Merge pull request #21 from Human-Augment-Analytics/rz/recruit1

Update more project metadata

Author: riyamzaman

projects/3d-generative-models.yml

@@ -17,6 +17,8 @@ summary: >
 tags:
   - 3D
   - Deep Generative Models
+  - Machine Learning
+  - Computer Vision
 links:
   github: null # placeholder, if public
   docs: null # Could be README, wiki, github pages, etc.

projects/ai-for-mental-health.yml

@@ -1,7 +1,7 @@
 id: ai-for-mental-health
 name: AI for Mental Health
 status: active      # active | completed | preprint | archived
-visibility: private # public | private
+visibility: public # public | private
 recruiting: false
 faculty:
   - Anind Dey
@@ -12,12 +12,16 @@ advisors:
   - 
   - 
 summary: >
-  Summary here.
+  Mobile phones and wearable devices are increasingly used to model human behavior and predict health outcomes. While classical machine learning has shown success in predicting behaviors, routines, depression, and sleep from passive sensing data, it often requires time-consuming manual feature extraction. Deep-CNN architectures, however, have revolutionized computer vision by learning features automatically.
+  This project aims to bridge this gap by encoding multi-variate time-series data from sensors into image representations (e.g., using Gramian Angular Fields or Markov Transition Fields). These images will then train robust Deep CNN models to predict well-being indicators like depression, sleep quality, and stress, as well as outcomes like GPA.
+  Our goal is to develop a "CT scan" for mental health—a non-invasive, accurate tool for diagnosis using passive sensing data.
 tags:
   - AI
+  - Machine Learning
+  - Deep Learning
 links:
   github: https://github.com/senseray-io/senseray.git # placeholder, if public
-  docs: https://example.com/docs # Could be README, wiki, github pages, etc.
-  publication: https://doi.org/... # If exists/completed
+  docs: null # Could be README, wiki, github pages, etc.
+  publication: null # If exists/completed
   forum: null # link to forum - @James Hennessy
-  contact: mailto:lab@example.edu # Contact to reach out to for interested parties (lab-level email distribution)
+  contact: null # Contact to reach out to for interested parties (lab-level email distribution)

projects/animal-detection-spatial.yml

@@ -13,9 +13,10 @@ advisors:
   - 
 summary: >
   This project focuses on the identification of species from camera trapping images using computer vision. 5 motion activated camera traps have been set up in different ecosystems across Stone Mountain. Previously, a team of researchers have tried using publicly available ML processes for species identification of individual animals. However, they found their solution to be more time consuming and less accurate than manual identification. Our objective is to find a regionally specific training data set and come up with a more effective strategy to identify species in the camera trap images. 
-  The current pipeline follows a two-stage architecture--identify animal vs. empty frame and then species classification.
+  The current pipeline follows a two-stage architecture—identify animal vs. empty frame and then species classification.
 tags:
-  - 
+  - Machine Learning
+  - Deep Learning
 links:
   github: https://github.com/cliff003/HAAG_Spatial_Camera_Trap_Fall2025 # placeholder, if public
   docs: null # Could be README, wiki, github pages, etc.

projects/knowledge-traceability.yml

@@ -1,7 +1,7 @@
 id: knowledge-traceability
 name: Knowledge Traceability
 status: active      # active | completed | preprint | archived
-visibility: private  # public | private
+visibility: public  # public | private
 recruiting: false
 faculty:
   - Anind Dey
@@ -16,8 +16,8 @@ summary: >
 tags:
   - 
 links:
-  github: https://github.com/org/... # placeholder, if public
-  docs: https://example.com/docs # Could be README, wiki, github pages, etc.
-  publication: https://doi.org/... # If exists/completed
+  github: null # placeholder, if public
+  docs: null # Could be README, wiki, github pages, etc.
+  publication: null # If exists/completed
   forum: null # link to forum - @James Hennessy
-  contact: mailto:lab@example.edu # Contact to reach out to for interested parties (lab-level email distribution)
+  contact: null # Contact to reach out to for interested parties (lab-level email distribution)

projects/nahpu.yml

@@ -14,7 +14,10 @@ advisors:
 summary: >
   NAHPU (NAtural History Project Utility) is a cross-platform application designed for cataloging natural history specimens. It modernizes data recording and management for field and museum work.
 tags:
-  - 
+  - Mobile App Development
+  - UI/UX
+  - Machine Learning
+  - Computer Vision
 links:
   github: https://github.com/nahpu/nahpu # placeholder, if public
   docs: https://nahpu.app # Could be README, wiki, github pages, etc.