Aboul Hassane Cisse

Hello, I'm

Aboul Hassane CISSE

PhD Candidate in Informatics · Multimodal AI · Learning Analytics · Educational Technology · Wearable & Ubiquitous Sensing

My research develops reliable self-supervised multimodal AI for wearable sensing. I work with physiological and behavioral signals to infer cognitive and learning-related states, and I study how these models can stay useful when information is missing, noisy, or redundant.

I apply this work to educational technology and multimodal learning analytics: methods that support real learning settings, where wearable, interaction, and sensor data are often incomplete or uneven.

"Indeed, in the creation of the heavens and the earth and the alternation of the day and night there are signs for people of reason."

إِنَّ فِي خَلْقِ السَّمَاوَاتِ وَالْأَرْضِ وَاخْتِلَافِ اللَّيْلِ وَالنَّهَارِ لَآيَاتٍ لِّأُولِي الْأَلْبَابِ

Quran, Surah 3 (Ali 'Imran), verse 190

Osaka Metropolitan University  ·  Graduate School of Informatics  ·  Japan

cisse.research@gmail.com

Updated August 2026

Current Role PhD Candidate · OMU
Latest Publication Sensors 2026 — Open Access
Languages EN · FR · JA · BM · SON
8+ Publications
8+ Citations
5+ Years Research
22 Countries visited

2026

Publications

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Research

Research Areas

Educational Technology

Designing technologies that improve learning, teaching, accessibility, and educational experience, including generative AI in education and ICT for resource-constrained contexts.

  • Generative AI
  • Accessibility
  • ICT

Multimodal Learning Analytics

Combining gaze, physiology, wearables, and interaction logs to model engagement, cognition, communication, and learning.

  • Gaze
  • Physiology
  • Engagement

Self-Supervised Learning

Learning representations from unlabeled physiological and wearable data, where large labeled multimodal datasets are expensive to collect.

  • Unlabeled data
  • Representations

Human-Centered AI

Designing AI around people: context-aware, data-efficient, and usable in the settings where humans actually learn and interact.

  • Context-aware
  • Data-efficient
  • Usable

Reliable Multimodal AI

Keeping multimodal inference robust when sensing is missing, noisy, or redundant, and using only the modalities that are actually needed.

  • Missing
  • Noisy
  • Redundant

Entrepreneurship & Digital Transformation

Supporting organizations through enterprise digital transformation and the design of management software and information systems that help businesses operate, grow, and work more effectively.

  • Digital transformation
  • Management software
  • Ventures

Research Collaboration

Let's collaborate

I welcome collaborations that combine AI methods with real-world human and learning data: wearable sensing experiments, multimodal datasets, learning environments, and cross-institutional studies. I also work with organizations on digital transformation and management software.

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Whether it's a research collaboration, a project idea, or just a hello — I'd love to hear from you.

Sakai-shi, Osaka, Japan
Osaka Metropolitan University
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