Past Performance & Project Highlights
AetherVision LLC, as a verified SDVOSB, brings a wealth of relevant experience to address complex national security challenges. The following highlights, drawn primarily from founder Dr. Sunil Kukreja's leadership roles in demanding environments like DARPA, major defense contractors (e.g., Raytheon), NASA, and international research institutions (NUS), directly demonstrate the technical expertise, program management rigor, and innovative capability that AetherVision offers its clients today. (Note: Experience attributed to specific previous employers reflects work performed during Dr. Kukreja's tenure there.)
DARPA FENCE Program: Neuromorphic Vision for Enhanced Situational Awareness
Client/Agency: DARPA Microsystems Technology Office (MTO)
Role (Dr. Kukreja at Raytheon): Concept Originator ('NOVEL'), Pitch Lead, Internal Approval Champion, Program Execution Lead
Program Value: $5.5M
Est. Period of Performance: ~2020 - ~2023
Key Technologies: Neuromorphic (Event-Based) Vision, Spiking Neural Networks (SNNs), Custom ROIC/FPA Design, Memristor Synapses (Explored), AI/ML Edge Processing, Low-SWaP Embedded Systems.
Challenge:
Significantly improve threat detection sensitivity, reduce false alarms, and lower operator cognitive burden in dynamic, cluttered battlefield environments, particularly under challenging low-light or high-speed conditions where traditional imaging sensors falter.
Leadership Contribution & Solution (Dr. Kukreja at Raytheon):
Dr. Kukreja conceptualized the core neuromorphic technology and successfully pitched the 'NOVEL' concept directly to DARPA MTO leadership. His technical advocacy and proactive engagement during internal Raytheon reviews were instrumental in the company's decision to pursue and influence the subsequent FENCE BAA. As program execution lead, he directed the $5.5M effort, managing a multidisciplinary team (21 FTEs) to engineer, fabricate, and test the novel bio-inspired neuromorphic camera system, driving the project from concept through successful TRL-6 prototype demonstration.
Impact & Results:
- Successfully delivered a TRL-6 neuromorphic camera prototype, exceeding all program technical milestones by an average margin of 20%.
- Demonstrated >60% improvement in threat detection sensitivity compared to state-of-the-art CMOS cameras in relevant operational scenarios.
- Achieved a 35% reduction in false alarm rates, enhancing system reliability and operational efficiency.
- Realized a 25% decrease in sensor power consumption compared to conventional high-speed cameras.
- Quantified a 30% reduction in human operator cognitive load during target detection tasks in simulated environments.
- Demonstrated Capability: Proven ability to lead high-risk DARPA programs, innovate novel sensor technology, and mature concepts to TRL-6 readiness.
Leadership: Secure AI/ML, Cloud & Edge Platforms (DoD Context)
Context (Dr. Kukreja at Raytheon): Executive Leadership (e.g., Associate Director, AI/ML/Robotics)
Program Scale Example: Managed $60M+ annual R&D portfolios; Led large teams (up to 87 personnel)
Est. Period of Performance: ~2017 - ~2020
Key Technologies: AI/ML (Deep Learning, CV, NLP), Secure Cloud (AWS GovCloud, Azure Gov), Edge Computing (FPGAs, SoCs), Robotics, Multi-INT Sensor Fusion, Secure Systems Architecture, Cybersecurity Compliance (NIST/RMF), Agile Methodologies (Scrum/Kanban), MLOps, Infrastructure as Code (Terraform).
Challenge:
Rapidly develop, securely deploy, and efficiently manage high-performance AI/ML capabilities for real-time analysis of multi-sensor data from unmanned systems (UxVs) across domains and for automated inspection of critical aerospace/defense assets, while adhering to stringent DoD security protocols (ITAR, NIST/RMF) and improving operational efficiency/cost-effectiveness.
Leadership Contribution & Solution Approach (Dr. Kukreja at Raytheon):
Directed large, cross-functional teams (up to 87 personnel including subordinate managers) in architecting, developing, and deploying secure, cloud-based (AWS GovCloud, Azure Gov) and edge-based AI/ML platforms. Systems supported real-time multi-INT ISR data analysis and automated robotic visual inspection. Championed and implemented robust cybersecurity (ITAR adherence, 100% DISA audit compliance achieved), Infrastructure as Code (IaC using Terraform, reducing provisioning time by 30%), and drove large-scale Agile transformations (Scrum/Kanban resulting in 35% cycle time reduction, 20% velocity increase). Led secure edge computing initiatives for DoD robotics, achieving 10% reduced development time, 20% faster computation, and 15% improved efficiency.
Impact & Results (Representative examples):
- Achieved significant measured improvements (e.g., 42%+) in mission success rates for autonomous systems via enhanced AI/ML capabilities.
- Realized substantial cost savings (est. $15-20M annually) through AI-powered automated inspection, reducing manual effort by up to 50%.
- Improved defect detection accuracy by up to 35% compared to manual methods in critical inspection tasks.
- Successfully delivered complex $60M+ DoD program portfolios 100% on-time and on-budget under leadership.
- Maintained 100% compliance with security audits and zero data breaches on developed platforms under leadership.
- Reduced infrastructure management costs by 20-30% through IaC and automation.
- Accelerated development cycles significantly (e.g., 35-50%) through Agile adoption.
- Demonstrated Capability: Proven ability to manage large-scale, secure AI/ML and edge computing programs for DoD, implement robust processes (Agile, IaC, Cybersecurity), and deliver tangible cost/performance benefits.
Leadership: NASA AI/ML for Flight Safety & Astronaut Health
Client/Agency: NASA
Role (Dr. Kukreja at NASA): Senior Principal Research Scientist / Team Lead
Program Scale Example: Led 8-person team, ~$7M-$10M project scopes
Est. Period of Performance: ~2004 - ~2014
Key Technologies: AI/ML (Predictive Modeling, HMM, SVM, KNN), Sensor Fusion (Accelerometers, Strain, Pressure, Physiological), Aerospace Engineering, Control Systems Synthesis, Structural Dynamics, Flight Test Data Analysis, Signal Processing (FFT, CNNs), Secure Data Governance, MBSE.
Challenge:
Enhance flight safety for high-performance aircraft (e.g., F/A-18) by accurately predicting flutter/LCO risks. Mitigate long-term health risks for astronauts during extended spaceflight by predicting physiological changes and developing countermeasures. Ensure security and integrity of highly sensitive human subject data.
Leadership Contribution & Solution Approach (Dr. Kukreja at NASA):
Led expert teams to develop advanced AI/ML models trained on extensive flight test data (F/A-18) and astronaut physiological data. Integrated cutting-edge sensor fusion techniques. Developed novel control synthesis for active flutter suppression. Architected HIPAA-compliant equivalent secure cloud platform (AWS) for analyzing real-time astronaut physiological data (using HMM, SVM, KNN algorithms), identifying biomarkers for changes (e.g., bone loss, muscle atrophy, cardiovascular health), and enabling personalized countermeasures. Established gold-standard data governance framework. Championed MBSE adoption.
Impact & Results:
- Achieved 15-20% improvement in F/A-18 control effectiveness for flutter suppression.
- Demonstrated 100% success rate in preventing flutter incidents during F/A-18 flight tests.
- Improved accuracy of predicting astronaut physiological changes by 25% compared to traditional methods.
- Enabled personalized countermeasures leading to measured improvements (e.g., 10% increased bone density retention, 15% reduced muscle atrophy).
- Achieved 100% compliance with NASA data security audits and zero data breaches for sensitive astronaut data.
- Increased divisional MBSE capability by 40%.
- Demonstrated Capability: Expertise in safety-critical AI/ML, complex sensor fusion, control systems, secure data governance for sensitive human data, and process improvement (MBSE).
National University of Singapore: DoD/IC Focused Neuromorphic & Cognitive Research
Context (Dr. Kukreja at NUS): Director, Neuromorphic Engineering & Robotics Center
Funding Example: Secured $10M+ competitive research funding (incl. ONR Global, ARL)
Est. Period of Performance: ~2015 - ~2017
Key Technologies: Neuromorphic Engineering (E-Skin, Sensors), Robotics (Bimanual Manipulation), Haptics, AI/ML (Pattern Recognition, Predictive Modeling), EEG Analysis, Cognitive Science, FPGA/SoC Implementation, ROS.
Challenge:
Enhance robotic capabilities for dangerous tasks (e.g., Explosive Ordnance Disposal - EOD) requiring fine dexterity and tactile feedback. Understand and mitigate cognitive overload in high-stress human operator scenarios (e.g., pilots, analysts) to improve performance and reduce errors.
Leadership Contribution & Solution Approach (Dr. Kukreja at NUS):
Directed a world-renowned research center, securing major funding from DoD entities (ONR, ARL) and others. Led pioneering R&D in: 1) Neuromorphic E-Skin: Developed high-density (100x improvement), low-power (90% reduction) flexible electronic skin with neuromorphic processing for rapid, accurate tactile perception, specifically targeting EOD robotic applications for safer object identification and manipulation. 2) Advanced Robotics: Directed research improving bimanual robotic manipulation (e.g., using UR10e platforms), achieving 20% faster and 15% more precise operations through novel control algorithms and tactile feedback integration. 3) Cognitive Load Analysis: Spearheaded research using EEG and other physiological data to identify neural biomarkers of cognitive load and stress. Developed AI/ML models predicting cognitive states with high accuracy (25% improvement), enabling potential real-time monitoring and interventions to reduce operator error rates (15% reduction demonstrated in simulated tasks).
Impact & Results:
- Developed neuromorphic e-skin technology demonstrating 100x density, 90% power reduction, and 70% improved tactile accuracy compared to previous benchmarks, directly applicable to enhancing EOD robot safety and effectiveness.
- Advanced bimanual robotic manipulation achieving 20% greater speed and 15% higher precision.
- Pioneered EEG-based cognitive load prediction models with 25% greater accuracy, leading to techniques reducing operator errors by 15% in high-stress simulations.
- Secured significant DoD-relevant funding (ONR, ARL) based on research merit and potential impact.
- Established NUS center as a leader in neuromorphic and cognitive engineering relevant to defense and intelligence applications.
- Demonstrated Capability: Proven ability to lead cutting-edge R&D relevant to DoD/IC needs in advanced robotics, neuromorphic sensing, and human cognitive performance enhancement.
This demonstrated history of innovation, leadership in high-consequence environments (DARPA, NASA, DoD R&D), and successful delivery of advanced technology solutions underscores AetherVision LLC's readiness to tackle today's most pressing national security challenges.