Alexander He Email & Phone Number

United States

• Designed and developed an algorithmic trading framework for intraday strategies using Python and Alpaca API, focusing on sideways trend detection and real-time risk management.
• Built backtesting capabilities to simulate and optimize strategy performance, incorporating stop-loss mechanisms and dynamic position sizing.
• Implemented a data collection system to track slippage during live trading, enabling analysis of execution performance and strategy improvement.
• Streamlined high-frequency data processing with Pandas and NumPy, enabling efficient handling of level 1 trade and quote data for precise execution.
• Leveraged AI-driven development tools for enhanced troubleshooting, code iteration, and strategy refinement, utilizing ChatGPT and Claude interfaces.

Indianapolis, Indiana, United States

• Created a workflow for Named Entity Recognition (NER) dataset annotation of patient lab result comments. Implemented fine-tuning of Spark NLP model pipelines.
• Optimized data processing with PySpark. Increased confidence scores of Hugging Face NER models by ~3% through tokenization improvements.
• Debugged, tuned, and compared classification model variants for Alzheimer's Early Signal Identification as part of an ongoing collaboration with Eli Lilly and Co.

• Implemented a Proof-of-Concept distributed ETL and stream processing pipeline on 3 Linux servers using Confluent for Apache Kafka.
• Collaborated with the Database team to ensure correctness of the ksqlDB data model for creating fast materialized views.
• Set up multithreaded Kafka connectors to introduce real-time incremental refresh to business-critical reporting tables in Microsoft SQL Server and Cassandra, reducing data latency from ~40 min to less than 1 min.

• Constructed training datasets of ~400,000 distinct container shipments by joining data from 10+ tables stored in Microsoft SQL Server.
• Identified impactful data fields and tables by organizing discussions with members of IT and Operations.
• Reduced Mean Absolute Error by 60% over baseline models using ML time series regression for predicting the Time-of-Arrival of container shipments.

• Participated in a training-focused internship, acquiring hands-on experience in FICO scorecard development and credit risk analysis for financial applications.
• Developed predictive models using Python, focusing on FICO scorecard techniques to assess the creditworthiness of loan applicants.
• Collaborated remotely with student teams in China, enhancing communication skills and cross-cultural understanding within the context of the financial industry.

• First author of conference publication: "Chronic Respiratory Disease: Risk Modeling Potential and Limitations," IEEE BigData 2021 [ https://ieeexplore.ieee.org/document/9672074 ]
• Combined spatiotemporal datasets of 10+ environmental and demographic variables into ~600K data points to predict monthly CRD mortality rates in the U.S.
• Aggregated GIS raster datasets into ~3,000 U.S. counties using GeoJSON shapefiles and geospatial Python libraries while ensuring algorithmic efficiency.
• Conducted iterative model evaluations and algorithmic feature selection to refine predictive accuracy, gaining practical experience in ML regression model optimization.
• Facilitated regular meetings with advisor, ensuring progress on milestones and effective guidance on research directions.

Master Of Science - Ms, Information, Data Track

Activities and Societies: Ultimate Frisbee

Bachelor Of Science - Bs, Computer Science - Information Technology And Web Science

Activities and Societies: Ultimate FrisbeeCS and ITWS dual degree, Data Science concentration

High School Diploma

Activities and Societies: Cross Country, Track (2014-2015); Ultimate Frisbee (2015-2018); Concertmaster of Camerata Symphony (2015-2016).

Applied Data Engineering Certificate Program

Analytical Engineering – Linux, AWS, Docker, Python, Airbyte, Snowflake, Data Warehousing, Data Modeling, ETL (SQL, dbt), Metabase. Big.