Machine Learning Data Analyst

HARSHITTA GANDHI

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SUMMARY

An experienced Data Analyst and Machine Learning Engineer with a strong background in data science, machine learning, artificial intelligence, and quantum computing, delivering innovative and impactful solutions across financial and scientific applications. Skilled in Python, MATLAB, C++, SQL, and various machine learning libraries, including TensorFlow, PyTorch, and Pandas. Adept in analyzing and optimizing large datasets, developing and implementing advanced machine learning models, and applying algorithms to real-world challenges such as sentiment analysis, flight delay prediction, and epidemic network analysis. Proven research contributions, published in top-tier conferences, demonstrate a capacity for solving complex problems using cutting-edge technologies. ACADEMIC QUALIFICATIONS

Master of Science - Computer Engineering

Northeastern University, Boston, MA, USA GPA: 3.45/4.00 Graduated: May 2024 Major: Computer Vision, Machine Learning and Algorithms Bachelor of Technology - Computer Science and Engineering Guru Gobind Singh Indraprastha University, New Delhi, India GPA: 8.10/10.00 Graduated: June 2021 PROFESSIONAL EXPERIENCE

Machine Learning Engineer February 2024 - Present

Qbit Solutions Research, Miami, FL, USA Remote.

• Designing quantum circuits for processing financial data, integrating machine learning techniques for optimization using Pennylane, Qiskit, and Python.

• Implementing advanced quantum NLP architectures on various quantum computing modalities to enhance financial data processing and improve outcomes. Research Assistant July 2022 - December 2023

Goodwill Computing Lab Northeastern University, Boston, MA, USA On-site.

• Published 5 machine learning papers in top-tier conferences by developing quantum algorithms for superconducting and photonics-based quantum computers.

• Led the analysis and improvement of machine learning-based classification methods, contributing to enhanced accuracy and performance in quantum systems.

• Designed and implemented circuits using Pennylane, TensorFlow Quantum, JAX, and Qiskit, focusing on data analysis and machine learning integration.

• Applied research insights to develop high-performance quantum machine learning models, advancing the accuracy and scalability of applications. Technical Consultant February 2022 - June 2022

Concur IP, NOIDA, UP, India On-site.

• Provided consultation on technical intellectual property matters by conducting a thorough analysis, leading to strategic insights and informed decision-making. RESEARCH EXPERIENCE

Research Intern May 2021 - December 2021

Quantum and Computer Engineering Lab, TU Delft, The Netherlands Remote.

• Developed the Quantum Knowledge Seeking Agent (QKSA) by extending universal reinforcement learning models into quantum environments, applying machine learning to optimize quantum process tomography (QPT) through dynamic cost functions. Research Intern May 2021 - October 2021

DY Patil International University, Pune, MH, India Remote.

• Applied machine learning techniques through the Variational Quantum Eigensolver (VQE) algorithm to optimize nuclear fusion processes in a tokamak, using data analysis to address optimization challenges and explore quantum solutions for high-energy physics applications. Data Analyst Intern July 2019 - August 2019

VCraft International, New Delhi, India On-site.

• Conducted comprehensive data audits on the company’s aviation services, using data analysis to identify trends and develop targeted re-engagement strategies for inactive customers which optimized SEO-driven content to enhance online visibility and customer engagement. RESEARCH PUBLICATIONS

ProxiML: Building Machine Learning Classifiers on Photonic Quantum Computer Link ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’24) Experimental Evaluation of Xanadu X8 Photonic Quantum Computer: Error Measurement, Characterization and Implications Link ACM/IEEE International Conference on High-Performance Computing, Networking, Storage and Analysis (SC ’23). Toward Privacy in Quantum Program Execution on Untrusted Quantum Cloud Computing Machines for Business Sensitive Quantum Needs Link arXiv preprint – 2023.

MosaiQ: Enabling High-Quality Image Generation on Quantum Computers Link International Conference on Computer Vision (ICCV ’23). SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers Link Association for the Advancement of Artificial Intelligence (AAAI ’23). Quantum Knowledge Seeking Agent: A resource-optimized reinforcement learning using quantum process tomography Link arXiv preprint – 2021.

ACADEMIC PROJECTS

Sentiment Analysis on Low-Resource Languages:

• Evaluated the effectiveness of different algorithms and machine learning models for sentiment analysis across languages with varying resource levels (Tamil vs. English), and helped identify the challenges and opportunities in applying NLP models to low-resource languages, improving their accuracy and adaptability. Verifiable Machine Learning on Quantum Circuits:

• Developed adversarially trained variational quantum circuits to assess the impact of adversarial attacks on quantum machine learning models, then tested different attack strategies and addressed key reliability and verifiability challenges, ensuring the robustness of quantum machine learning systems. Airplane Departure Prediction:

• Built and compared regression models to predict flight departure delays using weather and flight data, leveraged PySpark to parallelize gradient descent, improving data processing speed and model performance compared to traditional methods. Epidemics on Networks and Vaccination Strategies:

• Analyzed airport data using network theory to design a machine learning-based preventive strategy for mitigating pandemics, and identified key centrality measures to develop an SEIR (Susceptible-Exposed-Infectious-Recovered) model, which optimized vaccination strategies for outbreak prevention. TECHNICAL SKILLS & CERTIFICATIONS

Skills: Software Engineering, Machine Learning, Data Science, Quantum Computing, Cloud Computing, AutoCAD, SolidWorks. Programming Languages: Python, MATLAB, C++, SQL, Qiskit (Quantum Programming Language). Machine Learning Libraries: NumPy, PySpark, NetworkX, PyTorch, TensorFlow, Pandas, Pennylane, Tensorflow Quantum (Quantum Machine Learning).

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