Professor Line Roald is a faculty member in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. Her research focuses on power system optimization, renewable energy integration, grid resilience, and wildfire risk mitigation using stochastic optimization and data-driven methods. Education : PhD (2016), MS (2012), BS (2009) from ETH Zurich Key Research Areas : Power Systems Optimization, Renewable Energy Integration, Wildfire Risk Mitigation, Stochastic Programming, Grid Decarbonization Her work addresses critical challenges in sustainable energy systems, including balancing grid efficiency and risk, optimizing electrolyzer scheduling for flexibility, and predicting cascading blackout severity using graph neural networks. She has developed frameworks for carbon intensity comparison and wildfire risk assessment in power systems. Scientific Awards : 2024 Inclusion, Equity and Diversity in Engineering Award 2024 Vilas Faculty Early Career Investigator Award 2023 IEEE Power Tech Best Student Paper Award 2021 NSF CAREER Award 2019 MTLE Fellow Professor Roald mentors graduate students and teaches courses including Introduction to Optimization and On-Line Control of Power Systems . Her publications highlight innovative approaches to grid security, carbon-efficient energy markets, and climate resilience in infrastructure systems.
Travis Desell is a Professor in the Department of Software Engineering at Rochester Institute of Technology (RIT), part of the B. Thomas Golisano College of Computing and Information Sciences. His research focuses on data science and machine learning applied to large-scale datasets using high-performance and distributed computing. He specializes in neuro-evolution, combining evolutionary algorithms with neural networks, particularly through his EXACT and EXAMM algorithms. He leads the D2S2 Lab and has developed the SALSA programming language based on the actor model. Currently funded projects include the National General Aviation Flight Information Database (NGAFID) and an NSF award exploring contextual bandits for decision-making in cyber-physical systems. His work emphasizes practical scientific applications, including stock forecasting, power plant data prediction, and explainable time series models. Education details are not explicitly provided, but his roles and publications indicate advanced academic credentials. Research interests span neuro-evolutionary techniques, recurrent neural networks, and distributed computing frameworks. Key projects include EXAMM for time series forecasting and NGAFID for flight safety analysis. Collaborations involve students and teams at RIT and beyond, with a focus on advancing AI-driven solutions in dynamic environments. Lab affiliations include the D2S2 Lab, where he mentors students and conducts cutting-edge research. Current opportunities exist for PhD students with backgrounds in software engineering and expertise in areas like NLP, web development, and distributed systems.
Jiaoyan Chen is a Lecturer (Assistant Professor) in the Department of Computer Science at The University of Manchester, set to become a Senior Lecturer (Associate Professor) from July 2025. Previously, she served as a Senior Researcher at the University of Oxford and held postdoctoral roles at Heidelberg University. Her research focuses on neural-symbolic knowledge representation, ontology engineering, and integrating large language models with knowledge graphs. Education: PhD in Knowledge Reasoning and Predictive Analytics (Zhejiang University, 2011-2016) and BEng in Computer Science (Zhejiang University, 2007-2011). She also spent time as a visiting scholar at Zurich University (2014-2015). Research Interests include: Knowledge Graphs, Ontologies, Large Language Models, Retrieval Augmented Generation, and Machine Learning applications in knowledge-aware systems. She leads major grants such as the EPSRC New Investigator Award (EP/Y017706/1) and collaborates internationally through initiatives like the Manchester-Melbourne-Toronto Fund. Teaching: Leads units like 'Data Engineering Technologies' and 'Advanced Topics in Knowledge Representation'. She actively advises PhD students and co-develops tools like OWL2Vec* and DeepOnto. Service roles include Associate Editor of Transactions on Graph Data and Knowledge (TGDK), membership in the EPSRC Peer Review College, and leadership in ontology alignment initiatives like OAEI Bio-ML Track.
Wenhao Ding is a Research Scientist at NVIDIA's Autonomous Vehicle Group, focusing on enhancing the safety and robustness of physical autonomous systems, particularly autonomous vehicles. His research integrates multi-modal large language models, reinforcement learning, and causal discovery to improve model reasoning capabilities. He holds a Ph.D. from Tsinghua University's Department of Electronic Engineering, with a thesis on 'Generative AI for Critical Digital Twins.' Key research interests include safety-critical scenario generation, causal representation learning, and offline reinforcement learning. His work emphasizes closed-loop simulation for autonomous systems and has led to contributions like the SafeBench benchmarking platform and the RealGen scenario generation framework. He has received the 2022 Qualcomm Innovation Fellowship. Notable collaborations include projects with Prof. Marco Pavone at Stanford and internships at Amazon Lab126 (Astro team) and Bosch Center for AI. He actively reviews for top conferences (ICML, NeurIPS, CVPR) and journals (IEEE T-ITS, RA-L). His recent focus on privacy risks in robotics and causal-aware driving models underscores his commitment to trustworthy AI systems. He organizes conferences like the 2024 IEEE International Automated Vehicle Validation Conference and co-hosted the Secure and Safe Autonomous Driving (SSAD) Workshop at CVPR 2023. His interdisciplinary work bridges theory and practice, addressing critical challenges in autonomous systems' safety and generalization.
Professor Yizhou Sun is affiliated with the University of California Los Angeles (UCLA) and the Henry Samueli School of Engineering and Applied Science . Her academic work focuses on Machine Learning , Artificial Intelligence , and Graph Neural Networks within the Computer Science department. Her research spans High-Level Synthesis , Causal Inference , and Computational Biology , with recent publications addressing neural network compression, language model safety, and dynamical system modeling. The trends in her recent 2025 and 2024 publications emphasize Deep Learning , Graph Theory , and Language Model Optimization , reflecting interdisciplinary applications in Biomedical Data , Hardware Design , and Physical Simulation .
Dr. Arpan Man Sainju is an Assistant Professor and Internship Coordinator in the Department of Computer Science at Middle Tennessee State University (MTSU). He holds a PhD (2021) and MS (2020) from the University of Alabama, and a B.E. (2011) from Tribhuvan University. His research focuses on spatial big data analytics, spatiotemporal data mining, and GIS applications in environmental modeling, disaster management, and geospatial science. He develops innovative algorithms for Earth imagery segmentation, flood inundation mapping, and physics-aware machine learning models. Education: PhD in Computer Science, University of Alabama (2021) MS in Computer Science, University of Alabama (2020) B.E. in Computer Science, Tribhuvan University (2011) Key research interests include deep learning for geospatial tasks, semi-supervised learning with limited labels, and parallel computing for big spatial data. His work bridges computer science and environmental science, addressing challenges in hydrology, urban safety, and disaster response. He has published extensively in top journals like ACM TIST, IEEE TKDE, and Environmental Modelling & Software, focusing on applications like flood modeling, road safety analysis, and 3D shape analysis. Dr. Sainju collaborates on interdisciplinary projects involving physics-guided models, hidden Markov structures, and GPU-accelerated algorithms. His research has been applied to real-world scenarios such as hurricane flood analysis and malware detection through Windows log analysis.
Prof. Maosong Sun is a Professor at the Department of Computer Science and Technology, Tsinghua University, China. He holds additional leadership roles including Executive Vice Dean of the Institute for Artificial Intelligence and Deputy Director of the National Engineering Laboratory for Cyberlearning and Intelligent Technology. His research focuses on natural language processing (NLP), artificial intelligence, machine learning, and computational education. He leads interdisciplinary projects in computational humanities, knowledge graphs, and MOOC platforms like XuetangX, which has over 58.8 million registered learners. Key contributions include pioneering work in Chinese NLP tools, poetry generation systems like Jiuge, and large-scale research initiatives funded by Chinese and Singaporean programs. Awards include the Tsinghua University Education Award (2019) and the National Outstanding Practitioner Award (2007). Established NLP and Computational Humanities & Social Sciences Lab (2008) Co-director of the Joint Research Center for Extreme Search (2011-present) Over 200 publications with 11,000+ citations (h-index 47)
Dr. Teresa Wang is a Senior Lecturer in Data Science at Monash University's Faculty of Information Technology, specializing in entity/user modeling, relational/structural machine learning, and graph/network analysis. She holds a Ph.D. from the University of Queensland and degrees from Nanjing University. Currently, she directs the Master of Data Science Program and teaches courses like FIT5201 Machine Learning. Her research focuses on social, e-commerce, and health data modeling, with notable projects including the Knowledge Enriched Approach for Effective Personalization (2025–2027) and collaborations on AI in Mental Health and Site Safety. Dr. Wang has co-authored over 59 publications, emphasizing areas like ontology matching and multimodal data analysis. She actively supervises PhD students and contributes to initiatives like the CSIRO Next Generation Graduates Program for clean energy and sustainability. Education: Ph.D. in Computer Science (2017), University of Queensland Master of Computer Science (2013), Nanjing University Bachelor of Software Engineering (2010), Nanjing University Research Interests: Entity modeling, spatio-temporal data analysis, graph mining, recommender systems, and health/medical records mining. She explores applications in social media, e-commerce, and healthcare sectors. Projects: "Knowledge Enriched Approach for Effective Personalization" (2025–2027) "AI for Clean Energy and Sustainability" (2023–2027) "CSIRO Next Generation Graduates Program: AI in Mental Health" (2023–2027) "Large-scale multimodal knowledge management" (2022–2025) Grants & Collaborations: Engaged with CSIRO, Crank Group, and Pola Practice Pty Ltd. Her work aligns with UN SDGs in education and sustainable energy systems. Labs/Teams: Part of the Monash Energy Institute and Monash Data Futures Institute, contributing to interdisciplinary AI and energy research.
Elena Grigorescu is a Professor at the University of Waterloo, Department of Computer Science. She holds a Ph.D. from the Massachusetts Institute of Technology (2010), an M.S. from MIT (2006), and a B.A. from Bard College (2004). Her research focuses on sublinear-time algorithms, error-correcting codes, computational complexity, and learning theory. She explores foundational aspects of algorithms with constraints on time/space, privacy-preserving computation, and applications in graph theory and optimization. Her work includes advancements in spanner algorithms for network design, differential privacy in sublinear-time settings, and learning-augmented approaches for online optimization. Recent publications address trace reconstruction, privacy-utility trade-offs, and combinatorial optimization techniques. Grigorescu is actively involved in conferences like APPROX/RANDOM and IEEE Foundations of Computer Science, contributing to algorithmic theory and practical implementations. Her research emphasizes theoretical rigor while addressing real-world challenges in data analysis and distributed systems. No awards or formal advisees are explicitly listed in the provided information.
Anne J. Shiu is a Professor in the Department of Mathematics at Texas A&M University. She holds a Ph.D. in Mathematics (2010) from the University of California Berkeley with advisors Bernd Sturmfels and Lior Pachter. Her career includes postdoctoral positions at Duke University (2010-2011) and the University of Chicago (2011-2014), followed by a faculty role at Texas A&M since 2014. Research Focus: Algebraic, geometric, and combinatorial approaches to mathematical biology, specializing in biochemical dynamical systems, neural coding, parameter identifiability, algebraic statistics, and genomics. Academic Contributions: Over 15 recent publications spanning identifiability in compartmental models, multistationarity in reaction networks, convexity analysis of neural codes, and algebraic robustness in biochemical systems. Scientific Recognition: Association of Former Students Distinguished Achievement College-Level Award in Teaching (2019) Invited speaker for Ethel Ashworth-Tsutsui Memorial Lecture (2018-2019) Current Research: Investigates structural identifiability in biological models, focusing on compartmental systems, reaction networks, and neural codes through algebraic methods and computational tools. Her work bridges abstract algebra with practical biological applications, including parameter estimation and robustness analysis. Grant Support: Recipient of NSF CAREER award (2018-2023), prior NSF grants (2010-2017), and Simons Foundation Collaboration Grant (#521874, 2017-2018). Academic Leadership: Organized multiple international workshops/conferences including SIAM conferences and Banff workshop. Currently an Associate Editor for SIAM Journal on Applied Mathematics and serves on the AIM Scientific Research Board.
Dr Valdas Noreika is a Senior Lecturer in Psychology at the School of Biological and Behavioural Sciences, Queen Mary University of London. He serves as Head of The Centre for Brain and Behaviour and leads the Sleep and Cognition Lab. His work bridges cognitive neuroscience, psychology, and clinical applications, with a focus on understanding consciousness and its disorders. Dr Noreika's educational background includes: BA in Philosophy from Vilnius University, Lithuania MSc in Neurobiology from Vilnius University, Lithuania PhD in Psychology from the University of Turku, Finland, focusing on altered states of consciousness and temporal distortions Dr Noreika's research explores the cognitive and neural mechanisms underlying sleep, dreaming, and consciousness. Using techniques including electroencephalography (EEG), transcranial magnetic stimulation (TMS), and psychophysics, his work investigates both basic mechanisms of consciousness and their applications to neurodevelopmental and mental health conditions. His research spans multiple domains including time processing, inter-brain synchronization across species, and environmental decision-making. A key aspect of his work involves translational research focusing on sleep, subjective experiences, and well-being in conditions such as learning disabilities, ADHD, autism, and depression. Analysis of Dr Noreika's recent publications reveals a strong focus on consciousness studies, neural mechanisms of sleep and dreaming, and applications to neurodevelopmental conditions. His work frequently employs EEG and other neuroimaging techniques to study brain activity during various states of consciousness. Recent trends show increasing emphasis on inter-brain synchronization, particularly in infant-parent interactions and cross-species communication, as well as growing interest in environmental psychology and climate change-related decision making. Dr Noreika has secured significant research funding including: The neural basis of inter-species communication - £155,098 from the Biotechnology and Biological Sciences Research Council (2024-2026) Sleep and circadian interactions with sensory sensitivity in adults with intellectual disabilities - £103,229 from the Baily Thomas Charitable Fund (2023-2025) As a supervisor, Dr Noreika advises multiple PhD students working on diverse topics including Alzheimer's disease diagnosis using information theory, emotion recognition, thermal sensation in Parkinson's disease, cultural differences in cognitive processes, and time processing. His Sleep and Cognition Lab serves as a hub for interdisciplinary research bridging neuroscience, psychology, and clinical applications. Dr Noreika leads the Sleep and Cognition Lab at Queen Mary University of London, which focuses on investigating the neural mechanisms of sleep, dreaming, and consciousness. The lab brings together researchers from diverse backgrounds to study both fundamental aspects of consciousness and their applications to clinical populations. Current projects include investigations of sleep and sensory sensitivity in adults with learning disabilities and human-dog interaction studies.
Dr. Michael Gubanov is an Assistant Professor in Computer Science at Florida State University and founder of BigLab!, specializing in scalable data systems for scientific knowledge discovery. Research: Develops hybrid polystore/LLM systems for cancer research (CancerKG.ORG), COVID-19 knowledge graphs (COVIDKG.ORG), and aging studies (AgingGraph.ORG). Focuses on metadata classification, tabular embeddings, and web-scale knowledge extraction. Funding: Secured $1.8M+ from NSF, Florida Department of Health, and AWS for projects bridging data management and AI. Awards: IEEE ICDE Best Paper (2017), ACM SIGMOD Research Highlight (2018), CACM Research Highlight (2020). Elected to Sigma Xi. Education: PhD in Computer Science (University of Washington); Postdoc at MIT CSAIL.
Fattane Zarrinkalam is an Assistant Professor in the School of Engineering at the University of Guelph. She holds a PhD from Ferdowsi University of Mashhad, Iran, and completed a Postdoctoral Research Fellowship at Ryerson University (2018–2020). Her research focuses on social media mining, semantic technologies, and user modeling, with applications in healthcare, legal tech, and e-commerce. She is a Vector Institute Postgraduate Affiliate and serves on editorial boards for journals like Information Processing & Management and IEEE Transactions on Network Science and Engineering . Her work emphasizes actionable insights from social data, including sarcasm detection, user interest prediction, and fairness in social media analytics. Zarrinkalam has contributed to over 30 peer-reviewed publications and holds multiple patents in data analysis and social media sentiment modeling. Education: PhD, Ferdowsi University of Mashhad, Iran Postdoctoral Fellowship, Ryerson University Research Scientist, Thomson Reuters Labs Research Interests: Semantic interpretation of social content User modeling via temporal analysis Social good applications (e.g., mental health, telecommunication) Fairness in social media mining Recent Work Trends: Her articles span network representation learning, dynamic user interest prediction, and interdisciplinary applications. Notable themes include neural networks for sarcasm detection, heterogeneous graph embeddings, and leveraging Twitter data for psychological insights. Awards & Service: Co-chair, International Workshop on Mining Actionable Insights from Social Networks (MAISoN) Editorial board roles for top journals Labs & Teams: Involved in interdisciplinary collaborations at the Vector Institute and partnerships with industry on legal tech and social analytics projects.
Prof. Dr. Thomas Schick is a Professor of Mathematics at the Mathematical Institute of the University of Göttingen, leading the vibrant research group in Topology and Geometry. His work focuses on areas such as index theory, K-theory of C*-algebras, and geometry and analysis. He is a core member of the Research Training Group 2491 'Fourier Analysis and Spectral Theory', serving as its speaker, and has supervised numerous doctoral students in topics ranging from persistent cohomology to spectral engineering. His academic journey includes a PhD from Johannes Gutenberg University Mainz (1996) under Wolfgang Lück, followed by postdoctoral positions at the University of Münster and Penn State University before joining Göttingen in 2001. He has held visiting roles at institutions worldwide. Prof. Schick is an Ordentliches Mitglied of the Göttingen Academy of Sciences, a Fellow of the American Mathematical Society, and leads the Scientific Advisory Board of the Mathematisches Forschungsinstitut Oberwolfach. He edits several high-impact journals, including Annales Mathématiques Blaise Pascal and the Bulletin of the Iranian Mathematical Society. His research interests span topological and geometric analysis, with recent work exploring scalar curvature rigidity, T-duality, and coarse geometry. He regularly teaches advanced courses and seminars, including 'Index Theory and Theorems' and 'Topological Data Analysis', and actively mentors students through the RTG program.
Guido Montúfar is a Professor in the Departments of Mathematics and Statistics & Data Science at the University of California, Los Angeles (UCLA), effective since 2024. He also leads the Mathematical Machine Learning Group at the Max Planck Institute for Mathematics in the Sciences (MPI MIS) in Leipzig, Germany since 2018. His academic journey includes a PhD in Mathematics from Leipzig University (2012), and Diplom degrees in Physics and Mathematics from TU Berlin (2009 and 2007). Montúfar's research focuses on the theoretical foundations of deep learning, mathematical machine learning, and the interplay between geometry and learning. Key areas include neural network architecture theory, optimization landscapes, and information geometry. His work bridges algebraic statistics, graphical models, and topological data analysis. His grants and awards include an ERC Starting Grant (2018-2023), a Sloan Research Fellowship (2022), and an NSF CAREER Award. He has advised numerous PhD students and postdocs, contributing to significant advancements in machine learning theory and applications. Montúfar teaches courses on applied mathematics, optimization, and machine learning at UCLA. His research also explores topics like oversquashing in graph neural networks and the geometry of policy gradients in reinforcement learning.
