Computer Engineering Commons^™

Doing Less With More: A First-Principles Exploration Of The Suitability Of Agentic Computing Over Alternative Architectural Choices, Ritvik Garimella, Biplav Srivastava, Amit Sheth

Publications

There is growing interest in automating business activities with Agentic Artificial Intelligence (AI) due to latter's seeming ease of use. However, little is known on when they are suitable for a task over other alternatives developed over the years - local computation, REstful State Transfer (REST), and Simple Object Access Protocol (SOAP) - considering development speed, performance, and operational cost. We explore this with a small mathematical task evaluating five methods for automated mathematical expression evaluation across a benchmark of 1,000 equations where semantics of operator precedence has to be preserved. We ran this setup across a native Function Calling …

One Size Does Not Fit All: Revisitingworld Models And Neurosymbolic Ai, Amit P. Sheth, Madhur Thareja, Anushka Pawar, Niyati Rawal

Publications

World models are being built twice, from opposite ends, without a shared theory of how the two halves should meet. One lineage grounds the world model in perception: a self-supervised, latent-predictive encoder – exemplified by Joint Embedding Predictive Architectures (JEPA) – that learns the structure of sensory experi-ence. A second, older lineage grounds the world model in cognition: an explicit, inspectable structure of entities, rules, and constraints, ranging from knowledge graphs to formal logic to physical law. Neither lineage alone has produced a world model that is simultane-ously adaptive and auditable. We argue this is not solved by picking a …

Biologically-Inspired Multiscale Neuromorphic Architecture, Christian O'Reilly, Ramtin Zand

Publications

This white paper proposes a biologically-inspired multiscale neuromorphic architecture that bridges key gaps between artificial neural networks (ANNs), spiking neural networks (SNNs), and biological neural networks (BNNs). While SNNs offer promising energy efficiency, their broader adoption remains limited by suboptimal performance and the need for novel learning paradigms. To address these challenges, the proposed framework integrates structural and functional principles observed in the brain, including hierarchical organization, sparse and modular connectivity, predictive coding, and diverse neuronal dynamics.

The architecture operates across micro-, meso-, and macro-scales, incorporating neuron-level diversity (e.g., excitatory/inhibitory and principal/support cells), canonical microcircuits (CMCs), and large-scale hierarchical organization. …

In-Situ Eval: A Modular Framework For Custom And Real-Time Rag Benchmarking, Ritvik Garimella, Kaushik Roy, Chathurangi Shyalika, Amit Sheth

Publications

Retrieval-Augmented Generation (RAG) has become the standard approach for integrating domain knowledge into Large Language Models (LLMs). However, fair comparison of RAG pipelines remains difficult: data preparation is often ad hoc, subsampling methods are opaque, parameters vary across implementations, and evaluation is fragmented. We present In-Situ Eval, a unified and reproducible framework that operationalizes the full RAG pipeline with configurable subsampling strategies and both RAG-specific and generic evaluation metrics. The platform supports two execution modes: an offline Dataset mode for evaluating precomputed outputs, and a live Retrieval mode for benchmarking RAG variants with state-of-the-art LLMs. Users can flexibly select datasets, …

When Disasters Trigger Cyber Vulnerabilities: Mapping Physical-Digital Interdependencies In Critical Infrastructure Systems, Dikshya Panta, Sicheng Wang, Aditya Sapkota, Prakash Ranganathan

Faculty Publications

Critical infrastructure (CI) systems such as power, water, communications, and emergency services are increasingly exposed to compound risks in which natural disasters and cyber incidents interact and amplify one another. Traditional risk assessments often isolate physical and digital threats, overlooking the cascading dependencies that emerge when operational stress, emergency reconfiguration, and adversarial exploitation coincide. This study conducts a 2019–2025 scoping review and introduces a Geographic Information System (GIS) driven six-stage disaster cyber compounding framework that characterizes, maps, and operationalizes compound risk across interdependent CI sectors. The framework integrates a common operating picture, analytic situational understanding, exposure mapping, threat-fingerprint encoding, detection …

Toward Neurosymbolic Reinforcement Learning Via Editable Specifications, Vedant Khandelwal, Hong Yung Yip, Amit Sheth

Publications

Reinforcement learning systems are commonly adapted to new settings by retraining or fine-tuning policies. This default is costly, difficult to audit, and poorly aligned with structured requirement changes such as revised safety rules, new operational constraints, or updated user preferences. We argue for an alternative abstraction: adaptation via edits to an external, human-readable specification that the agent consults at execution time. We propose conditioning decision-making on an editable knowledge graph encoding (i) rules capturing action applicability and high-level effects, (ii) hard constraints defining feasibility, and (iii) soft preferences shaping tradeoffs among feasible behaviors. Requirement changes become graph edits, not policy …

Crisis Observatory: Extracting Credible Signals During A Crisis In The Age Of Llms, Kuan-Chieh Lo, Pranav Maneriker, Sriram Sai Ganesh, Dominik Winecki, Kelly Garrett, Ayaz Hyder, Arnab Nandi, Valerie Shalin, Shannon A. Bowen Ph.D., Amit Sheth, Srinivasan Parthasarathy

Publications

Systems for crisis response have required several different models for the analysis of unstructured text, such as identifying needs, locations, topics, routing, and matching of needs with available responders. Large Language Models (LLMs) have replaced task-specific models across various language processing tasks. However, LLMs are known to be limited by their training data, collected before the crisis. In this demo, we explore the use of LLMs for crisis response scenarios with rapidly evolving information environments. We show how the augmentation of these models with external reliable sources of crisis-specific information can help build adaptive systems for response. The demonstration video …

Hardware Accelerated Simulation Of Buck Converters Using Physics-Informed Neural Networks, James Clayton Crews

Theses and Dissertations

Physics-informed neural networks (PINNs) are an emerging machine learning method for learning the behavior of physical systems described by governing differential equations. Dc-dc power-electronic converters are used in a variety of industry applications such as motor drives or power supplies where real-time simulation is critical for control and safety. This thesis investigates physics-informed machine learning as an approach to develop a real-time digital twin for dc-dc power converters. Traditional numerical integration methods are used to approximate discretized behavior, and the results are compared with a trained PINN model. Modern ML frameworks (such as PyTorch and TensorFlow/Keras) are used to quickly …

From Assembly Lines To The Open Road: Predicting Rare Events In Autonomous Systems, Ruwan Wickramarachchi

Publications

In the age of embodied AI and smart automation, autonomous agents are increasingly deployed in high-stakes, real-world environments. Ensuring the robustness and resilience of these systems in the face of rare but critical failure events is essential for their safe and reliable operation. Accurate forecasting of such rare events is particularly crucial, as a single overlooked anomaly can lead to catastrophic consequences. In manufacturing, for instance, unplanned downtime due to rare failures costs industries over \$50 billion annually, with sectors like automotive losing more than \$2 million per hour—even with preventive maintenance systems in place.

However, the extreme rarity and …

Multiagent Copilot In Industrial Ai Applications, Chathurangi Shyalika, Renjith Prasad, Utkarshani Jaimini, Cory Henson, Fadi El Kalach, Amit Sheth

Publications

In the era of smart automation and digital transformation, achieving efficiency, precision, and adaptability is essential for industries to remain competitive. Sectors, including manufacturing, supply chain and logistics, healthcare, finance, and retail, face significant challenges in deploying Artificial Intelligence (AI) solutions tailored to their unique needs, particularly in critical, resource-constrained applications. According to Gartner’s 2024 Hype Cycle for Artificial Intelligence, composite AI, which integrates techniques like machine learning, knowledge graphs, and rule-based systems, is becoming foundational for industries, enhancing predictions, decisions, and scalability across complex environments.

The complexity of real-world systems requires Industrial AI solutions to be customizable to business …

Swvl: A Custom Ai-Powered Face Tracking Camera Gimbal, Alexander J. Anderson-Mcleod, Jakub Jerzmanowski, Michael Laitarovsky, Trevor Allison, Jagger Tanner

Senior Theses

In response to the growing demand for smarter, more responsive face tracking cameras in the post-pandemic world, our team designed SWVL, a custom AI-powered face tracking gimbal meant to address the limitations commonly encountered by the commercial models currently on the market. These commercially available gimbals come with several issues, such as frequently losing track of the person in the frame and requiring manual resets, which we sought to fix with our implementation. We designed a system with fully custom hardware and software including a 3D printed dual-axis camera gimbal driven by stepper motors, a control PCB based around an …

Cross-Layer Design And Optimization Of Analog In-Memory Computing Systems, Md Hasibul Amin

Theses and Dissertations

There has been a rapid growth in the computational demands of machine learning (ML) workloads in recent days. Conventional von Neumann architectures are not capable of keeping up with the high cost of data movement between the processor and memory, well-known as memory wall problem. In-memory computing (IMC) has been focused as a solution by the researchers, where the computation is performed inside the memory devices such as SRAM, MRAM, RRAM etc. Most commonly, the memory devices are arranged in a crossbar setting where the matrixvector multiplication (MVM) operation is performed through intrinsic parallelism of analog computations. The conventional IMC …

Neurosymbolic Knowledge-Grounded Planning And Reasoning In Ai Systems, Amit Sheth, Vedant Khandelwal, Kaushik Roy, Vishal Pallagani, Megha Chakraborty

Faculty Publications

To build AI systems capable of decision-support assistance, such as AI-assisted healthcare, it is essential to develop user-centric decision-making processes that are robust, interpretable, and capable of effectively processing and acting on natural language interactions. Instruction-based prompting of large language models has demonstrated considerable success in supporting humans with information assistance tasks, including creative writing and content generation. However, recent studies reveal that language models exhibit limitations in performing complex reasoning and planning tasks, such as constructing compositional or hierarchical plans involving multiple reasoning steps. To address these challenges, we propose a neurosymbolic framework that integrates large language models with …

Msbzip55 Regulates Salinity Tolerance By Modulating Melatonin Biosynthesis In Alfalfa, Tingting Wang, Jiaqi Yang, Jiamin Cao, Qi Zhang, Huayue Liu, Peng Li, Yizhi Huang, Wenwu Qian, Xiaojing Bi, Hui Wang, Yunwei Zhang

Faculty Publications

No abstract provided.

Lab: Developing Explainable Multimodal Ai Models With Hands-On Lab On The Life-Cycle Of Rare Event Prediction In Manufacturing, Chathurangi Shyalika, Ruwan Wickramarachchi, Revathy Venkataramanan, Dhaval Patel, Amit Sheth

Faculty Publications

In the age of Industry 4.0 and smart automation, unplanned downtime is costing industries over $50 billion annually. Even with preventive maintenance, industries like automotive lose more than $2 million per hour due to downtime caused by unexpected or "rare'' events. The extreme rarity of these events makes their detection and prediction a significant challenge for AI practitioners. Factors such as the lack of high-quality data, methodological gaps in the literature, and limited practical experience with multimodal data exacerbate the difficulty of rare event detection and prediction. This lab will provide hands-on experience to learn how to address these challenges …

A Survey On Food Ingredient Substitutions, Hyunwook Kim, Revathy Venkataramanan, Amit P. Sheth

Publications

Diet plays a crucial role in managing chronic conditions and overall well-being. As people become more selective about their food choices, finding recipes that meet dietary needs is important. Ingredient substitution is key to adapting recipes for dietary restrictions, allergies, and availability constraints. However, identifying suitable substitutions is challenging as it requires analyzing the flavor, functionality, and health suitability of ingredients. With the advancement of AI, researchers have explored computational approaches to address ingredient substitution. This survey paper provides a comprehensive overview of the research in this area, focusing on five key aspects: (i) datasets and data sources used to …

Exploring The Potential Of Large Language Models For Assisting With Mental Health Diagnostic Assessments: The Depression And Anxiety Case, Kaushik Roy, Harshul Surana, Darssan Eswaramoorthi, Yuxin Zi, Vedant Palit, Ritvik Garimella, Amit Sheth

Publications

Large language models (LLMs) are increasingly attracting the attention of healthcare professionals for their potential to assist in diagnostic assessments, which could alleviate the strain on the healthcare system caused by a high patient load and a shortage of providers. For LLMs to be effective in supporting diagnostic assessments, it is essential that they closely replicate the standard diagnostic procedures used by clinicians. In this paper, we specifically examine the diagnostic assessment processes described in the Patient Health Questionnaire-9 (PHQ-9) for major depressive disorder (MDD) and the Generalized Anxiety Disorder-7 (GAD-7) questionnaire for generalized anxiety disorder (GAD). We investigate various …

C 3 An: Custom, Compact And Composite Ai Systems - A Neurosymbolic Approach: 4Th-Generation Evolution Of Intelligent Systems, Amit P. Sheth, Kaushik Roy, Revathy Venkataramanan, Venkatesan Nadimuthu

Publications

Artificial Intelligence (AI) systems continue to evolve rapidly. From the architecture perspective, it is evolving from large, monolithic models trained on massive internet data to complex, multi-component “compound” systems and “agentic” frameworks capable of semi-autonomous decision-making. These systems show immense promise yet face numerous challenges in reliability, consistency, transparency, and alignment with user goals. In this article, we propose Custom, Compact and Composite AI with Neurosymbolic (C3AN) approach, a framework that paves way to 4th-generation of AI that integrates data, knowledge, and human expertise to build robust, intelligent and trustworthy AI systems defined by 14 foundation elements.

Custom emphasizes …

Safe Data-Enabled Control Of Human-In-The-Loop Robotic Manipulator Systems, Ritirupa Dey, Avimanyu Sahoo, Vignesh Narayanan

Publications

Safe control of human-in-the-loop (HIL) robotic manipulators is critical for applications such as assistive robotics, teleoperation in hazardous environments, and collaborative manufacturing. However, this remains challenging due to the lack of a unified framework that simultaneously addresses safety constraints, external disturbances, unmodeled dynamics, and dynamic role switching in the HIL setting. In this paper, we propose a novel NN-driven HIL control framework in which human–robot dyadic interaction occurs through the haptic channel. Using Lyapunov stability analysis, we theoretically show that the proposed NN-based controller ensures accurate joint trajectory tracking, compensates for system uncertainties, and adapts to human inputs modeled as …

Towards Rare Event And Anomaly Prediction In Manufacturing: Bridging Methodological Gaps In Industrial Applications, Chathurangi Shyalika, Renjith Prasad, Ruwan Wickramarachchi, Amit Sheth

Publications

Rare event prediction is critical in industrial applications, including real-world Industry 4.0 applications. These events, defined by their low occurrence frequency, are often difficult to predict due to the skewed data distribution, which complicates modeling and evaluation. In our research, we provide a comprehensive review of current approaches to rare event prediction across four key dimensions: rare event data, data processing techniques, algorithmic approaches, and evaluation methodologies [1]. By analyzing diverse datasets with multiple modalities, including numerical, image, text, and audio, we categorize the primary challenges and present the gaps in current research. Specifically, we present three novel research contributions …

Predicting Chaotic Systems With Quantum Echo-State Networks, Erik Connerty, Ethan N. Evans, Gerasimos Angelatos, Vignesh Narayanan

Publications

Recent advancements in artificial neural networks have enabled impressive tasks on classical computers, but they demand significant computational resources. While quantum computing offers potential beyond classical systems, the advantages of quantum neural networks (QNNs) remain largely unexplored. In this work, we present and examine a quantum circuit (QC) that implements and aims to improve upon the classical echo-state network (ESN), a type of reservoir-based recurrent neural networks (RNNs), using quantum computers. Typically, ESNs consist of an extremely large reservoir that learns high-dimensional embeddings, enabling prediction of complex system trajectories. Quantum echo-state networks (QESNs) aim to reduce this need for prohibitively …

A Benchmark Knowledge Graph Of Driving Scenes For Knowledge Completion Tasks, Ruwan Wickramarachchi, Cory Henson, Amit Sheth

Publications

Knowledge graph completion (KGC) is a problem of significant importance due to the inherent incompleteness in knowledge graphs (KGs). The current approaches for KGC using link prediction (LP) mostly rely on a common set of benchmark datasets that are quite different from real-world industrial KGs. Therefore, the adaptability of current LP methods for real-world KGs and domain-specific ap- plications is questionable. To support the evaluation of current and future LP and KGC methods for industrial KGs, we introduce DSceneKG, a suite of real-world driving scene knowledge graphs that are currently being used across various industrial applications. The DSceneKG is publicly …

Ontology Design Metapattern For Relationtype Role Composition, Utkarshani Jaimini, Ruwan Wickramarachchi, Cory Henson, Amit Sheth

Publications

RelationType is a metapattern that specifies a property in a knowledge graph that directly links the head of a triple with the type of the tail. This metapattern is useful for knowledge graph link prediction tasks, specifically when one wants to predict the type of a linked entity rather than the entity instance itself. The RelationType metapattern serves as a template for future extensions of an ontology with more fine-grained domain information.

Visual Causal Question And Answering With Knowledge Graph Link Prediction, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

The ability to answer causal questions is important for any system that requires robust scene under- standing. In this demonstration, we develop a prototype system that leverages our causal link prediction framework, CausalLP. CausalLP framework uses a visual causal knowledge graph and associated knowledge graph embedding for two visual causal question and answering tasks- (i) causal explanation and (ii) causal prediction. In the live demonstration sessions, the participants will be invited to test the efficiency and effectiveness of the system for visual causal question and answering.

Causal Neuro-Symbolic Ai For Root Cause Analysis In Smart Manufacturing, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

Root cause analysis is the process of investigating the cause of a failure and providing measures to prevent future failures. It is an active area of research due to the complexities in manufacturing production lines and the vast amount of data that requires manual inspection. We present a combined approach of causal neuro-symbolic AI for root cause analysis to identify failures in smart manufacturing production lines. We have used data from an industry-grade rocket assembly line and a simulation package to demonstrate the effectiveness and relevance of our approach.

Causal Knowledge Graph For Scene Understanding In Autonomous Driving, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

The current approaches to autonomous driving focus on learning from observation or simulated data. These approaches are based on correlations rather than causation. For safety-critical applications, like autonomous driving, it’s important to represent causal dependencies among variables in addition to the domain knowledge expressed in a knowledge graph. This will allow for a better understanding of causation during scenarios that have not been observed, such as malfunctions or accidents. The causal knowledge graph, coupled with domain knowledge, demonstrates how autonomous driving scenes can be represented, learned, and explained using counterfactual and intervention reasoning to infer and understand the behavior of …

Devices And Dining: A Cross-Cultural Analysis Of Mobile Device Use In Italian And American Restaurants, O'Malley Jenkins

Senior Theses

The field of technology ethics has seen increasing growth and interest over the years. Many have begun to consider the impacts of technology’s use and whether it is being employed in a healthy manner by users. However, there has been limited investigation into whether the ways in which technology use varies across cultures. This study begins to address this gap by researching Italian and American device usage in fast food settings. Non-participant naturalistic observations were used to record data for 89 Italian and 88 American customers of fast food restaurants. Analysis of the collected data indicates that Americans use their …

Real-Time Robot Pose Estimation For Industry 4.0: Enhancing Motion Validation And Monitoring With A Vision-Based Framework, Jad Samaha

Theses and Dissertations

Amidst the era of Industry 4.0, robots became integral to automated production lines by performing complex tasks with high precision and adapting to changing production needs in real-time. Therefore, ensuring their precise and reliable operation is paramount for maintaining high-quality standards and operational efficiency. This invoked the need for an automated motion validation tool that guarantees accurate program task execution, by detecting deviations or anomalies that may indicate mechanical faults or software errors. Given the exponential advancements in AI, particularly in Computer Vision, a vision-based solution is ideal for ensuring the correct positioning of a robot in real-time. Operating independently …

Neuro-Symbolic Ai For Deep Analysis Of Social Media Big Data, Vedant Khandelwal, Manas Gaur, Ugur Kursuncu, Valerie Shalin, Amit P. Sheth

Faculty Publications

This tutorial introduces a neuro-symbolic AI framework to analyze big data from social media platforms. Integrating human-curated knowledge through symbolic AI with the pattern recognition capabilities of neural networks enhances the adaptability and efficiency of traditional neural network approaches. Knowledge-guided zero-shot learning techniques enable swift adaption to new linguistic contexts and emerging events [6]. Participants will explore how to design, develop, and utilize these models in specific domains, such as public health surveillance, that require dynamic adaptation to new terminologies. This session The tutorial aims to equip attendees with practical skills and a deep understanding of how to apply neuro-symbolic …

Approximate Computing And In-Memory Computing: The Best Of The Two Worlds!, Mohammed Essa Fawzy Essa

Theses and Dissertations

Machine learning (ML) has become ubiquitous, integrating into numerous real-life applications. However, meeting the computational demands of ML systems is challenging, as existing computing platforms are constrained by memory bandwidth, and technology scaling no longer yields substantial improvements in system performance. This work introduces novel hardware architectures to accelerate ML workloads, addressing both compute and memory challenges. In the compute domain, we explore various approximate computing techniques to assess their efficacy in accelerating ML computations. Subsequently, we propose the Approximate Tensor Processing Unit (APTPU), a hardware accelerator that utilizes approximate processing elements to replace direct quantization of inputs and weights …