- Agentic AI Systems: I’m advancing multi-agent AI architectures designed for healthcare and biotech applications. These systems focus on distributed decision-making and swarm intelligence for tasks like hospital resource allocation and collaborative patient data analysis, driving improvements in care and operational efficiency.
- Generative AI: Building and deploying Generative AI models for various domains, including personalized healthcare treatments and UI/UX design automation. My work spans developing novel AI-powered tools to enhance customer interactions, diagnostic models, and visual experiences.
- AI-Driven Healthcare Innovation: I leverage AI for vocal biomarkers (such as DIYA, a COVID-19 vocal biomarker platform) to revolutionize digital health. My work aims to improve early diagnostics and preventive care, making healthcare more accessible and personalized.
- Blockchain for Healthcare: Developing secure, blockchain-based healthcare platforms that provide seamless health data management and interoperability while empowering patients to control their medical information.
- Quantum-Enhanced AI for Drug Discovery: Using quantum computing for molecular simulations to accelerate drug discovery, combining quantum and classical AI models to optimize drug candidate selection.
- Federated Learning in Healthcare: Investigating decentralized training models for medical AI systems to protect patient privacy while benefiting from large-scale data training.
- Multi-Modal AI in Medicine: Integrating medical data types (text, images, sensor readings) to create holistic, multi-modal AI models that provide comprehensive patient insights and improve diagnostic accuracy.
- Spearheading the development of AI-driven diagnostic tools that outperform traditional diagnostic methods by detecting diseases earlier and with higher accuracy.
- Building a blockchain-powered health data management system, enhancing data security and patient privacy.
- Leading AI-driven drug discovery initiatives to develop next-gen treatments, including mRNA vaccine candidates. My team utilizes AI for biotech process optimization, significantly advancing medical research during the COVID-19 pandemic.
- Created an AI-driven multilingual platform breaking communication barriers for over 1 billion users, leveraging advanced NLP and machine translation technologies. This platform is a lifeline for 500 million feature phone users, bridging the global digital divide.
- Generative AI: Developing cutting-edge generative models to automate design, content creation, and support systems.
- Multi-Agent AI Systems: Crafting AI agents that communicate and collaborate autonomously for complex problem-solving in healthcare and biotech.
- AI in Healthcare: Using AI for vocal biomarker analysis, personalized treatment platforms, and advanced diagnostics.
- Blockchain Integration: Building decentralized platforms for secure healthcare data management, driving clinical trials and patient empowerment.
- Agentic AI for Clinical Trials: I’m exploring how agentic AI systems can optimize patient enrollment, real-time monitoring, and data management in clinical trials.
- Generative AI in Healthcare: Collaborating with startups to implement Generative AI tools for improving patient care and enhancing user experiences.
- Blockchain for Healthcare Innovation: Partnering on projects that leverage blockchain for healthcare data security and operational transparency.
- Email: [email protected]
- LinkedIn: Vikram Jha
- GitHub: @invincible-jha
- Developed an AI-powered platform using vocal biomarkers for COVID-19 screening, tested with over 70,000 samples, achieving 85% accuracy. Currently undergoing multi-centric clinical trials across the US and other regions.
- Developed 200 novel molecular structures for COVID-19, leading to the discovery of 10 promising drug candidates now in pre-clinical trials.
- Designed a secure, blockchain-based data sharing system for patient-controlled health records. This platform enhances data interoperability and privacy, enabling seamless collaboration across healthcare providers and researchers.
- Federated AI Learning for Healthcare: Promoting privacy-preserving AI that respects patient data rights while providing high-quality medical insights.
- AI Governance and Policy: Contributing to frameworks ensuring Responsible AI deployment in healthcare, addressing ethical concerns around bias, transparency, and fairness.
- Quantum AI for Biotech: Exploring quantum computing for optimizing drug discovery pipelines.
- AI for Disease Progression Modeling: Using predictive analytics to model the course of diseases and customize treatment strategies.
- Multimodal AI: Merging text, images, and sensor data for comprehensive healthcare insights.
- Generative World Models: Developing physics-informed AI for biological simulations and complex medical problem-solving.
- He/Him
- I led a team in the DARPA Humanoid Robotics Challenge, pushing the boundaries of AI and robotics to solve real-world challenges.
Overview: DIYA is an AI-powered vocal biomarker screening tool designed for rapid, non-invasive COVID-19 detection. The platform leverages subtle vocal changes detectable in an individual’s voice, analyzed by AI algorithms to identify potential COVID-19 infections, even in asymptomatic patients.
- AI Technology: The tool is built on advanced machine learning and signal processing algorithms that detect unique vocal biomarkers. These biomarkers are indicative of respiratory system changes, which could signify infection.
- Dataset & Accuracy: The platform has been tested on over 70,000 samples, achieving an accuracy rate of 85% in detecting COVID-19. Ongoing clinical trials in the US and other countries are further validating its performance in real-world settings.
- Scalability: The DIYA platform is designed for scalable deployment, allowing it to be used across various devices, from smartphones to hospital-grade equipment. Its minimal hardware requirements make it ideal for rapid deployment in low-resource settings.
- Impact: DIYA’s rapid detection capabilities have the potential to significantly improve early intervention during pandemics, especially in low-resource areas or where PCR testing is limited. This tool could play a crucial role in flattening the curve during viral outbreaks.
Next Steps:
- Expanding the platform’s utility beyond COVID-19 by adapting the vocal biomarker algorithms to detect other respiratory conditions like influenza or pneumonia.
- Integrating DIYA with telemedicine platforms for seamless remote monitoring of patients.
Overview: In response to the COVID-19 pandemic, I led a team to develop an AI-powered platform to accelerate the discovery of novel antiviral drug candidates. Using deep learning models trained on extensive molecular datasets, the platform identified and optimized potential drug candidates.
- AI Technology: We utilized generative adversarial networks (GANs) and reinforcement learning to generate 200 novel molecular structures that were tested for their ability to inhibit SARS-CoV-2.
- Drug Candidate Identification: Of the 200 structures, 10 promising drug candidates were selected based on their structural similarities to known SARS-CoV-2 inhibitors. These candidates showed strong potential in pre-clinical trials.
- Collaboration with NIV: Out of the 10 candidates, 3 molecules were advanced into pre-clinical trials in collaboration with the National Institute of Virology (NIV). These trials focus on evaluating their efficacy and safety profiles.
Impact:
- This AI-driven approach reduced the time required for early-stage drug discovery from years to just months, showing the transformative power of AI in accelerating pharmaceutical research.
- The platform can be repurposed for other infectious diseases, such as Zika or Ebola, making it a versatile tool for future drug discovery efforts.
Next Steps:
- Expanding the platform to include quantum computing simulations for even more precise molecular modeling.
- Working on creating an open-source version of the platform for use by biotech startups and research institutions.
Overview: This project focused on developing a blockchain-based healthcare platform designed to enhance the security, privacy, and interoperability of health data across various healthcare providers, researchers, and patients.
- Blockchain Technology: Utilizing Hyperledger Fabric, this platform ensures tamper-proof storage and auditable sharing of health records. Patients can control who accesses their data via smart contracts, ensuring compliance with GDPR and HIPAA regulations.
- Interoperability: The system allows seamless data exchange between healthcare providers and researchers, enabling better patient care coordination. It addresses the fragmentation of medical records across different institutions, which often leads to misdiagnoses or delayed treatment.
- Patient Empowerment: Patients can directly control their health data, granting access only when necessary. This not only enhances data privacy but also fosters a patient-centric approach to healthcare, giving individuals more autonomy over their medical information.
Impact:
- This platform greatly reduces the risk of data breaches in healthcare, which is a critical issue given the sensitive nature of medical records.
- It enables faster, more accurate treatment decisions by providing healthcare professionals with complete and up-to-date medical histories.
- It supports decentralized clinical trials, allowing for better patient recruitment and data transparency during studies.
Next Steps:
- Integration with AI analytics tools to provide predictive insights into patient outcomes based on aggregated health data.
- Expanding use cases to include genomic data sharing, enabling breakthroughs in personalized medicine.
Overview: At Pucho Inc., I led the development of a multilingual AI assistant designed to break communication barriers for over 1 billion users. This platform leverages natural language processing (NLP) and machine translation technologies to offer seamless communication across diverse language groups.
- AI Technology: The platform utilizes transformer-based models for real-time language translation and NLP tasks, supporting over 22 Indian languages and multiple dialects. It is also optimized for both smartphones and feature phones, ensuring wide accessibility.
- Use Cases: From navigating government services to accessing educational content, this assistant has been instrumental in democratizing information access for communities with limited English proficiency or internet access.
Impact:
- This project has empowered users in rural and underrepresented communities by giving them access to crucial services in their native languages. The platform has bridged the digital divide for over 500 million feature phone users.
- By facilitating communication in multiple languages, the AI assistant promotes cross-cultural understanding and inclusivity, aligning with global digital literacy initiatives.
Next Steps:
- Expanding the assistant’s capabilities to include voice interaction using speech-to-text and text-to-speech features, increasing accessibility for those with literacy challenges.
- Integrating the platform with public sector services to enhance the delivery of government services in remote regions.
Overview: In this project, I spearheaded the development of a personalized treatment platform that leverages AI to tailor medical treatments based on an individual’s genetic makeup, medical history, and lifestyle data.
- AI Technology: Using deep learning models and genomic data analysis, this platform generates customized treatment plans by cross-referencing patient-specific data with global clinical datasets and research studies.
- Blockchain for Data Security: The platform integrates with blockchain technology to ensure that patients' sensitive data remains secure and private while still enabling healthcare providers to access real-time updates on treatment effectiveness.
Impact:
- The platform significantly improves treatment outcomes by providing highly individualized care recommendations, resulting in fewer side effects and higher patient satisfaction.
- It empowers healthcare providers to make data-driven decisions, improving both preventive care and chronic disease management.
Next Steps:
- Integrating the platform with wearable devices to provide continuous real-time monitoring and update treatment recommendations dynamically.
- Collaborating with pharmaceutical companies to optimize AI-driven drug adherence programs for patients using long-term medication therapies.
Overview: I developed a multilingual voice AI system to streamline form filling for insurance services. This system leverages natural language understanding (NLU) to process and understand user input in multiple Indian languages, guiding users through complex insurance forms.
- AI Technology: The platform uses a multilingual NLP engine capable of understanding over 10 Indian languages. It employs conversational AI to ask clarifying questions and guide users through the form, minimizing the need for manual inputs.
- Efficiency Gains: This voice-powered system has reduced form-filling time by 60% and improved data accuracy by 40%, revolutionizing the insurance customer experience.
Impact:
- The system has made insurance services more accessible, especially for users in rural areas who might struggle with literacy or technology. It has led to a significant increase in customer satisfaction and a decrease in operational costs.
Next Steps:
- Expanding the voice AI to handle more complex insurance processes such as claims submissions and policy renewals.
- Enhancing the AI to provide real-time feedback on errors during form filling, further improving data quality and customer experience.