How can we better analyze the large amounts of data collected until today, and help the insurance, financial services and healthcare sectors invent better data intelligence ?
DreamQuark puts its intelligence into developing the best algorithms, to detect, very efficiently, rare and otherwise invisible phenomena in a wide variety of data types (images, texts, audio files...)
DreamQuark develops a platform to facilitate access to this expertise of large datasets analysis by offering a transparent solution, with results accessible to all types of business users.
Reducing costs - AI has shown very efficient results at identifying costs across the value chain. Use cases include fraud detection, churn analytics, risk pricing, product segmentation and customer qualification. By gaining a better understanding of their costs, and prioritizing accordingly, professionals will be free to take on a more strategic role.
Targeting Emerging Risks & Product Innovation - New risks are emerging (such as cybersecurity, climate change), analyzing these trends and evaluating if there is a fitting insurance market for these risks are now natural machine learning tasks.
Automation - AI is improving abilities in customer interaction, resolution times, and delivery speed to market of new products. This efficiency is the result of AI accelerating decision-making (automated underwriting, auto-adjudicating claims, automated financial advice).
As former particle physicists, we use our expertise of large datasets analysis and efficient algorithm design to reinvent how data analysis is performed in the insurance, financial services and healthcare sectors. We know how to deal with complex signals, and how to detect even the smallest, invisible effects. Yet, our expertise is not enough, and we believe that it is the business knowledge of our clients' experts that give the final value to the results obtained!
Deep-learning models can be tricky to explain. We worked to develop a solution that not only comes up with results, but also explains the process through which a decision was reached. Learning which element triggered the decision will be a decisive factor in understanding your results, and focusing efforts on key parameters.
We currently develop technologies related to deep neural-networks with sparse architectures that can unveil new patterns inside the input data. This sparcity allows for an higher accuracy and speed. This work combines our knowledge of theoretical physics and artificial intelligence. We apply these new models to both structured and unstructured data, and we transform the initial data in order to create a perfect match between the data representation and the algorithm we want to finally use. Our goal has been to combine different algorithms to benefit from the different characteristics of these algorithms and create analyses that can easily generalize. We embed these algorithms, first trained on specific datasets, into innovative applications for insurance, financial services and healthcare professionals.
Images, time series, texts, audio recordings, Excel files... We treat datasets of all sizes, both structured and unstructured. We also automated a large part of the data pre-processing in order to obtain your results faster.
We develop dqANN, a machine learning framework with technologies intended to mimic the behavior of the human brain. We build lighter architecture to extract pattern and features in your data faster and with an increased accuracy.
Our platform is a White box: every decision made by the model can be explained, thanks to visualizations illustrating the variables responsible for building a specific model, a specific cluster, or even a specific profile. We help you tune parameters and explore new possibilities.
We help you embed custom models in your digital systems or in mobile applications. You can also use the model data generation abilities to explore new scenarios and build new strategies.
The early detection of retinal diseases could prevent blindness. DreamUp Vision's machine learning engine performs a quick detection of complicated patterns in retinal images, and is capable to detect the smallest effects, facilitating early diagnosis of different retina disorders. Deep-learning allows this detection with the performances of professional ophthalmologists in milliseconds. We have implemented this engine both in a mobile application and an online web-application.
To learn more about our project, please visit DreamUp Vision
Chief Executive Officer and FounderNicolas is the founder of DreamQuark. After finishing a PhD in both theoretical and experimental physics with a CNRS silver medal researcher and the director of the ATLAS experiment team in Paris, where some of his work has been presented in Harvard, Nicolas decided to apply what he has learned and developed during his PhD to help healthcare professionals use data to find better diagnostic, prevention and care approaches. He helped launch in parallel an initiative to improve the connection between companies and PhD students. Nicolas is concentrated on the market develoment of our company and on the most critical aspects of our solution such as early design of new data analysis architecture. Nicolas is an entrepreneur paradoxically a scientist and a dreamer. He is passionate about digital technologies, company strategy and science. He is very active contributing to associations. Kind of daredevil, he is practising Nanbudo, a Japanese martial art, althought conciliating this with the company creation is sometimes hard.
Chief Technology Officier and AssociateAxel has done his PhD work at the Laboratoire de Physique theorique of the Ecole Normale Superieure of Paris in theoretical particle physics. His analytical and programming skills are an important asset to develop DreamQuark solutions and to study innovative technologies. In his free time, Axel likes to unleash his creativity through cooking, music or video game development. To keep in shape he likes to hit the ball and plays "Bask Pelota".
Chief Operating Officer and AssociateKatia has a PhD degree in theoretical and experimental particle physics. During her PhD studies at the Institut de Physique Théorique of the CEA, she had high-quality training in using and developing Monte-Carlo models, in numerical simulation of hydrodynamic systems, in modelisation of the quark-gluon plasma created in heavy-ion collisions at the LHC (CERN, Geneva) and RHIC (BNL, Brookhaven) colliders, as well as in experimental analysis of particle physics data. Her ability and experience are fully used in the DreamQuark company for elaborating our models as well our numerical simulations. Big fan of Marie Curie since her childhood, she won many prizes during her study in the nuclear department of Moscow State University and graduated finally with the red diploma awarded to the best students of the promotion. She plays piano, practices dances and sports when she has some time.
Chief Research Officer and AssociateFormer student of Ecole Normale Supérieure of Lyon, PhD in Particle Physics, Adrien is very enthusiastic about the ideas developed by the DreamQuark company. With an experience of one year postdoctoral fellowship at the Service of Physique Nucléaire of the CEA and the PhD studies at the Laboratoire de Physique Théorique of Orsay, his competences in theoretical physics as well as in mathematics are the necessary asset in the development of the artificial intelligence engine and of the statistical models that are developed by the DreamQuark company. Adrien likes all kind of sports from martial arts to basque pelota, in which he finds the inspiration for new ideas.
R&DFormer student of École Polytechnique, Benoît ranked first in the Master's degree of Theoretical Physics of Ecole Normale Supérieure. During his PhD thesis at the Institut de Physique Théorique of CEA, he had to handle both abstract mathematical objects and numerical tools. Attracted to the theoretical fundations of deep learning and its wide range of application, he is now eager to apply his mathematical background to DreamQuark's projects. His practice of rugby for 18 years gave him a strong sense of team spirit. Always willing to surpass himself, he enjoys taking part in races and completed the Paris marathon.
Chief Sales and Marketing OfficerMarketing and Communication Expert, Cécile earned her Msc at the Pantheon Assas University. She worked for more than 100 companies and strategic business domains such as FMCG, Banking, Luxury brand, Cosmetic, Leisure...She developed tremendous brand equity management skills and very large helicopter view to handle with strategic affairs. She is definitively a process and result oriented manager. High achiever team player and team leader, Cecile has developed strong « let do it simple » communication and presentation skills humanizing big data.
CSO and AssociateMathias is an expert in strategy with a DBA with the highest grade. During his PhD, he studied the impact of work on the health and well-being aspects. He is an entrepreuneur and started his own company HumanBet. He brings his incredible vision and experience to our company !
Artistic Director and AssociateStephane is the artist of the company. Expert in image management and design, he worked in the past in huge projects such as the Evian trademark image, and created his own company Odysseus Communication. Stephane gives his genious touch to our projects!
Strategy analyst and AssociateRomain, with 3 years of experience at McKinsey, is an expert in corporate strategy and finance. He has worked in many industries (e.g., Aerospace and Defence, Steel manufacturing, Banking) on a large variety of topics. He graduated from Telecom ParisTech in 2012.
Sales Director and AssociateGabriel has a PhD in organic chemistry with lots of experience working with health professionals. He was the associate responsible of the in-vitro diagnostic companies federation and worked at Roche. He is now developing a company healthcare division.
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