Acuna lab is looking for students to optimize science using machine learning (new Fall 2019)

About the lab

Dr. Acuna is an Assistant Professor in the School of Information Studies at Syracuse University. He currently works on mathematical and computational models of scientific discovery, predictability, and integrity. Please take a moment to look at his background, research, and recent grants.

Professor Acuna teaches courses for the Applied Data Science and Information Management graduate degrees. He is currently the teacher and Professor of Record for the course IST 718: Big Data Analytics.

Past Master’s students have done internships in Silicon Valley (e.g., Airbnb, Google), are working in major consulting companies (e.g., Ernst & Young, Goldman Sachs), and are broadly working as data scientists. Please see the People section.

About the position

Assistant Professor Daniel Acuna from the School of Information Studies (https://acuna.io), leader of the newly-formed Science of Science and Computational Discovery (SOS+CD) Lab, is looking for Master’s students to work on quantitative analysis of big data. Broadly speaking, the SOS+CD Lab works on understanding how science works and semi-automatically generating scientific discoveries from vast, unstructured dataset of full-text publications, citations, and images. The SOS+CD Lab uses a variety of computational techniques including deep learning, natural language processing, graph analytics, image processing and causal inference. The ideal candidate should have an undergraduate major in Computer Science, Engineering, Applied Statistics, Mathematics, or a similar quantitative field.

Requirements

• Develop reproducible software and tools to optimally match reviewers and manuscripts based on mathematical objective functions
• Write method and result sections for scientific manuscripts
• Have advanced computer programming skills in languages such as Python and R. SQL is also desirable
• Understand linear algebra, calculus, probability and statistics
• Understand machine learning software tools and pipelines in scikit-learn, R, or Spark ML
• Understand basic concepts of software engineering
• Have good communication skills

Qualifications

• Undergraduate (for MS students) or graduate degree in Computer Science, Engineering, Applied Statistics, Applied Mathematics, or similar quantitative fields
• Minimum of 2 years of experience with coding in a major programming language such as Python, R, C, C++, or Java. Experience with handling big data with Apache Spark is a plus.
• Demonstrable knowledge of linear algebra, calculus, probability, and statistics

Apply

Otherwise, send an email to deacuna AT syr DOT edu and include:

1. A short introduction of yourself and why you want to work with me
2. A short CV or a 1-page resume
3. Your Github repository, preferably with code from a personal project rather than a “class project”.
4. Your transcripts
5. Your GRE, GMAT, or equivalent scores

Apply

If you have any questions, do not hesitate in contacting me. If you are thinking of applying to the Ph.D. program, we have a very competitive fully-funded program, and you should contact me first. Otherwise, apply to the Ph.D. program and mention my name in you materials.

Part of the funding for these positions has been generously provided by the National Science Foundation awards #1646763 and #1800956