RESEARCH SCIENTIST

Rahul Pandya

Rahul Pandya

Data Science & AI graduate with extensive hands-on experience in Machine Learning.

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"Advancing AI for solving complex problems"

"Pushing the boundaries of Deep Learning through interdisciplinary research"

Dmcie: Diffusion model with concatenation of inputs and errors for enhanced brain tumor segmentation in MRI images

📌 Goal:
Enhance brain tumor segmentation accuracy by combining multimodal MRI inputs with diffusion reconstructed error maps, enabling precise tumor region correction and improved clinical reliability.

🧠 Domain: Medical Imaging & Generative AI
🎯 Task: Brain Tumor Segmentation
📂 Dataset: BraTS 2020 – Multimodal MRI Dataset (T1, T1ce, T2, FLAIR + Ground Truth Masks)

Imaging

Dmcie: Diffusion model with concatenation of inputs and errors for enhanced brain tumor segmentation in MRI images

📌 Goal:
Enhance brain tumor segmentation accuracy by combining multimodal MRI inputs with diffusion reconstructed error maps, enabling precise tumor region correction and improved clinical reliability.

🧠 Domain: Medical Imaging & Generative AI
🎯 Task: Brain Tumor Segmentation
📂 Dataset: BraTS 2020 – Multimodal MRI Dataset (T1, T1ce, T2, FLAIR + Ground Truth Masks)

Imaging

Dmcie: Diffusion model with concatenation of inputs and errors for enhanced brain tumor segmentation in MRI images

📌 Goal:
Enhance brain tumor segmentation accuracy by combining multimodal MRI inputs with diffusion reconstructed error maps, enabling precise tumor region correction and improved clinical reliability.

🧠 Domain: Medical Imaging & Generative AI
🎯 Task: Brain Tumor Segmentation
📂 Dataset: BraTS 2020 – Multimodal MRI Dataset (T1, T1ce, T2, FLAIR + Ground Truth Masks)

Imaging

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Liver Tumor Segmentation with Deep Learning Architectures

Develop an automated segmentation model for liver tumors in CT scans and compare the effectiveness of U-Net and ResNet50 architectures in handling irregular boundaries and class imbalance.

🧠 Domain: Medical Imaging & Deep Learning
🎯 Task: Segmentation (Tumor vs. Background)
📂 Dataset: LiTS – Liver Tumor Segmentation Dataset (130 CT scans)

Imaging

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Liver Tumor Segmentation with Deep Learning Architectures

Develop an automated segmentation model for liver tumors in CT scans and compare the effectiveness of U-Net and ResNet50 architectures in handling irregular boundaries and class imbalance.

🧠 Domain: Medical Imaging & Deep Learning
🎯 Task: Segmentation (Tumor vs. Background)
📂 Dataset: LiTS – Liver Tumor Segmentation Dataset (130 CT scans)

Imaging

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Liver Tumor Segmentation with Deep Learning Architectures

Develop an automated segmentation model for liver tumors in CT scans and compare the effectiveness of U-Net and ResNet50 architectures in handling irregular boundaries and class imbalance.

🧠 Domain: Medical Imaging & Deep Learning
🎯 Task: Segmentation (Tumor vs. Background)
📂 Dataset: LiTS – Liver Tumor Segmentation Dataset (130 CT scans)

Imaging

🧠 ReCoSeg — Residual-Guided Cross-Modal Diffusion for Brain Tumor Segmentation

📌 Goal:
Improve brain tumor segmentation accuracy by synthesizing missing modalities using diffusion models and leveraging residual errors as attention cues for efficient, interpretable refinement.

🧠 Domain: Medical Imaging & Semi-Supervised Deep Learning
🎯 Task: Brain Tumor Segmentation (Whole Tumor)
📂 Dataset: BraTS 2020 — Multimodal MRI (355 subjects; FLAIR, T1, T2, T1ce)

Imaging

🧠 ReCoSeg — Residual-Guided Cross-Modal Diffusion for Brain Tumor Segmentation

📌 Goal:
Improve brain tumor segmentation accuracy by synthesizing missing modalities using diffusion models and leveraging residual errors as attention cues for efficient, interpretable refinement.

🧠 Domain: Medical Imaging & Semi-Supervised Deep Learning
🎯 Task: Brain Tumor Segmentation (Whole Tumor)
📂 Dataset: BraTS 2020 — Multimodal MRI (355 subjects; FLAIR, T1, T2, T1ce)

Imaging

🧠 ReCoSeg — Residual-Guided Cross-Modal Diffusion for Brain Tumor Segmentation

📌 Goal:
Improve brain tumor segmentation accuracy by synthesizing missing modalities using diffusion models and leveraging residual errors as attention cues for efficient, interpretable refinement.

🧠 Domain: Medical Imaging & Semi-Supervised Deep Learning
🎯 Task: Brain Tumor Segmentation (Whole Tumor)
📂 Dataset: BraTS 2020 — Multimodal MRI (355 subjects; FLAIR, T1, T2, T1ce)

Imaging

Machine Learning Specialist

Rahul Pandya, Your Research and Data Scientist

AI/ML , Data Expert

Available for work

Available for work

Hello I am Rahul Pandya,

An aspiring ML-AI researcher and data scientist.

Connect with me

I specialize in leveraging data, algorithms, and machine learning to create intelligent solutions. Passionate about problem-solving, I enjoy bridging the gap between technology and real-world applications.

Professional Experience

DePaul University

Data Analyst & Mathematics Tutor

Jan 2023 - Mar 2025

• Analyzed and managed Advancement Data Systems, SharePoint administration, and Web development. • Integrated automated forms and pipelines for handling user queries, laying the foundational work for data accessibility and user experience. • Spearheaded advanced data analysis initiatives, enhancing data accuracy and consistency across cross-functional datasets, resulting in a measurable reduction in discrepancies. • Tutored students at the DePaul ECC, mentoring and guiding them to solve complex mathematical and statistical problems. • Active Peer Mentor and volunteer with International Student Services (ISS).

DePaul University

Data Analyst & Mathematics Tutor

JAN 2000 - JAN 2020

• Analyzed and managed Advancement Data Systems, SharePoint administration, and Web development. • Integrated automated forms and pipelines for handling user queries, laying the foundational work for data accessibility and user experience. • Spearheaded advanced data analysis initiatives, enhancing data accuracy and consistency across cross-functional datasets, resulting in a measurable reduction in discrepancies. • Tutored students at the DePaul ECC, mentoring and guiding them to solve complex mathematical and statistical problems. • Active Peer Mentor and volunteer with International Student Services (ISS).

IIITDM Kancheepuram

Research Intern

Jan 2022 - July 2022

IIITDM Kancheepuram

Research Intern

JAN 2000 - JAN 2020

Sankey Solutions

Solution Analyst

June 2021 - Dec 2021

Sankey Solutions

Solution Analyst

JAN 2000 - JAN 2020

LinuxWorld Informatics Pvt Ltd

Learner Success Head

June 2020 - Aug 2021

LinuxWorld Informatics Pvt Ltd

Learner Success Head

JAN 2000 - JAN 2020

Robosync

Student Intern

Dec 2019 - Mar 2020

Robosync

Student Intern

JAN 2000 - JAN 2020

Education

DePaul University

Masters in Data Science

2022 - 2025

Core Machine Learning & AI: Advanced Machine Learning,Neural Networks and AI, Reinforcement Learning,Generative Artificial Intelligence Specialized Techniques: Diffusion Modelling, Graph Neural Networks (GNNs) Data Handling & Analysis: Data Analysis and Regression, Programming Machine Learning Applications, Data Visualization, Database Applications.

DePaul University

Masters in Data Science

JAN 2000 - JAN 2020

University of Mumbai

Bachelors of Engineering in Electronics and Telecommunications

2017 - 2021

University of Mumbai

Bachelors of Engineering in Electronics and Telecommunications

JAN 2000 - JAN 2020

How it works

Process Is Everything

Simple, streamlined process is what get's you results

1

Conceptualization & Framing

Define research goals, identify knowledge gaps, and align the study with existing literature to build a clear scientific foundation.

Establish clarity of purpose and theoretical foundation.

2

Dataset Design & Preparation

Curate and preprocess the datasets, ensuring data quality, reproducibility, and ethical integrity throughout.

Ensure data consistency and reproducibility for fair evaluation.

3

Model Development & Experimentation

Implement and refine architectures — from simple to advanced models — through systematic experimentation and evaluation.

Build explainable, high-performing, and reproducible systems.

4

Evaluation & Dissemination

Analyze metrics, interpret findings, and present validated results through visualizations, reports, or peer-reviewed publications.

Translate research outcomes into contributions for the broader scientific community.

1

Conceptualization & Framing

Define research goals, identify knowledge gaps, and align the study with existing literature to build a clear scientific foundation.

Establish clarity of purpose and theoretical foundation.

2

Dataset Design & Preparation

Curate and preprocess the datasets, ensuring data quality, reproducibility, and ethical integrity throughout.

Ensure data consistency and reproducibility for fair evaluation.

3

Model Development & Experimentation

Implement and refine architectures — from simple to advanced models — through systematic experimentation and evaluation.

Build explainable, high-performing, and reproducible systems.

4

Evaluation & Dissemination

Analyze metrics, interpret findings, and present validated results through visualizations, reports, or peer-reviewed publications.

Translate research outcomes into contributions for the broader scientific community.

1

Conceptualization & Framing

Define research goals, identify knowledge gaps, and align the study with existing literature to build a clear scientific foundation.

Establish clarity of purpose and theoretical foundation.

2

Dataset Design & Preparation

Curate and preprocess the datasets, ensuring data quality, reproducibility, and ethical integrity throughout.

Ensure data consistency and reproducibility for fair evaluation.

3

Model Development & Experimentation

Implement and refine architectures — from simple to advanced models — through systematic experimentation and evaluation.

Build explainable, high-performing, and reproducible systems.

4

Evaluation & Dissemination

Analyze metrics, interpret findings, and present validated results through visualizations, reports, or peer-reviewed publications.

Translate research outcomes into contributions for the broader scientific community.

I am with you in every step

Advancing AI research with precision, transparency, and impact.

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Research & Development Skills

My Skillset

Discover a diverse range of technical skills crafted for impactful solutions

  • Rahul Pandya Portfiolio
  • Rahul Pandya Portfiolio
  • Logo Representation of the NLTK

Core

My Expertise

Advancing AI through imaging, diffusion, and intelligent modeling.

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Image Processing & Machine Vision

Developing intelligent vision pipelines for feature extraction, 3D reconstruction, and automated visual understanding across domains.

Data Science & Analytics

Applying statistical modeling, predictive analytics, and visualization to extract insights from complex biomedical and global datasets.

GenAI, Diffusion & LLMs

Exploring generative diffusion models and large language frameworks for scientific discovery, multimodal learning, and explainable AI.

Medical Imaging & Deep Learning

Designing deep neural architectures for MRI and CT segmentation, disease detection, and medical image synthesis.

  • Predictive Modeling

  • Diffusion Models

  • Feature Extraction

  • 3D Reconstruction

  • Detection

  • GNNs

  • LLMs

  • Segmentation

  • Statistical Inference

  • Optimization

Teamwork

Recommendations & Collaborations

Endorsed by mentors, collaborators, and research peers across academia and AI-driven projects.

11

Major Publications

3550

Reads

30

Citations

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Vatsal Shah

ML Engineer SOTAES

5.0

Rahul's animosity towards group objectives and energy has carried trees to upgrade our exploration.

Neel Macwan

PhD grad at ASU

4.7

He is dedicated for his work and he always has a hunger to learn new skills.

Prof. Mayur Sevak

A. Professor at BVM

5.0

He knows how to bring best out of his team members by motivating them to the fullest.

Dr. Vedvyas Dwivedi

Vice-Chancellor

5.0

Rahul is always keen to learn new things and technology always admires him.

Let's Connect

Let's Work Together

Project Collaboration

Projects in Generative AI, ML and Imaging using advanced computational methods

Mentorship and Guidance

Open to join ongoing publications, supervision, and interdisciplinary projects exploring deep learning and scientific computing

See All Projects

Connect for Collaboration

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Let's Connect

Let's Work Together

Project Collaboration

Projects in Generative AI, ML and Imaging using advanced computational methods

Mentorship and Guidance

Open to join ongoing publications, supervision, and interdisciplinary projects exploring deep learning and scientific computing

See All Projects

Connect for Collaboration

Image banner

Let's Connect

Let's Work Together

Project Collaboration

Projects in Generative AI, ML and Imaging using advanced computational methods

Mentorship and Guidance

Open to join ongoing publications, supervision, and interdisciplinary projects exploring deep learning and scientific computing

See All Projects

Connect for Collaboration

Image banner