Home / Events / Data science and AI for sustainability conference 2022

Data science and AI for sustainability conference 2022

Wednesday, 27 April 2022, 8.45am to 6.30pm
Location: Howard Theatre, Downing College, Cambridge, CB2 1DQ

You are invited to the Data science and AI for sustainability conference 2022, hosted by Interdisciplinary Research Centres Energy and C2D3.

As we move towards a zero carbon economy to meet climate change targets, it is critical to identify and tackle data and AI challenges associated with the generation, storage and supply of energy.

This is increasingly important from a technology, policy and societal perspective. New technologies for energy networks, smart grids, future cities and future energy trading will require new ways of joined-up thinking about complex data sets, better policy frameworks and government regulation.

This conference aims to bring together academics, industrialists and policymakers to share their research, identify new opportunities and discuss areas where further collaborative work is required to decarbonise the energy sector.


Registration information

We welcome delegates from diverse backgrounds and particularly those from underrepresented groups.

For registration, please click here.



During each session we will hear three short presentations from our speakers, followed by a chair facilitated discussion with audience Q&A for 30 minutes.

08:45-09:15 Registration and arrival refreshments

09:15-09:30 Opening talk

Professor Colm-Cille Caulfield (Head of the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge)

09:30-11:00 Policy and regulation

What behavioural changes are required and how do we ensure that we create an energy system which is equitable and affordable? What are the policy instruments, frameworks and incentives needed to accelerate decarbonisation and scaling up renewable energy? What are the opportunities for the transition to low carbon generation? How do we prepare the grid and transmission system for future challenges?

Chaired by Dr Anil Madhavapeddy (Department of Computer Science and Technology, University of Cambridge)

  • Julian Critchlow (Senior Advisor Bain & Company; former DG Energy Transformation & Clean Growth, BEIS)
  • Dr Ronita Bardhan (Department of Architecture, University of Cambridge)
  • Lucy Yu (CEO, Octopus Energy’s Centre for Net Zero) 

Rapporteur: Arden Berlinger, Postgraduate Student, Department of Plant Sciences

11:00-11:30 Networking & refreshment break

11:30-13:00 Technologies: energy networks

What are the key challenges, innovations and opportunities for the UK electricity network?  How do we adapt and enhance existing networks? How do we create new efficient and effective ones? How do we integrate networks to optimise performance?

Chaired by Dr Teng Long (Department of Engineering, University of Cambridge)

  • Dr Fei Teng (Department of Electrical and Electronic Engineering, Imperial College)
  • Iulian Nitescu (Co-Founder and CTO, Graphmasters)
  • Dr Ioannis Lestas (Department of Engineering, University of Cambridge)

13:00-14:00 Networking lunch

14:00-15:30 Economics: energy market trading

How is data science and AI changing the energy market organisation and design? What are the factors affecting pricing? How do we ensure market stability and minimise cost? How do we embed market resilience and avoid future shocks?

Chaired by Professor Michael Pollitt (Judge Business School, University of Cambridge)

  • Dr Ramit Debnath (Judge Business School, University of Cambridge)
  • Ciaran Flynn (Head of Modelling & Analysis, Sembcorp Industries Ltd)
  • Steven Steer (Lead Data Consultant, Zuhlke)

15:30-16:00 Networking break

16:00-17:30 Future cities: buildings

How can we use AI to synchronise buildings energy supply and demand? How can we optimise building management systems? How can data visualisation help run buildings efficiently? How do we adjust building occupancy behaviour by combining technology with social data?

Chaired by Professor Ajith Parlikad (Institute for Manufacturing, University of Cambridge)

  • Ellissa Verseput (Data Team Lead, Sympower)
  • Dr Jim Scott (Chief Production Officer, Grid Edge) 
  • Dr Jethro Akroyd (Department of Chemical Engineering and Biotechnology, University of Cambridge)
  • Dr Isabella Gaetani (Senior Scientist Smart Buildings, Arup)

17:30-17:40 Closing

Professor Colm-Cille Caulfield (Head of the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge)

17:40-18:30 Networking drinks reception

Diana Scarborough, Conference Artist in Residence. Throughout the conference, Diana will be listening to the panel discussions and talking with speakers and delegates during the breaks. We have invited Diana to create a piece of art that reflects the interdisciplinarity of her discussions and observations, and we look forward to sharing the piece of art with you.

Organising Committee: C2D3: Ellen Ashmore, Almarie Williams; Energy: Shafiq Ahmed, Raheela Rehman, Lata Sahonta.


Biographies: Speakers, Chairs, and Conference Artist


Useful information


We thank the following organisations for kindly supporting this event.

  • Isaac Newton Trust
  • Natural Environment Research Council [Discipline Hopping for Environmental Solutions fund]
Event Poster with details, Carousel Image

Forthcoming talks

Achieving Consistent Low Latency for Wireless Real-Time Communications with the Shortest Control Loop

Thursday, 18 August 2022, 4.00pm to 5.00pm
Speaker: Zili Meng, Tsinghua Unversity
Venue: FW11 and

Real-time communication (RTC) applications like video conferencing or cloud gaming require consistent low latency to provide a seamless interactive experience. However, wireless networks including WiFi and cellular, albeit providing a satisfactory median latency, drastically degrade at the tail due to frequent and substantial wireless bandwidth fluctuations. We observe that the control loop for the sending rate of RTC applications is inflated when congestion happens at the wireless access point (AP), resulting in untimely rate adaption to wireless dynamics. Existing solutions, however, suffer from the inflated control loop and fail to quickly adapt to bandwidth fluctuations. In this paper, we propose Zhuge, a pure wireless AP based solution that reduces the control loop of RTC applications by separating congestion feedback from congested queues. We design a Fortune Teller to precisely estimate per-packet wireless latency upon its arrival at the wireless AP. To make Zhuge deployable at scale, we also design a Feedback Updater that translates the estimated latency to comprehensible feedback messages for various protocols and immediately delivers them back to senders for rate adaption. Trace-driven and real-world evaluation shows that Zhuge reduces the ratio of large tail latency and RTC performance degradation by 17% to 95%.

Speaker Bio: Zili is a 3rd-year PhD student in Tsinghua University. His current research interest focuses on real-time video communications. He has published several papers in SIGCOMM / NSDI and received the Microsoft Research Asia PhD Fellowship, Gold Medal of SIGCOMM 2018 Student Research Competition, and two best paper awards.

BSU Seminar: "Genome-wide genetic models for association, heritability analyses and prediction"

Monday, 22 August 2022, 4.30pm to 5.30pm
Speaker: David Balding, Honorary Professor of Statistical Genetics at UCL Genetics Institute and University of Melbourne
Venue: Seminar Rooms 1 & 2, School of Clinical Medicine, Hills Road, Cambridge CB2 0SP

Although simultaneous analysis of genome-wide SNPs has been popular for over a decade, the problems posed by more SNPs than study participants (more parameters than data points), and correlations among the SNPs, have not been adequately overcome so that almost all published genome-wide analyses are suboptimal. While there has been much attention paid to the shape of prior distributions for SNP effect sizes, we argue that this attention is misplaced. We focus on what we call the "heritability model": a low-dimensional model for the expected heritability at each SNP, which is key to both individual-data and summary-statistic analyses. The 1-df uniform heritability model has been implicitly adopted in a wide range of analyses. Replacing it with better heritability models, using predictors based on allele frequency, linkage disequilibrium and functional annotations, leads to substantial improvements in estimates of heritability and selection parameters over traits, and over genome regions, as well as improvements in gene-based association testing and prediction. Key collaborators Doug Speed, Aarhus, Denmark and Melbourne PhD student Anubhav Kaphle.

Statistics Clinic Summer 2022 III

Wednesday, 31 August 2022, 5.30pm to 7.00pm
Speaker: Speaker to be confirmed
Venue: Venue to be confirmed

If you would like to participate, please fill in the following "form": The deadline for signing up for a session is 12pm on Monday the 29th of August. Subject to availability of members of the Statistics Clinic team, we will confirm your in-person or remote appointment.

This event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.

Statistics Clinic Summer 2022 IV

Wednesday, 21 September 2022, 5.30pm to 7.00pm
Speaker: Speaker to be confirmed
Venue: Venue to be confirmed

Abstract not available

Title to be confirmed

Monday, 26 September 2022, 3.00pm to 4.00pm
Speaker: Christopher Yau, University of Manchester
Venue: CRUK CI Lecture Theatre

Abstract not available