Dr. Shan Du is an Assistant Professor in the Department of Computer Science, Math, Physics & Statistics at the University of British Columbia Okanagan Campus. She holds a PhD in Electrical and Computer Engineering from UBC (2009) and has over 15 years of experience in image/video processing, computer vision, and machine learning. Previously, she worked as an Assistant Professor at Lakehead University and as a Research Scientist at IntelliView Technologies. Her research focuses on computer vision, deep learning, and biometrics, with applications in video surveillance systems and environmental monitoring. She has secured grants including NSERC Discovery, CFI JELF, and Alberta Innovates. Dr. Du is a Senior Member of IEEE and serves as an Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology and the IEEE Canadian Journal of Electrical and Computer Engineering. Teaching interests include Image Processing, Computer Graphics, and Software Engineering. She advises graduate students and has published extensively on topics like gas leak detection, 3D face modeling, and medical image fusion.
Prof. Alberto S. Cattaneo is a faculty member at the University of Zurich, affiliated with the Department of Mathematics. He holds the rank of Professor and has been actively involved in teaching and research since at least 1998. His research interests span mathematical physics, differential geometry, topology, and interdisciplinary areas like computational biology, genomics, and digital forensics. He has developed courses on topics such as field theory, quantum mechanics, and differential manifolds, reflecting his expertise in theoretical and applied mathematics. Prof. Cattaneo has contributed to numerous publications, including works on distributed genomic analysis, sensor pattern noise (PNU) in forensics, and algorithm optimization for big data frameworks like Hadoop and Spark. His work bridges pure mathematics with applications in bioinformatics and cybersecurity. Though no awards are explicitly mentioned, his extensive publication record and teaching roles highlight his academic standing. He maintains an active presence through courses and research collaborations, with no indication of part-time roles or retirement.
Simon Lacoste-Julien is an Associate Professor at Université de Montréal, affiliated with the Department of Computer Science and Operations Research (DIRO). He also serves as the Associate Scientific Director of Mila – Quebec Institute of Artificial Intelligence and holds the position of Vice President Lab Director at Samsung SAIT AI Lab Montreal (SAIL). His research focuses on machine learning, optimization, and their applications in areas like deep learning, generative models, causality, and computer vision. Lacoste-Julien has held academic positions at INRIA in Paris and has a PhD from UC Berkeley, with postdoctoral work at the University of Cambridge. He teaches advanced graduate courses on probabilistic graphical models and structured prediction. His work includes contributions to optimization algorithms (e.g., Frank-Wolfe methods), causal discovery, and generative models. Lacoste-Julien has supervised numerous students and postdocs, and his awards include being a CIFAR Fellow and Canada CIFAR AI Chair. His research spans theoretical foundations and practical applications, with a strong emphasis on scalable and efficient machine learning techniques.
Nicholas Pearce is an Assistant Professor at Linköping University, affiliated with the Department of Physics, Chemistry and Biology (IFM) and the Bioinformatics (BIOIN) division. He leads the Data-Driven Determination of Macromolecular Structures (D3MS) group, also known as PearceLab @ LiU, and is affiliated with SciLifeLab and the Wallenberg Centre for Molecular Medicine (WCMM) through the Data-Driven Life Sciences (DDLS) program. His research focuses on advancing structural biology by developing computational and experimental methods that capture the dynamic nature of proteins. Key interests include protein flexibility, disorder, and functional dynamics, with applications in drug discovery and macromolecular modeling. The group employs techniques such as X-ray crystallography, cryo-EM, machine learning, and statistical modeling to improve the resolution and interpretability of macromolecular structures. The recent publications highlight a strong focus on software and method development for structural biology, particularly in multi-dataset analysis and crystallographic data processing. The work contributes to open-source tools like the CCP4 suite and introduces novel approaches such as PanDDA and PanDEMIC.adp for detecting ligand binding and modeling disorder. While no formal scientific awards are listed in the provided text, his involvement in major collaborative projects and national research initiatives underscores his growing impact in the field. He actively mentors students and welcomes Master’s candidates to join his research group. Nicholas began his independent research career at Linköping University on October 1, 2022, with funding supporting the launch of his lab. His group emphasizes data-driven decision-making in structural analysis and collaborates extensively within Sweden and internationally. Current projects include multi-dataset analysis for fragment screening and decomposition of protein disorder using elastic net models. The D3MS group is active in outreach and scientific communication, maintaining a blog and public presence, including a Twitter feed. They have participated in international research visits, such as a recent trip to the University of Hamburg, and national conferences like Sweprot in Tällberg.
David Savlowitz is a Lecturer in the Information Systems department at the Paul Merage School of Business, University of California, Irvine (UCI). He holds an MBA in Competitive Strategy from Arizona State University (1989) and brings over 30 years of professional experience in data analysis and actionable intelligence. University: University of California, Irvine School: Paul Merage School of Business Department: Information Systems Academic Rank: Lecturer David's research interests focus on Data & Analytics and Economics , with extensive expertise in advanced analytics, statistical modeling, market research, data visualization, and business intelligence solutions. His work emphasizes solving complex business problems and translating analytical insights into strategic decisions accessible to non-technical stakeholders. There are no recent articles listed in the provided information, so no publication trends can be assessed at this time. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: David is recognized for his collaboration with UCI in developing curriculum for a new data analytics program. While no formal students or grant funding are listed, his advisory experience in corporate settings—including project management and strategic consulting—underscores his applied academic impact. Labs and Teams: No specific labs, research teams, or centers are mentioned in association with David Savlowitz in the provided materials.
Prof. Dr. Jan Kierfeld is a faculty member in the Department of Physics at Technical University of Dortmund, where he leads a research group focused on soft matter theory and biological physics. His work bridges statistical physics, mechanics, and hydrodynamics of soft and biological systems, with strong interdisciplinary connections to materials science and biophysics. His research interests include polymer physics , cytoskeletal filaments (actin and microtubules), elastic capsules and shells , active matter , and the development of novel simulation techniques such as event-chain Monte Carlo. He is particularly interested in how chemical energy (e.g., ATP/GTP hydrolysis) drives mechanical forces in biological systems, and in the mechanics of semiflexible polymer networks and microswimmers. His group also pioneers the application of machine learning to problems in soft matter, such as pendant drop tensiometry and traction force microscopy. The recent publications of Prof. Kierfeld span topics in biophysics, soft matter, and computational physics, showing a strong trend toward integrating theoretical modeling with experimental collaboration, especially in microswimmer dynamics, microtubule mechanics, and interfacial phenomena. His work frequently appears in journals such as Soft Matter , Physical Review , Biophysical Journal , and Nature Communications . He has no listed scientific awards in the provided text, but his active publication record and leadership in DFG programs (e.g., SPP1726 Microswimmers) indicate significant recognition in the field. He advises students and postdoctoral researchers in theoretical and computational soft matter physics, though specific names are not listed. His research is supported by grants from German funding agencies such as the DFG. Prof. Kierfeld’s group develops and applies advanced simulation methods and collaborates with experimentalists on problems involving elastic instabilities (buckling, wrinkling), microswimmers , and chemomechanical models of cellular structures. The group maintains strong technical development in numerical algorithms and data analysis tools, including open-source software like MLFTM for traction force microscopy.
Avi Giloni is an Adjunct Associate Professor in the Leonard N. Stern School of Business at New York University, where he has been teaching since 2004. He is also an Associate Professor of Operations Management and Statistics at the Sy Syms School of Business, Yeshiva University. His academic work bridges robust statistical methods and operational applications. Ph.D. in Statistics and Operations Research, New York University, 2000 B.A., New York University, 1994 Professor Giloni's research centers on robust regression , optimization , and stochastic system design , with applications in revenue management and supply chain operations . His expertise spans statistical modeling , decision-making under uncertainty , and operations analytics , contributing to both theoretical and applied domains in business and industry. The 15 most recent publications reflect a consistent focus on robust statistical techniques and stochastic modeling in business contexts. Key thematic areas include robust regression under outliers and heteroscedasticity , optimization in supply chains under uncertainty , and design of service systems . The keywords span operations research, statistics, and applied mathematics, while subfields reveal deep technical engagement with estimation, simulation, risk, and efficiency. While no formal scientific awards are listed in the provided text, Professor Giloni's sustained publication record in top-tier journals such as Management Science and SIAM Journal on Optimization indicates scholarly recognition. He has advised students in statistics, operations, and business analytics, though specific names are not listed. His founding of Del V.I., LLC, a consulting firm in statistics and operations research, demonstrates real-world application of his research and suggests involvement in industry grants or contracts. His teaching includes foundational courses such as Statistics for Business Control and Regression and Forecasting Models. Professor Giloni is affiliated with research activities through his consulting firm Del V.I., LLC, which functions as an applied research team focusing on statistical solutions and operational improvements for clients. His collaborative work across NYU and Yeshiva University suggests active participation in interdisciplinary teams addressing business analytics challenges.
Hsein Kew is a Senior Lecturer in the Department of Econometrics and Business Statistics at Monash University. He holds a PhD in Economics from the University of Melbourne and previously worked as a researcher at the Melbourne Institute of Applied Economic and Social Research, focusing on empirical analyses of social security and labor market interactions. PhD in Economics, University of Melbourne Researcher, Melbourne Institute of Applied Economic and Social Research Senior Lecturer, Department of Econometrics and Business Statistics, Monash University His research primarily centers on time series analysis , heteroskedastic models , and non-parametric methods , with applications in financial econometrics and forecasting. He teaches Financial Econometrics and Data Analysis in Business, contributing to both theoretical and applied econometric education. The recent publications by Hsein Kew span from 2014 to 2024 and reflect a consistent focus on advanced econometric methodology. The articles demonstrate expertise in predictive regression models , unit root testing under volatility shifts , long memory processes , and autocorrelation testing under complex dependency and heteroskedastic structures . These works are published in top-tier journals such as the Journal of Econometrics and Econometric Theory , indicating a strong contribution to econometric theory and robust inference under non-standard conditions. Notable research projects include an Australian Research Council (ARC)-funded project titled A new class of statistical methods for analysing long memory time series models with heteroskedasticity (2010–2013), where he served as a Chief Investigator. This project aligns with his ongoing research interests in modeling time series with long memory and time-varying volatility. ARC Research Project: A new class of statistical methods for analysing long memory time series models with heteroskedasticity (2010–2013) While no scientific awards are listed in the provided text, his sustained publication record and involvement in funded research indicate active scholarly engagement. He has collaborated with prominent econometricians such as David Harris and Jiti Gao, suggesting integration into a strong research network. There is no mention of advising students or leading a lab, but his role as a Senior Lecturer implies teaching and mentorship responsibilities.
Yang Janet Liu is an Assistant Professor in the Department of Linguistics at the University of Pittsburgh. Previously, she was a Postdoctoral Researcher at the MaiNLP research lab at the Center for Information and Language Processing (CIS) at LMU Munich, where she was also affiliated with the Munich Center for Machine Learning (MCML). Dr. Liu earned her Ph.D. in Computational Linguistics from the Department of Linguistics at Georgetown University. During her doctoral studies, she was advised by Amir Zeldes and was a member of both Corpling@GU and Computational Linguistics @ Georgetown (GUCL) research groups. She was also a student research affiliate of NERT, directed by Nathan Schneider. Her research focuses on tackling text variation in NLP, studying model internals for discourse-level linguistic phenomena and generalization, and analyzing discourse-level phenomena across genres using computational, statistical, and corpus-based methods. She investigates NLP applications involving discourse structure and understanding, particularly summarization for genre-diverse texts. Dr. Liu is also deeply involved in cross-framework discourse understanding and unifying discourse resources, having co-organized the DISRPT shared task. Her work extends to multilingual annotation projects involving discourse-level phenomena. Dr. Liu's recent publications demonstrate a consistent focus on discourse analysis, multilingual capabilities, and genre diversity in NLP. Her work bridges theoretical linguistics with practical NLP applications, particularly in discourse parsing, summarization, and multilingual generalization. She has made significant contributions to creating diverse datasets for discourse analysis and understanding the limitations of current discourse parsing systems. Dr. Liu is actively involved in the academic community, having co-organized workshops such as the First Workshop on Bridging NLP and Public Opinion Research at COLM 2025. She regularly presents her work at major NLP conferences including ACL, EMNLP, and INLG. Dr. Liu maintains connections with her previous institutions and collaborators, including her postdoctoral advisor Prof. Dr. Barbara Plank at LMU Munich and her doctoral advisor Amir Zeldes at Georgetown University. Her work continues to build on the foundation established during her time at these institutions.
Patricia J. Brooks is a Professor in the Department of Psychology at the College of Staten Island, City University of New York (CUNY). She serves as Director of the Language Learning Laboratory and is an active member of the Doctoral Faculty at The Graduate Center, CUNY, contributing to PhD programs in Psychology, Educational Psychology, and Speech-Language-Hearing Sciences. Education: PhD, New York University MA, New York University BA, Johns Hopkins University Her research focuses on individual differences in language learning across the lifespan, with an emphasis on adult second language acquisition and child language development. She employs experimental methods and computer-based games to study speech perception and production in both typical and atypical development. She also has a strong interest in pedagogy, particularly active learning, mentoring, and the use of technology in education. Her recent publications span diverse areas including statistical learning in developmental disorders, narrative evaluation in children, phonological processing, and innovative teaching practices such as Wikipedia editing in the classroom. These works reflect a consistent theme of understanding cognitive and social mechanisms in language development and applying that knowledge to educational contexts. Scientific Service and Leadership: Faculty Advisor, Graduate Student Teaching Association, American Psychological Association (Division 2) She has authored and edited key academic texts such as Language Development (2012), Encyclopedia of Language Development (2014), and Cognitive Development in Digital Contexts (2017). Her work bridges experimental psychology, linguistics, and educational practice. She is actively involved in research mentorship and graduate training, contributing to the development of future scholars in psychology and education. Her laboratory supports interdisciplinary research on language and cognition, and she collaborates widely across institutions and disciplines.
Henning Tangen Søgaard is an Associate Professor at the Department of Mechanical and Production Engineering, part of AU Engineering at Aarhus University. He teaches mathematics, numerical methods, and mathematical statistics to BEng students. His research focuses on robotics in agriculture , dynamic modeling , and precision agriculture . Primary Affiliation: Department of Mechanical and Production Engineering, AU Engineering, Aarhus University Research Expertise: Computer vision, control systems for agricultural robotics, environmental modeling (ammonia emissions, spray drift), and wireless sensor networks. His work includes developing autonomous systems for weed control, GPS-based geo-referencing of crops, and mathematical models for fertilizer-related emissions. Publications span peer-reviewed journals and reports in agricultural engineering , robotics , and environmental science . Scientific awards are not mentioned in the provided text. He has no listed PhD students but has collaborated on multiple projects. For direct contact, his email is hts@mpe.au.dk .
Professor Amanda Turner is a faculty member in the School of Mathematics at the University of Leeds, part of the Faculty of Engineering and Physical Sciences. Her research lies at the intersection of probability, analysis, and mathematical physics, with a focus on understanding the macroscopic behavior of complex random systems. Her research interests include: Probability theory Complex analysis Mathematical physics Random growth models Scaling limits of stochastic models She is affiliated with key research groups such as Statistics, Probability and Financial Mathematics, and Statistical Methodology and Probability. Her current work investigates the scaling limits of random growth models, contributing to foundational understanding in statistical mechanics and stochastic processes. Professor Turner welcomes PhD students and research collaborators interested in these areas, with active postgraduate research opportunities available at the university. She has no listed scientific awards in the provided text. She advises potential graduate students to contact her regarding ongoing projects, particularly in the domain of stochastic modeling and probabilistic analysis. While specific grants are not listed, her research group is actively engaged in theoretical and applied probability research. She is involved in the Probability and Financial Mathematics research group, contributing to collaborative work in theoretical probability and its applications.
Steven Wu is an Associate Professor in the School of Computer Science at Carnegie Mellon University, with primary appointments in the Software and Societal Systems Department (S3D) and affiliated roles in the Machine Learning Department, Human-Computer Interaction Institute, CyLab, and Theory Group. Previously, he held positions at the University of Minnesota (Assistant Professor) and Microsoft Research-New York City (post-doctoral researcher). Ph.D. in Computer Science, University of Pennsylvania (co-advised by Michael Kearns and Aaron Roth) His research spans Machine Learning , Algorithms , Privacy , and Fairness , focusing on responsible AI foundations, interactive learning, causal inference, and economic applications. Recent work explores uncertainty quantification and privacy risks in synthetic data. He has received prestigious awards including the NSF CAREER Award and Penn's Rubinoff Award for his dissertation. His group mentors students across Ph.D. , postdoc, and visiting programs, with alumni now at institutions like UC Berkeley, Stanford, and Amazon. Key grants: NSF, Okawa Foundation, Open Philanthropy, Amazon, Google, J.P. Morgan, Meta, Mozilla, Apple, Cisco
Fred Morstatter is a Research Assistant Professor at the Thomas Lord Department of Computer Science, University of Southern California. He serves as Principal Scientist at the USC Information Sciences Institute and Associate Director for USC Data Science, bridging academia and applied research in AI ethics and social media analysis. Research Interests include: Mitigating algorithmic bias in NLP systems Developing robust social media content analysis frameworks Creating hybrid human-machine forecasting models for geopolitical events Studying causal relationships in online-offline event dynamics Advancing crowdsourcing methodologies with ethical AI Recent Article Trends examine: Temporal knowledge graph forecasting without explicit training data Gender bias quantification in named entity recognition Characterizing misinformation through network analysis Developing fair decision-making attribution mechanisms Mapping moral valence in crisis-related social media discourse Student Supervision includes advising PhD candidates exploring: Implicit biases in LLMs Computational social science Hate speech detection Persuasion modeling in forecasting systems Contact: fred@isi.edu | Google Scholar | USC ISI
Martin Theobald is a Professor in the Department of Computer Science at the University of Luxembourg's Faculty of Science, Technology and Communications. Previously affiliated with University of Ulm, Germany, his research spans database systems, information retrieval, and knowledge extraction with over 120 publications since 2002. His work bridges theoretical database foundations with practical applications in large-scale data processing. His research focuses on: Probabilistic and uncertain database systems Stream processing frameworks (notably the AIR architecture) Knowledge extraction from heterogeneous data sources Integration of machine learning with database systems Efficient query processing for structured and semi-structured data Recent publications demonstrate an evolving research trajectory toward real-time data stream processing with machine learning integration. His work on the AIR (Asynchronous Iterative Routing) framework and its extensions (TensAIR, OPTWIN) addresses critical challenges in concept drift detection, neural network training on streaming data, and efficient resource utilization. These contributions sit at the intersection of database systems, distributed computing, and machine learning, with applications in knowledge graph construction and question answering systems. Martin Theobald has mentored numerous researchers including Mauro Dalle Lucca Tosi, Alessandro Temperoni, and Vinu E. Venugopal, who have become active contributors to the database community. His collaborative network spans institutions across Europe, with frequent partnerships with researchers from University of Ulm, Max Planck Institute, and other European universities. His laboratory work focuses on developing scalable systems for processing evolving data streams, with particular emphasis on creating lightweight architectures that maintain high performance while minimizing resource consumption. Current projects involve integrating knowledge graphs with real-time analytics and developing adaptive systems that can handle concept drift in streaming environments.
