AIoT

I am a Research Engineer in the “Dynamic Distributed Systems” group and in this role I work on designing solutions to enable applications to meet their quality of service requirements when deployed on computing platforms. My job is particularly interesting since it lies at the conjunction of different disciplines including Cyber Physical systems, High Performance Computing architectures and Real-time scheduling theory. The focus is on designing and evaluating innovative methods and mechanisms for resource management which are correct-by-construction, formally sound and help in efficiently utilizing the underlying platforms, while also adhering to the application specific constraints.

I am a principal senior expert in computer vision at the Research and Technology Center in India. My research is motivated by the desire to organize and manage large scale video and multi-sensor data (Peta Bytes) with the goals of being able to smartly curate the right data desired by algorithm development. This includes automated approaches to select the most representative sub set of a large set of images, and search and retrieve scenes of interest from the stored images. Our research has multiple goals such as reducing the cost of ground truth generation by removing redundancies, supporting function development and testing to find difficult cases where algorithms do not work well, which can then be used to collect more of such cases or synthetically generate them. In order to achieve this we explore the structure and representation power of deep convolutional neural networks. We develop human computer interfaces that go hand in hand with the deep learning approaches to ensure ease of usage by the end users.

I am a Senior Expert in Robust Scalable Perception at the Bosch Center for Artificial Intelligence. My research is motivated by the desire to advance the understanding, robustness, and applicability of deep learning-based perception. In particular, I focus on evaluating and increasing the robustness of neural networks. While neural networks excel in classifying data coming from the distribution they have been trained on, they often perform poorly on data with slightly different properties. Addressing this shortcoming is crucial for applying neural network-based perception in safety-critical domains such as automated driving, where they will inevitably be faced with situations not encountered or foreseen during training.

I am the Vice President and Chief Scientist of Integrated Human- Machine Intelligence (HMI) at Bosch Research in North America. I am responsible for shaping strategic directions and developing cutting-edge technologies in AI focusing big data visual analytics, explainable AI, mixed reality/AR, computer perception, NLP, conversational AI, audio analytics, wearable analytics and so on for AIoT application areas such as autonomous driving, car infotainment and advanced driver assistance systems (ADAS), Industry 4.0, smart home/building solutions, and robotics, etc. As the responsible global head, I oversee these research activities for teams in the Silicon Valley (U.S.), Pittsburgh (U.S.), and Renningen (Germany). I have won the Bosch North America Inventor of the Year Award for 3D maps (2016), Best Paper Award (2018, 2020), and Honorable Mention Award (2016) for big data visual analytics in IEEE Visualization.

I’m the head of IoT@Life program and group leader for IoT & I4.0 at the Bosch Research and Technology Center in China. Before joining Bosch, I was an Assistant Professor at Shanghai Jiao Tong University focusing on medical robotics and sensor systems. I received my PhD from King's College London and did a postdoc at the University of Southampton in the field of sensor systems and lab-on-chip. As an alumni of the Bosch Accelerator Program, I not only collaborate closely with Business Units for joint development, but also explore commercialization of IoT products and solutions with scalable, repeatable and profitable business models.