Catalin Tiseanu Email & Phone Number

Remote First, Us

As the first ML Engineer, I co-founded the modern machine learning efforts at Coinbase, acting as the team's tech lead for the last three years over a 40+ team. I had a role in building, designing, or advising every ML model running in production currently at Coinbase, as well as the technical and strategic direction. Highlights:* Landed double-digit increases in site-wide net revenue and engagement across the Feed, Growth & Notifications, and user spend limits; strategic independence of 3rd-party vendors and a massive reduction in fraud for the ATO & Risk domain; and improving support agent response time.* Created Auto-ML-like lego-blocks around tabular data, sequence & text data (using Transformers - the T in ChatGPT - from scratch & deep learning), and support for blending these models. Machine learning engineers and data scientists across the company use these lego-blocks to deliver impact.* Set long-term technical direction for the team, owned large parts of the interview process, published a blog post around our stack, contributed to a KDD presentation, and ran a knowledge-sharing group and boot camp.* Proposed a Responsible AI framework around model cards inspired by academia, which is used by new models.* Working on the first ML + Crypto use cases.

Remote First, Us

San Francisco, California, Us

Worked on applying NLP on Slack chat logs - expert finding, topic modeling, text classification. Improved search quality. Later on, built full-stack features such as web search and external note sharing.Machine learning===============Expert finding:• Tf-idf based model for recommending experts based on a query stringTopic modeling:• Pipeline for cleaning text data, parsing out stop-words and first names, keeping only nouns• Langid-based classifier for keeping only english-speaking teams• Clustered the cleaned-up text messages using LDA in 50 clusters * Visualized results using pyLDAvisText classification:• Collected a labeled dataset for classifying messages into design/non-design• Built additional filtering step for design-messages using a vocabulary from design-related Wikipedia pages• Used fastText as an initial baseline (both with pretrained Glove word vectors and with training the word vectors). Got 0.83 f1-score• Reproduced colleagues Bidirectional LSTM classifier using Keras. Got 0.86 f1-scoreTechnologies used: jupyter, pandas, fasttext (text classification), gensim (lda), keras (bidirectional LSTM)Search quality============• Collected gold dataset of queries and expected positives for those queries• Built search quality eval tool• Iterated on the ElasticSearch model • Improvement top3 recall from an initial 30% to 90% for that gold dataset * Had follow-up call with an ElasticSearch outside expert consultantFull-stack========• Web-search• External note sharing (via public link)• Invite flow• Note Reactions (similar to the Facebook ones) Technologies used: Python, React, CSS, HTML

Bucuresti, Romania, Ro

* In charge of building the big data system in charge of making sense of terabytes of diverse information* Worked on building a one box type of search system for the various data collected by BitDefender, using Cassandra, Spark and Django.* Aim of the system was two-fold: easily search through terabytes of data and automatically generate forensic leads for the analysts to investigate.

College Park, Md, Us

* TA for Algorithms & Data Structures, Complexity Theory and Randomized Algorithms* Held weekly TA hours for explaining difficult course material, at both undergrad and grad level* Responsibilities also included grading homework

San Francisco, Ca, Us

* Shipped a drastically improved version of the signup account recommendation system.* Designed and implemented features for improving the experience new users have on the site, by suggesting more relevant accounts to follow after they just signed up. * The project relied heavily on understanding trends in big data.* To that end, I used Pig (Big Data) and Scala (Backend).* I used one of the features as part of a Hackweek project, which ended winning one of the top prizes.

* Worked in the Ads optimization team.* Implemented a feature which helped increase CTR ... this needed both infrastructure work as well as some Machine Learning models, all coded in C++.* Implemented a text feature as a Hackathon project to help make indexing easier.* I also used Big Data technologies such as Hive.

* Interned in the Business Optimization group on a research project about risk management in the pharmaceutics industry.* Worked on the interface of stochastic programming and combinatorial algorithms in order to formulate a model which minimized risk.* The linear programming model was implemented in ILOG CPLEX C++ and compared against other methods. * The joint results of this work were published in the Operations Research Proceedings 2010.

Mountain View, Ca, Us

* Worked on the Google Books team.* Implemented an user facing feature as well as a backend one related to the scanning of books* Used Python for prototyping and C++ for the code in production.

Research Master, Computer Science

Masters, Computer Science

Bachelor, Computer Science