Prof. Dr. Jochen Garcke is a faculty member at the Institute for Numerical Simulation, University of Bonn, with a dual affiliation at Fraunhofer SCAI's Department of Numerical Data-Based Prediction. His work bridges numerical simulation and machine learning, focusing on high-dimensional problems, sparse grids, and optimal control. Key research themes: Sparse grids, machine learning for simulations, reinforcement learning, uncertainty quantification Teaching includes courses on Numerical Methods in Science and Technology and Scientific Computing , emphasizing practical machine learning applications. Recent publications explore hybrid models combining data-driven and physics-based approaches in automotive engineering, wind turbines, and geoscientific modeling. His group employs adaptive sparse grids, graph algorithms, and spectral methods to tackle challenges in crash simulations, fluctuating renewable energy systems, and turbulent flow analysis. Collaborations span Fraunhofer SCAI and industry 4.0 initiatives.
Raghavendra Selvan, an Assistant Professor (Tenure Track) at the University of Copenhagen, holds joint appointments in the Machine Learning Section (Department of Computer Science), Kiehn Lab (Department of Neuroscience), and the Data Science Laboratory. His academic journey includes a PhD in Medical Image Analysis (2018), MSc in Communication Engineering (2015), and BSc in Electronics and Communication Engineering (2009). PhD - Medical Image Analysis, University of Copenhagen (2018) MSc - Communication Engineering, Chalmers University (2015) BSc - Electronics and Communication Engineering, BMS Institute of Technology, India (2009) His research focuses on Bayesian Machine Learning with emphasis on Medical Image Analysis, Graph-based Learning, Tensor Networks, Approximate Inference, and Multi-Object Tracking Theory. Recent publications highlight his contributions to environmentally sustainable AI practices, efficient deep learning in medical imaging, and novel applications of tensor networks. Key research areas: Green AI and Environmental Sustainability Medical Image Analysis Graph Neural Networks Crystal Structure Prediction Model Compression Materials Science Applications
Isabelle Augenstein is a Professor at the University of Copenhagen, Department of Computer Science (DIKU), where she heads the Copenhagen Natural Language Understanding (CopeNLU) research group and the Natural Language Processing section. She is also a co-lead of the Danish Pioneer Centre for Artificial Intelligence, Denmark's largest research center initiated by the Danish Ministry of Higher Education and Science. In October 2022, she became Denmark's youngest ever female full professor. Dr. Augenstein earned her undergraduate degree in Computational Linguistics and Psychology from Heidelberg University, followed by a Master's in Computational Linguistics. She completed her PhD in Computer Science at the University of Sheffield under the supervision of Dr. Diana Maynard and Prof. Fabio Ciravegna. In 2021, she earned a Habilitation at the University of Copenhagen in Explainable Fact-checking. Professor Augenstein's primary research focuses on fair and accountable Natural Language Processing, with particular emphasis on explainability, factuality, and bias detection. Her work spans multiple subfields including automated fact-checking, stance detection, gender bias analysis, and cultural bias in language models. She has pioneered research in explainable fact-checking, developing methods that not only predict claim veracity but also provide meaningful explanations of the decision-making process. Her research group has produced numerous influential papers on measuring model fragility, quantifying gender biases, and developing robust fact-checking systems that account for distribution shifts. Her significant contributions have been recognized with several prestigious awards: ERC Starting Grant on 'Explainable and Robust Automatic Fact Checking' DFF Sapere Aude Research Leader fellowship on 'Learning to Explain Attitudes on Social Media' Karen Spärck Jones Award from the British Computing Society and Bloomberg Hartmann Diploma Prize from the Hartmann Foundation Member of the Royal Danish Academy of Sciences and Letters since 2024 Professor Augenstein has secured significant research funding including her ERC Starting Grant supporting five years of blue-sky research. She actively mentors PhD students and postdoctoral researchers through her 'ExplainYourself' project. She served as President of SIGDAT (which organizes the EMNLP conference series), having previously held leadership roles as Vice President and Vice President-Elect. She is a co-founder of Widening NLP (WiNLP), an initiative to increase diversity in the NLP community, and maintains the BIG Directory of underrepresented groups in NLP. She leads the Copenhagen Natural Language Understanding (CopeNLU) research group, which relocated to the historic Østervold Observatory in Copenhagen's Botanical Gardens in 2023. The group focuses on developing methods for explainable and robust natural language understanding, with applications in fact-checking, bias detection, and social media analysis. Professor Augenstein also co-leads the Speech and Language collaboratory at the Pioneer Centre for Artificial Intelligence, where her team investigates how language models can better serve diverse populations while maintaining accountability and transparency.
Zachary Tatlock is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he leads the Programming Languages & Software Engineering Group (PLSE) and the SAMPL Group. His research spans programming languages, formal verification, compilers, and computational fabrication. He is also an Amazon Scholar with AWS's Automated Reasoning Group and previously advised OctoML. Tatlock's work bridges theoretical foundations with practical systems, focusing on making it easier to write tricky code while ensuring correctness through rigorous proofs and measurements. PhD in Computer Science & Engineering, University of California, San Diego (2014) Thesis: Reducing the Costs of Proof Assistant Based Formal Verification Advisor: Sorin Lerner BS in Computer Science (Honors) and Mathematics, Purdue University (2007) Professor Tatlock's research focuses on the intersection of programming languages, formal methods, and systems. His work in compilers and formal verification aims to make it easier to write tricky code while ensuring correctness through rigorous proofs. He explores computational fabrication techniques that bridge digital design with physical manufacturing. His recent work on equality saturation (via the egg framework) has transformed program optimization and synthesis. Tatlock also investigates floating-point numerics, distributed systems verification, and hardware/software co-design, always seeking to balance theoretical rigor with practical implementation. Tatlock's recent publications demonstrate a strong focus on equality saturation techniques (egg framework), computational fabrication, and verified systems. His work increasingly integrates machine learning with program analysis and synthesis. There's a clear trajectory toward more practical applications of formal methods in real-world systems, particularly in numerical computing and fabrication. His research group has made significant contributions to e-graph technology, floating-point accuracy, and the verification of distributed systems. Distinguished Paper Award for Rewrite Rule Inference Using Equality Saturation (OOPSLA 2021) Spotlight Paper Award for Dynamic Tensor Rematerialization (ICLR 2021) Distinguished Paper Award for egg: Fast and Extensible Equality Saturation (POPL 2021) Faculty Appreciation for Career Education & Training (FACET) Award (2020) NSF CAREER Award: Verifying Distributed System Implementations (2017) Distinguished Paper Award for Automatically Improving Accuracy for Floating Point Expressions (PLDI 2015) Distinguished Teaching Award Nomination (2015) Professor Tatlock has advised numerous doctoral, master's, and undergraduate students who have gone on to prominent positions in academia and industry, including faculty positions at the University of Utah and Brown University, and leadership roles at companies like OctoML and Certora. His research is supported by significant funding from NSF, DARPA, DOE, and industry partners, totaling millions of dollars. Current grants include projects on computer-aided reasoning, formal verification, computational fabrication, and machine learning systems. He has served on numerous program committees and organized workshops including FPTalks, EGRAPHS, and PNW PLSE. As co-leader of the Programming Languages & Software Engineering (PLSE) research group and affiliate of the SAMPL Group at the University of Washington, Tatlock has developed influential tools including egg (an equality saturation toolkit), Carpentry Compiler, and Odyssey. His group actively collaborates with industry partners including Amazon Web Services, where he serves as an Amazon Scholar. The group has made significant contributions to equality saturation, floating-point accuracy, program synthesis, and computational fabrication, with applications ranging from compiler optimization to 3D printing.
Jieh Hsiang is a Distinguished Professor at National Taiwan University , with affiliations in the Department of Computer Science and Information Engineering, the Digital Archives and Automatic Inference Laboratory, and the Digital Humanities Research Center. He holds concurrent roles at the Institute of Information Science, Academia Sinica, and the Higher Education Research & Development Office, National Taiwan University. Education PhD in Computer Science, University of Illinois at Urbana-Champaign (1979–1982) BS in Mathematics, National Taiwan University (1972–1976) Research Interests Hsiang's work spans automated reasoning , digital libraries , digital humanities , and information retrieval . His research focuses on integrating computational methods with cultural heritage preservation , particularly through tools like DocuSky and databases such as the Taiwan Historical Digital Library . He explores AI applications in patent analysis , historical text mining , and semantic relationships in legal documents . Recent Trends in Publications His recent articles highlight advancements in BERT and GPT-2 fine-tuning for patent classification , LARGE language models for legal automation , and GIS-based analysis of historical archives . Themes include digital preservation , AI-driven legal text analysis , and cross-disciplinary computational tools for humanities scholars. Scientific Awards 2019 Ministry of Science and Technology Distinguished Research Fellow 2009 National Taiwan University Outstanding In-House Service Award 2008 Chinese Library Association Special Contribution Award 2006 IEEE Test-of-Time Award 1997 & 1999 National Science Council Outstanding Research Award 1997 Ministry of Education Outstanding Industrial-Academic Collaboration Award 1998–2001 Founder and First Chair of IFIP WG1.6 Labs and Collaborations Hsiang leads the Digital Archive and Automatic Inference Laboratory , developing platforms like DocuSky for digital humanities, Taiwan Historical Digital Library , and QGIS Cloud Maps for spatial analysis. His team collaborates internationally on projects involving historical document digitization , patent automation , and cross-domain knowledge integration .
Jungeun (Jenny) Won is an Assistant Professor of Research in the Department of Biomedical Engineering at the School of Engineering and Applied Sciences, University at Buffalo. Her research focuses on optical imaging , biomedical device development , medical image analysis , and artificial intelligence in OCT . She leads the Translational Biophotonics Laboratory , where she develops advanced OCT techniques for medical applications such as diabetic retinopathy , otitis media , and biofilm analysis . Contact: 215J Bonner Hall, Buffalo NY 14260, jungeunw@buffalo.edu Related Links: CV PDF , Google Scholar , Lab Website Her recent work involves high-resolution OCT for longitudinal studies on retinal degeneration, VISTA OCTA for blood flow analysis, and 3D motion correction algorithms to enhance image quality. She also explores multimodal imaging combining OCT with Raman spectroscopy for bacterial differentiation and microplasma-based therapies for ear infections.
Peng Gao is an Assistant Professor in the Department of Computer Science at Virginia Tech. He is affiliated with the Virginia Tech Security & Intelligence Lab and holds a Ph.D. in Electrical Engineering from Princeton University. Prior to his faculty position, he was a postdoctoral researcher at UC Berkeley and held research internships at Microsoft Research, Facebook, and NEC Laboratories America. Education includes a B.Eng. from Shanghai Jiao Tong University (2009-2013), M.A. and Ph.D. from Princeton (2013-2019), and an exchange program at the University of Hong Kong (2012). Key roles include Technical Program Committee memberships for conferences like IEEE S&P, USENIX Security, and CCS. Research focuses on Cybersecurity (APT prevention, network security with P4/eBPF) AI applications (LLMs for security, AI safety) Systems security (attack investigation, threat intelligence) AI for science (molecular/materials prediction) Notable awards include the 2022 Amazon-VT Initiative Faculty Award, CCI Fellowships, and multiple best paper nominations. His lab has received grants from CCI, NSF, and industry partners like Google Cloud and Cisco. Teaching includes courses on Principles of Computer Security (CS 4264) and Blockchain Technologies (CS 5594). He advises ~20 students across Ph.D., MS, and undergraduate levels.
Chinthaka Pathum Dinesh Herath Gedara is an Assistant Teaching Professor in the Multidisciplinary Graduate Engineering Programs at Northeastern University. He holds a PhD in Signal and Image Processing from Simon Fraser University (2021). His research focuses on graph signal processing, machine learning, computer vision, and smart grids. He has published extensively in top journals like IEEE Transactions on Image Processing and Pattern Analysis and Machine Intelligence. His recent work addresses challenges in 3D point cloud processing, image super-resolution, and power system forecasting. Education: PhD, Signal and Image Processing, Simon Fraser University, 2021 Research interests include developing novel algorithms for graph-based data analytics, generative AI applications, and optimization techniques. His work bridges theoretical foundations and practical applications in computer vision and smart energy systems. While no grants or awards are explicitly listed, his publications reflect active engagement in high-impact interdisciplinary research. No student advisees or lab affiliations are documented in the provided materials.
Hoseung Song is an Assistant Professor at KAIST (Korea Advanced Institute of Science & Technology), affiliated with the Department of Industrial and Systems Engineering and the Graduate School of Data Science. His research focuses on statistical data science, decision making, and biomedical applications, particularly in areas like change-point analysis, two-sample tests, and spatial clustering. His work bridges theoretical statistics with practical biomedical and healthcare challenges. Research interests include advanced statistical methodologies for analyzing complex biological and healthcare data, such as viral genomics, microbiota associations, and immune cell clustering. He develops scalable algorithms and kernel-based methods to address high-dimensional and non-Euclidean data challenges. Recent work highlights applications in infectious diseases (e.g., HSV-2) and postmenopausal health through association studies and differential analysis. His publications emphasize robust statistical testing frameworks, including permutation-based limitations, batch effect corrections, and graph-based methodologies. These contributions enhance reliability in biomedical research and safety-critical data applications. His lab likely integrates computational statistics with real-world healthcare datasets to drive translational insights.
Alexander Rodríguez is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan. His research focuses on advancing AI methods for modeling complex spatiotemporal dynamics, particularly in applications related to population health and community resilience. He specializes in machine learning, time series analysis, uncertainty quantification, and multi-agent systems, with an emphasis on scientific modeling and data-driven decision-making. Recent contributions include keynote talks at AAMAS 2025 (Autonomous Agents for Social Good workshop), presentations at the US National Academies Symposium, and invited talks at AAAI 2025 on topics like knowledge-guided machine learning and public health prediction. He co-organizes AAMAS 2025 as sponsorship co-chair and leads initiatives in AI for science and epidemic forecasting. His publications emphasize neural networks for time series forecasting, biomedical foundation models, and epidemic surveillance systems. Notable work includes 'Neural Conformal Control for Time Series Forecasting' (AAAI 2025) and 'Deepcovid: An operational deep learning-driven framework for explainable real-time forecasting' (2021). No scientific awards explicitly listed in available texts. His research group actively collaborates on grants related to AI applications in public health and infrastructure resilience, with a focus on data-centric methodologies and multi-agent systems.
Zahraa Abdallah is a Senior Lecturer at the School of Engineering Mathematics and Technology, University of Bristol. She holds a PhD and BSc in relevant fields. Her research focuses on Machine Learning, Data Science, Time Series Analysis, and their applications in Health Informatics, Neuroscience, and Bioinformatics. She leads projects on wearable technology integration for diabetes management and EEG-based disease classification. Her work emphasizes explainable AI and multimodal approaches. Zahraa is affiliated with the Bristol Doctoral College Initiative (BDFI) as an Academic Co-Director and collaborates with experts like Prof. Raul Santos-Rodriguez. Contact: zahraa.abdallah@bristol.ac.uk | Website: zahraa-abdallah.com Research Interests: Time Series Clustering & Forecasting EEG-based Disease Detection (Parkinson’s, Alzheimer’s) Smartwatch-Driven Healthcare Systems Explainable AI in Biomedical Applications Key Projects: Development of the CSTS benchmark for time series clustering Investigating insulin needs using automated delivery data Gene essentiality classification via graph neural networks Collaborations: Professor Raul Santos-Rodriguez (BDFI) Lucia Marucci (Systems & Engineering Biology)
Michael A. Newton is a Professor and Chair of the Department of Biostatistics and Medical Informatics at the University of Wisconsin–Madison, School of Medicine and Public Health. His research focuses on statistical methodologies for high-dimensional biomedical data, including cancer biology, immunology, and genomics. He is renowned for developing empirical Bayesian methods, stochastic models, and computational tools for analyzing molecular data. His work integrates statistical theory with interdisciplinary collaborations, contributing to advancements in translational biomedicine. Newton has held prestigious awards, including the Mortimer Spiegelman Award (2003) and the COPSS Presidents' Award (2004). He is an elected Fellow of the American Statistical Association and an elected Member of the International Statistical Institute. He leads the Biostatistics and Epidemiology Research and Design (BERD) core at the Institute for Clinical and Translational Research and is affiliated with the Carbone Comprehensive Cancer Center and the Center for Genome Science and Innovation. His teaching includes advanced courses in computational statistics, Bayesian analysis, and statistical methods in molecular biology. Newton directs graduate programs in Statistics and Biomedical Data Science, emphasizing interdisciplinary training.
Dr. Zhenghao Chen is an Assistant Professor at the University of Newcastle. He holds a B.Eng. H1 and Ph.D. from the University of Sydney (2017 and 2022). His research focuses on Computer Vision, NLP, and Machine Learning, with expertise in Generative AI. He has published in top conferences like CVPR and journals such as IEEE T-IP. Awards include the Google Australia Prize and ACM SIGMM Outstanding Thesis Award. He previously worked at TikTok and Disney Research, and serves on program committees for major conferences. Research interests emphasize generative models and industrial applications. His publications span topics like image compression, facial recognition, and medical imaging. Awards highlight academic and industrial recognition. Teaching includes courses on visual signal understanding and video intelligence. Current roles include HDR recruitment and organizing international workshops.
Kourosh Davoudi is an Associate Professor of Computer Science at Ontario Tech University's Faculty of Science. He holds a PhD in Computer Science from York University with a focus on Machine Learning and Data Mining. Prior to joining Ontario Tech in 2019, he was a postdoctoral research fellow at the University of Waterloo's Department of Management Sciences. His research interests span Natural Language Processing, Deep Learning, Reinforcement Learning, Graph Mining, and Machine Learning. He actively supervises graduate students in these areas and teaches courses such as Data Mining and Artificial Intelligence. His research emphasizes practical applications of AI techniques in areas like outbreak detection, sentiment analysis, and automated grading systems. Recent work includes innovations in neural document segmentation, vision-language models, and hybrid outbreak detection using social media data. His publications consistently address challenges in algorithm design, explainable AI, and domain-specific NLP applications. Dr. Davoudi has contributed to conferences such as COLING, EMNLP, and IEEE transactions, focusing on interdisciplinary applications of machine learning. His work bridges theoretical advancements with real-world problems in healthcare, education, and social media analysis.
Nianwen Xue is a Professor in the Computer Science Department and Language & Linguistics Program at Brandeis University, leading the Chinese Language Processing Group. He holds affiliations with the Michtom School of Computer Science, Benjamin and Mae Volen National Center for Complex Systems, and serves on editorial boards of Computational Linguistics and Lingua Sinica. His research focuses on computational linguistics, machine learning, and cross-linguistic meaning representations. He previously held roles at the University of Colorado and University of Pennsylvania. Education: Ph.D. in Linguistics from University of Delaware, M.A. and B.A. in Linguistics from Nankai University. Research projects include the Chinese Treebank, OntoNotes, and initiatives funded by NSF, DARPA, and IARPA. Teaching includes courses on Natural Language Systems and Computational Corpus Linguistics. He has organized major NLP conferences like CoNLL Shared Tasks and DMR workshops. Research interests emphasize semantic parsing, discourse analysis, and computational social science, with over 100 publications in top venues like ACL, EMNLP, and Computational Linguistics. His work bridges theoretical linguistics and applied NLP, advancing cross-lingual resources and formal meaning representations.
