Get to know our researchers

I initially came to Bosch research as an expert in thin film solar cells. After that I did research on a whole range of different topics and now, since the start of 2019, I am an Agile Master for various research teams. Here, I use agile working methods to support my teams in producing top results. Agile methods allow us to react flexibly to surprising findings and changing conditions.

I am a senior 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.

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.

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.

Prior to joining the Energy Technologies Department in Bosch Research and Technology Center - North America, I have worked on a number of different materials synthesis and simulation methods in the industry, a national laboratory and in academia. At Bosch, I am mainly responsible for analysing technical challenges experienced by other researcher(s) or business units, and then coming up with (an) innovative material solution(s). In 2018 and 2019, I led the Energy Technologies Department in Bosch Research and Technology Center - North America in the number of U.S. patent filings.

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 am a lead engineer at the Russian Technology Office in St-Petersburg. I organize and coordinate R&D projects in Russia, developing novel functional materials (thermoelectrics, metamaterials, composites for energy storage and conversion devices) and computational methods, such as Discrete Element Method, Viscous Vortex Domain method as well as generative neural networks, such as disentangled Variational AutoEncoders.

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 Corporate 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 BHCS.

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.

I am a research scientist at the Computer Vision Lab of Robert Bosch GmbH. I am interested in object detection and tracking, sensor fusion, scene understanding, and machine learning. For me, automated driving at all levels is currently the most interesting challenge in computer vision and machine learning. My daily work is to push the borders for vision-based automated driving, making algorithms more robust and thus cars safer.

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 research engineer in the “Life Science” working group with a focus on the area of molecular diagnostics. I mainly focus on the characterization & integration of biological analysis and proof processes into the Vivalytic lab-on-a-chip system, which was introduced onto the automated laboratory diagnostics market by the Bosch Healthcare Solutions GmbH (BHCS) division in spring 2020. In close dialog with BHCS, in the strategic Healthcare Solutions portfolio we enable tapping into new market segments by making innovative clinical sample materials processable and new biomarkers in Vivalytic LoC cartridges detectable.

In addition, owing to my microbiological background, I have responsibility at the Renningen location for infection protection and the safe operation of the Life Science laboratory (Biosafety Level 2).

Before Bosch, I worked in the field of “interfacial biology,” in which we researched the targeted use but also the elimination of biofilms. New assessment methods were developed here, adapted to the respective issue, for processes where established standard methods reach their limits.

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.”

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 radar perception 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 2016. I give lectures in Computer Vision and Machine Learning.

I am very proud of the fact that in 5 years I have accumulated 1000 voluntary hours and introduced the fascinating subject of machine learning to more than 500 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
~60 patents

I work on knowledge-based intelligent systems. My primary interest is to investigate how semantic resources like computational ontologies and lexical databases can be used to augment learning algorithms, and help machines to make sense of the world. I strive to make progress in explainable AI, which I’m convinced can be achieved only by designing systems that integrate human-accessible machine representations with neural networks.

In 2012, I started working in the control engineering research group @ Robert Bosch GmbH. I focus on model- based, adaptive and learning control methods ready for implementation on electronic control units. My colleagues and I already improved performance and calibration efficiency on real-world hydraulic, thermodynamic, and energy-generating systems and I am currently working on control algorithms for highly automated driving. I am glad for being part of Bosch, as Bosch allows me not only to do cutting-edge research but also to combine work and family life.

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.

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.

Together with my team at Bosch Research and the colleagues in the business unit Automotive Electronics development, I work on a better understanding of our future microsystems and their production processes. With our technology, we can demonstrate high-precision micro- and nanostructures from thin films down to the monolayer level in the research cleanroom. This level of precision is mandatory for the reliable operation of today's smart, high-performance, optical and mechanical microsystems.

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 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.

In my group, we work on energy infrastructure focusing electrochemical systems. My tasks include the multiphysical simulation of solid oxide fuel cell systems. We also develop models for determining the lifetime of the systems and to evaluate optimal operation within the specifications to achieve high electrical efficiency.

Optimization of energy systems towards CO2 neutrality has always been my favorite subject. During my PhD funded by the Bosch Research Foundation, I started to do research into sustainable technologies that enable transformation of the emission-intensive chemical industry. I have been active in Bosch Research since 2016 where I continue my search for system and technology solutions towards CO2-free living.

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.

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.

I am now senior expert in electrical drive control systems at Bosch Research and Technology Center in China. My research topics focus on motor control, sensorless control algorithms of electrical drives for electromobility. I am also looking at connected vehicle applications to further investigate big data based fault diagnosis and intelligent control strategies. I have won China Top Inventor 2019 and Bosch Inventor 2020 for data based sensor fault diagnosis.