Machine Learning Engineer | NeuroAI Enthusiast 🎯

📖 Graduated with M.S. in Artificial Intelligence at University at Buffalo,NY (Graduating 2026).

💻 Currently working as a Machine Learning Engineer @CYBRA Corporation, NY, USA.

🔍 Worked as Research Scientist Intern at Clinical Translational Research Center, Buffalo, NY for building neuroimaging analysis pipeline and appiyng Machine Learning and deep learning algorithms for early detection of Alzheimer's.

💻 I’m a Machine Learning Engineer and Researcher passionate about building intelligent systems at the intersection of Artificial Intelligence, Neuroscience, and Brain-Computer Interface.

✅️ How to reach me: gawalevaishnavi@gmail.com | LinkedIn | Portfolio

I’m deeply interested in the intersection of AI and neuroscience, especially in:

• 🧩 Neuroprosthetics and Brain–Computer Interfaces (BCI)
• 🧬 Motor function restoration through neural decoding
• 🧠 Machine Learning for neural signal interpretation
• 🤖 Reinforcement learning and Computer Vision for adaptive decision-making and intelligent agent design

Master of Science in Artificial Intelligence
University at Buffalo – SUNY, New York, USA (Aug 2024 – Jan 2026)
Courses: ML • AI Fundamentals • Numerical Math • Algorithms • Data-Intensive Computing • Computer Vision • RL • Pattern Recognition

Master of Technology in Computer Engineering
University of Mumbai – VJTI, India (Aug 2017 – Jul 2019)
Publication: Firewall Algorithm Approach for DDoS Attack Mitigation on Cloud, IJSRD (2019)
Graduate TA: Python and C Programming (Fall 2017–Spring 2018)

Bachelor of Engineering in Computer Science & Engineering
Dr. BAMU University, Aurangabad, India (Aug 2014 – Jul 2017)

Jan 2025 – Jan 2026
Project: CEST MRI-Based Biomarker Development for Early Alzheimer’s Diagnosis

• Built an automated CEST MRI pipeline for APT signal correction & regional amyloid-beta quantification
• Conducted MRI & PET preprocessing, Centiloid SUVR quantification, and ROI-level statistical correlation
• Applied CNNs, ROI-patch classification, and segmentation models for biomarker prediction
• Tools: Python (NiBabel, Nilearn, Plotly) • MATLAB (SPM12) • ANTs, FSL, FreeSurfer, 3DSlicer

Sept 2024 – Dec 2024
Project: 3D Shape Reconstruction in Computer Vision

• Implemented 3D CNNs, Autoencoders, and Transformers for object reconstruction
• Datasets: OmniObject3D, ShapeNet
• Tools: PyTorch, Open3D, CUDA, Git

May 2026 – Present

• Developing an AI-driven RFID validation framework for warehouse automation using Python and machine learning.
• Working with cross-functional teams to integrate AI solutions into enterprise RFID software.

Jan 2024 – Jul 2024

• Led plant disease prediction research using CNNs and TensorFlow
• Taught Data Structures, Logic Design, and Python Programming
• Launched a YouTube channel for Data Structures education
• Mentored undergraduate students in AI and programming fundamentals

Aug 2019 – Jan 2024

• Built ML models to forecast telecom traffic (↑25% accuracy)
• Automated ETL pipelines using Python + SQL + Shell (↓20% processing time)
• Developed RESTful APIs in Java improving backend performance by 30%
• Collaborated cross-functionally for requirement gathering and client demos

• Implemented DQN & A2C models on the MIND dataset for personalized news delivery
• Achieved 9% CTR uplift, outperforming baseline recommenders
• Integrated NER, lemmatization, TF-IDF, and GloVe embeddings

• Developed real-time parking detection using SVM & OpenCV
• Achieved 90%+ accuracy on 2K+ custom parking spot images
• Overlaid live predictions on video feed for intuitive visualization

• Trained XGBoost model (ROC AUC: 0.779) on 30K+ customer accounts
• Used SMOTE + SHAP explainability for class imbalance and interpretability
• Delivered dashboards using Plotly + Power BI

Programming: Python, C, C++, MATLAB, Bash
Frameworks: TensorFlow, PyTorch, Keras, Scikit-learn, OpenCV
ML/AI: CNN, RNN, LSTM, Transformers, RL (DQN, A2C), SHAP/LIME, XAI
Neuroimaging: NiBabel, Nilearn, MNE, SPM12, FSL, ANTs, FreeSurfer
Data & Visualization: Pandas, NumPy, Plotly, Power BI, Tableau
Databases: MySQL, PostgreSQL, SQL Server, MongoDB
DevOps: Docker, MLflow, Git, Streamlit, FastAPI
Stats: Hypothesis Testing, ANOVA, A/B Testing, Bayesian Inference

• 🧩 Computational Neuroscience — University of Washington (Coursera)
• 🤖 Machine Learning Specialization — DeepLearning.AI
• 🧠 TensorFlow Developer Specialization — DeepLearning.AI
• 🦾 Generative AI with LangChain & Hugging Face — Udemy
• ⚙️ MLOps Bootcamp: End-to-End ML Projects — Udemy

• 🧠 Attended Brain–Computer Interface Symposium 2025, New York, USA
• 🥉 3rd place – CSE Demo Day Poster Presentation, UB Spring 2025
• 🖼️ Presented “Facial Emotion Recognition using Deep Learning” – UB Demo Day 2025
• 💪 Most Valuable Employee 2023 – Vegayan Systems Pvt. Ltd.
• 👩‍🔬 Member, Women in Science and Engineering (WiSE)
• 🥈 2nd place, Codiac Coding Competition – AGNITIO’15, JNEC

Feel free to reach out if you're looking for a researcher, have a question, or just want to say hi. I'm always open to discussing new projects and ideas.