Hi, I'm Rishabh Sharma, I did my Ph.D. in Computer Science from the University of Houston.
Hi, I'm Rishabh Sharma, I did my Ph.D. in Computer Science from the University of Houston.
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About Me
About Me
Hello there! I'm Rishabh Sharma, a computer scientist originally from New Delhi, India, currently based in the beautiful Bay Area, California. I hold a PhD in Computer Science from the esteemed University of Houston.
Hello there! I'm Rishabh Sharma, a computer scientist originally from New Delhi, India, currently based in the beautiful Bay Area, California. I hold a PhD in Computer Science from the esteemed University of Houston.
My professional journey has led me to explore and specialize in captivating fields such as machine learning, computer vision, and medical imaging. I firmly believe that technology, especially AI, can be harnessed to address real-world challenges and make a positive impact.
My professional journey has led me to explore and specialize in captivating fields such as machine learning, computer vision, and medical imaging. I firmly believe that technology, especially AI, can be harnessed to address real-world challenges and make a positive impact.
As a researcher and practitioner, my key strengths lie in computer vision, explainable AI, and a strong foundation in statistics. The pursuit of knowledge and a PhD in computer science have honed my research skills, making me passionate about driving innovation and discoveries.
As a researcher and practitioner, my key strengths lie in computer vision, explainable AI, and a strong foundation in statistics. The pursuit of knowledge and a PhD in computer science have honed my research skills, making me passionate about driving innovation and discoveries.
My specialization revolves around medical image enhancement, MRI acquisition, and the fascinating world of computer vision applied to radiology. I find joy in working on projects that have a significant impact on healthcare and people's lives.
My specialization revolves around medical image enhancement, MRI acquisition, and the fascinating world of computer vision applied to radiology. I find joy in working on projects that have a significant impact on healthcare and people's lives.
Beyond my work, I indulge in various interests and hobbies. Soccer, chess, and immersing myself in the world of movies are some of my favorite pastimes. These activities keep me balanced and energized.
Beyond my work, I indulge in various interests and hobbies. Soccer, chess, and immersing myself in the world of movies are some of my favorite pastimes. These activities keep me balanced and energized.
To stay updated and engaged in my domain, I make a habit of regularly diving into books and research papers. This continuous learning fuels my creativity and sharpens my abilities to tackle challenging problems.
To stay updated and engaged in my domain, I make a habit of regularly diving into books and research papers. This continuous learning fuels my creativity and sharpens my abilities to tackle challenging problems.
If you share similar interests, are curious about AI's potential, or simply want to connect, I'd love to hear from you. Let's collaborate and explore the endless possibilities of technology together!
If you share similar interests, are curious about AI's potential, or simply want to connect, I'd love to hear from you. Let's collaborate and explore the endless possibilities of technology together!
My Research
My Research
News
News
2023
09/2023: I joined Hewlett Packard Labs as a Post-Doctoral Research Associate in Milpitas, California.
07/2023: I succesfully defended my PhD dissertation titled: Statistical Analysis and Deep Learning Techniques for Enhancing Low Quality Magnetic Resonance Imaging
05/2023: We submitted a paper to esteemed MAGMA journal
05/2023: We submitted an abstract to RSNA.
2022
11/2022: my paper "A Deep Learning Approach to Upscaling “Low-Quality” MR Images: An In Silico Comparison Study Based on the UNet Framework", is accepted by Applied sciences journal.
11/2022: I successfully defended my PhD proposal and is promoted to PhD candidate.
10/2022: We submitted our work "A Deep Learning Approach to Upscaling “Low-Quality” MR Images: An In Silico Comparison Study Based on the UNet Framework", to Applied Science Journal.
09/2022: We will be presenting our abstract "Deep learning in upscaling low quality MR images: A comparison study of three UNet architectures with and without priors employment" in Panhellenic Congress of Medical Physics 2022, in Athens, Greece.
08/2022: Completed Internship at Siemens Healthineers. Developed algorithms for Positron Emission Topography (PET) image Super Resolution.
07/2022: Our abstract "Deep learning in upscaling low quality MR images: A comparison study of three UNet architectures with and without priors employment" got accepted in Panhellenic Congress of Medical Physics 2022.
07/2022: Presented my work at Alzheimer's Association International Conference (AAIC) 2022.
05/2022: I moved to Knoxville, TN to start my internship with Siemens Healthineers.
04/2022: Our abstract "Multi-branch Convolutional Neural Network for Alzheimer’s Disease versus Normal Control Classification using PET Images" got accepted in Alzheimer's Association International Conference.
03/2022: Our paper "Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge." got accepted to the Medical Image Analysis journal.
03/2022: Submitted a paper to the Journal of Digital Imaging.
01/2022: Accepted an internship offer from Siemens Healthineers.
2021
12/2021: Submitted a paper to AAIC conference
08/2021: Submitted a paper to the Medical Image Analysis Journal
08/2021: Completed summer internship at Siemens Healthineers
08/2021: Presented my internship work with multiple teams at Siemens Healthineers. Produced two research abstracts and file one patent.
07/2021: Started a patent application with Siemens Healthineers.
05/2021: Started my internship with Siemens Healthineers.
03/2021: Reached my highest rating at chess.com
2020
12/2021: Accepted Internship Offer from Siemens Healthineers.
10/2020: Our paper "Myocardial Infarction Segmentation in Late Gadolinium Enhanced MRI Images using Data Augmentation and Chaining Multiple U-Net" got accepted in IEEE BIBE Conference
10/2020: Our paper "SM2N2: A Stacked Architecture for Multimodal Data and Its Application to Myocardial Infarction Detection" got accepted in the STACOM workshop in MICCAI 2020
08/2020: Our paper "Logistic LASSO regression for dietary intakes and breast cancer" got accepted in Nutrients.
06/2020: Started playing Chess in the middle of the pandemic.
05/2020: I was general chair for UH CS GradCon 2020.
2019:
08/2019: Started PhD program at the University of Houston
05/2019: Graduated with a Master of Science in Computer Science from California State University Fullerton.
03/2019: Accepted PhD offer from University of Houston.
Publications
Publications
A Deep Learning Approach to Upscaling “Low-Quality” MR Images: An In Silico Comparison Study Based on the UNet Framework
A Deep Learning Approach to Upscaling “Low-Quality” MR Images: An In Silico Comparison Study Based on the UNet Framework
Rishabh Sharma, Panagiotis Tsiamyrtzis, Andrew G. Webb, Ioannis Seimenis, Constantinos Loukas, Ernst Leiss, and Nikolaos V. Tsekos
Multi-branch Convolutional Neural Network for Alzheimer’s Disease versus Normal Control Classification using PET Images
Multi-branch Convolutional Neural Network for Alzheimer’s Disease versus Normal Control Classification using PET Images
Rishabh Sharma, Ludovic Sibille, Rachid Fahmi
Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge.
Lalande et al.
Myocardial Infarction Segmentation in Late Gadolinium Enhanced MRI Images using Data Augmentation and Chaining Multiple U-Net.
Rishabh Sharma, Christoph F. Eick, Nikolaos V. Tsekos
SM2N2: A Stacked Architecture for Multimodal Data and Its Application to Myocardial Infarction Detection.
Rishabh Sharma, Christoph F. Eick, Nikolaos V. Tsekos
Logistic LASSO regression for dietary intakes and breast cancer
Archana J. McEligot, Valerie Poynor, Rishabh Sharma, Anand Panangadan
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