جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Online Learning
• Algorithmic Game Theory
• +۴ مورد دیگر

Andrea Celli is an Assistant Professor in the Department of Computing Sciences at Bocconi University. He is affiliated with the ELLIS Society and the Bocconi Institute for Data Science and Analytics. Previously, he was a postdoctoral researcher at Meta (formerly Facebook Research) in London. He holds a Ph.D. in Computer Science from Politecnico di Milano and conducted research at Carnegie Mellon University's Electronic Marketplaces Lab. His research focuses on intersections of computer science, machine learning, and economics, particularly in strategic interactions and online learning environments. Notable achievements include a NeurIPS 2020 Best Paper Award and the 2017 Lesmo Prize for his MSc thesis. His research is supported by grants including an ERC Starting Grant (2024) for the PLA-STEER project and an MUR-PRIN grant (2022). He teaches courses such as Deep Learning and Reinforcement Learning, Machine Learning, and Optimization at the Ph.D. level. His work spans topics like equilibrium finding, online learning under constraints, and mechanism design. Key publications include contributions to ICLR, NeurIPS, EC, and other top venues. Celli advises Master’s students including Annalisa Barbara, Emanuele Coccia, Davide Drago, and Antonio Preiti. He collaborates with postdocs like Riccardo Poiani and Martino Bernasconi. He actively organizes academic events, such as the EC 2025 workshop on Online Learning and Economics. His research emphasizes theoretical foundations while addressing practical challenges in algorithmic game theory and data-driven decision-making.

Ingrid Bauer-Hänselمشاهده پروفایل

استادیار

• Blockchain Applications
• Data Markets and Platforms
• Agile IT Management
• +۳ مورد دیگر

Ingrid Bauer-Hänsel is an Assistant Professor of IT Management at the University of St. Gallen (HSG), affiliated with the Information Management (IWI-HSG) department. Her academic career includes roles as a Senior Technology Manager at Chage:ON (AMAG Group) and a doctoral researcher at the University of Zurich. She holds a Ph.D. in Blockchain Information Systems from the University of Zurich and a M.Sc. in Business Administration and Information Systems from Copenhagen Business School. Her research focuses on blockchain technologies, data markets, agile IT systems, and interorganizational systems design. Notable projects include the InnoSuisse Cardossier Project and studies on blockchain consortia. Bauer-Hänsel teaches courses such as Agile IT and Organization , Information Management , and Business Informatics 1 at both bachelor and master levels. She received the 2018 2nd Runner-up Best Paper Award at the ICIS conference. Her work explores socio-technical systems, tokenization, and AI-driven business models, with a particular emphasis on automotive and financial sectors. Current initiatives include designing blockchain-based data markets and exploring generative AI’s impact on knowledge work. Bauer-Hänsel’s professional background includes IT consulting at PwC, risk management roles at Danske Bank, and academic research at IBM and the Karlsruhe Institute of Technology. She actively contributes to bridging academic research with industry applications through collaborative projects and advisory roles.

Alexander Miserlis Hoyleمشاهده پروفایل

پژوهشگر

• Computational Social Science
• Natural Language Processing
• Machine Learning
• +۶ مورد دیگر

Alexander Hoyle is a Researcher at the ETH Zürich AI Center, concurrently contributing to natural language processing/machine learning and social science groups. He holds a PhD in Computer Science from the University of Maryland (advised by Philip Resnik) and a Master's in Computational Statistics from University College London (advised by Sebastian Riedel and Jeff Mitchell). His research focuses on computational social science, emphasizing methods for latent construct identification (e.g., topic models, ideal point models) and evaluation frameworks grounded in validity. Key areas include bias/fairness in AI, political science applications, and mental health constructs like suicidality. He pioneered frameworks like PairScale (attitude measurement via pairwise comparisons) and TopicGPT (prompt-based topic modeling). Education: PhD in Computer Science, University of Maryland (2020-2023) MS in Computational Statistics & Machine Learning, University College London (2018) Bachelor's degree (pre-PhD details omitted) Research Interests: Combining NLP with social science needs, particularly in evaluation rigor and interpretability. Active in interdisciplinary work between NLP and computational social science (e.g., measuring attitude evolution on Reddit, improving topic model validity). Advocates for human-in-the-loop approaches to address LLM limitations in tasks like document clustering and sentiment analysis. Grants & Projects: Contributed to a landmark $2.2B DOJ settlement on NYC public housing via econometric modeling at The Brattle Group. Active in graduate labor advocacy (Maryland state legislature testimony) and mentorship (Científico Latino's mentorship program). Labs & Teams: Leads initiatives at the ETH Zürich AI Center, collaborating with groups like Microsoft Research (FATE) and AI2's AllenNLP. Involved in multi-university projects (e.g., University of Maryland's Computational Linguistics lab).

Partha Lahiriمشاهده پروفایل

استاد

• Survey Sampling
• Small-Area Estimation
• Bayesian Inference
• +۳ مورد دیگر

Partha Lahiri is a Professor at the University of Maryland, College Park, holding dual appointments in the Joint Program in Survey Methodology (JPSM) and the Department of Mathematics . He also serves as an Adjunct Research Professor at the Institute of Social Research, University of Michigan, Ann Arbor. Previously, he was the Milton Mohr Distinguished Professor of Statistics at the University of Nebraska-Lincoln. His expertise spans Survey Sampling , Small-Area Estimation , Bayesian and Empirical Bayes Inference , and Resampling Methods . He has advised organizations such as the United Nations Development Program, World Bank, and Gallup, and contributed to panels like the U.S. Census Advisory Committee. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. Research interests include methodological advancements in small area estimation, survey data integration, and statistical linkage. His work emphasizes practical applications in policy and public health, including poverty measurement and pandemic-related studies (e.g., mask effectiveness perception during the pandemic). He has authored influential papers on topics like multi-goal prior selection, probabilistic record linkage, and resampling techniques for uncertainty quantification. His leadership roles, including directing JPSM, highlight his commitment to training future survey methodologists. Awards: ASA and IMS Fellowships, ISI Elected Membership Advisory Roles: U.S. Census Advisory Committee, National Academy of Sciences panels Publications Trends: Focus on small area estimation, Bayesian methods, and policy-relevant applications Grants/Projects: Collaborations with international organizations and academic institutions on statistical methodologies Lahiri’s research bridges theoretical statistics and practical survey challenges, with a strong emphasis on data integration and uncertainty estimation in complex systems.

Kang Zhaoمشاهده پروفایل

استاد

• Data Science
• Social Network Analysis
• Business Analytics
• +۱۲ مورد دیگر

Kang Zhao is the Leonard A. Hadley Professor in Business Analytics and Senior Associate Dean at the Tippie College of Business, University of Iowa. He holds a faculty appointment in the Department of Business Analytics and is affiliated with the Interdisciplinary Graduate Program in Informatics. He previously served as Department Executive Officer (2023–2025) and Faculty Director of the Master of Business Analytics program (2020–2023). His educational background includes: PhD in Information Sciences and Technology, Pennsylvania State University MS in Computer Science, Eastern Michigan University BE in Electrical and Information Engineering, Beijing Institute of Technology Dr. Zhao's research lies at the intersection of data science and network analytics, focusing on social and business networks. His work explores social influence, engagement, and health outcomes in online health communities; develops advanced recommendation systems for social media and live streaming platforms; applies 'people analytics' to scholarly and sports data; models information and behavior diffusion; and analyzes supply chain network resilience. His research has been featured in prominent media outlets such as MIT Technology Review, Forbes, BBC, and The Washington Post. The 15 most recent publications reflect a strong trend in applying graph neural networks, dynamic representation learning, and deep learning to model complex networked systems in health, finance, and social media. Key themes include user engagement prediction, misinformation diffusion, stock movement forecasting, and personalized recommendations using temporal and structural network data. His scientific achievements have been recognized with numerous awards: Top Cited Article, Journal of the Association for Information Science and Technology (2024, 2023) Journal of Operations Management Ambassador Award (2024) Charles A. Taff Research Excellence Award, Tippie College of Business (2023) Best Paper Award, INFORMS Workshop on Data Science (2017) Early Career Faculty Research Award, Tippie College of Business (2016) Dr. Zhao has successfully led multiple research grants as Principal or Co-Principal Investigator from the National Science Foundation (NSF), National Institutes of Health (NIH), and the Institute of Museum and Library Services. His projects span topics including gender gaps in academic science, smoking cessation networks, misinformation in health communities, and disaster resilience in public libraries. He actively mentors a cohort of PhD and master’s students, many of whom have secured positions in academia and leading industry firms. He also contributes to the academic community through editorial roles at top journals such as Decision Support Systems , Electronic Commerce Research and Applications , and Journal of the Association for Information Science and Technology . He leads the Data and Network Analytics Research Group at the University of Iowa, which focuses on cutting-edge research in network science, machine learning, and data mining applied to real-world social and business problems.

Eric W. Kleeمشاهده پروفایل

استاد

• Omics Data Analysis
• Rare and Undiagnosed Diseases
• Genomic Medicine
• +۳ مورد دیگر

Eric W. Klee, Ph.D., is a Professor of Biomedical Informatics at Mayo Clinic, leading translational research in omics data integration and precision medicine. He holds key roles including Scientific Director of Research Data and Digital Innovation, Enterprise Co-Leader of Cancer Informatics & Data Science at the Mayo Clinic Comprehensive Cancer Center, and Director of Digital Omics in the Center for Individualized Medicine. His work focuses on rare disease diagnosis, genomic data infrastructure, and machine learning applications in healthcare. Education: Ph.D. in Health Informatics, University of Minnesota MS in Health Informatics, University of Minnesota BS in Electrical Engineering, Iowa State University Research Interests: Dr. Klee’s research integrates multi-omics profiling, AI-driven analytics, and cloud-based platforms to advance diagnostics and treatment for rare genetic disorders. He leads initiatives like RADIaNT (RNA sequencing for rare diseases), SAVI (automated variant interpretation), and RENEW (continuous genomic data reanalysis). His work bridges lab discoveries with clinical practice, emphasizing precision medicine and scalable genomic solutions. Publications Trends: His recent work highlights RNA-based diagnostics, AI in variant prioritization, and infrastructure for population-level genomic screening. Key areas include drug repositioning for tobacco dependence and molecular mechanisms of Mendelian diseases. Awards: Research Award from the Minnesota Partnership for Biotechnology and Medical Genomics (2023) Advising & Grants: Leads the Mayo Clinic’s Digital Omics initiative and co-leads cancer informatics efforts. His lab collaborates on NIH-funded studies and industry partnerships to translate genomic insights into clinical tools. Labs/Teams: Directs the Advanced Diagnostics Laboratory’s bioinformatics team and chairs the Undiagnosed Diseases Network International board, fostering global rare disease research collaboration.

Nabil J. Sarhanمشاهده پروفایل

دانشیار

• Computer Systems
• Deep Learning
• Video Streaming
• +۵ مورد دیگر

Dr. Nabil J. Sarhan is an Associate Professor of Electrical and Computer Engineering at Wayne State University , where he directs the Computer Systems and Deep Learning Research Laboratory and the interdisciplinary M.S. Program in Artificial Intelligence (Systems and Hardware Track) . He previously served as Graduate Program Director and College of Engineering Faculty Assembly Chair. Education : Ph.D. and M.S. in Computer Science and Engineering from Pennsylvania State University (GPA 4.0), B.S. in Electrical Engineering from Jordan University of Science and Technology Internationally recognized for expertise in computer systems , deep learning , video streaming , and AI hardware accelerators , Dr. Sarhan's research bridges theoretical and applied domains in automated video surveillance and medical AI systems . His work includes novel approaches to epileptic seizure prediction , energy-efficient neural network accelerators , and mixed-signal circuit design for edge computing. Recent grant highlights include a $418,907 National Science Foundation award for reconfigurable AI accelerator development. His patent (US Patent 9,313,463) on automated video surveillance systems demonstrates practical technology impact. Scientific Awards IEEE SEM Outstanding Professional of the Year Wayne State President's Excellence in Teaching Award College of Engineering Excellence in Teaching Award WSU Academy of Teachers (2022) IEEE Senior Member (2016) As an educational reform leader , he has chaired over 20 ABET accreditation panels in Saudi Arabia and Palestine, evaluating programs in electrical engineering, computer science, and software engineering. His intellectual property expertise spans significant patent infringement cases including ACQIS v. ASUSTek and VideoShare v. Google .

Saravanan Venkatachalamمشاهده پروفایل

دانشیار

• Stochastic Programming
• Large Scale Optimization
• Robust Optimization
• +۶ مورد دیگر

Saravanan Venkatachalam is an Associate Professor in the Department of Industrial and Systems Engineering at Wayne State University. His research focuses on stochastic programming, robust optimization, and discrete event modeling with applications to supply chain management, healthcare, energy systems, and unmanned vehicle operations. Education Ph.D. in Industrial and Systems Engineering, Texas A&M University M.S. in Industrial and Systems Engineering, Texas A&M University B.E. in Production Engineering, PSG College of Technology, India His work addresses decision-making under uncertainty through decomposition algorithms and data-driven approaches. Recent projects include autonomous vehicle path planning with uncertain parameters, community-aware electric vehicle charging networks, and optimization models for radio advertisement placements. Current research trends emphasize stochastic programming for resource allocation in dynamic environments, robust optimization for transportation and energy systems, and multi-vehicle routing under uncertainty. He has supervised multiple graduate students in thesis work related to optimization models and taught core courses such as Operations Research, Deterministic Optimization, and Stochastic Programming at both undergraduate and graduate levels.

• Immunology
• Genetics
• Dermatology
• +۲ مورد دیگر

Michele Boniotto is a researcher at the University of Verona's Department of Molecular and Translational Medicine, focusing on immunology, genetics, and dermatology. His work spans molecular mechanisms in skin diseases and evolutionary aspects of host defense peptides. Research Focus Dr. Boniotto's research examines: Genetic factors in hidradenitis suppurativa Beta-defensin evolution and function Aquaporin-3 role in skin homeostasis Photobiomodulation therapies HLA-DR expression in septic shock His publications show interdisciplinary approaches combining molecular biology, clinical dermatology, and bioinformatics to understand complex disease mechanisms. Scientific Contributions 2025: Keratin filament-melanin interactions 2025: Polygenic risk scoring for HS 2024: Aquaporin-3 dysregulation in HS 2023: NCSTN mutations in familial HS 2022: Holistic HS health records 2020: Photobiomodulation for HS

Stefan Leykمشاهده پروفایل

استاد

• GIScience
• Spatial Uncertainty Modeling
• Land Cover Change Modeling
• +۵ مورد دیگر

Stefan Leyk is a Professor of Geography at the University of Colorado Boulder within the Department of Geography in the College of Arts and Sciences. His research focuses on GIScience, spatial uncertainty modeling, and historical landscape analysis, with significant contributions to cartographic pattern recognition from historical maps and spatial dynamic modeling in public health. He holds a Ph.D. from the University of Zurich and the Federal Research Institute for Forest, Snow and Landscape (2005). His primary research interests include uncertainty in GIScience and spatial uncertainty modeling, land cover change modeling using historical spatial information, cartographic pattern recognition from historical maps, and spatial dynamic modeling approaches in public health. His work bridges historical geography with advanced computational methods, particularly in extracting settlement patterns from historical map archives and developing spatiotemporal datasets spanning centuries. Leyk's recent publications demonstrate strong trends in historical settlement analysis, with major projects like CHRONEX-US and HISDAC-US creating century-long datasets of urban infrastructure and settlement evolution. His work increasingly integrates machine learning with historical map processing, focusing on uncertainty quantification, built-up land validation, and environmental justice applications related to flood risk and coastal hazards. Key thematic areas include long-term urban growth patterns, rural poverty dynamics, and wildfire risk assessment at the wildland-urban interface. Leyk has received significant research funding through collaborative grants including 'HNDS-I: Building Long-term, National-scale Spatiotemporal Data Collections from Historical Map Archives' (2025) and 'HNDS-I: Data Infrastructure for Research on Historical Settlement and Population Growth in the United States' (2021). He actively mentors graduate students including Alek Berg, Caitlin McShane, and Yuying Ren, and teaches advanced GIS courses such as GEOG 4303/5303 GIS: Spatial Programming and GEOG 4103/5303 GIS: Spatial Analytics. His laboratory work centers on geospatial modeling of historical settlement and landscape analysis, with a focus on developing automated methods for processing historical map archives and creating linked spatiotemporal data. Current projects involve machine learning applications for feature extraction from historical maps, uncertainty prediction in built-up land layers, and the development of fine-grained datasets measuring 200 years of land development in the United States.

Haleh Sadeghiمشاهده پروفایل

پژوهشگر

• Occupational Safety and Health
• Construction Project Management
• Risk Assessment
• +۳ مورد دیگر

Dr. Haleh Sadeghi is a researcher affiliated with the Department of Mechanical, Aerospace & Civil Engineering at the University of Manchester, contributing to the Thomas Ashton Institute. She specializes in occupational safety and risk assessment in construction, with a focus on fuzzy logic methodologies and sustainable practices. Her work aligns with UN Sustainable Development Goals related to safe and sustainable infrastructure. Education: Holds a Doctor of Philosophy (PhD) in Mechanical, Aerospace & Civil Engineering. Research Interests: Her research revolves around safety risk assessment in construction sites, decision-making frameworks, modular construction safety, and the integration of fuzzy logic systems. She explores deterrents to green technologies like green walls and evaluates the impact of Building Information Modeling (BIM) on safety and project efficiency. Article Trends: Recent work emphasizes predictive safety models for tower cranes, automated risk assessment systems, and hybrid methodologies for analyzing accidents and deterrents. She also investigates the adoption barriers of sustainable practices in construction projects. Grants and Advising: No specific grants or student advising details are listed in the provided sources. Collaborations include international partners on projects involving fuzzy-based systems and safety frameworks. Labs/Teams: Active within the Thomas Ashton Institute, focusing on engineering solutions for safer construction practices and sustainable infrastructure development.

Tijn Fleurenمشاهده پروفایل

پژوهشگر

• Operations Research
• Supply Chain Management
• Production-Inventory Planning
• +۷ مورد دیگر

Dr. Tijn Fleuren is a researcher and teacher at the Department of Econometrics and Operations Research within the Tilburg School of Economics and Management (TiSEM) at Tilburg University. His work focuses on optimizing production-inventory systems under uncertainty, with applications in high-tech, low-volume manufacturing supply chains. Institution: Tilburg University School: Tilburg School of Economics and Management Department: Department of Econometrics and Operations Research Email: T.W.A.Fleuren@tilburguniversity.edu ORCID: https://orcid.org/0000-0002-0925-6892 Office: Koopmans Building, Room K 423, Warandelaan 2, 5037 AB Tilburg, Netherlands His research lies at the intersection of operations research and supply chain management, with a strong emphasis on stochastic modeling, risk-aware decision-making, and integrated planning under uncertainty. Key themes include production-inventory optimization, customer portfolio selection, procurement planning, and dynamic supply chain configurations. His work often addresses real-world challenges in collaboration with industry leaders like ASML. The recent publications demonstrate a consistent focus on advanced optimization techniques applied to complex supply chain environments. These include multi-stage stochastic programming, conic quadratic programming heuristics, sampling-based methods, and deep reinforcement learning. The research spans both theoretical contributions—such as optimality conditions and bounds—and practical applicability, particularly in high-tech sectors with long lead times and customization requirements. Dr. Fleuren completed his doctoral studies at Tilburg University in January 2025, contributing significantly to the field through his dissertation on stochastic approaches in production-inventory planning. While no formal scientific awards are listed, his research outputs have been published in top-tier journals such as International Journal of Production Economics and Omega–International Journal of Management Science . He is actively involved in teaching courses such as Supply Chain Analytics and Introduction to Data Analysis. As a recent PhD graduate, he may be mentoring students informally, though no formal advisees are listed. His research has been supported through collaboration with industrial partners and academic grants, likely via CentER and the Operations Research Center at Tilburg University. Dr. Fleuren is affiliated with the Operations Research Center at TiSEM, where he contributes to a vibrant research environment focused on quantitative methods in economics and management. His ongoing work continues to advance methodologies for decision-making under uncertainty in complex, real-world supply chains.

Sanjay K Dharمشاهده پروفایل

استاد

• Strategic Marketing Management
• Brand Management
• New Product Development
• +۱۲ مورد دیگر

Sanjay K. Dhar, the James M. Kilts, Jr. Professor of Marketing at the University of Chicago Booth School of Business, is a leading scholar in strategic marketing management and retail dynamics. His work bridges empirical economic modeling with actionable business insights. Education: B.Tech (IIT), MBA (IIM), PhD (UCLA Anderson School) Affiliation: Faculty member at Chicago Booth since 1992 Research interests span brand management , consumer behavior , and retail strategy , with a focus on how economic factors and marketing mix variables influence household budget allocations and brand performance . His empirical work leverages massive datasets like Nielsen Homescan to uncover geographic patterns and long-term consumer trends . Recent publications examine on-screen cigarette product placement effects, WIC program impacts on cereal consumption, and retail format competition . His 2025 under-review paper extends these insights to multi-category budget reallocation during macroeconomic shifts. Scientific Recognition includes: William O’Dell Award Finalist (2012) Paul Green Award (2008) John D. C. Little Award (1995) McKinsey Teaching Excellence Award (2000) Hillel Einhorn Teaching Awards (2008, 2012) Dhar’s teaching philosophy emphasizes mastering fundamentals to solve complex marketing problems. He advises firms on strategic marketing and new product development while maintaining active research in consumer packaged goods (CPG) industries.

Ashwinkumar Venkatanaga Machanavajjhalaمشاهده پروفایل

دانشیار مدعو

• Differential Privacy
• Data Privacy
• Secure Multi-Party Computation
• +۳ مورد دیگر

Ashwinkumar Venkatanaga Machanavajjhala is an Adjunct Associate Professor of Computer Science at Duke University's Trinity College of Arts & Sciences since 2024, with prior roles as Associate Professor (2018-2024) and Assistant Professor (2012-2018). His research focuses on differential privacy, secure multi-party computation, and privacy-preserving data analysis frameworks. Education: Ph.D. from Cornell University (2008) His work spans privacy-preserving algorithm design, synthetic data generation, and privacy-utility trade-offs. Recent research themes include differential privacy for aggregate queries, foreign key constraints in database systems, and policy-aware privacy frameworks . Grant projects like RAISE: C-Accel Pilot and RAPID: Poirot highlight his leadership in privacy and data security initiatives. Scientific contributions include groundbreaking work on ϵktelo for differentially private algorithms, DP-Sync for update pattern privacy, and Blowfish Privacy for customizable privacy definitions. Awards include the NSF CAREER Award (2013) for early-career impact. In outreach, he led the Bass Connections Faculty Team (2018-2019) addressing vaccine misinformation in Durham. Teaching includes COMPSCI 891: Special Readings in Computer Science (Summer 2022).

Jason Poulosمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Causal Inference
• Health Policy
• +۵ مورد دیگر

Jason Poulos is a data scientist and researcher currently working at the intersection of machine learning, causal inference, and health policy. He holds a Ph.D. in Political Science from the University of California, Berkeley, with a designated emphasis in Computational Science and Engineering. His academic journey includes postdoctoral positions at Harvard Medical School's Department of Health Care Policy and Brigham and Women's Hospital. Dr. Poulos's research focuses on developing and applying machine learning methods to address challenges in complex, high-dimensional longitudinal health data. His work spans several interconnected areas including extending causal inference methods for multi-valued treatments, developing frameworks for adversarial machine learning, applying neural networks for health outcome prediction, and evaluating deep learning approaches for missing data imputation in survey data. His recent publications demonstrate expertise in targeted learning methodologies, particularly in observational studies with multi-level treatments, with applications in pharmacoepidemiology. His research output shows a consistent pattern of interdisciplinary work bridging political science, statistics, and health policy. The majority of his recent publications (2021-2024) appear in high-impact journals across biostatistics, political methodology, and machine learning domains, reflecting his ability to contribute to multiple academic communities. His work often employs advanced statistical techniques to address real-world policy questions, particularly in healthcare settings. As evidenced by his publications and research trajectory, Dr. Poulos has established himself as a methodological innovator in causal inference with multi-valued treatments, with particular expertise in applying these methods to evaluate the safety and effectiveness of antipsychotic medications. His work combines deep methodological rigor with practical policy applications.