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Documentation and Code for SeeR, leveraging the power of deep learning for cybersecurity.

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SeeR

Seer

SeeR is leveraging the power of deep learning to maximize the security of cloud-connected embedded devices.

Team Song

Code Repository

Team Members

Member Email Photo
Karan Singhal [email protected] Karan Singhal
Pratyaksh (Praty) Sharma [email protected] Praty Sharma
John Kamalu [email protected] John Kamalu
Sarah Tollman [email protected] Sarah Tollman
Alan Flores-Lopez [email protected] Alan Flores-Lopez

Team Skills Matrix:

Member Hat Technical Skills Non-Technical Skills Personal Traits Desired Growth Weaknesses
Karan Blue AI/deep learning, Python, some systems Leadership skills, rapid prototyping, experimentation, interviewing people Logical, cautious, motivated Product management skills, embedded systems, cybersecurity Presentation skills, time management
Praty White Deep learning, Systems, Python, some cybersecurity Team facilitator, leadership skills Logical, flexible, optimistic Project management and presentation skills UI/UX, time management
John Black/Yellow Python, AI/ML, quick prototyping Small-team work, international work environments Planner, group-oriented, cautious UI/UX research and design, security, AI/ML lifecycle Presentation, prioritization, systems
Sarah Blue Systems, networking, testing Decision making, some project management, time management Motivated, logical, direct Embedded systems & cybersecurity / launching attacks AI, can be too blunt
Alan Black Python, systems design, systems security, some AI/deep learning Facilitating groups, managing schedules and tasks Precise, consistent, reliable, introverted, thinks of the larger picture IoT security, applying deep learning, group dynamics, managing a budget Can be fickle, sometimes hard to convince, introverted

Team Communication:

  1. Email us at [email protected]

  2. Slack: cs210hella.slack.com (sign up here).

  3. Documentation: Github Wiki.

  4. Google Calendar

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  • Python 43.8%
  • Jupyter Notebook 42.5%
  • JavaScript 7.5%
  • HTML 2.8%
  • Shell 1.7%
  • CSS 1.7%