Hisham Cholakkal has received MBZUAI’s inaugural Award for Teaching Excellence, launched by the University’s Center for Teaching and Learning. Cholakkal, an Assistant Professor of Computer Vision who joined MBZUAI in 2020, was recognized for his innovative teaching methods and positive impact on students. The award considers course evaluations and student feedback to recognize impactful, student-centered teaching. Why it matters: This award highlights MBZUAI's commitment to recognizing and promoting excellence in AI education within the region.
MBZUAI recognized seven faculty members for outstanding contributions in research, teaching, and mentorship at its annual Faculty Recognition and Welcome Reception. Associate Professor Salman Khan received the Distinguished Research Award for his work on multimodal models for remote Earth observation, including projects like AI4Weather and the AI Global Agriculture Advisory. Assistant Professor Alham Fikri Aji received the Early Career Researcher Award for his contributions to low-resource NLP and international collaborations. Why it matters: The awards highlight MBZUAI's focus on advancing AI for global challenges and recognizing faculty contributions to research and education.
MBZUAI faculty member Ekaterina Kochmar and postdoctoral researcher Kaushal Kumar Maurya won a Google Academic Research Award for their research on an intelligent tutoring system. The project, "2σ-ITS," aims to develop an educational foundation model for personalized learning and to support tutors in reaching students with limited access to mainstream education. The Google award provides funding and collaboration opportunities for researchers, with Kochmar and Maurya being the only team from the Middle East to win. Why it matters: This award highlights the growing recognition of AI's potential to improve educational equity and access in the region and beyond.
MBZUAI NLP master's graduate Hasan Iqbal developed OpenFactCheck, a framework for fact-checking and evaluating the factual accuracy of large language models. The framework consists of three modules: ResponseEvaluator, LLMEvaluator, and CheckerEvaluator. OpenFactCheck was published at EMNLP 2024 and accepted at NAACL 2025 and COLING 2025, with Iqbal playing an active role at COLING in Abu Dhabi. Why it matters: The development of automated fact-checking frameworks is crucial for ensuring the reliability and trustworthiness of information generated by increasingly prevalent LLMs, especially in the Arabic-speaking world.
MBZUAI researchers demonstrated a low-latency, multilingual multimodal AI system at GITEX that integrates speech, text, and visual capabilities for more lifelike human-machine conversation. The demo, led by Dr. Hisham Cholakkal, includes a mobile app where users can point their camera at an object and ask questions, receiving spoken answers in multiple languages. They are also integrating the model into a robot dog that can respond to voice commands. Why it matters: This work addresses key challenges in deploying LLMs to real-world applications in the Middle East, such as multilingual support and real-time responsiveness.