategies to balance precision, reliability, and computational efficiency. This thesis explores machine learning (ML)-driven methodologies to address these challenges, focusing on scalable optimization frameworks for next-generation integrated circuits. Central to this work is the development of advanced machine learning (ML) models suited for circuit data modeling. A chained architecture is introduced to capture process-induced variations in leakage power, voltage, and current across diverse technology nodes and operating conditions. By integrating temperature and voltage fluctuations into its design, this framework significantly enhances prediction accuracy, enabling robust modeling of nanoscale devices across tech-nodes. Complementing this, a novel automated system leverages ML to streamline analog circuit optimization, reducing manual iterations and simulation overhead while maintaining compliance with designer specifications. The thesis further proposes optimization strategies accelerated by ML to address transistor sizing and aging mitigation. A multi-objective evolutionary algorithm framework is designed to rapidly optimize digital logic cells, achieving substantial improvements in power-delay metrics without reliance on traditional simulator-heavy workflows. For FinFET-based systems, an aging-aware optimization methodology dynamically adjusts device parameters to counteract reliability degradation from NBTI and HCI effects, ensuring sustained performance across operational lifetimes. These approaches collectively bridge the gap between computational efficiency and design robustness. By unifying ML-driven modeling with intelligent optimization, this research advances the integration of automation in electronic design. The proposed frameworks not only enhance circuit reliability and efficiency but also empower designers to navigate the complexities of advanced CMOS and FinFET technologies. These contributions lay a foundation for adaptive, high-performance systems in applications ranging from IoT devices to industrial electronics, underscoring the transformative potential of machine-learning (ML) in modern VLSI design.

Abstract

Path tracing is ubiquitous for photorealistic rendering of various real-world appearances. It follows the principles of light transport adapted from physics, which describe light propagation as a set of integral equations. These equations are stochastically evaluated by tracing light rays in virtual scenes. Such stochastic evaluations with ray-tracing form the bulk of the path tracing algorithm, which is widely used in the industry. Stochastic evaluations in path tracing converge to the correct answer in time that is inversely pro- portional to the square root of the number of iterations. This coupled with the fact that the underlying integrals are often complex and high dimensional results in large compute complexity. Research efforts have thus largely focused on accelerating path tracing by improving the stochastic sampling processes. However, it is interesting to look at efficient analytic approximations by making reasonable assumptions on the nature of these light transport integrals. Such analytic methods have the potential to achieve zero variance at the outset. Practically, they are often used in conjunction with stochastic methods thereby achieving lower variance than the fully stochastic counterparts. The primary focus of this thesis is to develop new (semi-)analytic methods and improve existing ones to accelerate direct lighting computations in path tracing. We base our research on the theory of Linearly Transformed Cosines (LTC) applied for direct lighting from area lights. The LTC method produces plausible renderings by building on the principles of light transport from the ground up and has proved useful for tasks other than real-time rendering We make the following three contributions that either build on LTCs or improve it. We first explore fully-analytic direct lighting for arbitrarily shaped area lights, built on LTCs at the core. Due to assumptions of the LTC method, it can only handle polygonal area lights. Furthermore, rendering shadows with LTCs require stochastic evaluations - our contribution here relaxes these as- sumptions, enabling fully-analytic direct lighting with shadows from an arbitrary shaped area light. We show that our method achieves plausible and noise-free renderings compared to semi-analytic LTCs and ground truth ray-tracing, given equal compute budget. Our second contribution improves the LTC formulation by relaxing an assumption that restricts their usage to isotropic GGX reflection distribution functions. Isotropic GGX restricts the kind of reflectance that can be modeled, ultimately restricting the visual fidelity of rendering with LTCs. We identify the core issues that prevent the use of anisotropic GGX, and propose solutions for each. Our contribution not only benefits (semi-)analytic rendering with LTCs but also other related methods that use LTCs at their core.In our third contribution, we extend the theory of Linearly Transformed Spherical Distributions (LTSDs), which are a superset of and core to LTCs, to work with phase functions. This enables us to analytically compute in-scattered radiance, which we build on to semi-analytically render single scattering. Like the original LTC method, we ground our derivations and formulations on the Volume Rendering Equation (VRE) which paves the way for plausible photorealistic renderings despite the bi- ased nature of our method. In effect, our work enables semi-analytic volumetric rendering with area lights. Another focus of this thesis is to accelerate path tracing computations with neural approximations. Path tracing poses a challenge for (semi-)analytic methods as the underlying integrals become increas- ingly more complex and high-dimensional. In this setting, it is beneficial to use a neural network to ap- proximate parts of these complex integrals for improved efficiency. Previous works have demonstrated the usage of neural networks in this manner, however their main focus was on scenes with relatively shorter light paths. We instead focus on scenes where longer paths are required for accurate rendering. More specifically, we tackle the problem of accelerating multiple scattering computations in hair using neural networks. The contributions of this thesis range from exploring analytic and semi-analytic methods to neural approximations for physically based rendering. Moreover, our contributions advance the state of the art, in terms of efficiency, fidelity and capability. We believe our work can inspire further research in computer graphics and adjacent fields.

Abstract

Decision Transformers (DT) have emerged as a powerful offline reinforcement learning architecture, demonstrating substantial successes across various complex tasks. Despite their impressive performance, the interpretability and adaptability of these models remain areas requiring significant exploration, particularly when applied to continuous control and multi-discrete action environments. This thesis provides a comprehensive study of Decision Transformers by addressing these two critical aspects: interpretability in continuous control environments and adaptability through novel tokenisation strategies in multi-discrete action spaces. In the first part, we perform a detailed interpretability analysis of Decision Transformers trained on MuJoCo environments, focusing on continuous control tasks known for their complexity and realism. We utilize multiple interpretability techniques such as positional encoding (PE) analysis, return-to-go (RTG) examination, embedding and hidden state analyses, attention visualizations, and perturbation studies. Our results reveal crucial insights: positional encoding significantly impacts performance only in environments demanding precise temporal coordination (e.g., Hopper and Walker2d), whereas in simpler, velocity-based tasks (HalfCheetah), performance remains stable without explicit positional encoding. Additionally, RTG analysis indicates that the model’s ability to achieve specified returns is closely linked to the distribution of training data rewards, highlighting its adaptability. Embedding analysis uncovers the hierarchical abstractions and structured representations learned by the models, and attention visualization reveals the nuanced role of attention mechanisms in guiding behavior. Perturbation studies further emphasize the robustness of Decision Transformers, identifying key action dimensions (joints) critical for successful task execution. The second part of the thesis addresses Decision Transformer performance limitations in environments characterized by multi-discrete action spaces, specifically image-based domains like ViZDoom. Here, we introduce Multi-State Action Tokenisation (M-SAT), an innovative method that enhances decision-making performance by tokenizing actions at the individual action level and incorporating auxiliary state information. M-SAT’s tokenization strategy significantly improves agent performance and interpretability within attention layers, thereby providing clearer insights into agent decisions and enhancing transparency in dynamic environments involving complex, multi-discrete action choices. Crucially, we demonstrate that M-SAT can achieve superior performance in challenging scenarios such as Deadly Corridor, My Way Home, and Death Match without the necessity of positional encoding, occasionally even benefiting from its removal. The granular action tokenization approach employed by M-SAT facilitates more efficient learning and enables detailed interpretability of individual actions, showcasing marked improvements in sample efficiency and adaptability over baseline Decision Transformers. Through the integration of these two studies, this thesis presents a unified narrative emphasizing the necessity and benefits of interpretability and innovative tokenization strategies in Decision Transformers. Our insights are critical for the future development of more interpretable, adaptable, and reliable transformer-based decision-making systems capable of excelling across diverse and complex environments. This combined analysis deepens our understanding of the internal mechanisms driving Decision Transformer performance, setting the groundwork for further advancements in reinforcement learning architectures.

Abstract

Integrating traffic cameras with deep learning facilitates real-time multi-camera vehicle path reconstruction, benefiting smart city initiatives such as traffic management and public safety. The high demands on latency, bandwidth, and privacy underscore the necessity of edge computing platforms. However, the continuous operation of compute-intensive deep learning algorithms on round-the-clock camera streams can exceed the compute and power capacities of edge data centers. Therefore, it is critical to strategically deploy and activate camera streams to balance compute demands with performance requirements. Currently, there is a lack of tools to support this planning process. This thesis presents Seer, a comprehensive suite of tools and algorithms that aids traffic planners in optimizing traffic camera placement and deep learning model deployment, considering compute and budgetary constraints. Seer encompasses (1) a graph-based algorithm for efficient camera placement incorporating road network topology, (2) a collaborative algorithm for vehicle path reconstruction that enables the use of less accurate but resource-efficient deep learning models, and (3) a scalable traffic simulation framework derived from open-source projects that model city road networks. Evaluations of Seer on two real-world road networks, each with thousands of intersections and simulated vehicle paths, show a 6.3x reduction in compute requirements. This is achieved through a combination of sparse camera deployment and the use of lightweight models, with an average 55% increase in the Hausdorff distance for the path reconstruction algorithm, translating to an absolute increase of 135 to 195 meters.

Abstract

Understanding 3D structure from sparse visual observations is a foundational capability required for robotic perception. In many real-world settings, objects are observed in arbitrary poses, under occlusion, and from limited viewpoints. Inferring complete 3D shape and appearance from such inputs enables downstream tasks like robotic manipulation, scene understanding, and 3D generation. This thesis explores representation learning methods that operate over both explicit and implicit 3D representations to recover structured geometry from minimal input, enabling category-level generalization across unseen instances in challenging conditions. To enable robust 3D inference in such scenarios, we first focus on the challenge of completing object geometry from partial point cloud observations when objects appear in arbitrary orientations. Traditional shape completion methods assume inputs are aligned to a canonical frame, which limits applicability in robotics where objects may be present in non-canonical poses. To address this, we propose SCARP—a method for Shape Completion in ARbitrary Poses. SCARP disentangles shape and pose using a multi-task formulation; it learns rotation-equivariant features for pose estimation and rotation-invariant features for shape reasoning. The network jointly predicts a canonical full point cloud and its 6D pose. Unlike multi-stage pipelines that depend on external canonicalization, SCARP is a single network that learns to complete shapes directly in the observed pose. We demonstrate SCARP’s utility in robotic grasping by showing that completing shapes before grasp prediction reduces invalid and colliding grasps by over 70%, and outperforms prior methods by 45% on completion metrics across several categories. While point clouds offer a compact and geometric view of 3D structure, they are inherently sparse and do not capture appearance or high-frequency detail. To model richer, dense 3D structure directly from images, we turn to implicit neural fields—specifically, Neural Radiance Fields (NeRFs). We introduce HyP-NeRF, a framework for learning category-level priors over Neural Radiance Fields (NeRFs). Our key insight in HyP-NeRF is to use a hypernetwork to not just parameterize a neural network (NeRF) but also generate learnable input encodings, resulting in learning higher frequency details while reducing the computation cost. The hypernetwork predicts both the NeRF MLP weights as well as parameters of a multi-resolution hash encoding(MRHE) of a NeRF conditioned on an instance code, enabling efficient synthesis of NeRFs across unseen objects in a class. To overcome rendering artifacts and improve fidelity, we propose a denoising and finetuning pipeline that leverages a learned 2D denoiser followed by view-consistent NeRF optimization. This formulation supports diverse downstream tasks, including single-view NeRF generation, text-to-NeRF synthesis via CLIP, and retrieval from real-world images. Experiments on high-resolution (512x512) object datasets show that HyP-NeRF achieves state-of-the-art performance in generalization, compression, and instance retrieval, while maintaining photorealistic quality and fast inference. In summary, this thesis presents methods for learning robust and generalizable 3D structure from sparse inputs. SCARP addresses the challenge of shape completion at arbitrary poses through rotationaware modeling of partial point clouds, while HyP-NeRF develops scalable priors over neural fields for instance-conditioned NeRF generation. Together, these approaches offer complementary pathways toward unified, learnable 3D perception in robotic environments. Moreover, the ideas and methods of representation learning presented in these papers have the scope of being scalable and being integrated in future 3D foundation models which require the sufficient inductive biases to be able to account and compensate for the limited availability of training data.

Abstract

Puns, as a form of wordplay, play a significant role in humor, language comprehension, and cultural expression. They rely on phonetic and semantic ambiguities to create humor and are widely used in entertainment, advertising, and literature. While computational approaches to pun generation and detection have advanced in English, pun generation in code-mixed languages remains largely unexplored. Code-mixing, the blending of linguistic elements from multiple languages within a single utterance, presents unique challenges due to its syntactic and semantic complexities. As multilingual communication continues to evolve, developing computational models that can understand and generate humor in such settings is increasingly important. This thesis presents a comprehensive study on the generation of puns in Hindi-English code-mixed text. We begin by constructing a resource of pun-alternate word pairs, which serve as the foundation for pun generation. These pairs are collected by experimenting with various phonetic similarity matching strategies designed to identify humorously interchangeable words across Hindi and English. Using these pairs, we generate puns and analyze their linguistic plausibility and humor potential. We evaluate several pre-trained multilingual language models for their effectiveness in generating syntactically and semantically coherent code-mixed text. Based on these insights, we propose several novel structured prompt-based methods for generating puns in the Hindi-English code-mixed setting. To assess the quality of generated puns, we design a comprehensive human evaluation framework and collect detailed annotations across multiple dimensions of humor and fluency. Human evaluation suggests that our proposed methods significantly outperform baseline approaches in terms of humor quality and contextual relevance. The annotated outputs from our evaluations form the Hindi-English Code-mixed Pun (HECoP) dataset, comprising 2000 human-annotated sentences. We leverage this dataset to compare various multilingual models on the task of pun detection. This resource provides a valuable benchmark for future research in multilingual pun generation and humor detection. Beyond pair-based methods, we further introduce a structured pun generation pipeline capable of generating puns from a single input word without relying on predefined pun-alternate lists. This pipeline integrates phonetic similarity analysis, compatibility scoring, and sentence filtering to enhance the coherence and humorousness of generated content. To the best of our knowledge, this is one of the first comprehensive computational studies focused on pun generation in code-mixed or low resource setting. The methodologies, evaluation frameworks,and datasets introduced in this work lay a strong foundation for future advancements in computational humor and multilingual NLP.

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, yet they exhibit critical inconsistencies that fundamentally undermine their reliability in real-world applications. This thesis addresses two fundamental challenges in LLM reasoning: the evaluation and enhancement of consistency in moral and logical reasoning tasks. We first tackle moral consistency evaluation, where traditional accuracy-based methods fail due to the subjective nature of moral reasoning. We introduce SaGE (Semantic Graph Entropy), an information-theoretic framework that quantifies moral consistency by analyzing the semantic coherence of underlying “Rules of Thumb” (RoTs) inferred from LLM responses. To support this evaluation, we construct the Moral Consistency Corpus (MCC), containing 50,000 moral reasoning instances across diverse scenarios. Our comprehensive evaluation reveals widespread moral inconsistencies across state-of-the-art LLMs, with the maximum observed SaGE score being only 0.681, indicating substantial reliability concerns. We then investigate logical consistency, focusing on the pronounced difficulties LLMs encounter when reasoning with counterfactual premises that conflict with their parametric knowledge. Through the CounterLogic benchmark, a systematically designed dataset spanning 9 formal inference schemas, we demonstrate substantial performance degradation (27% on average) when models reason against their parametric knowledge compared to knowledge-consistent scenarios. To address these logical consistency challenges, we propose Self-Segregate, a metacognitive intervention inspired by human cognitive strategies for handling conflicting information. This two-phase prompting technique first assesses the factual alignment of premises before performing logical reasoning, enabling epistemic compartmentalization. Self-Segregate significantly reduces counterfactual reasoning performance gaps from 27% to 11% while improving overall logical accuracy by 7.5% across multiple models and tasks. Our findings establish consistency as a critical dimension of LLM performance that is orthogonal to accuracy, revealing that models can achieve high task performance while remaining fundamentally unreliable. This thesis contributes essential methodologies for developing more robust and trustworthy language models through novel evaluation frameworks, systematic benchmarks, and effective intervention strategies.

Abstract

Cinema, as a pervasive cultural medium, wields profound influence over societal perceptions, yet its role in perpetuating objectification remains underexplored in non-Western contexts. This thesis bridges this gap by investigating how cinematic techniques in Indian ”item songs”—a genre marked by provocative choreography and strategic camera framing—to induce objectifying gaze behavior in viewers. Integrating this with computer vision model, we present a dual-methodological approach: an empirical eye-tracking study and a novel multi-modal deep learning framework for understanding and detecting visual objectification in videos. In the experimental component, 91 participants viewed sexualized (SV) and non-sexualized (TV) music videos while their gaze metrics—fixation duration, visit counts, and scanpaths—were recorded. Results revealed that sexualized framing significantly redirected attention toward objectified body regions (torso, lower body), with gaze synchronization rates 6× higher in SV than TV (p < 0.001). Dynamic segmentation and ScanGraph analyses demonstrated that camera techniques such as close-ups and rapid editing overrode individual differences, homogenizing gaze patterns across viewers. These findings empirically validate theoretical frameworks like objectification theory (Fredrickson & Roberts, 1997) and the male gaze (Mulvey, 1975), highlighting how exogenous cinematic cues force sexual gaze objectification. Complementing this, our computational contribution introduces an interpretable multi-modal AI framework. By fusing video (LLaVA-NeXT-Video-7B-hf), audio (Whisper-large-v2), and text (allmpnet-base-v2) embeddings via contrastive learning, the model quantifies objectification intensity by dynamically weighting multi-modal cues, achieving state-of-the-art objectification detection (F1: 0.783, Acc: 0.826). A concept bottleneck mechanism further links predictions to human-interpretable cinematic elements (e.g., ”male gaze framing,” AUC: 0.803). This work advances interdisciplinary research by quantifying the cognitive impact of cultural media practices and providing scalable tools for bias detection caused by directors. Its implications extend to AI-driven content moderation, policy frameworks, ethical cinematography, and cross-cultural studies of media effects, establishing a foundation for mitigating objectification in increasingly visual digital ecosystems.

Abstract

In this thesis, we study the problem of Security and Decentralization in distributed systems when incentive driven players are present. The challenge of security is crucial in distributed systems to ensure agreement of the current state among different players distributed across the globe. Distributed systems is used for critical infrastructures such as communication, finance and banking, data warehousing and storage. Therefore, the presence of security vulnerabilities allows for an attacker to exploit such infrastructure and cause disruptions — which can indirectly lead to severe consequences. Similarly, decentralization is another critical requirement from democratized distributed systems such as Blockchains, to ensure that no single player or a small set of like-minded players hold disproportionate control over the system. Centralization of a democratized distributed system such as blockchains can cause challenges such as censorship, reversing transactions, double payment, unilateral changes in the protocol, etc. Therefore, the challenges of security and decentralization of distributed systems are of critical importance. Recent literature has tackled this problem against a set of completely disruptive player (called Byzantine player) and a set of honest (altruistic) players that follow the protocol honestly. However, a more realistic model of the system would involve a discussion on incentive-driven players, which will follow a profit-maximizing strategy. This is the behavior of real-world players involved in such distributed protocols. Decentralization in Proof-of-Work (PoW) based blockchain protocols (which is a type of distributed consensus protocol) is challenged by Rational players – who aim to reduce risk in rewards through the formation of mining pools — where a group of miners give their mining power to a single pool manager in exchange for more frequent payments. This causes centralization of such PoW blockchains. Among existing works that address the problem of security in the presence of rational players, we cover the gaps and show that these bounds can be improved. We also show that a higher level of security is achievable through a clever design of the protocol. On the decentralization front, existing work aims to reduce centralization in PoW blockchains through protocol design. However, we propose a change in the class of reward mechanisms used in PoW blockchains that eliminates any incentives of centralization through pool formation. Overall, this thesis studies and solves the problem of security and decentralization in distributed systems. We identify that accountability and penalty are crucial components in designing such a mechanism, and show that we can improve the adversarial tolerance bounds by designing a consensus/agreement protocol using these two properties, while also guaranteeing security. In decentralization of PoW blockchains, we categorize the Block Reward Mechanisms (BRMs) into two categories — Memoryless and Retentive BRMs. We show that most existing blockchains use Memoryless BRMs and it is impossible to achieve true decentralization using such BRMs. We then propose our own retentive BRM — decentBRM which achieves true decentralization. This proves through construction that true decentralization can be achieved using Retentive BRMs. Our work shows changes in reward/penalty schemes can achieve a higher level of security and decentralization in the presence of rational players. The goal of this thesis is to demonstrate the possibility of solving challenges in cryptographic protocols involving rational players through changes in reward/penalty schemes.

Abstract

The Internet of Things (IoT) has transformed how devices interact, enabling seamless data exchange. However, traditional terrestrial networks that rely on cellular towers, Wi-Fi, and LPWAN technologies can only reach areas with existing infrastructure. This limitation leaves vast rural, remote, and oceanic regions without reliable connectivity, restricting the full potential of IoT applications. To overcome this challenge, satellite-based IoT (Sat-IoT) has emerged as a promising solution, extending connectivity beyond the reach of ground-based networks. By leveraging Low Earth Orbit (LEO) satellite constellations, Sat-IoT enables direct communication between IoT devices and satellites, ensuring global coverage. This thesis explores the performance of Sat-IoT networks using stochastic geometry, a mathematical framework that models the random placement of satellites and IoT devices in large-scale environments. The study focuses on coverage probability and meta-distribution, providing deeper insights into network reliability under different channel conditions. This thesis considers a direct-access LEO satellite-IoT architecture where IoT devices communicate directly with multiple satellites. The satellites selectively decode and forward the information with dedicated orthogonal resources, avoiding interference. Signals from each satellite are coherently combined at the GS using maximal ratio combining (MRC). The satellites are considered to be distributed at a fixed altitude around Earth following a Binomial point process. Derived generalized closed-form expressions for coverage and Meta-distribution(MD). The coverage and MD as a function of SNR threshold T, reliability threshold τ and the altitude rmin are analyzed. The results demonstrate the impact of mega-LEO constellations on coverage and reliability, guiding 6G architecture design to improve connectivity and data offloading in smart cities and dense urban environments and also highlights that the proposed architecture can achieve high coverage and reliability with 8-10 satellites at altitudes between 800 km and 1400 km, as seen in upcoming mega-LEO constellations like Starlink and OneWeb