Microsoft Azure 900

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Currently pursuing Masters in Computer Engineering.
·      Projects:1.**Predictive Supply Chain Analytics System** - Developed a predictive supply chain analytics solution using machine learning to analyze historical data, real-time sensor data, and external factors affecting the supply chain to predict potential disruptions and recommend proactive measures. - Implemented on AWS cloud infrastructure for real-time monitoring and alerts.
2\. **Natural Language Processing Chatbot** - Created a chatbot using NLP techniques to provide automated customer support. - Integrated with SQL databases to retrieve customer information.
3\. **Automated Quality Control System using Computer Vision**\- Developed an automated quality control system using computer vision techniques to inspect and classify products on a production line. -Utilized convolutional neural networks (CNNs) for accurate classification of product defects and anomalies, thereby ensuring high-quality manufacturing standards.
4\. **Industrial Equipment Health Monitor using Machine Learning**\- leveraged  RNN neural network-based models to analyze sensor data and identify potential issues, thereby reducing 74% of false positives and minimizing downtime in manufacturing processes.

·      Relevant Subjects and Areas of Study: 1. Machine Learning and Deep Learning 2. Artificial Intelligence and Neural Networks 3. Cloud Computing and Infrastructure 4. SQL and Database Management 5. Natural Language Processing 6. Computer Vision 7. Distributed Systems 8. Data Mining and Big Data Analytics 9. Software Engineering 10. Cloud SQL Databases 11. Cloud Infrastructure (e.g., AWS, Azure, GCP) 12. Data Warehousing and Data Modeling 13. Advanced Algorithms and Data Structures.

Iowa State University

·      Developing an AI-powered predictive model that is aimed at optimizing traffic flow dynamics within urban transportation networks.
·      Implemented advanced NLP techniques for data processing  such as tokenization, lemmatization, and named entity recognition, to extract relevant information from unstructured textual data.
·      Random Forests, Gradient Boosting Machines, and Long Short-Term Memory (LSTM) networks algorithms are used for development and fine tuning the model.
·      Developed hybrid models combining traditional ML algorithms with deep learning architectures such as Recurrent Neural Networks (RNNs) and Transformer-based models like BERT (Bidirectional Encoder Representations from Transformers).
·      Utilized AWS SageMaker for feature transformation and extraction, enabling seamless integration of structured and unstructured data into ML models.
·      Integrated textual features extracted through NLP techniques with numerical features to capture the contextual information and semantic relationships embedded in the textual data. TensorFlow and PyTorch were employed for model development and training.
·      scikit-learn was used for model evaluation and optimization tasks.

Graduate Research Assistant

·      Designed a machine learning model to predict the churn rate among 10K customers based on historical data; reduced churn rate by 71% in the ﬁrst week of implementation.
·      Trained an encoder/decoder grammar error correction model using synthetic data methods; outperformed open-source baseline by 92%.
·      Initiated a character-based transformer spelling correction model using Triton and ONNX; reduced inference latencies below 178ms at 35 req/s.
·      Developed an NLP system that automatically classiﬁed 7.5K emails as spam or advertising mail using 11+ natural language processing methods.
·      Led efforts in machine learning anti-spam initiatives for Google Cloud mail and calendar, which involved tasks such as training and assessing spam models, managing training pipelines, contributing to model enhancements, and ensuring compliance with Google’s data privacy and security policies.
·      Successfully deployed the inaugural machine learning-based anti-spam model for Google Cloud mail and calendar into production.
·      Implemented the capability to conduct A/B experiments in the calendar and mail systems, which are now utilized for assessing model performance before deployment.
·      Automated the process of generating datasets for labeling and collaborated on integrating them with Google’s labeling tools. Established an effective method for evaluating spam models even when ground truth data was unavailable.
·      Collaborated cross-functionally with various teams within GCS to create reports and experimentation dashboards for monitoring the performance of deployed ML models.
·      Took responsibility for maintaining and continuously enhancing production models post-deployment.
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Artificial Intelligence Engineer

·      Designed and built an alerting model to detect anomalous data in the incoming data streams. The model consists of a forecasting component which utilizes regression with regularization to generate predicted values of key performance indicators. The model has high sensitivity and specificity and requires minimal human intervention leading to a more robust tracking system.
·      Built forecasting models to predict road accidents.
·      Demonstrated advanced proficiency in Python programming, serving as the primary language for research activities. Leveraged Python libraries, including NumPy, pandas, scikit-learn, TensorFlow, and PyTorch, to develop machine learning models and data analysis pipelines.
·      Developed novel machine learning algorithms and models tailored to address transportation challenges. Employed techniques spanning from traditional statistical approaches to cutting-edge deep learning methods to solve complex problems.
·      Designed and executed well-engineered, easy-to-maintain, reliable, and bug-free code for various company applications in collaboration with other Al engineers, data scientists, programmers, and software personnel.
·      Continuously improved gameplay with iOS and Android gaming applications by working with the existing code base and enhancing its different phases, including Al strategies, rules, and player-world interactions.
·      Sophisticated and optimized codes for different games to boost the reusability of standard modules.

Reshmi Jayashree Radhakrishnan

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