Interview scorecard template

Affective Computing Researcher interview scorecard

Evaluate Affective Computing Researcher candidates across 4 weighted areas: theoretical command, from theory to hardware or code, research judgement, and explaining it to non-specialists. Theoretical command leads at 35%, so probe command of appraisal theory versus dimensional models: ask how they treat valence-arousal regression against discrete labels, FACS action units, and inter-rater. Use the rubric to compare role-specific evidence consistently.

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frontier research deep techaffective computingfacs annotationmultimodal emotion recognitionphysiological signals
TL;DR
For theoretical command, look for evidence the candidate argues models of emotion with citations, names annotation schemes and their reliability limits, and distinguishes expression from felt state. For from theory to hardware or code, look for evidence the candidate points to shipped models or open datasets with reported metrics, plus the sensor rig or capture protocol they personally assembled. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework

What to assess and how to score it

Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.

01
Evaluation factor

Theoretical command

35% weight

Probe command of appraisal theory versus dimensional models: ask how they treat valence-arousal regression against discrete labels, FACS action units, and inter-rater reliability on IEMOCAP or AffectNet.

Evidence to listen for

  • Explains the underlying theory at the level the role demands, and can go a layer deeper when pushed
  • Knows which results are established and which are contested
  • Distinguishes their own contribution from the field's
  • Comfortable saying where the theory runs out

Five-point scoring guide

1
Poor

Recites terminology without understanding; cannot go one layer deeper.

2
Needs Improvement

Surface familiarity; conflates established results with speculation.

3
Satisfactory

Solid grasp of the core theory; thin at the frontier.

4
Very Good

Strong command; separates settled results from open questions.

5
Excellent

Argues models of emotion with citations, names annotation schemes and their reliability limits, and distinguishes expression from felt state.

02
Evaluation factor

From theory to hardware or code

30% weight

Ask what they built: multimodal fusion pipelines over EDA, ECG/HRV, EEG, video and prosody; wearable data collection rigs; released code, datasets or benchmark results.

Evidence to listen for

  • Has built, simulated, or run something real, not only published about it
  • Knows the gap between the idealised model and the actual apparatus or system
  • Names the practical constraint that dominates in real conditions
  • Can describe a result that did not match prediction

Five-point scoring guide

1
Poor

Purely theoretical; no contact with implementation.

2
Needs Improvement

Some exposure but unaware of practical constraints.

3
Satisfactory

Has implemented work; understands the main real-world limits.

4
Very Good

Strong practical record; articulate about theory-versus-reality gaps.

5
Excellent

Points to shipped models or open datasets with reported metrics, plus the sensor rig or capture protocol they personally assembled.

03
Evaluation factor

Research judgement

20% weight

Test how they choose studies: participant sample size, IRB or ethics approval, subject-independent versus subject-dependent splits, and handling of culturally biased or imbalanced affect labels.

Evidence to listen for

  • Chooses problems by tractability and value, not novelty alone
  • Knows when to abandon a line of work
  • Reads and evaluates others' results critically
  • Can say what would falsify their own approach

Five-point scoring guide

1
Poor

Chases novelty; no sense of tractability or when to stop.

2
Needs Improvement

Weak problem selection; persists past the point of value.

3
Satisfactory

Reasonable judgement within a defined programme.

4
Very Good

Selects problems well and knows when to abandon a line.

5
Excellent

Explains discarded directions, defends subject-independent evaluation, and treats demographic bias and consent as design constraints rather than caveats.

04
Evaluation factor

Explaining it to non-specialists

15% weight

Judge how they brief clinicians, UX teams or product owners on what emotion inference can and cannot claim, including false positive costs and deployment misuse.

Evidence to listen for

  • Explains the work to an engineer, an executive, or a funder without either mystifying or dumbing it down
  • Writes clearly
  • Collaborates across disciplines
  • Makes the case for resources in terms the audience cares about

Five-point scoring guide

1
Poor

Cannot communicate outside their specialism.

2
Needs Improvement

Explanation is either impenetrable or hollow.

3
Satisfactory

Adequate with technical peers; less effective with lay audiences.

4
Very Good

Explains clearly to specialists and non-specialists alike.

5
Excellent

Translates confusion matrices into plain consequences, states model limits unprompted, and refuses overclaiming when stakeholders want certainty.

Evidence-led prompts

Interview questions for a Affective Computing Researcher

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Can you describe a research project you have worked on in this field?

  2. 02

    Which projects or publications have you contributed to here?

  3. 03

    Describe applying your expertise to solve a significant problem.

  4. 04

    Have you developed algorithms for emotion detection?

  5. 05

    What is your experience with physiological sensors or emotion datasets?

See the complete Affective Computing Researcher question set
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