Get to know our researchers

Internet of things (IoT) and artificial intelligent connected innovation are my passion; placing Bosch’s connected systems research ideas in various connectivity standardization is my ultimate goal. I am a wireless communication and standardization research expert, playing a key role in wireless research, innovation, and idea creation for 5G communication standards and beyond. I joined Bosch in 2019 focusing on driving our research along with the wireless standardization evolution in 5G/6G and WiFi. My main research interest is reliable, low-latency, and safe communication for artificially intelligent connected mobility and industries. The main challenge that our research needs to resolve is to allow fully connected intelligent systems and devices with a reasonable cost of ownership, affordable operational complexity, and reduced power consumption/CO2 emission. Our pre-standardization research activities focus on creating innovative ideas and solid research foundation tackling the aforementioned challenges and impacting the 5G evolution. Furthermore, having Bosch’s artificial intelligent device-centric approach in mind allows us to optimize for artificial intelligence (AI) enabled connected systems, which is the gate for the revolutionary 6G communication.

I am a PhD student at the Bosch Computer Vision Lab, where my research is focused on camera calibration. Having studied physics with a focus on computational neuroscience, I then moved to the more applied field of computer vision. The goal of my research is to accurately model how cameras map the 3D world onto a 2D image. Such a model is needed in automated driving and robotics, where cameras are used to perceive the surroundings and to navigate safely.

I am a lead research scientist at Bosch Research and Technology Center with a focus on applying knowledge representation and semantic technology to enable autonomous driving. I also hold an Adjunct Faculty position at Wright State University. Prior to joining Bosch, I earned a PhD in Computer Science from WSU, where I worked at the Kno.e.sis Center applying semantic technologies to represent and manage sensor data on the Web.

After studying and gaining some research experience in computer science and cognitive science, I started at Bosch in 2016 as a PhD student. In my dissertation, I investigated methods to improve sample efficiency, i.e. how to speed up learning, for self-learning robots in contact-rich manipulation tasks in production settings.

In 2019 I took a position as research engineer with Bosch Research in the same field, so ever since I get to investigate pragmatic ways how to apply and enhance state-of-the-art machine learning methods on data from and for Bosch production - for self-learning robots and beyond.

I manage and contribute to research in electrochemistry and materials, which includes fuel cells, batteries, and emerging topics. Previously at LANL, I focused on fuel cells, especially catalysts, supports, and electrodes. At Bosch, my team engages in electrochemical material and device development (e.g. fuel cell, battery, water purification), material property measurement, and advanced characterization.

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've been with Bosch in the Electric Drives and Electric Machines group since 2017. I am involved in the simulation and design of electrical powertrains. This includes traction motors for electric cars, but also steering drives, e-bike motors and electrified braking systems.

The current focus of my work is on the modeling and evaluation of electrical insulation systems in traction machines in the environment of increasing battery voltages and their supply with fast-switching power electronics.

My name is Dr. Helerson Kemmer, and I am a thoroughgoing system engineer working with fuel cells for mobility applications. My function is to anticipate and shape the future systems and derive research projects to prepare my company for that. To accomplish these tasks, I am project leader and chief expert for future fuel cell systems.

Making manufacturing smarter with the help of AI, digital twins, and semantic technologies is my passion.

I have been with Bosch since 2018. My work aims at developing AI methods that combine -- via symbolic reasoning and machine learning -- manufacturing knowledge captured as semantic conceptual models, digital twins, and knowledge graphs with production data. Such methods allow us to enhance and democratize industrial data analytics and analyses as well as develop industrial AI solutions, for example, semantically-enhanced machine learning pipelines for monitoring discrete manufacturing operations. I aim for solutions that are based on solid theory and have high impact in Industry 4.0. I am active in international scientific and engineering communities. For example, I have more than 130 scientific publications and several of them were awarded with or nominated for the best paper award at top-tier venues. In 2021 I was ranked Nr 18 among ""AI 2000 Knowledge Engineering Most Influential Scholars"" according to AMiner. Moreover, I participate in publicly funded projects with multiple academic and industrial partners and currently I am running three projects like this at Bosch.

My work always focused on topics around AI and robotics. Either in academia or industry. In Academia, I completed my doctorate, post-doctoral degree and habilitation in this area. In industry, I worked in Los Angeles, California at iRobot on the Roomba and Terra robots, and at FaceMap I developed deep learning solutions. To date, I have published almost 100 papers and designed more than a dozen successful robot systems taking first place at international competitions. I was an Executive at RoboCup, General Chair and PC Chair of the IEEE SSRR Symposium, and regularly work as an Associate Editor at the AAMAS, IROS and ICRA conferences.

I established and led the direction on atomistic computational materials design at Bosch Research in North America. Our team uses algorithms, based on quantum physics, and high-performance computers to understand and design materials for next-generation alloys, batteries, semiconductors, coatings, sensors, and other technologies. In 2018 I started a new research group at Harvard University, focused on combining physics and machine learning models to describe transport properties and time evolution of complex materials. In this dual role, I work to bring together the talented researchers from the Bosch and Harvard teams and form close collaborations, creating new cutting edge methods to reveal the atomic details of how materials work and change.

I am a passionate chemist, with focus areas in synthesis, characterization, and electrochemical device design, especially for energy storage and conversion. I have developed materials for energy storage applications and I study the structure-property-performance relationships of such novel solids using synchrotron radiation and various state-of-the-art spectromicroscopic techniques. The insights gained from my work are used to develop cost-effective and efficient solutions for energy storage and conversion, specifically for the widespread commercialization of zero-emission vehicles. I have also developed a new concept together with our team for an electrochemical water purification system.

In 1990, I started as a physicist in microsystems technologies at Bosch Research. After more than 10 years of successful work on new MEMS sensors for automotive and consumer electronics, I stepped into the development of microfluidics solutions for molecular diagnostics in healthcare. Currently I am a research fellow/senior chief expert, identifying and developing new applications for our Lab-on-Chip platform "Vivalytic", together with our business unit Bosch Healthcare Solutions.

I am Director of the Department "Intelligent and Connected Sensors and Systems" at our Research and Technology Center in Silicon Valley. My teams and I develop solutions in the field of sensors including integrated circuits, wireless connectivity, microsystems and medical diagnostics. I have worked in positions as researcher and development engineer at the University of Kaiserslautern, for Bosch in Reutlingen, for the start-up SiTime and for Bosch in Silicon Valley.

As senior expert and team leader in accident research, I carry the overall responsibility for Bosch accident research worldwide.

On the basis of real-world traffic accidents, I derive the strategic goals for products of the business and regional units of Bosch. In addition to my international activities, I also participate in committees and publicly funded projects.

My current research focus is the conception of a simulation environment for the evaluation of automated driving functions. To do this, I build a link between data from traffic observations and conventional on-the-spot investigated crash data.

I am a senior expert for power electronics at Bosch Research and Technology Center in China. My research focuses on power conversion technology in different application fields, especially in electrified mobility. In the past three years, my team and I have mainly worked on key electronic components in the fuel cell electric vehicle, such as the motor controller for 120,000 RPM high speed electric air compressor system and the high voltage high power DC/DC converter.

As a systems engineer, I have been working on the challenging task of automated driving for 15 years. During the course of my PhD with the Honda Research Institute in Offenbach, I researched the roots of human intelligence and the plasticity of the human brain together with an interdisciplinary team. One of the tangible applications of my work on a system that works in a “brain-like” or “human-like” manner was the creation of an “attentive co-pilot” which supports humans in the task of driving. As a trainee at Daimler AG with a focus on R&D for automated driving, I became closely acquainted with the perspective of a vehicle manufacturer, for which the challenging task lies in the robust integration of numerous hardware and software modules into the complete vehicle system. Differences in the corporate culture which were crucial to me and the strong focus on algorithms by a leading supplier led me to Bosch and into research. Thanks to significant progress in the field of machine learning, I can connect with the conceptual world of my PhD here and, in the process, provide support for guiding our mobility into the future with “technology for life.”

Throughout my career, I have focused on different aspects of cyber-physical systems (CPS). From software development, communications and security, to abstractions & modeling. At Bosch Research, I head the Information & Communication Technologies area. By combining modern software and communication technologies with CPS, we open up a new world of possibilities for embedded applications, making them portable, flexible, and high-performing.

More than 20 years working on the different algorithms related to automated driving. These include perception, motion planning and control, localization etc. Currently investigating the very interesting and challenging field of the anticipatory driving with deep learning.

I have been a guest professor at the Free University in Tbilisi, Georgia (Institute of Mathematics and Applied Computer Science - MACS) since 2015. I give lectures in Computer Vision and Machine Learning.

I am very proud of the fact that in 6 years I have introduced the fascinating subject of machine learning to more than 600 students.

In the past, I was often able to prove that 'nothing is more practical than the result of the complex theory'.

~40 journal and conference publications
~65 patents

I work in the area of Intelligent Assistance, with a focus on decision support systems that combine machine perception and reasoning. My primary interest is to investigate how semantic resources, either structured or unstructured, can be integrated with data-driven algorithms, and help machines make sense of the physical and digital worlds. I strive to make progress in the area of Human Machine Collaboration, which can benefit greatly from designing AI-based systems that infuse powerful neural models with transparent knowledge representations.

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.

Engineering devices typically rely on sensors to measure necessary inputs. Machine-learning methods trained on sensor data can increase the utility of such devices by improving their efficiency or enabling exciting new features. However, modeling sensor data presents a number of challenges: real-world data often exhibits multimodal dynamics; some sensors are usually sampled at irregular time intervals; and most industrial datasets are unlabeled. Learning from sensor data requires methods that are flexible enough to capture the underlying complexity while handling various types of noise and uncertainty. For this reason, the main technical theme in my research is how to best combine deep learning with probabilistic modeling.

After completing my degree in physics, I worked for eight years in the field of climate and polar research, where I obtained valuable knowledge about the climate system and climate change. I then transferred to Bosch, where I first served in the Automotive Electronics Business Unit in Reutlingen (Germany) and Plymouth (USA) – first as project and later as group manager in series development of electronic control units for products such as ABS and ESP. In 2015, I transferred to Bosch Research as an expert for climate change and the energy transition. In this role I consult in strategic issues across all levels, from research projects to the CEO and Supervisory Board. Additionally, since 2020 I have been the co-chair of our Strategic Research Portfolio for Sustainability.

We use quantum mechanical simulations for predicting properties of materials at the atomic scale. The properties are varied (conductivity, stability, reactivity, sensitivity, selectivity) and application specific (thermoelectrics, batteries, fuel cells, sensors). We perform a computational screening of candidate materials for the desired property and suggest promising candidates for experimental synthesis and characterization.

I am a Senior Expert with a broad background from mechanical engineering to medical diagnostics. Currently, I am a Senior Expert in the bioelectronics team. Our team is developing new technologies for next-generation medical diagnostics, especially for point-of-care applications. Our vision is to revolutionize healthcare by combining Bosch competencies such as MEMS sensors, circuit design, wireless communication, and biochemistry.

I’ve been focusing on the topic of computational material science as a research engineer since 2011. Right now, I am working as a senior expert on micromechanical fatigue simulation in metals. Together with a team, I am developing models of relevant factors influencing cyclic mechanical damage in materials and components. This allows us to make material lifetime predictions or identify micromechanical factors that are responsible for material damage. In this way, we are contributing to the reliability and sustainability of our products.

I am a senior research scientist at Bosch Research in Renningen for Quantum Technologies and coordinating Quantum Technologies at Bosch. My focus areas are Quantum Computing and Quantum Random Number Generators and I'm very much interested in the foundations of quantum physics and in modeling quantum systems. Quantum Technologies is a research activity which is still in an early phase and, therefore, cooperation with the academic community and public funding initiatives is important. For this reason, I am very active in the European Quantum Flagship, as a member of its Coordination Office and as a representative of German industry in its European Quantum Community Network.

Research on HVAC, IAQ & heat pump systems have been my passion for the last 15 years. I have been working on sustainable HVAC systems, model-based design and controls development for Bosch Research since 2018.

Me and my team deal with HVAC efficiency & thermal comfort, indoor air quality, filtration and sensing challenges for commercial buildings and 3rd living spaces. As Bosch has carbon neutrality targets, we are supporting this vision with our advanced energy management functions that are enriched with AIoT enablers. We use model-based engineering, machine learning models and human perception techniques to optimize and control HVAC demand for higher sustainability.

I am a research scientist at Bosch Research and Technology Center in China, and joined Bosch in 2019. Before joining Bosch, I gained 16 years of R&D working experience on internal-combustion engines (ICEs) and fuel cell systems in FAW (First Automobile Workshop). As a team leader for research on fuel cell and hydrogen systems, I am currently working together with our team and cooperating closely with local BU on fuel cell system adaption to local market needs, focusing on deriving innovative concepts and bringing "WoW" ideas to keep the Bosch fuel cell system and BoPs competitive on the market.

I am a research engineer at Bosch Research and Technology Center in China. My studies focus on the design and characterization of membrane electrode assembly for PEMFC fuel cells. I am also working on the modeling of coupling heat, species, and charge transfer processes inside the fuel cell. The knowledge obtained from combined experiment and modeling study is useful to develop a high-performance and durable fuel cell stack and system for vehicles.

Engineer for development of new sensor fabrication processes to be used in next-generation products. I review new materials and processes and, depending on how promising they look, integrate them into prototypes for enhancement of sensor functionality. Using the cleanroom Stanford University wafer fabrication facility, parts are fabricated for proof of concept. If successful and there is market demand, they are transferred for mass production. Some of these parts are now commercialized for use in timing references by the spin-off company, SiTime.

Research and development of computer vision solutions and algorithms for IoT applications

Throughout my career, I have been researching and developing artificial perception to improve the mobility of humans and machines. As team leader in Intelligent Connected Vehicle (ICV) at Bosch Research Shanghai, our team and I jointly develop solutions for intelligent perception and autonomous navigation in ADAS, and robotics for I4.0. We use cross-domain sensor fusion and machine learning to build rich models of the world guiding autonomous vehicles’ decisions. I believe that by pooling information from diverse sources via wireless communication technology, we should give ourselves and our machines the ability to extend our “vision” to make better choices - be it at home, in factories, or on the road.