Interview with Zahra Ghorrati: growing frameworks for human exercise recognition utilizing wearable sensors

On this interview sequence, we’re assembly a number of the AAAI/SIGAI Doctoral Consortium contributors to search out out extra about their analysis. Zahra Ghorrati is growing frameworks for human exercise recognition utilizing wearable sensors. We caught up with Zahra to search out out extra about this analysis, the facets she has discovered most attention-grabbing, and her recommendation for potential PhD college students.

Inform us a bit about your PhD – the place are you finding out, and what’s the subject of your analysis?

I’m pursuing my PhD at Purdue College, the place my dissertation focuses on growing scalable and adaptive deep studying frameworks for human exercise recognition (HAR) utilizing wearable sensors. I used to be drawn to this subject as a result of wearables have the potential to rework fields like healthcare, aged care, and long-term exercise monitoring. In contrast to video-based recognition, which may elevate privateness considerations and requires mounted digital camera setups, wearables are moveable, non-intrusive, and able to steady monitoring, making them best for capturing exercise knowledge in pure, real-world settings.

The central problem my dissertation addresses is that wearable knowledge is commonly noisy, inconsistent, and unsure, relying on sensor placement, motion artifacts, and machine limitations. My purpose is to design deep studying fashions that aren’t solely computationally environment friendly and interpretable but additionally strong to the variability of real-world knowledge. In doing so, I purpose to make sure that wearable HAR methods are each sensible and reliable for deployment exterior managed lab environments.

This analysis has been supported by the Polytechnic Summer season Analysis Grant at Purdue. Past my dissertation work, I contribute to the analysis neighborhood as a reviewer for conferences corresponding to CoDIT, CTDIAC, and IRC, and I’ve been invited to evaluate for AAAI 2026. I used to be additionally concerned in neighborhood constructing, serving as Native Organizer and Security Chair for the twenty fourth Worldwide Convention on Autonomous Brokers and Multiagent Techniques (AAMAS 2025), and persevering with as Security Chair for AAMAS 2026.

Might you give us an summary of the analysis you’ve carried out to date throughout your PhD?

To date, my analysis has centered on growing a hierarchical fuzzy deep neural community that may adapt to numerous human exercise recognition datasets. In my preliminary work, I explored a hierarchical recognition strategy, the place less complicated actions are detected at earlier ranges of the mannequin and extra advanced actions are acknowledged at larger ranges. To reinforce each robustness and interpretability, I built-in fuzzy logic ideas into deep studying, permitting the mannequin to raised deal with uncertainty in real-world sensor knowledge.

A key power of this mannequin is its simplicity and low computational price, which makes it significantly nicely fitted to real-time exercise recognition on wearable gadgets. I’ve rigorously evaluated the framework on a number of benchmark datasets of multivariate time sequence and systematically in contrast its efficiency in opposition to state-of-the-art strategies, the place it has demonstrated each aggressive accuracy and improved interpretability.

Is there a side of your analysis that has been significantly attention-grabbing?

Sure, what excites me most is discovering how completely different approaches could make human exercise recognition each smarter and extra sensible. For example, integrating fuzzy logic has been fascinating, as a result of it permits the mannequin to seize the pure uncertainty and variability of human motion. As a substitute of forcing inflexible classifications, the system can purpose by way of levels of confidence, making it extra interpretable and nearer to how people really assume.

I additionally discover the hierarchical design of my mannequin significantly attention-grabbing. Recognizing easy actions first, after which constructing towards extra advanced behaviors, mirrors the way in which people usually perceive actions in layers. This construction not solely makes the mannequin environment friendly but additionally offers insights into how completely different actions relate to at least one one other.

Past methodology, what motivates me is the real-world potential. The truth that these fashions can run effectively on wearables means they may finally help customized healthcare, aged care, and long run exercise monitoring in folks’s on a regular basis lives. And for the reason that methods I’m growing apply broadly to time sequence knowledge, their affect might lengthen nicely past HAR, into areas like medical diagnostics, IoT monitoring, and even audio recognition. That sense of each depth and flexibility is what makes the analysis particularly rewarding for me.

What are your plans for constructing in your analysis to date in the course of the PhD – what facets will you be investigating subsequent?

Shifting ahead, I plan to additional improve the scalability and flexibility of my framework in order that it may possibly successfully deal with giant scale datasets and help real-time purposes. A significant focus will probably be on bettering each the computational effectivity and interpretability of the mannequin, guaranteeing it isn’t solely highly effective but additionally sensible for deployment in real-world eventualities.

Whereas my present analysis has centered on human exercise recognition, I’m excited to broaden the scope to the broader area of time sequence classification. I see nice potential in making use of my framework to areas corresponding to sound classification, physiological sign evaluation, and different time-dependent domains. It will permit me to show the generalizability and robustness of my strategy throughout numerous purposes the place time-based knowledge is essential.

In the long term, my purpose is to develop a unified, scalable mannequin for time sequence evaluation that balances adaptability, interpretability, and effectivity. I hope such a framework can function a basis for advancing not solely HAR but additionally a broad vary of healthcare, environmental, and AI-driven purposes that require real-time, data-driven decision-making.

What made you need to research AI, and particularly the world of wearables?

My curiosity in wearables started throughout my time in Paris, the place I used to be first launched to the potential of sensor-based monitoring in healthcare. I used to be instantly drawn to how discreet and non-invasive wearables are in comparison with video-based strategies, particularly for purposes like aged care and affected person monitoring.

Extra broadly, I’ve at all times been fascinated by AI’s capacity to interpret advanced knowledge and uncover significant patterns that may improve human well-being. Wearables supplied the proper intersection of my pursuits, combining cutting-edge AI methods with sensible, real-world affect, which naturally led me to focus my analysis on this space.

What recommendation would you give to somebody pondering of doing a PhD within the subject?

A PhD in AI calls for each technical experience and resilience. My recommendation can be:

• Keep curious and adaptable, as a result of analysis instructions evolve shortly, and the power to pivot or discover new concepts is invaluable.
• Examine combining disciplines. AI advantages vastly from insights in fields like psychology, healthcare, and human-computer interplay.
• Most significantly, select an issue you might be actually captivated with. That keenness will maintain you thru the inevitable challenges and setbacks of the PhD journey.

Approaching your analysis with curiosity, openness, and real curiosity could make the PhD not only a problem, however a deeply rewarding expertise.

Might you inform us an attention-grabbing (non-AI associated) reality about you?

Outdoors of analysis, I’m captivated with management and neighborhood constructing. As president of the Purdue Tango Membership, I grew the group from simply 2 college students to over 40 energetic members, organized weekly courses, and hosted giant occasions with internationally acknowledged instructors. Extra importantly, I centered on making a welcoming neighborhood the place college students really feel related and supported. For me, tango is greater than dance, it’s a technique to carry folks collectively, bridge cultures, and stability the depth of analysis with creativity and pleasure.

I additionally apply these expertise in educational management. For instance, I function Native Organizer and Security Chair for the AAMAS 2025 and 2026 conferences, which has given me hands-on expertise managing occasions, coordinating groups, and creating inclusive areas for researchers worldwide.

About Zahra

Zahra Ghorrati is a PhD candidate and instructing assistant at Purdue College, specializing in synthetic intelligence and time sequence classification with purposes in human exercise recognition. She earned her undergraduate diploma in Laptop Software program Engineering and her grasp’s diploma in Synthetic Intelligence. Her analysis focuses on growing scalable and interpretable fuzzy deep studying fashions for wearable sensor knowledge. She has offered her work at main worldwide conferences and journals, together with AAMAS, PAAMS, FUZZ-IEEE, IEEE Entry, System and Utilized Delicate Computing. She has served as a reviewer for CoDIT, CTDIAC, and IRC, and has been invited to evaluate for AAAI 2026. Zahra additionally contributes to neighborhood constructing as Native Organizer and Security Chair for AAMAS 2025 and 2026.