== pre-class to do:
post video:
calendar email invitation:
homework assignment, data camp,
socrative sign in
update Canvas course materials, update learning objectives. assignments as needed:
Test-run code: skip.
kindle book. using ipad to highlight key points.
== In-class to do:
clean up destktop space, calendars,
ZOOM, live transcript (start video recording).
Socrative sign in,
github: rotary encoding, multihead latent attention,
R1 nature paper
https://amandalazar.net/Research.html?utm_source=chatgpt.com
Summary of Amanda Lazar’s Research Page
The lab studies how researchers and designers conceptualize marginalized populations, especially around vulnerability, disability, and wellness.
Their work includes designing and building novel interactive systems and conducting long-term mixed-method evaluations.
A major focus is on technologies for older adults, especially individuals with cognitive impairments such as dementia.
Investigates how technologies for meaningful engagement are currently used by people with dementia.
Includes interviews with practitioners, people with dementia, and caregivers.
Aims to identify barriers to long-term technology use and unmet needs.
Goal is to guide the design of technologies that better support meaningful engagement.
Led by Emma Dixon (PhD student, iSchool).
Funded by U.S. Administration for Community Living, HHS (Grant 90REGE0008).
Focuses on how to design technologies (particularly VR/MR) to support the sharing of embodied skills (e.g., woodworking, knitting, gardening) between generations.
Addresses how technology can help people access meaningful hobbies despite disabilities or constraints.
Current work centers on gardening, with participant observation and interviews.
Studies how experienced older gardeners can mentor younger novices remotely.
Led by Teja Maddali (PhD student, Computer Science).
Examines why older adults adopt or abandon IoT technologies.
Explores integrating maker technology so retirees can build IoT devices that matter to them.
Developing a modular toolkit to allow older adults to design their own IoT systems.
Includes a human-rights-based co-design study involving people with dementia in intergenerational workshops.
Projects explore smart home technologies in cohousing contexts.
Led by Alisha Pradhan (PhD student, iSchool).
Funded by NSF Award #1816145.
Here’s a streamlined protocol you can follow next time you submit GRA support in the Research Foundation portal.
Have these items ready:
GRA’s full name and ODU email
Their home academic department/program (critical for routing)
Employee type: GR (Graduate Research Assistant)
Pay basis: semester or annual (you used semester basis)
Stipend for the semester (e.g., $11,000)
Hours per week: usually 20 hours
Funding project (RF project number)
Whether there is a tuition exemption, and if so:
Source (e.g., ODU Research Foundation)
Level (Master’s or Doctoral)
Important: You must know the student’s home department/program. The EPASS routes to that chair/dean for approval and cannot be changed later. If it’s wrong, the assignment must be deleted and recreated.
Log in to the Research Foundation portal. https://hera.odurf.odu.edu/RFPortal
Go to “Research Assignments”.
In the blue bar, click “Add Assignment”.
Next to Employee ID, click “Select”.
Try typing the student’s name:
If found: select them.
If not found:
Click “Start a new employee” at the bottom.
Enter first name, last name, and email.
Save.
Then click “Select” again and choose the new employee.
Set Employee Type to GR.
Choose Pay Basis:
For GRA by term, select Semester basis.
Select Employee Department from the dropdown: (eg 6093 Computer Science)
This must be the student’s home department/program (not your department if they’re different).
Do not proceed until you are sure this is correct; it controls the routing path.
Select the semester (e.g., Fall).
Click “Save and Next”.
If the wrong department is chosen at this step, it cannot be edited later. The EPASS must be deleted and recreated.
In Annual/Term Salary, enter the semester stipend amount (since you selected semester basis).
Enter Hours per Week = 20.
Locate the Tuition Exemption section.
Select the appropriate option (e.g., ODU RF Tuition Exemption).
Choose the degree level: Master’s or Doctoral (for your case: Doctoral).
Indicate if you are covering 100% of tuition or another percentage, as required.
(Note: this tuition entry is separate from salary and fringe.)
Scroll down to Payline and click “Add Payline”.
Select the correct project from the list.
For the payline details:
You can enter hours/week (e.g., 20) for the project.
Do not manually type the budget amount.
To calculate salary for that payline:
Click “Calc” next to Budget on the right.
The system will calculate the salary based on the previously entered stipend and hours.
Adjust any rounding (e.g., remove a $0.01 extra) if needed.
Click “Create” to finalize the payline.
(This covers salary only – no tuition, no fringe.)
Click “Save” to save the assignment.
To review or change details:
Go to the top and click “Edit Assignment” (green button).
Confirm:
Employee type = GR
Correct home department
Semester basis and semester
Stipend amount and 20 hours/week
Tuition exemption details
Correct project and calculated payline
When everything looks correct, click “Submit”.
The status will show pending chair approval, routed through the student’s home department.
The department cannot be edited in an existing assignment.
The RF staff must delete the assignment, and you must create a new one with the correct department.
If you are unsure of the student’s department:
Check the offer letter, the program catalog, or contact the Graduate School / program.
You can also coordinate with RF staff to help verify if needed.
RF will request new hire paperwork from the student if needed.
Remind the student to complete all HR documents promptly so the GRA appointment can be processed on time.
https://workshopamia2025.github.io/AMIA-KDDM-2025/
chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://workshopamia2025.github.io/AMIA-KDDM-2025/slides/AMIA%202025%20KDDM%20Workshop_Demo_Balu_Version%202.pdf
Elastic Network Models (ENMs) are deeply connected to spectral analysis, both mathematically and conceptually.
In fact, the core of ENM theory is spectral analysis of the system’s stiffness (Hessian) matrix.
Here’s a detailed explanation that connects ENM theory to classical and modern spectral methods across physics, engineering, and applied mathematics:
When you build an ENM, you define a stiffness matrix ( H ) that encodes spring interactions between all node pairs (atoms, residues, or coarse elements).
ENM analysis then solves the eigenvalue problem:
[
H \mathbf{u}_k = \lambda_k \mathbf{u}_k
]
This is precisely a spectral decomposition of the network’s Laplacian-like operator:
( \mathbf{u}_k ): eigenvectors (normal modes)
( \lambda_k ): eigenvalues (mode stiffness or squared frequency)
So the “spectrum” of an ENM — the ordered list of eigenvalues — represents the vibrational frequency spectrum of the molecular or mechanical network.
The ENM is mathematically equivalent to a weighted graph Laplacian:
[
L_{ij} =
\begin{cases}
k_{ij} & i \neq j, \
\sum_{m \neq i} k_{im} & i = j
\end{cases}
]
where ( k_{ij} ) are spring constants.
In this form:
(L) is symmetric and positive semidefinite.
Its eigenvectors describe collective deformation patterns.
Its eigenvalues describe mode stiffness (λ) or oscillation frequencies (ω²).
This is directly analogous to spectral graph theory, where eigenvectors of (L) define smooth “vibrations” over a network — the same concept used in graph signal processing and diffusion geometry.
In ENM:
The lowest nonzero eigenvalues correspond to soft, large-scale motions (e.g., hinge bending in proteins).
The high-frequency spectrum corresponds to local vibrations (e.g., bond stretching).
Spectral analysis isolates these frequencies, enabling dimensionality reduction:
Instead of using 3N coordinates, ENM keeps only the first ~20–50 eigenmodes — just as in principal component analysis (PCA) or Fourier decomposition.
This is why ENM-based normal mode analysis (NMA) is sometimes called “spectral mode decomposition of structure.”
| Field | Operator | Eigenvectors represent | ENM Equivalent |
|---|---|---|---|
| Quantum mechanics | Schrödinger operator | stationary states | normal modes |
| Graph theory | Laplacian | diffusion patterns | elastic vibrations |
| Image processing | Graph Laplacian | texture or segmentation basis | molecular domain partitioning |
| Mechanics | Stiffness/mass matrices | vibration shapes | residue motion |
| Data science | Covariance matrix | PCA directions | soft collective modes |
Thus, the ENM spectrum is analogous to a vibrational fingerprint or energy landscape basis — widely used for reduced modeling, clustering, and dynamics inference.
| Spectral Quantity | Meaning / Use |
|---|---|
| Eigenvalues ((λ_k)) | Mode stiffness or frequency; related to energy curvature |
| Eigenvectors ((u_k)) | Collective motion directions |
| Spectral density | Distribution of stiffness over frequency bands |
| Spectral gap | Rigidity vs flexibility contrast; useful for detecting modular domains |
| Participation ratio | Localization measure of each mode |
| Mode overlap | Projection between observed conformational changes and ENM modes |
| Spectral entropy | Quantifies complexity of the motion spectrum |
Dimensional reduction – Keep the top 10–20 softest modes to represent conformational changes efficiently.
Domain decomposition – Detect flexible regions by analyzing spectral gaps or nodal structure of eigenmodes.
Allosteric communication – Use mode correlation spectra to identify dynamic coupling between residues.
Multiscale analysis – Compare spectra across coarse-grained and fine-grained ENMs to infer scale invariance.
AI/ML integration – Use ENM eigenvalues as spectral graph features for learning protein motion embeddings.
| Concept | ENM Term | Spectral Analogy |
|---|---|---|
| Stiffness matrix (H) | Elastic coupling | Graph Laplacian |
| Normal modes | Eigenvectors | Basis functions |
| Mode frequencies | Eigenvalues | Spectrum |
| Collective motion | Low-frequency subspace | Smooth eigenfunctions |
| Rigidity transition | Spectral gap | Connectivity change |
✅ Key Insight:
The Elastic Network Model is not just similar to spectral analysis — it is a spectral analysis of molecular mechanics.
It’s a graph-based eigen-decomposition of the protein’s elastic energy landscape.
Would you like me to show a small numerical example (e.g., a 5-node ENM stiffness matrix and its spectral decomposition) to visualize how the eigenmodes correspond to motion patterns?
