MIDAS
Competition 2026
Mohanlab Image and Data Analysis Scholarship
Put your analytical, computational, and problem-solving skills to the test for an opportunity to join the Mohan Lab as a Research Assistant and receive the $12,000 MIDAS Scholarship.
About MIDAS
A challenge-based competition designed to identify Master's students with strong analytical, computational, and problem-solving abilities.
Candidates may focus on the track that best matches their strengths and interests or attempt both tracks.
Final selected candidates will have the opportunity to join the Mohan Lab as Research Assistants and receive the MIDAS Scholarship, contingent upon satisfactory progress.
Research Assistant Scholarship Opportunity
Final selected candidates will receive a total MIDAS Scholarship award of $12,000, disbursed at $1,500 per month over two semesters, contingent upon satisfactory progress as a Research Assistant in the Mohan Lab.
Who Should Participate?
The MIDAS Competition is intended for Master's students with relevant technical and analytical experience who are interested in computational biomedical research.
Recommended Background
Participants should have sufficient subject-matter knowledge to complete the competition challenges. Depending on the selected track, candidates should have experience in one or more of the following areas:
- Statistics / Biostatistics
- Data Analysis & Data Interpretation
- R and/or Python
- Plot and Figure Interpretation
- Biological / Biomedical Data Analysis
- Machine Learning Fundamentals
- Artificial Intelligence / Neural Networks
- Computational or Algorithmic Problem Solving
Expertise in every area is not required. Candidates may focus on the Data Analytics track, the Artificial Intelligence / Machine Learning track, or both, depending on their background and strengths.
In addition to technical knowledge, strong candidates should demonstrate:
Computational Research at Mohan Lab
The MIDAS Competition reflects the types of computational and biomedical data challenges encountered in research at the Mohan Lab.
The Mohan Lab works with large and complex biomedical datasets generated from a variety of omics-based studies, including bulk proteomics, spatial proteomics, spatial transcriptomics, and histopathological data.
We are particularly interested in students who can apply computational approaches to biomedical data and help develop or adapt analytical methods to address emerging bottlenecks in large-scale biological data analysis.
Candidates are encouraged to explore the projects listed on the Mohan Lab website for more context on the type of computational research and biomedical data-analysis projects conducted in the lab.
Competition Tracks
Participants may attempt one track or both tracks based on their skills, background, and interests.
Data Analytics
This track focuses on the ability to analyze, interpret, and communicate findings from biomedical and quantitative datasets.
Relevant knowledge may include:
- Statistics and biostatistical concepts
- Data interpretation and analytical reasoning
- Plot, figure, and graphical-output interpretation
- R and/or Python for data analysis
- Basic Machine Learning concepts
- Biological and biomedical data analysis
- Working with large datasets
- Selection and interpretation of appropriate statistical approaches
A small portion of this track may include one or two questions related to Data Engineering concepts.
Relevant experience may include data pipelines, workflow automation, databases, data infrastructure, servers, computational environments, or website and web-based system management.
Artificial Intelligence & Machine Learning
This track evaluates analytical thinking, computational reasoning, and the ability to approach AI and Machine Learning problems effectively.
- Analytical and computational thinking
- Logical problem solving
- Machine Learning concepts
- Artificial Intelligence concepts
- Neural-network fundamentals
- Creative approaches to unfamiliar computational problems
- Selection of appropriate AI/ML approaches
- Interpretation of model outputs and results
- Ability to adapt algorithms to new analytical challenges
Previous experience with Machine Learning, Artificial Intelligence, neural networks, programming, algorithm development, or related projects will be beneficial.
Recommended AI/ML Preparation Resources
Applicants are encouraged to explore these resources to develop high-level familiarity with the mathematical foundations and mechanics of AI. The goal is conceptual understanding—not memorization of equations or complete mathematical mastery.
A quick introduction to notation commonly encountered in AI and technical literature.
Builds intuition for vectors and matrices used throughout Machine Learning and neural networks.
Introduces derivatives and rates of change underlying optimization and model learning.
Connects the mathematical foundations to neural-network architecture, training, and backpropagation.
Competition Format
The MIDAS Competition consists of two rounds. Participants may manage their time between the two rounds according to their preferred strategy.
Round 1 — Closed-Book Challenge
The primary component of the competition evaluates independent reasoning, conceptual understanding, analytical thinking, and problem-solving ability.
The round will include two sections:
-
Section A:
Data Analytics
May include one or two Data Engineering questions. - Section B: Artificial Intelligence & Machine Learning
Participants may attempt Section A, Section B, or both.
Questions may include:
- Multiple-choice questions
- Conceptual questions
- Short-answer questions
- Approach-based technical questions
- Problem-solving scenarios
Closed-Book: AI tools, internet resources, notes, and other external reference materials may not be used during this round.
Round 2 — Open-Book Data Challenge
Participants will receive a dataset and a practical use case or problem statement.
Candidates will be expected to:
- Understand the given problem
- Explore and interpret the dataset
- Select an appropriate analytical or computational approach
- Analyze the data
- Generate meaningful results and visualizations
- Interpret findings
- Develop conclusions
- Communicate the approach clearly
Open-Book: Participants may use AI tools, internet resources, documentation, programming environments, analytical software, and other appropriate resources.
Final Deliverable: A presentation summarizing the problem, approach, analysis, results, interpretation, and conclusions.
How Will You Be Evaluated?
Selection will emphasize independent thinking, conceptual understanding, analytical ability, and effective problem solving.
Register for MIDAS 2026
Complete the registration form below to participate in the MIDAS Competition. Registration closes on September 7, 2026.
Questions?
For questions regarding the MIDAS Competition or Scholarship, please contact the Mohan Lab at the University of Houston.
vmaruvad@cougarnet.uh.edu