Hamza Salih Erden is an Associate Professor (Docent) at the Informatics Institute of Istanbul Technical University in Turkey. His research focuses on energy optimization in data centers, thermal management systems, and computational fluid dynamics applications. With over 34 research outputs and an h-index of 12, he leads projects in energy-grid integration and carbon-aware load management. Research Focus Dr. Erden's work centers on improving energy efficiency in technological infrastructure through: Advanced cooling techniques for data centers Integration of thermal energy storage systems Computational fluid dynamics modeling Demand-response optimization for smart grids AI-driven monitoring of energy systems Publication Trends Recent works (2022-2025) demonstrate strong focus on sustainable energy technologies, particularly optimization of data center operations through thermal management innovations, integration of renewable energy solutions, and AI applications for system monitoring. Economic assessments of energy-saving techniques feature prominently. Awards and Recognition Technical Paper Award (2016) Multiple International Scientific Publication Incentive Awards (2017-2021) Poster Award (2012) Graduate Student Grant (2007) Projects and Funding Leads multiple energy research projects including: Carbon-aware load management in data centers (2025) Grid-integrated energy system modeling for data centers (2021-2023) CFD analysis of CRAH bypass methods (2018-2020) Economizer applications in Turkish data centers (2016-2017)
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۳۱-۴۵ از ۱٬۲۱۹ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Sherry Tongshuang Wu is an Assistant Professor at Carnegie Mellon University's School of Computer Science, with primary appointments in the Human-Computer Interaction Institute (HCII) and secondary affiliation with the Language Technology Institute (LTI) . Trained at the University of Washington under Jeffrey Heer and Dan Weld, she bridges HCI and NLP to study human interactions with AI systems across diverse user groups. Her educational background includes a Ph.D. (2016-22) and M.S. (2016-18) in Computer Science and Engineering from the University of Washington, and a B.Eng. (2012-16) from Hong Kong University of Science and Technology. Industry experience includes research internships at Google Brain, Microsoft Research, and Apple. Wu's research focuses on three interconnected pillars: Real-world AI Evaluation (developing frameworks like SPHERE for systematic assessment), Task-specific AI Test & Distill (optimizing general-purpose models for specific use cases), and Human-AI Task Delegation (designing optimal collaboration between humans and AI). Her work emphasizes practical deployment, user-specific net gains, and error recovery mechanisms. Analysis of her 15 most recent publications reveals a strong trend toward evaluation frameworks (35%), human-AI collaboration systems (30%), and specialized model distillation (25%), with growing emphasis on educational applications (10%). Key methodological themes include checklist-based evaluation, perspective-aware retrieval, and structural analysis of AI outputs. Google Academic Research Award (2024) Amazon Research Awards (2024) AIED 2024 Best Paper Award ACL 2020 Best Paper Award Rising Stars in EECS Workshop (2020) Wu actively mentors 19 students across PhD, Master's, and undergraduate levels, with notable projects including synthetic data generation, LLM literacy tools, and retrieval system optimization. She leads significant grant-funded work through Amazon Research Awards and Google Academic Research Awards, focusing on deployable model generation and human-AI collaboration frameworks. Her lab develops practical tools like Promp2Model and SPHERE that bridge theoretical research with industry applications.
Sheryl Graceمشاهده پروفایل
دانشیار
- Aerodynamics
- Fluid Dynamics
- Acoustics
- +۴ مورد دیگر
Sheryl Grace is an Associate Professor of Mechanical Engineering at Boston University, leading the Unsteady Fluid Mechanics & Acoustics Laboratory (UFMAL). Her primary appointment is in the Department of Mechanical Engineering within the College of Engineering. She holds a PhD from the University of Notre Dame. Her research focuses on unsteady aerodynamics, aeroacoustics, and fluid-structure interactions, with applications in aerospace systems, propulsion technologies, and biological acoustics. Notable projects include NASA-funded work on quieter vertical lift vehicles and computational modeling of gerbil hearing mechanics. Professor Grace’s research interests span aerodynamics, fluid dynamics, and acoustics. She develops analytical and computational models to predict sound and vibration generated by unsteady flows interacting with solid structures. Recent studies include noise reduction in aircraft wings, turbine blade fatigue analysis, and acoustic scattering in gerbil ears. Her work bridges theoretical models with practical engineering solutions, emphasizing cost-effective predictive tools for next-generation systems. Her publications highlight advancements in shock-droplet interactions, cavitation modeling, and machine learning applications in aeroacoustics. Collaborative projects include multi-institutional efforts to address urban air vehicle noise challenges. While no explicit awards are listed, her contributions to computational acoustics and fluid dynamics are recognized through extensive peer-reviewed output. Advising and grants: Professor Grace leads the UFMAL lab and has secured funding from agencies like NASA. Her research integrates fluid mechanics, acoustics, and computational methods to address industrial and environmental noise issues. She collaborates across disciplines, including mechanical engineering, aerospace, and biomedical acoustics.
Florence d'Alché-Buc is a Professor at Télécom Paris (Institut Polytechnique de Paris), holding an Isaac Newton Institute Simons Chair (2025) and leading the Data Science and Artificial Intelligence for Digitalized Industry & Services (DSAI) Chair. She heads the Image, Data, and Signal Department and is part of the Signal, Statistics, and Learning (S2A) team at the LTCI laboratory. Her research focuses on machine learning, bioinformatics, and industrial applications, emphasizing kernel methods, structured prediction, and reliable AI. Education: Previously a professor at Université d’Evry and deputy director of the IBISC lab. Co-director of the Paris-Saclay Data Science Master and creator of specialized AI programs (e.g., Certificate of Specialized Studies in AI). Research highlights include contributions to operator-valued kernel methods, graph prediction, and frugal AI. She actively collaborates with institutions like Inria, École Polytechnique, and industry partners (Airbus, Engie, etc.). Notable roles: Scientific director of Digicosme Labex, Ellis Fellow, and board member of IVADO (Montreal). Her recent work addresses AI explainability, robustness, and sustainability, including projects on interpretable networks and energy-efficient models.
Changyou Chenمشاهده پروفایل
دانشیار
Dr. Changyou Chen is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York. His research focuses on Multi-Modal Learning Foundation Models Deep Generative Models Large-scale Bayesian Sampling with applications in document understanding, music-AI integration, and molecular representation learning. Research Trends revealed through his recent publications include Optimizing Multimodal Large Language Models Developing Novel Retrieval-Augmented Generation Frameworks Creating Benchmark Datasets for Visual Text Understanding Advancing Diffusion Models with Domain-Specific Constraints across domains from music sheets to biomedical documents. Scientific Contributions : UB Young Investigator Award (2020) Architect of LoCAL Framework for Long Document Understanding Co-developer of MusiXQA Benchmark Pioneering Work in Probability Contrastive Learning Academic Leadership includes mentoring 10+ graduate students and serving as Area Chair for major AI conferences (ICML, NeurIPS, AAAI, IJCAI). His Labs develop scalable solutions for multimodal reasoning, with recent work demonstrating practical GPU memory optimization through LoRA adapter sharing.
Amin Barariمشاهده پروفایل
استاد مدعو
Amin Barari is an Adjunct Professor at the School of Engineering, RMIT University, Australia. His research focuses on geotechnical and offshore engineering, particularly in foundation systems for offshore wind turbines, soil-structure interaction, and seismic liquefaction mitigation. He has extensive experience in experimental and numerical analysis of pile foundations, bucket foundations, and caisson structures. His work integrates advanced computational methods (e.g., machine learning, finite element modeling) to predict foundation behavior under extreme conditions. Research interests include: offshore wind energy foundations, soil liquefaction, cyclic stability diagrams, and probabilistic hazard assessment frameworks. He has supervised multiple PhD/Masters projects on topics like resilient foundations in calcareous deposits and pile foundation dynamics in expansive soils. His publications span over 147 research outputs, emphasizing geotechnical challenges in coastal and offshore environments. Dr. Barari collaborates with international institutions and has expertise in experimental testing (e.g., large-scale load testing, centrifuge modeling) and advanced AI-driven frameworks for geohazard prediction. His work contributes to sustainable infrastructure design and risk mitigation strategies for renewable energy systems.
Luigi Acerbiمشاهده پروفایل
دانشیار
Luigi Acerbi is an Associate Professor in the Department of Computer Science at the University of Helsinki, where he leads the Machine and Human Intelligence research group. He is also an active member of the Finnish Center for Artificial Intelligence (FCAI) and ELLIS (European Laboratory for Learning and Intelligent Systems). His research focuses on probabilistic machine learning and computational neuroscience, particularly on developing efficient methods for statistical inference, Bayesian models of perception, and resource-constrained rationality. His work bridges machine learning and cognitive science, with applications in Bayesian optimization, simulation-based inference, and image completion. The recent publications highlight a strong trend toward unifying probabilistic conditioning across diverse tasks using transformer-based meta-learning frameworks like the Amortized Conditioning Engine (ACE). These works emphasize amortized inference, flexible latent variable modeling, and the integration of prior knowledge at runtime, enabling efficient and scalable Bayesian methods for complex problems. Scientific Affiliations: University of Helsinki, Department of Computer Science Finnish Center for Artificial Intelligence (FCAI) ELLIS (European Laboratory for Learning and Intelligent Systems) Education: PhD in Computational Neuroscience, Doctoral Training Centre, Edinburgh, UK Advisor: Sethu Vijayakumar and Daniel Wolpert Visiting work at Computational and Biological Learning Lab, Cambridge Postdoctoral Experience: Alex Pouget’s lab, University of Geneva, Switzerland Wei Ji Ma, New York University, USA Collaboration with the International Brain Laboratory Luigi Acerbi mentors PhD students including Daolang Huang and Nasrulloh Loka, and collaborates widely with researchers such as Samuel Kaski. He has contributed to open-source tools like PyVBMC and is involved in community initiatives such as the EurIPS conference. His work is supported by grants from the Research Council of Finland, Business Finland, and the UKRI Turing AI World-Leading Researcher Fellowship. He leads a research lab focused on amortized probabilistic inference, with ongoing projects including PriorGuide and Stacked VBMC, aiming to make Bayesian methods more practical and accessible for real-world scientific and engineering applications.
George Athanasopoulos is Professor and Head of the Department of Econometrics and Business Statistics at Monash University, a position he has held since 2022. He was appointed Professor in 2019 and has established himself as an internationally recognized expert in forecasting, time series analysis, and applied econometrics. He serves as Past President (since 2024) and former Director (2014-2024) of the International Institute of Forecasters, and is Associate Editor of the International Journal of Forecasting since 2014. His research focuses on hierarchical and grouped time series forecasting, where he has pioneered methods for forecast reconciliation and cross-temporal coherence. His work has significantly influenced forecasting practices across diverse fields including national statistics offices, energy markets, and public health. He is particularly renowned for his contributions to tourism forecasting and macroeconomic modeling in big data environments. Awarded the Australian Awards for University Teaching in 2022 for outstanding contributions to student learning, Professor Athanasopoulos has also received multiple Dean's Awards from Monash Business School for research excellence, teaching innovation, and publication quality. His research output includes over 49 publications and leadership of six major research projects, including the ARC-funded 'Macroeconomic forecasting in a Big Data world' and the RACE for 2030 CRC project on clean energy forecasting. His work contributes to UN Sustainable Development Goals through applications in economic forecasting, energy modeling, and sustainable tourism development. He has supervised numerous research students and collaborated extensively with institutions including Australian National University, Griffith University, and international partners across multiple continents.
- Shear Strength
- Geomembranes
- Slope Stability
- +۳ مورد دیگر
Timothy D Stark is a Professor in the Department of Civil and Environmental Engineering at the University of Illinois Urbana-Champaign, where he maintains an active research program focused on geotechnical engineering challenges related to infrastructure safety and environmental protection. His work bridges theoretical soil mechanics with practical applications in dam engineering, landfill design, and slope stability analysis. Stark's research centers on shear strength characterization of soils and geosynthetic materials, particularly examining geomembrane interfaces, landfill liner systems, and failure mechanisms in earth structures. His fingerprint analysis reveals dominant expertise in Shear Strength (100%), Geomembranes (98%), and Stability Analysis (63%), with significant contributions to Landslide Analysis (43%) and Landfill Engineering (35%). Current investigations address liquid effects on interface behavior, flow failure assessment in embankments, and advanced soil parameter determination through cone penetration testing. Recent publications demonstrate consistent innovation in predictive modeling for geotechnical failures, with 2024-2025 works focusing on landfill stability under liquid exposure, dam safety assessment methodologies, and three-dimensional strength characterization of compacted fills. These studies advance practical engineering solutions while contributing to fundamental understanding of soil-geosynthetic interaction mechanisms. Honors recognizing his scholarly impact include: ASCE Distinguished Member (2024) Norman Medal (2019) for seminal technical contributions to civil engineering Walter L. Huber Civil Engineering Research Prize (1999) While specific details about student advising and grant funding are not provided in the source material, his extensive publication record (268 research outputs) and fingerprint analysis indicate sustained leadership in advancing geotechnical engineering practice through rigorous experimental and analytical research.
Yongxiang Li is a Professor in the School of Engineering at RMIT University, Australia, specializing in advanced materials and nanotechnology. His research focuses on piezoelectric materials, 2D functional materials, and energy harvesting systems. He is actively involved in supervision of PhD and Master’s projects, including topics like flexible piezoelectric nanogenerators, 2D heterostructures, and low-temperature co-fired ceramic (LTCC) sensors. Research Interests: Materials Engineering, Electrical Engineering, Nanotechnology, Condensed Matter Physics, and Functional Ceramic Design. Key projects include developing lead-free piezoelectric materials, integrating sensors for lithium-ion batteries, and exploring gas sensors using liquid metal-derived oxides. Teaching Interests: Dielectric materials, ferroelectric systems, sensor technologies, and LTCC applications. His interdisciplinary work bridges materials science with optoelectronics and energy storage. Publications span over 400 outputs, emphasizing machine learning-driven materials discovery, defect engineering in 2D materials, and sensor innovation. He leads projects in collaboration with industry, focusing on practical applications of advanced ceramics and nanomaterials.
Ryan Caverlyمشاهده پروفایل
دانشیار
- Control Theory
- Dynamic Modeling
- Aerospace Systems
- +۸ مورد دیگر
Ryan Caverly serves as an Associate Professor in the Department of Aerospace Engineering and Mechanics at the University of Minnesota, Twin Cities, holding the prestigious McKnight Land-Grant Professorship. His research bridges theoretical control frameworks with practical aerospace and robotics applications, focusing on dynamic modeling and system control. Education: BS in Honours Mechanical Engineering from McGill University MS in Aerospace Engineering from the University of Michigan PhD in Aerospace Engineering from the University of Michigan Professor Caverly's research centers on input-output stability, robust control of nonlinear systems, and computationally efficient modeling of flexible structures. His work spans aerospace vehicles, spacecraft, and robotic manipulators, emphasizing theoretical rigor alongside real-world implementation challenges in structural flexibility and control precision. Recent publications reveal strong emphasis on predictive control for orbital mechanics, hypersonic vehicle dynamics, and cable-driven systems. His work consistently integrates convex optimization, state estimation, and structural dynamics to solve complex problems in solar sail technology, UAV navigation, and hypersonic flow measurement. Scientific Awards: McKnight Land-Grant Professor Caverly leads multiple externally funded projects including NASA-sponsored research on solar sail momentum management, UAV state estimation with Honeywell, hypersonic bow shock measurements with the Air Force, and deployable space structure control. His grants portfolio demonstrates significant industry and government collaboration in aerospace innovation. He directs the Aerospace, Robotics, Dynamics, and Control (ARDC) Lab, which specializes in the intersection of dynamic modeling and control theory for flexible multi-body systems, with particular focus on cable-driven mechanisms and lightweight aerospace structures.
Loris D'Antoniمشاهده پروفایل
دانشیار
Loris D'Antoni is an Associate Professor in the Department of Computer Science and Engineering at the University of California at San Diego (UCSD) . He is also a Visiting Academic at Amazon Web Services (AWS) . His research focuses on helping people write trustworthy software through techniques in program synthesis, formal verification, and machine learning robustness. Bachelor and Master in Computer Science from University of Torino (2008, 2010) PhD in Computer Science from University of Pennsylvania (2015) His research integrates programming languages , automata theory , and formal methods to ensure software reliability. Recent work explores semantics-guided synthesis and specification-aligned LLMs , with applications in network security, machine learning fairness, and automated code repair. Key trends in his publications include program synthesis , formal verification , and trustworthy AI systems . He has contributed to tools like AutomataTutor and SemGuS , a framework for customizable synthesis problems using constrained Horn clauses. Phillip R. Certain-Gary D. Sandefur Distinguished Faculty Award NSF CAREER Award Microsoft Research Faculty Fellowship Google and Facebook Faculty Awards Best Paper Award at ICDCN 2023 Distinguished Paper Award at SBES 2021 D'Antoni actively contributes to academic community service as a committee member in PLDI , OOPSLA , POPL , and CAV . He leads the Programming Systems Group at UCSD and collaborates with SemGuS research team on synthesis frameworks.
Kimberly B. Rogers is an Associate Professor of Sociology at Dartmouth College , where she has been employed since 2015. She is affiliated with the Quantitative Social Science Program and the Neukom Institute for Computational Science . Her research focuses on how inequalities are produced, maintained, and resisted through behavior and emotion dynamics in social interactions , with particular attention to status and power hierarchies , occupational inequality , and mental health outcomes . She teaches courses such as Introductory Sociology , Research Methods , and Status and Power in Social Interaction , integrating computational tools into her pedagogy. PhD in Sociology, Duke University (2013) MA in Sociology, Duke University (2008) MA in Psychology, Wake Forest University (2005) BA in Psychology, Randolph-Macon Woman's College (2003) Rogers' research spans three key areas: cultural consensus in identity sentiments , behavioral/emotional responses to inequality , and computational modeling of social processes . Her work reveals how micro-social mechanisms perpetuate or disrupt status hierarchies across contexts like occupational roles , racialized interactions , and digital collaboration . Recent publications examine technological co-diffusion , pandemic-induced identity shifts , and gendered occupational perceptions . Her scholarly output follows trends in computational social science and cross-cultural emotion analysis . She employs Bayesian affect control theory to model dynamic identity processes and uncertainty in interactions , while her 2025 work on violence against women demonstrates applications of general strain theory to contemporary social issues. Senior Faculty Grant (2023-24) , Dartmouth College Wilson Fellowship (2021-22) , Dartmouth College Outstanding Article Award (2017) , American Sociological Association Seed Funding Grant (2016-17) , Dartmouth Provost Doctoral Dissertation Grant (2010-11) , National Science Foundation Rogers has received multiple grants for collaborative projects like THEMIS.COG , examining identity and sentiment modeling in groups . She mentors students through engaged scholarship and social impact practicums , though specific advisees aren't listed. Her 2019-2020 involvement with Campus Compact and 2016 DCSI teaching grant highlight her commitment to community-engaged pedagogy .
Prof Jean Van Den Elsen is a Professor of Biochemistry at the University of Bath , affiliated with the Department of Life Sciences and several research centers including the Centre for Sustainable Chemical Technologies (CSCT) , Centre for Therapeutic Innovation , and Milner Centre for Evolution . They are actively accepting doctoral students and leading multidisciplinary projects at the intersection of structural biology and immunology. Education: Not explicitly mentioned Appointments: University of Bath (current), with collaborations across Chemistry, Biology & Biochemistry departments Research Focus: The laboratory investigates protein structural biology in two major areas: Complement System Interactions: Studying how pathogenic microbes like Staphylococcus aureus evade host immunity through complement system manipulation , with structural analysis of proteins such as C3d and C5 complexes. This work directly informs vaccine design and autoimmune disease treatment . Glycation Mechanisms: Developing diagnostic tools for detecting non-enzymatic sugar modifications linked to diabetes , Alzheimer's , and aging, with commercial partnerships like Abcam . Article Trends: Recent publications demonstrate: Structural analysis of complement evasion proteins (2022-2025) Engineering knob domains as miniature antibodies (2023) Evolutionary studies on cooperative behavior (2021-2025) Advancements in peptide synthesis platforms (2022-2023) Applications of AI protein folding (2020) Scientific Contributions: ORCID: 0000-0002-0367-1956 BBSRC Funding recipient (2023) 100+ citations across 76 research outputs Student Mentorship: Directly supervising 4 current PhD students and 1 postdoc, with a track record of mentoring 11 previous students including Daphne Jackson Fellows and MSCA researchers. Collaborations: Maintains active partnerships with Dr. Tony James (Chemistry), Dr. Rob Williams (Biochemistry), and industry partners UCB Celltech and Porton Biopharma Ltd. Their work contributes to UN Sustainable Development Goals 3 (Good Health), 12 (Responsible Consumption), and 13 (Climate Action).
Olivia Guمشاهده پروفایل
دانشیار
- Asset Pricing
- Return Predictability
- Innovation
- +۴ مورد دیگر
Olivia Gu is an Associate Professor of Finance at Sy Syms School of Business, Yeshiva University. She holds a PhD in Finance from the University of Illinois at Urbana-Champaign and has previously taught investments and risk management at The University of Hong Kong (HKU). Her research spans asset pricing, corporate governance, ESG, and machine learning applications in finance. PhD in Finance, University of Illinois at Urbana-Champaign Research interests include: Asset Pricing Return Predictability Innovation Finance ESG Investing Corporate Governance Machine Learning Applications Gender Discrimination Analysis Recent publications analyze venture capital human capital impacts, price rigidities, market competition effects, and corporate diversification strategies. She received multiple prestigious awards including the Trefftzs Award and SAC Capital PhD Candidate Award. Trefftzs Award SAC Capital PhD Candidate Award for Outstanding Research HKU Research Output Prize Professor Gu serves on the editorial board of Review of Financial Economics and participates in finance conference committees.
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