Reflexion - Learning Launch 

 Challenge 

 I am a first year PhD student and my challenge is to successfully complete my thesis defense at the end of three years of work . This defense must show the realization of an applied project allowing to advance the research in the field of Artificial Intelligence applied to connected objects ( Internet of Things i.e “ IoT ” ) . Previously , I worked 3 years in the industry . According to me , many relevant concepts can be applied in academic research . I have two thesis supervisors , coming from two different backgrounds ( Applied Mathematics and Computer Networks ) . My supervisors are the equivalent of my managers and my clients , they have both roles . In addition to my defense , I have to realize a publication ( at least ) and in a field related to Artificial Intelligence and “ IoT ” accompanied by a real application ( a prototype ) . My research is not fundamental ( i.e theoretical ) , but applied . 

 Selection 

 Coming from the industrial world , in the field of Artificial Intelligence , I often had to used prototype , visualization and other " Design Thinking " tools for clients . I apply the concept of " Learning Launch " in the academic world because I find it relevant to applied research . Broadly speaking , the learning launch is an experiment meant to quickly test an idea and prototype . 

 Application 

 In the applied research we need to quickly see if an idea can be realizable and useful in a practical world . Reflection is done in groups but application and realization is done lonely . First , we discuss the idea , the possibility and how it can improve some aspect of the world of connected objects . This exploratory phase is done in a team through the laboratory , through research in the scientific literature but also around discussion with my thesis supervisors ( i.e my clients and partners ) . The discussion in the team is every two weeks and the discussion with my thesis supervisors every week . 

 The first phase lasts maximum 1 month in order to avoid long and theoretical debates . After this part , I set a short deadline , in agreement with my thesis supervisors ( i.e. managers and clients ) to build a first prototype which is the realization in practice of the hypothesis and deduction made in the previous part . This is a very iterative process where we focus on one big idea and try to put it into practice . Every week , a review is done with my thesis supervisor ( i.e. managers and clients ) to see if the schedule is respected and if the research and the result are valid and consistent with the hypothesis made with the theory . 

 Insight 

 The use of the “ Learning Lauch ” tools allows us to quickly realize a prototype . Thus , each researcher according to his specialty ( Artificial Intelligence or Connected Object / Computer network ) will be able to quickly apprehend the objective and the interest or the drawbacks of the tool by using it quickly in a quasi - real situation . The development of a rapid tool allows us 

 to cross the theoretical barriers and to allow each specialty to quickly apprehend its relevance . The regular feedback from users allows me to determine the best research directions , to " fail early " and focus my limited time on functional ideas . The objective is very similar to industry . However , the objective here is not to produce a product version that can be delivered to a client but to the research community in what is called " reproducible research " . In other words , to show through a product that the research is part of a practical framework and that it can easily be reused . 

 Approach 

 Due to my practical research and my experience in industry we are not a lot to practice this " Learning Launch " . Nevertheless , after having integrated this concept with my thesis supervisors ( i.e my managers and clients ) I am much more relevant in my research and quickly locate points of interest for the scientific community . I am much faster at making applied and relevant publications for the industrial world . Thanks to this course I am also able to formalize this concept better and apply it more efficiently . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Reflexion - Learning Launch 

 Challenge 

 I am a first year PhD student and my challenge is to successfully complete my thesis defense at the end of three years of work . This defense must show the realization of an applied project allowing to advance the research in the field of Artificial Intelligence applied to connected objects ( Internet of Things i.e “ IoT ” ) . Previously , I worked 3 years in the industry . According to me , many relevant concepts can be applied in academic research . I have two thesis supervisors , coming from two different backgrounds ( Applied Mathematics and Computer Networks ) . My supervisors are the equivalent of my managers and my clients , they have both roles . In addition to my defense , I have to realize a publication ( at least ) and in a field related to Artificial Intelligence and “ IoT ” accompanied by a real application ( a prototype ) . My research is not fundamental ( i.e theoretical ) , but applied . 

 Selection 

 Coming from the industrial world , in the field of Artificial Intelligence , I often had to used prototype , visualization and other " Design Thinking " tools for clients . I apply the concept of " Learning Launch " in the academic world because I find it relevant to applied research . Broadly speaking , the learning launch is an experiment meant to quickly test an idea and prototype . 

 Application 

 In the applied research we need to quickly see if an idea can be realizable and useful in a practical world . Reflection is done in groups but application and realization is done lonely . First , we discuss the idea , the possibility and how it can improve some aspect of the world of connected objects . This exploratory phase is done in a team through the laboratory , through research in the scientific literature but also around discussion with my thesis supervisors ( i.e my clients and partners ) . The discussion in the team is every two weeks and the discussion with my thesis supervisors every week . 

 The first phase lasts maximum 1 month in order to avoid long and theoretical debates . After this part , I set a short deadline , in agreement with my thesis supervisors ( i.e. managers and clients ) to build a first prototype which is the realization in practice of the hypothesis and deduction made in the previous part . This is a very iterative process where we focus on one big idea and try to put it into practice . Every week , a review is done with my thesis supervisor ( i.e. managers and clients ) to see if the schedule is respected and if the research and the result are valid and consistent with the hypothesis made with the theory . 

 Insight 

 The use of the “ Learning Lauch ” tools allows us to quickly realize a prototype . Thus , each researcher according to his specialty ( Artificial Intelligence or Connected Object / Computer network ) will be able to quickly apprehend the objective and the interest or the drawbacks of the tool by using it quickly in a quasi - real situation . The development of a rapid tool allows us 

 to cross the theoretical barriers and to allow each specialty to quickly apprehend its relevance . The regular feedback from users allows me to determine the best research directions , to " fail early " and focus my limited time on functional ideas . The objective is very similar to industry . However , the objective here is not to produce a product version that can be delivered to a client but to the research community in what is called " reproducible research " . In other words , to show through a product that the research is part of a practical framework and that it can easily be reused . 

 Approach 

 Due to my practical research and my experience in industry we are not a lot to practice this " Learning Launch " . Nevertheless , after having integrated this concept with my thesis supervisors ( i.e my managers and clients ) I am much more relevant in my research and quickly locate points of interest for the scientific community . I am much faster at making applied and relevant publications for the industrial world . Thanks to this course I am also able to formalize this concept better and apply it more efficiently .
### Output: 