プロフィール詳細

プロジェクトを作成

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Dr. Aditya S.に依頼

United Kingdom

Computational Scientist at Imperial College London | AI, biotech, catalysis, physics | 10+ years research experience

プロフィール概要

I'm a computational scientist and machine learning engineer with 10+ years of experience developing predictive models for molecular systems. My work spans from first-principles simulations to state-of-the-art generative AI for protein dynamics and drug discovery. Currently, as a Postdoctoral Researcher at EPFL, I develop generative models for all-atom protein dynamics. My recent work, LD-FPG, is a novel framework that uses graph neural networks and latent diffusion to generate and simulate the full conformational ensembles of complex drug targets like GPCRs. This research is complemented by my previous postdoctoral work at Imperial College London, where I built simulation pipelines using the oxDNA model to explain the kinetics and thermodynamics of DNA nanotechnology systems. As a consultant through my company, Sengar Consulting BV, I apply my expertise to solve diverse real-world challenges. Over the past 4 years, I have delivered over 30 projects for clients in biotech, fintech, and cybersecurity across the USA, Europe, and India. My expertise enables me to effectively address a wide range of projects, including: CADD/SBDD: Designing and optimizing docking workflows for a US-based biotech startup, reducing runtimes by 35% while increasing hit enrichment, and contributing to molecular property prediction pipelines. Fintech: Developing an explainable AI (XAI) credit risk model with fairness constraints, cutting disparate impact by 18%. Cybersecurity: Engineering an API anomaly detection ensemble (Isolation Forest + Autoencoder) that achieved a ROC-AUC of 0.94. Forecasting: Deploying LSTM models for demand and price prediction, achieving a Mean Absolute Percentage Error (MAPE) below 5% and saving one client an estimated ~12 analyst hours per week. Strategic Consulting: Delivering a 6-month strategic roadmap for a client on integrating hybrid quantum computing (VQE) and AI workflows for oncology drug discovery.

専門分野

• ★ Biotechnology & Bioengineering
• ★ Machine Learning
• ☆ Chemistry
• ☆ Statistics
• ☆ Microfluidics
• ☆ Mathematical Models
• ☆ Marketing Analytics

サービス

• Writing
• Research
• Consulting
• Data & AI
• Product Development

Writing Technical Writing

Research Market Research, Feasibility Study, Fact Checking, Systematic Literature Review

Consulting Scientific and Technical Consulting

Data & AI Statistical Analysis, Algorithm Design-Non ML, Data Visualization

Product Development Formulation

職務経験

Postdoctoral Research Associate

Imperial College London

3月 2020 - 現在

Postdoctoral Research Associate

Eindhoven University of Technology

7月 2019 - 3月 2020

PhD

Delft University of Technology

7月 2015 - 4月 2019

Junior Researcher

Tsinghua University

5月 2013 - 7月 2013

学歴

PhD in Chemical Engineering and Chemistry

Delft University of Technology

7月 2015 - 4月 2019

Bachelor of Technology

Indian Institute of Technology, Delhi

7月 2011 - 5月 2015

認定資格

• 認定資格の詳細は未入力です。

出版物

JOURNAL ARTICLE

(2020). Deactivation Kinetics of the Catalytic Alkylation Reaction . ACS Catalysis.

(2019). Towards a particle based approach for multiscale modeling of heterogeneous catalytic reactors . Chemical Engineering Science.

(2018). Deactivation Kinetics of Solid Acid Catalyst with Laterally Interacting Protons . ACS Catalysis.

(2018). The challenge of catalyst prediction . Faraday Discussions.

(2017). Particle-based modeling of heterogeneous chemical kinetics including mass transfer . Physical Review E.

(2016). Residence time estimates for asymmetric simple exclusion dynamics on strips . Physica A: Statistical Mechanics and its Applications.