You will get Generative AI and Synthetic Data
Project details
Utilizing Generative AI to empower “Data Collaboration Intelligence”. We developed a data sharing platform that allows private data sharing, predictive analytics and model building among different data parties, in essence, the “Data Clean Room” to enhance Click-through Rate (CTR) predictions using privacy-preserving synthetic data.
In both of the Parts, i.e., Implementation and Evaluation, we used various technologies including Azure Confidential Cloud VM, imbibing TDVM was well as TEE (Trusted Execution Environments),specifically Intel® TDX and SGX, GAN neural network, Gen AI, and Machine Learning Models. Similarly, several concepts like Hashing, Trusted Computing, Synthetic Data Fidelity and Synthetic Data Utility, Verification of Quote and Return Key.
In both of the Parts, i.e., Implementation and Evaluation, we used various technologies including Azure Confidential Cloud VM, imbibing TDVM was well as TEE (Trusted Execution Environments),specifically Intel® TDX and SGX, GAN neural network, Gen AI, and Machine Learning Models. Similarly, several concepts like Hashing, Trusted Computing, Synthetic Data Fidelity and Synthetic Data Utility, Verification of Quote and Return Key.
Machine Learning Tools
Azure Machine Learning, BERT, GitHub Copilot, Microsoft Excel, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, Scrapy, XGBoostWhat's included $1,000
These options are included with the project scope.
$1,000
- Delivery Time 10 days
- Number of Revisions 2
- Number of Model Variations 3
- Number of Scenarios 4
- Number of Graphs/Charts 6
- Model Validation/Testing
- Model Documentation
- Data Source Connectivity
- Source Code
Optional add-ons
You can add these on the next page.
Additional Revision
+$150
Additional Model Variation
(+ 2 Days)
+$80
Additional Scenario
(+ 1 Day)
+$100
Additional Graph/Chart
(+ 2 Days)
+$50About Arnav
Research at NIH (US), Harvard, IIT BBS, USC, UCLA , Ex- ML at Julia
Mumbai, India - 5:35 pm local time
Currently, I'm juggling multiple roles that showcasing my diverse interests and skills. As a Research Intern at Harvard University, I'm working on optimizing LLM inference for VIDUR, my work being for optimization of inferencing. Simultaneously, I'm contributing to the medical imaging field as a Machine Learning Intern at JuliaHealth, where I'm developing GPU-accelerated containers to enhance healthcare technology.
My passion for bridging technology and healthcare is exemplified by my role as a Research Intern at the National Institute of Health (USA). Here, I've successfully identified biomarkers for Lung Cancer with high accuracy, working with both serum and plasma samples. This experience builds upon my previous internship at UCLA, where I tackled Click Through Rate Models and synthetic data generation, honing my skills in handling large datasets and privacy-preserving techniques.
Throughout my journey, I've had the opportunity to work on impactful projects. One such project is "Synthia," a synthetic data generation tool I developed using Phi-3 and NVIDIA AI Workbench.
Experienced multiple hackathon wins, including the Live-AI Global Harvard-Duke Hackathon and Stanford Biohacks. These achievements reflect my ability to apply my knowledge in competitive, fast-paced environments and collaborate effectively with diverse teams.
I'm also proud to have contributed to the academic community through publications. I've authored a chapter on "Integration of IoT and Quantum Computing in Manufacturing" published by IGI Global, and first-authored papers on "HepaScope: Densely Connected UNet for CT Volume-Based Liver and Tumor Segmentation" and " Enhancing Privacy in Digital Marketing: A Data Clean Room Framework for Synthetic Data Generation and CTR Prediction" currently under review.
If you're interested in discussing potential collaborations, research opportunities, or simply sharing insights about the exciting developments in our field, please don't hesitate to reach out.
Steps for completing your project
After purchasing the project, send requirements so Arnav can start the project.
Delivery time starts when Arnav receives requirements from you.
Arnav works on your project following the steps below.
Revisions may occur after the delivery date.
Synthetic Data
Amount and quality of Data
ML model for evaluation
Any kind of Machine Learning and Deep Learning experience for evaluation