About Me.
I am Lakshmi Narayana Aditya Akilesh Mantha, a passionate AI & ML Researcher and Data Science Professional. My work focuses on building intelligent systems for Forecasting, Anomaly Detection, Predictive Analytics, and cutting-edge Generative AI applications. I specialize in crafting end-to-end, scalable solutions that turn complex datasets into real-world impact and actionable insights.
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With a strong foundation in AI/ML and Data Science Research, I bring hands-on expertise in designing and deploying solutions using platforms like SQL, Tableau, Azure ML, and Power BI. I have hands-on experience working with ML/DL models, as well as Large Language Models (LLMs) and Generative AI frameworks, focusing on building solutions that are scalable, explainable, and tailored to business needs. My approach blends advanced Machine Learning techniques with interactive visualizations to bridge the gap between data and decision-making.
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Beyond technical development, I am deeply committed to research-driven problem-solving and continuously exploring the latest advancements in AI, LLMs, and data-driven systems. I thrive in collaborative environments, working alongside cross-functional teams to solve complex challenges. Through this portfolio, I aim to showcase a blend of innovative thinking, technical expertise, and a passion for building impactful AI solutions.
Work Experience
January 2025 - Present
Data Analytics and Machine Learning Fellow Trainee​
ElevateME
As a Data Analytics & Machine Learning Fellow Trainee at ElevateMe, I have completed over 150 hours of intensive, hands-on learning focused on solving real-world business problems using data-driven techniques. I worked on diverse projects, including real estate data analysis, financial health assessment, and HR attrition analysis. My responsibilities involved cleaning and analyzing large datasets, applying feature engineering, handling missing data and anomalies, and building insightful visualizations to derive meaningful business insights. I led the financial data analysis project to identify key cost drivers and uncovered that operational costs accounted for 61% of total expenses. Additionally, I conducted an employee attrition analysis by writing advanced SQL queries to segment workforce data and calculate attrition metrics.​
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Key Skills & Technologies:
Data Cleaning & Preprocessing (Handling Missing Values, Feature Engineering), Data Visualization (Matplotlib, Seaborn, Correlation Heatmaps, Trend Analysis), Statistical Analysis (Descriptive Statistics, Cost Driver Analysis), SQL (Advanced Queries, Data Segmentation, Aggregation), Python (Pandas, NumPy, Matplotlib, Seaborn)
May 2024 - December 2024
Energy Systems Intern
Eaton Corpoation | Eaton Reserch Labs
As a Grid Intelligence Intern at Eaton Corporation, I worked on cutting-edge AI solutions to optimize microgrid operations. I implemented advanced Reinforcement Learning (RL) algorithms, including A2C and PPO, using CYME and Stable Baselines to optimize voltage control, reducing switching operations by 10%. I also built and deployed time-series forecasting models such as LSTM and ARIMA for load and solar energy prediction, achieving less than 5% forecast error. Additionally, I trained a Variational Autoencoder (VAE) model for switchgear failure prediction with an accuracy of 85%, supporting proactive maintenance strategies. Throughout the internship, I collaborated with cross-functional teams, delivered progress updates through presentations, and maintained project workflows using GitHub.​
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Key Skills & Technologies:
Reinforcement Learning: A2C, PPO (Stable Baselines3), Time-Series Forecasting: LSTM, ARIMA (TensorFlow), Anomaly Detection: Variational Autoencoder (VAE), Microgrid Simulation & Optimization: CYME Software, Python Programming, Data Analysis & Model Evaluation, Collaboration & Communication: Project Management, Technical Presentations, Version Control: GitHub
May 2023 - December 2024
Graduate Research Assistatnt
Iowa State University
As a Graduate Research Assistant at Iowa State University, I developed and implemented advanced Machine Learning techniques for anomaly detection in complex Cyber-Physical Systems (CPS). I designed a Python-based framework to simulate faults, cyberattacks, and perturbations in real-time environments using Hardware-in-the-Loop (HIL) simulations. Leveraging TensorFlow and Scikit-learn, I trained and optimized ML models, achieving a 99.1% F1 score in detecting anomalies. Additionally, I conducted analytical studies on control strategies in distributed systems to better understand system behavior under various conditions. My responsibilities included collaborating with research teams, presenting weekly progress updates, and contributing to peer-reviewed IEEE conference publications.
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Key Skills & Technologies:
Machine Learning: Anomaly Detection, Model Training & Evaluation, Python Programming, ML Frameworks: TensorFlow, Scikit-learn, Data Simulation & Generation: Fault & Attack Injection, Perturbation Modeling, Data Analysis: Behavioral Analysis of Complex Systems, Collaboration & Research: Weekly Presentations, IEEE Publications, Technical Writing & Communication
August 2023- December 2023
Graduate Teaching Assistatnt
Iowa State University
As a Graduate Teaching Assistant at Iowa State University, I facilitated lab sessions for the course SE 185: Problem-Solving in Software Engineering, mentoring over 100 students in programming fundamentals and logical problem-solving using the C language. I evaluated student performance by grading assignments and exams and providing constructive feedback to enhance their technical skills. Additionally, I conducted office hours to offer one-on-one academic support, fostering a positive and productive learning environment.
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Key Skills & Technologies:
Programming Fundamentals: C Language, Technical Mentoring & Teaching, Problem-Solving Techniques, Assessment & Feedback, Student Support & Communication, Academic Collaboration & Teamwork
December 2021- May 2022
Data Science Intern
Rubixe PVT. LTD.
As a Data Science Intern at Rubixe Pvt. Ltd., I led the development of deep learning models, including CNNs and RNNs, for heart disease prediction and leaf disease classification, achieving a model accuracy of 92%. I collaborated with cross-functional teams to improve predictive workflows and data-driven decision-making. Additionally, I performed data analysis and visualization using Python libraries, delivering insights and presenting key findings through weekly presentations to support strategic initiatives. I actively contributed to model performance tuning and hyperparameter optimization to enhance predictive accuracy. This experience strengthened my practical knowledge of applying machine learning techniques to real-world healthcare and agriculture datasets.
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Key Skills & Technologies:
Deep Learning: CNN, RNN, Python Programming, Data Analysis & Visualization: Pandas, Matplotlib, Seaborn, Predictive Modeling & Evaluation, Model Optimization & Tuning, Cross-functional Collaboration
Skills
Python
C
R
SQL
Tableau
PowerBI
NumPy
Git
MATLAB
CUDA
TensorFlow
PyTorch
scikit learn
Pandas
matplotlib
Seaborn
Azure
Azure ML
Linux
Apache Spark
Jira
Gymnasium
Education
August 2022 - December 2024
Master of Science in Computer Engineering
Iowa State University | Ames, Iowa
August 2015 - May 2019
Bachelor of Technology in Electrical & Electronics Engineering
Gayatri Vidya Parishad College of Engineering | India
Completed my Master’s degree in Computer Engineering with a focus on Machine Learning and Deep Learning applications. The program emphasized real-world problem-solving using data-driven and AI techniques. Conducted research on anomaly detection, developing and evaluating ML models that achieved a 99.1% F1 score, with the findings published in IEEE conferences. Actively contributed as a Graduate Research and Teaching Assistant, participating in academic research, mentoring students, and enhancing technical and communication skills.
Completed my undergraduate program in Electrical & Electronics Engineering, building a strong foundation in core engineering principles, circuit design, and power systems. Developed problem-solving and analytical skills through coursework and hands-on projects involving electrical systems, control systems, and embedded technologies. Actively participated in technical workshops and team-based projects, which enhanced collaboration, critical thinking, and technical communication abilities. This program also introduced me to programming concepts and data analysis, sparking my interest in pursuing advanced studies in Machine Learning and Data Science.