DINAH PUNNOOSE
Fremont,CA
Summary
- 7+ years of academic and industry experience with 4.8 years of experience as Data Scientist using ML algorithms, Computer Vision and NLP.
- Working experience as in building ML models using various algorithms and deep learning frameworks such as TensorFlow, Scikit Learn, and Keras.
- Worked on tools like- PyCharm, Visual Studio, Jupyter Notebook.
- Developed models for various applications, including computer vision, predictive analytics, natural language processing, anomaly detection, and recommendation engines.
- Implemented a variety of optimization techniques such as cross-validation, hyper parameter tuning, and grid search to improve the accuracy of my models.
- Working with GenAI capabilities via textual and multimodal RAG pipeline
- Experience in deploying models to production and have automated the process using technologies such as Docker, Kubernetes, and AWS.
- Recognized and got appreciated by clients to deliver efficient solutions.
- Have excellent communication and agile team working experience
Overview
12
12
years of professional experience
1
1
Certification
Work History
Data Scientist
IQZ Systems
Atlanta, GA
01.2022 - 02.2023
- Designed an innovative algorithm that automated the resume review process, increasing productivity by 30%.
- Involved in developing an LLM chatbot that can assist hiring managers in the resume screening process.
- Involves the use of hybrid retrieval methods to augment the LLM agent with suitable resumes as context:
- Similarity-based retrieval: When a job description is provided, the retriever utilizes RAG/RAG Fusion to search for similar resumes to narrow the pool of applicants to the most relevant profiles.
- Keyword-based retrieval: When applicant information is provided (IDs), the retriever can also retrieve additional information about specified candidates.
- The retrieved resumes are then used to augment the LLM generator, so it is conditioned on the data of the retrieved applicants.
- The generator can then be used for further downstream tasks, like cross-comparisons, analysis, summarization, or decision-making.
Research Associate
HITS
Chennai, India
08.2018 - 04.2021
- Deep authenticator, image embedding (POC)
- Tech: Python, NodeMCU, MongoDB, DeepFace, FastAPI, Docker, ACR, App Services, Terraform, Azure.
- Designed an embeddings-based remote application for a client to provide permission-based access to restricted areas.
- Selected MTCNN for face detection, and FaceNet for embedding generation, utilized MongoDB as a feature store.
- I used FastAPI as an interface for the model and checked it similarly using loss.
- Implemented CI/CD in a monolith architecture using GitHub Actions, and deployed the web application on App Services on Azure Cloud.
R & D Engineer
Modern High Tech
Seoul, South Korea
09.2017 - 07.2018
- Responsible for 2D object detection and classification projects, which include license plate detection, face and ID anonymization, vehicles, and person monitoring.
- Requirement gathering and analyzing data of over 4,000 custom images.
- Performed annotation and data engineering on the custom images.
- Used OCR to fetch the number from the number plate detected.
- Performed model selection and built a model on the images.
- Achieved an accuracy of 86% after training the model on YOLOv4.
- Deployed deep learning models as microservices using Docker, Flask/Nginx, and AWS.
- Unit testing the data on customer datasets.
- Created BI reports to present the data predictions.
Postdoctoral Research Fellow
Pusan National University
Busan, SOUTH KOREA
02.2017 - 08.2017
- Engineered a fast, adaptable machine-learning model that improved the efficiency of solar cell accuracy by 30%.
- Led a team of five to design and implement a predictive model using deep learning algorithms, increasing the performance of solar cells by 40%.
- Developed a robust ML-based algorithm that reduced manual work by 50%.
- Maintained accurate records of all fellows' progress throughout their tenure in the program.
Test Engineer
Nokia Pvt Ltd.
Bangalore, India
05.2011 - 02.2013
- Worked on UMTS UE Protocol Conformance Testing (3GPP 34.123), with a focus on RRC, RLC, and MAC protocols.
- Involved in GCF/PTCRB certification of Renesas chipsets.
- Experience in testing the Renesas UE platforms on various network simulators, like Anite SAT6, Anritsu MD8480C, and Rohde and Schwarz (CMW500).
Education
Doctor of Philosophy (PhD) - Computer Science and Engineering
Pusan National University
Busan, South Korea12.2017
Master of Technology (M.Tech) - Computer Science and Engineering
Sastra University
Tanjore, India12.2012
Bachelor of Engineering(B.E) - Computer Science and Engineering
Saraswathi Velu College of Engineering
Shollinghur, India12.2010
Skills
- Machine Learning Expertise: Regression Modeling, Random Forest, XGBoost, Gradient Boost, kNN Classifier, K-means Clustering, AutoEncoder, Feature Extraction (PCA, Factor Analysis), Advanced Natural Language Processing (Text Analytics, Encoder-Decoder, BERT, Transformer Architecture, etc), GANs (Generative Adversarial Networks), VAE (Variational Auto Encoder)
- Deep Learning Frameworks: TensorFlow, PyTorch, Keras, CUDA
- Programming: Python (NumPy, Pandas, NLTK, spaCy, Keras, scikit-learn, Matplotlib, Seaborn, TensorFlow)
- Version Control Systems (VCS), DevOps, and Miscellaneous Tools: Jupyter Notebook, PyCharm, VSCode, Git, Flask, and REST API
- Tools & Platforms: AWS (SageMaker, Lambda, EC2), Azure ML, Docker, Kubernetes, Jupyter
Certification
- Certification on 'Online Summer Training Program/FDP on AI&ML and Blockchain Technology' through Finland Labs.
- One Week Training On 'Image Processing Using OpenCV Python' offered through Finland Labs.
- Obtained certification on 'Artificial Intelligence and Machine Learning' from Finland Labs.
- Done certification course on 'Introduction to Machine Learning' through NPTEL.
- Done certification course on 'Machine Learning' through Coursera.
- Obtained certification on 'Resume Selection using Naive Bayes' using Coursera.
- Obtained certificate on Online FDP on 'Data Sciences' from IIT Vadodara.
Projects
- Feedback analysis based on sentiment review analysis on native language (telugu).
- Detection Of Brain Tumor In MRI Images Using Anisotropic Filtering And Kernel SVM Classification
- Computer vision related applications like Face Mask Detector in still images and video.
- NLP related applications like Text Classification using Bidirectional LSTM.
Publications
- Feedback analysis based on sentiment review analysis on native language (telugu), International Journal of Advanced Science and Technology Vol. 29, No. 5, (2020), pp. 11589-11596.
- Two-Way Traceability using Smart Tags (QR): DL Tags, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9 Issue-5, June 2020.
- Detection Of Brain Tumor İn MRI Images Using Anisotropic Filtering And Kernel SVM Classification, International Journal of Advanced Science and Technology Vol. 29, No. 8, (2020), pp.3836-3844.
- A comparative study on the performance of rule engines in automated ontology learning: a case study with erythematosquamous disease (ESD),DOI 10.1108/IJIUS-08-2019-0047, International Journal of Intelligent Unmanned Systems © Emerald Publishing Limited 2049-6427.
- For more publications: https://drive.google.com/file/d/1yLruW9m_KIm5M6UJ2iUF8XJo bkTCX5t-/view?usp=sharing
Timeline
Data Scientist
IQZ Systems
01.2022 - 02.2023
Research Associate
HITS
08.2018 - 04.2021
R & D Engineer
Modern High Tech
09.2017 - 07.2018
Postdoctoral Research Fellow
Pusan National University
02.2017 - 08.2017
Test Engineer
Nokia Pvt Ltd.
05.2011 - 02.2013
Doctor of Philosophy (PhD) - Computer Science and Engineering
Pusan National University
Master of Technology (M.Tech) - Computer Science and Engineering
Sastra University
Bachelor of Engineering(B.E) - Computer Science and Engineering
Saraswathi Velu College of Engineering
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