Abstract

AI-driven data processing is revolutionizing the way researchers analyze vast datasets, enabling intuitive interactions and insightful answers. However, the integration of AI in data processing also introduces several challenges that must be addressed to ensure its reliability and trustworthiness. Key issues include hallucinations, where AI systems generate incorrect information; bias, which can lead to unfair outcomes; ethical considerations, such as privacy and misuse; explainability, to ensure transparency; and answer provenance, to verify the accuracy of AI outputs. These challenges can undermine the effectiveness of AI systems and potentially lead to incorrect or even harmful decisions. This talk will explore these issues and discuss strategies to mitigate them, ensuring the full potential of AI in data analysis is harnessed responsibly.

Biography

Dr. Georgia Koutrika is Research Director at Athena Research Center in Greece. Before that, she was a Senior Research Scientist at HP Labs, researcher at IBM Almaden, and postdoctoral researcher at Stanford. Her research emerges at the intersection of data management and deep learning, focusing on Conversational Data Exploration, Fair and Ethical Algorithmic Systems, and Learnt Data Management. Her work has been incorporated in commercial products, described in 19 granted patents and 26 patent applications in the US and worldwide, and published in more than 140 papers in top-tier conferences and journals. She is a member of the VLDB Endowment Board of Trustees, coordinating EiC for VLDB Journal, and chair of the ACM Europe Working Group on Seasonal Schools. She is VLDB 2027 General Chair, EDBT2025 Demo Chair, and she was PC co-Chair for VLDB 2023, co-EiC of Proceedings of the VLDB (PVLDB) Vol 16, General Chair for ACM SIGMOD 2016, and member of the PVLDB Advisory board. She has served in various other organization roles, including EDBT 2023 and ICDE 2021 sponsorship chair. She has served or serves as associate editor for top conferences (such as ACM SIGMOD and VLDB) and journals (TKDE, VLDB Journal). She has received the EDBT 2023 Test-of-Time award, 2 best demo awards, and several industrial recognitions.

Abstract

Massive continuous data streams arise naturally in several dynamic big data analytics applications, such as enabling observability for complex distributed systems, network-operations monitoring in large ISPs, or incremental federated learning over dynamic distributed data. In such settings, usage information from numerous devices needs to be continuously collected and analyzed for interesting trends and real-time reaction to different conditions (e.g., anomalies/hotspots, DDoS attacks, or concept drifts). Streaming data raises important memory-, time-, and communication-efficiency issues, making it critical to carefully optimize the use of available computation and communication resources. In this talk, I will provide a (biased) overview of some key algorithmic tools in the space of streaming data analytics, along with relevant applications and challenges.

Biography

Minos Garofalakis is the Director of the Information Management Systems Institute (IMSI) at the ATHENA Research Center and a Professor at the School of ECE at the Technical University of Crete (TUC). He also works as a (part-time) senior research consultant for Huawei ISR/ERC and is the Co-founder and Director of Research at Agora Labs, a startup company bringing state-of-the-art data privacy technologies to the healthcare domain. Minos received the MSc and PhD degrees from the University of Wisconsin-Madison, and previously held senior/principal researcher positions at Bell Labs (1998-2005), Intel Research Berkeley (2005-2007), and Yahoo! Research (2007-2008); in parallel, he held an Adjunct Professor position at the EECS Department of UC Berkeley (2006-2008). Between 2/2022-2/2023, he also worked as a consulting Senior Principal Scientist for Amazon Web Services (AWS). Minos’s research interests lie in the broad area of Big Data Analytics. He has published over 170 papers that have received more than 17,000 citations (h-index=70) according to Google Scholar. Minos is an ACM and IEEE Fellow, a Member of Academia Europaea, and a recipient of several awards, including the TUC “Excellence in Research” Award (2015), the Bell Labs President’s Gold Award (2004), two Best Research Paper Awards (VLDB’2024, ICDE’2009), and ten "best of" conference paper selections.

Abstract

The technology ecosystem has experienced a profound and disruptive transformation in recent years. Groundbreaking technological innovations such as cloud computing, the Internet of Things (IoT) and Artificial Intelligence (AI) have paved the way for applications that were once deemed inconceivable, significantly enhancing quality of life and empowering businesses to make more informed decisions. The long-term potential of these technologies presents immense opportunities. The question we face now is how to automate the machine learning workflows and build systems that can meet the demands of scalability and efficiency, bridging the gap between AI innovations and end users to make AI widely accessible to diverse audiences. In this talk, we explore recent developments and highlight exciting prospects for supporting AI pipelines across the evolving computing continuum. In particular, we will discuss a series of research challenges and introduce novel techniques focused on resource management, energy efficiency, reliability, and privacy. Our discussion is motivated by critical application areas within the SmartCity domain, specifically: (a) transportation systems and (b) urban disaster and emergency response.

Biography

Vana Kalogeraki is the Dean of the School of Information Sciences and Technology, a Professor at the Department of Informatics and a Director of the Computer Systems and Communications Laboratory at Athens University of Economics and Business. Previously she has held positions as an Associate and Assistant Professor at the Department of Computer Science at the University of California, Riverside and as a Research Scientist at Hewlett-Packard Labs in Palo Alto, CA. She received her PhD from the University of California, Santa Barbara. Prof. Vana Kalogeraki has been working in the field of distributed and real-time systems, big data systems, stream processing systems, participatory sensing systems, peer-to-peer systems, crowdsourcing, mobility, resource management and fault-tolerance for over 25 years and has authored and co-authored over 200 papers in journals and conferences proceedings, including co-authoring the OMG CORBA Dynamic Scheduling Standard. Prof. Kalogeraki was invited to give keynote talks at CLOSER 2023, PerFoT2018, MoVid2015, DNCMS 2012, SN2AE 2012, PETRA 2011, DBISP2P 2006 and MLSN 2006 in the areas of IoT, participatory sensing systems and sensor network middleware and delivered tutorials and seminars on peer-to-peer computing. She has served as the General co-Chair of EuroSys 2024, GEC 2023, MDM 2021, SEUS 2009 and WPDRTS 2006 and as a Program co-Chair of MobiQuitous 2023, ACSOS 2021, DASFAA 2021, Middleware 2019, MDM 2017, DEBS 2016, MDM 2011, ISORC 2009, ISORC 2007, ICPS 2005, WPDRTS 2005 and DBISP2P 2003, a Tutorial Chair for IEEE ICDE 2020, ACM DEBS 2015, a Workshops Chair for IEEE SRDS 2015, a Demo Chair for IEEE MDM 2012, a Poster Chair for GEC2021, in addition to other roles such as Area Chair (IEEE ICDCS 2016, 2012) and as program committee member on over 200 conferences. She was also awarded an ERC Starting Independent Researcher Award, a Marie Curie Fellowship, three best paper awards at the 11th ACM International Conference on Distributed Event-Based Systems (DEBS 2017), 24th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2009) and the 9th IEEE Annual International Symposium on Applications and the Internet (SAINT 2008), a best poster award at EuroSys 2024, a best technical paper award at ACM PETRA 2018, a Best Student Paper Award at the 11th IEEE/IPSJ International Symposium on Applications and the Internet (SAINT 2011), an IBM best student paper award runner up at MDM 2014, a UC Regents Fellowship Award, UC Academic Senate Research Awards and a research award from HP Labs. She has also received an Award for Excellence in Teaching for the academic year 2018-2019 from the Department of Computer Science, Athens University of Economics and Business. Her research has been supported by an ERC Starting Independent Researcher Grant, the European Union, joint EU/Greek "Aristeia" grant, a joint EU/Greek "Thalis" grant, NSF and gifts from SUN and Nokia.

Abstract

The exponential growth of cloud computing has been a defining trend of our time, fueled by rapidly growing demands from data-intensive and machine learning workloads. Despite the end of Denard scaling, the cloud's energy demand grew more slowly than expected over the past decade due to the aggressive implementation of energy-efficiency optimizations. Unfortunately, there are few significant remaining optimization opportunities using traditional methods, and moving forward, the cloud's and AI's continued exponential growth will translate into rising energy demand, which, if left unchecked, will translate to an increasing carbon footprint.
In this talk, I will discuss recent developments in decarbonizing cloud platforms and workloads. I will discuss how AI workloads have contributed to the rising demand for cloud computing and the promise that AI holds for enhancing the sustainability of cloud platforms. I will discuss how recently proposed approaches for designing carbon-efficient systems introduce new tradeoffs in performance, cost, and energy-efficiency when reducing the carbon footprint of modern cloud applications. I will present approaches for navigating these tradeoffs and end with open research challenges in the emerging field of computational decarbonization.

Biography

Prashant Shenoy is currently a Distinguished Professor and Associate Dean in the College of Information and Computer Sciences at the University of Massachusetts Amherst. He received the B.Tech degree in Computer Science and Engineering from the Indian Institute of Technology, Bombay and the M.S and Ph.D degrees in Computer Science from the University of Texas, Austin. His research interests lie in distributed systems and networking, with a recent emphasis on cloud and sustainable computing. He has been the recipient of several best paper awards at leading conferences, including two ACM Test of Time Awards. He is a fellow of the ACM, IEEE, AAAS, and AAIA.

Abstract

Distributed and event-based systems are central to today's real-time computing. But behind these technical infrastructures lie political and social structures that shape how they function and who they benefit. These systems rely on the large-scale extraction of labor, resources, and data. The human work that sustains them, such as data labeling, content moderation, and data cleaning, is often outsourced, poorly compensated, and rendered invisible. At the same time, the environmental footprint of distributed infrastructures, from high energy consumption to the mining of materials for hardware, is significant and disproportionately impacts vulnerable regions. Yet both forms of extraction remain largely overlooked in mainstream systems research. In this talk, we examine how power operates in the design and deployment of distributed systems. We analyze how current practices prioritize speed, automation, and control, often reinforcing global inequalities and ecological harm. Drawing on research in data justice, intersectional feminist studies, and political ecology, we argue that distributed systems are shaped by social and political choices, not just technical ones. As an alternative, we explore participatory design and community-centered approaches as strategies for redistributing power and building infrastructures that serve the common good.

Biography

Amir H. Payberah is an Associate Professor of Computer Science at KTH Royal Institute of Technology in Sweden. He earned his PhD in distributed systems from KTH in 2013. Following his doctoral studies, he worked as a postdoctoral researcher at the Research Institute of Sweden (RISE), focusing on data-intensive computing platforms. From 2017 to 2018, he served as a machine learning researcher at the University of Oxford before returning to KTH as an Assistant Professor. He currently serves as the program director of the ICT doctoral program at EECS and leads the WASP cluster on Legal, Ethical, and Societal Aspects of AI. Amir's research focuses on the intersection of equity and justice in AI, particularly on large language models. To explore these questions, he initiated the Co-Liberative Computing research group to promote critical consciousness in computer science research and education. The group is grounded in recognizing that computing is not neutral; it shapes and is shaped by societal power structures. Their work centers on exposing systemic inequalities embedded in technical infrastructures and developing alternatives rooted in justice and equity. At the core of this approach is the principle of co-liberation, a collective framework for building technologies that promote mutual well-being and redistribute power. Rather than treating inclusion as charity, this perspective reframes computing as a space of relational struggle, where collaboration across lines of difference can lead to more inclusive, equitable, and transformative outcomes.