Jessica Lin is an Associate Professor in the Department of Computer Science at George Mason University, with a focus on data mining and time series analysis. She has published extensively on topics including motif discovery, anomaly detection, clustering, and symbolic representation of time series data. Ph.D., M.S., and B.S. in Computer Science from UC Riverside (2005, 2002, 1999) Her research spans efficient algorithms for mining massive time series datasets, extending to multimedia data like images and texts. She has developed tools such as GrammarViz and SAX for pattern visualization and symbolic analysis. Recent publications highlight advancements in variable-length motif discovery, interpretable classification frameworks, and anomaly detection. Her work appears in top conferences like AAAI, ICDM, and SDM, as well as journals including Knowledge and Information Systems and Data Mining and Knowledge Discovery . Dr. Lin has advised numerous Ph.D. students, many of whom have taken academic or industry positions. She has served on editorial boards and program committees for conferences such as KDD, ICDM, and ECML-PKDD.
Dr. Jean-Christophe Nave is an Associate Professor in the Department of Mathematics and Statistics at McGill University, specializing in applied mathematics, numerical analysis, and computational methods. His research focuses on numerical methods for partial differential equations, fluid mechanics, interface problems, and computer graphics. He holds a PhD from UCSB (2004) and has held academic positions at MIT and McGill since 2005. Currently, he serves on committees such as the Steering Committee of the Institut des Sciences Mathematiques and the CRM Applied Mathematics Lab. His educational background includes a PhD under Professors Xu-Dong Liu and Sanjoy Banerjee. Key research areas include level set methods, fluid-structure interaction, and invariant numerical methods. Notable works include the Correction Function Method for interface problems and the Characteristic Mapping Method for advection problems. Nave’s publications span topics like Poisson equations with discontinuous coefficients, fluid dynamics simulations, and high-order numerical schemes. He has advised numerous graduate and undergraduate students, contributing to their research in applied mathematics and computational science. His work bridges theoretical rigor and practical applications in engineering and physics. He teaches advanced courses such as Numerical Analysis I/II and Computational Methods in Applied Mathematics. His research group collaborates on projects involving fluid dynamics, elasticity, and geometric algorithms, with a focus on developing robust numerical tools for complex systems.
Rahul Sarkar is a Postdoctoral Fellow at the University of California, Berkeley, affiliated with the Department of Mathematics . He was previously a Ph.D. student in the Institute for Computational and Mathematical Engineering (ICME) at Stanford University, graduating in 2022 under the advisement of Biondo Biondi and András Vasy. Research Interests : Quantum information theory, inverse problems, machine learning, microlocal analysis, and numerical methods for PDEs. Scientific Contributions : Developed novel quantum computing algorithms and numerical schemes for geophysical imaging, with applications in seismic tomography and quantum signal processing. Teaching : Taught courses at Stanford including Introduction to Quantum Computing and 3D Seismic Imaging , with roles as instructor and course assistant. Awards : Schlumberger Innovation Fellowship (2019-2020). His work bridges mathematical analysis and quantum computation , with a focus on solving real-world problems through interdisciplinary approaches. He has collaborated with institutions like IBM and Schlumberger to apply quantum algorithms to geoscience and financial optimization.
Prof. Waldemar Kolanus leads the Molecular Immunology and Cell Biology department at the University of Bonn's Life & Medical Sciences Institute (LIMES) . His research bridges immunoregulation , stem cell dynamics , and metabolic stress responses in immune cells. Unit 2 member at LIMES Principal investigator in SFB 704 and ImmunoSensation Cluster Leads a multidisciplinary lab with postdocs, PhD students, and technical staff His work focuses on intracellular signaling pathways connecting immune activation to tissue homeostasis, particularly through: Cytohesin proteins in integrin-mediated adhesion and migration TRIM71 in stem cell regulation and congenital hydrocephalus High-salt environments affecting macrophage function Publication trends show expertise in immune cell migration , genetic models , and chemical inhibition , with frequent use of mice and zebrafish for in vivo studies. Key articles explore: TRIM71's dual role in auditory development and germ cell maintenance Cytohesin family's Golgi regulation and insulin signaling Ruxolitinib's off-target migration inhibition of dendritic cells Contact details: Address: LIMES Institute, Carl-Troll-Straße 31, Bonn Email: kolanus.sekretariat@uni-bonn.de Phone: +49 228 73-62788
David Cash is a Professor in the Department of Computer Science at the University of Chicago. His research focuses on applied and theoretical cryptography, computer security, and theoretical computer science. He joined UChicago in 2018 and has held roles such as teaching courses in cryptography, computer security, and discrete mathematics. Cash has advised numerous PhD and master’s students, including Sam Everett, Alexander Hoover, and Jesse Stern. His work includes constructing quantum-secure cryptography systems, analyzing encrypted data navigation, and foundational theoretical results. He has received notable awards like the 2025 Quantrell Award for Teaching and multiple Best Paper awards at Eurocrypt. Cash's research also explores secure computation, oblivious RAM, and cryptographic agility. His affiliations include the Systems Group at UChicago, focusing on interdisciplinary systems research. Education details are not explicitly provided in the text. However, his career trajectory suggests advanced degrees in computer science or related fields. His teaching spans undergraduate and graduate courses, emphasizing both theoretical foundations (e.g., discrete mathematics) and applied topics like cryptocurrencies and secure systems. Cash actively engages in academic service, including organizing conferences and reviewing research. His work bridges theoretical insights with practical applications, addressing modern computational security challenges. His research contributions span cryptographic protocols, secure data structures, and privacy-preserving technologies. Notable projects include work on searchable encryption, leakage-abuse attacks, and cryptographic systems resilient to quantum computing. Cash collaborates with institutions like Rutgers University and has mentored postdoctoral researchers such as Alexander Hoover. His grants include NSF CAREER awards and Simons Institute fellowships, supporting research in secure outsourcing and cryptographic data protection.
John F. Canny is the Paul and Stacy Jacobs Distinguished Professor of Engineering at the University of California, Berkeley, in the Department of Electrical Engineering and Computer Science (EECS). He joined the faculty in 1987 and is affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Center for New Media (BCNM), and other research institutes. His research focuses on artificial intelligence, robotics, human-computer interaction, and computational geometry. Canny is renowned for developing the Canny edge detector and contributions to motion planning, privacy-preserving algorithms, and real-time simulation. Education: B.Sc. in Computer Science and Theoretical Physics (Adelaide University, 1979), B.E. (Hons) in Electrical Engineering (Adelaide University, 1980), M.S. and Ph.D. in Electrical Engineering (MIT, 1983 and 1987). Research Interests: His work spans AI, robotics (including universal planar manipulation), HCI (e.g., MultiView video conferencing), and security (privacy in collaborative filtering). He has pioneered algorithms for edge detection, non-linear FEM simulation, and computational algebra. Awards: ACM Fellow (2020), Okawa Research Grant (2003), AAAI Classic Paper Award (2002), NSF PYI (1989), Packard Fellowship (1988), and ACM Doctoral Dissertation Award (1987). Advising & Grants: Advised over 30 Ph.D. students, many of whom hold prominent roles in academia and industry. His grants include NSF and Packard funding for foundational research in robotics and computational methods. He currently teaches CS188: Introduction to Artificial Intelligence. Labs/Teams: Leads research at the Berkeley Institute of Design (BiD), focusing on RISC robotics, activity-based computing, and privacy-enhanced telepresence systems.
Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .
Padhraic Smyth is a Distinguished Professor and Hasso Plattner Endowed Chair in Artificial Intelligence at the University of California, Irvine (UCI), holding joint appointments in the Department of Computer Science and Department of Statistics. He leads the DataLab research group, focusing on machine learning, AI, and their applications in climate science, healthcare, and education. His research spans probabilistic modeling, deep learning, and human-AI collaboration. Education: PhD in Electrical Engineering from the California Institute of Technology (1988), MSEE (1985), and BEng (1984). Prior to UCI, he worked at NASA's Jet Propulsion Laboratory (1988–1996). Research Interests: Machine learning, AI, pattern recognition, Bayesian methods, climate science applications, algorithmic fairness, and human-AI interaction. He has published over 200 papers and co-authored textbooks like Modeling the Internet and the Web . Awards: ACM Fellow, IEEE Fellow, AAAI Fellow, AAAS Fellow, and ACM SIGKDD Innovation Award recipient. He has held leadership roles in UCI's Center for Machine Learning and Data Science. Key Projects: Human-AI collaboration frameworks, robustness in deep learning, climate modeling using spatio-temporal data, and AI fairness with missing attributes. Collaborates with institutions like NASA and industry partners (e.g., Google, eBay). Labs/Teams: Director of UCI’s Data Science Initiative and HPI Research Center in Machine Learning. Supervises a vibrant PhD program with over 30 alumni in academia and industry.
Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.
Jeremy Rau is a Professor of Linguistics and Classics at Harvard University's Department of Linguistics, part of the Faculty of Arts and Sciences. He currently serves as Director of Graduate Studies for Fall 2024 and is on leave for Spring 2025. His office is located in Boylston Hall, Cambridge, MA. Rau specializes in Greek and Latin historical linguistics with a particular focus on Homeric language, Indo-European linguistic systems, and Indo-Iranian comparative studies. His research explores ancient language structures, verb morphology, and diachronic phonological changes in pre-classical texts. Research Interests: Proto-Indo-European root analysis Mycenaean Greek linguistic features Homer's language and epic conventions Indo-Iranian comparative linguistics Verb derivation mechanisms Publications reflect deep engagement with ancient language structures, including studies on Mycenaean contract verbs, Homeric noun inflections, and Proto-Germanic lexicon. He has contributed to journals like Die Sprache , Glotta , and Historische Sprachforschung , as well as edited volumes such as Verba Docenti . Administratively, Rau oversees graduate programs and participates in departmental governance. His work bridges historical linguistics with philological scholarship, emphasizing systematic analysis of ancient language data.
Yasushi Sakurai is a Professor in the Department of Translational Datability at Osaka University's Institute of Scientific and Industrial Research, co-leading the Sakurai and Matsubara Laboratory within the Center for Industrial Science and AI. His research mission focuses on transforming society through real-time prediction of natural and social phenomena using large-scale data analytics, with emphasis on practical technological implementation. His research spans time-series big data analysis, dynamic learning systems, and real-time information provision. Key areas include tensor stream mining, EEG-based healthcare applications, cybersecurity anomaly detection, and multi-omics cancer subtyping. The lab specializes in developing deployable technologies that optimize social activities through predictive modeling of evolving data streams. Recent publications (2023-2025) reveal concentrated innovation in time-series data stream processing, with dominant themes in tensor analytics, frequency-domain forecasting, and causal modeling. His team produces high-impact work accepted at premier AI venues (ICLR, AAAI, KDD, WWW), consistently featuring oral presentations that highlight technical novelty and societal relevance. Scientific Awards: FY2024 Minister of Education, Culture, Sports, Science and Technology Award for Science and Technology (Research Category) for dynamic learning and real-time data stream analysis Professor Sakurai mentors graduate students including Naoki Chihara (DEIM2024 Outstanding Paper Award winner), Yuka Tamura (DEIM2024 Student Presentation Award winner), and Ren Fujiwara. His lab maintains active industry-academia partnerships focused on practical technology deployment, with research directly addressing real-world challenges in healthcare monitoring and cybersecurity. The Sakurai and Matsubara Laboratory operates as a dynamic research unit within Osaka University's Center for Industrial Science and AI, structured around specialized teams for tensor stream analysis, medical data mining, and network dynamics. Current projects emphasize real-time prediction systems with immediate societal applications, supported by strong industry collaboration frameworks.
Mariana Resener is an Assistant Professor in the School of Sustainable Energy Engineering at Simon Fraser University (SFU). She holds a Ph.D. in Electrical Engineering (2016) from the Federal University of Rio Grande do Sul, Brazil, alongside M.Sc. (2011) and B.Sc. (2008) degrees in the same field. Her research focuses on optimizing power systems, particularly in distributed energy resources, energy storage, and volt/var control. She teaches courses like Power Electronics and Power Systems Analysis & Design. Her work emphasizes sustainable development in grid planning and energy infrastructure. As a Senior Member of IEEE and an Associate Editor for the Energy Systems Journal (Springer), she contributes to advancing smart grid technologies and renewable integration. Her research spans metaheuristic optimization, stochastic modeling, and grid resilience strategies for distributed systems. Recent projects include hybrid renewable energy systems for substations, EV charging station optimization, and fault analysis in unbalanced grids. She collaborates with industry on practical solutions for grid modernization and reliability enhancement.
Professor Phil Clark is a Professor of International Politics at SOAS University of London, specializing in conflict and post-conflict issues with a focus on transitional justice, peacebuilding, and reconciliation after mass atrocity in Rwanda, Uganda, the Democratic Republic of Congo, and Burundi. He holds a DPhil in Politics from the University of Oxford, where he studied as a Rhodes Scholar, and a Bachelor of International Studies from Flinders University. His research focuses on international, national, and community-based responses to mass violence in Central Africa, particularly examining the gacaca community courts in post-genocide Rwanda and the International Criminal Court (ICC). Professor Clark has advised multiple governments (Danish, Sudanese, Swedish, Ugandan, and UK), international organizations (ICC, International Criminal Tribunal for Rwanda), and NGOs (Human Rights Watch, Crisis Group) on conflict issues in Africa. Professor Clark's work spans several key areas including the History, Politics and Law of the African Great Lakes; Transitional Justice Theory and Practice; the Politics of Violence; Community-Based and Customary Law; Conflict Resolution and Reconciliation; the Politics of International Criminal Justice; Peacebuilding, Security and Post-Conflict Reconstruction; and Moral and Political Philosophy. His approach often combines theoretical analysis with practical fieldwork in post-conflict societies. His publications reveal a consistent focus on the tension between international legal frameworks and local justice mechanisms, particularly examining how post-genocide societies navigate complex reconciliation processes. Over time, his work has evolved from documenting specific cases (like the gacaca courts) to analyzing broader patterns of transitional justice and international criminal law across the African continent. Shortlisted for the Raphael Lemkin Award for best book on genocide and mass violence (for "Distant Justice: The Impact of the International Criminal Court on African Politics") Regular contributor to major media outlets including The New York Times, Foreign Affairs, Washington Post, The Guardian, and BBC Frequent media commentator on international news networks including BBC, France 24, Al Jazeera, CNN, and Deutsche Welle Professor Clark has supervised multiple PhD students working on topics related to conflict analysis, women's access to transitional justice, China-Africa relations, and conflict transformation. His current research includes "Intergenerational Justice in Eastern Africa" and "Reframing Justice after Atrocity," which examines justice interactions beyond the linear international-to-national transmission of norms and practices. He has led significant research projects, including serving as technical advisor and co-author of a 2007 UN Office of the High Commissioner for Human Rights project surveying popular perceptions of transitional justice and peacebuilding in northern Uganda. His ongoing work continues to shape academic and policy discussions about justice mechanisms in post-conflict societies.
Huazheng Wang is an Assistant Professor in the School of Electrical Engineering and Computer Science at Oregon State University. His research focuses on reinforcement learning, information retrieval, and trustworthy AI. He received his Ph.D. from the University of Virginia (2021) and B.E. from the University of Science and Technology of China (2015). He holds awards including the 2025 EECS Fabulous Teacher Recognition and SIGIR 2019 Best Paper Award. His work addresses challenges in robust reinforcement learning, adversarial attacks on bandit systems, and applications in scientific discovery. Education: Ph.D., Computer Science, University of Virginia (2021) B.E., Computer Science and Technology, University of Science and Technology of China (2015) Research interests emphasize developing efficient algorithms for reinforcement learning, multi-armed bandits, and their applications in recommendation systems, protein optimization, and security. Notable contributions include provably efficient risk-aware reinforcement learning frameworks and adversarial attack analysis on bandit systems. Recent work includes NSF-funded research on neural bandits (IIS-2403401) and publications in top venues like ICML, NeurIPS, and AAAI. His lab explores embodied LLM agents for team cooperation and federated collaborative online monitoring frameworks.
Xiaoming Hu is a Professor at the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH Royal Institute of Technology (Kungliga Tekniska Högskolan) in Stockholm, Sweden. Born in Chengdu, China, he received his B.S. degree from University of Science and Technology of China in 1983, followed by M.S. and Ph.D. degrees from Arizona State University in 1986 and 1989 respectively. After serving as a research assistant at the Institute of Automation, Chinese Academy of Sciences (1983-1984), he was a Gustafsson Postdoctoral Fellow at KTH (1989-1990) before becoming a faculty member. His educational background includes: B.S. in Engineering, University of Science and Technology of China, 1983 M.S. in Engineering, Arizona State University, 1986 Ph.D. in Engineering, Arizona State University, 1989 Xiaoming Hu's research primarily focuses on multi-agent systems, nonlinear feedback stabilization, nonlinear observer design, and sensing and active perception. His work bridges theoretical control theory with practical applications in robotics and autonomous systems. He has made significant contributions to geometric control theory, mathematical systems theory, and nonlinear systems analysis and control. His research often involves developing theoretical frameworks for distributed control, formation control, and cooperative behavior in multi-robot systems. Professor Hu's publication record shows a consistent research trajectory with numerous high-impact publications in top-tier journals like Automatica, IEEE Transactions on Automatic Control, and Systems & Control Letters. His research has evolved from fundamental control theory to more applied problems in robotics and multi-agent systems, while maintaining strong mathematical foundations. Recent work shows increasing focus on safety-critical control, inverse problems in estimation, and networked systems. His scientific contributions include: Development of theoretical frameworks for multi-agent coordination and formation control Advances in nonlinear observer design for robotic systems Contributions to geometric control theory and systems theory Research on distributed estimation and control algorithms Applications of control theory to robotics and autonomous systems Professor Hu teaches several advanced courses including Mathematical Systems Theory, Geometric Control Theory, and Nonlinear Systems: Analysis and Control. He has supervised numerous degree projects at both undergraduate and graduate levels in mathematics, optimization, systems theory, and scientific computing. His teaching reflects his research expertise, providing students with both theoretical foundations and practical applications of control theory.
