Picture of Damon Wischik Damon Jude Wischik

Personal details

Born 5 August 1974. British, French & Australian nationalities
Email— damon.wischik⦿cl.cam.ac.uk. Web— www.cl.cam.ac.uk/~djw1005. Phone— +44 1223 334417

Research

Mobility data science

funded by Toyota Mobility Foundation, £350k in 2020. There is no Excel for mobility data, no common platform for testing ideas and sharing them. The issue is that mobiility data is oriented around user histories, whereas Excel is oriented around tabular data. I am building a new class of tool which will enable end users to make sense of mobility data, especially new services such as rideshare and dockless bikes. I am also developing new theory for modelling such services: theory that combines models of user choice with models of system dynamics.

Probabilistic machine learning.

I am interested in how neural networks can be used as building blocks for probabilistic modelling, building on the way that generations of statisticians have used linear models. In particular, I have developed a new way to train neural networks, to enable them to express how confident they are in their predictions.

Explainability and the philosophy of machine learning.

Over the past year I have collaborated with a lawyer, and uncovered some fascinating parallels between machine learning and legal philosophy.

Employment

Computer Laboratory, Cambridge, 2017–: university lecturer

, working in the field of data science.

Alan Turing Institute, 2018–: fellow,

working on data science to model shared vehicle fleets and the future of transport, as part of the Urban Analytics research theme.

Urban Engines (a startup based in Los Altos, CA), 2011–2016: chief data scientist.

We built a platform for visualising and analysing big data about things that move: commuters, trains, buses, taxis, delivery fleets, etc. Customers included several major cities and transport infrastructure providers. Technologies used include Spark, d3, Postgres, and Amazon's AWS. My work involved all levels of the stack: talking with customers, designing system capabilities, data architecture, devising visualisations and inference algorithms, and detailed coding in R, Python, Scala, Javascript. The company had 22 employees, 10 of them in the data science group. Urban Engines was acquired by Google in 2016.

Electrical Engineering, Stanford: visiting professor in 2011, consulting professor in 2016.

In 2011 I worked with Prof. Balaji Prabhakar on societal networks—networks that combine real-world infrastructure and people. I worked on health incentives for Accenture employees, and transit incentives for Singaporean commuters. (This work led into Urban Engines, which Prof. Prabhakar cofounded.) In 2016, Prof. Prabhakar and I are co-teaching a course on Big Data for Things that Move, to graduate students in computing and engineering.

UCL, London, 2004–2011: Royal Society university research fellow,

based in the Networks and Systems group in the Computer Science department. I came to UCL with the goal of translating theoretical work on congestion control, begun by Prof. Frank Kelly in Cambridge, into a practical system. With Prof. Mark Handley and others in the EU-funded Trilogy project, we created MPTCP (Multipath TCP), which became an IETF standard and is used by Siri on the iPhone.

Statistical consulting, 2006–

for TauRx Therapeutics, a biotech startup based in Aberdeen, working on Alzheimer's Disease. This has involved interacting with medics, scientists, clinical research organisations, regulators, valuation consultants, and investment bankers, and has spanned Phase 2 and Phase 3 clinical trials. I have analysed data and advised on clinical trials, psychometrics, animal experiments, business development, risk and valuation. This work has given me a deep appreciation of statistics as a form of rhetoric, not just a tool for mathematical modelling.

Trinity College, Cambridge, 1999-2004: Junior Research Fellow,

  an independent research position. I was based in the Statistical Laboratory in the University of Cambridge. I worked on probability theory for queueing networks, with application to Internet switches.

Selected outputs

T.D.Grant and D.Wischik (2020). The path to AI: law's prophecies and the conceptual foundations of the machine learning age. Palgrave. The big novelty of machine learning—the elevation of prediction over explanation—was prefigured by Oliver Wendell Holmes Jr, a judge who reshaped American legal thinking at the end of the 19th century. The ensuing jurisprudential debates have much to offer machine learning.
T.D.Grant and D.Wischik (2020). Show us the data: privacy, explainability, and why the law can't have both. To appear in George Washington University Law Review. The GDPR requires that explanations be given of decisions given by machines—but for machine learning algorithms the only meaningful explanation involves sharing the training data.
D.Wischik (2018). The price of choice: models, paradoxes, and inference for ‘mobility as a service’. Allerton. This paper unifies discrete choice modelling with network flow optimization, via information theory. It points the way to a comprehensive economic understanding of Mobility as a Service.
N.Gomes, D.Merugu et al. (2012). Steptacular: an incentive mechanism for promoting wellness. COMSNETS NetHealth. This is the only publication to come out of my work on incentives—but the system I built is running at www.travelsmartrewards.com, has 330,000 users, and has paid out $10M Singapore dollars over four years.
D.Wischik, C.Raiciu, and A.Greenhalgh (2011). Design, implementation and evaluation of congestion control for multipath TCP. NSDI, winner of best paper award. This work has been standardized as an Internet Experimental Standard, RFC 6356.
D.Shah and D.Wischik. Switched networks with maximum weight policies: fluid approximation and multiplicative state space collapse. Annals of Applied Probability (2012). Fluid models of congestion collapse in overloaded switched networks. Queueing Systems (2011).
C.M. Wischik, D.J. Wischik, J.M.D. Storey, C.R. Harrington (2010). Rationale for tau aggregation inhibitor therapy in Alzheimer's disease and other tauopathies. Chapter in Emerging drugs and targets for Alzheimer's disease, vol. 1, ed. A. Martinez, RSC Drug Discovery Series. I am a co-author on three patents relating to this work.
G.Raina, D.J.Wischik (2005). Buffer sizes for large multiplexers: TCP queueing theory and instability analysis. This work lead to a DARPA grant, and to a series of letters in ACM Computer Communication Review, co-authored with Nick McKeown and Don Towsley.
A.Ganesh, N.O'Connell, D.J.Wischik (2004). Big Queues, a book. Awarded the 2004 Best Publication Award by the Applied Probability Society of INFORMS.

Awards & education

Awards


Royal Society University Research Fellowship, 2004–2011.
Trinity College Junior Research Fellowship, 1999–2003
Best Publication Award from the Applied Probability Society of INFORMS in 2005, for Big Queues, a book arising from my PhD work.
Best Paper Award at NSDI 2011, for Design, implementation and evaluation of congestion control for multipath TCP

Trinity College, Cambridge, 1996-1999: PhD

  in the Statistical Laboratory, supervised by Prof. Frank Kelly. My thesis was titled Large Deviations and Internet Congestion.

[degree certificate]

Trinity College, Cambridge, 1992-1996: BA in Mathematics.

Specialising in applied probability, statistical inference, and optimization.

Awards

—  Mayhew Prize for top final-year result in Applied Mathematics.

[academical record]

Teaching & supervision

Lecturing.

I teach 1st year Algorithms, and Scientific Computing; 2nd year Data Science; 3rd year Data Science and Visualization; and masters courses in Autoencoders, and Probabilistic Machine Learning.

Students.

Two completed PhD students (Cambridge maths; UCL computer science), one ongoing (Cambridge computer science). Assorted masters dissertations.

Skills and interests

Computing

—  Spark and Scala for big data; R for statistics and visualization; Python and Javascript for web services

Data

—  Recreational statistics & visualisations relating to election results, recipes, psychometrics, international affairs, etc.