Quantum Technologies
This project, in collaboration with the RQT Center at Stanford University, aims to conduct a comprehensive analysis of the innovation landscape in the emerging field of quantum technologies (QT). Anticipating the potential implications of quantum technologies in real-world products, commentators have started proposing governance and regulatory strategies, raising concerns, and suggesting policy recommendations focused on quantum technologies. These proposals range from incentives (e.g. push incentives and IPRs) to calls for standardization (e.g. ISO/IEC/IEEE) and regulation (e.g. technology governance). Such academic contributions and proposals can be better judged by examining them in conjunction with empirical data to facilitate evidence-based policy decisions [Aboy 2021 IIC]. More info
Digital Innovation & Healthcare
This area of research explores the key tenets of digital innovation in healthcare with a focus on the digital health, biotech, medical device and pharmaceutical industries. This research seeks to understand the drivers of innovation, R&D intensity and incentives, technology and IP roadmapping, and the determinants of how emergent medical technologies are funded, developed, regulated, adopted and used in practice. It investigates the intersection of digital innovation, digital medicine, competitive strategy, regulation, IP incentives, policy, and economics of healthcare. My research is currently focused on investigating the transformation of medical technology for healthcare delivery, and the associated policy, strategic and managerial questions raised by the growth of cloud-based information systems, medical AI/ML, advanced big data algorithms, blockchain, decentralisation, decentralised multi-party homomorphic encryption systems, and biotech innovation for personalised medicine. I'm particularly interested in addressing ‘grand challenge’ topics examining both digital entrepreneurship and incentives to promote innovation in 1) orphan drugs to address rare diseases, 2) novel antibiotics to address antimicrobial resistance, 3) molecular diagnostics for early detection of cancer, 4) drug repurposing to find new indications for known compounds, 5) multi-party homomorphic encryption information systems to facilitate enhanced cybersecurity and data protection in collaborative healthcare settings, and 6) the impact of quantum technologies.
Law & Regulatory Science Research Summary: Patent, IP, Privacy/Data Protection & Medical Device/Pharma Law
This area of research is focused on intellectual property, information law, and medical device/pharmaceutical law. Currently, I am actively conducting research on fundamental patent law questions involving subject matter eligibility of information age inventions (e.g., biomarkers, diagnostics, algorithms, AI) and the impact of recent US Supreme Court case law including Myriad v AMP, Mayo v Prometheus and Alice Corp v CLS Bank. A parallel area of research is focused on medical device law and regulation, including research on: FDA Breakthrough Devices Program, FDA De Novo Program, Medical Device IP & Pre-Market Pathways, cGMP, cGMP, GxP, ISMS, PIMS, and privacy/data protection (EU GDPR, US HIPAA, APEC CBPR).
This research has resulted in several Feature Patent Articles published in Nature Biotechnology, as well as legal papers on medical device regulation (Journal of Law & Biosciences) and privacy/data protection (European Pharmaceutical Law Review). For additional details see the publication section.
Data Science Research Summary: Data Science, AI & Applications
This area of research is focused on the development and application of computational data science & artificial intelligence (AI & NLP) to solve data-intensive problems of public and private interest. The research projects in this area typically lie at the interface between data science, law, and management sciences. The overall objective is to apply computational data science in order to conduct evidence-based (empirical) studies to guide public policy and/or private strategy. Sample areas of research include: Patent Landscaping, Empirical Legal Research (Impact Empirical Analysis of IP Decisions), Computational Data Science & IP, Legal/IP Informatics & Computational Analytics, Automated Patent Search Algorithm Development, Machine Learning & IP (e.g., Automatic Analysis of Patent Claims to Determine Similarity, Scope, and/or Validity), Empirical Patent Valuation, Empirical Licensing & Technology Transfer, Intellectual Capital & IP Strategy, and Computational Law. For additional details visit the LML.
This research has resulted in the design and development of the search algorithms and associated patent landscaping methodologies that enabled several evidence-based IP studies published in Nature Biotechnology For additional details see publication section.
Engineering Research Summary: DSP, BSP & Applications
This research area is focused on solving clinically relevant medical technology problems in which the extracted physiological information and biomarkers can help physicians or smart medical devices make better critical decisions and improve patient outcome. I’m particularly interested in problems involving the application of advanced statistical signal processing & data science techniques to develop novel digital medicine systems to analyze and extract information from physiologic signals that can help improve patient outcomes (e.g., improved diagnostics, clinical trials, precision medicine, and personalized medicine). Additionally, I’m interested in the development of innovative medical devices and clinical trial solutions that have the potential to improve patient treatment and quality of life while reducing the overall cost of healthcare.
This research has resulted in 50+ scholarly articles (including 13 papers published in IEEE Transactions in Biomedical Engineering and other leading biomedical engineering peer-reviewed journals), 20+ medical device inventions that received patent protection before the USPTO, and technology transfer of the created IP. As part of technology transfer efforts to commercialize part of these research outputs, I co-founded APDM Inc, a medical technology company focused on precision motion analytics for biomarker discovery (clinical research), clinical trial optimization (precision CNS, neurology, and mobility-based safety & efficacy endpoints), and precision/personalized medicine for CNS, neurodegenerative diseases, and movement disorders. For additional details visit APDM Wearable Technologies
This project, in collaboration with the RQT Center at Stanford University, aims to conduct a comprehensive analysis of the innovation landscape in the emerging field of quantum technologies (QT). Anticipating the potential implications of quantum technologies in real-world products, commentators have started proposing governance and regulatory strategies, raising concerns, and suggesting policy recommendations focused on quantum technologies. These proposals range from incentives (e.g. push incentives and IPRs) to calls for standardization (e.g. ISO/IEC/IEEE) and regulation (e.g. technology governance). Such academic contributions and proposals can be better judged by examining them in conjunction with empirical data to facilitate evidence-based policy decisions [Aboy 2021 IIC]. More info
Digital Innovation & Healthcare
This area of research explores the key tenets of digital innovation in healthcare with a focus on the digital health, biotech, medical device and pharmaceutical industries. This research seeks to understand the drivers of innovation, R&D intensity and incentives, technology and IP roadmapping, and the determinants of how emergent medical technologies are funded, developed, regulated, adopted and used in practice. It investigates the intersection of digital innovation, digital medicine, competitive strategy, regulation, IP incentives, policy, and economics of healthcare. My research is currently focused on investigating the transformation of medical technology for healthcare delivery, and the associated policy, strategic and managerial questions raised by the growth of cloud-based information systems, medical AI/ML, advanced big data algorithms, blockchain, decentralisation, decentralised multi-party homomorphic encryption systems, and biotech innovation for personalised medicine. I'm particularly interested in addressing ‘grand challenge’ topics examining both digital entrepreneurship and incentives to promote innovation in 1) orphan drugs to address rare diseases, 2) novel antibiotics to address antimicrobial resistance, 3) molecular diagnostics for early detection of cancer, 4) drug repurposing to find new indications for known compounds, 5) multi-party homomorphic encryption information systems to facilitate enhanced cybersecurity and data protection in collaborative healthcare settings, and 6) the impact of quantum technologies.
Law & Regulatory Science Research Summary: Patent, IP, Privacy/Data Protection & Medical Device/Pharma Law
This area of research is focused on intellectual property, information law, and medical device/pharmaceutical law. Currently, I am actively conducting research on fundamental patent law questions involving subject matter eligibility of information age inventions (e.g., biomarkers, diagnostics, algorithms, AI) and the impact of recent US Supreme Court case law including Myriad v AMP, Mayo v Prometheus and Alice Corp v CLS Bank. A parallel area of research is focused on medical device law and regulation, including research on: FDA Breakthrough Devices Program, FDA De Novo Program, Medical Device IP & Pre-Market Pathways, cGMP, cGMP, GxP, ISMS, PIMS, and privacy/data protection (EU GDPR, US HIPAA, APEC CBPR).
This research has resulted in several Feature Patent Articles published in Nature Biotechnology, as well as legal papers on medical device regulation (Journal of Law & Biosciences) and privacy/data protection (European Pharmaceutical Law Review). For additional details see the publication section.
Data Science Research Summary: Data Science, AI & Applications
This area of research is focused on the development and application of computational data science & artificial intelligence (AI & NLP) to solve data-intensive problems of public and private interest. The research projects in this area typically lie at the interface between data science, law, and management sciences. The overall objective is to apply computational data science in order to conduct evidence-based (empirical) studies to guide public policy and/or private strategy. Sample areas of research include: Patent Landscaping, Empirical Legal Research (Impact Empirical Analysis of IP Decisions), Computational Data Science & IP, Legal/IP Informatics & Computational Analytics, Automated Patent Search Algorithm Development, Machine Learning & IP (e.g., Automatic Analysis of Patent Claims to Determine Similarity, Scope, and/or Validity), Empirical Patent Valuation, Empirical Licensing & Technology Transfer, Intellectual Capital & IP Strategy, and Computational Law. For additional details visit the LML.
This research has resulted in the design and development of the search algorithms and associated patent landscaping methodologies that enabled several evidence-based IP studies published in Nature Biotechnology For additional details see publication section.
Engineering Research Summary: DSP, BSP & Applications
This research area is focused on solving clinically relevant medical technology problems in which the extracted physiological information and biomarkers can help physicians or smart medical devices make better critical decisions and improve patient outcome. I’m particularly interested in problems involving the application of advanced statistical signal processing & data science techniques to develop novel digital medicine systems to analyze and extract information from physiologic signals that can help improve patient outcomes (e.g., improved diagnostics, clinical trials, precision medicine, and personalized medicine). Additionally, I’m interested in the development of innovative medical devices and clinical trial solutions that have the potential to improve patient treatment and quality of life while reducing the overall cost of healthcare.
This research has resulted in 50+ scholarly articles (including 13 papers published in IEEE Transactions in Biomedical Engineering and other leading biomedical engineering peer-reviewed journals), 20+ medical device inventions that received patent protection before the USPTO, and technology transfer of the created IP. As part of technology transfer efforts to commercialize part of these research outputs, I co-founded APDM Inc, a medical technology company focused on precision motion analytics for biomarker discovery (clinical research), clinical trial optimization (precision CNS, neurology, and mobility-based safety & efficacy endpoints), and precision/personalized medicine for CNS, neurodegenerative diseases, and movement disorders. For additional details visit APDM Wearable Technologies
"Innovation has nothing to do with with how many R&D dollars you have." Steve Jobs
LML, University of Cambridge
RQT, Stanford University
PFC, Harvard University
CeBIL, University of Copenhagen
Publications List - Google Scholar
Publications - Research Gate
Publications - ORCID
Publications - Mendeley
Publications - SCOPUS
Patented Inventions