Week 2: Collaborative Research 
Monday, June 21st to Friday, June 25th 

Examples possible problems and their sources: 

(Based on current and previous research collaborations that CIMAT researchers and research events with industry.)

  1. Bioinformatics. Data from sequencing technology used to answer questions regarding the RNA exchange between different organisms. Source: Langebio, (https://langebio.cinvestav.mx/en/), a research center in genomics and biodiversity located in Irapuato, MX,  45km from Guanajuato.
  2. Biodiversity and ecology. Large database of a specific group of plants of the world, to relate the physiology and morphology with climatic characteristics of the plant’s native distribution. Source: Langebio.
  3. Metrology. Diverse data and problems related to the standardization and physical measurement of physical quantities such as length, luminosity, weight, and atomic time. Source: Cenam (https://www.gob.mx/cenam), Mexico’s national laboratory of standards and measurements, located in Queretaro, MX, 150km from Guanajuato.
  4. Consumer Preference Mapping 2015 (http://spiempresas2018.eventos.cimat.mx/node/862)
  5. Motion capture OptiTrack integration with GC Engine 
  6. Reduction of waiting periods for patients in the emergency room. 2010 Public Sector (http://spiempresas2018.eventos.cimat.mx/node/862
  7. Market risk measures

Week two: What to expect? 

Technical, mathematical modeling and computational aspects directed towards the solution to the problem will be discussed with faculty members in breakout groups. Students will work in groups to develop, evaluate and test potential solutions for successful completion of the project.

An important component of this hands-on experience will be to hold face-to-face meetings with the client who originated the problem. The first of such meetings will amount to a simulated first reunion with the client in order to understand the context of the problem and the research questions proposed. Subsequent periodical meetings with the client will occur for questions and answers, and for communicating partial and final results. 

To summarize, in addition to techniques in mathematics and computation, two-way communication and interdisciplinary collaboration, the preparation of oral and written reports aimed at a non-mathematical audience are also among the intended goals of our program.

Learning outcomes: 

To gain or improve a student's comprehension of a specific set of techniques, topics and type of mathematical problem-solving. (This set will be determined once the partner organization and the problem(s)/data are determined and assessed by MSSG faculty.)

To conduct research in a collaborative and international format. 

To experience solving problems related to data science in a supportive and guided environment.

To realize that the nature of data is by far the most important aspect in data science problems and that it highly conditions the selection of analytical techniques  

To analyze a problem, develop a solution and present that solution to a client, all in a group environment.  

To gain experience communicating with non-mathematical partners in translating their problem into mathematical terms, and thereafter working through to a solution, translating the data and the results into terms that can be understood and applied in the client's context.   

Benefits of participation:

A chance to gain experience working with industry with support from highly-qualified researchers.
A certification of completion from CIMAT.  
The opportunity to connect, collaborate and learn from and with professionals in the field. 
A chance to gain experience working with industry with support from highly-qualified researchers. 
New connections with peers from other institutions, countries and cultures.