Pushing AI frontiers, globally.
Leading with deep domain expertise.
At K2 Research, we push the boundaries of AI and machine learning. Our researchers collaborate with the world's best academic institutions and combine a varied set of deep domain skills.
K2 Research is paramount to K2's mission in general. Expertise fuels excellence - our experts operate across our AI execution hub, coming up with solutions for the most challenging of problems.
WHAT WE DO
Our Projects
EDUCATED AT
WHO WE ARE
Our People
Andrey Bryutkin
Deep Learning Engineer
Deep learning for partial differential equations, designing efficient network architectures, and biological modelling.
#scientificML #biologicalModelling
Massachusetts Institute of Technology
PhD Candidate, Applied Mathematics
Sarah Cechnicka
Healthcare Specialist
Deep-learning-based generation and segmentation for outcome prediction in post-transplant kidney biopsis.
#graphNeuralNetworks #medicalImaging
Imperial College London
PhD Candidate, AI4HEALTH
Lisanne Blok
Climate Data Scientist
Multivariate modeling of global and local sea level changes with far-term interdependent prediction using Gaussian processes and deep learning.
#climateModels #deepLearning
University of Cambridge
PhD Candidate, AI4ER
Gordon J. Köhn
AI Partner
Trained physicist & biostatistician. Bayesian data analysis, machine learning and high-performance computing.
#genomics #computationalNeuroscience
ETH Zurich
Dual-MSc Holder
Manith Adikari
Robotics Researcher
Human-robot interaction and applied AI research: looking into using Social Robots for collaboration and mediation.
#robotics #deepLearning
University of Manchester
PhD Candidate, Cognitive Robotics
Mark Eid
Healthcare ML Engineer
Research in ultrasound scan reconstruction, specialising in fetal brains.
#medicalImaging #NeRF
University of Oxford
PhD Candidate, AIMS
Dhruv Menon
Computational Materials Scientist
Discovery, design and optimisation of porous reticular materials for the drug delivery to the pancreatic cancer cell environment using ML and GenAI.
#drugDelivery #scientificML
University of Cambridge
PhD Candidate, NanoDTC
SCIENTIFIC CONTRIBUTIONS
Selected Research
01
Cechnicka et al. present a novel method to enhance robustness in histopathological image segmentation, addressing challenges of limited datasets and imbalanced object representation. Their approach utilizes diffusion models to enrich datasets with realistic examples, improving performance and interpretability. Validation on two datasets demonstrates its efficacy for clinical applications.
02
A. Obuchowski et al. introduce an innovative approach to intent recognition, merging transformer architecture with capsule networks. Their method outperforms traditional capsule-NLU networks, achieving state-of-the-art results on key datasets like ATIS, AskUbuntu, and WebApp. This advancement holds significant promise for enhancing intelligent conversation design within NLU systems.
03
Renal tumor malignancy classification profoundly impacts urological decisions, yet misdiagnoses lead to unnecessary nephrectomies. A machine-aided diagnosis system, presented by A. Obuchowski et al., predicts malignancy post radiological diagnosis to mitigate false-positives. Achieving an F1-score of 0.84, their method incorporates colorization-based preprocessing for improved knowledge transfer, enhancing accuracy by up to 1.8pp.