Spring 2025 Presentation Schedule
Jan 30 - DRZ
Feb 6 - Raihan
Feb 13 - Christian
Feb 20 - DRZ
Feb 27 - Reza (MS Project, Spring 2025)
Mar 6 - Sonu (MS Project, Spring 2025)
Mar 13 - DRZ
Mar 20 - Spring Break!
Mar 27 - Fairuz
Apr 3 - Sonu
Apr 10 - Reza
Apr 17 - Raihan
Apr 24 - Christian
May 1 - Fairuz
May 8 - DRZ
Date: Jan 30 | Presenter(s): DRZ
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Welcome (DRZ)
DeepSeek - AI, Stock Market, Future!?
CAPPLab website (useful info/links)
Presentation Schedule
Updates (Professional Achievements)
DRZ - four+1 grants, two+3 journals, two+4 conferences
Raihan - one+1 journals, two+1 conferences, two course projects
Sonu - MS Project report
Reza - MS Project report
Fairuz - N/A
Christian - one+1 journals, one+1 conferences
Yoel - Absent
Dr. Merve - Absent
Lab/Group Activities
Advancing Knowledge and Innovation
Collaboration and Interdisciplinary Work
Pathway to Higher Education and Careers
Technical Writing
Suggested Venues to Publish
English and Grammar
Using Template
Figures and Tables (no copy/paste!)
References (formatting)
Author Sequence
As May Arise
Send MS Thesis template to Sonu and Reza
// DRZ on Jan. 30, 2025
Date: Feb 6 | Presenter(s): Raihan and DRZ
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Presentation 1: 鈥淒istributed Computing Systems for Efficient ML Model Training鈥� by
Raihan
Summary: A distributed computing system integrates devices, edge servers, and cloud servers
to collaboratively execute computational tasks, enhancing scalability, and resource
utilization. In the context of machine learning, deploying resource-intensive models
on edge devices is challenging due to their limited computational capabilities. To
address this, efficient training frameworks are essential, involving intelligent workload
distribution, dynamic task offloading, and resource-aware scheduling to balance computations
across devices, edge servers, and cloud servers.
Questions: (i) Should we offload the full ML model to ES or partially to device and ES or partially to ES and CS? (ii) How can an ML model be effectively partitioned for improved performance and resource optimization? (iii) How can we model/simulate the problem and what optimization strategy should be used to solve the problem? (iv) Is the ML model in the devices is independent or dependent? (v) Is the training of those independent ML models in the ES are also independent or aggregated (dependent)?
Questions: (i) Should we offload the full ML model to ES or partially to device and ES or partially to ES and CS? (ii) How can an ML model be effectively partitioned for improved performance and resource optimization? (iii) How can we model/simulate the problem and what optimization strategy should be used to solve the problem? (iv) Is the ML model in the devices is independent or dependent? (v) Is the training of those independent ML models in the ES are also independent or aggregated (dependent)?
Outcomes: (i) Partially offloading to ES and CS. (ii) By applying optimization techniques.
(iii) Develop mathematical models from existing literature/optimization strategies---DNN
based and RL based. (iv) Independent. (v) Training is independent.
Presentation 2: 鈥淪imulating Wireless Network-on-Chip (WNoC) Systems鈥� by DRZ
Summary: The presentation begins by outlining the challenges associated with WNoC systems
and provides background information on the evolution from single-core to multicore
architectures, culminating in WNoC. It then introduces a proposed approach for simulating
WNoC systems, which comprises two main steps: (i) Node Selection: identifying WNoC
nodes suitable for wireless routers, and (ii) Job Scheduling: selecting jobs and cores,
and assigning jobs to the appropriate cores. After that, the presentation delves into
simulation issues, detailing the equations used to calculate: heat indicator, hop
count, communication latency, and power consumption. Finally, it discusses questions
related to heat indicators parameters and their respective values.
Questions: (i) Are the task types acceptable? (ii) How about the task execution times? (iii) Is the equation for calculating heat indicator values suitable for this work?
Questions: (i) Are the task types acceptable? (ii) How about the task execution times? (iii) Is the equation for calculating heat indicator values suitable for this work?
Outcomes: (i) Task types are acceptable. (ii) Task execution times are adjusted per discussion.
(iii) The current equation for calculating the heat indicator is suitable for cores
executing tasks but requires modification for idle cores.
==============================
Title: 鈥淓nhancing Skin Disease Treatment: High-Speed Machine Learning Models to Boost
the Effectiveness of CADx Systems鈥�
Summary: We have been investigating machine learning approaches to enhance the performance
of Computer-Aided Diagnosis (CADx) systems for skin disease detection. In our ACS
Omega paper, we have utilized techniques such as Generative Adversarial Networks (GAN),
Exploratory Data Analysis (EDA), and resampling methods. However, we have not captured
the models' training or inference time. While ensemble models are known to require
more time to train, they do not always guarantee improved accuracy. To develop high-speed
machine learning models, we aim to apply dimensionality reduction techniques such
as Recursive Feature Elimination with Cross-Validation (RFECV) and Principal Component
Analysis (PCA) to reduce both training and inference times without compromising model
performance.
Questions: (i) How to implement noise removal for pixel-based data? (ii) What are the main sources of error in these models when detecting skin cancer? What preprocessing methods should we apply? (iii) We have applied an ensemble CNN-SVM model and plan to apply an ensemble RF-XGBoost. Do you think there are other combinations that we can try? (iv) Could combining more than two models (e.g., RF, SVM, CNN, and XGBoost) create a better ensemble for skin cancer detection?
Questions: (i) How to implement noise removal for pixel-based data? (ii) What are the main sources of error in these models when detecting skin cancer? What preprocessing methods should we apply? (iii) We have applied an ensemble CNN-SVM model and plan to apply an ensemble RF-XGBoost. Do you think there are other combinations that we can try? (iv) Could combining more than two models (e.g., RF, SVM, CNN, and XGBoost) create a better ensemble for skin cancer detection?
Outcomes: (i) Use image-based data, and use statistical measures like mean and median to identify
and remove outliers to effectively reduce noise. (Also think about matching the pixel
data with a reference/patient-ID key and comparing it to real images to identify the
noise.) (ii) Since models can develop biases leading to incorrect predictions, explore
if autoencoders can do noise reduction and feature extraction. (iii) Implement a CNN-RF
model as it can leverage CNN's capabilities in feature extraction and RF's classification
power to improve the accuracy of CADx systems. (iv) Perform a literature review on
tri-ensemble models to understand the impact of using three or more models on classification
models (training time, inference time, accuracy, etc.).
==========================
Title: 鈥淔uture Research Challenges and Funding Opportunities鈥�
Summary: With funding cuts affecting federal agencies such as the NSF and NIH, it is essential
to explore grant opportunities from industry that bridge academic research with real-world
technology challenges. The Cisco Distributed Systems, SONY Faculty Innovation Award,
Alternatives Research & Development Foundation (ARDF)'s Annual Open Grant Program,
and Samsung Research America (SRA) Strategic Alliance for Research and Technology
(START) programs are some promising avenues for funding. Key AI/ML focus areas include
deep learning, on-device AI, generative AI, and multi-modal machine learning. Our
goal is to submit proposals that deliver high-performance, real-time, power-efficient,
and always-available solutions. To achieve this, researchers are developing AI/ML
models that require minimal disk space, runtime memory, and computational resources.
They are also advancing the field by designing deep learning models compact enough
to run on-chip with ultra-low power consumption, memory usage, and latency.
Questions: (i) What/How can we apply to Cisco Distributed Systems Program? (ii) What/How can we apply to SONY Faculty Innovation Award Program? (iii) What/How can we apply to ARDF's Annual Open Grant Program? (iv) What/How can we apply to SRA START AI Program?
Questions: (i) What/How can we apply to Cisco Distributed Systems Program? (ii) What/How can we apply to SONY Faculty Innovation Award Program? (iii) What/How can we apply to ARDF's Annual Open Grant Program? (iv) What/How can we apply to SRA START AI Program?
Outcomes: (i) Raihan will work with DRZ and collect results for the Cisco (Distributed Systems)
proposal. (ii) Christian will work with DRZ and collect results for the SONY (Biomedical
and Life Science | Cell Biology | AI-assisted High-speed Cell Image Analysis) proposal.
(iii) DRZ will submit the ARDF LOI after discussing it with Christian. (iv) Fairuz
will work with DRZ to identify challenges/topics for Samsung's (Artificial Intelligence
| Multi-modal life-long memory augmentation system with fast retrieval) proposals.
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