Organizational
Behavior
Research
j
Research Proposal
The topic I’d like to research is small team dynamics, and specifically in a non-corporate/ startup environment. The question I’d like to answer is how does a small startup team operate and interact with each other? What are some key aspects of an organization that influences that team dynamic: leadership, culture, symbolic mission, institutional effects, politics, social capital etc.
Preliminary Research Question
Through my fieldnotes and interviews, I’ve observed various phenomena unique to SC in terms of cultural norms, leadership, interpersonal behaviors, and external effects. The very particular form of leadership SC’s CEO operate with is very integral to creating a flexible and open environment for the team. He is very receptive to feedback and listens and acts according to the needs of the employees. That funnels down to the company-wide culture that is respectful and casual. This form of leadership has been discussed in articles on group dynamics of high-functioning teams and could be a reason why SC performs so well as a 7-person team. At the same time, the CEO’s personal traits contribute to a high level of pressure to perform within the company. That constant vigilance sometimes burns out the team members which brings them to vent behind doors, a cultural observation I’ve made over the past 5 weeks. From this, I’d argue that the leader has an essential influence on the team dynamic including culture and interpersonal relations.
A Raspberry Pi Arducam 5MP Camera was used
to capture the images in real-time
Building the Feature Vectors
To help with the facial identification and categorization process, I built a “Face” object that hosted attributes such as “height” “width” “color” etc. These dimensions were then converted into numerical representations of the face by having each element in the training set mapped onto a feature vector comprising of integer or string values representing the facial features. To automate this process, I developed an algorithm that derived the feature vectors in real-time as it detects faces and receive input.
The Raspberry Pi is very portable and easily
attachable to a range of clothings
With the upgraded WiFi and Bluetooth capabilities, we were able to migrate the database to an offline server and reduce overheating
Matching Faces
Working with another teammate of mine, we also created a matching algorithm to determine the similarity between the feature vectors I built out. The facial analysis used the library model VGG-Face and it imported pre-trained weights and deep learning networks. Distance values were assigned to indicate the numerical difference between two faces. Once all the training data is prepared (using imagery data collected from our consented classmates), I helped program the Rapsberry Pi to interact with the facial recognition API. This process included the process of assigning unique face_tokens and user_ids for each image received.
Takeaways
This project brought me an abundance of takeaways. It not only offered a practical application to my technical skills in algorithm design and development (which was definitely a great challenge for me at the time, but deeply appreciated). It also developed further my team collaboration and communication abilities, as well as task delegation skills as the team leader. I learned to develop a full machine learning product, from ideation, design, implementation, to a testable wearable (which garnered even the attention of the Mayor of Chicago herself when she visited our Demo Day). I am deeply proud of and thankful for Team Momento and our EECS 338 Practicum professor, Kris J Hammond, as well as our many teaching assistants who offered us valuable insights on the way.