The talk entitled “The Cognitive and Human Factors of Formal Methods” will be given on 23 July at 11:00 am in room B025, moderated by Prof. Alexandra Mendes (DEI).

About the Talk:

“As formal methods improve in expressiveness and power, they create new opportunities for non-expert adoption. In principle, formal tools are now powerful enough to enable developers to scalably validate realistic systems artifacts without extensive formal training. However, realizing this potential for adoption requires attention to not only the technical but also the human side—which has received extraordinarily little attention from formal-methods research.
This talk presents some of our efforts to address this paucity. We apply ideas from cognitive science, human-factors research, and education theory to improve the usability of formal methods. Along the way, we find misconceptions suffered by users, how technically appealing designs that experts may value may fail to help, and how our tools may even mislead users.”

About the Speaker:

Shriram Krishnamurthi is a Professor of Computer Science at Brown University. With collaborators and students, he has created several influential systems like DrRacket, Margrave, Flapjax, LambdaJS, Flowlog, and Pyret. He has also written multiple widely-used books. He also co-directs the Bootstrap integrated computing outreach program. For his work he has received SIGPLAN’s Robin Milner Young Researcher Award, SIGPLAN’s Software Award (jointly), SIGSOFT’s Influential Educator Award, SIGPLAN’s Distinguished Educator Award (jointly), and Brown’s Wriston and Philip J. Bray teaching awards. He has authored over twenty papers recognized for honors by program committees. He has an honorary doctorate from the Università della Svizzera Italiana.

The talk entitled “Trustworthy AI Under Constraints: From Small Language Models to Adaptive Antibiotic Treatment” will be given on 14 July at 4:00 pm in room B001, moderated by Prof. Gil Gonçalves (DEI).

About the Talk:

“This talk presents two current research directions in trustworthy and efficient AI systems for sensitive domains. The first part focuses on small language models as a practical alternative to large language models in settings where cost, energy use, privacy, local deployment, and governance are important. I will present our recent work on privacy risks in chatbots built on small language models, showing that standard template-based evaluations can underestimate personally identifiable information leakage. Our results motivate a broader view of reliability, where prompts, role formatting, decoding choices, and deployment settings are treated as part of the AI system itself.
The second part introduces OptiRegi, a project that uses reinforcement learning to study optimized dynamic antibiotic dosing regimens. The goal is to explore how treatment decisions can adapt over time while balancing bacterial killing, toxicity, resistance development, and changing infection dynamics. Together, these two projects highlight a common theme: building AI systems that remain trustworthy under real-world constraints, from privacy and deployment challenges in small language models to safe and adaptive treatment optimization.”

About the Speaker:

Dr. Vi Ngoc-Nha Tran is an Associate Professor of Computer Science at UiT The Arctic University of Norway and a member of the steering group of NORA.startup, the innovation network of the Norwegian Artificial Intelligence Research Consortium. She holds a Ph.D. in Computer Science from UiT, was a visiting scholar at Rutgers University, USA, and received an Erasmus Mundus scholarship for a European M.Sc. in Software Engineering. Her research spans machine learning, health technology, high-performance computing, and energy-efficient computing. She currently leads two projects as principal investigator: a UiT-funded project on trustworthy small language models for regulated domains, and OptiRegi, a Research Council of Norway-funded project on optimized dynamic antibiotic dosing using reinforcement learning.

The talk entitled “Natural and artificial trust for decision-making in human-machine teamwork” will be given on 25 June at 11:00, in room B011.

About the Talk:

“Human-machine teams count on both humans and artificial agents to work together collaboratively. In human-human teams, we use trust to make decisions, such as which teammate should do which task, based on what we believe might be successful. Our prediction of task success is based on our beliefs of others’ trustworthiness, which can be divided into several dimensions, e.g., competence, willingness, external factors, etc.

As artificial teammates’ autonomy increases, the variation of interdependences in human-machine teams increases too. As such, team members need to consider the different possibilities to achieve task success as a team, making the best use of human-machine collaboration. It is then important that all members involved, both humans and machines, have the necessary beliefs of trust and trustworthiness to make decisions that ensure the team’s goal and mitigate possible risks. By formalising trust and trustworthiness beliefs, we can increase the transparency of decisions, either made by humans or machines.

In this talk, I will go over notions of trust, trustworthiness, interdependence and coactive design, all in the context of decision-making in human-machine teams. I will present some of our multidisciplinary research which allow us to increase our (and the machine’s) understandability of the human teammate when collaborating with a machine and, consequently, make the machine teammate more understandable to the human.”

About the Speaker:

Carolina Centeio Jorge is a Senior Data Scientist at Glintt Next in Portugal. She completed her PhD in the Interactive Intelligence group at Delft University of Technology (TU Delft), where she researched mental models and trust in human-AI teams, and was a visiting researcher at the University of Michigan. Carolina has been actively involved in academic service and research communities, including co-founding the MultiTTrust workshop series and serving on TU Delft’s Integrity Board. Having lived in Tokyo, Barcelona, and Delft, she values multidisciplinary and multicultural environments. Today, she combines her research background with industry practice, focusing on customer intelligence.

Entrance is free, no registration required.

The talk entitled “Fifty Years of the Algorithm Selection Problem: John Rice’s Enduring Legacy” will be given on 27 May at 11:00 am in room B008, moderated by Prof. Carlos Soares (DEI).

About the Talk:

“The seminal paper “The Algorithm Selection Problem” by John Rice was published 50 years ago. This paper formalizes the problem of selecting the best algorithm for a given problem, which is important in Machine Learning, Optimization and many areas of Computer Science. In this talk, I discuss the paper, its impact and challenges it raises which are still open today.”

About the Speaker:

Kate Smith-Miles AO FAA is Pro Vice-Chancellor (Research Capability) and a Melbourne Laureate Professor in the School of Mathematics and Statistics at The University of Melbourne. She is also Director of the ARC Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA). She has previously held a five year Laureate Fellowship from the Australian Research Council with a Georgina Sweet Award, and has held positions as President of the Australian Mathematical Society (2016-2018), a member of the Australian Research Council College of Experts (2017-2019), and Associate Dean (Enterprise and Innovation) in the University’s Faculty of Science (2019-2023). Prior to joining The University of Melbourne in September 2017, she was Professor of Applied Mathematics at Monash University, where she was also Head of the School of Mathematical Sciences (2009-2014), and inaugural Director of the Monash Academy for Cross & Interdisciplinary Mathematical Applications (MAXIMA) from 2013-2017. She was also previously Head of the School of Engineering and Information Technology at Deakin University (2006-2009) with a Chair in Engineering. She obtained her first Professorship in Information Technology at Monash University, where she worked from 1996-2006. Professorships in three disciplines (mathematics, engineering, and information technology) have given her an interdisciplinary breadth reflected in much of her research.

The talk entitled “The 2025 Turing Award: Secret Key Sharing Using Quantum Mechanics” will take place on June 3rd at 14:30, in room B004 . The session will be moderated by Prof. Ana Paiva (DEI).

About the Talk:

The 2025 Turing Award was awarded this year to Charles H. Bennett and Gilles Brassard for their work in quantum cryptography. In their 1984 paper, they introduced the famous BB84 protocol, showing how two parties can securely share a cryptographic key. The security of this protocol is guaranteed by the laws of quantum physics themselves.
In this talk, the speaker will explain this protocol and its significance. No advanced knowledge of quantum mechanics is required.

About the Speaker:

Sagar Silva Pratapsi is an Assistant Professor at the Department of Informatics Engineering (DEI) at FEUP. His research focuses on quantum computing and quantum information, particularly on the fundamental principles of quantum mechanics, quantum system control, and related algorithms.
He completed his PhD at Instituto Superior Técnico in 2024 and served as a Visiting Assistant Professor at the University of Coimbra until 2026, where he taught courses in quantum computing and quantum information. He was also a visiting research student at the University of California, Berkeley in 2024, funded by the Luso-American Development Foundation, and a visiting student at the University of Maryland, Baltimore in 2023, supported by a doctoral fellowship from the “la Caixa” Foundation.
Before his PhD, he worked as a Research Analyst at the European Central Bank. He has received several distinctions, including a scholarship from the Gulbenkian Foundation’s New Talents in Mathematics Programme, and is a medalist in the International Physics Olympiad.

The talk, entitled “Hybrid neural systems: neuromorphic computing for real-time control of brain activity“, will be given on 15 June at 2:00 pm in room I-105. Henrique Lopes Cardoso (DEI) will chair the session.

Abstract:

The brain is a powerful model for fast, adaptive and energy-efficient information processing. Translating these principles into computing systems is a major challenge with direct relevance for the next generation of intelligent neural interfaces. In this talk I will present work from my research group where we developed a hybrid neural system combining biological neurons with neuromorphic hardware, where artificial spiking neurons process ongoing brain activity in real time. By converting biological signals into spike-based representations, we trained spiking neural networks (SNNs) to identify short-lived electrophysiological patterns and deployed them on neuromorphic hardware. This system was integrated with open-source electrophysiology tools to create a closed-loop pipeline capable of detecting ongoing brain states and triggering targeted stimulation with low latency. Using this approach, we show that hybrid systems linking artificial and biological neurons can support real-time interaction with living neural circuits. These results provide a proof-of-concept for accessible neuromorphic neural interfaces and open new possibilities for adaptive, low-power and biologically inspired computing technologies.

About the Speaker:

Paulo Aguiar graduated in Physics (U. Lisbon, PT), and completed his PhD in Computational Neuroscience at the Institute for Adaptive and Neural Computation (U. Edinburgh, UK). Since 2016, he leads the Neuroengineering and Computational Neuroscience Lab (i3S-UPorto, PT). His group uses a bottom-up approach, focusing on understanding how neural circuits process and store information. The team combines expertise in neurobiology, advanced analytical methods and computational models, to reveal and repair neural function. He has already been the (co)PI/Task Leader in 20+ national and international research projects obtained through competitive calls.

The talk entitled “nanoML: Pushing the Limits of Edge AI with Weightless Neural Networks”, will be given by Prof. Lizy Kurian John (University of Texas at Austin) on 6 May at 2.30 pm in room I-105. The session will be moderated by Prof. Pedro Diniz (DEI).

About the Talk:

“Mainstream artificial neural network models, such as Deep Neural Networks (DNNs) are computation-heavy and energy-hungry. Weightless Neural Networks (WNNs) are natively built with RAM-based neurons and represent an entirely distinct type of neural network computing compared to DNNs. WNNs are extremely low-latency, low-energy, and suitable for efficient, accurate, edge inference. The WNN approach derives an implicit inspiration from the decoding process observed in the dendritic trees of biological neurons, making neurons based on Random Access Memories (RAMs) and/or Lookup Tables (LUTs) ready-to-deploy neuromorphic digital circuits. WNNs are a natural fit for edge AI due to the low area, energy and latency properties offered by them. This talk will describe the state of the art of Weightless Neural Networks, and their applications for edge inferencing.”

About the Speaker:

“Lizy Kurian John is Truchard Foundation Chair in Engineering at the University of Texas at Austin. Her research interests include workload characterization, performance evaluation, and high performance architectures for emerging workloads. She is recipient of many awards including Joe J. King Professional Engineering Achievement Award (2023), and The Pennsylvania State University Outstanding Engineering Alumnus Award (2011). She has authored 3 books and has edited 4 books including a book on Computer Performance Evaluation and Benchmarking. She holds 18 US patents and is an IEEE Fellow (Class of 2009), ACM Fellow, AAAS Fellow and Fellow of the National Academy of Inventors (NAI).”

Acknowledgement: Lizy K. John is currently a Fulbright Specialist. Her research is supported in part by the Unites States National Science Foundation (NSF) Grants #2326894, #2425655, Semiconductor Research Corporation (SRC) Task 3148.001, and NVIDIA Applied Research Accelerator Program Grant.

The talk entitled “When AI meets Performance Engineering: Challenges and Opportunities”, will be given by Prof. Lizy Kurian John (University of Texas at Austin) on 29 April, at 2.30 pm, in room L119 (DEMec). The session will be moderated by Prof. Pedro Diniz (DEI).

About the Talk:

“Artificial Intelligence/Machine Learning (AI/ML) has transformed hardware design, software systems, and system architectures. Emerging hardware for AI accelerators have become heterogeneous and complex, that traditional modeling methodologies, benchmarks, and metrics for prior systems may not work for assessing AI models and AI hardware. The single-most commonly used operation in AI is matrix multiplication. Hardware accelerators like GPUs have created Tensor Cores to support matrix multiplication. Google TPUs support matrix multiplication by the hardware systolic arrays. However, matrix multiplication cannot be a reliable benchmark for benchmarking, due to its high sensitivity to optimizations. The MLPerf consortium creates ML benchmarks, however those benchmarks are prohibitively difficult to manage for many small companies and academic researchers. What are good benchmarks for AI/ML? How can you evaluate AI hardware in pre-silicon and post-silicon stages? Complete system simulation was a popular mode of pre-silicon evaluation but prohibitively expensive. This talk will describe some of the opportunities and challenges when AI meets Performance Engineering.”

About the Speaker:

“Lizy Kurian John is Truchard Foundation Chair in Engineering at the University of Texas at Austin. Her research interests include workload characterization, performance evaluation, and high performance architectures for emerging workloads. She is recipient of many awards including Joe J. King Professional Engineering Achievement Award (2023), and The Pennsylvania State University Outstanding Engineering Alumnus Award (2011). She has authored 3 books and has edited 4 books including a book on Computer Performance Evaluation and Benchmarking. She holds 18 US patents and is an IEEE Fellow (Class of 2009), ACM Fellow, AAAS Fellow and Fellow of the National Academy of Inventors (NAI).”

Acknowledgement: Lizy K. John is currently a Fulbright Specialist. Her research is supported in part by the Unites States National Science Foundation (NSF) Grants #2326894, #2425655, Semiconductor Research Corporation (SRC) Task 3148.001, and NVIDIA Applied Research Accelerator Program Grant.