Mahasweta Bhattacharya
Baltimore,MD
Summary
A Postdoctoral researcher with 5+ years of experience in developing AI/ML-based models for complex large-scale biological datasets. Skilled in data science, machine learning, and bioinformatics with a strong aptitude for learning new technologies, adapting them, resulting in scientific contributions. Highly motivated to work in a cross-functional collaborative team, with a particular interest in contributing to novel therapeutics, drug discovery, and precision medicine.
Overview
9
9
years of professional experience
1
1
Certification
Work History
Postdoctoral Research Fellow
Johns Hopkins University School Of Medicine
01.2023 - Current
- Technical Stack: CUDA, C, C++, Artificial Intelligence (AI), Reinforcement Learning, Python, R, Convolutional Neural Networks (CNN), High-Performance Computing (HPC), Clinical CT images
- Developed CNN models on CT images to automate radiotherapy planning; employed reinforcement learning; achieved accuracy improvement by 30% in plan accuracy
- Created CUDA-enabled radiation dose package in Python for dose computation for radiotherapy planning using CT image data achieving 99.5% accuracy with 2-fold time improvement
Research Assistant
University At Buffalo
08.2017 - 01.2023
- Technical Stack: Machine Learning, Deep Neural Networks, Bioinformatics, Supervised Learning, Unsupervised Learning, Generative Models, Regression, Classification, HPC, Python, MATLAB, R
- Devised and executed comprehensive analysis of TabulaSapiens' scRNAseq data using R to identify gene clusters, cell types, and pathways; created interactive visualizations that facilitated easy interpretation
- Constructed Python pipeline to process pre-clinical multi-omics data from mice, utilizing autoencoders and dimensionality-reduction algorithms; identified biomarkers for kidney disease with 90% accuracy and developed diagnostic tool for early detection
- Implemented random forest classification model in Python on large-scale transcriptomics data, achieving 97% accuracy in predicting chronic diabetes, and performed statistical analysis to identify diabetes biomarkers
- Spearheaded collaborative effort with neuroscientists to preprocess and analyze calcium imaging data from brain using Python; implemented RNN-based regression model that predicted muscle movements with over 90% accuracy
- Designed linear regression model in MATLAB to infer causal networks of neurons from large-scale neuron activity data, achieving 125% improvement in AUROC post-benchmarking
- Developed Finite Element Models in COMSOL to study thermal effect of optical brain stimulation to understand optical dose response of brain tissue, leading to better understanding of optimal stimulation parameters
Teaching Assistant
University At Buffalo, The State University Of New York
08.2017 - 12.2019
- Knowledge Base: Biosignal Acquisition, Signal Preprocessing, Statistical Analysis, Biosensor Circuits, Experimental Design, Hypothesis Testing, Data Visualization
- Instructed and mentored undergraduate students in Biomedical Instrumentation (BE 403 - UG) and Biomedical Engineering - Biosignals (BE 312 - UG) courses, improving technical proficiency and learning outcomes of students
Summer Intern
Siagnos LLC
05.2019 - 07.2019
- Technical Stack: Time-series Data, Unsupervised Learning, Multi-modal Data Integration
- Engineered integration of EEG signals and Near-Infrared Spectroscopy data in MATLAB to study ischemic stroke effect on blood-brain barrier resulting in development of portable brain-scanner device
- Investigated commercial potential of a portable brain monitoring device in NSF I-Corps program using customer discovery techniques; identified pain points and conceptualized a minimum viable product
Programmer Analyst Trainee
Cognizant
10.2014 - 08.2015
- Technical Stack: Documentation, Microsoft Visual Studios, SQL, HTML, CSS
- Developed Online Air Ticket Reservation System, utilizing SQL for backend database design; developed front-end user interface using HTML5 and CSS3, while conducting testing and maintenance for operational efficiency
Summer Intern
Hindustan Aeronautics Limited
05.2014 - 07.2014
- Technical Stack: State Estimation Modeling, Signal Processing, MATLAB, Outlier Identification
- Improved flight navigation accuracy by 30% employing Kalman Filter-based data fusion model in MATLAB using navigational sensor data in collaboration with Mission Combat Systems team
Education
Ph.D. - Biomedical Engineering
University At Buffalo
Buffalo, NY01.2023
Master of Science - Electrical Engineering
University At Buffalo
Buffalo, NY05.2017
Bachelor of Technology - Electronics and Communication Engineering
West Bengal University of Technology
Kolkata, India05.2014
Skills
- Programming Languages: Python, MATLAB, SQL, R, C, C, Bash
- Environment: Jupyter, PyCharm, Eclipse, LaTeX
- Libraries: Keras, TensorFlow, Scikit-Learn, Numpy, Pandas, Matplotlib, Scanpy, Biopython, Seurat, ggplot2
- Softwares: GraphPad Prism, MiniTab, Tableau, COMSOL
- Version Control: Git, Github, Bitbucket
- HPC Tools: CUDA, Slurm
- Operating Systems: Linux, Windows, MacOS
- Knowledge Base: Machine Learning, Computational Biology, Data Science, Bioinformatics, Systems Biology
Publications
- S. Meamardoost, E. J. Hwang, M. Bhattacharya, et al., ”Rewiring Dynamics of Functional Connectome in Motor Cortex during Motor Skill Learning”, bioRxiv, 2022
- S. Meamardoost, M. Bhattacharya, et al., ”FARCI: Fast and Robust Connectome Inference” , Brain Sciences, 2021
- Z. Rezaee, S. Ranjan, D. Solanki, M. Bhattacharya, et al., ”Feasibility of combining functional near-infrared spectroscopy with electroencephalography to identify chronic stroke responders to cerebellar transcranial direct current stimulation-a computational modeling and portable neuroimaging methodological study”, The Cerebellum, 2021
- M. H. Othman, M. Bhattacharya, et al., ”Resting-State NIRS-EEG in Unresponsive Patients with Acute Brain Injury: A Proof-of-Concept Study”, Neurocritical Care, 2020
- A. Dutta, S. S. Karanth, M. Bhattacharya, et al., ”A proof of concept ’phase zero’ study of neurodevelopment using brain organoid models with Vis/near-infrared spectroscopy and electrophysiology”, Nature Scientific Reports, 2020
- M. Bhattacharya, et al., ”Computational Modeling of the Photon Transport, Tissue Heating, and Cytochrome C Oxidase Absorption during Transcranial Near-Infrared Stimulation”, Brain Sciences, 2019
Presentations
- Decrypting your Brain: Quest for Smarter Machines - 3 Minute Thesis Final, Buffalo, NY, 2022
- Stability of Motor Cortex Decoders during Learning - Society for Neuroscience, 2022, San Diego, CA
- Does Learning Alter Neural Decoders of Motor Cortex? - Society for Neuroscience Global Connectome, 2021 - Virtual
- Development of bidirectional ‘mini-Brain’ computer interface (mBCI) to modulate functional neural circuits – stimulation and recording from a cerebral organoid - Society for Neuroscience, 2019, Chicago, IL
Certification
- Introduction to Systems Biology, Icahn School of Medicine at Mount Sinai - ongoing
- Introduction to Genomic Technologies - Johns Hopkins University, Apr, 2023
Work Availability
monday
tuesday
wednesday
thursday
friday
saturday
sunday
morning
afternoon
evening
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Timeline
Postdoctoral Research Fellow
Johns Hopkins University School Of Medicine
01.2023 - Current
Summer Intern
Siagnos LLC
05.2019 - 07.2019
Research Assistant
University At Buffalo
08.2017 - 01.2023
Teaching Assistant
University At Buffalo, The State University Of New York
08.2017 - 12.2019
Programmer Analyst Trainee
Cognizant
10.2014 - 08.2015
Summer Intern
Hindustan Aeronautics Limited
05.2014 - 07.2014
Ph.D. - Biomedical Engineering
University At Buffalo
Master of Science - Electrical Engineering
University At Buffalo
Bachelor of Technology - Electronics and Communication Engineering
West Bengal University of Technology
- Introduction to Systems Biology, Icahn School of Medicine at Mount Sinai - ongoing
- Introduction to Genomic Technologies - Johns Hopkins University, Apr, 2023
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