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Chao Liu is an Associate Professor at the Department of Biomedical Engineering , Southern University of Science and Technology (2024.11–present). Previously, he served as an Assistant Professor at the same institution (2019–2024.11) and completed a Postdoctoral Fellowship at New York University (2016–2019) with joint appointments in the Department of Biomedical Engineering and Orthopedics at NYU Langone Health . He holds a PhD in Biomedical Engineering (2016) and MASc in Mechanical Engineering (2010) from University of Toronto , following a BASc in Engineering Science (2008). Education : PhD (IBBME, U of Toronto, 2016), MASc (Mechanical Engineering, U of Toronto, 2010), BASc (Engineering Science, U of Toronto, 2008) His research focuses on mechanobiology of stem cells , bone tissue engineering , and functional implants . He investigates how mechanical forces modulate osteogenic and angiogenic stem cells, designs implants with micro-geometry to enhance cellular activity, and develops 3D real-time imaging modalities for bone repair processes. His work spans biomechanics , regenerative medicine , and cellular mechanotransduction . The selected publications from 2022 to 2010 reveal trends in mechanical force applications for bone regeneration, pharmacological impairments in healing, nanostructured biomaterials for stem cell differentiation, and microfluidic platforms for osteocyte studies. These works are published in journals like Bone , FASEB Journal , and ACS Applied Materials & Interfaces . Scientific Awards : International Conference BME Young Investigator Award (2019), IFMRS Travel Grant (2017), ASBMR Young Investigator Grant (2016), CIHR Travel Awards (2010–2014), Ontario Graduate Scholarship (2011–2013), Barbara and Frank Milligan Fellowship (2008, 2010) Chao Liu mentors graduate students, including Zhang Jianing (2022 Master’s graduate). His lab ( https://abtrl.bme.sustech.edu.cn/ ) recruits postdocs and research assistants for projects in mechanical force applications , micro-geometric implants , and 3D imaging . He has contributed to 14 journal articles with 714 citations and presented at 6 international conferences in the past three years.

• Stem Cell Mechanobiology
• Bone Tissue Engineering
• Regenerative Medicine
• +3 more

Prof. Hans van Lint is a Professor of Traffic Simulation and Computing at Delft University of Technology (TU Delft), where he holds the Anthony van Leeuwenhoek Chair since 2013. He is affiliated with the Department of Transport & Planning within the Faculty of Civil Engineering and Geosciences. His research focuses on the intersection of traffic flow theory, data analytics, and traffic simulation, with applications in estimating and predicting traffic states in networks. He has supervised numerous PhD students and contributed to valorization projects translating research into practical solutions. Van Lint earned his MSc in Civil Engineering in 1997 and returned to TU Delft for his PhD, which he completed in 2004 on 'Freeway Travel Time Prediction.' He has held roles including Assistant Professor (until 2009), Associate Professor, and has served as Director of Education for the MSc Transport, Infrastructure and Logistics program from 2010–2016. His research interests include traffic simulation frameworks, data assimilation techniques, and the development of tools for traffic state estimation. He has authored influential papers on topics such as microscopic traffic modeling, congestion pattern analysis, and macroscopic fundamental diagrams. His work emphasizes bridging theoretical models with real-world applications, enhancing traffic management and infrastructure planning. Van Lint teaches courses like 'Transport & Planning' and 'Interdisciplinary Fundamentals,' reflecting his commitment to both research and education. He actively contributes to TU Delft's labs, including the Traffic Dynamics, Modelling and Control Lab, advancing interdisciplinary approaches to mobility challenges.

• Traffic Simulation
• Transportation Engineering
• Data Analytics
• +3 more

Vladimir Spokoiny is a Professor at the Departments of Mathematics and Economics of the Humboldt University of Berlin and Head of the Research Group "Stochastic Algorithms and Nonparametric Statistics" at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, Germany. His research spans multiple areas of statistics, machine learning, and financial mathematics, with significant contributions to nonparametric statistics, high-dimensional data analysis, and statistical methods in finance. Spokoiny received his M.Sc. in applied mathematics from the Moscow Institute of Railway Engineering in 1981 and his Ph.D. in mathematics from Lomonosov Moscow State University in 1988. He completed his Habilitation at Humboldt University in 1996. His academic career includes positions at the All-Union Institute of Railway Transport in Moscow, the Institute for Information Transmission Problems in Moscow, and the Institute for Applied Analysis and Statistics in Berlin before joining the Weierstrass Institute and Humboldt University where he has been a professor since 2002. Spokoiny's research focuses on adaptive nonparametric smoothing and hypothesis testing, high dimensional data analysis, statistical methods in finance, image analysis with applications to medicine, classification, and nonlinear time series. His work often addresses the challenges of nonstationarity in time series data and develops innovative methods for volatility estimation and risk management. He has made significant contributions to the development of adaptive weights smoothing procedures, which have applications in image processing, community detection, and manifold learning. His recent work has expanded into high-dimensional statistics, Bayesian inference, and optimization methods for machine learning, with publications demonstrating novel approaches to Gaussian approximation, Laplace methods, and statistical inference in non-Euclidean spaces. Spokoiny has supervised numerous PhD students including Oliver Reiss, Danilo Mercurio, Ying Chen, Elmar Diederichs, and Mstislav Elagin, whose research has focused on mathematical finance, time series analysis, and statistical methods. He serves as an Associate Editor for The Annals of Statistics (since 2004) and Statistics and Decisions (since 2002), and has previously served on the editorial board of the Journal of Statistical Planning and Inference. His professional activities include reviewing for major statistical journals including Annals of Statistics, Bernoulli, Econometrica, and Journal of American Statistical Association, as well as reviewing grant proposals for the National Science Foundation (USA), German Research Foundation, and Netherlands Organisation for Scientific Research. Spokoiny is a member of several professional societies including the International Statistical Institute, American Statistical Association, Institute of Mathematical Statistics, and Bernoulli Society. He is fluent in Russian (mother tongue), English, and German, and has good knowledge of French. His research group at WIAS focuses on developing novel statistical methodologies with applications across various scientific domains, particularly emphasizing adaptivity and robustness in complex data environments. The group's work has significant implications for financial risk management, medical imaging, and machine learning applications, with recent publications addressing fundamental questions in high-dimensional statistics and nonparametric inference.

• Nonparametric Statistics
• High Dimensional Data Analysis
• Statistical Methods in Finance
• +7 more

Nati Srebro is a Professor at the Toyota Technological Institute at Chicago with a cross-appointment as a Part-Time Professor in the Department of Computer Science and Committee on Computational and Applied Mathematics at the University of Chicago. He earned his PhD from MIT in 2004 and has held previous positions including post-doctoral fellow at the University of Toronto, Visiting Scientist at IBM, and Associate Professor at the Technion. Professor Srebro's research focuses on methodological, statistical and computational aspects of Machine Learning and Optimization. His work spans foundational contributions to learning theory, matrix reconstruction, and optimization techniques. He is particularly known for introducing the use of nuclear norm for machine learning, work on wider Markov networks, and advancing our understanding of the relationship between learning and optimization. His current research interests include understanding deep learning through optimization, distributed and federated learning systems, algorithmic fairness, and practical adaptive data analysis. His publication record shows consistent contributions to core machine learning conferences and workshops, with recent work focusing on symmetric and asymmetric hashing techniques, matrix parameter learning, and optimization methods. The publications demonstrate a strong theoretical foundation with practical applications across various machine learning domains. Professor Srebro has been actively involved in several research programs including the Federated and Collaborative Learning program (Spring 2026, as Visiting Scientist and Program Organizer), Modern Paradigms in Generalization (Fall 2024), and multiple summer clusters on Deep Learning Theory and Fairness. His program participation reflects his leadership in emerging areas of machine learning research. Contact: nati@ttic.edu | (773) 834-7493 | Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave., Chicago, IL 60637

• Machine Learning
• Optimization
• Deep Learning
• +4 more

S. Yaser Samadi is an Associate Professor in the Department of Mathematics at the School of Mathematical and Statistical Sciences, Southern Illinois University Carbondale. He holds a Ph.D. in Statistics from the University of Georgia (2014) and maintains an active research program in advanced statistical methodologies. Education: Ph.D. in Statistics, University of Georgia, 2014 Research Interests: Dr. Samadi specializes in multivariate time series analysis, high-dimensional statistical inference, and tensor data analysis. His work addresses critical challenges in big data, symbolic data, and dimension reduction for time series through Bayesian analysis and sequential methods for dependent and independent data, yielding robust models for complex data structures. Publication Trends: His recent publications (2014-2023) emphasize time series analysis, dimension reduction, and innovative approaches for interval-valued and matrix-valued data. Key contributions include envelope models for vector autoregression, copula-based count data modeling, and sequential analysis techniques, bridging theoretical statistics with econometrics and data science applications. Scientific Awards: Outstanding Teacher of the Year, School of Mathematical and Statistical Sciences (2021) Advising: Dr. Samadi has mentored four Ph.D. students to completion: Rukayya Ibrahim (Assistant Professor, Penn State Harrisburg), Wiranthe Herath (Assistant Professor, Drake University), Tharindu De Alwis (Postdoctoral Fellow, WPI), and Hadi Safari Katesari (Teaching Assistant Professor, Stevens Institute of Technology). His Master's students Samira Zaroudi (CUNY) and Reginald Ziedzor (Amplify) have also achieved notable career placements.

• Multivariate Time Series Analysis
• High Dimensional Statistical Inference
• Tensor Data Analysis
• +5 more

Emily M. Bender is the Thomas L. and Margo G. Wyckoff Endowed Professor in the Department of Linguistics at the University of Washington. She also holds adjunct appointments in the School of Computer Science and Engineering and the Information School. Her research spans multilingual grammar engineering, computational linguistics, societal impacts of language technology, and sociolinguistic variation. She directs the Computational Linguistics Laboratory (The Treehouse) and leads the CLMS program. Bender is a Fellow of the AAAS (2022) and previously served as Howard and Frances Nostrand Endowed Professor (2019–2022). She has authored influential textbooks on NLP fundamentals and pioneered work on data statements to mitigate bias in NLP systems. Her work integrates linguistic theory with computational methods, emphasizing ethical AI and language documentation. Education: PhD in Linguistics from Stanford University (advisor: Ivan A. Sag), AB in Linguistics from UC Berkeley, with studies at Tohoku University. Past roles include NAACL Executive Board Chair (2016–2017) and current roles in the Association for Computational Linguistics leadership. Her Erdős number is 4. Research focuses on the LinGO Grammar Matrix, automatic grammar inference from interlinear glossed text (AGGREGATION project), and societal implications of NLP technologies like large language models. She co-leads the RAISE initiative and contributes to labs like the Tech Policy Lab and Value Sensitive Design Lab. Over 30 advisees have completed PhD and MS degrees under her mentorship. Teaching includes courses on syntax for NLP, societal impacts of language tech, and computational linguistics. Her 2020 ACL paper on form-meaning distinctions in NLP has been influential in ethical discussions. Current projects include The AI CON (2025) on combating tech hype.

• Multilingual Grammar Engineering
• Linguistics in NLP/Computation in Linguistics
• Societal Impacts of Language Technology
• +3 more

Andrea Montanari is a Professor of Mathematics and Statistics at Stanford University, affiliated with the Department of Mathematics and Statistics. His research focuses on high-dimensional statistics, machine learning theory, optimization algorithms, and statistical physics, with applications to neural networks and complex systems. He has contributed extensively to understanding generalization in overparametrized models, spin glass theory, and algorithmic methods like approximate message passing. His work bridges theoretical computer science and mathematical physics, addressing challenges in data analysis and learning from high-dimensional datasets. Notable themes include analyzing neural network dynamics, optimizing high-dimensional landscapes, and developing efficient algorithms for sparse and low-rank matrix estimation. Montanari’s publications explore topics such as the interplay between statistical and computational limits, the behavior of gradient-based methods, and the theoretical foundations of modern machine learning. His recent research demonstrates a focus on fundamental questions in learning theory, including the study of phase transitions in statistical estimation, the role of overparametrization in generalization, and the mathematical underpinnings of contemporary algorithms. While no specific awards are listed here, his contributions reflect significant impact in interdisciplinary fields.

• High-dimensional statistics
• Machine learning theory
• Statistical physics
• +4 more

Tengyao Wang is a Professor in the Department of Statistics at the London School of Economics and Political Science (LSE), serving as the MSc Statistics (Financial Statistics) Programme Director. Prior to LSE, he held positions as a Lecturer at University College London and a Research Fellow at the Cantab Capital Institute for the Mathematics of Information, University of Cambridge. His research focuses on high-dimensional statistics, computational efficiency, and statistical limitations imposed by computational constraints. Education: PhD in Statistics under Prof Richard Samworth at the University of Cambridge, with earlier studies including a Part III Essay in Empirical Process Theory. Research interests include sparse signal detection, change-point analysis, dimension reduction, robust statistics, and applications in medical statistics, financial data analysis, and material discovery. Key contributions include methodologies for handling missing data, high-dimensional change-point detection algorithms, and statistical learning techniques. Publications span theoretical advancements and applied innovations, with recent work emphasizing deep learning with missing data, residual permutation tests, and semi-supervised learning via random projections. His work has been recognized with awards such as the Royal Statistical Society Research Prize (2019) and the Guy Medal in Bronze (2023). He is an Associate Editor of the Journal of the Royal Statistical Society, Series B (JRSS B), and actively contributes to open-source tools like the 'ocd' and 'MissInspect' R packages for changepoint detection and missing data analysis.

• High-dimensional Statistics
• Change-point Analysis
• Sparse Signal Detection
• +7 more

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.

• Reinforcement Learning
• Information Retrieval
• Machine Learning
• +4 more

Ashley M R Montanaro is a Professor of Quantum Computation at the School of Mathematics, University of Bristol . Active in quantum computing research since at least 2014, they lead projects at the intersection of quantum algorithms , computational complexity , and quantum information theory , affiliated with the Bristol Quantum Information Institute. Research interests focus on quantum algorithm design , computational complexity analysis , and quantum simulation . Key work includes developing variational quantum algorithms for phase transition detection, Hamiltonian simulation techniques, and quantum-classical hybrid methods for solving complex problems in physics and optimization. Recent publications demonstrate expertise in: Quantum phase diagram simulation with low-depth circuits Quantum speedups for constraint satisfaction problems Quantum communication complexity of machine learning tasks Quantum-enhanced optimization heuristics Hamiltonian simulation with time-dependent product formulas Quantum algorithm complexity analysis Scientific awards include: EPSRC Fellowship (2014-2019) - "New insights in quantum algorithms and complexity" Active in quantum software development through projects like: "Quantum Algorithms from Foundations to Applications" (ERC-2018-COG) "Quantum Computing and Simulation Hub" (2019-2024) "Prosperity Partnership in Quantum Software" (2019-2023)

• Quantum Computation
• Quantum Algorithms
• Computational Complexity
• +3 more

Dr. William V. Giannobile serves as the Dean and A. Lee Loomis, Jr. Professor of Oral Medicine, Infection, and Immunity at Harvard School of Dental Medicine (HSDM). He leads the institution as its chief academic and administrative officer while maintaining an active research program and clinical practice in periodontics and implant dentistry at the Harvard Dental Center in Cambridge, MA. Dr. Giannobile received his DDS and MS in oral biology from the University of Missouri, followed by a certificate in periodontology and Doctor of Medical Sciences (DMSc) in oral biology from Harvard School of Dental Medicine. He completed postdoctoral training in molecular biology at the Dana-Farber Cancer Institute and Harvard Medical School. With over 30 years of continuously NIH-funded research, Dr. Giannobile's work focuses on oral and periodontal regenerative medicine, tissue engineering, and precision medicine. He serves as principal investigator of the NIDCR U-Award Harvard Clinical Practice-based Research Program for Dental Students (H-CREDENT), which evaluates the genomics of oral pain in a multi-institutional academic setting. His research has produced over 300 manuscripts, textbook chapters, and patents focused on periodontology, regenerative medicine, and oral health research. He has edited or co-edited over 10 books in clinical and translational research, periodontology, and regenerative medicine. His publications demonstrate consistent focus on periodontal regeneration, biomaterials, tissue engineering, and the molecular mechanisms underlying oral diseases. ADA Gold Medal Award for Excellence in Dental Research (2024) International Association for Dental Research Distinguished Scientist Research in Periodontal Disease Award (2022) Norton M. Ross Clinical Research Award from the American Dental Association (2018) William J. Gies Award (2014) Jan Lindhe International Periodontology Research Award (2011) As an educator and mentor, Dr. Giannobile completed a 10-year term as editor-in-chief for the Journal of Dental Research, the official journal of the International Association for Dental Research. He has served as a consultant to the U.S. Food and Drug Administration for Dental Devices and as an associate editor for the NIDCR Report on Oral Health of the U.S. in 2021. His laboratory continues to advance research in periodontal regeneration and precision medicine approaches to oral health, with ongoing NIH funding supporting innovative translational research.

• Periodontology
• Regenerative Medicine
• Tissue Engineering
• +3 more

Dr. Jia Zhang is the Inaugural Robert H. Dedman Jr. Endowed Department Chair and Professor of Computer Science at Southern Methodist University (SMU Lyle School of Engineering). She holds the Cruse C. and Marjorie F. Calahan Centennial Chair in Engineering and has a courtesy appointment in the Department of Operations Research and Engineering Management. Her research focuses on applying machine learning, natural language processing, and information retrieval to data science infrastructure, particularly scientific workflows, provenance mining, software discovery, knowledge graphs, cloud computing, immune AI, and applications in earth science and healthcare. Education: Ph.D. in Computer Science, University of Illinois at Chicago M.S. in Computer Science, Nanjing University B.S. in Computer Science, Nanjing University Dr. Zhang's work emphasizes data science infrastructure and machine learning for scientific workflows and knowledge graphs. Her recent publications highlight deep learning , graph neural networks , and optimization algorithms in cloud computing, cybersecurity, and environmental applications. Key trends include spatiotemporal modeling , hybrid neural architectures , and AI-driven service ecosystems . Scientific Awards: Best Paper Awards IEEE SCC (2011, 2017) Best Student Paper Awards IEEE ICWS (2014, 2018), IEEE ICCC (2018) Distinguished Paper Award ICSOC (2023) First Outstanding Service Award IEEE Technical Committee on Services Computing (2016) She has secured over $5 million in federal grants (as PI) and $11 million as PI/Co-PI from NSF, NASA, NIH, UTSW, Ericsson, SAP, and Google. Her lab (Caruth Hall 308) actively recruits research assistants. She previously served as a faculty member at Carnegie Mellon University, Northern Illinois University, and Nanjing University, and worked in industry as a software architect.

• Machine Learning
• Natural Language Processing
• Information Retrieval
• +10 more

Sean Howe is an Assistant Professor in the Department of Mathematics at the University of Utah, where he has been employed since July 2019. His research is supported by NSF grants DMS-2201112 and DMS-2501816. In the academic year 2023-2024, he was a Friends of the Institute for Advanced Study Member at the special year on p-adic arithmetic geometry at the Institute for Advanced Study. Dr. Howe received his PhD from the University of Chicago in 2017 under the supervision of Matt Emerton. Prior to his position at Utah, he was an NSF Postdoctoral Scholar at Stanford University from September 2017 to June 2019. He earned a joint master's degree from Leiden University and Universite Paris-Sud 11 through the ALGANT program in 2012 and completed his undergraduate studies at the University of Arizona. Dr. Howe's research spans arithmetic and algebraic geometry, representation theory, and number theory, with a particular focus on p-adic aspects. His work often explores the connections between geometry and number theory through the lens of p-adic methods, including p-adic Hodge theory, perfectoid spaces, and the Langlands program. He has made significant contributions to understanding cohomological structures in mixed characteristic settings, the geometry of moduli spaces, and the statistical properties of L-functions. His extensive publication record demonstrates a strong trajectory in advancing p-adic geometry and its applications. Recent work shows increasing focus on cohomological smoothness in mixed characteristic, p-adic periods, and the interplay between random matrix theory and arithmetic statistics. His research often bridges abstract theoretical frameworks with concrete computational approaches. NSF Postdoctoral Scholar NSF grants DMS-2201112 and DMS-2501816 Dr. Howe is an active mentor, currently advising five PhD students: Minhua Cheng, Madison Delmoe, Shea Engle, Abhay Goel, and Suo Jun Tan. He has successfully graduated two PhD students: Matthew Bertucci (2025) and Hanlin Cai (2024). He also regularly mentors undergraduate researchers, with notable projects including Emil Geisler's work on stable multiplicities in configuration space cohomology and Daniel Koizumi's software for computing braid monodromy of cubic surfaces. His teaching portfolio includes advanced courses in algebraic topology, number theory, and algebra, reflecting his broad expertise across pure mathematics. He has taught courses such as Math 6950 (Topics in Algebraic Topology), Math 4400 (Introduction to Number Theory), and Math 6320 (Modern Algebra II).

• arithmetic geometry
• algebraic geometry
• representation theory
• +2 more

Associate Professor Steven Lu is a faculty member at the University of Sydney Business School, serving as Deputy Head of Discipline (Education). He holds a PhD in Marketing from the University of Toronto, an MA in Economics from York University, and a BA from Nankai University. His research focuses on quantitative modeling, machine learning, and big data analytics applied to digital economy challenges such as digital retailing, search advertising, and blockchain. He co-directs the Consumer Insights Research Group and is affiliated with the Sydney Institute of Agriculture. Dr. Lu has published in top journals including Marketing Science , Production and Operations Management , and Journal of Retailing . His awards include the CNS Vithala Rao Award, ANZMAC Best Paper Awards (2022-2024), and the 2021 Vice Chancellor's Teaching Award. He teaches courses on machine learning in marketing, marketing research, and new product development. He leads research grants such as 'The Era of Mobile Payment' (2021) and 'Digital Transformation of Food Sensory Quality' (2017). His advising focuses on topics like neural recommender systems, e-coupon effectiveness, and heterogeneous treatment effects analysis.

• Quantitative Modeling
• Machine Learning
• Big Data Analytics
• +6 more

Julie C. Liu is an Associate Professor of Chemical Engineering at Purdue University, with a courtesy appointment in Biomedical Engineering. She joined Purdue in 2008 and holds a B.S.E. from Princeton University, M.S. and Ph.D. from Caltech, and completed a postdoc at the University of Massachusetts Medical School. Her research focuses on protein-based biomaterials for tissue engineering, regenerative medicine, and surgical adhesives, with a particular emphasis on recombinant proteins and natural matrix molecules like collagen and hyaluronic acid. Key awards include the Purdue Faculty Scholar (2019-2024), ELATES Fellowship (2019), and Outstanding Mentor Award (2018). Her work has been funded by NSF, NIH, and industry grants. She advises multiple graduate and undergraduate students, mentoring over 20 researchers. Her lab collaborates on outreach, including Introduce a Girl to Engineering Day, reaching 500+ students since 2014. Liu’s lab designs biomaterials with tailored properties for cartilage repair, drug testing models, and adhesives. Recent advances include mussel-inspired adhesives and redox-responsive hydrogels. She leads a team focused on translational biomedical innovations, bridging engineering and clinical applications.

• Tissue Engineering
• Regenerative Medicine
• Biomedical Adhesives
• +7 more