Machine Learning Engineer

Jayanth Bhargav, Machine Learning Engineer

West Lafayette, 47906, USA, +1-267-***-****, ********@******.***, jayanthbhargav.github.io SUMMARY Dynamic Machine Learning Engineer with over 3 years of experience in developing advanced models for real-time machine learning applications. Expertise in reinforcement learning, game-theoretic algorithms, and resource optimization, achieving up to a 30% performance improvement in previous roles. A proven track record of innovative problem-solving and algorithm design, as demonstrated in large-scale projects. Eager to leverage unique skills in a challenging environment to drive impactful solutions in machine learning and data science.

WORK EXPERIENCE

05/2023 – Present Graduate Research Assistant, Purdue University West Lafayette, USA Spearheaded a project focused on large-scale machine learning for real- time inferencing and classification tasks with limited labeled data Engineered game-theoretic and machine learning frameworks to optimize resource allocation, achieving a 30% enhancement in system performance Formulated computationally efficient algorithms for identifying the most informative data sources for hypothesis testing and A/B testing tasks 06/2022 – 08/2022 PhD Intern, Machine Learning Optimization and Controls Group, Pacific Northwest National Laboratory (PNNL) Richland, USA

Developed a novel architecture utilizing an attention-based framework to learn robust policies within adversarial multi-agent reinforcement learning environments

Integrated an innovative anomaly detection unit into the attention-based inferencing model, effectively filtering adversarially corrupted multi-agent messages

Executed rigorous testing and validation of the proposed algorithm on an autonomous multi-agent navigation task within the Traffic Junction v0 environment of OpenAI Gym

05/2020 – 05/2021 Machine Learning Researcher, RoboRacer.ai Philadelphia, USA Devised a multi-agent reinforcement learning (MARL) framework aimed at competitive decision-making in dynamic, high-stakes environments Revamped sequential policy learning by optimizing self-play and employing game-theoretic methods to enhance adaptation in adversarial settings EDUCATION

08/2021 – Present Purdue University

Doctor of Philosophy (Ph.D.), Electrical and Computer Engineering

West Lafayette, USA

Cumulative GPA: 3.70/4.0

Dissertation: Efficient Algorithms for Information Gathering and Decision- Making in Large-Scale Systems; Research Areas: Machine Learning, Reinforcement Learning & Bandits, Optimization & Game Theory, Algorithms & Complexity, Human-AI Interaction

08/2019 – 05/2021 University of Pennsylvania

Master of Science in Engineering (M.S.E.), Electrical Engineering; Track: Information & Decision Systems Philadelphia, USA

Cumulative GPA: 3.70/4.0

Outstanding Master’s Student Award for Service; Certificate in University Teaching; President of ESE Graduate Association (ESEGA), Board Member of Graduate and Professional Student Assembly (GAPSA) 08/2015 – 05/2019 RV College of Engineering

Bachelor of Engineering (B.E.), Electrical and Electronics Engineering

Bengaluru, IND

Cumulative GPA: 9.45/10.0

Gold Medal (Rank 1/Summa Cum Laude); Sorroco Award for the Best Outgoing Student - Class of 2019

SKILLS Python C

C++ MATLAB

SQL (PySQL, PandasSQL,

SparkSQL)

HTML/CSS

PyTorch TensorFlow

NumPy Pandas

PyKDL OpenCV

Huggingface Transformers REST APIs

Leadership Professional Services

Team Collaboration Problem Solving

Machine Learning Data Analysis

Statistical Modeling Algorithms

Cloud Platforms Communication

Deep Learning Hadoop

Mentoring Adaptability

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