Aishik Konwer5th year PhD candidate Department of Computer Science, Stony Brook University Email: akonwer@cs.stonybrook.edu, Mobile: +1-6317471244 Resume • Google Scholar • Github • Linkedin |
I am Research Assistant at Imagine lab under the supervision of Prof. Prateek Prasanna.
Prior to joining SBU, I did my Bachelor in Technology (Btech) from the Institute of Engineering & Management, Kolkata (India) majoring in Electronics and Communication Engineering. During undergraduate, I had particularly worked on research problems like Scene Text Detection and Recognition, Handwriting Recognition, Writer Identification, Script Identification.
Research Interest: My research interests lie in computer vision and medical image analysis. In the recent past, my focus was on applying deep learning techniques to build predictive and prognostic models in various clinical domains. Currently I am working on designing efficient algorithms that can handle limited annotations and/or missing modalities. I am also interested in application of multi-modal data for medical vision. One such example is vision-language models.
Coursework: Data Science, Machine Learning, Computer Vision, HCI, Data Visualization
I am currently on the job market for Research Scientist roles.
Stony Brook University, USA
PhD in Computer Science & Engineering Aug 2019 - June 2024 |
Institute of Engineering & Management, India
Btech in Electronics and Communication Engineering Aug 2013 - June 2017 |
Indian Statistical Institute, Kolkata, India
Under Prof. Umapada Pal Sept 2016 - Sept 2017 [Certificate] |
Indian Institute of Technology Roorkee, India
Under Prof. Partha Pratim Roy Sept 2017 - March 2018 [Certificate] |
GE Healthcare, USA
AI Scientist Intern May 2024 - August 2024 |
SRI International, USA
Deep Learning Research Intern May 2023 - August 2023 |
Roche, USA
Advanced ML Research Intern May 2022 - August 2022 |
Cognizant, India
Datawarehouse developer Dec 2017 - July 2019 |
MetaStain: Stain-generalizable Meta-learning for Cell Segmentation and Classification with Limited Exemplars
|
|
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation
|
|
Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations
|
|
Attention-based Multi-scale Gated Recurrent Encoder with Novel Correlation Loss for COVID-19 Progression Prediction
|
|
Predicting COVID-19 Lung Infiltrate Progression on Chest Radiographs Using Spatio-temporal LSTM based Encoder-Decoder Network
|
|
A New GVF Arrow Pattern for Character Segmentation from Double Line License Plate Images
|