Explainable Process Recommendation through Multi-Contextual Grounding of Dynamic Multimodal Process Knowledge Graphs

  • 72 views
Friday, March 21, 2025 - 10:30 am
Zoom and AI Institute, Seminar Room 529

DISSERTATION DEFENSE

Author : Revathy Venkataramanan Chandrasekaran
Advisor: Dr. Amit Sheth
Date: March 21, 2025
Time: 10:30 am
Place: Zoom and AI Institute, Seminar Room 529
Meeting Link: https://sc-edu.zoom.us/j/8440139296
Meeting ID: 844 013 9296

Abstract

Can I eat this food or not, and why? Which AI pipeline is best for a task and dataset? These questions differ from factual questions and answering tasks as they involve processes with interacting entities. Recipes consist of ingredients, methods, and interactions, while AI pipelines include datasets, models, and tasks. Each entity must be analyzed independently, and a collective inference, known as compositional reasoning, is required to draw the conclusion.

Existing process recommendation methods rely on the availability of structured data but struggle with unstructured data like recipes and AI pipelines. These datasets are often lengthy and noisy, making it hard to capture interactions and derive relevant insights. Additionally, natural language descriptions don’t provide necessary domain knowledge. For example, recipes don’t state that potatoes are healthy carbs with a high glycemic index. Domain-specific knowledge is needed for effective analysis and recommendations.
While neural networks excel in pattern recognition, they struggle with compositional reasoning. This work introduces a neurosymbolic framework for explainable process recommendation using Dynamic Multimodal Process Knowledge Graphs (DMPKGs). DMPKGs provide structured process representations grounded in multi-contextual knowledge for reasoning, explainability, and traceability while utilizing neural networks for pattern recognition. They enable modular entity inference and capture interactions for dynamic decision-making. DMPKGs allow continuous updates and store multimodal data, improving recommendation accuracy and explainability. Two use cases, recipe suitability analysis and AI pipeline recommendation, are explored to demonstrate the effectiveness of this approach in process recommendation.

Hallucinations in Large Foundation Models: Characterization, Quantification, Detection, Avoidance, and Mitigation

  • 24 views
Tuesday, March 18, 2025 - 09:00 am
Online

DISSERTATION DEFENSE
Department of Computer Science and Engineering

University of South Carolina

Author : Vipula Rawte
Advisor: Dr. Amit Sheth
Date: March 18, 2025
Time:  9:00 am – 11:00 am
Place: Zoom and AI Institute, Seminar Room 529
Meeting Link: https://sc-edu.zoom.us/j/83442966750
Meeting ID: 834 4296 6750

Abstract

Deception is inherent in human interactions, and AI systems increasingly exhibit similar tendencies, mainly through hallucinations - plausible yet incorrect outputs stemming from their design, memory limitations, and statistical nature. As AI progresses into Wave 2 - Generative AI, as outlined by Mustafa Suleyman in The Coming Wave, models like GPT and DALL-E are revolutionizing fields like healthcare and education. However, their rapid adoption brings misinformation, safety, and ethics challenges. Notable cases, such as Air Canada’s chatbot providing false information, highlight the real-world impact of AI hallucinations, a phenomenon so prevalent that hallucinate was named Cambridge Dictionary’s Word of the Year for 2023.

This dissertation tackles AI hallucinations through six key areas: (i) Characterization - developing a taxonomy and benchmark (HILT); (ii) Quantification - introducing evaluation metrics (HVI and HVI_auto); (iii) Detection - proposing a span-based Factual Entailment method to improve accuracy; (iv) Avoidance - creating techniques like “Sorry, Come Again?” (SCA) and [PAUSE] injection for better responses; (v) Mitigation - developing RADIANT, a retrieval-augmented framework for entity-context alignment; and (vi) Multi-modal - constructing VHILT and ViBe datasets for hallucination analysis in image-to-text and text-to-video models. This research makes generative AI more reliable and trustworthy by systematically addressing AI hallucinations.

Exploiting structures in reinforcement learning: multi-agent homogeneity, euclidean symmetry, and natural languages

  • 18 views
Tuesday, March 11, 2025 - 03:00 pm
Online
DISSERTATION DEFENSE
 
Author : Dingyang Chen
 
Advisor: Dr. Qi Zhang
 
Date: March 11, 2025
 
Time:  3:00 pm – 5:00 pm
 
Place: Zoom
 

Abstract

Reinforcement learning (RL) has emerged as a powerful paradigm for decision-making in complex environments. However, many RL tasks exhibit inherent structural properties—such as homogeneity, symmetry, and linguistic patterns—that are often underutilized, leading to inefficiencies in learning and generalization. This dissertation systematically exploits these structures to improve the efficiency, scalability, and robustness of RL algorithms across multi-agent and sequential decision-making settings.

First, we investigate homogeneity in multi-agent systems, where agents share similar roles and objectives. By leveraging this structure, we develop communication-efficient actor-critic methods for homogeneous Markov games, enabling scalable learning with reduced coordination overhead.
Second, we introduce Euclidean symmetry in RL, demonstrating how equivariant function approximators can significantly enhance sample efficiency and generalization in spatially structured tasks, such as robotic control.
Third, we integrate large language models (LLMs) into RL to improve sequential decision-making while avoiding expensive retraining. Our framework efficiently combines LLM inference with RL-based optimization, leading to better adaptability and reduced computational costs in contextual decision-making tasks.
Finally, we explore Markov Potential Games (MPGs), a subclass of multi-agent RL with inherent homogeneity. We develop best-response learning dynamics that mitigate non-stationarity and improve equilibrium quality, providing theoretical guarantees on convergence and the first known Price of Anarchy (POA) bounds for policy gradient methods in MPGs.
Through extensive theoretical analysis and empirical validation on diverse benchmarks, this work demonstrates the power of structural exploitation in RL. By leveraging homogeneity, symmetry, and natural languages, this research lays the foundation for more efficient, generalizable, and scalable RL algorithms, with applications in multi-robot coordination, traffic management, recommendation systems, and strategic game playing.

Knowledge and Ontology Enhanced Approach to Natural Language Understanding (KOE-NLU) in Computational Social Media and Healthcare

  • 15 views
Monday, March 10, 2025 - 11:00 am
Online
DISSERTATION DEFENSE
 
Author : Naga Usha Gayathri Lokala
Advisor: Dr. Amit Sheth
Date: March 10, 2025
Time:  11:00 am – 1:00 pm
Place: Zoom and Room 529, AI Institute

Meeting ID: 824 6502 7458

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Abstract


 

Natural Language Understanding (NLU) faces both opportunities and challenges as the amount of social media and healthcare data grows. This is particularly evident in context-sensitive applications such as evaluating cognitive health, identifying mental health symptoms, and monitoring drug abuse. Even though traditional NLU models work well for processing language in a wide range of areas, they often lack the ability to understand language in a specific domain, reason in context, and incorporate structured external knowledge. This dissertation talks about the Knowledge and Ontology Enhanced Approach to Natural Language Understanding (KOE-NLU), a new framework that is meant to make NLU systems better at understanding semantic depth, contextual awareness, and drawing conclusions in computational social media and healthcare informatics. The KOE-NLU framework is built upon three fundamental pillars: There are three types of methods. The first is Ontology-Driven Semantic Representation, which puts domain-specific knowledge into formalized ontologies to help with making sense of things based on their context and drawing conclusions from them. The second is Knowledge Graph Integration, which links different types of data sources to improve structured representation and reasoning over unstructured text. The third type is transformer-based language models with knowledge augmentation. This type of modeling uses domain-specific prompts to teach transformer based models, generative AI models structured knowledge sources. These components work in synergy to enable more accurate and context-aware natural language predictions. To demonstrate the efficacy of the KOE-NLU framework, this dissertation presents four major application areas. In the first place, the framework is used in cognitive health informatics to predict Mild Cognitive Impairment (MCI) by putting together MoCA test scores and automated discourse analysis. This is done by using a Cross-Cognitive Domain Attention (CCDA) model to pull out linguistic markers that show cognitive decline. Second, we present process knowledge-guided language prompting as a way to automate Main Concept Analysis (MCA) for discourse assessment. It is shown that process-aware language models are better than traditional text-based classifiers at detecting speech coherence in people with neurodegenerative conditions. Third, in mental health analytics, a knowledge-infused multi-task learning framework is developed to extract mental health symptoms linked to cardiovascular disease (CVD) from social media discourse, employing hierarchical attention networks combined with expert-curated knowledge bases. Finally, the dissertation introduces a Drug Abuse Ontology (DAO) that was created using semi-automated ontology engineering methods. We can use this ontology to identify patterns in substance use disorders, monitor illicit drug trends on social media, and examine the evolution of drug-related emotions over time. A rigorous experimental framework is implemented to evaluate the KOE-NLU models across these applications. The role of ontology-enhanced reasoning in model performance is looked at by comparing supervised and self-supervised learning paradigms. The results show that knowledge-infused transformer architectures do better than baseline deep learning models by as much as 17% in the F1 score and are better at interpreting clinical discourse in terms of context. Structured ontological constraints also make it much easier to classify substance use disorders and pull out mental health symptoms. This cuts down on false positives in automated detection systems by over 12%. This dissertation makes computational social media analytics and healthcare informatics better by giving us a knowledge-driven NLU framework that can be used on a large scale. This framework connects data-driven machine learning with symbolic AI methods. The KOE-NLU framework creates the foundation for the next generation of AI models that can be explained. These models will use process knowledge, ontological reasoning, and knowledge graphs to help computers understand language better in important healthcare settings. More work can be built on top of this by including speech, physiological, and neuroimaging signals, making domain-adaptive self-learning ontologies, and setting up real-time clinical decision support systems. This research marks a significant advancement in KOE-NLU, with important real-world applications in healthcare AI, clinical decision support, and public health monitoring on social media.

 

CSE Graduate Research Symposium

  • 94 views
Friday, March 7, 2025 - 04:00 pm
Storey. 550 Assembly St, Room 2277, Columbia, SC 29201

Below is a tentative agenda for the day. If you plan to attend the luncheon, please RSVP by at this link https://forms.office.com/r/2fayB7SPqA by Tuesday, March 4 to ensure we have an accurate lunch count.

Agenda:
10:00 am - 12:00 pm-  Industrial Advisory Board meeting in room 2277
10:30 am - 12:00 pm-  Even number graduate student poster session, 2nd floor hallway
12:00 pm - 1:30 pm-    Lunch in room 2277

1:30 pm - 2:30 pm-      Industrial Advisory Board meeting continues in room 2277
1:30 pm - 3:00 pm-      Odd number graduate student poster session, 2nd floor hallway
3:00 pm - 4:00 pm -     Talk and poster session winners, room 1400 (1st floor)

Posters:

NumberTimePresenting Author(s)Poster Title
AI18MorningRojina PantaFinding Reaction Mechanism Pathways with Deep Reinforcement Learning and Heuristic Search
AI19MorningMahsa Majdzadeh ArdakaniLLMPi: Optimizing LLMs for Realtime Conversational AI on Raspberry Pi
AI20MorningBharath Muppasani On Generalized Planning for Controlling Opinion Networks: Interpreting Human-AI Dialog States and Beliefs
AI21MorningProtik NagTargeted Layer Recalibration: Enhancing Interpretability with TCAV-Based Retraining
AI22MorningSadman Sadeed OmeePolymorphism-Aware Crystal Structure Prediction via Neural Network Potential and Genetic Algorithm with Adaptive Space Group Control
CHS2MorningMd Hasibul AminCrossNAS: A Cross-Layer Neural Architecture Search Framework for PIM Systems
CHS3MorningSuyash Vardhan Singh;Iftakhar Ahmad;Resource Scheduler for Real-time Machine Learning
CHS4MorningJinendra MalekarPIM-LLM: A High-Throughput Hybrid PIM Architecture for 1-bit LLMs
CV5MorningHuaqiang GuoAn End-to-end Flooding Depth Detection System Based on SAM Model 
CV6MorningLili WangDiffusion-based CT Image Segmentation for Intracerebral Hemorrhage
CV7MorningRenjith PrasadPic2Prep:A multimodal conversational agent for cooking assistance
CV8MorningHongpeng YangUnlocking Dark Vision Potential for Medical Image Segmentation
HB10MorningAlireza Bagheri RajeoniDeep Learning For Human Vascular Analysis
HB11MorningHamed AbdollahiGenome Methylation Array Analysis Using Iterative Random Forest Classification
HB12MorningAndrew Smith; Kuba JerzmanowskiMonitoring Opioid Use Disorder Treatment Adherence Using Smartwatch Gesture Recognition
HB13MorningChristopher LeeReducing Diagnostic Barriers Using Neural Networks for Trypanosoma cruzi Detection in Blood Smears
HB14MorningSavannah Noblitt and Kayly TranThe Creation and Implementation of a Novel Database for Medical School, Research Tracking and Student Research Opportunities
QN32MorningErik ConnertyPredicting Chaotic Systems with Quantum Echo-state Networks
QN33MorningRabins WostiQuantum fanout and GHZ states using spin-exchange interactions
QN34MorningHasti Zanganeh, Blake SeekingsSpikeFed: Exploring the Landscape of Federated Learning for Spiking Neural Networks
RA27MorningRitirupa DeyDynamic Role Transition in Human-in-the-Loop Robotic System with Neural Network Controllers
RA28MorningMohammadreza Mohammadi; Aishneet JunejaTRACE: Efficient Object Tracking via In-Sensor Compression and Near-Sensor Template-Matching
RA29MorningChathurangi ShyalikaTime Series Foundational Models: Their Role in Anomaly Detection and Prediction
RA30MorningJian LiuReinforcement Learning Driven Channel Switching Strategy for Connected Vehicles under Extreme Weather
AI16AfternoonJinzhu LuoReinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control
AI17AfternoonNitin Gupta, Bharath MuppasaniTowards Enhancing Road Safety in South Carolina Using Insights from Traffic and Driver-Education Data
AI23AfternoonQinyang LiOut-of-distribution materials property prediction using adversarial learning based fine-tuning
AI24AfternoonYong Yang;Xiang GuanMimicking Aphasia through Controlled Neural Perturbations in Multimodal Large Language Models
AI25AfternoonAndrew SmithToward Concurrent Identification of Human Activities with a Single Unifying Neural Network Classification: First Step
AI35AfternoonVansh Nagpal, Rae Sara Jones,  Kausik LakkarajuDisseminating Authentic Public Messages using Chatbots - A Case Study  with ElectionBot-SC to Understand and Compare Chatbot Behavior for Safe Election Information in South Carolina
CHS1AfternoonTiffany YuEnhancing Security and Efficiency in Computational Hardware Through Intelligent Optimization
HB9AfternoonSavannah NoblittWeb Application for Searching and Displaying Cancer Patient Data
HB15AfternoonMusa AzeemDihedral Angle Adherence: Evaluating Protein Structure Predictions in the Absence of Experimental Structures
QN31AfternoonMichael Cluver, Eli HatcherEfficient Quantum Circuit Synthesis with DeepXube
RA26AfternoonMisagh SoltaniPlanning in Large Action Spaces Using Invertible Policy Networks
    

Building Trustable Methods for Group Recommendations: Advancing Fairness and Robustness Across Domains

  • 23 views
Friday, February 14, 2025 - 10:00 am

DISSERTATION DEFENSE

Department of Computer Science and Engineering

University of South Carolina

Author : Siva Likitha Valluru
Advisor: Dr. Biplav Srivastava
Date: February 14, 2025
Time:  10 am – 11:30 am
 
Place: AI Institute, Seminar Room 529 and Teams
 
 

Abstract

On the internet, where the number of available choices is exponentially growing, there is a need to prioritize and efficiently deliver relevant results to users, on demand. Recommendation systems (RSs) address this need by searching and filtering through large amounts of dynamically generated information and providing users with recommendations tailored to them. These systems have primarily focused on (1) single-user models, where recommendations are tailored towards a specific individual, or (2) single-item models, where items are recommended based on a broader appeal to users and similarities in item metadata, in the past. In many real-world scenarios, however, recommendation systems are often tailored to groups of users rather than individual users. This shift from single-user and towards group recommendation brings forth unique challenges such as addressing the diverse needs of a group, resolving conflicts and reaching consensus, assuring that groups receive fair recommendations without bias towards protected characteristics such as race, gender, and socioeconomic status; and ensuring that recommendations remain stable and adaptable even in response to dynamic and evolving environments. Such systems are growing increasingly relevant in disciplines such as team management, healthcare, online communities, and entertainment.

This dissertation explores and addresses the unique challenges of group recommendation systems (GRSs), such as increased computational complexity, varying group dynamics, and principles of trustworthy artificial intelligence (AI) related to bias, fairness, and robustness. The research makes contributions towards developing novel methods and metrics for GRSs, evaluating them with respect to performance through real-world case studies, such as team formation (project management) and meal planning (healthcare), and improving fairness and robustness of group recommendations. For team recommendation, the built system, called ULTRA, has been evaluated to demonstrate its effectiveness and generality in the context of research funding at academic institutions in the United States (US) and India. This work releases comprehensive datasets to aid in developing adaptable and reliable GRSs that optimize both performance and user satisfaction in dynamic environments. Beyond research, the dissertation is opening new avenues in entrepreneurship, intellectual property, and teaching resources.

Augmenting Deep Learning for Efficient NextG Wireless Communication and Sensing Systems

  • 9 views
Friday, January 31, 2025 - 11:00 am
Online

DISSERTATION DEFENSE

Department of Computer Science and Engineering

University of South Carolina

Author : Hem Kanta Regmi
Advisor: Dr. Sanjib Sur
Date: Jan 31, 2025
Time:  11 am – 1:00 pm
Place: Teams: Meeting Link
 
Meeting ID: 234 373 819 493
Passcode: mC6WG6me
 

Abstract

  Wireless networks have become an integral aspect of our daily lives. Over the years, earlier generations of wireless networks have enabled some innovative applications, such as wireless gaming, fast internet browsing, and home automation, which were previously unattainable. However, to support emerging technologies like autonomous driving, virtual reality, telemedicine, and intelligent manufacturing, which require high data throughput and low latency, there is a need for advanced wireless networks. Legacy networks like WiFi/LTE, which operate below 6 GHz, have limited bandwidth and are insufficient to fulfill the high data throughput demands of several applications.

Millimeter-wave (mmWave) networks, operating between 30 GHz to 300 GHz, promise to offer a broader contiguous spectrum and low latency and might solve various applications’ data throughput and latency requirements. However, due to the directional nature of mmWave, it is prone to frequent outages while using Line-of-Sight (LoS) paths only, and the network deployment needs to consider Non-Line-of-Sight (NLoS) paths to provide reliable network connectivity. Additionally, due to the high operating frequency of mmWave network devices, these devices can also work as sensors to detect objects in their surroundings and work in all weather conditions. However, the specularity of mmWave and the weak reflectivity of objects cause most object details to be lost, making objects imperceptible. Besides, sharing a single mmWave device for networking and sensing applications reduces data throughput and sensing accuracy.

In this dissertation, we first design and evaluate a deep learning-based model to find optimal locations of 5G base stations (called “picocell”) for indoor and outdoor environments. Due to the lack of open-source data samples at high frequency, we collect actual data samples and verify our deep-learning models. Our model uses semantic features of the environment, such as the wall, ceiling, floor, and door, to make it generalizable and requires only a few data samples from the new environment to fine-tune, and it removes the need for an extensive site survey for optimal deployment. We then enable accurate sensing and high-throughput networking with a single mmWave device. We use conditional Generative Adversarial Networks to get an accurate depth image of the objects and residual networks to predict unobservable data samples from previously observed ones during networking. Finally, we design and implement a deep learning framework to detect the 3D bounding boxes of vehicles and pedestrians from mmWave devices in harsh weather conditions and compare their performance with vision-based methods. We use the non-anchor bounding box prediction method to enable 3D object detection for outdoor environments.

Neural Network-Based Low-Level 3D Point Cloud Processing

  • 10 views
Thursday, December 19, 2024 - 09:00 am
Online

DISSERTATION DEFENSE

Department of Computer Science and Engineering
University of South Carolina
Author : Pingping Cai
Advisor: Dr. Song Wang

 
Date: Dec 19, 2024
Time:  9 am – 10: 30 am
 
Place: Teams Link
 
Meeting ID: 240 720 185 444
Passcode: Lj6ot2X7 

Abstract

  3D computer vision is a promising research field with the potential to revolutionize future lifestyles. Among various 3D representation formats, point clouds stand out for their efficiency in depicting 3D objects using a set of coordinates, enabling advancements in fields such as autonomous driving, virtual reality, and robotics. Due to the limitations of sensor fields of view and scanning trajectories, the collected point clouds are usually sparse, noisy, and incomplete, impeding the performance of many downstream applications. Thus, the tasks of low-level point cloud processing are proposed to refine and generate dense, clean, and complete point clouds.

To accomplish these tasks, traditional algorithms rely on manually designed rules for processing point clouds in 3D coordinate space, but they often struggle with new or complex shapes. In contrast, neural network-based algorithms extract and manipulate geometric features in a high-dimensional feature space and have made substantial progress in point cloud processing. Nevertheless, outputs from existing neural networks frequently exhibit ambiguous shapes and excessive noise, indicating significant room for improvement. Therefore, we focus on advancing neural network-based low-level point cloud processing algorithms, including upsampling, completion, and denoising. A key contribution of this dissertation is the integration of task-specific properties, such as geometric surface constraints and 3D shape knowledge, into neural networks, resulting in significant improvements over previous methods.
We begin our research with the task of point cloud upsampling, a fundamental problem in 3D analysis. A number of attempts achieve this goal by establishing a point-to-point mapping function via deep neural networks. However, these approaches are prone to produce outlier points due to the lack of explicit surface-level constraints. To solve this problem, we introduce a novel surface regularizer into the upsampler network by forcing the neural network to learn the underlying parametric surface represented by bicubic functions and rotation functions, where the newly generated points are then constrained on the underlying surface.
Then, we focus on the point cloud shape completion task, which aims to reconstruct the missing regions of the incomplete point clouds with accurate shapes. Prior approaches address this task by generating a coarse but complete seed point cloud through an encoder-decoder network. However, the encoded features often suffer from information loss in the missing portions, leading to an inability of the decoder to reconstruct the seed point cloud with detailed geometric features. To overcome this challenge, we propose a novel dictionary-guided shape completion network. It consists of orthogonal dictionaries that can learn shape priors from training samples, thereby compensating for the information loss in missing portions during inference and enhancing the representation capability of seed points.
Finally, we continue our research on the point cloud denoising task, where the denoised point clouds are expected to well represent the underlying object shape, as well as exhibit better point distributions on the object surface. Previous methods iteratively shift noisy points toward the underlying surface using fixed directions, resulting in poor efficiency and distribution. To address this problem, we introduce a novel direction-guided denoising pipeline, where each point is shifted to the underlying surface using optimally predicted directions and distances. It includes the newly designed direction-guided projection blocks, based on neural implicit functions, to facilitate efficient point movement.

Ethical and effective use of AI in academic settings. How should we use AI effectively in our classrooms?

  • 9 views
Friday, November 1, 2024 - 10:00 am
Online

You are invited to attend an AI Roundtable event this Friday, November 1. This event is entitled “Ethical and effective use of AI in academic settings. How should we use AI effectively in our classrooms?”

We will have representatives from various departments across campus (Law/Academic Integrity/Library Sciences) and the panel will discuss the use of Gen AI in classrooms. You will have the opportunity to provide feedback to them as both faculty and students.

If you are interested in attending, please register here: https://forms.office.com/r/n4UznxruNg

Held online and in person at the AI Institute 
Room 513, 1112 Greene St. Columbia, SC 29208 (Science and Technology Building)
10 am ET to 12 PM ET (refreshments will be provided)

*Limited to 50 in-person participants