Dr. Parimala Kancharla
| Email: | parimala[at]iitmandi[dot]ac[dot]in |
|---|---|
| Address: | School of Computing and Electrical Engineering (SCEE) |
| Indian Institute of Technology, Mandi | |
| A17.03.06,North Campus | |
| Mandi, Himachal Pradesh, India - 175005 |
To Prospective Students
- IIT Mandi - MS/PhD/BTech (EE or CS): If you are interested in working on any of the research areas mentioned below, drop me an email or meet me in my office. I am looking for motivated students with strong mathematical and coding background.
- Non IIT Mandi Students: Reach out if you need any guidance on problems related to Computer Vision and Deep Learning.
Research Interests
- Computer Vision
- Machine Learning and Deep Learning
- Generative Adversarial Networks
- Understanding Visual Cortex
- Video Quality Assessment
- Learned Video Compression
- Machine Learning for Robotics
Teaching
- EE608 - Digital Image Processing (Spring 2023)
- DS403 - Introduction to Statistical Learning (Fall 2023)
- DS401 - Optimization for Data Science (Spring 2024)
Research
Education
- Ph.D - Electrical Engineering, Indian Institute of Technology Hyderabad, 2022
- M.Tech - Electrical Engineering, Indian Institute of Technology Hyderabad, 2018
- B.Tech - Electronics and Communication Engineering, RGUKT Basar, 2015
Work Experience
Academic Experience
- Assistant Professor, SCEE IIT Mandi, (Nov 2022 - Present)
Industrial Experience
- Research Scientist, Intel Labs, Bangalore (Sep 2021 - Nov 2022)
- Research Intern, Intel Labs, Bangalore (Feb 2021 - July 2021)
- Research Intern, Intel Labs, Bangalore (May 2020 - October 2020)
- Graduate Research Assistant, IIT Hyderabad (August 2015 - July 2017) (Sponsored M.Tech student by LVPEI, Hyderabad)
Awards, Honors, and Invited Talks
- Super winner for Qualcomm Innovation Fellowship(QIF) -2020 for the project “ Blind Video Quality assessment ” (one team out of ten winning teams of QIF 2019)
- Recipient of Qualcomm Innovation Fellowship(QIF) -2019 for the project “Blind Video Quality assessment ”
- Received NeurIPS travel award to present our work at NeurIPS 2019
- Selected to attend Doctoral Symposium at ICVGIP 2021
- Selected to attend Google Research India Graduate Symposium 2021
- Participated in the IIT-H and RIKEN-AIP Joint workshop on AI
- All India 9th rank in National Creativity Aptitude Test (NCAT) conducted by IIT Delhi in 2012
Invited Talks
- Speaker in Online Faculty Development Programme (FDP) on "Machine Learning Algorithms for Signal Processing and Communications" organized by NIT, Warangal (December 29th & 30th,2022)
- Speaker in One Week Online Faculty Development Programme (FDP) on "Deep Learning Approaches for 5G and Software Defined Networks" organized by Department of DS&AI, IcfaiTech (FST), IFHE Hyderabad, (April-3rd,2022)
- Delivered a talk in Doctoral Symposium at ICVGIP 2021
Publications
Journal Articles
- P. Kancharla, S. S. Channappayya, “Completely Blind Quality Assessment of User Generated Video Content,” IEEE Transactions on Image Processing. DOI: 10.1109/TIP.2021.3130541,2021. [Abstract, Code]
- P. Kancharla, S. S. Channappayya, “Improving the Visual Quality of Video Frame Prediction Models Using the Perceptual Straightening Hypothesis,” IEEE Signal Processing Letters. DOI: 10.1109/LSP.2021.3118639,2021.[Abstract, Code]
Conference Publications
- P. Kancharla, S. S. Channappayya, “Improving the Visual Quality of Generative Adversarial Network (GAN) - generated Images Using the Multi-scale Structural Similarity Index”, In 25th IEEE International Conference on Image Processing (ICIP)(pp.3908-3912), 2018.
- P. Kancharla, S. S. Channappayya, “Quality Aware Generative Adversarial Networks”, In Advances in Neural Information Processing Systems. pp 2948-2958, 2019. [Abstract, Code]
- P. Kancharla, S. S. Channappayya,“A weighted optimization for Fourier Ptychographic Microscopy,” Proc. of NCC 2019, IISc Bangalore, February, 2019.
- F. K. Joseph, A. Arora, P. Kancharla, M. K. A. Singh, W. Steenbergen, S. S. Channappayya “Generative adversarial network-based photoacoustic image reconstruction from bandlimited and limited-view data,” Proc. SPIE Photons Plus Ultrasound: Imaging and Sensing, 2021.
Professional Services
- Reviewer : IEEE Transactions on Neural Networks and Learning Systems, IEEE Signal Processing Letters, IEEE SP-COM, IEEE NCC
.