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A high level overview of my past work. If any of this is interesting, reach out!

Basics

Name Aditya Mangalampalli
Label Research Scientist / Quant Researcher
Email amangalampalli@berkeley.edu
Url https://linkedin.com/in/aditya-mangalampalli
Summary Berkeley student focused on Machine Learning applied to large language models and AI. Incoming Research Scientist at OpenAI. Experienced in quantitative research, statistical modeling, and software engineering.

Work

  • 2026.07 - Present

    San Francisco, CA

    Incoming Member of Technical Staff
    OpenAI
    Yet to be matched with a team, but Research Scientist/Engineer
    • Large Language Models
    • High Dimensional Statistics
  • 2025.11 - 2026.02

    San Francisco, CA

    Machine Learning Intern
    Mosaic Intelligence Labs
    Working on CT Angiogram Reconstruction to map a 2D Scan into 3D mesh space.
    • 3D reconstruction
    • Computer vision
    • Deep learning
  • 2025.06 - 2025.08

    New York, NY

    Software Engineering Intern
    Schonfeld Strategic Advisors
    Built low-latency market data and telemetry systems and monitoring to support real-time trading workflows.
    • Real-time data pipelines
    • Order book reconstruction
    • Statistical anomaly detection
  • 2024.05 - 2024.08

    Chicago, IL

    Quantitative Research Analyst Intern
    Deutsche Bank Asset Management
    Applied machine learning and statistical methods to portfolio research and data pipelines for systematic strategies.
    • Portfolio ML + optimization
    • Stat arb research
    • Automated data pipelines
  • 2023.05 - 2023.08

    Austin, TX

    Software Developer Engineering Intern
    General Motors
    Worked on robotics calibration and localization using ML/optimization on real-time sensor data.
    • Robotics + perception
    • Optimization for calibration
    • Production deployment
  • 2023.04 - 2023.05

    London, UK

    Quantitative Research & Trading Intern
    Unsigned Research
    Built and tested systematic crypto trading ideas using volatility modeling and mixed feature sets.
    • Volatility modeling
    • Backtesting
    • Systematic strategy research
  • 2022.12 - 2025.06

    Berkeley, CA

    Undergraduate Researcher (High Performance Computing)
    UC Berkeley EECS — RISELab
    Research on how reasoning and symbolic structure emerge in large language models; built analysis tooling for embeddings and evaluations.
    • LLM reasoning experiments
    • Dataset curation
    • Representation analysis

Volunteer

  • 2022.08 - 2026.05

    Berkeley, CA

    Staff Member
    Open Computing Facility (UC Berkeley)
    Led engineering and taught Linux systems concepts in a student-run computing organization. Was formerly DeCal Head, Finance Head, Internal Head, and led an LLM project.
    • Systems leadership
    • Linux instruction
    • Community support

Education

  • 2022.08 - 2026.05

    Berkeley, CA

    B.A., B.A.
    University of California, Berkeley
    Statistics, Data Science
    • Machine Learning
    • Data Engineering
    • Statistical Modeling
    • Computer Vision

Awards

Skills

Programming
Python
C/C++
R
Java
Rust
TypeScript
JavaScript
SQL
PostgreSQL
Systems & Data
Low-latency systems
Data pipelines
Streaming telemetry
Market data
Machine Learning & Quant
Statistical modeling
Optimization
Time series
Backtesting
Tools & Frameworks
React
Kotlin
Swift

Languages

English
Fluent
French
Business Proficient
Dutch
Business Proficient

Interests

Poker
Chess
Academic research
Travel
Volunteering
Open-source

Projects

  • 2022.05 - Present
    Optimized Trading Platform (Commodity Cluster)
    Algorithmic trading platform designed for live data processing and automated execution on compute infrastructure.
    • Live market data ingestion
    • Research-to-execution workflow
    • Performance-focused implementation
  • 2020.03 - Present
    Open-Source Contributions
    Ongoing contributions to scientific computing and ML ecosystems.
    • Scientific computing libraries
    • Developer tooling
    • Community collaboration