,
"In the silent whispers of dawn's embrace,
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Incoming MS in CS at University of Massachusetts Amherst
Greetings! I'm an experienced AI-ML professional specializing in Computer Vision, NLP, and ML solutions, with a strong background in developing impactful projects and upholding ethical AI practices. I hold a Bachelor of Technology in Computer Science and Engineering from VIT, where I was honored as the Best UG Researcher. My professional journey includes research internships at prestigious institutions like the University of Toronto, Harvard University, and Apple. I've contributed to advancements in areas such as breast cancer detection, cardiac arrest prediction, and psychiatric illness recognition. With a commitment to ethical considerations and continuous learning, my objective is to contribute my expertise to innovative teams, advancing AI's potential to address complex challenges through collaboration and cutting-edge technologies.
I am currently pursuing my Master's Degree in Computer Science with Data Science Concentration at the University of Massachusetts Amherst
- Master of Science (MS) in Computer Science
- Courses: Advanced Machine Learning, Advanced Natural Language Processing, 3D Computer Vision, Reinforcement Learning, Systems for Data Science, Applied Statistics
- B.Tech in Computer Science and Engineering
- Courses: Data Structures & Algorithms, Advanced Mathematics, Digital Image Processing, Linear Algebra
With 2+ years of experience in AI and ML, I have been actively involved in creating and deploying innovative solutions across various domains. My academic journey at VIT, where I received the Best UG Researcher Award, and research internships at prestigious institutions like the University of Toronto and Harvard University, have honed my skills in areas such as breast cancer detection, cardiac health monitoring, and mental health analysis. I have led several AI-driven projects, utilizing deep learning and hybrid models, to tackle complex challenges in healthcare, finance, and beyond. My work with Apple’s AI/ML team and other research labs has solidified my expertise in developing practical, impactful machine learning applications. Passionate about advancing AI, I am committed to continuous learning and ethical practices in technology.
- Surveyed on various methods to evaluate the AI-based breast cancer detection and constructed CancLite.
- Developed a simple technique requiring less computation for breast cancer detection with good accuracy.
- Category: Healthcare, Machine Learning
- Designed an ML model with a sensitivity of 85% and specificity of 97% to predict the occurrence of cardiac arrests using heart rate variability (HRV) data.
- Developed a dynamic time-warping algorithm to improve HRV data alignment and analysis, leading to more accurate cardiac arrest predictions.
- Category: Healthcare, Machine Learning
- Conducted research on applying federated learning techniques to improve Siri's performance while ensuring user privacy.
- Developed a framework to enable decentralized training of Siri's natural language processing models across multiple devices.
- Category: AI, Machine Learning
Nurturing a strong aspiration to share my research findings, I am thrilled to share that my dedication has yielded noteworthy results. I am delighted to report the acceptance and publication of two of my papers at the CCVPR 2021 conference, published by Springer. My commitment to advancing knowledge remains unwavering, further highlighted by my recent submission to EMNLP 2023, which is currently undergoing review.
Deep Learning based Techniques for Neuro-Degenerative Disorders Detection, EAAI, Elsevier
GNN based Model for Aroma Prediction using Molecular Structures, GCAT, IEEE Xplore
Early Detection of Forest Fire Using Mixed Learning Techniques and UAV, CIN, Hindawi
Early Detection of ColoRectal Cancer Using Patch-Based Hybrid Model, CVBIC, Springer
PsychNet: Explainable DNN for Psychiatric Disorders and Mental Illness,CICT, IEEE Xplore
A Novel Deep Learning Framework for Diabetic Retinopathy Detection, CICT, IEEE Xplore
A Novel Approach to Detect Fake News Using eXtreme Gradient Boosting, ISDFS, IEEE Xplore.
Gastric Cancer Detection using Hybrid-Network and SHAP Analysis, Book Chapter, IEEE Xplore
RoadSDNet: Algorithm for Road Boundary Detection using MN and HT, ACM, IEEE Xplore
Certain Integral Representations of Hypergeometric Polynomials, EMEA, American Inst. of Physics
Neural architecture of 3D face modelling using GANs, EMEA, American Inst. of Physics
VitaDNet: A Deep Learning-based approach for Vitamin-D Deficiency Prediction, JIKM, World Scientific
I have keen interest towards Software Development, Machine Learning and Artificial Technologies, and Design.
- Python
- Java+
- SQL
- Javascript
- HTML CSS
- R
- TensorFlow
- Keras
- PyTorch
- OpenCV
- Scikit-Learn
- NLTK
- Numpy
- Pandas
- Spacy
- Flask
- LLMs
- Airflow
- Databricks
- Adobe Photoshop
- Adobe Illustrator
- Adobe InDesign
- Figma
- MongoDB
- MySQL
- PostgreSQL
- Tableau
- Grafana
- Kibana
- Splunk
- Power BI
- AWS
- Azure
- GCP
- Linux
- Docker
- Spark
- Hadoop
- MapReduce
- Kubernetes
- ElasticSearch
- RESTful APIs
- GIT
- CI/CD
- SSH
- Unity
- JIRA
I am just a ping away.
Kasyap Varanasi
Address: Amherst, Massachusetts, USA
Email: lvaranasi@umass.edu
Phone number: +91 8074582936