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Yingqi ZhaoView profile

Professor

• Statistical methods for personalized medicine
• Dynamic treatment regimes
• Clinical trials design
• +7 more

Yingqi Zhao, PhD is a Professor in the Biostatistics Program within the Public Health Sciences Division at Fred Hutchinson Cancer Center. She is also a Member of the Translational Data Science Integrated Research Center (TDS IRC) at Fred Hutch. Dr. Zhao leads the Zhao Group, which focuses on developing novel statistical and machine learning methods for personalized medicine, dynamic treatment regimes, disease screening and surveillance, clinical trial design, and electronic health records analysis. Dr. Zhao earned her PhD in Biostatistics from the University of North Carolina Chapel Hill in 2012 and completed her BS in Statistics at Wuhan University in 2006. Her research spans multiple areas of biostatistics with applications in cancer detection and treatment, diabetes care, and public health surveillance. Dr. Zhao's work focuses on developing statistical methods for precision treatment and precision prevention, clinical trial design for therapeutic and early detection purposes, methods for analyzing real-world data from electronic medical records and administrative data, and public health surveillance. Her research has significant applications in early cancer detection, cancer therapeutics, cancer survivorship, and diabetes care. She develops innovative approaches to improve cancer immunotherapy trial design, early detection of cancer, healthcare delivery for complex Type 2 diabetes patients, and childhood obesity surveillance. Her recent publications demonstrate a strong focus on individualized treatment rules, dynamic treatment regimes, cancer screening trial design, and analysis of electronic health records data. These works show an evolving trend toward more personalized and cost-effective healthcare solutions with applications across multiple disease areas, particularly cancer and diabetes. 2020 - UNC Biostatistics Grizzle Alumni Award 2011 - Best Paper in Biometrics, International Biometrics Society SWOG Coltman Fellowship early-career award ENAR Distinguished Student Paper Award Society for Clinical Trials (SCT) Thomas C. Chalmers Student Scholarship Finalist Dr. Zhao mentors numerous PhD students and postdoctoral fellows, with alumni now holding faculty positions at institutions like MD Anderson Cancer Center and Georgia State University. Her research is supported by significant grants from NIH, PCORI, and the Hope Foundation, including a 2025 R01 grant from the National Cancer Institute for pancreatic cancer early detection research and a 2024 PCORI grant for improving randomized screening trial design. The Zhao Group operates within the Fred Hutch ecosystem, collaborating extensively with the SWOG Cancer Research Network and the Early Detection Research Network. The group maintains active research projects focused on pancreatic cancer early detection, cancer immunotherapy trials, diabetes care for complex patients, and childhood obesity surveillance, leveraging electronic medical records data and advanced statistical methods to address critical healthcare challenges.

Ellen OldenburgView profile

Researcher

• Marine Microbiology
• Arctic Ocean Ecology
• Microbial Community Dynamics
• +5 more

Dr. Ellen Oldenburg is a Researcher at the Institute for Quantitative and Theoretical Biology, Heinrich Heine University Düsseldorf, where she investigates microbial community dynamics in polar marine ecosystems using computational methodologies. Her work integrates long-term Arctic Ocean time-series data with advanced analytical frameworks to understand biogeochemical cycles and ecosystem stability under environmental change. Her research specializes in applying network analyses—including co-occurrence networks, convergent cross mapping (CCM), and energy landscape analysis (ELA)—to unravel seasonal microbial interactions. She examines how winter conditions function as ecological resets and tracks causal relationships across taxonomic clusters (e.g., Syndiniales, Dinophyceae, Bacillariophyta). Key focus areas include Marine Microbiology, Arctic Ecosystem Resilience, and Computational Ecology, with visualizations mapping ASV abundance peaks and community membership across seasonal transitions (high-light summer, low-light winter, spring/autumn transitions). Dr. Oldenburg's methodological innovations in causal inference provide critical insights for predicting climate-driven ecosystem responses in vulnerable polar regions, emphasizing the role of microbial networks in global biogeochemical processes.

• Natural Language Processing (NLP)
• Deep Learning
• Data Science
• +3 more

Bang LIU is an Associate Professor at the University of Montreal's Faculty of Arts and Science, specifically within the Department of Computer Science and Operations Research. His research focuses on Natural Language Processing (NLP), Deep Learning, and Data Science with applications in healthcare, materials science, and embodied AI systems. He leads multiple research projects funded by organizations like NSERC and MITACS, exploring causal inference, multimodal reasoning, and AI-driven material discovery. Education: PhD in Computer Science (University of Montreal). Research Highlights : Developing causal discovery frameworks (e.g., CausalNET, OCDB) Pioneering materials science applications via AI agents (MatExpert, Honeycomb) Advancing embodied instruction following systems (OPEx) Grants & Projects : NSERC Discovery Grants (2021-2027) MITACS Accelerate Projects (2022-2025) focusing on healthcare LLM trust, CAD generation, and procedural knowledge extraction He supervises Master's students in low-resource NLP tasks and serves as a lead researcher in over 15 active projects. Affiliated with the CRM and RALI labs, his work bridges theoretical AI advancements with practical industrial applications.

Arghya PalView profile

Lecturer

• Causality
• Generative Models
• Computer Vision
• +3 more

Dr. Arghya Pal is a Lecturer at the Malaysia School of Information Technology, Monash University. His research focuses on causality, generative models, and computer vision, with notable contributions in medical imaging optimization, bias detection in AI systems, and cryptographic applications of autoencoders. He has led or co-investigated projects such as Æinstein: Adversarial AI amongst Materials Discovery Domains (2024–2026) and Memory Efficient Fast Sampling of Diffusion Model (2023–2024). Research Interests: Causality and bias analysis in machine learning Generative models for multimedia applications Computer vision techniques in medical imaging Cryptography and AI-driven security systems Awards: Magna cum laude (2022) Grants/Projects: Primary Investigator: Memory Efficient Fast Sampling of Diffusion Model (2023–2024) Chief Investigator: Æinstein Project (2024–2026)

James SharpnackView profile

Adjunct Associate Professor

• Statistical Machine Learning
• Graph Signal Processing
• Anomaly Detection
• +1 more

James Sharpnack is an Adjunct Associate Professor in the Statistics Department at UC Davis and an AI Research Scientist at Duolingo. His research focuses on statistical machine learning applied to anomaly detection, graph signal processing, and epidemiological modeling. His methodological work includes developing scan statistics for network data, trend filtering techniques, and domain adaptation approaches. Applied projects involve COVID-19 wastewater monitoring, gravitational lens detection in astronomy, and transportation data analysis. Sharpnack contributes to educational AI systems through his industry role.

Sanjay KrishnanView profile

Assistant Professor

• Data Science
• Databases
• Machine Learning
• +5 more

Sanjay Krishnan is an Assistant Professor of Computer Science at the University of Chicago. His research focuses on managing uncertain data in databases and information systems, including accuracy guarantees in incomplete databases, query evaluation under corruption, and data leakage detection. He leads the ChiDATA research group within the University of Chicago's Department of Computer Science, collaborating with researchers like Michael Franklin and Raul Castro Fernandez on large-scale data analysis and systems. His academic work spans foundational areas such as AI/ML integration with data systems, scalable data processing, and resource-efficient database design. He is affiliated with the CERES Center for Unstoppable Computing and the Systems Group, exploring resilient computing systems and software engineering challenges. His lab's research has been featured in UChicago CS news for innovations like geometric database query processing and interdisciplinary collaborations with the Museum of Science & Industry. Key contributions include frameworks like EdgeServe for decentralized model serving, CrocodileDB for resource-efficient query execution, and ActiveClean for interactive data cleaning in ML pipelines. His work bridges theoretical computer science with practical system implementations, emphasizing real-world data challenges in IoT, healthcare, and environmental monitoring.

• Computer Networks
• Internet Video Distribution
• Network Synthesis
• +6 more

Sanjay Rao is a Professor in the School of Electrical and Computer Engineering at Purdue University, leading the Internet Systems Laboratory. His research focuses on network synthesis/design/verification and Internet video distribution. He holds a B.Tech from IIT Madras and a Ph.D from Carnegie Mellon University. He has held visiting roles at Google, AT&T Research, and Princeton University. Research & Awards: His work includes groundbreaking contributions like End System Multicast (winner of the ACM SIGMETRICS Test of Time Award), Oboe (ABR auto-tuning), and Veritas (causal video streaming analysis). He received the NSF CAREER Award (2010) and is an ACM Distinguished Member (2021). Teaching: Courses include Computer Networking (ECE 463), Object-Oriented Programming (ECE 39595), and Computer Network Systems (ECE 595). Service: Chair, ACM Sigcomm Doctoral Dissertation Award Committee (2022); Associate Editor, IEEE/ACM Transactions on Networking (2016–2020). Labs & Teams: Directs the Internet Systems Lab (ISL) with active projects on video streaming, network resilience, and intent-based design. Graduate students collaborate on these initiatives.

Rajdeep GrewalView profile

Professor

• Marketing Strategy
• Business Markets
• Customer Relationship Management
• +9 more

Rajdeep Grewal is the Townsend Family Distinguished Professor of Marketing and Area Chair of Marketing at UNC Kenan-Flagler Business School. His research focuses on strategic marketing issues using advanced causal models, combining quasi-experimental methods with statistical learning techniques. He teaches Customer Value Strategies and Marketing Analytics in executive programs and PhD-level courses on Empirical Economic Models. As Marketing Area Chair, he leads faculty and program strategy, and previously co-chaired Phase 2 of the Rethink Committee to redefine the business school's strategic vision. Dr. Grewal earned a PhD in Marketing (with econometrics minor) from the University of Cincinnati, a MS in Quantitative Analysis, a postgraduate diploma from IIM Lucknow, and a B.Tech in Textile Technology from IIT Delhi. His academic career includes roles at Penn State University, Indian School of Business, Wharton, and others. He served as Editor-in-Chief of the Journal of Marketing Research (2016-2020), advancing rigorous research standards. His research addresses customer relationship management, peer effects, agency theory, and B2B marketing dynamics. Notable contributions include frameworks for evaluating customer satisfaction impacts on sales costs and pioneering studies on chief marketing officer roles. His work appears in top journals like Journal of Marketing, Marketing Science, and Management Science. Awarded the Sheth Foundation/Journal of Marketing Award (2020), Louis W. Stern Award (2012), and AMA's Early Career and Lifetime Achievement Awards, his research has shaped interorganizational studies and marketing strategy. He has led initiatives like Penn State's Institute for the Study of Business Markets (2005-2015), driving B2B marketing-strategy intersections. Current research explores generative AI in education, digital disruptions in retail, and socio-political activism's economic consequences. His work bridges theoretical rigor with practical industry applications, influencing both academia and business practices globally.

• Artificial Intelligence
• Optimization
• Machine Learning
• +3 more

Sebastian Pokutta is a Professor at Technische Universität Berlin, Vice President at Zuse Institute Berlin (ZIB), and Chair of the Cluster of Excellence MATH+ and MODAL. His research focuses on artificial intelligence, optimization, and machine learning, with emphasis on algorithmic development, decision-making systems, and real-world applications in areas like satellite data analysis, quantum computing, and combinatorial mathematics. He leads a research group exploring AI-driven methodologies and their interdisciplinary applications. He holds academic positions including leadership roles in major institutions and has contributed to numerous high-impact publications in optimization, machine learning, and quantum computing. His work has been recognized with awards such as the Science Prize of the Association for Pediatric Orthopedics (VKO). Pokutta actively engages in academic outreach, delivering talks on optimization and AI, and contributes to open-source projects like the FrankWolfe.jl library. His research bridges theoretical foundations with practical implementations, addressing challenges in computational efficiency, mathematical modeling, and AI ethics.

Conrad TuckerView profile

Professor

• Artificial Intelligence
• Machine Learning
• Robotics
• +6 more

Conrad Tucker is a Professor of Mechanical Engineering at Carnegie Mellon University (CMU) and Director of CMU-Africa, with courtesy appointments in Machine Learning, Robotics, and Biomedical Engineering. He serves as Associate Dean for International Affairs (Africa) and holds an endowed Trustee Professorship. His research focuses on AI-driven systems design, data mining, cybersecurity, and education innovation, with grants from NSF, DARPA, and the Gates Foundation. Education: Ph.D. in Industrial & Systems Engineering, University of Illinois, 2011 MBA in Business Administration, University of Illinois, 2009 M.S. in Industrial & Systems Engineering, University of Illinois, 2007 B.S. in Mechanical Engineering, Rose-Hulman Institute of Technology, 2004 Research Interests: Dr. Tucker’s work bridges AI, engineering design, and societal impact. Key areas include: AI/ML for security and privacy Optimization of additive manufacturing AI applications in healthcare and education Data-driven product innovation His lab, AiPEX, develops ML methods for personalized design solutions and addresses challenges like deepfake detection in education. Grants & Awards: Endowed Trustee Professorship (CMU, 2023) NAE Frontiers of Engineering Education Advisory Committee (2016–present) Over $10M in grants from NSF, AFOSR, DARPA, and others Labs & Collaborations: Leads the AiPEX Lab, advancing AI in product design and cybersecurity. Collaborates with institutions globally, including the African Engineering and Technology Network (Afretec), to promote STEM education and digital infrastructure in Africa.

• Applied Mathematics
• Computational Mathematics
• Machine Learning
• +4 more

Yue Yu is a Professor in the Department of Mathematics at Lehigh University, specializing in applied mathematics and computational mathematics. Their research focuses on hybrid modeling, physics-informed machine learning, and nonlocal operators for material science and engineering applications. Yu has developed innovative methods for data-driven constitutive law discovery, peridynamic modeling, and causal graph learning. Their work bridges computational mathematics with real-world challenges in materials, biomedical systems, and fluid-structure interactions. Key research directions include operator learning frameworks (e.g., neural operators, nonlocal attention mechanisms), physics-guided smoothing techniques for material modeling, and scalable causal inference algorithms. Yu has pioneered methods like the Peridynamic Neural Operator (PNO) and Embedded Nonlocal Operator Regression (ENOR), advancing nonlocal calculus applications. Their contributions span interdisciplinary areas such as metamaterial design, fracture mechanics, and biomedical tissue modeling using digital image correlation. Publications emphasize methodological advancements in operator networks, DAG learning, and asymptotically compatible numerical methods. Yu has explored meta-learning for physics discovery and developed frameworks for integrating finite elements with deep neural operators. Current work addresses high-dimensional systems, data-scarce scenarios, and uncertainty quantification in complex material systems. Lab activities focus on computational modeling, numerical analysis, and AI-driven scientific computing. Collaborations involve engineering, physics, and biomedical domains. Yu's research is supported by leading journals in computational mathematics and has been recognized in conferences like the ASME Summer Bioengineering Conference.

Dawei ZhouView profile

Assistant Professor

• Open-World Machine Learning (OpenML)
• AI for Material Science
• AI for Finance
• +8 more

Dr. Dawei Zhou is an Assistant Professor at the Department of Computer Science, Virginia Tech, and the Director of the Virginia Tech Learning on Graphs (VLOG) Lab. His research focuses on Open-World Machine Learning (OpenML), with applications in AI for Material Science, Finance, and Healthcare. He holds a Ph.D. from the University of Illinois Urbana-Champaign (UIUC). Education: Ph.D., Computer Science, UIUC (2021) M.S., Electrical and Computer Engineering, University of Rochester (2014) B.S., Electrical Engineering, Nanjing University of Posts and Telecommunications (2012) Research Interests: Long-tail category analysis, transfer learning across graphs, symbolic reasoning on knowledge graphs, trustworthy learning on graphs, graph generative models for scientific domains, and AI for finance. Grants & Awards: NSF Career Award (2024) AAAI New Faculty Highlights Roster (2024) Cisco Faculty Research Award (2023) Virginia Tech Outstanding Assistant Professor Award (2025) Amazon and Google Cloud Computing Grants (2024-2025) Advising & Lab: Directs the VLOG Lab, actively recruiting Ph.D. students and postdocs. Notable advisees include Shuaicheng, Haohui, Longfeng, and others. The lab focuses on cutting-edge research in graph learning and AI-driven scientific discovery. Service: Serves as Proceedings Chair for ACM SIGKDD 2026, Workshop Chair for IEEE BigData 2025, and on program committees for top conferences (NeurIPS, ICML, KDD).

Leon BoegmanView profile

Associate Professor

• Civil Engineering
• Maritime Engineering
• Transport Engineering
• +9 more

Leon Boegman is an Associate Professor in the Department of Civil Engineering at Queen's University. He holds cross-appointments at the Beaty Water Research Centre and serves as the Graduate Program Coordinator for Smith Engineering. His research focuses on aquatic transport processes, including hydrodynamic modeling of lakes and coastal systems, sediment resuspension, and impacts of climate change on water resources. Boegman earned his Ph.D. in Environmental Fluid Dynamics from the University of Western Australia, followed by an M.A.Sc. from the University of Toronto and a B.Eng. from McGill University. Research interests span marine platform hydrodynamics, geophysical fluid dynamics, and limnology. He develops process-based models to address water quality challenges, such as dissolved oxygen dynamics and harmful algal blooms. Boegman also advises on infrastructure projects for Environment and Climate Change Canada and provincial ministries. His work integrates field observations, numerical simulations, and experimental studies, with a focus on stratified flows and internal wave dynamics. Teaching responsibilities include courses on environmental fluid dynamics, hydraulics, and coastal engineering. He serves on editorial boards for Limnology and Oceanography and the Journal of Great Lakes Research . Recent publications emphasize lake management under climate change, wave-driven mixing, and sediment transport mechanisms.

LiuView profile

Professor

• Electrochemical Materials Science
• Energy Storage Systems
• Lithium Metal Batteries
• +5 more

Prof. Liu is a Professor in the Department of Nanoengineering at the University of California, San Diego. His research focuses on advancing electrochemical materials science for sustainable energy storage systems, including lithium metal batteries, solid electrolytes, and safety mechanisms. The Liu Research Group explores conversion reactions for nanomaterial synthesis and multifunctional electrochemical systems to address global climate challenges. Recently awarded the William Coles Endowed Chair, Prof. Liu leads initiatives such as the SPEC Battery Boot Camp and collaborates on projects like superamphiphobic surfaces and corrosion-resistant composites. The group’s mission emphasizes transitioning to carbon-neutral energy solutions through interdisciplinary innovation. Research themes include improving energy density in batteries, mitigating side reactions in solid electrolytes, and developing scalable nanomaterial production. Notable recent work spans materials characterization, corrosion analysis, and numerical simulations of fractional diffusion processes. The group also engages in cross-disciplinary studies, including peer victimization analysis and mental health interventions, reflecting Prof. Liu’s broader scientific impact.

• Algorithm Design and Analysis
• Algorithmic Game Theory
• Approximation and Online Algorithms
• +5 more

Dimitrios Fotakis is a Professor at the School of Electrical and Computer Engineering at the National Technical University of Athens (NTUA), where he has been serving since February 2009. He also collaborates with the "Archimedes" research unit as an experienced researcher since 2023. His academic journey includes previous positions as a Senior Research Scientist at Yahoo Research (2017-2019), Assistant Professor at the University of the Aegean (2004-2009), and Postdoctoral Researcher at the Max-Planck Institut für Informatik (2001-2003). National Technical University of Athens (2009-present): Professor "Archimedes" Research Unit (2023-present): Collaborating Senior Researcher Yahoo Research (2017-2019): Senior Research Scientist University of the Aegean (2004-2009): Assistant Professor Max-Planck Institut für Informatik (2001-2003): Postdoctoral Researcher Education: He holds a Diploma (1994) and a PhD (1999) from the Department of Computer Engineering and Informatics at the University of Patras, Greece. Dimitrios Fotakis specializes in Theoretical Computer Science with a focus on Algorithmic Game Theory and Approximation Algorithms. His research centers on the algorithmic properties of congestion games, the design of approximate mechanisms without monetary exchanges, and direct algorithms with emphasis on service location problems. His work has produced significant results including optimal algorithms for service location problems, potential functions for generalizations of congestion games, and optimal approximate truth-based mechanisms. With over 120 publications in major international conferences and journals and more than 3,000 citations according to Google Scholar, his research has had substantial impact in the field. His recent publications (2023-2025) demonstrate a continued focus on cutting-edge topics at the intersection of algorithms, game theory, and machine learning. These works span diverse areas including learning-augmented algorithms, graph neural networks, facility location problems, mechanism design, opinion dynamics, and fairness in ranking systems. The breadth of his research shows his ability to bridge theoretical computer science with practical applications in energy systems, social networks, and AI ethics. Teaching: Professor Fotakis teaches courses including Algorithms and Complexity, Discrete Mathematics, Computer Programming, Algorithmic Game Theory, Network Algorithms and Complexity, and Convex Optimization with Applications in Machine Learning. Research Leadership: He has served as Principal Investigator for several significant research projects including BALSAM (Beyond Worst-Case Analysis in Approximation Algorithms and Mechanism Design, 2019-2023), LEADAlgo (Learning-Augmented and Data-Driven Online Algorithms, 2020-2022), and Selfish Resource Allocation through Game Theoretic Models (2009-2021). He has also contributed as a Senior Researcher to multiple THALES projects through the years. Mentorship: Professor Fotakis has supervised numerous PhD students who have gone on to prestigious postdoctoral positions at institutions including MIT, Stanford, Yale, and UT Austin. His academic lineage extends through many successful students now working in top universities and research institutions worldwide.