MSc, Computer Science
Stockholm, SE
KTH Royal Institute of Technology
Aug 2019 | Aug 2021
MSc, Computer Science
Berlin, DE
Technical University of Berlin
Oct 2018 | Nov 2020
Experience
-
Huawei
Singapore, SG
Staff Machine Learning Engineer
Sep 2023 | Present
- Developed deep learning-based super-sampling SDK in C++ to augment the game’s frame rate: engineered
GPU/NPU-accelerated inference pipeline achieving real-time performance on desktop and mobile through
low-level optimization.
- Drove ML research-to-production initiatives: optimized model architectures through systematic
bottleneck analysis, established performance benchmarks, and mentored team on SDK delivery.
Senior Machine Learning Engineer
Jun 2022 | Sep 2023
- Led C++ SDK development for a metaverse product with multiple deep learning models,
achieving a 24x speedup to real-time performance and earning a Best Product Delivery Award.
- Developed the candidate generation and ranking models for video and music services, enhancing
the system capability and performance to real-time serving using Redis and Vector dBs.
-
Taiger
Singapore, SG
Senior Machine Learning Engineer
Nov 2021 | Apr 2022
- Research, implement, and optimize deep learning algorithms for information extraction/retrieval in
unstructured documents, while handling software engineering and model
deployment tasks.
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Sony AI
Tokyo, JP
Research Intern
Apr 2021 | Oct 2021
- Collaborated with Sony AI and Polyphony Digital to create a high-performance PS4
GranTurismo racing controller using a SOTA vision-based reinforcement learning algorithm,
achieving consistent performance across three simulators.
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Fraunhofer Heinrich Hertz Institute
Berlin, DE
Machine Learning Research Assistant
Aug 2020 | Oct 2021
- Created a deep learning-based 3D super-resolution pipeline in PyTorch, reconstructing 3D models from
2D images from 36 viewpoints, rendering multi-view images in 3D, and training a generative model with
PyTorch3D for diffused lighting and textures.
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Electronic Arts
Stockholm, SE
Research Intern
Jan 2020 | Aug 2020
- Developed a deep learning-based video super-resolution pipeline in PyTorch, upscaling game cinematics
from 1K to 4K quality for Battlefield, Mass Effect, and Anthem in collaboration with studios from Los
Angeles, Montreal, and Redwood Shores.
Research & Deployment
-
Monitoring Unrest From Space
- Contributed to MOUNTS, an operational monitoring system that processes European Space Agency Sentinel-1
satellite data to detect early signs of volcanic activity across 100+ active volcanoes worldwide.
Developed a machine learning-based despeckling pipeline to denoise satellite imagery, improving
detection reliability. The system is deployed and actively used for volcanic hazard monitoring.
Website |
Paper |
System
Technical Skills
- Languages: Python, C++, Scala
- Frameworks: PyTorch, Tensorflow, Spark
- Databases: MySQL, Hive
- Others: Git, Docker, Linux, LaTeX, OpenGL
Honors & Awards
-
DBS Hack2Hire
March 2022, Singapore
- Won DBS hackathon among 45 teams by developing a data visualization tool for customer spends
using machine learning and AWS services to analyze income, age, and background features.
-
Bosch BCX19
July 2019, Germany
- Semi-finalist in Bosch Hackathon19. Developed a solution for Cobi’s UI dashboard that performs
navigation assistance and predictive analysis for E-bikers in real-time.
-
2050 Tencent Conference
May 2019, China
- One of the 25 students selected across Europe for the conference organized by Alibaba in Hangzhou,
China. Proposed a start-up idea and gave a talk on using AI in healthcare.
-
Steam School
Dec 2017, India
- One of the 100 applicants selected to work on UN SDGs from applicants of 7 countries. Developed a
project named CURA, which was showcased during the UNESCO tech conference in Vizag, India.
Activities
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EEML 2020
- Attended the virtual EEML 2020 summer school on deep learning and reinforcement learning.
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Wikimania 2019
- Part of the volunteering team for the Wikimania 2019 conference held in Stockholm, Sweden.
References