Umang Gupta
B.Tech + M.Tech, Process Optimisation & Controls Engineering. AIR 1248 in IIT-JEE; TPA Certificate from TOTAL, France. Techfest Overall Coordinator — grew participation 30%, managed $15K+ in funds
Contributed to international research on non-carcinogenic, green PCB components — international publication, PhD invitation, and a 44% stipend hike. Reduced experimentation cycle time by 17%.
Manager → Executive Assistant to President, Commercial & Corporate Affairs. Increased jetty utilisation 18% ($1.8M incremental revenue); $400K profit improvement via production optimisation; chaired 300+ senior engagements
Led ERP implementation delivery and cross-functional coordination. Improved process efficiency 8%; reduced Oracle ERP SDLC delivery timelines by 22%
Unanimously elected Class Representative; Overall Coordinator, Student Activity Council. Highest pre-placement offer of the class
Founding member & GM — scaled to $5.6M+ in new sales ahead of a strategic exit into Joulon.
$254M+ deployed across 60+ M&A evaluations; 11 majority-control acquisitions, 4 growth investments. Scaled a 14-company, $200M+ ARR platform — 22% EBITDA expansion, 45% revenue CAGR. Org grew from 8 to 2,500+
AI roadmap fusing ultrasound and electromagnetic imaging into diagnostic-grade scans. ~4–8dB signal-to-noise improvement over single-modality baseline; detects cancer, calcification and fluid formation invisible to conventional ultrasound
Promotion. AI roadmap, strategy, fundraising, clinical, regulatory and commercialisation
UWB microwave imaging — reconstruction in milliseconds vs. minutes for classical methods, via a physics-informed deep learning approach
30+ product partnerships across India and the UK — ~$350K ARR, 80% YoY growth, 15% net margins. Team scaled to 55+
$2–10M co-investments in deeptech and science-led companies.
Tata-backed — board-level product, technical and commercial leadership for the Edge AI hearing-aid platform. guiding CNN/RNN model design for real-time speech enhancement on ultra-low-power MCUs, sub-10ms inference latency. Model compression strategy targeting 30–60% lower processing power and 18→24hr battery life
Appointed CEO; Now Chairman & Board Director as the business matured
My journey didn't begin with venture capital or startups. It began with understanding how complex systems behave — and how seemingly independent parts can work together to create extraordinary outcomes.
At IIT Bombay, I studied Process Optimization & Controls Engineering, where I learned to view the world as interconnected systems rather than isolated events.
Engineering taught me that behind every complex outcome lies a model waiting to be understood. This mindset of breaking problems into first principles has shaped every investment, company, and strategic decision I've made since.
While at IIT Bombay, I became fascinated by peer-to-peer (P2P) networks and distributed computing long before they became mainstream — no central authority, every node equal, trust established between peers rather than handed down by a hierarchy.
In early days, I mined a few BTCs using my assembled PC while experimenting with the protocol — but later sold them when Bitcoin first crossed into $ double digits. Financially, it wasn't my best decision, but it taught me one of my earliest investing lessons.
Far more important than the price was the protocol itself. The idea that a decentralized network could establish trust without a central authority fundamentally changed how I thought about incentives, coordination, and scale.
Today, the same principles power AI infrastructure, distributed computing, and modern venture ecosystems.
My Master's thesis focused on stochastic processes — the mathematics of uncertainty. Given enough historical data, very little is actually random; cause and effect can be recovered, and the probability of an outcome estimated.
One foundational idea, grown into many disciplines.
Still how I approach investing today: identify signal, quantify uncertainty, and make asymmetric bets.
Understanding systems wasn't enough. I wanted to understand how organisations scale.
Executive Assistant to the President (Commercial & Corporate Affairs) — redesigning port jetty logistics and turnaround operations at one of India's largest industrial sites.
ERP implementation delivery, cross-functional execution — enterprise transformation.
MBA, General Management. Unanimously elected Class Representative — highest pre-placement offer in the class.
This wasn't a career change. It was the next layer of the same operating system — learning how to build, lead, and scale organisations as effectively as the systems behind them.
First proof point: build a functioning commercial operation from zero, to a specification someone else would acquire.
Founding member, LCAM — industrial asset management and services, Dubai. Built commercial operations, finance, supply chain, and project delivery across multiple verticals, from zero.
Scaled to $11.6M revenue, $1.2M EBITDA. Acquired into a KKR-backed platform — Joulon. That acquisition became the entry point into the next chapter.
Not only evaluation. Structured the deals, then operated the businesses those deals created.
As VP, Investment, Finance, Operations & Corporate Development at Joulon, a KKR-backed industrial platform, I structured the deals, then ran what they created.
Capital markets fluency, proven at global scale. Then the operating parameters changed.
I started building in deeptech, where AI & technology could be used to make healthcare accessible, affordable, and safer.
Same rigor, applied at build stage. The evaluator became the operator being evaluated.
Co-founder / VP, AI Product & Engineering across two UK deeptech/HealthTech ventures in parallel, raised early-stage & growth capital to build AI-driven medical imaging technologies from R&D through fundraising and commercialisation, across product, engineering, IP licensing and investor relations.
Owned the AI product roadmap to combine ultrasound and radio wave signal data into MRI/CT-equivalent diagnostic-grade images — resulting in a ~4–8dB signal-to-noise improvement over single-modality baseline and enabling detection of cancer, calcification and fluid formation undetectable by conventional ultrasound.
Fusing ultrasound and electromagnetic signal data into diagnostic-grade images — ~4–8dB SNR improvement over single-modality baseline.
Conditional GAN with PatchGAN discriminator converting fused data into MRI/CT-equivalent diagnostic images — ~0.70–0.80 SSIM against paired MRI ground truth.
Transfer-learned ResNet/EfficientNet backbone distinguishing malignant tissue, calcification and fluid formations — ~82–86% sensitivity, AUC ~0.85 in early clinical validation.
Built 3 prototypes, raised non-dilutive funding and successfully licensed the technology to Neko Health, the preventive-healthcare company backed by Spotify founder Daniel Ek.
Owned the technical strategy for solving the inverse scattering problem in UWB microwave tissue imaging, prioritising reconstruction speed as a core product requirement and championing a physics-informed neural network approach to cut required training data.
Trained and validated deep learning models across local GPU workstations (prototyping) and AWS cloud infrastructure (distributed multi-GPU training) for both ventures.
Physics-informed deep learning delivering single-pass reconstruction in milliseconds, versus seconds-to-minutes for classical iterative methods — ~0.65–0.75 SSIM against phantom ground truth.
Embedding electromagnetic wave-propagation constraints into model training — reducing required training data by an estimated 30–40% versus purely data-driven baselines.
Distinguishing malignant/benign tissue from dielectric-contrast signatures — ~80% sensitivity, AUC ~0.82, signal-to-clutter improved ~8–12dB.
Co-founder, Chairman & Board Director (Part-time), 2022–Present. Built and scaled a pharmaceutical and medical-device partnerships platform with WHO-GMP/USFDA-certified manufacturers across India and the UK — from zero, entirely bootstrapped, personally owning partner economics, pricing tiers and channel strategy.
Scaled the portfolio to 30+ product partnerships across diabetes, pain management, gynaecology and nutraceuticals through strategic licensing, manufacturing and distribution agreements — owning partner segmentation, ICP definition, pricing architecture and exclusivity terms by category and geography.
Expanded into medical devices, sourcing and commercialising global partner technologies (Edge AI hearing aids, smart rings, smart cuffs, glucose monitoring, non-contact optical screening) for the Indian market. Grew the team to 55+, scaling revenue to ~$350K ARR at 80% YoY growth and 15% net margins.
Transitioned from founder-operator to Chairman & Board Director as the business scaled, with appointed CEOs now running day-to-day commercial execution while retaining strategic oversight of partnerships and capital allocation.
The same evaluator's eye, redirected: board seats, applied AI, and capital deployed as principal — not employee.
Providing board-level product, technical and commercial leadership for the company's Edge AI hearing-aid platform, spanning £2M seed+ financing preparation, scale-up, India commercialisation agreements, product roadmap and regulatory pathway.
Building a suite of AI-native products for the venture ecosystem, spanning founders, investors and evaluators, combining LLM-based product architecture with hands-on GTM ownership.
Deploying his own capital, alongside a network of family offices and strategic investors, into breakthrough science and deeptech.
Designed and built an end-to-end fundraising automation system: a scraper layer that sources and enriches investor data, a knowledge graph of investor mandates and holdings, and a ranker that scores investor-deal fit.
Built a proprietary reinforcement learning model delivering investor-grade pitch evaluation across 10 scored dimensions.
Built a self-improving diligence model that ingests unstructured data-room files and folders and generates:
Investor and operator. Dealmaker and founder. Multimillion-dollar programmes led, companies built from zero — not a choice between the two, both, concurrently.