2022 CMP Industrial Projects

• Prototype graph data mode applied to multiple clinical trials
• Graph analytics aimed at providing insight into clinical trial operations and outcome
• Improve clinical trial enrollment lifecycle
• Develop methods to model clinical trials using a graph structure, apply and test these methods using data from multiple clinical trials including different phases of study and therapeutic area
• Develop machine-learning enabled graph analytics to provide insight into clinical trial outcome, clinical trial operations and overall clinical trial design
• Prototype machine learning autoencoder based methods for working with clinical trial data

• Student will gain experience in biomedical and healthcare data mining and variety of skills to develop analytics strategy that leverage biomedical data
• The incoming student will get a co-mentorship from different departments of AstraZeneca, including therapeutic areas, and Data Science & AI.
• The student will be part of collaborative projects and publications that will improve hands- on skills in data science, machine learning, digital biology and bioinformatics
• Develop algorithms to improve therapeutic responder/non-responder analytics
• Understand key drivers or responders/non-responders for different AZ medicines
• Improve the performance of TheraSign modules

• Machine learning in cardiovascular medicine: are we there yet? https://heart.bmj.com/content/104/14/1156.
• Long Mammography Assessment using Multi-Scale Deep Classifiers https://arxiv.org/abs/1807.03095
• Functional genomics predictive network model identifies regulators of inflammatory bowel disease. Nat Genet. 2017 Oct;49(10):1437-1449. doi: 10.1038/ng.3947. Epub 2017 Sep 11. PubMed PMID: 28892060; PubMed Central PMCID: PMC5660607.
• Glicksberg BS, et. al; Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks. Bioinformatics. 2016 Jun 15;32(12):i101-i110. doi: 10.1093/bioinformatics/btw282. PubMed PMID: 27307606; PubMed Central PMCID: PMC4908366.
• Shameer K et. al; Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams. Brief Bioinform. 2017 Jan;18(1):105-124. doi: 10.1093/bib/bbv118. Epub 2016 Feb 14. PubMed PMID: 26876889; PubMed Central PMCID: PMC5221424.

AIMS AND EXPECTATIONS

• Develop methods to model patient sub-typing
• Prototype machine learning methods for working with public data
• The incoming student will gain experience in predictive modelling, healthcare analytics, data science, integration and mining large-scale, real-world clinical data from public and proprietary database
• The incoming student will work closely with an AstraZeneca team to develop a novel phenotype or endpoint related to NASH or other diseases
• The student will gain experience in compiling data, building models, and contributing to publishing the model in leading conferences/journals

• Shameer K, et. al; PREDICTIVE MODELING OF HOSPITAL READMISSION RATES USING ELECTRONIC MEDICAL RECORD-WIDE MACHINE LEARNING: A CASE-STUDY USING MOUNT SINAI HEART FAILURE COHORT. Pac Symp Biocomput. 2017;22:276-287. doi: 10.1142/9789813207813_0027. PubMed PMID: 27896982; PubMed Central PMCID: PMC5362124.
• Shameer K, et. al; Machine learning in cardiovascular medicine: are we there yet? Heart. 2018 Jan 19. pii: heartjnl-2017-311198. doi: 10.1136/heartjnl-2017-311198. [Epub ahead of print] Review. PubMed PMID: 29352006.
• Badgeley MA, et. al; EHDViz: clinical dashboard development using open-source technologies. BMJ Open. 2016 Mar 24;6(3):e010579. doi: 10.1136/bmjopen-2015-010579. PubMed PMID: 27013597; PubMed Central PMCID: PMC4809078.

• The incoming candidate will be part of the Machine Learning Research Team within DS & AI's Applied Analytics and Artificial Intelligence Organization. The team is currently working on a portfolio of projects across multiple therapeutic areas with a common goal of optimizing clinical trials using machine intelligence methods.
• We aim to cross-train the incoming student in the areas of digital biology, drug discovery, precision medicine, multi-scale, and data science. We expect the student to leverage high- performance computing and biomedical informatics facilities in AZ to assist in develop data- driven methods to analyze large multi-scale, multi-omics data sets.
• The student will be part of collaborative efforts across microbial science, artificial intelligence, and drug development. This unique collaborative nature of the project will help to improve hands-on skills in clinical data, biomedical data analytics, and data science.
• The incoming student will contribute to the design, development, and deployment of predictive models that help to organize, analyze and interpret large-scale clinical and omics

• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5221424/ https://rinpharma.com/publication/rinpharma_209/
• https://www.frontiersin.org/articles/10.3389/fonc.2021.672916/full

• Student will gain experience in digital drug positioning, a new drug development strategy that leverage biomedical data
• The incoming student will get a co-mentorship from different departments of AstraZeneca, including therapeutic areas, Emerging Innovations Unit, and Data Science & AI.
• The student will be part of collaborative projects and publications that will improve hands-on skills in data science, machine learning, digital biology and bioinformatics
• Develop methods to model drug repositioning
• Develop machine-learning enabled drug repositioning methods
• Prototype machine learning methods for working with public data

• Shameer K, et. al; Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning. Brief Bioinform. 2017 Feb 15. doi: 10.1093/bib/bbw136. [Epub ahead of print] PubMed PMID: 28200013.
• Yadav KK et. al; Systems Medicine Approaches to Improving Understanding, Treatment, and Clinical Management of Neuroendocrine Prostate Cancer. Curr Pharm Des. 2016;22(34):5234-5248. Review. PubMed PMID: 27174811.
• Hodos RA, et. al; In silico methods for drug repurposing and pharmacology. Wiley Interdiscip Rev Syst Biol Med. 2016 May;8(3):186-210. doi: 10.1002/wsbm.1337. Epub 2016 Apr 15. Review. PubMed PMID: 27080087; PubMed Central PMCID: PMC4845762.

The objectives of this internship project are:

1. to summarize relevant background literature and results applicable to the design of late phase clinical studies with time-to-event endpoint,
2. to examine generalizations of the RMST optimal under asymmetric loss,
3. to implement and demonstrate the operating characteristics of studies designed using estimates of survival differences between active and control arms under asymmetric loss.

The intern will be supported by industrial mentors as appropriate to ensure that the project objectives can be reached within the limitations of a summer project.

Numerical methods and illustrative analyses can be implemented in any popular programming language, including R, SAS, Matlab, Python.

Project deliverables will include a final report, potentially providing material of appropriate quality for publication with the student as first author, a final presentation to the industrial and academic supervisors, and computer code developed to implement estimators and data analysis.

[1] Bayesian approach to life testing and reliability estimation using asymmetric loss function, Journal of Statistical Planning and Inference, Volume 29, Issues 1-2, September-October 1991, Pages 21-31
[2] Bayesian Estimation and Prediction Using Asymmetric Loss Functions: Journal of the American Statistical Association: Vol 81, No 394

Depending on the background and interests of the intern, the project will take one of three possible forms:

(1) developing theoretical analytical bounds on the effectiveness of finite-dimensional representations of multivariate random variables;
(2) developing new algorithms that exploit the ability of uncertainty-tracking computing platforms;
(3) develop new variants of variational quantum algorithms that exploit uncertainty-tracking computer hardware architectures.

• Stone, Edmund Keith, and Gary Pearce. 'A network of Mode-S receivers for routine acquisition of aircraft-derived meteorological data.' Journal of Atmospheric and Oceanic Technology 33.4 (2016): 757-768.
• Stone, E. K. "A comparison of Mode-S Enhanced Surveillance observations with other in situ aircraft observations." Quarterly Journal of the Royal Meteorological Society 144.712 (2018): 695-700.
• Mirza, Andrew K., et al. "Comparison of aircraft-derived observations with in situ research aircraft measurements." Quarterly Journal of the Royal Meteorological Society 142.701 (2016): 2949-2967.
• de Haan, Siebren. An improved correction method for high quality wind and temperature observations derived from Mode-S EHS. KNMI, 2013.

•  "A survey on shape-constraint deep learning for medical image segmentation" https://arxiv.org/abs/2101.07721
•  "clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation" https://arxiv.org/abs/2003.07311
•  "A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology" https://arxiv.org/abs/1910.01877

Based on multiple sites in Chilton Industrial Estate, Sudbury, we design and assemble blood, urine and diabetes analysis instruments. With our point-of-care testing systems, Siemens Healthineers delivers lab-accurate, actionable, and timely results on the spot. Siemens Healthineers has been Certified™ as a 'great place to work'. We are inspired to transform the way things are done - because we want what is best for our people, our customers, and ultimately to help everyone live longer and healthier lives. The initial focus of this will be developed in the context of spares data, it would be a further aim that the model developed through this project would provide an exemplar to support release of data from other assets for the business. The placement would suit an individual with a good analytical background who enjoys problem solving and attention to detail. Offering an opportunity to support a programme of work with clearly defined expectations and delivering operational solutions. The outputs from this placement will improve the efficiency of planning & inventory, finance & forecast and operational demand and focus. It will provide an excellent intern opportunity to develop skills and knowledge whilst also demonstrating competency through successful project delivery.

The aim of this placement will be to
1) assess the seasonality data of spares and cross check internal vs external actuals vs demand,
2) using the statistical evidence created to establish a forward planning trend and alert system for product,
3) utilise successful analysis to extend out to other needs such as lead times and safety stock.

A combination of the following is required:

• Structured querying of available database and business data.
• Analyse and assess trends in data and to produce new models of projection.
• Liaise with site-specific leads to discuss expectation of the results.
• Compare new developed method to current methods and results.
• Conduct a sensitivity analysis where appropriate. Produce a 'toolkit' programme for multiplying the process out to other data analysis needs of the business.
• Sharing of technical knowledge with the wider team through documentation and peer-to-peer learning.
• Supporting other analytical work within the team through findings.
• Develop visualisations of the data and identification of markers set by trend analysis.

We are AutoFill Technologies, a high-performance team, striving to tackle the toughest challenges in Computer Vision and Machine Learning. AutoFill is a company where Deeptech meets Hardtech to develop the best systems for cutting edge inspections of objects, powered by Artificial Intelligence. We are proud that we work on the edge of what is possible and bring theoretical research to life. We are confident that our technology will become the worldwide standard for automated object inspections. Our team is at the forefront of the artificial intelligence revolution. If you're seeking to work with experienced professionals who are excited to create impact in multiple industries and if you like Deeptech, hardware and pushing some serious boundaries, then you're ready to join our team. What we do, really makes a difference. Amazing opportunities like joining AutoFill just don't come around every day. Be part of something big, from the early days.

At AutoFill, we have developed an automated object inspection system that automatically captures large, high quality multimodal scans from a vehicle in only a few seconds. We use Computer Vision and Machine Learning to optimise the quality and efficiency of the data collected, as well as to process the data into valuable information for our customers. With our multi-sensor solution, we are able to fuse the data from different types of sensors and from different viewing angles. With our systems deployed at customer locations, and our own test setup at our AutoFill Research Lab, we continuously generate large representative datasets that are used for the development and training of new AI models and algorithms.

With our automated vehicle inspection systems, located at customer locations in Europe, we collect thousands of datasets, containing images of vehicles, captured from multiple angles, using the RGB and polarization sensors. According to recent studies the polarization modality provides a very rich description of the abnormalities in very challenging conditions such as poor illumination and strong reflection (Blin et al.). We built our in-house data annotation team which ensures consistent high standard annotations. However, the process of data annotation in the world of AI applications needs a large amount of labour work. Your main contribution as a researcher is to investigate whether self-supervised learning in the domain of vehicle damage detection on the multimodal data can reduce the cost of annotation while still performing as well as fully-supervised models. The literature review of self-supervised learning proved model distillation opened a new way of learning which provides a decent representation using labelled and un-labelled data. In a very recent work (Koohpayegani et al.), a self-supervised AlexNet has outperformed the supervised one on ImageNet classification.

With this research project, you have the opportunity to work in a high-tech company active in building automated machine vision solutions for vehicle fleets and rail in close collaboration with AutoFill Technologies AI experts. You will also have access to large datasets of RGB and polarization images from different vehicles captured by AutoFill Technologies. Last but not least, you will have access to the Google Cloud environment for developing and testing of AI solutions.

Outline of planned activities:

• To perform a literature study on existing methods and computational models for self-supervised learning; 
• To select two methods and implement them in a software environment, using the datasets that are generated by the automated vehicle inspection systems of AutoFill;
• To validate and benchmark the performance of the newly developed computational models, using the test setup at the AutoFill Research Lab
• To write your report and/or publications.

You'll have to have affinity with programming and familiarity with python. A strong background and interest in image processing and machine learning is a must. Experience with deep learning libraries (Pytorch or Tensorflow) is preferred.

• Blin, Rachel, et al. "A new multimodal RGB and polarimetric image dataset for road scenes analysis." CVPR 2020. 
• Koohpayegani, S. A., Tejankar, A., & Pirsiavash, H. (2020). Compress: Self-supervised learning by compressing representations. NeurIPS 2020.

We are AutoFill Technologies, a high-performance team, striving to tackle the toughest challenges in Computer Vision and Machine Learning. AutoFill is a company where Deeptech meets Hardtech to develop the best systems for cutting edge inspections of objects, powered by Artificial Intelligence. We are proud that we work on the edge of what is possible and bring theoretical research to life. We are confident that our technology will become the worldwide standard for automated object inspections. Our team is at the forefront of the artificial intelligence revolution. If you're seeking to work with experienced professionals who are excited to create impact in multiple industries and if you like Deeptech, hardware and pushing some serious boundaries, then you're ready to join our team. What we do, really makes a difference. Amazing opportunities like joining AutoFill just don't come around every day. Be part of something big, from the early days.

At AutoFill, we have developed an automated object inspection system that automatically captures large, high quality multimodal scans from a vehicle in only a few seconds. We use Computer Vision and Machine Learning to optimise the quality and efficiency of the data collected, as well as to process the data into valuable information for our customers. With our multi-sensor solution, we are able to fuse the data from different types of sensors and from different viewing angles. With our systems deployed at customer locations, and our own test setup at our AutoFill Research Lab, we continuously generate large representative datasets that are used for the development and training of new AI models and algorithms.

With our automated vehicle inspection systems, located at customer locations in Europe, we collect thousands of datasets, containing images of vehicles, captured from multiple angles, using the RGB and polarization sensors. According to recent studies the polarization modality provides a very rich description of the abnormalities in very challenging conditions such as poor illumination and strong reflection (Blin et al.). We built our in-house data annotation team which ensures consistent high standard annotations. In a very recent work Xiang et al. presented an Efficient Attention-bridged Fusion Network to exploit complementary information coming from different optical sensors. Specifically, they incorporate polarization sensing to obtain supplementary information, considering its optical characteristics for robust representation of diverse materials. Further, Ouali et al. proposed a cross-consistency training, where an invariance of the predictions is enforced over different perturbations applied to the outputs of the encoder. Such approaches help greatly to have highly accurate yet compact models for complex visual recognition tasks.

Your main contribution as a researcher will be implementing a semi-supervised learning approach by leveraging various perturbations along with exploiting the supplementary information such as polarization data for a semantic segmentation task. You have the opportunity to work in a high-tech company active in building automated machine vision solutions for vehicle fleets and rail and close collaboration with Autofill Technologies' AI experts. You will have access to large datasets of RGB and polarization images from different vehicles captured by Autofill Technology. You will have access to the Google Cloud environment for developing and testing of AI solutions.

Outline of planned activities:

• To perform a literature study on existing deep learning approaches for multi-modal semantic segmentation tasks ;
• To select two methods and implement them in a software environment, using the datasets that are generated by the automated vehicle inspection systems of AutoFill;
• To validate and benchmark the performance of the newly developed computational models, using the test setup at the AutoFill Technologies Research Lab To write your report and/or publications.

You'll have to have affinity with programming and familiarity with python. A strong background and interest in image processing and machine learning is a must. Experience with deep learning libraries (Pytorch or Tensorflow) is preferred.

• Blin, Rachel, et al. "A new multimodal RGB and polarimetric image dataset for road scenes analysis." CVPR 2020.
• Xiang, Kaite, Kailun Yang, and Kaiwei Wang. "Polarization-driven semantic segmentation via efficient attention-bridged fusion." Optics Express 29.4 (2021): 4802-4820.
• Ouali, Yassine, Céline Hudelot, and Myriam Tami. "Semi-supervised semantic segmentation with cross-consistency training." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.

The analogy between Deep Neural Networks (DNN) and human brains is traditionally evoked through the identification of artificial neurons in the DNN with neurons in the human brain. A Deeply Interacting Learning Systems (DILS) [1] develops this analogy further. Using Interaction Diagrams (ID) ([2]) as a common framework for both DNNs and Interacting Dynamical Systems (IDS), a DILS is defined as an ID with the dynamic re-wiring of a DNN and the non-trivial internal wiring of an IDS. In the broadest sense, a DILS proposes to move away from the discrete phases of learning, testing, and inference in DNNs towards a system which is continuously online. It also supports a more collaborative approach towards learning than a "shouting" match between weights and biases. In this way, we should be more able to understand the relationships between data as it flows through the learning system. Suppose that we have a supervised learning problem for image classification, where the problem is to classify images of lung tissue as either showing cancerous tissue or not. A basic example of the DILS framework would be to train a DNN with the added structure of OR gates to support classification in this problem. The inclusion of the OR gates in this case already furnishes the learning system with the knowledge that it is solving a classification problem, coming closer to how a human brain would approach this task.

This project will bring these ideas above to life, with the overarching goal to make more explicit and precise the definition of DILS. Along the way, the student should develop an understanding of the limitations and advantages of this novel approach over conventional methods for supervised learning, in their application to real-world problems. The ideas underpinning this proposed approach build on the foundations established in [2] and [3]. The project will start from a literature review. This will involve experimentation with the Python package [4] implemented as part of the work in [3]. Following this, several directions could be taken depending on the interests of the student undertaking this project. This might encompass a more detailed analysis and comparison of the framework in [3] against conventional neural networks and on a wider range of examples to better understand its advantages and limitations. It could also involve generating explicit descriptions of the simple examples of neural networks which are the basis of experiments in [3] and [4] in the language of Interaction Diagrams. At the conclusion of the project, the student should understand how to formulate examples of neural networks in these novel Category Theoretic frameworks, and be able to implement and experiment with these examples, with a focus on comparison against conventional implementations.

What you will do:

• Develop, devise and research algorithms and software to enhance Riverlane's capabilities, contributing to one or more projects that are core to Riverlane's goals
• Discuss ideas with colleagues and communicate work in the form of presentations and reports
• Develop an understanding of quantum computers and their industrial applications

What we need:

• A current student studying for a masters degree (including the final year of an integrated undergraduate and masters degree) or PhD in physics, chemistry, mathematics, computer science, electrical engineering, or a related technical field
• Proven ability in computational and/or theoretical work
• Experience with at least one programming language
• Excellent critical thinking and problem-solving ability
• Strong communication skills, both written and verbal
• Ability to take initiative and to work well as part of a team
• An interest in quantum computing (extensive knowledge or experience is not required)

For more information and to apply, please visit our website: https://www.riverlane.com/internships/

This project will investigate the optimal modelling methods to evaluate the effects of congestion control systems on 2 simple traffic scenarios; one macroscopic traffic flow fluid dynamics analogy, and one microscopic 4-way junction model. In the former, a student will examine a basic traffic density model representing a motorway section. A set of rules for traffic control will be implemented to understand the impact on various congestion metrics. In this case study the traffic density will be controlled using adaptive speed limits to suppress the average traffic speed to optimal levels. The objective is to maximise the outflow of traffic over the simulation's duration using minimal rules. The latter model involves the implementation of individual cars on a 4-way junction, with traffic lights controlling the flow of traffic in any direction. Cars will enter the junction from one of 4 directions and join a queue. Each will have a destination in one of the remaining 3 lanes, determined at random. The control system will have the goal of minimising the traffic build-up, in queues adjacent to traffic lights, by governing the durations with which red lights are held.

This project requires game theoretic experimentation to understand the effect on results when modifying the control system's behaviour. Students will be encouraged to decide on suitable congestion and wait-time cost metrics in order to evaluate the control system's performance. This project will most appeal to students interested in control theory, applied fluid dynamics, applied game theory, programming, and experimentation through mathematical modelling.

• https://doi.org/10.1093/bib/bbv004
• https://www.liebertpub.com/doi/abs/10.1089/adt.2009.0242

• Conduct cutting-edge quantitative research using real-world market datasets to develop models that underlie and enhance electronic execution and market making
• Work with our quant team globally (Hong-Kong, London, New-York) and learn about multiple financial markets

Develop a machine learning tool applied to Veterinary CT scans

Machine learning, deep learning, AI, computer science, CNN, diagnostic imaging, CT, veterinary, innovation

Hauser Forum, Broers Building, 21 JJ Thomson Avenue, CB30FA, Cambridge

8 weeks between end June/July to September 2022