I2S Masters/ Doctoral Theses


All students and faculty are welcome to attend the final defense of I2S graduate students completing their M.S. or Ph.D. degrees. Defense notices for M.S./Ph.D. presentations for this year and several previous years are listed below in reverse chronological order.

Students who are nearing the completion of their M.S./Ph.D. research should schedule their final defenses through the EECS graduate office at least THREE WEEKS PRIOR to their presentation date so that there is time to complete the degree requirements check, and post the presentation announcement online.

Upcoming Defenses

Master's Thesis Defenses

An Empirical Evaluation of Multi-Resource Scheduling for Moldable Workflows

Student Name: Sandhya Kandaswamy

Defense Date: Friday, December 9, 2022 at 3:30 PM

Location: Eaton Hall, Room 2001B

 

Degree: MS Thesis Defense (CS)

Degree Field: Computer Science

 

Chair: Hongyang Sun
Committee Member 1: Suzanne Shontz
Committee Member 2: Heechul Yun

 

Resource scheduling plays a vital role in High-Performance Computing (HPC) systems. However, most scheduling research in HPC has focused on only a single type of resource (e.g., computing cores or I/O resources). With the advancement in hardware architectures and the increase in data-intensive HPC applications, there is a need to simultaneously embrace a diverse set of resources (e.g., computing cores, cache, memory, I/O, and network resources) in the design of runtime schedulers for improving the overall application performance. This thesis performs an empirical evaluation of a recently proposed multi-resource scheduling algorithm for minimizing the overall completion time (or makespan) of computational workflows comprised of moldable parallel jobs. Moldable parallel jobs allow the scheduler to select the resource allocations at launch time and thus can adapt to the available system resources (as compared to rigid jobs) while staying easy to design and implement (as compared to malleable jobs). The algorithm was proven to have a worst-case approximation ratio that grows linearly with the number of resource types for moldable workflows. In this thesis, a comprehensive set of simulations is conducted to empirically evaluate the performance of the algorithm using synthetic workflows generated by DAGGEN and moldable jobs that exhibit different speedup profiles. The results show that the algorithm fares better than the theoretical bound predicts, and it consistently outperforms two baseline heuristics under a variety of parameter settings, illustrating its robust practical performance.


Monkeypox Detection Using Computer Vision

Student Name: Samyak Jain

Defense Date: Tuesday, November 29, 2022 at 2:25 PM

Location: Eaton Hall, Room 2001B

 

Chair: Prasad Kulkarni
Committee Member 1: David Johnson, (Co-Chair)
Committee Member 2: Hongyang Sun

As the world recovers from the damage caused by the spread of COVID-19, the monkeypox virus poses a new threat of becoming a global pandemic. The monkeypox virus itself is not as deadly or contagious as COVID-19, but many countries report new patient cases every day. So it wouldn't be surprising if the world faces another pandemic due to lack of proper precautions. Recently, deep learning has shown great potential in image-based diagnostics, such as cancer detection, tumor cell identification, and COVID-19 patient detection. Therefore, since monkeypox has infected human skin, a similar application can be employed in diagnosing monkeypox-related diseases, and this image can be captured and used for further disease diagnosis. This project presents a deep learning approach for detecting monkeypox disease from skin lesion images. Several pre-trained deep learning models, such as ResNet50 and Mobilenet, are deployed on the dataset to classify monkeypox and other diseases.

I2S Defenses
StudentTitleDefense TypeAdvisor(s)Year

Farzad Farshchi

Deterministic Memory Systems for Real-time Multicore ProcessorsDoctoralDr. Heechul Yun2020
Alex Amir ModarresiNetwork Resilience Architecture and Analysis for Smart HomesDoctoralDr. Victor S. Frost2018

Michael Stees

Optimization-based Methods in High-Order Mesh Generation and UntanglingDoctoral

Dr. Suzanne Shontz

2020

Jacob FustosAttacks and Defenses against Speculative Execution Based Side ChannelsDoctoralDr. Heechul Yun2020
Matthew TaylorDefending Against Typosquatting Attacks In Programming Language-Based Package RepositoriesDoctoralDr. Drew Davidson2020
Mohammad Saad AdnanCorvus: Integrating Blockchain with Internet of Things Towards a Privacy Preserving, Collaborative and Accountable, Surveillance System in a Smart CommunityMastersDr. Bo Luo2020
Qiaozhi WangTowards the Understanding of Private Content -- Content-based Privacy Assessment and Protection in Social NetworksDoctoralDr. Bo Luo2020
Sandip DeyAnalysis of Performance Overheads in DynamoRIO Binary TranslatorMastersDr. Prasad Kulkarni2020
Eric ScheisbergerOptical Limiting via Plasmonic Parametric AbsorbersMastersDr. Alessandro Salandrino2020
Hao XueTrust and Credibility in Online Social NetworksDoctoralDr. Fengjun Li2020
Naveed MahmudTowards Complete Emulation of Quantum Algorithms using High-Performance Reconfigurable ComputingDoctoralDr. Esam El-Araby2022
Lumumba HarnettMismatched Processing for Radar Interference CancellationDoctoralDr. Shannon Blunt2022