Xiao Qin's Students
[Total: 115 | Current Students: 9 | Student Alumni: 99 | Other Students: 7 ]
Students
I have graduated 40 Ph.D. students, 40 master's students (14 theses and 26 projects). Among these students, 37 doctoral students have graduated from Auburn University since 2007; 3 doctoral students graduated from New Mexico Tech between 2006 and 2008. In addition, I served on committees for 54 Ph.D., 38 M.S., and 4 undergraduate students. Currently, I am supervising 8 Ph.D. students and 6 master's students.
Current Students
[ Total: 9 | Doctoral Students: 7 | Master's Students: 2 ]
Doctoral Students
Chenran Zhang [Ph.D. Student, Summer 2031 Expected]
Dissertation Research: Energy-Efficient Adaptive File Replication in Data-Intensive Clusters.
This research investigates energy-efficient file replication for data-intensive clusters. File systems such as HDFS typically store a fixed number of replicas for every file on randomly selected servers, ignoring differences in file popularity and server capability; as a result, popular files may lack enough replicas to serve bursts of read requests, while replicas of rarely accessed files waste storage and energy. This study aims to develop replication techniques that adapt the number of replicas to time-varying file popularity, place cold data on low-power servers, and select replica servers according to their network bandwidth and service capacity, thereby reducing file read latency, replication time, and power consumption.
Zewei Zhang [Ph.D. Student, Summer 2030 Expected]
Dissertation Research: Aerial Multimodal Sensing and Inference: Green Edge Services with Multi-Dimensional Resource Orchestration.
This research investigates energy-efficient edge services for uncrewed aerial vehicle (UAV)-assisted multimodal mobile edge computing (MEC) networks, in which a UAV collects images and textual data from ground nodes, performs onboard multimodal fusion and inference, and harvests energy from a ground base station through wireless power transfer (WPT). We formulate a joint optimization problem that minimizes overall system energy consumption by orchestrating computing capacity, semantic compression ratio, transmission power, node association, UAV trajectory, and WPT time allocation. To solve this problem, we design an adaptive projection-augmented proximal policy optimization (AP-APPO) algorithm that uses a pre-computed lookup table and closed-form derivations to reduce the action space of deep reinforcement learning, together with a constraint projection layer and a forced service mechanism that keep the learned solutions feasible.
Libo Sun [Ph.D. Student, Spring 2028 Expected]
Dissertation Research: When Does Added Machinery Pay? Empirical Boundaries in Neural and Multi-Agent Systems.
We ask when added architectural machinery – routers, attention, and inter-expert communication channels – earns its cost once the comparison is made at a matched computing budget. Across six studies spanning sparse Mixture-of-Experts routing in machine translation and in vision, sequence architectures in cooperative multi-agent reinforcement learning, key-value cache retention and training-time cache geometry for long-context inference, and multi-agent network intrusion detection, we find that added machinery paid only when it expressed something the existing computation could not, and only in the evaluation regime where that expressiveness was the binding constraint. We report the unfavorable side of each comparison, so that the boundary is located rather than a single effect size.
Corey McDaniels [Ph.D. Student, Summer 2028 Expected, Co-Advisor: Dr. Laurence Rilett]
Dissertation Research: A Data-Driven Approach to Vehicle Performance Assessment.
This project involves the development of a predictive model for vehicle traversal speed and time across road segments, aimed at creating an advanced traffic analysis system. The outcome is intended to be a set of analytical tools to assess vehicle performance and inform traffic management strategies.
Mohammad Joghataee [Ph.D. Student, Summer 2027 Expected, Co-Advisor: Dr. Ashish Gupta]
Dissertation Research: AI-Driven Diagnosis of Rare Infectious Diseases Using EHR Data.
This study aims to develop a robust AI-driven clinical decision support system that leverages EHR data to detect rare and atypical infections within the first few days of hospital admission. By analyzing data from patients admitted across multiple Mayo Clinic hospitals between January 2010 and March 2023, we intend to build models that can identify patients at risk of rare infections based on their initial clinical and demographic information. The dataset used in this study includes both patients with confirmed rare infections and a control group of patients admitted on an urgent or emergent basis for other conditions, ensuring a relevant comparison in terms of admission acuity.
Maryam Bigonah [Ph.D. Student, Summer 2027 Expected, Co-Advisor: Dr. Daniela Marghitu]
Dissertation Research: A Gamified Exploration of Augmented Reality (AR) and Virtual Reality (VR) Technologies in Agricultural Education.
We introduce a platform utilizing Augmented Reality (AR), Virtual Reality (VR) and Artificial Intelligence (AI) in agricultural education, specifically focusing on CEA. Our platform incorporates dynamic stochastic elements to enhance engagement and understanding, ensuring accessibility for all students, regardless of physical or cognitive abilities. The system relies on a robust evaluation process, encompassing qualitative and quantitative assessments.
Chris White [Ph.D. Student, Spring 2027 Expected]
Dissertation Research: AI-Enabled RTLA and Real-Time Tuning.
This dissertation explores real-time performance in virtualized environments. It focuses on (a) extending the Real-Time Linux Analysis (RTLA) codebase and KVM architecture with a feasibility prototype, and (b) evaluating tools for real-time metrics collection in distributed systems and exploring data analysis from bare metal and virtualized sources.
Master's Students
Carlos Berrios [M.S. Student, Thesis Option; co-advised with Dr. David Bevly]
Griffin Herring [M.S. Student, Thesis Option; co-advised with Dr. Larry Rilett]
Student Alumni
[Total: 99 | Doctorates: 40 | Master's Students: 40 | Undergraduate Students: 19 | Back to Top]
Doctoral Student Alumni
Liangliang Xu (Ph.D., Summer 2026, Co-Advisors: Dr. Hao Chen and Dr. Zongliang Yue; Auburn University)
Dissertation Research: Interpretable and Risk-Aware Machine Learning for Environmental Suitability, Exposure, and Decision Systems.
This dissertation develops interpretable and risk-aware machine-learning approaches for environmental prediction and decision-making under uncertainty. The first study builds a climate-regeneration-fire informatics framework that maps the future climatic suitability of loblolly, shortleaf, slash, and longleaf pine across the southeastern United States, drawing on more than 100,000 adult and seedling occurrence records from the USDA Forest Inventory and Analysis database and seven species distribution modeling algorithms. The second study examines whether airborne pollen, alongside air pollution and humidity, carries useful information for county-level lung cancer incidence and mortality prediction, using Random Forest, SHAP interpretation, and county-aware validation. The third study presents TOPS, a transition-based volatility-controlled policy search framework for risk-averse reinforcement learning that learns from short transition segments and provides global convergence guarantees under over-parameterized neural network policies.
Peixiong He (Ph.D., Summer 2026; Auburn University)
Current Employment: Assistant Professor of Computer Science at Tuskegee University
Dissertation Research: An Energy-Aware SQL Database System.
This dissertation presents GreenDB_DM, an energy-aware SQL database system that uses workload-aware data mining to improve table placement and reduce unnecessary wakeups of cold nodes. Its first component, GARMT, groups consecutive SQL queries and applies a modified FP-Growth algorithm (GFP-Growth) to discover tables that are frequently accessed together, reducing pattern-mining runtime by up to 40% while preserving rule quality. GreenDB_DM then combines these co-access patterns with a greedy set-cover algorithm that places complete table groups on the hot node, improving the full-query hit rate by 5–15 percentage points over the strongest placement baseline and reducing total cluster energy by an additional 2.4–4.7% beyond GreenDB. A complementary study of recommender systems, based on a network Susceptible-Infected model with time-varying transmission rates, supports the broader argument that modeling temporal and structural behavior leads to more efficient systems.
Taha Khalid Al Tekreeti (Ph.D., Spring 2026; Auburn University)
Employment: Senior Technical Architect at Accenture.
Dissertation Research: Towards Energy-Efficient and Real-Time Cloud Computing.
This dissertation research addresses the high energy consumption and environmental impact of cloud data centers by introducing two complementary resource management frameworks, EGRET and VMaestro. The first framework, EGRET, optimizes IT-side energy by combining virtual machine consolidation with dynamic voltage and frequency scaling to reduce IT energy by 41.6% while maintaining strict service-level agreements. The second framework, VMaestro, extends these savings to the facility level by co-optimizing IT workloads and adaptive cooling systems based on thermal feedback, achieving a 22.9% reduction in total facility energy and establishing a holistic roadmap for sustainable cloud computing.
Rushabh Patel (Ph.D., Spring 2026, Co-Advisor: Dr. Ashish Gupta; Auburn University)
Employment: Lead Data Scientist at VNS Health.
Dissertation Research: Enhancing Healthcare Operations Through Generative AI: Contact Center Intelligence and Clinical Note Quality Assessment.
This dissertation introduces two distinct advancements in AI-driven healthcare documentation to improve operational efficiency and evaluation rigor in high-stakes environments. First, it presents an end-to-end operational pipeline for Medicare Advantage contact centers that integrates speech recognition, sentiment analysis, and LLM summarization into a functional dashboard to automate quality monitoring and protect performance ratings. Second, it establishes SOAP-QualiVal, a multi-agent framework that resolves the “evaluator quality paradox” by utilizing multi-criteria optimization to accurately rank and validate LLM evaluators based on their intrinsic ability to detect safety-critical clinical errors.
Gabrielle Taylor (Ph.D., Summer 2023, Co-Advisor: Dr. Ashish Gupta; Auburn University)
Starting Employment: Data Scientist at MultiPlan Inc.
Dissertation Research: Enhancing Aspect-Based Sentiment Analysis: Investigating Aspect Term Extraction and Annotation Schemes.
We provide one case study that exposes the limits of topic modeling as an approach to ATE in aspect-based sentiment analysis (ABSA). Through our experiments, we develop a framework for Sentiment and Emotion Analysis (SEA) that highlights the limitations of topic modeling, which is commonly used for Aspect-Based Sentiment and Emotion Analysis. We offer a novel approach to Aspect-Term Extraction (ATE) – a vastly unexplored research area. The ATE design provides guidance and direction that can lead to more fine-grained details for companies and organizations that desire user feedback.
Hung Nguyen (Ph.D., Spring 2023, Co-Advisor: Dr. Yi Zhou; Auburn University)
Employment: Sr. Director, Head of Software Engineering at Yield Engineering Systems
Dissertation Research: From Edge to Equipment: Design and Implementation of a Machine-Learning-Enabled Smart Manufacturing System.
We investigate an offloading strategy to shift a selection of computation tasks toward the equipment layer. We devise a novel scheduler that allocates edge computing resources with workload-awareness at the equipment level. As the last piece in this dissertation, we design an advanced protocol on the SECS/GEM interface to facilitate the transfer of computational tasks from the edge to the equipment.
Aditya Tadakaluru (Ph.D., Spring 2023; Auburn University)
Employment: Software Engineer at Southern Company
Dissertation Research: A Geospatial Approach to Preserving Location Privacy.
We propose a novel context-optimized and spatial-aware (COSA) dummy locations generation framework for location privacy, built and evaluated on real-world geospatial data.
Next, we propose a novel enhanced parcel-based location privacy framework - PLP+ - to construct spatially similar dummy locations anchored on the real-world spatial context of locations such as parcels, building footprints, and road proximity. Finally, we introduce a temporal-constraint attack that exploits temporal constraints associated with the semantic category of locations to eliminate dummy locations and identify the true location.
Chengfei Wang (Ph.D., Fall 2022, Co-Advisor: Dr. Ashish Gupta; Auburn University)
Starting Employment: Assistant Professor at Penn State University Green Valley
Dissertation Research: User Feedback Analysis for Business Intelligence: Semantics, Sentiment and Model Robustness.
Business intelligence is a set of enterprise decision support tools designed to help managers, analysts, and executives rapidly make wise decisions. In this dissertation research, we put forward an AI-driven framework that supports application design. We utilize deep unsupervised learning models to build a framework for harnessing potential customer feedback information. The first component of the framework applies Bidirectional Encoder Representations from the transformers (BERT)-based topic modelling approach to identify topics and key themes that emerge from user reviews of mobile applications belonging to the health and fitness genre. Sentiment analytics integrates the accompanying ratings to reveal the market acceptance of various aspects of product design.
Xiaopu Peng (Ph.D., Summer 2022; Auburn University)
Current Employment: Assistant Professor at Talladega College
Starting Employment: Assistant Professor at Lander University
Dissertation Research: Exploiting Renewable Energy and UPS Systems with a Renewable-Aware.
We propose a renewable-energy manager called REDUX, which offers a smart way of managing server energy consumption powered by a distributed UPS system and renewable energy. REDUX maintains a desirable balance between renewable-energy utilization and data center performance. REDUX makes judicious use of UPS devices to allocate energy resources when renewable energy generation is low or fluctuate condition. REDUX not only guarantees the stable operation of daily workload, but also reduces the energy cost of data centers by improving power resource utilization. Compared with existing strategies, REDUX demonstrates a prominent capability of mitigating average peak workload and boosting renewable-energy utilization.
Ting Cao (Ph.D., Summer 2022; Auburn University)
Current Employment: Assistant Professor at Utah Valley University
Starting Employment: Assistant Professor at Truman State University
Dissertation Research: Popularity-Aware Storage Systems for Big Data Applications.
Recommendation algorithms play an increasingly dominant role in big data services like Netflix and YouTube. In streaming applications, it becomes unavoidable that trillion tons of personal and industrial data are flooded into the data center. This dissertation is focused on popularity-aware techniques anchored on recommendation algorithms to bolster the performance of data processing. In this dissertation study, we make three contributions centered around data reconstruction, cache replacement, malware detection, and distributed denial of service (DDoS) detection.
Tathagata Bhattacharya (Ph.D., Summer 2022; Auburn University)
Starting Employment: Assistant Professor at Auburn University at Montgomery
Dissertation Research: Energy-Efficient Computing and Modeling in Cloud Infrastructures.
We perform experiments on virtual machines running on a cloud computing infrastructure to measure the resource utilization for several most commonly used benchmark applications on a cloud computing infrastructure. Based on the data we train several resource utilization models to predict the resource usage of virtual machines in a cloud computing infrastructure. Next, we construct a power consumption model of a traditional center by incorporating more clean energy resources and cutting back the brown energy resources while maintaining the data center power demand. Deploying this optimized power model that includes mostly clean energy resources, traditional data centers are expected to meet their power demand and protect the environment.
Jianzhou Mao (Ph.D., Summer 2022; Auburn University)
Current Employment: Assistant Professor at Morgan State University
Starting Employment: Research Assistant Professor at Morgan State University
Dissertation Research: Improving Energy Efficiency and Security of DVFS-Enabled Clouds.
We propose a frequency-aware management strategy, which controls dynamic power and static power of processors running virtual machines in data centers. Unlike existing dynamic voltage and frequency scaling schemes, our strategy simply incorporates frequency requirements rather than task execution times. We build a frequency-aware model to derive an optimal frequency ratio that minimizes processors' energy consumption. We design a management approach to judiciously adjust frequency ratio to conserve energy without violating the frequency requirements imposed by virtual machines. In addition, we articulate novel energy-aware scheduling policies customized for virtual machines running on clouds, in which service-level agreements (SLAs) are fulfilled.
Kenan Xiao (Ph.D., Summer 2022, Co-Advisor: Dr. Ashish Gupta; Auburn University)
Starting Employment: Member of Technical Staff at TikTok
Dissertation Research: Understanding Misinformation: The Tale of Fake News and Fake Reviews.
We develop FNEPP - a unique framework that explicitly combines multiple social context perspectives like news contents, user engagements, user characteristics, and the news propagation path. The FNEPP framework orchestrates two collaborative modules - the engagement module and the propagation path module - as composite features. The engagement module captures news contents and user engagements, whereas the propagation path module learns global and local patterns of user characteristics and news dissemination patterns.
Longwei Wang (Ph.D., Summer 2022, Co-Advisor: Dr. Ashish Gupta; Auburn University)
Current Employment: Assistant Professor at University of South Dakota
Starting Employment: Assistant Professor at Nanjing University of Information Science & Technology, China
Dissertation Research: Behavior Analysis and Enhancement of Robustness for Deep Neural Networks.
We study the neuron activation behaviors of a well-trained classification model. An information theoretical method is leveraged to examine the behavior of layer-wise neurons in deep neural networks. We discover that in a well-trained classification model, the randomness level of a neurons activation pattern is curtailed with the depth of fully connected layers. This finding suggests that the neuron activation patterns of deep layers are more stable than those of shallow layers. Next, we implement an approach to incorporating a diversity of symmetries, such as rotation and scaling, into an existing CNN model to enhance the robustness of deep neural network models.
Wei Li (Ph.D., Dec. 2021, Co-Advisor: Dr. Wei-Shinn Ku; Auburn University)
Starting Employment: Senior Lecturer at Auburn University at Montgomery
Dissertation Research: Crowdsourcing for Smart Cities.
We investigate a challenging geo-task scheduling problem under the various spatial and temporal constraints in real-world mobile crowdsourcing applications. Given the location of a worker, the goal is to find an optimal task execution sequence that maximizes the number of tasks that could be finished. Since the exact solution to the maximum task scheduling is computationally intractable, we propose two sub-optimal approaches using the particle filtering and the DBSCAN clustering.
Chaowei Zhang (Ph.D., Summer 2021, Co-Advisor: Dr. Ashish Gupta; Auburn University)
Starting Employment: Assistant Professor at Yangzhou University
Dissertation Research: Real-Time Fake News Detection using Objectivity Extraction and Analytics Approaches.
We propose the framework of our proposed approach and the underlying mathematical foundations followed by the implementation of our proposed approach and its validation based on a corpus built using scraped fake news and ground truth events. We collect legitimate news, refine news from word-based dataset to topic-based dataset, detect fake news through a two-layered filter which includes the first-layer (fake topic detection) and the second-layer (fake event detection).
Christian Kauten (Ph.D., Summer 2021, Co-Advisor: Dr. Ashish Gupta; Auburn University)
Starting Employment: Research Scientist at Vision and Emerging Technologies Sensory, Inc.
Dissertation Research: An Augmentation System for Autonomous Vehicles.
We develop a system prototype for perception augmentation in autonomous vehicles. The system is built using a fully convolutional deep encoder-decoder architecture to map pixels with depth measures to semantic class labels. Class labels recombine with depth measures to produce a 3-dimensional semantic map of the objects in front of the vehicle. The map, simplified to highlight areas of importance (e.g., other vehicles, pedestrians), is shown to the passenger using a novel user interface. The map is also analyzed for potential risks to queue alerts to the passenger. Alerts are both: (1) shown to the passenger using an addressable LED strip around the windshield, and (2) delivered to the passenger through a speaker.
Ye Wang (Ph.D., Dec. 2020, Co-Advisor: Dr. Li Chen; Auburn University)
Current Employment: Research Scientist at Biogen Inc.
Starting Employment: Postdoctoral Fellow at Biogen Inc.
Dissertation Research: Novel Machine Learning Algorithms for Analyzing Large-scale Genomic and Genetic Data.
In this dissertation, we take advantage of this and explore these association patterns using machine learning methods. We first design a deep learning method MDeep for microbiome-based prediction by considering both the taxon abundance and phylogenetic tree. MDeep models the taxonomic rank by the convolutional layers and captures the phylogenetic correlation on each taxonomic rank via the convolutional operation. In order to explore the diseases/phenotypic associations with the human genome, we propose two machine learning frameworks. The first framework, WEVar, is a supervised learning framework by integrating the pre-computed scores from representative existing scoring methods. The second framework, DeepMFIVar, is a deep multimodal learning framework for the functional interpretation of genetic variants.
Shubbhi Taneja (Ph.D., Summer 2018; Auburn University)
Current Employment: Assistant Teaching Professor at Worcester Polytechnic Institute
Starting Employment: Assistant Professor at Sonoma State University
Dissertation Research: Energy-efficiency, Thermal-aware, Scheduling, Approximate Computing, MapReduce, Modeling.
We show that CPU-intensive and I/O-intensive jobs exhibit various thermal and performance impacts on multicore processors and hard drives of Hadoop cluster nodes. After we quantify the thermal behaviors of Hadoop jobs on the master and data nodes of a cluster, we propose our scheduler that performs job-to-node mappings for CPU-intensive and I/O-intensive jobs. We apply our strategy to several MapReduce applications with different resource consumption profiles.
Yi Zhou (Ph.D., May 2018; Auburn University)
Starting Employment: Assistant Professor at Columbus State University
Dissertation Research: Energy-Efficient Database Operations on Multicore Servers.
Evaluating energy efficiency of database applications running on multicore systems becomes an indispensable and strategic component of building green data centers. We discuss the criteria and challenges of building an energy efficiency benchmark for databases. We construct a benchmark to evaluate the energy consumption of operations on multicore processors.
Yangyang Liu (Ph.D., Fall 2017; Auburn University)
Starting Employment: Software Engineer at Salesforce
Dissertation Research: Profiling Energy Usage of Web-Service Applications on Clusters and Topology-based scheduler on Apache Storm.
In the first part of dissertation, we are diving into this key issue and evaluating energy-efficiency based on TPC-W benchmark: a notable web transaction e-commerce benchmark. We simulate the web transaction with different database sizes and collect the energy data by KILL-A-WATT. In the second part of this dissertation, we propose a novel scheduler for Apache Storm, topology-based scheduler or TOSS. Compared to the default round-robin scheduler, TOSS handles the tight-bind components and balances the workloads by introducing self-tuning mechanism in the deployment stage.
Ajit Chavan (Ph.D., Fall 2017; Auburn University)
Current Employment: Assistant Professor at Cornell College
Starting Employment: Assistant Professor at Earlham College
Dissertation Research: Thermal-Aware File Assignment in Cluster Storage Systems.
In this research, we present a thermal-aware file assignment technique called TIGER for reducing cooling cost of storage clusters in data centers. The TIGER scheme aims to lower peak inlet temperatures of storage clusters by dynamic thermal management through file placements. TIGER makes use of cross-interference coefficients to estimate the recirculation of hot air from the outlets to the inlets of data nodes. We evaluate performance of TIGER in terms of both cooling energy conservation and response time of a storage cluster. Our results confirm that TIGER reduces cooling-power requirements for clusters by offering about 10 to 15 percent cooling-energy savings without significantly degrading I/O performance.
Yuanqi Chen (Ph.D., May 2016; Auburn University)
Starting Employment: Member of Technical Staff at Equifax Inc.
Dissertation Research: Thermal Management and Data Archiving in Data Centers.
We proposed a thermal efficiency benchmark called ThermoBench for clusters. ThermoBench evaluates the thermal efficiency of computing and storage clusters deployed in data centers. we also built a self-adjusting model called TERN to predict thermal behaviors of hardware resources for client sessions. In the last part of this research, we proposed an erasure-coded data archival system called aHDFS for Hadoop clusters, where RS(k+r,k) Codes are employed to archive rarely accessed replicas in the Hadoop distributed file system or HDFS to achieve storage efficiency in data centers.
Liang Tang (Ph.D., May 2016; Auburn University; Co-Advisor: Dr. Wei-Shinn Ku)
Current Employment: Software Engineer, Facebook
Starting Employment: Member of Technical Staff, LinkedIn
Dissertation Research: A MapReduce Framework for Probabilistic Skylines over Uncertain Data.
We are focusing on how to leverage the MapReduce programming model to process the exact p-skylines on uncertain data in a highly efficient and scalable way.
Tausif Muzaffar (Ph.D., Summer 2015; Auburn University)
Starting Employment: Member of Technical Staff, Salesforce
Dissertation Research: An I/O-Aware Thermal Model for Data Centers.
With ever-growing cooling costs of large-scale data centers, thermal management of must be adequately addressed. Thermal models play a critical role in thermal management that helps in reducing cooling costs in data centers. However, existing thermal models for data centers overload I/O activities. To address this issue, we develop an I/O-aware thermal model called iTad for data centers. The iTad model captures the thermal characteristics of servers in a data center, offering a much finer granularity than the existing models. In addition to CPU workloads, iTad incorporates the I/O load in order to accurately estimate the thermal footprint of the servers with I/O-intensive activities. We validate the accuracy of the iTad model using real-world temperature measurements acquired by an infrared thermometer.
Xunfei Jiang (Ph.D., Aug. 2014; Auburn University)
Current Employment: Assistant Professor at California State University Northridge (CSUN)
Starting Employment: Assistant Professor at Earlham College
Dissertation Research: Thermal Modeling of Data Storage Systems.
We address in this study the thermal impact of storage systems. In the first phase of this work, we generate the thermal profile of a storage server containing three hard disks. The profiling results show that disks have comparable thermal impacts as processing and networking elements to overall storage node temperature. We develop a thermal model to estimate the outlet temperature of a storage server based on processor and disk utilizations. The thermal model is validated against data acquired by an infrared thermometer as well as build-in temperature sensors on disks. Next, we apply the thermal model to investigate the thermal impact of workload management on storage systems.
Yun Tian (Ph.D., May 2013; Auburn University)
Starting Employment: Assistant Professor at California State University, Fullerton.
Dissertation Research: Security and Performance Improvement in Distributed Systems.
In this project, we develop a fragment allocation scheme called S-FAS to improve security of a distributed system where storage sites have a wide variety of vulnerabilities. In the S-FAS approach, we integrate file fragmentation with the secret sharing technique in a distributed storage system with heterogeneous properties in vulnerability. Storage sites in a distributed system are classified into a variety of different server types base upon vulnerability characteristics. Given a file and a distributed system, S-FAS allocates fragments of the file to as many different types of nodes as possible in the system.
Ji Zhang (Ph.D., May 2013; Auburn University; Co-Advisor: Dr. Wei-Shinn Ku)
Current Employment: Senior Software Engineer, Doxpop
Starting Employment: Member of Technical Staff, VT iDirect, Inc.
Dissertation Research: Context-Based File Systems and Spatial Query Applications.
This dissertation presents studies related to I/O techniques in data-intensive computation and advanced solutions of spatial queries. There is a lack of general mechanisms for integrating multiple file system techniques and, therefore, the dissertation first illustrates a framework for Context-Based File Systems (CBFSs), which simplifies the development of context-based file systems and applications. Unlike existing informed-based context-aware systems, the framework provides a unifying informed-based mechanism that abstracts context-specific solutions as views, thereby allowing applications to make view selections according to their behaviors. In the second part of this dissertation, we propose two novel spatial queries, multi-criteria optimal location query and keyword-spatial query.
Shu Yin [Awards | Publications] (Ph.D., May 2012; Auburn University)
Starting Employment: Assistant Professor at Hunan University, Hunan, China
Dissertation Research: Reliability Models for Energy-Efficient Disk Systems.
This project contributes to reliability modeling techniques for fault-tolerant and energy-efficient parallel disk systems by developing a reliability analysis modeling toolkit accompanied with a set of novel mathematical reliability models. The innovative models investigated in this project include disk power consumption models, a reliability model for parallel disk systems with redundancy techniques, a reliability model for repairable and energy-efficient parallel disk systems, a fault recovery model for energy-efficient parallel disk systems.
Yixian Yang (Ph.D., May 2012; Auburn University)
Current Employment: Member of Technical Staff, Oracle Corporation
Starting Employment: Member of Technical Staff, VMWare Inc.
Dissertation Research: Improve I/O Performance of Hadoop Clusters.
This project will contribute to reliability modeling techniques for fault-tolerant and energy-efficient parallel disk systems by developing a reliability analysis modeling toolkit accompanied with a set of novel mathematical reliability models. The innovative models investigated in this project include disk power consumption models, a reliability model for parallel disk systems with redundancy techniques, a reliability model for repairable and energy-efficient parallel disk systems, a fault recovery model for energy-efficient parallel disk systems.
Jiong Xie (Ph.D., Dec. 2011; Auburn University)
Current Employment: Senior Engineer, Inner Mongolia Power (Group) Co.,Ltd., China.
Starting Employment: Assistant Professor at Hunan University, Hunan, China.
Dissertation Research: Improving Performance of Hadoop Clusters.
This project addresses the problem of how to place data across nodes in a way that each node has a balanced data processing load. Apart from the data placement issue, we also design a prefetching and predictive scheduling mechanism to help Hadoop in loading data from local or remote disks into main memory.
Maen M. Al Assaf (Ph.D., Dec. 2011; Auburn University)
Starting Employment: Assistant Professor at University of Jordan, Amman, Jordan.
Dissertation Research: Informed Prefetching in Distributed Multi-Levels Storage Systems. [Abstract | PDF | Slides]
We study pipelined prefetching mechanisms that use application disclosed access patterns to prefetch hinted blocks in multi-level storage systems. The fundamental concept in our approach is to split an informed prefetching process into a set of independent prefetching steps among multiple storage levels (e.g., main memory, solid state disks, and hard disk drives).
Xiaojun Ruan (Ph.D., Summer 2011; Auburn University)
Current Employment: Assistant Professor at California State University, East Bay.
Starting Employment: Assistant Professor at West Chester University of Pennsylvania.
Dissertation: Improving Energy Efficiency and Security in Cluster Computing Systems. [Abstract | PDF]
It is a major challenge to conserve energy for parallel disks and energy efficiently coordinate I/Os of hundreds or thousands of concurrent disk devices to meet high-performance and energy-saving requirements. The goal of this research is to develop energy conservation techniques that will provide significant energy savings while achieving low-cost and high-performance for parallel disks.
Zhiyang Ding (Ph.D., Summer 2011; Auburn University)
Starting Employment: Member of Technical Staff, China Investment Corporation.
Dissertation: An Active and Hybrid Storage System for Data-Intensive Applications. [Abstract | PDF]
In our multicore-based disk architectures, data processing code can be performed by embedded multicore processors where associated data reside. We will achieve this overall objective by carrying out the following two basic research tasks: (1) designing hardware and software architectures for multicore-based parallel disk systems and (2) developing multicore-based data processing techniques.
Adam Manzanares [Awards | Publications] (Ph.D., Spring 2010; Auburn University)
Current Employment: Research Staff Member at Western Digital.
Previous Employment: Assistant Professor at California State University, Chico.
Starting Employment: Postdoctoral Fellow, Los Alamos National Laboratory.
Dissertation: Energy Efficient Prefetching – From Models to Implementation. [Abstract | PDF]
The goal of this research is to bring down the cost of operating parallel disk systems. A computer hard drive can be in several states, including active, idle, or standby, and these states consume various amounts of energy. The project attempts to prefetch popular data into a small subset of the parallel disks (buffer disks) and allow the other disks in the disk system (data disks) to be placed in the standby state. When the data is moved it may cause the buffer disks to become a bottleneck, so the buffer disks must be carefully managed to prevent unacceptable degradations to response times.
Kiranmai Bellam [Awards | Publications] (Ph.D., Summer 2009; Auburn University)
Current Employment: Associate Professor at Prairie View A&M University.
Starting Employment: Assistant Professor at Prairie View A&M University.
Dissertation: Power, Fault Tolerance, and Security Issues in Real-Time Systems. [Abstract | PDF]
Power, fault tolerance, and security issues in modern real-time systems are of critical importance. This work is intended to seamlessly integrate security services and energy conservation techniques for real-time systems while endeavoring to achieving high system reliability.
Ziliang Zong [Awards | Publications] (Ph.D., Summer 2008; Auburn University)
Current Employment: Professor at Texas State University.
Starting Employment: Asst. Prof. of Comp. Sci., South Dakota School of Mines and Tech.
Dissertation: Energy-Efficient Resource Management for High-Performance Computing Platforms. [Abstract | PDF]
Minimizing power dissipation is an important requirement in developing resource management systems for clusters. In this work, we investigate resource allocation solutions that conserve energy in clusters while retaining high performance. Our resource allocation approaches will judiciously allocate resources of a cluster computing system to satisfy performance needs of parallel applications and achieve significant energy savings.
Mohammed Alghamdi [Awards | Publications] (Ph.D., Summer 2008; New Mexico Tech)
Starting Employment: Assistant Prof. of Comp. Sci., Al-Baha University, Saudi Arabia.
Dissertation: Energy-Efficient Packet Transmissions in Real-Time Wireless Networks.
Reducing energy consumption has become a major goal in designing modern real-time wireless networks. The focus of this study is to investigate the power and real-time issues in wireless networks. The study aims to develop a rich variety of scheduling schemes to reduce energy dissipation while meeting timing constraints of real-time applications in wireless networks.
Mais Nijim [Awards | Publications] (Ph.D., Summer 2007; New Mexico Tech)
Current Employment: Associate Professor at Texas A&M University-Kingsville.
Starting Employment: Assistant Prof. of Computer Sci., University of Southern Mississippi.
Dissertation: Adaptive Quality of Security Control in Storage Systems. [Abstract | PDF]
The purpose of this study is to address a novel approach to achieving high performance and high quality of security at the same time. We proposed an array of adaptive quality of security control schemes that makes it possible for storage systems to adapt to changing security requirements and workload conditions.
Tao Xie [Awards | Publications] (Ph.D., May 2006; New Mexico Tech)
Current Employment: Prof. of Computer Science, San Diego State University
Starting Employment: Assistant Prof. of Computer Science, San Diego State University.
Dissertation: Security-Aware Scheduling for Real-Time Systems. [Abstract | PDF]
An increasing number of real-time systems like aircraft control and medical electronics systems require high quality of security to assure confidentiality, authenticity and integrity of information. This work investigates scheduling approaches to improving security of real-time systems.
Master's Student Alumni
Garrett Scott (M.S., Summer 2026; Thesis Option; co-advised with Dr. Larry Rilett)
Thesis: Minimum Viable Demonstrations: Data Requirements for Zero-Shot Track Transfer in Hybrid Imitation and Reinforcement Learning for Autonomous Vehicles.
This thesis investigates how much human demonstration data is needed to train autonomous driving agents that combine imitation learning and reinforcement learning. Built on the Learning Agents plugin in Unreal Engine 5, the framework first trains driving policies by behavioral cloning on human demonstration laps and then applies Proximal Policy Optimization (PPO) to extend that behavior. The study identifies the minimum number of demonstrations needed for a competent driving policy (20 laps on the easy track, whereas no tested amount up to 50 laps sufficed on the hard track), evaluates zero-shot transfer to an unseen track, and analyzes sensitivity to demonstration quantity and track difficulty. The results show that adding more demonstrations from a single track neither guarantees competence nor improves transfer to new road geometries, suggesting that the diversity of demonstration data matters more than its volume.
Keanna Tennyson (M.S., Summer 2026; Thesis Option; co-advised with Dr. Larry Rilett)
Thesis: Integrating an AI-Driven Chatbot for Natural Language Traffic Safety Analysis within the SAFE-AL Dashboard.
This thesis designs and implements an AI-driven chatbot within the SAFE-AL traffic safety dashboard, allowing users to explore transportation safety data through natural language questions rather than dashboard filters, SQL queries, or geospatial tools. The chatbot combines rule-based parsing with a large language model to interpret requests about time, location, and roadway, and then applies the dashboard's existing callbacks and stored procedures to zoom to selected counties, highlight results on the map, and open related charts. Its read-only design prevents query generation or changes to the underlying data, keeping answers consistent with approved dashboard outputs. In a comparison of two language models, OpenAI GPT-4.1-mini outperformed Google Gemini 2.5 Flash in filter accuracy (87% versus 77%), intent understanding, hallucination rate, and response time (2.67 versus 3.81 seconds on average).
Shrey Patel (M.S., Spring 2025; Project Option)
Project: Building an Academic Program Assessment Dashboard.
Our project involves building an Academic Program Assessment Dashboard using Microsoft Power BI. By leveraging Power BI's powerful visualization tools, we aim to enhance data analysis and reporting capabilities, making it easier for departments in various colleges to assess and improve academic programs.
Jeronime Houndonougbo (M.S., Spring 2025; Project Option)
Project: Using GenAI to Generate Academic Program Assessment Reports.
Our project leverages Google AI Studio to streamline the generation of academic program assessment reports. We utilize MongoDB for efficient data storage and management, ensuring robust and scalable performance.
Corey McDaniels (M.S., Fall 2025; Thesis Option; co-advised with Dr. Laurence Rilett and Dr. Adrian Cottam)
Thesis: Development of a Reinforcement Learning Environment for Optimized Connected Autonomous Vehicle Performance.
Vennela Akula (M.S., Fall 2024; Project Option; co-advised with Dr. Beverley Rilett)
Dheeraj Bhaskaruni (M.S., Fall 2024; Project Option; co-advised with Dr. Beverley Rilett)
Nayanika Reddy Rajoli (M.S., Fall 2024; Project Option; co-advised with Dr. Beverley Rilett)
Le Cai (M.S., Summer 2023; Thesis Option; co-advised with Dr. Song-Yul Choe)
Thesis: A Data-Driven Lithium-Ion Concentration Estimator for Electrode Solid Phase in Battery Models.
This thesis introduces a data-driven approach for lithium-ion concentration estimation in battery cells, leveraging a deep learning model based on Long Short-Term Memory (LSTM) networks.
Bayu Sukmanto (M.S., Spring 2023; co-advised with Dr. Ashish Gupta)
Chris White (M.S., Spring 2022; Project Option; co-advised with Dr. Ashish Gupta)
Project: Porting and Refactoring MaruOS for Pixel 2/XL.
We develop in this project the architecture of a system that provides both a robust application ecosystem and a UI that is familiar enough for the end-user to use.
Ornela Hogu (M.S., Spring 2022; Thesis Option; co-advised with Dr. Ashish Gupta)
Thesis: Understanding Long Covid-19 Patterns in Pediatric Patients using Network Analytics.
We apply network analytics approaches to model various chronic and non-chronic conditions that pre-exists in patients diagnosed with Long COVID. We then applied two community detection algorithms, Louvain and Leiden algorithms, on these projections to identify clustering patterns of diseases.
Mahmoud Abdalkarim (M.S., Spring 2022; Thesis Option; co-advised with Dr. Ashish Gupta)
Thesis: Using V2X and reinforcement learning to improve autonomous vehicles algorithms with CARLA.
This research aims to study the impact of connecting self-driving cars with their surrounding through Vehicle-to-Everything (V2X) data. We introduced a reinforcement learning (RL) algorithm that benefits from the use of V2X and trains a car in a simulated testbed on various maneuvering scenarios to emphasize the impact of using V2X compared to the use of traditional sensors alone.
Bibhav Bhattarai (M.S., Spring 2021; Thesis Option; co-advised with Dr. Daniela Marghitu)
Meenakshi Das (M.S., Spring 2021; Thesis Option; co-advised with Dr. Daniela Marghitu)
Christopher Tillery (M.S., Summer 2020; co-advised with Dr. Haiquan Chen)
Huaizhi Wang (M.S., Summer 2020; co-advised with Dr. Haiquan Chen)
Ahila Muthuswamy (M.S., Spring 2020; Project Option; co-advised with Dr. Haiquan Chen)
Project: TraitSpot: Improving Personality Detection using Social Relationships in Latent Neural Embedding Space.
We analyze how reviews posted by a user's friends in social media can be used to determine the personality of a user. Specifically, we improve personality detection using social relationship by predicting personality traits of users with their friend's reviews.
Xiaopu Peng (M.S., Dec. 2019)
Jianzhou Mao (M.S., Dec. 2019)
Tong Li (M.S., May 2019)
Chaowei Zhang (M.S., May 2019)
Gaoxiang Li (M.S., May 2019, co-advised with Dr. Li Chen)
Robin Ward (M.S., May 2019, co-advised with Dr. Xing Fang)
Yifei Nie (M.S., Summer 2019; Auburn University)
Project: A Time Series Forecasting Algorithm based on LSTM.
In this project, we focus on time series forecasting. After building a time-series model using the historical solar energy, we devise a LSTM-based algorithm to predict future solar energy.
Tong Li (M.S., Summer 2019; Auburn University)
Project: Performance Analysis of Erasure Coding Schemes.
We compare the performance of the reed-solomon coding (RS) and the local reconstruction coding (LRC) schemes. Our experimental results show that LRC is faster than RS in terms of coding performance at the cost of reliability.
Xiao Li (M.S., Dec. 2016; Auburn University)
Project: A Hadoop-based Personality Prediction System.
We develop a parallel personality prediction system on a Hadoop cluster. The system optimize the prediction performance through the MapReduce programming model coupled with the Hadoop distributed file system.
Chetan Prakash Somani (M.S., Dec. 2015; Auburn University)
Thesis: Item-based Recommendation Algorithms using Hadoop.
We start this research by implementing a recommendation algorithm using a sequential approach for a large dataset. We then develop a parallel algorithm to derive recommendations by using the Hadoop framework along with an item-based similarity collaborative filtering technique.
Anirudh Sivapurapu (M.S., Dec. 2015; Auburn University)
Project: Detecting Trends from Twitter Social Data.
We propose two schemes for trending topics on real time twitter data. The first scheme is done by using a Chi-Square metric, which is a statistical approach commonly used to compare observed data with the data we would expect to obtain according to a specific hypothesis. The second approach is by using Min Hashing, which is a technique for quickly estimating how similar the two sets are.
Hasitha Athota (M.S., Dec. 2015; Auburn University)
Project: Content Based Recommendation System on Hadoop Clusters.
Recommendations made by our system are driven by analyzing the contents of users. We focus on content-based filtering methods.
Sudha Varanasi (M.S., May 2015; Auburn University)
Starting Employment: Software Engineer, Infogroup
Thesis: A User-based Collaborative Filtering Recommender Algorithm on Hadoop Clusters.
In this study, we focus on neighborhood-based collaborative filtering methods, which are well known techniques used in recommender systems on Hadoop clusters. Neighborhood-based collaborative filtering methods are user-based and item based, meaning user preferences are inferred solely from what items they and other users in the dataset have interacted with. Experiments prove that user similarity based recommender systems on Hadoop has higher performance with the increase in number of data nodes when compared to the results of implementation in a single node.
Sanket Reddy Chintapalli (M.S., Dec. 2014; Auburn University)
Starting Employment: Software Engineer, Yahoo
Thesis: Thermal Management in Hadoop Distributed File Systems.
We design and implement thermal management policies in Hadoop Distributed file systems or HDFS. Coupled with our new thermal management policies, HDFS becomes more thermal friendly in large-scale data centers.
Gautam Dudeja (M.S., May 2014; Auburn University)
Starting Employment: Software Engineer, Texas Instruments
Thesis: Thermal-Aware Scheduling in Hadoop Clusters.
In this project, we develop thermal-aware scheduling mechanisms in Hadoop clusters. Our design aims to reduce outlet temperatures of data nodes in a cluster.
Tausif Muzaffar (M.S., May 2014; Auburn University)
Starting Employment: Doctoral Student at Auburn University
Thesis: iTad: An I/O-Aware Thermal Model for Data Centers.
Thermal models can play a critical role in thermal management that helps in reducing cooling costs in data centers. However, existing thermal models for data centers can overload I/O activities. To address this issue, we develop an I/O-aware thermal model called iTad for data centers. The iTad model captures the thermal characteristics of servers in a data center, offering a much finer granularity than the existing models. In addition to CPU workloads, iTad incorporates the I/O load in order to accurately estimate the thermal footprint of the servers with I/O-intensive activities. We show that thermal management mechanisms can quickly retrieve the thermal information of servers from iTad before making important workload placement decisions in a real-time manner.
Sanjay Kulkarni (M.S., May 2013; Auburn University)
Starting Employment: Member of Technical Staff, Symantec Corporation
Thesis: Cooling Hadoop: Temperature Aware Schedulers in Data Centers.
Based on thorough investigations of Hadoop's existing schedulers, we propose a couple of new thermal aware schedulers that schedules tasks to balance the outlet temperature across all nodes and reduce AC costs in data center. The experimental results show that our schedulers achieve average outlet temperature saving by 2°C over the default FIFO scheduler that saves about 15% of cooling cost with little performance overhead.
James Majors (M.S., May 2011; Auburn University)
Starting Employment: Member of Technical Staff, Greenway Medical Technologies, Inc.
Thesis: Secdoop: A Confidentiality Service on Hadoop Clusters. [Abstract | PDF]
We implemented two Hadoop applications that distribute the cryptographic process over a trusted cluster. The first application handles encryption of an input file placed inside HDFS (a.k.a., the Hadoop Distributed File System). The second application handles decryption of an input file that is located on HDFS. These two applications demonstrate the effect of utilizing cryptography while distributing processing over a Hadoop cluster.
Jianguo Lu (M.S., May 2011; Auburn University)
Project: MPI-Velvet: A Next Generation Sequence Assembler.
We developed MPI-Velvet – a parallel assembler software tool – using MPICH-2 (a message passing interface implementation). MPI-Velvet can process high coverage data sets and quickly reconstruct the underlying sequences. This project contributes to the bioinformatics research community with a new parallel computing tool.
Gourav Tilve [Publications] (M.S., May 2007, New Mexico Tech)
Research Project: Energy-Efficient Storage Systems.
Improving the energy efficiency of storage systems is an intrinsic requirement of next generation large-scale computing systems, because a storage subsystem can represent 27% of the energy consumed in a data center. The goal of this research is to investigate energy conservation techniques that will provide significant energy savings while achieving high-performance for storage systems.
Kiranmai Bellam [Awards | Publications] (M.S., Dec. 2006)
Starting Employment: Doctoral Student at Auburn University.
Now: Associate Professor at Prairie View A&M University.
Research Project: Fault tolerance and security management in real-time systems.
The purpose of this study is to address a novel approach to achieving strong fault tolerance and high quality of security at the same time. We will seamlessly integrate a variety of fault tolerant techniques with an array of security mechanisms in the context of real-time systems.
Adam Roth [Publications] (M.S., Dec. 2005)
Starting Employment: Member of Technical Staff, BigTribe Corporation
Thesis: Power Aware Disk Scheduling Algorithms for Real-Time Systems. [Abstract | PDF]
This work addresses the crucial issue of energy conservation in real-time storage systems. The thesis presents two energy-aware power management policies, namely, I/O Burstiness for Energy Conservation (IBEC) and Speed-Aware Real-time Disk Scheduling for energy conservation (SARDS), which integrate power management policies into disk scheduling algorithms for I/O-intensive applications.
B.S. Student Alumni (16 from Auburn University; 3 from New Mexico Tech)
Justin Lovell (B.S. 2015, REU Student; Auburn University)
Research Topic: Parallel Recommendation Systems.
James Sentell (B.S. 2015, REU Student; Auburn University)
Research Topic: Parallel Recommendation Systems.
Chris Muschek (B.S. 2014, REU Student; Auburn University)
Research Topic: iOS Application Development.
Collin McAtee (B.S. 2014, REU Student; Auburn University)
Research Topic: Parallel Computing for Light-Field Photography.
Garrett Walden (B.S. 2013, REU Student; Auburn University)
Joshua Lewis (B.S. 2012, REU Student; Auburn University)
Research Topic: Predictive Prefetching in Multiple-Layer Storage Systems.
We implement predictive prefetching algorithms for multiple-layer storage systems. A prefetching mechanism is implemented in a data storage system containing solid-state disks, hard drives, and disk arrays.
Alfred Nilson (B.S. 2012, REU Student; Auburn University)
Research Topic: A Development Framework for Security-Sensitive Applications.
Greg Poirier (B.S. 2012, REU Student; Auburn University)
Research Topic: A Parallel Encryption System.
Alexander Luchs (B.S. 2012, REU Student; Auburn University)
Research Topic: A Parallel Encryption System.
Riley Spahn (B.S. 2012, REU Student; Auburn University)
Research Topic: A Parallel Encryption System.
Kathryn Catlett (B.S. 2012, REU Student; Auburn University)
Research Topic: A Parallel Encryption System.
Andrew Pitchford (B.S. 2011, REU Student; Auburn University)
Research Topic: Secure Banking Systems.
John Barton (B.S. 2011, REU Student; Auburn University)
Research Topic: A Development Framework for Security-Sensitive Applications.
Eric Ingram (B.S., 2010, REU Student; Auburn University)
Research Topic: Measuring Energy Efficiency of Parallel Disks.
James Majors (B.S., 2009, REU Student; Auburn University)
Research Topic: Middleware-Based Approach to Teaching Computer Security Courses.
In this project we implement a middleware framework for courses on computer security. The software framework allows teachers to help students learn the rapid development of critical security software and is the first educational material of its kind designed to teach real-world computing system security to undergraduate students.
Christopher M. Monsanto (B.S., 2009, REU Student)
Research Topic: Dynamic Power Management for Parallel Disk Systems.
In this research project we design and implement a dynamic power management mechanism to conserve energy in parallel disk systems. The prototype of this mechanism will be integrated into an energy-efficient parallel disk system with buffer disks.
Matthew E. Briggs [Publications] (Spring 2006 – Spring 2007)
Research Topic: Energy-efficient disk buffer disks.
The goal of this research is to develop a novel buffer-disk architecture that will provide significant energy savings while achieving low-cost and high-performance for parallel disks. In this project we will take an organized approach to implementing energy-saving techniques for parallel disks and simulating energy-efficient parallel disk systems.
Brian Stinar [Awards | Publications] (B.S., Dec. 2006 (Now: Graduate student, University of New Mexico))
Research Topic: Energy consumption models for high-performance clusters.
This study addresses the issue of modeling energy dissipation in computational nodes and network interconnections in high-performance clusters. Brian's work on energy consumption models for clusters appeared in the proceedings of the 8th IEEE International Conference on Cluster Computing (Cluster'06), Sept. 2006. Brian is a winner of the IEEE TCSC's prestigious Student Travel Award.
Adam Manzanares [Awards | Publications] (B.S. with Honors, May 13, 2006 (Now: Doctoral student, Auburn University))
Research Topic: Random and regular benchmark task graphs for parallel programs.
In this study we simulated an array of real world parallel applications running on energy-efficient clusters. Adam's work on energy conservation for high-performance clusters appeared in the proceedings of the 8th IEEE International Conference on Cluster Computing (Cluster'06), Sept. 2006.
Other students who have worked with me
[Total: 7 | Doctoral Students: 6 | Master's Students: 1 | Back to Top]
CJ Short (Ph.D. Student, Fall 2020 - Fall 2022)
Dissertation Research: Navigating Controversial Debate on Facebook using Reactions.
This research seeks to demonstrate a rudimentary measurement of curve feeding as a proof of concept for capturing controversy on Facebook using the reactions of its user base toward controversial topics.
Thomas Heckwolf (Ph.D. Student, Fall 2019 - Spring 2022)
Dissertation Research: CBlock - A Dual Blockchain Approach for IoT Devices.
We propose a solution to create a new compressed version of the blockchain in which embedded IoT devices with limited memory can be full participants within the blockchain network.
Anand Paturi (01/2007-07/2007 (Ph.D. Student, New Mexico Tech))
Dissertation: QoS-Aware Energy-Efficient Wireless Sensor Networks.
Wireless sensor networks are likely to support a wide spectrum of next-generation applications such as wildlife monitoring and earthquake monitoring applications. In this project, we will study quality-of-service (QoS) and energy conservation issues in modern wireless sensor networks. We aim to develop an approach to minimizing energy dissipation in wireless sensor networks subject to QoS constraints.
Wei Luo [Publications] (06/2005 – 07/2008 (Ph.D. Candidate, Huazhong University of Science and Technology))
Research Topic: Fault-tolerant scheduling for heterogeneous systems.
In this project we first proposed novel reliability models tailored for preemptive periodic tasks. Next, we developed an array of real-time fault-tolerant scheduling algorithms for heterogeneous systems.
Nicholas W. O'Connor [Publications] (01/2006 – 07/2007 (M.S. Student))
Research Topic: Energy consumption models for disk systems.
This study addresses the issue of modeling energy consumption in server and embedded disk systems.
Menglei Tang (08/2006 - 12/2006 (Ph.D. Student))
Research Topic: Parallel database systems on clusters.
Xinfa Hu (08/2005- 12/2005, Ph.D. student (Ph.D. Student transferred to New Jersey Institute of Technology))
Research Topic: Communication-aware utility-based resource allocation in distributed real-time systems.
This study is focused on the development of a novel utility model that is aware of communication cost and benefits. We designed a resource allocation algorithm that aims at maximizing both communication and computation utility of a distributed real-time system.