Leman Akoglu

Professor at Carnegie Mellon University

Aaron Klein

Senior Scientist
at Amazon Web Services

Mitra Baratchi

Professor
at Leiden University

Irina Rish

Professor
at Université de Montréal

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Leman Akoglu

Leman Akoglu is the Heinz College Dean’s Associate Professor of Information Systems at Carnegie Mellon University. She has also received her Ph.D. from CSD/SCS of Carnegie Mellon University in 2012. Dr. Akoglu’s research interests are graph mining, pattern discovery and anomaly detection, with applications to fraud and event detection in diverse real-world domains. She is a recipient of the SDM/IBM Early Career Data Mining Research award (2020), National Science Foundation CAREER award (2015) and US Army Research Office Young Investigator award (2013). Her early work on graph anomalies has been recognized as the The Most Influential Paper (PAKDD 2020), which was previously awarded the Best Paper (PAKDD 2010), along with several “best paper” awards at top-tier conferences. Her research has been supported by the NSF, US ARO, DARPA, Adobe, Capital One Bank, Facebook, Northrop Grumman, PNC Bank, PwC, and Snap Inc.

Aaron Klein

Aaron Klein is a senior scientist at Amazon Web Services (AWS), where he leads the scientific efforts on guardrails for large language models, deployed in several AWS services. 
His research focus is on automated machine learning (AutoML) for deep neural networks, specifically neural architecture search and hyperparameter optimization. Recently, he works on data-driven approaches to make large language models more resource efficient.
Aaron joined AWS in 2019 as a Post-Doc in the long-term science team of SageMaker, AWS’s machine learning cloud platform, where he worked on large-scale solutions for AutoML. He holds a Ph.D. from the University of Freiburg (Germany) (2020) under the supervision of Prof. Dr. Frank Hutter. 
Together with his collaborators from the University of Freiburg, he won the ChaLearn AutoML Challenge in 2015. He received with his co-authors the best paper award at the AutoML Conference in 2021. He co-organized the neural architecture search workshop at ICLR in 2020 and 2021 and served as the local chair of the AutoML conference in 2022. Since 2020, he also co-hosts the virtual AutoML Seminar as part of the ELLIS Units Berlin and Freiburg, featuring regular talks and discussions with experts from the research community.

Mitra Baratchi

Mitra Baratchi is assistant professor of artificial intelligence at Leiden University, where she leads the Spatio-temporal data Analysis and Reasoning (STAR) and co-leads of the Automated Design of Algorithms research group. Her research interests lie in spatio-temporal, time-series, and mobility data modelling. She strongly focuses on developing algorithms for wearable sensors data, Earth observations and other open spatio-temporal data sources. Specifically, she explores the design of algorithms that can automatically handle all necessary data processing tasks from the point of data collection to high-level modelling, extraction of information, and effective decision-making from such data. Her research targets applications in a broad range of urban, environmental, and industrial domains, for which she has collaborated, notably with the European Space Agency, Netherlands Institute for Space Research, Honda Research Institute, various municipalities, and researchers in other scientific disciplines.

Irina Rish

Irina Rish is a Full Professor in the Computer Science and Operations Research Department at the Université de Montréal (UdeM) and a core faculty member of MILA – Quebec AI Institute, where she leads the Autonomous AI Lab. Dr. Rish holds a Canada Excellence Research Chair (CERC) in Autonomous AI and a Canadian Institute for Advanced Research (CIFAR) Canada AI Chair. Her extensive research career spans multiple AI domains, from automated reasoning and probabilistic inference in graphical models, to machine learning, sparse modeling, and neuroscience-inspired AI. Her current research endeavors concentrate on continual learning, out-of-distribution generalization, and robustness of AI systems, as well as understanding neural scaling laws and emergent behaviors, w.r.t. both capabilities and alignment, in large-scale foundation models – a vital stride towards achieving maximally beneficial Artificial General Intelligence (AGI). She teaches courses on AI scaling and alignment, and runs Neural Scaling & Alignment workshop series. Dr. Rish is a recipient of the INCITE 2023 and other compute grants by the US Department of Energy, and leads several projects on Scalable Foundation Models on Summit & Frontier supercomputers at the Oak Ridge Leadership Computing Facility, focusing on developing open-source large-scale AI models. She is also a co-founder and the Chief Science Officer of nolano.ai, a company focused on training, compression and fast inference in large-scale AI models.