NeuroSoph’s philosophy is guided by the following core principles:
- Client business and policy needs always drive the work
- Organizational goals are achieved by using the right technology stack
- Technology is used as a tool to help achieve your goals
- Provide transparent, flexible, and adaptable solutions
Provide high-quality artificial intelligence solutions and services to increase efficiency, improve analysis and cut costs for customers.
Maximize the return on data assets (ROD) to improve decision making and increase operational efficiency.
Augment human skills while reducing the error associated with manual repetitive processes, and allow humans to focus on tasks that require creativity and problem solving.
Complete commitment to customers’ needs while consistently providing high-quality artificial intelligence solutions and services at or below the prescribed price point.
Responsiveness to clients is vital at NeuroSoph and we will respond to all customer inquiries as soon as possible. The customer experience is one of the most important things at NeuroSoph and we promise high quality service to all our customers.
Artificial Intelligence Knowledge
NeuroSoph possess both a breadth of business and a depth of technical knowledge in artificial intelligence (AI); this enables us to provide effective AI solutions for customers and allows us to adapt to customers’ needs.
Meet the Team
Matt Pallone (CTO)
Matt is an innovative engineer with over ten years of research experience in biomedical imaging systems design and five years of experience managing high-technology start-ups. He received his Ph.D. in Engineering Sciences from Dartmouth College in 2013, concentrating in electrical and biomedical engineering. Matt has led the development of several medical imaging technologies related to breast cancer screening, and his background in digital image and signal processing gives him a unique perspective on the challenges common to high-dimensional data analysis and machine learning. His research interests include image processing, deep learning and artificial intelligence systems, medical device development, robotics and machine vision, and other projects with a multidisciplinary focus.
Tushar is a co-founder of NeuroSoph and is currently applying various applications and tools of Artificial Intelligence such as machine learning and natural language processing with a focus on the organizational and managerial implications of these technologies that include the Business Implications of AI and designing AI systems for competitive advantage. Tushar has 7+ years of overseeing and implementing technology solutions with a strong background in identifying business problems, associated use cases, user experience design, and recommending the right technology solution.
Kyle Johnson (COO)
Kyle has over a decade of experience in fields ranging from real estate and start up investments to building advanced algorithms designed to capitalize on inefficiencies in the financial and sports markets. Kyle’s background uniquely positions him to understand the needs of clients from a business and entrepreneurial angle while possessing the familiarity with technology required to execute the client’s vision. Kyle graduated from the University of Alberta with a degree in Business and a specialization in Finance.
Jing Yang (AI Scientist)
Jing is an AI scientist at NeuroSoph who specializes in image processing. She has more than 8 years of academic and industry related research experience, in which she has earned her PhD and has numerous publications in the field. She has designed algorithms for super-resolution, deblurring, periodic video detection, object detection, and image clustering. Jing’s PhD is in Applied Mathematics from Northwestern Polytechnical University. She has also worked in the Robotics-Vision lab at the University of Alberta for 3 years. Her extensive research experience makes her a valuable scientist at NeuroSoph.
Kevin Wang (AI Scientist)
Kevin completed his Ph.D. at the University of Alberta in the Department of Electrical and Computer Engineering. He has developed several computer-aided detection techniques for screening epileptogenic lesions in brain MRI images and published over 10 papers in refereed journals and conferences. Kevin’s research interests include image processing, machine learning, deep learning, and applying related technologies to various applications. Kevin is a quick learned and can adapts new research methods into Specto’s core processes in areas such as image processing, extraneous mark detection, and mobile vision.