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Jonas SchreyöggView profile

Professor

• Healthcare Management
• Health Economics
• Health Policy
• +5 more

Prof. Dr. Jonas Schreyögg is a Professor of Healthcare Management and Scientific Director of the Hamburg Center for Health Economics (HCHE) at the University of Hamburg since 2010. He holds the Chair of Healthcare Management and serves as Deputy Chairman of the German Council of Economic Experts for Health and Nursing Care. His roles also include memberships on scientific advisory boards and associated research at Stanford University. Education & Career: Doctorate and Habilitation (2008) from Technical University of Berlin Former Professor at Ludwig-Maximilians-University of Munich (LMU Munich) Junior Professor at Technical University of Berlin Harkness Fellow at Stanford University (2006–2007) Research Interests: Focus on healthcare systems, health economics, hospital payment reforms, pandemic policy responses, and healthcare workforce optimization. Key areas include DRG systems, healthcare competition, and socioeconomic disparities in health outcomes. He has conducted extensive research on the impact of policy interventions during the COVID-19 pandemic, including lockdown perceptions and vaccine acceptance. Grants & Awards: Recipient of the Harkness Fellowship, German National Academic Foundation doctoral scholarship, and multiple research grants. His work has been published in leading journals like Health Affairs , Journal of Public Health , and Social Science & Medicine . Professional Activities: Engaged in international collaborations and teaching in Norway, Singapore, Taiwan, and the U.S. He advises on healthcare policy and has contributed to high-profile reports on healthcare workforce sustainability and pandemic response strategies.

Yuri BazilevsView profile

Professor

• Computational Mechanics
• Isogeometric Analysis
• Fluid-Structure Interaction
• +5 more

Yuri Bazilevs is the E. Paul Sorensen Professor of Engineering at Brown University's School of Engineering and Co-Director of the Mechanics of Undersea Science and Engineering Center. His research focuses on computational mechanics, isogeometric analysis, fluid-structure interaction, and high-performance computing. Prior to Brown, he held positions at UC San Diego, where he advanced to Full Professor in 2014 after a rapid tenure. He earned his PhD in 2006 and postdoc training in computational engineering at UT Austin's ICES. Research interests span computational fluid dynamics, solid mechanics, and advanced discretization methods like isogeometric analysis (IGA) and meshfree approaches. He has developed novel formulations for complex phenomena such as underwater explosions, composite material failure, and hypersonic flow dynamics. His work integrates cutting-edge numerical methods with practical engineering applications in aerospace, energy, and biomedical systems. Recent publications highlight advancements in IGA for architected materials, RKPM-based crack modeling, and stabilized formulations for compressible flows. His contributions bridge theoretical mechanics with computational innovation, addressing challenges in multiphysics coupling and large-scale simulations. Collaborations span academia and industry, emphasizing practical validation and real-world impact. Bazilevs' expertise includes variational multiscale methods, peridynamics for fracture mechanics, and immersive particle methods for fluid-structure interaction. His work has been applied to wind turbine aerodynamics, gas turbine optimization, and cardiovascular flow analysis. He actively contributes to computational infrastructure development, such as the tIGAr software framework for IGA automation.

Mohammad Mohammadi AmiriView profile

Assistant Professor

• Large Language Models
• Data Valuation
• Federated Learning
• +5 more

Mohammad Mohammadi Amiri serves as an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), appointed in Fall 2023. His research focuses on advancing artificial intelligence through strategic data utilization, with emphasis on large language models, data valuation, federated learning, and deep learning. Previously, he held postdoctoral appointments at Princeton University and MIT Media Lab, building on his strong educational foundation from Imperial College London, University of Tehran, and Iran University of Science and Technology. Education: Ph.D. in Electrical and Electronic Engineering, Imperial College London (2019) - Best Ph.D. Thesis Award recipient M.Sc. in Electrical and Computer Engineering, University of Tehran (2014) - Ranked 1st among all M.Sc. students B.Sc. in Electrical Engineering, Iran University of Science and Technology (2011) - Ranked 1st among all B.Sc. students Dr. Amiri's research centers on optimizing artificial intelligence systems through innovative data strategies. His work addresses critical challenges in large language models including efficiency, memory usage, alignment, and reasoning capabilities. In data valuation, he develops principled methods to quantify data worth for fair trading platforms. His federated learning research tackles privacy concerns, heterogeneous data distribution, and communication overhead in decentralized environments. The deep learning component explores theoretical foundations to improve model interpretability and robustness. Analysis of his recent publications reveals a strong focus on making AI systems more efficient and accessible, with particular emphasis on large language model optimization, federated learning advancements, and data valuation frameworks. His work bridges theoretical foundations with practical applications in wireless communications and distributed computing environments. Scientific Awards: IEEE Communications Society Young Author Best Paper Award (2022) Best PhD Thesis Award from IEEE Information Theory Chapter of UK and Ireland (2019) Eryl Cadwallader Davies Prize for Outstanding PhD Thesis (2019) EEE Departmental Scholarship at Imperial College London (2015-2019) Ranked 1st among M.Sc. students at University of Tehran (2014) Ranked 1st among B.Sc. students at Iran University of Science and Technology (2011) Dr. Amiri actively mentors graduate students, currently supervising five Ph.D. candidates and one M.Sc. student working on efficient LLM fine-tuning, inference, and storage. His research has attracted significant attention, evidenced by numerous keynote invitations at prestigious institutions including Bell Labs, MIT, King's College London, and various IEEE conferences. He serves on program committees for major conferences including IEEE Globecom and ICC, demonstrating his growing influence in the academic community. His research group operates at the intersection of machine learning and wireless communications, developing innovative solutions for resource-constrained environments while addressing fundamental theoretical challenges in AI systems. Current projects focus on making advanced AI more scalable and accessible through efficiency improvements in model training and inference.

• Coastal Engineering
• Coastal Hazards Mitigation
• Nature-based Solutions (NbS)
• +5 more

Jon Miller is a Research Associate Professor in the Department of Civil, Environmental and Ocean Engineering at Stevens Institute of Technology. He holds dual roles as Director of the NJ Coastal Protection Technical Assistance Service and NJ Sea Grant Coastal Processes Specialist. Miller earned a B.E. in Civil Engineering from Stevens (1999), followed by M.S. and Ph.D. in Coastal Engineering from the University of Florida (2001, 2004). His research focuses on coastal hazard mitigation, nature-based solutions, and numerical modeling of coastal systems. Education: - Ph.D. Coastal Engineering, University of Florida (2004) - M.S. Coastal Engineering, University of Florida (2001) - B.E. Civil Engineering, Stevens Institute of Technology (1999) Research Interests: Miller's work emphasizes coastal resilience through innovative engineering approaches. Key areas include: - Wave attenuation mechanisms of natural/nature-based features - Climate change impacts on coastal erosion - Living shoreline design and implementation - Sediment management strategies for inlets and beaches - Dune system vulnerability analysis Grants & Awards: - $1M+ funding from NOAA, NSF, and state agencies - 2024 ASCE Educator of the Year Award - 2023 Robert G. Dean Coastal Award - Over 20+ technical reports guiding coastal policy Professional Leadership: - Editorial roles in Shore & Beach and Journal of Coastal Research - Leadership in NJ Coastal Resilience Collaborative - Advisor for 3 Technogenesis Summer Scholars Labs & Projects: - Principal Investigator for SEECPRS disaster response system - Co-developed NJ Living Shorelines Engineering Guidelines - Conducts fieldwork on Hudson River shoreline restoration

• IoT Security
• AI Security
• Wireless Networks
• +2 more

Xiaojiang Du is the Anson Wood Burchard Endowed Professor at Stevens Institute of Technology, directing research in IoT security, AI security, and wireless networks. An IEEE Fellow and ACM Distinguished Member, he leads NSF-funded projects on secure IoT systems and cross-platform security vulnerabilities. Education PhD in Electrical Engineering, University of Maryland MS in Electrical Engineering, Tsinghua University BE in Electrical Engineering, Tsinghua University Research Focus: Develops security frameworks for IoT ecosystems and adversarial machine learning, with recent breakthroughs in smart home security anomaly detection. Honors: IEEE Fellow, ACM Distinguished Member, multiple best paper awards at IEEE conferences. Graduated PhD students hold faculty positions at UNC Charlotte, UL Lafayette, and ShanXi University. Professional Service: IEEE ComSoc Distinguished Lecturer, Associate Editor for IEEE Transactions, and General Co-Chair for IEEE/ACM IWQoS 2023. Secured $9M+ in research funding from NSF, NSA, and DOD.

Natalia RybczynskiView profile

Research Professor

• Paleontology
• Evolutionary Biology
• Functional Morphology
• +7 more

Natalia Rybczynski is an Adjunct Research Professor in the Department of Biology at Carleton University, affiliated with the Faculty of Science. Her primary research focuses on Paleobiology and Paleoenvironments of the Cenozoic High Arctic and functional morphology of vertebrate feeding and locomotion systems. She holds offices at the Canadian Museum of Nature in Gatineau and coordinates interdisciplinary projects on Arctic paleoenvironments, pinniped evolution, and vertebrate herbivore feeding mechanisms. Education: B.Sc. in Biology, Carleton University M.Sc. in Zoology, University of Toronto Ph.D. in Evolutionary Anthropology, Duke University Research Interests: Reconstructing Arctic paleoenvironments during the Neogene Evolutionary transitions in marine mammals (e.g., pinnipeds) Feeding kinematics in herbivorous vertebrates like hadrosaurs Integration of field-based paleontology with biomechanical and anatomical analyses Her publications span paleoclimatology, fossil analysis, and evolutionary studies, emphasizing Arctic ecosystems and vertebrate adaptations. Collaborative work includes geoscientists to reconstruct ancient Arctic climates and anatomists to model feeding systems in extinct species. Active in field expeditions and lab-based experimental studies. Labs/Teams: Collaborates with Canadian Museum of Nature researchers and international teams in Arctic paleontology projects. Engages in interdisciplinary initiatives like the PoLAR-FIT project studying Pliocene Arctic ecosystems.

Cangbai WangView profile

Professor

• Transnational migration
• Chinese diasporas
• Migration-related museum studies
• +3 more

Cangbai Wang is a Professor of Migration, Heritage and Language at the University of Westminster and co-founder of HOMELandS (Hub On Migration, Exile, Languages and Spaces). He holds a PhD in Asian Studies from the University of Hong Kong (2003) and previously worked as a Post-doctoral Fellow/Honorary Assistant Professor at the University of Hong Kong and Visiting Lecturer at City University of Hong Kong. Academic focus: Transnational migration, Chinese diasporas, cultural heritage Co-Director of HOMELandS since 2014 (elevated to Research Centre in 2023) External Examiner for 20 institutions including University of Birmingham (2024), University of Cambridge (2020), and Nanyang Technological University (2024) His research explores the intersection of migration and material culture , emphasizing how diasporic communities negotiate identity through heritage practices. Recent work examines museum representations of Chinese diasporas and language dynamics in multicultural London. He has led the AHRC-funded Global Diasporic Chinese Museums Network Initiative (2023-2025) and contributed to policy discussions on diaspora engagement with Chinese municipal governments. Key publication: Museum Representations of Chinese Diasporas (Routledge 2021) and Negotiating Identities, Language and Migration in Global London (Multilingual Matters 2024). Supervised PhDs include Denise Kwan’s The talking of objects: migratory experiences and Chinese women in UK (2020) and Xiao Ma’s ongoing London Chinatown: heritage making and diasporic identities . Active in academic leadership: Co-editor: Routledge Research on Museums and Heritage in Asia Editorial board: Social Transformations in Chinese Societies (since 2024) International editorial board: Chinese Overseas Review (since 2020) UKRI FLF Peer Review College member

Andreas GulyasView profile

Assistant Professor

• Macroeconomics
• Labor Economics
• Search Theory
• +1 more

Prof. Andreas Gulyas is an Assistant Professor of Macroeconomics at the Department of Economics, University of Mannheim, and a research affiliate at IZA. He holds a Ph.D. from UCLA and a M.Sc. from the University of Vienna. His research focuses on Macroeconomics, Labor Economics, Search Theory, and Machine Learning applications in economics. Education: Ph.D. in Economics, UCLA, Los Angeles, USA M.Sc. in Economics, University of Vienna, Austria Research Interests: Gulyas examines labor market dynamics, monetary policy impacts, wage transparency, and the consequences of job displacement. His work combines theoretical models with empirical analysis, often employing field experiments and machine learning techniques to explore labor market inefficiencies and policy implications. Notable areas include cross-country comparisons of economic inequality and pandemic-induced unemployment effects. Articles Trends: His recent work emphasizes labor market policies, wage structures, and macroeconomic shocks. Key themes include the role of fringe benefits in job search behavior, monetary policy's heterogeneous effects, and pandemic-related unemployment disparities. Machine learning is increasingly used to analyze heterogeneous outcomes in displacement scenarios. Awards: No scientific awards listed. Advising/Grants: No advising records or grant information provided in available texts. His current research is likely supported through institutional funding and affiliations like IZA. Labs/Teams: Affiliated with the AEE Seminar at Mannheim, contributing to collaborative economics research initiatives.

Mehmet Esat BelviranliView profile

Assistant Professor

• Heterogeneous Architectures
• Runtime Systems
• Performance Modeling
• +5 more

Mehmet Esat Belviranli is an Assistant Professor in the Computer Science Department at the Colorado School of Mines, where he directs the High Performance Systems and Software Lab (HyperSys). His research focuses on increasing resource utilization in heterogeneous architectures through runtime systems, scheduling algorithms, and performance modeling, with publications in top venues including MICRO, PPoPP, and SC. Education: Ph.D. in Computer Science, University of California, Riverside (2016) M.S. in Computer Science, Bilkent University (2009) B.S. in Computer Science, Bilkent University (2006) Belviranli's research spans heterogeneous architectures, runtime systems, performance modeling, parallel programming, autonomous computing, deep learning acceleration, cyber-physical systems, and edge-cloud platforms. His work develops analytical models and programming abstractions to address resource management, scheduling, and security challenges in diversely heterogeneous systems, with applications in edge computing, autonomous systems, and machine learning acceleration. Recent projects emphasize real-world constraints and security implications. His publication trends reveal increasing focus on edge-cloud resource management (e.g., HARNESS), security vulnerabilities in heterogeneous systems (e.g., MC3), and deep learning acceleration under resource constraints. Key themes include memory contention modeling, scheduling for cyber-physical systems, and concurrent DNN execution, reflecting a shift toward practical deployment in security-sensitive edge environments. Scientific Awards: U.S. Air Force Research Lab Summer Faculty Fellowship Award (2022) U.S. Air Force Research Lab Summer Faculty Fellowship Award (2021) Oak Ridge National Laboratory Significant Event Award (2019) Best Paper Finalist, IEEE HPEC 2018 Outstanding Paper Award, DATE 2024 Belviranli mentors Ph.D. students Ismet Dagli (MLCommons Rising Star 2024, CGO'24 SRC finalist) and Justin Davis (DATE'24 Outstanding Paper Award winner). He has secured $2M+ in funding from NSF, DoE, and SRC, including an NSF-SaTC grant on mobile security (2024), a DoE grant on superconductive systems (2023), and an NSF FuSe grant on graphene nanoribbons (2023), often leading multi-institutional teams from Rochester, Virginia, Arizona, and Minnesota. The HyperSys Lab develops ecosystems for high-performance heterogeneous systems, with recent projects including HARNESS for edge-cloud resource management and MC3 for mobile SoC security. The lab has received equipment donations from Google Coral.ai and Xilinx, and collaborates with national labs on security challenges and next-generation semiconductor technologies.

• Energy Efficiency
• Waste Heat Recovery
• Organic Rankine Cycle (ORC)
• +7 more

Dr. Muhammad Imran is a Reader and Lecturer in Mechanical, Biomedical & Design Engineering at Aston University, UK. He is affiliated with the Energy and Bioproducts Research Institute (EBRI) and the College of Engineering and Physical Sciences. His research focuses on energy efficiency, waste heat recovery, and low-temperature power cycles such as Organic Rankine Cycle (ORC) and Supercritical CO₂ systems. He has contributed to the commercialization of ORC systems and collaborates internationally on hybrid energy systems, solar-thermal integration, and district heating networks. Dr. Imran holds a PhD in Energy System Engineering (2016), MSc in Thermal Power Engineering (2012), and BEng in Mechanical Engineering (2009). He has held academic roles at institutions in Pakistan, South Korea, and Denmark, including a Marie Curie Fellowship at the Technical University of Denmark. His awards include the Marie Curie Fellowship (EU), Innovation Award (South Asia Triple Helix), and multiple Research Excellence Awards from South Korea. He leads funded projects on hybrid energy systems for agriculture, waste heat recovery in industries, and sustainable energy solutions in developing countries. His editorial roles include associate editorships in Frontiers in Thermal Engineering and Resources, Environment and Sustainability . He supervises PhD students in renewable energy and low-temperature thermodynamic systems, with ongoing projects on solid-state heat pumps and advanced ORC control strategies. Dr. Imran’s work bridges engineering, data science, and environmental science to address energy challenges. Notable collaborations include projects in Ethiopia, Kenya, Nigeria, and Sudan, focusing on off-grid cold storage, smart irrigation, and biomass energy systems. His research outputs include over 130 peer-reviewed articles, patents, and contributions to international conferences.

Jonathan Shihao JiView profile

Associate Professor

• Deep Learning
• Computer Vision
• Natural Language Processing
• +5 more

Jonathan Shihao Ji is an Associate Professor in the School of Computing at the University of Connecticut (UConn), leading the Intelligent Systems Lab. He holds a Ph.D. in Electrical and Computer Engineering from Duke University and previously served as an Associate Professor at Georgia State University and Director of the DoD Center of Excellence (CiARE). His research focuses on deep learning applications in computer vision, NLP, robotics, and high-performance computing, with over 50 publications in top venues like CVPR, NeurIPS, and IEEE journals. He has secured grants from NSF, NIH, DoD, and industry partners including VMware and Nvidia. His work emphasizes efficient algorithms for large-scale data processing, parameter-efficient model fine-tuning (e.g., VB-LoRA), and 3D perception benchmarks for UAVs (UAV3D). Notable contributions include sparse network optimization (Dep-L0), energy-based models (M-EBM), and robust defenses against adversarial attacks (Defense-VAE). He is a Senior Member of IEEE and has developed open-source tools like Parallel Word2Vec and WordRank. Recent projects include accelerating Llama2 models on FPGAs (LlamaF) and improving text-to-image synthesis via contrastive learning. His research spans theoretical advancements and practical applications, with industry collaborations in healthcare, robotics, and embedded systems.

Konstantin BaumanView profile

Associate Professor

• Data Science
• Machine Learning
• Recommender Systems
• +3 more

Dr. Konstantin Bauman is an Associate Professor in the Department of Management Information Systems at Temple University's Fox School of Business. He holds a PhD in Mathematics (Geometry and Topology) from Moscow State University and dual Master’s degrees in Mathematics and Machine Learning from prestigious Russian institutions. His research focuses on machine learning, data science, and context-aware recommender systems, emphasizing novel methods for predicting customer preferences and designing personalized recommendation frameworks. Education: PhD in Mathematics (Geometry and Topology), Moscow State University MS in Mathematics, Moscow State University MS in Machine Learning, Moscow Institute of Physics and Technology/Yandex School of Data Analysis Research Interests: Data Science and Analytics Machine Learning and Recommender Systems Context-Aware Systems and Text Mining Technology-Enhanced Learning Recent Work Trends: His publications emphasize context-aware recommendation algorithms, privacy concerns in personalized systems, and applications of hyperbolic embeddings. He also explores device impact on employee feedback and cryptocurrency investor behavior using multimodal data analysis. Awards: None explicitly listed in the provided materials. Advising/Grants: No formal advisees listed; his work at Yandex and NYU involved leading machine learning teams and tackling large-scale data science challenges. Labs/Teams: Active in the MIS department at Temple, contributing to research on adaptive learning systems and enterprise machine learning applications.

Chris De SaView profile

Associate Professor

• Machine Learning
• Distributed Systems
• Optimization Algorithms
• +5 more

Chris De Sa is an Associate Professor in the Department of Computer Science at Cornell University, affiliated with the Cornell Machine Learning Group and leading the Relax ML Lab. His research focuses on algorithmic, software, and hardware techniques for high-performance machine learning, particularly relaxed-consistency stochastic algorithms like asynchronous and low-precision stochastic gradient descent (SGD). He earned his Ph.D. from Stanford University under advisors Kunle Olukotun and Chris Ré. His work emphasizes constructing efficient, parallel, and distributed machine learning frameworks for deep learning and data analytics. Education: Ph.D. in Computer Science, Stanford University (2017) Research Interests: Algorithmic techniques for scalable ML, quantization, distributed optimization, hyperbolic geometry in ML, and reliable measurement of ML systems. His group develops frameworks for efficient inference/training and explores the intersection of ML with domains like agriculture and plant science through courses like PLSCI 7202. Recent Highlights: DARPA YFA Grant (2024), NSF CAREER Award, Google Research Scholar Award, and multiple best paper recognitions. Key contributions include QuIP quantization methods, Coneheads attention mechanisms, and theoretical advances in decentralized training. Awards: NSF CAREER Award DARPA YFA Grant (2024) Google Research Scholar Award Mr. & Mrs. Richard F. Tucker Teaching Award Grants & Advising: Advises 8 Ph.D. students (including Ruqi Zhang, Yucheng Lu, A. Feder Cooper) and holds leadership roles in MLSys conferences. Active in grant-funded research (e.g., NSF Robust Intelligence). Labs/Teams: Leads the Relax ML Lab and participates in Cornell’s Institute for Digital Agriculture (CIDA).

Annalisa ManeraView profile

Professor

• Nuclear Engineering
• Thermal-Hydraulics
• Computational Fluid Dynamics (CFD)
• +5 more

Prof. Annalisa Manera is a Full Professor at ETH Zurich's Department of Mechanical and Process Engineering since July 2021, specializing in nuclear systems and multiphase flows. Previously, she held a professorship at the University of Michigan's Nuclear Engineering Department from 2011 to 2021. Her research focuses on advanced experimental techniques for single-phase and multiphase flows, high-resolution CFD validation, and computational tools for nuclear systems. She co-directs the Experimental and Computational Multiphase Flow (ECMF) Lab and the High Resolution Imaging Lab. Education: M.Sc. in Nuclear Engineering (University of Pisa, summa cum laude) and Ph.D. in Nuclear Engineering (Delft University of Technology). Awards include the ANS Bal-Raj Sehgal Memorial Award (2022) and the US DOE CASL Director’s Award (2016), alongside being an American Nuclear Society Fellow. Her work bridges nuclear safety, thermal-hydraulics, and computational modeling, with contributions to polaron physics, electron-phonon interactions, and material simulations. Courses taught include Nuclear Energy Conversion and Beyond-Design-Basis Safety.

Christopher O'DonnellView profile

Professor

• Productivity and Efficiency Analysis
• Econometrics
• Agricultural Economics
• +5 more

Christopher O'Donnell is a Professor of Econometrics at the University of Queensland's School of Economics. He holds a PhD from the University of Sydney and has held academic leadership roles including Director of the Centre for Efficiency and Productivity Analysis. His research focuses on productivity and efficiency analysis, econometric methods, and their applications in agriculture, public policy, and environmental economics. Education: PhD (University of Sydney), MCom (University of New South Wales), BAgEc (Hons) (University of New England). Research interests include economic and statistical methods for measuring productivity changes, stochastic frontier analysis, and metafrontier frameworks. He has authored/co-authored three books and over 80 journal articles, with notable contributions in the American Journal of Agricultural Economics , Journal of Econometrics , and European Journal of Operational Research . Scientific awards include being a Distinguished Fellow of the Australian Agricultural and Resource Economics Society. His work has been applied in sectors like healthcare, fisheries, and public utilities through collaborations with organizations such as the World Bank and Asian Productivity Organization. Key projects include measuring agricultural productivity in China, analyzing hospital efficiency, and evaluating climate impacts on farming. Grants include studies on productivity measurement in Australian universities and Northern Grains Region farms. Labs/Teams: Former Director of the Centre for Efficiency and Productivity Analysis, collaborating with global institutions on productivity benchmarking and policy analysis.