Computer Science Colloquium
Organisers: Markus Brill, Yu Guan
Colloquia normally take place fortnightly on Wednesday 2pm at CS1.01 (but there are many exceptions, see below). Additional titles and abstracts will be added as details become available.
Upcoming Colloquia
21st March 202315:00  16:00 
Petar VelickovicLink opens in a new window (DeepMind)Title: Reasoning Algorithmically: from Toy Experiments to AGI Modules Abstract: Neural networks that are able to reliably execute algorithmic computation may hold transformative potential to both machine learning and theoretical computer science. On one hand, they could enable the kind of extrapolative generalisation scarcely seen with deep learning models. On another, they may allow for running classical algorithms on inputs previously considered inaccessible to them. Over the past few years, the pace of development in this area has gradually become intense. As someone who has been very active in its latest incarnation, I have witnessed these concepts grow from isolated 'toy experiments', through NeurIPS spotlights, all the way to helping detect patterns in complicated mathematical objects (published on the cover of Nature) and supporting the development of generalist reasoning agents. In this talk, I will give my personal account of this journey, and especially how our own interpretation of this methodology, and understanding of its potential, changed with time. It should be of interest to a general audience interested in graphs, (classical) algorithms, reasoning, and building intelligent systems.

24th May 2023 
Yali Du (King's College London)Title/Abstract: TBD 
Past Colloquiua
10th March 202314:0015:00 MSB 2.23 
Debmalya Mandal (Max Planck Institute) Title: AI for Societal DecisionMaking Abstract: Largescale machine learning models trained on human preferences have led to significant advances in AI capabilities. The next frontier of AI lies in building reliable decisionmaking systems that society can utilize to support consequential decisions. In this talk, I will first present a framework of AIenabled societal decisionmaking and provide a brief overview of my research in several stages of the framework ranging from information elicitation to evaluation. The second part of the talk will focus on the preference aggregation stage, and I will outline my research on designing improved voting rules for aggregating diverse preferences. I will conclude by highlighting some exciting research directions at the intersection of reinforcement learning and preference aggregation with applications focused on societal problems. Bio: Debmalya Mandal is a postdoctoral researcher at the Max Planck Institute for Software Systems in Saarbrücken, Germany. Previously, he was a postdoctoral fellow at the Data Science Institute of Columbia University. He obtained his Ph.D. in Computer Science from Harvard University where he was part of the EconCS group. He works on problems in AI and multiagent systems, with an emphasis on theory and modeling, and with applications focused on societal problems. His paper has been selected for Oral presentation at the NeurIPS conference and he also received a Certificate of Distinction in Teaching from Harvard.

8th March 202314:00  15:00 CS1.01 
Jose CamachoCollados (Cardiff) Title: Natural Language Processing and Social Media: Challenges, Applications and TweetNLP Abstract: Social media represents a fundamental tool to understand the interactions and progress of society in the 21st century. Despite the large amount of information generated in social media platforms, understanding what is going on is not an easy task, even after the significant NLP progress in recent years. Its multilingual, dynamic, informal and multimodal nature means that standard techniques are seldom optimal. In this talk, I will summarise some of the latest advances on social media processing, and in particular the development of specialised language models. I will also explain our latest project that addresses the temporal and dynamic nature in social media, with evaluation studies involving language modelling, named entity recognition and topic classification. Finally, I will present our latest release, TweetNLP (tweetnlp.org), stemming from an international collaboration between academia and industry. TweetNLP is an integrated platform that enables a simple usage of cuttingedge NLP models for social media. I will quickly showcase TweetNLP’s online demo and will discuss a few applications for social media research, in particular for analysing political communication. Bio: Jose CamachoCollados is a Senior Lecturer and UKRI Future Leaders Fellow at Cardiff University, leading the Cardiff NLP group. Before joining Cardiff University, he completed his PhD in Sapienza University of Rome and was a Google AI PhD Fellow. Until recently, his research has focused on various semantics aspects in NLP with a distributional perspective. He wrote the “Embeddings in Natural Language Processing" book and is the current program cochair of *SEM 2023. More related to the topic of the talk, in the last few years Jose has been working in social media analysis and applications, developing NLP tools specifically targeted to this domain. 
24th Feb. 202314:0015:00 (Friday) CS1.01 
Matthias CaroLink opens in a new window (Caltech)Title: Quantum Computing Meets Machine Learning  A Math/TCS Perspective Abstract: Machine learning, based on computational and statistical learning theory, constitutes a recent example of how influential applications arise from theoretical innovation. Quantum computing, rooted in quantum information theory, promises to be a future instance of the same phenomenon, providing a new paradigm of computation using features of quantum physics. In this talk, I will give an introduction to the theories underlying machine learning and quantum computing, and I will present recent developments at the intersection of the two fields. I will discuss machine learning models based on trainable quantum circuits, quantum extensions of computational learning theory, and learning from data collected in quantum experiments. 
23rd Feb. 202314:0015:00 (Thursday) MB2.22 
Grzegorz Lisowski (Warwick)  Viva SpecialTitle: An Algorithmic Analysis of Deliberation and Representation in Collective Behaviour Abstract: The selection of a nominee by a group of players in the process of selecting a winner is present in many contexts. In sports, it is a major strategic problem to select the best team members. Crucially, in politics, this problem is essential for the process of primaries. We study the strategic behaviour of coalitions from the gametheoretic perspective. More precisely, we analyse the existence of a pure Nash equilibrium in the games capturing the strategic nomination problem. First, we adapt the wellknown HotellingDowns model, capturing the strategic behaviour of political parties in primaries. Subsequently, we explore this problem for tournamentbased rules. Nominee selection can also be influenced by the deliberation between the voters. To account for that, we investigate the complexity of checking the convergence of a synchronous, thresholdbased protocol. Furthermore, we explore computational aspects of majority illusion, which occurs when a large number of agents in a network perceives the opinion, which is a minority view, as the one which is held by the majority of agents. 
17th Jan. 202314:0015:00 (R0.21Link opens in a new window) 
*Joint DIMAP seminar and DCS Colloquium*Adi ShamirLink opens in a new window (Weizmann Institute of Science)Title: Efficient Detection of High Probability Cryptanalytic Properties of Boolean Functions Abstract: A central problem in cryptanalysis is to find significant deviations from randomness in a given $n$bit cryptographic primitive. When $n$ is small (e.g., an $8$bit Sbox), this is easy to do, but for large $n$, the only practical way to find such statistical properties was to exploit the internal structure of the primitive and to speed up the search with a variety of heuristic rules of thumb. However, such bottomup techniques can miss many properties, especially in cryptosystems which are designed to have hidden trapdoors. Joint work with Itai Dinur, Orr Dunkelman, Nathan Keller, and Eyal Ronen. Speaker Bio (Royal Society): Professor Adi Shamir was born in Israel in 1952, and received his PhD from the Weizmann Institute of Science in 1977. He is one of the founders of modern cryptography, and had made significant contributions to many of its branches. In 1977 he coinvented (together with Ron Rivest and Len Adleman) the RSA cryptosystem, which remains the best known and most commonly used public key encryption and signature scheme. Among his other inventions are secret sharing schemes, identitybased schemes, Zeroknowledge identification and signature schemes, ring signatures, and a variety of both classical and sidechannel attacks on cryptosystems including differential cryptanalysis, cache attacks, bug attacks, and acoustic attacks. For these contributions he received the Pius XI Gold Medal in 1992, the Turing Award in 2002, the Israel Prize in 2008, and the Japan Prize in 2017. He is a member of the Israeli Academy, the US National Academy of Science, the Academia Europaea, the French Academy of Science, and the Royal Society.

11th Jan. 202314:00  15:00 (CS1.01) 
Title: Sequential persuasion and information design Abstract: In Bayesian persuasion, a moreinformed principal influences the actions of a lessinformed agent by signaling information. We introduce a dynamic model of Bayesian persuasion, where the principal and the agent interact over time. The agent takes actions in each time step based on the current state, the principal's advice/signal, and their belief about the external parameter. Meanwhile, the action of the agent updates the state according to a stochastic process. The model is a generalization of the Markov decision process while it can also be seen as a special type of Markov games. It arises naturally in a number of applications, e.g., an app (the principal) can advise its user (the agent) on possible choices between actions based on additional realtime information the app has. In this talk, I will introduce this sequential model and present several complexity and algorithmic results about sequential information design, i.e., the problem of designing an optimal signaling strategy for the principal in this sequential model. 
7th Dec. 202214:0015:00 
Qiang Zhang (Oxford)Title: MRI Enhancement with AI Virtual Contrast Dye: Deep learning at forefront of cardiac imaging Abstract: Since their first launch in 1988, intravenous (IV) contrasts, such as gadoliniumbased contrast agents, have been commonly used in current MRI clinical practice, to enhance image contrast and detection of disease. However, IV contrast is cautioned in some patient groups, and increases scan time and costs. After three decades of IV contrasts in MRI clinical practice, AI deep learning emerges to provide a new perspective to rethink contrast enhancement approaches in diagnostic imaging. In this talk, I will present a new concept of enhancing MRI with AI instead of IV contrasts, a framework termed "Virtual Native Enhancement". We have validated it on two heart diseases, and are further extending it to the whole spectrum of commonly encountered heart diseases. This work aims to shift the paradigm of cardiovascular MRI practice from IV contrast enhancement to needlefree AI enhancement, improving its diagnostic utility, making it faster, safer and accessible for more centres and countries.

16 November 202214:00  15:00 (Cancelled) 
Jose CamachoCollados (Cardiff) Title: Natural Language Processing and Social Media: Challenges, Applications and TweetNLP Abstract: Social media represents a fundamental tool to understand the interactions and progress of society in the 21st century. Despite the large amount of information generated in social media platforms, understanding what is going on is not an easy task, even after the significant NLP progress in recent years. Its multilingual, dynamic, informal and multimodal nature, including the use of emojis, means that standard techniques are seldom optimal. In this talk, I will present advances on social media processing, and in particular the development of language models specialised on social media, as well as unified benchmarks for standard NLP tasks such as sentiment analysis or offensive language identification. I will also explain our latest project that addresses the temporal and dynamic nature in social media, which is one of the most important NLP challenges nowadays.

2 November 202214:00  15:00 
Title: Generative Language Model, Deepfake, and Fake News 2.0: Scenarios and Implications Abstract: The recent explosive advancements in both generative language models in NLP and deepfakeenabling methods in Computer Vision have greatly helped trigger a new surge in AI research and introduced a myriad of novel AI applications. However, at the same time, these new AI technologies can be used by adversaries for malicious usages, opening a window of opportunity for fake news creators and statesponsored hackers. In this talk, I will present a few scenarios where adversaries could exploit these cuttingedge AI techniques to produce more sophisticated fake news by synthesizing realistic artifacts or evade detection of fake news from stateoftheart detectors. I will conclude the talk by discussing the important implications of the new type of fake news (i.e., Fake News 2.0) and some future research directions. 
19 October 202214:00  15:00 
Title: The learnability of quantum observables. Abstract: In this talk, I will argue that every balanced quantum Boolean function has a geometrically influential variable. I will also derive a quantum analogue of the related Friedgut junta theorem about the approximation of Boolean functions of small total influence by functions of few variables. These results are based on the joint use of recently studied hypercontractivity and gradient estimates. Such generic tools also allow us to derive generalizations of these results in a general von Neumann algebraic setting beyond the case of the quantum hypercube, including examples in infinite dimensions relevant to quantum information theory such as continuous variables quantum systems. I will then comment on the implications of our results as regards to noncommutative extensions of isoperimetric inequalities, lower bounding the circuit complexity of quantum circuits, and the learnability of quantum observables.

5 October 202214:00  15:00 
Title: How can we improve explanation faithfulness in NLP? Abstract: Large pretrained language models (LLMs) currently dominate performance across language understanding tasks. However, their complexity and opaque nature have opened up new challenges on how to extract faithful explanations (or rationales), which accurately represent the true reasons behind a model’s prediction when adapted to downstream tasks. In this talk, I will present recent work from my group on how we can improve the faithfulness of LLM predictions and a study of explanation faithfulness in outofdomain settings.

23 August 202214:00  15:00 
Title: Identifying low dimensional data in high dimensional spaces. Abstract: Assume a Boolean function f in the ndimensional space such Joint work with Elchanan Mossel and Joe Neeman. 
9 June 202214:00  15:00 
Title: Extending Generalization Theory Towards Addressing Modern Challenges in Machine Learning
Abstract: Recent years have witnessed tremendous progress in the field of Machine Learning (ML). However, many of the recent breakthroughs demonstrate phenomena that lack explanations, and sometimes even contradict conventional wisdom. One main reason for this is because classical ML theory adopts a worstcase perspective which seems too pessimistic to explain practical ML: in reality data is rarely worstcase, and experiments indicate that often much less data is needed than predicted by traditional theory. In this talk we will discuss two variations of classical learning theory. These models are based on a distribution and datadependent perspective which complements the distributionfree worstcase perspective of classical theory, and is suitable for exploiting specific properties of a given learning task. A common theme of these models is their combinatorial nature. This can be seen as a continuation of the fruitful link between machine learning and combinatorics, which goes back to the discovery of the VC dimension more than 50 years ago. Based on joint works with Noga Alon, Olivier Bousquet, Steve Hanneke, Ron Holzman, Ramon vanHandel, and Amir Yehudayoff 
26 May 202216:00  17:00 
Title: Reproducibility in Learning Abstract: Reproducibility is vital to ensuring scientific conclusions are reliable, but failures of reproducibility have been a major issue in nearly all scientific areas of study in recent decades. A key issue underlying the reproducibility crisis is the explosion of methods for data generation, screening, testing, and analysis, where only the combinations producing the most significant results are reported. Such practices can lead to erroneous findings that appear to be significant, but don’t hold up when other researchers attempt to replicate them. In this talk, we introduce a new notion of reproducibility for randomized algorithms. This notion ensures that with high probability, an algorithm returns exactly the same output when run with two samples from the same distribution. Despite the exceedingly strong demand of reproducibility, there are efficient reproducible algorithms for several fundamental problems in statistics and learning. We will introduce these problems and the techniques used to design reproducible algorithms for them. Finally, we discuss connections to other wellstudied notions of algorithmic stability, such as differential privacy. This talk is based on joint work with Russell Impagliazzo (UCSD), Rex Lei (UCSD), and Toniann Pitassi (Columbia University), to appear in STOC 2022. 
17 March 202214:00  15:00 
Title: The Limits of Symmetric Computation Abstract: The most famous open problem in theoretical computer science, Bio: Anuj Dawar is Professor of Logic and Algorithms at the University of Cambridge, where he has worked since 1999. He has degrees from IIT, Delhi, the University of Delaware and obtained his PhD at the University of Pennsylvania. Before moving to Cambridge, he worked in Swansea. His research focuses on investigating computational complexity through the lens of logic. He served for five years as president of the European Association for Computer Science Logic. He has chaired (among others) the committees that award the Ackermann award, the Gödel prize, the Barry Cooper prize and the IPECNerode award. 
10 March 202214:00  15:00 
Title: Programming for our Future  The role of computer science in climate science Abstract: Climate science and computer science are both relatively young fields of study that essentially "grew up" together; some of the first Bio: Dominic Orchard is a researcher in both the theory and practice of 
10 February 202214:00  15:00 
YvesAlexandre de Montjoye (Imperial) Title: Using Data while Protecting Privacy in the Digital Era Abstract: We live in a time when information about most of our movements and actions is collected and stored in real time. The availability of largescale behavioural data dramatically increases our capacity to understand and potentially affect the behaviour of individuals and collectives. The use of this data, however, raises legitimate privacy concerns. Anonymization is meant to address these concerns: allowing data to be fully used while preserving individuals' privacy. In this talk, I will first describe a line of research in attacks against deidentified datasets showing how traditional data protection mechanisms mostly fail to protect people's privacy in the age of big data. I will then describe what I see as a necessary evolution of the notion of data anonymization towards an anonymous use of data and discuss the pros and cons of some of the modern privacy engineering techniques currently developed ranging from Differential Privacy to QueryBased Systems. I will conclude by describing how, when combined, I think they will allow largescale behavioural data to be used while giving individuals strong privacy guarantees. Bio: YvesAlexandre de Montjoye is an Associate Professor at Imperial College London. He currently is a Special Adviser on AI and Data Protection to EC Justice Commissioner Reynders and a Parliamentappointed expert to the Belgian Data Protection Agency (APDGBA). In 20182019, he was a Special Adviser to EC Competition Commissioner Vestager coauthoring the Competition Policy for the Digital Era report. YvesAlexandre worked for the Boston Consulting Group and acted as an expert for both the Bill and Melinda Gates Foundation and the United Nations. He received his PhD from MIT in 2015 
13 January 202214:00  15:00 
Title: Complexity of total search problems Abstract: There are many theorems of the form "for all, there exists" (such as all games have a Nash equilibrium, or all numbers have a prime factorisation). Bio: Since 2013 I have been a professor at the Dept of Computer Science, University of Oxford. Previously I was a professor at the Dept of Computer Science, University of Liverpool, where I moved in 2006. I was founding head of the ECCO research group. At both Oxford and Liverpool, I’ve worked extensively on algorithmic game theory and related problems in algorithms and computational complexity. Prior to this I was at the Department of Computer Science, University of Warwick, from 1997. I have also worked at Aston University and Sandia National Labs (working on algorithmic problems from computational biology), USA. PhD (Edinburgh, ’92) in computational learning theory; BA in mathematics (Oxford, ’88). 
07 October 202114:00  15:00 
Mohamed Abdelfattah (Cornel Tech) Title: Rethinking Deep Learning Computations: From AutoML to Hardware Abstract: The explosion and availability of data has created a new computation paradigm based on deep learning. Consequently, we need to rethink both software and hardware to make these computations possible and to keep up with the everincreasing computation demand. In this talk, I will describe how we use automated machine learning (AutoML) to enable ondevice AI through neural architecture search, compression, hardware modelling and efficient search algorithms. Next, I will give an overview of my experience in designing hardware and compilers for deep learning acceleration – I will focus on a new automated codesign methodology that simultaneously improves efficiency and accuracy. Finally, I will focus on reconfigurable devices like FPGAs. I will describe how an embedded networkonchip can transform FPGAs into a generalpurpose computation platform wellsuited for deep learning. Bio: Mohamed is an incoming assistant professor of ECE at Cornell University and Cornell Tech. He is currently a research team lead at the Samsung AI Center in Cambridge UK, working on the codesign of deep learning algorithms and hardware. Before that, he was at Intel building an FPGAbased accelerator and compiler for deep neural networks. Mohamed did his PhD at the University of Toronto, during which he was awarded the Vanier Canada Graduate scholarship and three best paper awards for his work on embedded networksonchip for FPGAs. 
21 October 202114:00  15:00 
Title: Learning to Schedule Abstract: In this talk, I will present new results for two scheduling problems with unknown parameters that require using a learning algorithm along with a scheduling policy. The first problem is for a multiclass, multiserver queueing system with stochastic rewards of assignments of jobs to servers, with mean rewards depending on the features of jobs and servers according to a bilinear model. The goal is to maximize the expected reward of assignments while maintaining the queueing system stable. The second problem is scheduling jobs with stochastic holding costs with unknown mean values in a single server system. The goal in this problem is to minimize the expected total job holding cost. For both these problems, I will present new algorithms and regret bounds. The motivating application scenarios for these problems arise in data processing platforms, crowdsourcing systems, and other online platforms. Joint work with JungHun Kim and Dabeen Lee Bio: Milan Vojnovic is a Professor of Data Science, with the Department of Statistics at LSE. He was a Researcher at Microsoft Research from 2004 to 2016. He received a Ph.D. degree EPFL in 2003. He was an affiliated lecturer at the University of Cambridge, from 2014 to 2016. He held a visiting researcher position with Facebook from 2019 to 2021. Milan’s research interests are in optimisation, machine learning, and design of datadriven algorithms. His research work was recognised by various awards including a 2010 ACM Sigmetrics Rising Star Researcher award and a 2005 ERCIM Cor Baayen Award, and several best paper awards at various conferences. He received a Facebook Faculty Research Award in 2019 and a Criteo Faculty Research Award in 2018. 
04 November 202117:00  18:00 
Divyakant Agrawal (UC Santa Barbara) Title: On the power of summary statistics in largescale data management Abstract: During the past two decades we have seen an unprecedented increase in the amount of data that is being generated from numerous internetscale applications. Before this data can be subject to modelling and analysis, it becomes necessary to obtain summary statistics such as the cardinality of unique visitors, frequency counts of users from different states or countries, and in general finding the quantile and median information from the dataset. Efficient algorithms exist for computing the exact information over the data. Unfortunately, these algorithms require a considerable amount of time, scanning the data multiple times, or require additional storage that is linear to the size of the dataset itself. Approximation methods, with guaranteed error bounds, developed in the context of streaming data are extremely effective to extract useful and relatively accurate knowledge from big data. In this talk, we will start with a review of one such approximation technique for estimating frequency counts over streaming data where only insert operations are permitted. We will then present a refinement of this technique in the context of large datasets that need to handle both insert and delete operations. 
25 November 202114:00  15:00 
Claus Brabrand (ITU Copenhagen) Title: How to Increase the Diversity in Computer Science Abstract: Women are widely underrepresented in Computing studies. 
13 May 202114:00  15:00 
Title: Optimising Computer Systems in Complex and Dynamic Parameter Space Abstract: Performance tuning of computer systems is challenging for a variety of reasons. Modern computer systems expose many configuration parameters in a complex and massive parameter space. The systems are nonlinear and there is no method for quantifying or modelling such systems by performance tuning to the level of precision required. Autotuning has emerged using a blackbox optimiser such as Bayesian Optimisation (BO). However, BO has limited scalability. Reinforcement Learning (RL) could be applied for combinatorial optimisation problems, but there is a gap between current research and practical RL deployments. I will introduce our framework to tackle these issues and demonstrate the potential of machine learning based methodologies for computer system optimisation. Bio: Eiko Yoneki is a senior researcher in the Systems Research Group of the University of Cambridge Computer Laboratory and a Turing Fellow at the Alan Turing Institute. Her research interests span distributed systems, networking and databases, including largescale graph processing. Her group’s current research focusses on autotuning of data processing/analytics framework to deal with complex parameter space using ML. 
27 May 202114:00  15:00 
Title: Constructive Cryptography Abstract: Modularization is a key principle in any constructive discipline. One wants to obtain complex constructions as the composition of simpler Bio: Ueli Maurer is Full Professor of Computer Science at ETH Zurich. His research interests include the theory and applications of cryptography, information security, theoretical computer science, information theory, and discrete mathematics. One of his longterm research goals is to establish a constructive theory of cryptography and to apply it to the modular design of provablysecure cryptographic protocols. He has served as EditorinChief of the Journal of Cryptology from 2002 to 2010. He is an IEEE Fellow, an ACM Fellow, an IACR Fellow, and a member of the German Academy of Sciences (Leopoldina). He received the 2013 Vodafone Innovation Award for Mobile Communications, the 2016 RSA Award for Excellence in Mathematics, and the 2016 Theory of Cryptography (TCC) TestofTime Award. Maurer has served many companies and startups as board member and consultant and has advised many government organsations. In 2018 he cofounded the Concordium project and foundation aiming at creating a global, universally trusted, provably incorruptible and secure transaction platform. 
24 June 202114:00  15:00 
Title: Stop talking to me (again): some communicationavoiding programming patterns Abstract: In this talk, we generalise the term communicationavoiding. Beyond its wellestablished meaning from linear algebra, we make it comprise (i) the reduction of data volume, (ii) the elimination of (meta) data generation, (iii) the reduction of data exchange frequency, (iv) the homogenisation of data access, (v) data access hiding and (vi) the localisation of data transfers. These criteria apply to both classic data exchange between compute nodes as well as data movements on the chip. Communicationavoiding then tackles the problematic divergence sketched above. Bio: Tobias Weinzierl is Professor in the Department of Computer Science at Durham University, Durham, UK. He has served at the Munich Centre for Advanced Computing (see the Springer edited book, Advanced Computing) before, and holds a PhD and habilitation from the Technical University Munich. 
21 January 202114:00  15:00 
Arthur Gervais (Imperial College; Liquidity Network) Title: Flash Loans for Fun and Profit
Abstract: Credit allows a lender to loan out surplus capital to a borrower. In the traditional economy, credit bears the risk that the borrower may default on its debt, the lender hence requires upfront collateral from the borrower, plus interest fee payments. Due to the atomicity of blockchain transactions, lenders can offer flash loans, i.e., loans that are only valid within one transaction and must be repaid by the end of that transaction. This concept has lead to a number of interesting attack possibilities, some of which were exploited in February 2020. In this talk we will explore the implication of transaction atomicity and flash loans for the nascent decentralized finance (DeFi) ecosystem. We will analyze two existing attacks with ROIs beyond 500k% and formulate finding the attack parameters as an optimization problem over the state of the underlying Ethereum blockchain and the state of the DeFi ecosystem. We will show how malicious adversaries can efficiently maximize an attack profit and hence damage the DeFi ecosystem further. Specifically, we will present how two previously executed attacks can be “boosted” to result in a profit of 829.5k USD and 1.1M USD, respectively, which is a boost of 2.37× and 1.73×, respectively. Bio: Arthur Gervais is a Lecturer (equivalent Assistant Professor) at Imperial College London, and the founder of Liquidity Network. He's passionate about information security and worked since 2012 on blockchain related topics, with a recent focus on Decentralized Finance (DeFi). 
4 February 202114:00  15:00 
Title: Local computation algorithms Abstract: Consider a setting in which inputs to and outputs from a computational problem are so large, that there is not time to read them in their entirety. However, if one is only interested in small parts of the output at any given time, is it really necessary to solve the entire computational problem? Is it even necessary to view the whole input? We survey recent work in the model of "local computation algorithms" which for a given input, supports queries by a user to values of specified bits of a legal output. The goal is to design local computation algorithms in such a way that very little of the input needs to be seen in order to determine the value of any single bit of the output. Though this model describes sequential computations, techniques from local distributed algorithms have been extremely important in designing efficient local computation algorithms. In this talk, we describe results on a variety of problems for which sublinear time and space local computation algorithms have been developed  we will give special focus to finding maximal independent sets and generating random objects. Bio: Ronitt Rubinfeld is a Edwin Sibley Webster Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, and is on the faculty at the University of Tel Aviv. Her research interests include sublinear time algorithms, local algorithms and algorithms for testing discrete distributions over large domains. Ronitt Rubinfeld has been an ONR Young Investigator, a Sloan Fellow, an invited speaker at the Internal Congress of Mathematics (2006) and an ACM Fellow. 
15 March 202115:00  16:00 
Title: Abstract: Bio: 
29 October 202014:00  15:00 
Title: A primer on PACBayesian learning, followed by News from the PACBayes frontline Abstract: PACBayes is a generic and flexible framework to address generalisation abilities of machine learning algorithms. It leverages the power of Bayesian inference and allows to derive new learning strategies. I will briefly present the key concepts of PACBayes and illustrate a few of its recent successes, for deep neural networks, for coherent risk measures, contrastive learning and robust learning. Bio: Benjamin Guedj is a Principal Research Fellow (~Associate professor) at University College London (Centre for Artificial Intelligence and Department of Computer Science). He is also a tenured research scientist at Inria, the top French research institute in mathematics and computer science, and a visiting researcher with the Alan Turing Institute. Since Sept. 2020, he is the founder and scientific director of The Inria London Programme, a joint lab between France and the UK. He holds a PhD in mathematics from Sorbonne Université (Paris, France) and focuses on statistical learning theory, PACBayes, computational statistics, theory for deep learning, among other topics. 
19 November 202017:00  18:00 
Rocco Servedio (Columbia University) Title: Testing noisy linear functions for sparsity Abstract: This talk explores a question at the intersection of two wellstudied topics: (1) sparse recovery, and (2) property testing. Consider the following basic sparse recovery problem: An algorithm is given noisy inputoutput examples of a linear function over a highdimensional space. Given the promise that the linear function is sparse, breakthrough work of Candes, Romberg and Tao shows that the linear function can be recovered from a number of examples that scales logarithmically in the dimension (and this dependence is required). We look at this problem from the vantage point of property testing, by studying the complexity of merely determining whether the unknown linear function is sparse versus "far from" sparse. We show that this decision problem can be solved with a number of samples which is completely independent of the dimension if the background highdimensional distribution of samples has i.i.d. components that are not Gaussian. Bio: Rocco Servedio is a professor and chair of the Department of Computer Science at Columbia University. His research is in theoretical computer science, and his main research interests lie in computational complexity theory, computational learning theory, property testing, and the role of randomness in computation. 
26 November 202014:00  15:00 
John Daugman (University of Cambridge) Title: Neural Computing, Identity, and Visual Recognition of Persons Abstract: Brains and computers have long been used as metaphors for each other. In recent years the link has grown stronger, with the explosion of results in machine learning based on big multilayer networks, and with data from wet neurobiology showing that each cubic millimeter (mm^3) of brain tissue contains about 3 kilometers of "wiring" (neural connections). This talk discusses vision both in machines and in mammals, focusing on pattern encoding mechanisms and their efficiency. The main application areas discussed are face recognition and the IrisCode, which has recently been used to enroll the entire 1.3 billion population of India in a national ID system. Unifying theoretical ideas are entropy, and visual encoders that are optimal under the Uncertainty Principle. Bio: John Daugman received his degrees at Harvard University and then taught at Harvard before coming to Cambridge University, where he is Professor of Computer Vision and Pattern Recognition. His areas of research include computer vision, information theory, neuro computing and statistical pattern recognition. Awards for his work in science and technology include the Information Technology Award and Medal of the British Computer Society, the "Time 100" Innovators Award, and the OBE, Order of the British Empire. He is the founder and benefactor of the Cambridge Chrysalis Trust. 
10 December 202014:00  15:00 
Ava Khamseh (University of Edinburgh) Title: Higherorder interactions in statistical physics, machine learning and biomedicine 
Third term, 20192020:
2 July 202014:00  15:00 
Title: Blockchain Scalability and Privacy via Scalable and Transparent zkSTARKs
Abstract: Two of the biggest challenges of blockchains like Bitcoin and Ethereum are (1) scalability  growing thoughput 10x, 100x, 1000x, ... and (2) maintaining privacy. Scalable ZK proof systems can solve both problems. This talk will recount the theorytopractice journey of ZK proofs and describes questions in algebraic coding theory whose resolution will help improve scalability and privacy even more. Bio: Eli is a cofounder and president of StarkWare, and Chairman of its Board of Directors. Eli has been passionate about the theory (“moon math”) and realization of transparent computational integrity since 2001, when he was a postdoctoral researcher at MIT and Harvard University, after completing his PhD in CS at the Hebrew University. Prior to cofounding StarkWare, Eli was a Professor of Computer Science at Technion – Israel Institute of Technology, which he joined in 2005. He is also a coinventor of the Zerocash decentralized anonymous payment system and a founding scientist of the Electric Coin Company (aka the Zcash company). 
18 June 202015:00  16:00 
Title: Are All Features Created Equal? 
16 January 202014:00  15:00 MSB2.22 
Title: Automated Fact Checking: a natural language processing perspective Abstract: 
4 February 202010:00  11:00 
Hanan Samet (University of Maryland)  ACM Distinguished Speaker Title: Reading News with Maps by Exploiting Spatial Synonyms Abstract: 
6 February 202014:00  15:00MSB2.22 
Title: Multiparty Privacy and AI Security & Privacy Abstract: 
27 February 202014:00  15:00 
Title: Modulated Bayesian Optimisation Abstract: 
12 March 202014:00  15:00 
Title: Testing Discrete Distributions  a CS Retrospective Abstract: Making inferences about the distributional properties of data is arguably one of the most fundamental classes of data analysis tasks. The study of such inferences has been a scientific endeavour for a long time, typically, by statistics and information theory. In the last two decades, testing problems for distributions, especially, on discrete domains have been studied also in the theoretical computer science community, often from a slightly different perspective. The primary objective in this line of research is characterising the number of samples needed, in terms of the domain size or some function of the distributions involved, to perform a given inference task reliably. In this talk, I will present an overview of this very active field of research, describing some basic models and problems, landmark results, and future directions. 
First term, 20192020:
10 October 201914:00  15:00 
Sunil Prabhakar (Purdue University) 
24 October 201914:00  15:00 
Evangelia Kalyvianaki (Cambridge)Title: THEMIS  Fairness in Federated Stream Processing under Overload Abstract: 
7 November 201914:00  15:00 
Ata Kaban (Birmingham)Title: Uncovering structure with randomness for learning in high dimensions Abstract: 
21 November 201914:00  15:00 
Samson Abramsky (Oxford)Title: Nonclassicality, quantum resources and quantum advantage (slides) Abstract: Our focus here is on quantum resources which take the form of nonlocal, or more generally \emph{contextual}, correlations. Contextuality is one of the key signatures of nonclassicality in quantum mechanics and has been shown to be a necessary ingredient for quantum advantage in a range of information processing tasks. We will describe a notion of simulation between quantum resources, and more generally between resources described in terms of contextual correlations, in the ``sheaftheoretic'' framework for contextuality (AbramskyBrandenburger). The talk will be essentially selfcontained, introducing the various notions we will discuss. 
3 December 201915:00  16:00 
Jakob Nikolas Kather (University Hospital Aachen)Title: Deep Learning for Precision Oncology Abstract: 
Thu 2 May, '1914:00  15:00 
Sarah Meiklejohn (UCL)Title: Anonymity in Cryptocurrencies
(This talk will take place in CS1.04)
Abstract: A long line of recent research has demonstrated that existing cryptocurrencies often do not achieve the level of anonymity that users might expect they do, while at the same time another line of research has worked to increase the level of anonymity by adding new features to existing cryptocurrencies or creating entirely new cryptocurrencies. This talk will explore both of these lines of research, demonstrating both deanonymization attacks and techniques for anonymity that achieve provably secure guarantees. 
Thu 9 May, '1914:00  15:00 
Edith Elkind (Oxford)Title: Fair Division of a Graph Abstract: We consider fair allocation of indivisible items under an additional constraint: there is an undirected graph describing the relationship between the items, and each agent's share must form a connected subgraph of this graph. This framework captures, e.g., fair allocation of land plots, where the graph describes the accessibility relation among the plots. We focus on agents that have additive utilities for the items, and consider several common fair division solution concepts, such as proportionality, envyfreeness and maximin share guarantee.In particular, we prove that for acyclic graphs a maximin share allocation always exists and can be computed efficiently. We also discuss a continuous variant of this problem, where agents need to divide a collection of interconnected "cakes" and each agent's piece has to be connected, or, more generally, have a small number of connected components. Based on joint work with Sylvain Bouveret, Katarina Cehlarova, Ayumi Igarashi, Dominik Peters, Xiaohui Bei and Warut Suksompong 
Thu 6 Jun, '1914:00  15:00 
Andreas Damianou (Amazon, Cambridge)(This talk will take place in CS1.04) Title: Probability and uncertainty in Deep Learning Abstract: 
Thu 20 Jun, '1914:00  15:00 
Mario Berta (Imperial College London)Title: Quantum Technologies for Cryptography Abstract: 
Thu 11 Jul, '1911:00  12:00 
Radu Calinescu (University of York)Title: Learning, synthesis and efficient analysis of probabilistic models Abstract: 