Nikhil Yadala

Co-Founder

San Francisco, California, United States10 yrs experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in machine learning and its applications in healthcare.
Co-founder of a startup focused on biological aging.
Experience in high-frequency trading algorithms.

Stackforce AI infers this person is a Healthcare and Fintech specialist with a strong focus on machine learning and data science.

Contact

nikhil.yadala@gmail.com LinkedIn

Skills

Other Skills

AJAXCC++CSSDeep LearningFuzzy LogicHTML5JavaJavaScriptMachine LearningMathematical StatisticsMatlabMicrosoft OfficeMySQLPHP

About

Building tech for personalised health and biomarker discovery I worked as a Data & Applied Scientist at Microsoft Bing search. Prior to that, I was a research assistant at EPFL working at the intersection of distributed computing and genetics. My interests include Machine learning, parallel computing and their applications to Genetics, Cell Biology, Building scalable software systems. My long term goal is to help accelerate drug discovery and healthy longevity by tackling ageing.

Experience

10 yrs

Total Experience

2 yrs 6 mos

Average Tenure

4 yrs

Current Experience

Curedao alliance for the acceleration of clinical research

Software Engineer

May 2022 – Present · 4 yrs · San Francisco, California, United States

CureDAO that's working to establish collaborations and data sharing between the 350k data silos that already have enough data to produce far more statistically significant Outcome Labels.

Carnegie mellon university

Graduate Research Assistant

May 2022 – Dec 2022 · 7 mos · Pittsburgh, Pennsylvania, United States

Building Brain Aging models via single-cell RNA Seq for human neurons

Healome one

Co-Founder and CEO

Feb 2021 – Present · 5 yrs 3 mos · San Francisco, California, United States

AI copilot to measure and slowdown your biological aging

Fusion research capital

Partner & Quant

Aug 2020 – Apr 2021 · 8 mos · India

Building ML-based strategies for ultra-high-frequency trading with ~30 micros full trade latency in crypto and Indian equity and derivatives markets

Microsoft

Data & Applied Scientist

Jul 2018 – Jul 2021 · 3 yrs · Hyderabad, Telangana, India

Developed Retrieval and Ranking models, ML model compression and distillation for Bing Search in the Core Relevance team of AI&R. Built ranking models for petabytes of internet serving results on Bing across the countries with sub-millisecond latency via optimized LLMs

Epfl (école polytechnique fédérale de lausanne)

Research Assistant at Very Large Scale computing laboratory

May 2018 – Aug 2018 · 3 mos · Lausanne Area, Switzerland

Working on fastening all-pair protein matching algorithms.

Microsoft

Research Engineering Intern

May 2017 – Jul 2017 · 2 mos · India

Developed, implemented and analyzed methods for unsupervised classification of web-pages using deep neural networks. I had written the models using Microsoft's CNTK python.
Worked on developing and implementing custom loss functions for deep neural networks for hierarchical classification problems

American express

Research Intern

Nov 2016 – Dec 2016 · 1 mo · Bengaluru Area, India

I have worked on devising and implementing a distributed hybrid bagging and boosted version of Gradient boosting machine for Decision trees training and achieved on par accuracy with baseline models with many factors decrease in computation time.

Hanyang university

Research Assistant at Computational Vision and Fuzzy systems lab

May 2016 – May 2018 · 2 yrs · Ansan, Gyeonggi-do, Korea

Extended the Karnik-Mendel type reduction algorithm to multidimensional space, so the type reduction could retain correlation among the features to an extent better than the traditional type reduction where each dimension is type reduced independently. I have proved theorems guaranteeing the optimality and convergence of the proposed algorithm. Likewise devised a multidimensional EIASC ( Enhanced KM algorithm).
Worked on data preprocessing for improving the performance of neural nets by developing four methods of generation of Type-2 Fuzzy membership functions and proposed a new variant of KM algorithm for defuzzification and type reduction to use the resultant vectors as support vectors to transform the data into a new space.