Ankit Chauhan

CEO

Gujarat, India12 yrs 8 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

CTO leading AI-driven innovations at InfyU Labs
Winner of multiple prestigious startup competitions
Expert in machine learning and computer vision technologies

Stackforce AI infers this person is a leader in AI-driven technology solutions for the food processing and research sectors.

Contact

Skills

Core Skills

Machine LearningAiAlgorithmsData Structures

Other Skills

Amazon Web Services (AWS)CC++Cloud InfrastructureComplexity TheoryComputer ScienceComputer VisionData AnalysisData EngineeringData ScienceEmbedded SystemsGame TheoryGitIoTLaTeX

About

CTO at InfyU Labs | Leading AI-driven innovations | Building real-time, scalable systems for quality assessment using computer vision and embedded tech

Experience

Infyu labs

Co-Founder | CTO

Dec 2018 – Present · 7 yrs 3 mos · Gandhinagar, Gujarat, India

InfyU Labs is developing IoT-based smart technical innovations at each level of the supply chain of food processing industries to help the quality manager take quick purchasing decisions on the consignment.
The technology, based on UV-vis-NIR spectroscopy powered by AI, is capable of performing non-invasive rapid testing of fruits for their chemical composition.
Selected for Google for Startups SDG program 2021.
National winner of Youth Co:Lab 2020 co-organized by Niti Aayog and UNDP India. Representing India at Youth Co: Lab International platform.
Winner of Boeing BUILD competition 2019, organized by Boeing India
Selected in top 10 startups in GreenTech vertical in Fifty Founder's Battle by Techchill.
Supported by CIIE, IIM Ahmedabad, IIT Gandhinagar, IIT Madras, ThinkAg, Murugappa Chettiar Research Centre.

Machine LearningAIEmbedded SystemsIoTComputer VisionData Analysis+1

Groovytek

Software Developer

Nov 2018 – Jan 2020 · 1 yr 2 mos · India

GroovyTek provides trainers for people who do not have much knowledge of the current technology. At GroovyTek, I am responsible for developing the new feature for the website.

Hasso plattner institute

Research Assistant

Nov 2015 – Oct 2018 · 2 yrs 11 mos · Potsdam Area, Germany

Analysis of Local Search and Greedy Algorithms for the NP-Hard Optimization Problems.
Analyzed the local search and the greedy algorithms for the NP-Hard optimization problems like Vertex Cover, Independent Set, etc. on Scale-Free Networks.
Experimentally compared the Greedy algorithm and a variant of Local search known as (1+1) EA on artiﬁcially generated scale-free networks and real-world instances from the Stanford Network Analysis Project SNAP .
Experiments showed that (1+1) EA is faster than the Greedy algorithm. Furthermore, the solution produced by the (1+1) EA is 1.5 times better than the Greedy algorithm.
Theoretically proved that the run-time of (1+1) EA algorithm is polynomial in the size of the graph better than the solution of a Greedy algorithm.
2. Game-Theoretic Modelling of the Networks.
The real-world networks e.g Facebook have structural properties such as : small-world phenomenon and heavy-tailed degree distribution.
Introduced a Game-Theoretic model to generate networks with above properties.
3.Real-World Networks and NP-Hard Optimization Problems.
We made an attempt to understand: Why can we solve NP-hard optimization problems faster than the theoretically known lower-bounds on real-world networks.
We considered two properties of the real-world networks, the heavy-tailed degree distribution and the community structure of the networks.
We proved that many NP-Hard problems such as Dominating set, Independent set etc., can be solved faster and eﬃciently on the networks with these properties.
4. IP Based Geolocation.
We are using delay based method, i.e., the measurement between client and servers that could help to locate users original location.
The primary goal is to identify that whether the user is accessing the data from a trusted location or not.