David Lo (Singapore Management University, Singapore) is the OUB Chair Professor of Computer Science and Director of the Center for Research in Intelligent Software Engineering (RISE) at Singapore Management University. Since the mid-2000s, he has championed the area of AI for Software Engineering (AI4SE), demonstrating how AI — including data mining, machine learning, information retrieval, natural language processing, and search-based algorithms — can be used to transform software engineering (SE) data into automation and insights. Additionally, through interviews and surveys, he has (i) sought to understand practitioners’ pain points, which highlight requirements for further automation and research, and (ii) explored the requirements and acceptance thresholds for adopting AI-powered tools. His works have been recognized by over 20 awards, including two Test-of-Time awards; nine ACM/IEEE Distinguished Paper awards; and have gathered more than 30k citations. He is an ACM Fellow, IEEE Fellow, ASE Fellow, and National Research Foundation Investigator (Senior Fellow), and is serving as a PC Co-Chair for ASE’20, FSE’24, and ICSE’25. More information is available at: Link to Website
Xi He (University of Waterloo, Canada) holds the position of Canada CIFAR AI Chair at the Vector Institute and serves as an Assistant Professor at the University of Waterloo’s School of Computer Science. Her research is dedicated to enhancing privacy and security within the realm of big data. This involves developing accessible, dependable tools for data analysis and machine learning, ensuring strong security and privacy (S&P) protections. Her foundational strategy focuses on setting and achieving strict S&P criteria, leading to innovative optimizations in S&P-conscious database and machine learning systems. Her contributions have been recognized at leading conferences in databases, privacy, and machine learning, such as SIGMOD, VLDB, CCS, PoPets, and AAAI, where she has also conducted esteemed tutorials on privacy. In 2021, she co-authored the book “Differential Privacy for Databases” with Joseph Near, further cementing her expertise and influence in the field.” More information is available at: Link to Website
Denys Poshyvanyk (William & Mary, USA) is a Chancellor Professor and a Graduate Director in the Computer Science Department at William & Mary. He currently serves as a Guest Editor-in-Chief of the AI-SE Continuous Special Section at the ACM Transactions on Software Engineering and Methodology (TOSEM) and a Program Co-Chair for FSE’25. He is a recipient of multiple ACM SIGSOFT Distinguished paper awards, the NSF CAREER award (2013). He is an IEEE Fellow and an ACM distinguished member. More information is available at: Link to Website
Negar Rostamzadeh (Google, Canada) is a Senior Research Scientist at Google Responsible team, where she studies the social impact of machine learning technologies and evaluation systems. Prior to that, Negar was a research scientist at Element AI, where she worked on efficient sampling and labeling approaches in a variety of multimedia and computer vision problems. Negar obtained her PhD from the University of Trento, where she was advised by Dr. Nicu Sebe. Her main area of research during her PhD was on large scale video understanding problems. During her PhD, she spent two years at MILA, where she worked on attention mechanisms in videos, video captioning and generation under the supervision of Dr. Aaron Courville. Negar was a co-founder of Women in Deep Learning (WiDL) in 2016, and a co-organizer of WiML, WiCV and WiDL in 2017. She served as a DEI chair for ICCV 2021. She was also co-founder and chair of multiple workshop series in CVPR, such as Computer Vision for Fashion, Art and Design, Ethical Considerations in Creative Applications of Computer Vision, and Responsible Generative AI, as well as Science meets Engineering of Deep Learning workshop series at ICLR, NeurIPS and FAccT. More information is available at: Link to Website
Prem Devanbu (UC Davis, USA) is Distinguished Professor of Computer Science at UC Davis. He received his B.Tech from IIT Madras, and his Ph.D from Rutgers University under Alex Borgida. After a career in Industrial R&D at Bell Labs in New Jersey, he joined UC Davis. He has worked in several areas, including Software tools, Secure Data Outsourcing, Empirical Software Engineering, and the Naturalness of Software, and applications thereof. Five of his papers (in MSR 2006, MSR 2009, ESEC/FSE 2008, ESEC/FSE 2009, ESEC/FSE 2011) have won “test-of-time” or “10 year most influential paper” awards. He also won the ACM SIGSOFT Outstanding Research Award in 2021, “for profoundly changing the way researchers think about software by exploring connections between source code and natural language”. He is an ACM Fellow. More information is available at: Link to Website
Lingming Zhang (University of Illinois Urbana-Champaign, USA) is an Associate Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign. His main research interests lie in Software Engineering and Programming Languages, as well as their synergy with Machine Learning, aiming to improve developer productivity and software quality. For example, his work has found over 1000 new bugs/vulnerabilities in critical software systems, including deep learning libraries/systems, C/C++ compilers, Java virtual machines, operating systems, and quantum computing systems. He is the recipient of ACM SIGSOFT Early Career Researcher Award, NSF CAREER Award, and UIUC Dean’s Award for Excellence in Research. Additionally, he has received multiple distinguished/best paper awards and various awards/grants from leading tech companies such as Alibaba, Amazon, Google, Kwai Inc., Meta, NVIDIA, and Samsung. He currently serves as program co-chair for ASE 2025 and LLM4Code 2024, and associate chair for OOPSLA 2024. More information is available at: Link to Website
Jie M. Zhang (King’s College London, England) is a lecturer (assistant professor) of computer science at King’s College London, UK. Before joining King’s she was a Research Fellow at University College London and a research consultant for Meta. She got her PHD degree at Peking University in 2018. Her main research interests are software testing, software engineering and AI/LLMs, and AI trustworthiness. She has published many papers in top-tier venues including ICLR, ICSE, FSE, ASE, ISSTA, TSE, and TOSEM. She is a steering committee member of ICST and AIware. She is a Program co-chair of AIware 2024, Internetware 2024, ASE 2023 NIER track, SANER 2023 Journal-First Track, PRDC 2023 Fast Abstract Track, SBST 2021, Mutation 2021&2020, and ASE 2019 Student Research Competition. Over the last three years, she has been invited to give over 20 talks at conferences, universities, and IT companies, including four keynote talks. She has also been invited as a panelist for several seminars on large language models. She has been selected as the top-fifteen 2023 Global Chinese Female Young Scholars in interdisciplinary AI. Her research has won the 2022 Transactions on Software Engineering Best Paper award and the ICLR 2022 spotlight paper award. More information is available at: Link to Website
Golnoosh Farnadi (McGill University, Canada) is an Assistant Professor at McGill University’s School of Computer Science, an Adjunct Professor at Université de Montréal, and a visiting researcher at Google. She’s a key member of MILA, holds a Canada CIFAR AI Chair, co-directs McGill’s McCAIS, and leads the EQUAL lab at Mila/McGill, focusing on algorithmic fairness and responsible AI. Previously, she was at HEC Montréal and held postdoctoral positions at Université de Montréal/MILA and the University of California, Santa Cruz. Golnoosh completed her Ph.D. at KU Leuven and Ghent University in 2017, with research visits to several prestigious institutions. She has received awards from Google and Facebook, recognized as a Rising Star in AI Ethics, and in 2023, won a Google inclusion research award and was acknowledged as a leading woman in AI Ethics. More information is available at: Link to Website
Anh Nguyen-Duc (South Eastern Norway and Norwegian University of Science and Technology, Norway) is a Full Professor of Software Engineering at University of South Eastern Norway and Norwegian University of Science and Technology. He has more than 150 peer-reviewed publications in high-ranked journals, conferences, and workshops in software engineering fields, including software startups, software security and AI Engineering. Currently, he is leading several research initiatives on evaluating the adoption of Generative AI for software development and business. More information is available at: Link to Website
Elnaz Kanani K. (Deloitte, Canada) is Partner in Omnia – Artificial Intelligence (AI) practice at Deloitte. She is a mathematician at heart with a Doctorate degree in Management Sciences, Quantitative Methods, from University of Montreal for which she was the recipient of the best doctoral thesis of 2015. She leads advanced analytics and AI initiatives for Government and Public Services and has worked with various government entities in these topics. Her expertise spans data analytics, machine learning, and the implementation of AI (and Generative AI) solutions to drive efficiency and transparency in government operations and processes. Known for her commitment to ethical AI practices, she has successfully navigated the intersection of technology and public service, contributing to the advancement of data-driven governance and policy-making. She has pioneered numerous proof of concepts, minimum viable products (MVPs), and fully functional AI solutions for the public sector. She is also an Adjunct Professor at the University of Ottawa’s Telfer School of Management, where she teaches multiple courses focused on Business AI, Big Data Analytics, and Digital to MBA and Executive-MBA candidates. Elnaz was also recognized as a rising business star by the Ottawa Board of Trade in 2021 as part of their annual Forty under 40 awards.
Karime Maamari (Distyl AI, USA) is a Research Engineer at Distyl AI, where he specializes in building LLM-powered structured querying and optimization systems for Fortune 500 customers. Prior to that, Karime was a Research Associate at Argonne National Laboratory, where he worked on synthetic data generation and a Simulation Engineer at the NASA Langley Research Center, where he worked on simulation development and analysis. Karime holds a B.S in Physics from the University of Southern California.
Abstract: Anomaly detection plays an important role in management of modern large-scale distributed systems. Logs, which record system runtime information, are widely used for anomaly detection. However, Unsupervised anomaly detection algorithms face challenges in addressing complex systems, which generate vast amounts of multivariate time series data. Timely anomaly detection is crucial for managing these systems effectively and minimizing downtime. This proactive approach minimizes system downtime and plays a vital role in incident management for large-scale systems. To address these challenges, a method called Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED) has been developed for detecting anomalies in CN PTC system logs. MSCRED leverages the power of multivariate time series data to perform anomaly detection and diagnosis. It creates multi-scale signature matrices that capture different levels of system statuses across various time steps. The method utilizes a convolutional encoder to capture inter-sensor correlations and a Convolutional Long-Short Term Memory (ConvLSTM) network with attention mechanisms to capture temporal patterns.
Abstract
Abstract: Language models such as RoBERTa, CodeBERT, and GraphCodeBERT have gotten much attention in the past three years for various Software Engineering tasks. Though these models are proven to have state-of-the-art performance for many SE tasks, such as code summarization, they often require to be fully fine-tuned for the downstream task. Is there a better way for fine-tuning these models that require training fewer parameters? Can we impose new information on the current models without pre-training them again? How do these models perform for different programming languages, especially low-resource ones with less training data available? How can we use the knowledge learned from other programming languages to improve the performance of low-resource languages? This talk will review a series of experiments and our contributions to answering these questions.
