NESTiD Seminar
NESTiD Seminar
NESTiD Seminar Coordinator: George Mertzios
Here you can find the links to the dedicated webpages for the NESTiD Seminars and videos from previous years: Past NESTiD Seminars
NESTiD Seminar 2025/26
The seminar talks will be streamed online on Teams. Whenever the speaker is physically present in Durham, the presentation will also be in the room MCS3052 at the MCS building (in addition to Teams streaming). Whenever a speaker is not physically present, all people at Durham are invited to watch the Teams seminar in the above room. Below you can find the list of speakers and talks for the year 2025/26. In the schedule of talks below is in UK time.
NOTE: Please refer to the schedule below for any room changes for some selected talks.
Link for 2025-26 Seminar videos: click here
Term 1 of 2025/26:
(online on teams)
| Date and Time | Talk |
|---|---|
| Friday 10 Oct 2025 13:00 – 14:00 (Inaugural talk of the NESTiD seminar series) | Gopal Pandurangan (University of Houston, USA) Quantum Communication Advantage in Leader Election and Agreement This talk posits a new quantum framework for designing and analyzing communication-efficient distributed quantum algorithms. Using this framework, we present distributed quantum algorithms for two fundamental problems in distributed computing, namely, leader election and agreement. Our quantum algorithms are significantly more message-efficient compared to their classical counterparts, and breach the classical lower bounds. This is joint work with Fabien Dufoulon (Lancaster University) and Frédéric Magniez (Université Paris Cité, CNRS, IRIF). |
| Friday 17 Oct 2025 13:00 – 14:00 | No seminar |
| Friday 24 Oct 2025 13:00 – 14:00 | Ayalvadi Ganesh (University of Bristol) Social learning in multi-agent multi-armed bandits Consider a large number of agents, N, faced with the problem of choosing amongst a large number of options, K. The problem occurs repeatedly, and every time an agent chooses an option, it receives a random reward or payoff whose distribution depends on the option but not on the agent. The goal is to maximize the long-run payoff. The problem involves a trade-off between exploitation – choosing the option currently believed to be the best – and exploration – choosing possibly sub-optimal options in order to gain more information about their payoffs. The challenge is to optimize this trade-off. If there were a single agent, then this is an instance of the multi-armed bandit problem with K arms, which has been studied extensively for decades. If no communication is allowed between agents, then it is N parallel instances of the multi-armed bandit problem. If there are no communication constraints, then the agents act in aggregate as if they were a single agent. We are interested in the intermediate case where limited communication is allowed. We show that, even with limited communication, in the long run the system behaves in aggregate as if there were a single agent, i.e., as if there were no communication constraints. This is a joint work with Abhishek Sankararaman, Ronshee Chawla, Sanjay Shakkottai, Conor Newton and Henry Reeve. |
| Friday 31 Oct 2025 12:00 – 13:00 Room MCS 3055 NOTE: different time AND room! | Frank Krauss (Durham University, UK) Introducing JUNE – an open-source epidemiological simulation [slides] |
| Friday 7 Nov 2025 13:00 – 14:00 | Rizwan Asghar (University of Surrey, UK) Advancing Cyber Resilience in IoT: Strategies, Challenges, and Future Directions The rising number of cyber attacks targeting IT infrastructure and IoT networks—especially in critical domains such as healthcare and smart homes—reveals the limitations of traditional security approaches in countering adaptive and sophisticated threats. As attackers increasingly bypass conventional intrusion detection and prevention systems, the focus has shifted toward cyber resilience: designing systems that can anticipate, withstand, recover from, and adapt to cyber disruptions. This talk explores key cyber resilience factors and presents strategies to enhance resilience, particularly in IoT environments. Case studies from medical systems and smart home networks illustrate how these strategies can be practically applied to minimise service disruption and maintain operational integrity. Finally, we will discuss key strategies, research challenges, and future directions to advance the state of cyber resilience. |
| Friday 14 Nov 2025 13:00 – 14:00 | Francesco d’Amore (Gran Sasso Science Institute, Italy) Limits of distributed quantum computing Theoretical quantum advantage is well-established in centralized computing, where computation is performed on a single computer. However, its potential in distributed computing is less understood. In this talk, we consider the LOCAL model of computation (Linial 1987), a model of synchronous distributed computation where the complexity measure is the number of communication rounds needed to solve a problem, with no constraints on communication bandwidth or local computation capabilities. The classical LOCAL model is well-studied, especially regarding Locally Checkable Labeling (LCL) problems (Naor and Stockmeyer 1993), which include problems like graph coloring, finding maximal independent sets, etc. However, determining whether quantum computers and quantum communication can speed up computation in this setting remains an open question. Currently, no tools specific to quantum-LOCAL exist to address this question. Nonetheless, arguments based on independence and causality have been useful in “sandwiching” quantum-LOCAL between classical LOCAL and super-quantum models. This talk will discuss recent findings in this area, highlighting settings where quantum advantage is impossible, where current arguments fail to rule out quantum advantage, and where quantum advantage is, instead, established. |
| Friday 21 Nov 2025 12:00 – 15:00 in the Business School, Durham NOTE: different time AND room! | Joint NESTiD – Business School workshop Location: Executive Hub (WB-4002), 4th floor, Waterside Building |
| Friday 28 Nov 2025 13:00 – 14:00 | No seminar |
| Friday 5 Dec 2025 13:00 – 14:00 | Mor Harchol-Balter (Carnegie Mellon University, USA) [slides: [pdf-file] or [Powerpoint file]] Optimal Core Allocation for Parallel Speedup Jobs Most queueing-theory models assume that each job runs on a single server. But this one-server-per-job model is not a good representation of today’s compute jobs. In this talk we define the parallel speedup job model, which is representative of many machine learning training jobs, database queries, and scientific computing jobs. Each job can run on any number of cores, but the job’s speed depends on the number of cores on which the job is run, where there is typically a depreciating benefit from each additional core. The question is to understand how to best share a limited number of cores among a stream of jobs, each governed by a potentially different speedup function. We discuss some recent optimality results in this nascent area. This talk is designed to be accessible to all (including students) and also features many open problems. |
| Friday 12 Dec 2025 13:00 – 14:00 | Raman Singh (University of West Scotland, UK) Quantum-Safe Blockchains: Cryptographic Vulnerabilities and the Role of No-Sum Sequences [slides] Blockchain security relies on cryptographic primitives such as digital signatures, hash functions, Merkle structures, and verifiable randomness. These components ensure transaction validity, data integrity, and distributed consensus. However, advances in quantum computing pose a significant threat: Shor’s algorithm breaks elliptic-curve–based signatures. In contrast, Grover’s algorithm weakens common hashing schemes, making current blockchain systems vulnerable to future quantum adversaries. This seminar examines where cryptography is used within blockchain architecture and why these primitives fail against quantum attacks. It then introduces No-Sum Sequences, a combinatorial construct with strong resistance to known quantum algorithmic speedups, making them promising for quantum-secure hashing and commitment schemes. |
Term 2 of 2025/26:
(online on teams)
| Friday 16 Jan 2026 13:00 – 14:00 | Kavitha Telikepalli (Tata Institute of Fundamental Research, Mumbai, India) Assignments, Arborescences, and Popularity [pdf slides] Matching problems with one-sided preferences are seen in applications such as campus housing allocation in universities. Popularity is a well-studied notion of optimality in this model. There is a simple algorithm to solve the popular assignment problem. A related problem asks for a popular arborescence in a directed graph where vertices have preferences over their in-neighbors. Such arborescences are relevant in liquid democracy. We will see that the popular arborescence problem can be solved by a generalization of the popular assignment algorithm. |
| Friday 23 Jan 2026 13:00 – 14:00 | Dominik Schallmoser (Technical University Hamburg, Germany) NOTE: This talk has been moved to 13 March 2026. |
| Friday 30 Jan 2026 13:00 – 14:00 | David Oswald (Durham University, UK) How confidential is confidential computing? In this talk, we will walk through a range of hardware-software vulnerabilities on widely used trusted execution environments, including Intel SGX and AMD SEV-SNP. We particularly look at how fault and side-channel attacks, which were traditionally developed in an embedded context, can be adapted to fully-fledged CPUs. We discuss the commonly assumed threat model of confidential computing (where the adversary includes the cloud provider) and what current technology lacks to uphold this promise. Finally, we consider mitigations and future directions in this field. |
| Friday 6 Feb 2026 13:00 – 14:00 | Peter Davies (Durham University, UK) On the Locality of the Lovász Local Lemma The Lovász Local Lemma is a versatile result in probability theory, characterizing circumstances in which a collection of n ‘bad events’, each occurring with probability at most p and dependent on a set of underlying random variables, can be avoided. It is a central tool of the probabilistic method, since it can be used to show that combinatorial objects satisfying some desirable properties must exist. While the original proof was existential, subsequent work has shown algorithms for the Lovász Local Lemma: that is, in circumstances in which the lemma proves the existence of some object, these algorithms can constructively find such an object. One main strand of these algorithms, which began with Moser and Tardos’s well-known result, involves iteratively resampling the dependent variables of satisfied bad events until none remain satisfied. In this talk, we will discuss a novel analysis that can be applied to resampling-style Lovász Local Lemma algorithms. This analysis shows that an output assignment for the dependent variables of most events can be determined only from low-radius local neighborhoods, and that the events whose variables may still require resampling can be identified from these neighborhoods. This allows us to improve randomized complexities for the constructive Lovász Local Lemma in several parallel and distributed models. |
| Friday 13 Feb 2026 13:00 – 14:00 | Sourav Chakraborty (Indian Statistical Institute, Kolkata, India) Distinct Elements in Streams and the Klee’s Measure Problem The distinct elements problem in streaming algorithms refers to estimating the number of unique elements in a data stream while using limited memory. A generalization of this problem is Klee’s Measure Problem, where the goal is to estimate the size of the union of sets when the sets arrive in a data stream, all while using limited memory and having restricted access to the sets. While the distinct elements problem is well-studied in streaming algorithms, with tight bounds already established, Klee’s Measure Problem has remained a major open problem in the field for many years. We will present the first-ever efficient streaming algorithm (from PODS ’21) for Klee’s Measure Problem. This algorithm also provides a remarkably simple streaming solution for the distinct elements estimation problem, which even caught the attention of Donald E. Knuth (https://www-cs-faculty.stanford.edu/~knuth/papers/cvm-note.pdf). This talk is based on joint works with N. V. Vinodchandran and Kuldeep S. Meel across multiple articles, notable the following: – Estimating the Size of Union of Sets in Streaming Models. PODS 2021 – Estimation of the Size of Union of Delphic Sets: Achieving Independence from Stream Size. PODS 2022 – Distinct Elements in Streams: An Algorithm for the (Text) Book. ESA 2022 – Improved Streaming Algorithm for the Klee’s Measure Problem and Generalizations. (jointly with Mridul Nandi, Arijit Ghosh and Soumit Pal) APPROX 2024 |
| Friday 20 Feb 2026 13:00 – 14:00 | Victor Valls (IBM Research, UK) A brief introduction to quantum algorithms and their applications Quantum computing is an emerging computational paradigm that aims to solve problems that are intractable classically. However, designing algorithms for quantum computers can be challenging, as they represent and manipulate information in a fundamentally different way from classical computers. In this talk, I will give a brief introduction to the core components of quantum computing (such as superposition and entanglement) and show how they can be used in the design of quantum algorithms. I will also discuss some of the practical challenges of implementing these algorithms on current quantum computers, including noise, limited qubit counts, and hardware constraints. |
| Friday 27 Feb 2026 12:00 – 15:00 in the Business School, Durham NOTE: different time AND room! | Joint NESTiD – Business School workshop Location: Executive Hub (WB-4002), 4th floor, Waterside Building |
| Friday 6 Mar 2026 13:00 – 14:00 | Behzad Kazemtabrizi (Durham University, UK) TBA |
| Friday 13 Mar 2026 13:00 – 14:00 | Dominik Schallmoser (Technical University Hamburg, Germany) Heterogeneous Federated Learning Federated learning is a machine learning paradigm where multiple distributed clients train a common model without the need to share their data. From a distributed systems perspective, federated learning introduces fundamental challenges, including data and model heterogeneity and evolving data distributions. This talk provides an overview of federated learning from a distributed computing viewpoint. I will outline core algorithmic ideas, discuss key system-level considerations, and highlight selected research directions, with a focus on the effects of client selection, heterogeneity, and non-stationarity of data distributions on performance and convergence. |
| Friday 20 Mar 2026 13:00 – 14:00 | Arpita Patra (IIS Bangalore, India) TBA |
Term 3 of 2025/26:
(online on teams)
| Friday 1 May 2026 13:00 – 14:00 | Rajmohan Rajaraman (Northeastern University, USA) TBA |
| Friday 8 May 2026 13:00 – 14:00 | Tarun Mangla (IIT Delhi, India) TBA |