Computer vision (CV) focuses on visual functionalities that give rise to semantically meaningful interpretations of the visual world. Our CV research aspires to develop intelligent algorithms that perform important visual perception tasks such as object recognition, scene categorization, semantic segmentation, integrative scene understanding, human motion recognition with applications to autonomous driving, medical imaging and security, just to name a few.

National language processing (NLP) focuses on algorithms that allow computers to process, generate, and understand human languages. Our NLP research ranges from basic research in textual representation learning to key applications in human language technology, and covers areas such as information retrieval, sentence understanding, automatic question answering, machine translation, sentiment analysis, dialogue agents, and models of text and visual scenes.

In open-world AI, to produce the unexpected behavior, attackers create “adversarial examples” that often resemble normal inputs, but instead are meticulously optimized to break the model’s performance. Our trustworthy AI research includes developing adversarially robust, explainable, secure, and privacy-aware models and algorithms to enable at-scale deployment of AI systems in security-critical and safety-critical application domains, such cybersecurity, healthcare, and autonomous driving.

Legacy industrial cities face numerous barriers to creating an equitable transportation system for all residents. Such is the case of Detroit, whose once vibrant public transportation system has weakened amidst decades of population loss, suburbanization, the relocation of job hubs away from the central city, leading to aggravated access poverty particularly for low-income population. Our smart transportation and mobility research leverage cutting-edge AI to enhance our capability to integrate and leverage the existing resources and infrastructure where residents can access multiple modes of transport such as bike shares, e-scooters, ride hail, electric vehicle charging stations, microtransit, and public transit.

AI in healthcare refers to the utilization of robust machine learning models, explainable algorithms and edge intelligence to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data. We conduct novel AI healthcare research in Electronic Health Record (EHR) based human disease prognosis; novel sensing technology-based disease prevention and patient management; medical imaging interpretation-based diagnosis; and molecular profiling data-based biomarker screening and discovery, just to name a few.

Integrating AI and immersive computing can propel future Intelligent Reality (IR), providing real-time intelligent assistant user experience for the immersive environment. Also, to make machine learning inferencing more robust with realistic content rendering, leveraging big data for handling Volume and Velocity of data from a single source or Varieties of data from multiple sources would provide the IR the perfect playing field. Emerging technology such as augmented reality (AR), virtual reality (VR), mixed reality (MR), extended reality (XR), and Digital Twins, increasingly blur the line between the physical world and the digital world. We perform original research in IR to address both device and application challenges such as energy awareness, latency minimization and user experience enhancement.