جستجوی استاد راهنمای دانشگاهی

• Human Geography
• Science and Technology Studies
• Climate Change Adaptation
• +۴ مورد دیگر

Dr. Sophie Adams serves as an ARC DECRA Fellow in the Department of Sociology at La Trobe University, where she conducts interdisciplinary research at the intersection of human geography and Science and Technology Studies. Her work critically examines the social dimensions of climate change mitigation and adaptation, with particular emphasis on renewable energy transitions and the societal implications of technological solutions to environmental crises. Adams' research program centers on the politics of environmental knowledge , investigating how climate science informs policy formation and the negotiation of social objectives in decarbonisation processes. She employs qualitative and participatory methodologies to analyze climate change adaptation in energy systems , social license for technological interventions , and the redistribution of expertise in public health and environmental governance. Her scholarship consistently addresses power dynamics in knowledge production and the democratic challenges of sustainability transitions. Analysis of her publication trajectory (2019-2025) reveals increasing focus on energy-social interfaces, with significant contributions to understanding virtual power plants, demand-side management, and the role of modeling in climate policy. Her work demonstrates methodological innovation through participatory approaches while maintaining critical engagement with resilience thinking and adaptation frameworks. Scientific recognition includes: Australian Research Council Discovery Early Career Researcher Award (ARC DECRA) Supported by her DECRA fellowship, Adams leads independent research projects examining the societal dimensions of energy transitions. Her collaborative work spans geography, public health, and energy engineering, as evidenced by interdisciplinary co-authorship patterns. Current projects investigate Australian householders' conditions for participating in virtual power plants and the social implications of climate modeling in adaptation planning, reflecting her commitment to socially relevant environmental research.

Xia Ben Huمشاهده پروفایل

استاد

• Machine Learning
• Automated Machine Learning
• Deep Learning
• +۷ مورد دیگر

Xia Ben Hu is a Professor in the Department of Computer Science at Rice University's Brown School of Engineering. He leads research in automated and interpretable machine learning algorithms with applications across social informatics, health informatics, and information security. His work has resulted in widely adopted systems including AutoKeras, TODS, and RLCard. Education: PhD from Arizona State University (supervised by Dr. Huan Liu) Master and Bachelor degrees from Beihang University Prof. Hu's research focuses on developing automated and interpretable machine learning algorithms for large-scale, networked, dynamic and sparse data. His work spans automated machine learning (AutoML), deep learning, fairness in AI, time series analysis, and interpretable AI. He has made significant contributions to neural architecture search, collaborative filtering, anomaly detection, and reinforcement learning in imperfect information games. His publication record shows a clear progression from foundational work in network embedding and collaborative filtering (2017) to more recent work on LLM optimization, quantization, and extending context windows (2024). A consistent thread throughout his research is the focus on making complex machine learning systems more accessible, efficient, and interpretable. Selected Awards: ACM SIGKDD Rising Star Award (2021) NSF CAREER Award (2018) Teaching + Research Excellence Award, Rice University (2023) Multiple Best Paper Awards at top venues including ICML, CIKM, and AMIA Prof. Hu has successfully mentored numerous graduate students, with recent graduates securing tenure-track positions at major universities. His research is generously supported by federal agencies including DARPA (XAI, D3M, NGS2), NSF (CAREER, III, SaTC), NIH, and industrial sponsors such as Adobe, Apple, Google, LinkedIn, and JP Morgan. He has served as General Co-Chair for WSDM 2020 and ICHI 2023, and Program Chair for AIHC 2024. He leads the DATA Lab at Rice University, which develops open-source systems for automated machine learning and reinforcement learning. The lab's AutoKeras system has over 8,000 GitHub stars and 1,000 forks, and their work has been integrated into TensorFlow, Apple production systems, and Bing production systems.

Parviz Moinمشاهده پروفایل

استاد

• Computational Physics
• Turbulent Boundary Layers
• Hypersonic Flows
• +۴ مورد دیگر

Parviz Moin holds the Franklin P. and Caroline M. Johnson Professorship in Stanford University's School of Engineering. As founding director of the Center for Turbulence Research (CTR)—a NASA-Stanford consortium established in 1987—he has pioneered computational methods for turbulence physics, including direct numerical simulation and Large Eddy Simulation (LES) techniques. CTR serves as an international hub for turbulence studies across engineering, mathematics, and physics disciplines. Moin's research encompasses computational physics of turbulent flows, with emphasis on boundary layer control, hypersonic aerodynamics, propulsion systems, and aircraft icing. His recent work advances high-fidelity simulations for aerospace applications, particularly developing wall models for LES that accurately capture separation phenomena under complex pressure gradients and Reynolds number effects. Recent publications demonstrate extensive applications of LES to aircraft design challenges, including transonic buffet prediction, high-lift configuration analysis, and icing aerodynamics. Investigations consistently address fundamental turbulence physics while developing practical computational tools for aerospace engineering, with particular focus on hypersonic boundary layers, flow separation mechanisms, and conjugate heat transfer in iced environments.

Themistoklis Sapsisمشاهده پروفایل

استاد

• Probabilistic Prediction
• Nonlinear Systems Analysis
• Machine Learning in Fluid Dynamics
• +۳ مورد دیگر

Themistoklis Sapsis is a Professor in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where he also holds an affiliation with the MIT Institute for Data, Systems, and Society. He earned his Ph.D. in Mechanical Engineering from MIT in 2011 and previously served as an Assistant Research Scientist at NYU’s Courant Institute of Mathematical Sciences. His research focuses on developing analytical, computational, and data-driven methods to predict and quantify extreme events in high-dimensional nonlinear systems, such as turbulent fluid flows and mechanical systems. Key areas include probabilistic modeling of climate extremes, machine learning for climate simulation corrections, and uncertainty quantification in complex dynamical systems. Recent work emphasizes applications in ocean engineering (e.g., vortex-induced vibrations, wave energy systems) and environmental science (e.g., spatially resolved climate extremes, bias correction in Earth system models). His methodologies combine stochastic emulators, Bayesian experimental design, and neural networks to address challenges in data sparsity and model fidelity. Notable contributions include frameworks for correcting coarse-scale climate simulations using machine learning, real-time ocean temperature reconstruction from satellite data, and data-driven modeling of hydrodynamic interactions in marine risers. His research bridges theoretical developments with practical applications in energy systems, structural monitoring, and autonomous systems. Prof. Sapsis collaborates with interdisciplinary teams and has contributed to initiatives such as FIRSTLING-DIGIMAR (a marine riser digital twin) and multi-fidelity frameworks for autonomous seakeeping. His work is supported by grants focused on advancing machine learning in scientific modeling and extreme event prediction.

• Antibiotic Resistance
• Micropollutants
• Pyrolysis
• +۷ مورد دیگر

Dr. Patrick J. McNamara is an Associate Professor in the Department of Civil, Construction and Environmental Engineering at Marquette University's College of Engineering. He directs the McNamara Research Group, which focuses on understanding how chemicals from consumer products impact public health and the environment once they pass through water treatment systems. His research bridges environmental engineering and microbiology to address critical water quality challenges facing modern infrastructure. Dr. McNamara's educational background includes: Ph.D., 2012, Civil Engineering, University of Minnesota, Twin Cities M.S., 2008, Environmental and Water Resources Engineering, University of Texas at Austin B.S., 2006, Civil Engineering (Minor - Spanish for the Business Professions), Marquette University His research program investigates how consumer product chemicals impact engineering treatment processes that rely on healthy bacteria to treat water. The McNamara Research Group develops non-traditional treatment processes to remove these chemicals from water and mitigate their environmental effects. His work spans antibiotic resistance in water systems, micropollutant removal technologies, pyrolysis of biosolids, PFAS contamination, and electrochemical treatment processes. Specific areas include the impact of corrosion inhibitors on antibiotic resistance, removal of chemicals via drinking water treatment, environmental antibiotic resistant bacteria, beneficial biosolids reuse, and pyrolysis applications. Dr. McNamara's publication record demonstrates a strong focus on emerging water quality challenges, particularly the intersection of chemical contaminants and antibiotic resistance. His recent work examines corrosion inhibitors' impact on antibiotic resistance in drinking water, PFAS mitigation through advanced treatment processes, and environmental drivers of antibiotic resistance in stormwater systems. His research combines fundamental microbiology with practical engineering solutions to address complex water quality issues. Dr. McNamara has received numerous honors and awards: 2022 OCOE Outstanding Researcher Award from Marquette University Marquette University's Campus 2020 KEEN Rising Star Faculty Scholar Award from Provost Office (2019) Central States Water Environment Association Bill Boyle Outstanding Educator Award (2018) Way Klingler Young Scholar Award (Marquette University, 2018) Excellence in Review Award – Environmental Science & Technology (2017) Dr. McNamara has secured significant research funding as Principal Investigator on multiple projects, including NSF grants focused on mitigating antibiotic resistance in drinking water and studying the environmental impacts of quaternary ammonium compounds. His current research portfolio includes projects on PFAS removal through novel electrocoagulation-peroxidation processes, designing green stormwater infrastructure to combat antibiotic resistance, and removing contaminants from greywater using electrocoagulation technology in collaboration with industry partners like Kohler Company. The McNamara Research Group at Marquette University maintains strong collaborations with researchers across multiple institutions and works closely with water utilities and industry partners to translate research findings into practical solutions for water treatment challenges. Their work addresses critical infrastructure needs while protecting environmental and public health through innovative engineering approaches.

Jeffrey Guastoمشاهده پروفایل

دانشیار

• Biophysics
• Soft Matter Physics
• Microscale Fluid Mechanics
• +۵ مورد دیگر

Jeffrey Guasto , Associate Professor at Tufts University, holds joint appointments in the School of Engineering (Mechanical Engineering) and School of Arts and Sciences (Physics & Astronomy). His work bridges engineering, physics, and biology to study transport properties in complex systems. Ph.D., Engineering (2009), Brown University Sc.M., Engineering (2004), Brown University Dual B.S. in Physics and Mechanical Engineering (2003), Lehigh University Research Interests focus on: Biophysics : Flagellar mechanics, chemotaxis, cell-fluid interactions Soft Matter : Active suspensions, colloids, viscoelastic materials Microfluidics : Device design for cell motility studies and gradient generation Environmental Transport : Microbial ecology in porous systems Scientific Trends from his 77+ publications show emphasis on microscale fluid dynamics, bacterial transport mechanisms, and viscoelastic flow instabilities. His 2024 Nature Microbiology work reveals phage-infected bacteria driving marine chemotaxis, while 2023 PNAS research explores stress topology in viscoelastic flows. Scientific Awards : NSF CAREER Award (2016) for cell dispersal mechanisms Collaborative NSF grants (2015-2023) Advising includes mentoring 15+ students and postdocs. His grants portfolio features 9+ awards, notably NSF grants for viral-microbe interactions (2018) and flagellar mechanics (2020). Labs & Teams : Leads the Guasto Laboratory at Tufts, integrating microfluidics and high-speed imaging for studying microbial transport, while collaborating with MIT, Harvard, and international institutions.

WANG Qinghaiمشاهده پروفایل

دانشیار

• Non-Hermitian Quantum Mechanics
• Quantum Field Theory
• Mathematical Physics
• +۳ مورد دیگر

WANG Qinghai is an Associate Professor (Educator Track) at the National University of Singapore (NUS), specializing in Non-Hermitian PT-symmetric quantum mechanics, quantum field theory, and mathematical physics. His research explores the stability of non-Hermitian systems through periodic driving, time-dependent PT-symmetric frameworks, and applications of 2×2 matrices in quantum dynamics. Recent publications focus on advanced topics in quantum mechanics, thermodynamics, and cosmological instantons, reflecting his interdisciplinary expertise. While no formal student lists or scientific awards are documented in the provided texts, his work bridges theoretical physics and applied mathematics.

Crystal Leeمشاهده پروفایل

استادیار

• Ethical Technology
• Data Visualization
• Disability Studies
• +۴ مورد دیگر

Crytal Lee is an Assistant Professor in Computational Media and Design at MIT, with a joint appointment in the Schwarzman College of Computing and Comparative Media Studies/Writing. She is also a Faculty Associate at Harvard's Berkman Klein Center for Internet & Society, co-leading the Ethical Tech Working Group, and a Senior Fellow at Mozilla's Responsible Computing Challenge. Her research focuses on data visualization, disability studies, and ethical technology, emphasizing the 'life-cycle of data representations.' Education: PhD in History, Anthropology, Science, Technology, and Society from MIT (2022); MA and BA (High Honors) in History of Science from Stanford University (2016 and 2015). Research Interests : Crystal examines how data is curated, visualized, and contested, with a particular lens on disability justice and accessible design. Her work bridges STS, HCI, and critical data studies, addressing issues like misinformation, algorithmic bias, and inclusive technology. Awards & Grants : Honorable Mentions at EuroVis 2022 and CHI 2021; NSF Dissertation Improvement Grant; SSRC Social Data Fellowship. Advising & Mentorship : Advised projects on accessible visualization, participatory AI, and disability inclusion. Current book project: Crip Computation . Labs & Teams : Co-leads Ethical Tech Working Group at Berkman Klein; involved in MIT's Data + Feminism Lab and Accessible Interactions projects.

Max Alekseyevمشاهده پروفایل

دانشیار

• Computational Graph Theory
• Enumerative Combinatorics
• Computational/Algorithmic Biology
• +۱ مورد دیگر

Max Alekseyev is an Associate Professor in the Mathematics Department and Computational Biology Institute. His research spans computational graph theory, enumerative combinatorics, computational/algorithmic biology, and comparative genomics. He focuses on interdisciplinary problems, blending mathematics with biological applications, particularly in genome assembly and analysis. His work includes advancements in genome scaffolding algorithms, combinatorial sequence analysis, and mathematical biology. Notable contributions involve genome assembly tools like CAMSA and studies on ancestral genome reconstruction. He also explores theoretical topics such as Bernoulli series generalizations and modular data classification. His research trends highlight a blend of pure mathematics (e.g., number theory, graph theory) and applied computational methods, addressing challenges in genomics and evolutionary biology. He secured an NSF Student Travel Grant in 2018 for computational molecular biology.

Natalie Simpsonمشاهده پروفایل

استاد

• Emergency Response Operations
• Supply Chain Logistics
• Educational Technology

Dr. Natalie Simpson is Professor and Associate Dean for Graduate Programs at the University at Buffalo's School of Management, Department of Operations Management and Strategy. She holds a PhD and MBA from the University of Florida, and a BFA from North Carolina School of the Arts. Her research explores emergency response systems , supply chain logistics , and educational technology , with particular focus on operational challenges in crisis management. She investigates hyper-project coordination in emergency contexts and resource allocation frameworks for incident commanders. Simpson's scholarly contributions show strong emphasis on: Modeling emergency response operations and supply chain vulnerabilities Developing pedagogical innovations for operations management education Analyzing healthcare workflow efficiency and disaster management systems Her extensive recognition includes: Decision Sciences Institute's Best Case Studies Award (2005) National Instructional Innovation Award (2004) SUNY Chancellor's Award for Excellence in Teaching (2002) Grinter Fellowship and Matherly Scholarship As Academic Director of Digital Access Education, she leads technology-enhanced learning initiatives and advises graduate programs. Administrative responsibilities include heading the Digital Access Working Group and serving on editorial boards for Decision Sciences.

Nina Balcanمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Theoretical Computer Science
• +۵ مورد دیگر

Nina Balcan is the Cadence Design Systems Professor of Computer Science at Carnegie Mellon University's School of Computer Science, with affiliations in both the Machine Learning Department (MLD) and Computer Science Department (CSD). She maintains her office in Gates Hillman Center (GHC) 8205 and is a prominent figure in theoretical machine learning and algorithmic game theory. Her research spans multiple critical areas in computer science, with a strong focus on the theoretical foundations of machine learning, algorithm design and analysis, and computational approaches to game theory and economics. Balcan has made significant contributions to developing principled algorithms for deep learning, learning with limited supervision, representation learning, and life-long learning. Her work uniquely bridges theoretical computer science with practical applications, particularly in the analysis of complex objects and processes, including algorithmic learning and multi-agent systems. Analysis of her recent publications reveals a strong trend toward data-driven algorithm design, with particular emphasis on learning to optimize combinatorial algorithms, revenue-maximizing mechanisms, and robust learning frameworks. Her work consistently demonstrates how theoretical guarantees can inform practical algorithm development across diverse domains from optimization to economics. Major Awards and Honors: ACM Fellow AAAI Fellow Simons Investigator 2019 ACM Grace Murray Hopper Award (awarded to the outstanding young computer professional of the year) Winner of Outstanding Student Paper Award at UAI 2024 Winner of Exemplary Artificial Intelligence Track Paper Award at ACM EC 2019 Runner Up Best Paper Award at COLT 2012 Professor Balcan has served as Program Committee Co-chair for major conferences including NeurIPS 2020, ICML 2016, and COLT 2014, demonstrating her leadership in the machine learning community. Her teaching portfolio at CMU includes foundational courses such as 10-701 Machine Learning, 10-315 Machine Learning, and 10-715 Advanced Introduction to Machine Learning, where she has mentored numerous students in both theoretical and applied aspects of the field. Her research group focuses on developing theoretically sound yet practically applicable machine learning algorithms, with particular attention to algorithm configuration, data-driven optimization, and learning in strategic environments. Current projects involve learning to improve combinatorial algorithms, designing revenue-maximizing mechanisms, and developing robust learning frameworks that can operate effectively in challenging environments.

AI Xinمشاهده پروفایل

مدرس

• Artificial Intelligence
• Multi-Agent Systems & Algorithmic Game Theory
• Machine Learning and Deep Learning
• +۵ مورد دیگر

AI Xin is a Lecturer in the School of Computing at the National University of Singapore (NUS), specializing in Artificial Intelligence and Data Science. She teaches courses such as machine learning, deep learning, and data mining, including advanced modules like CS4225 and CS5425. Education: Ph.D. in Electrical and Computer Engineering from NUS; B.Eng. from Xidian University, China. Her research spans Game Theoretical Modelling , Optimization Methods , Algorithm Design , and Wireless Networks . She has contributed to multi-agent systems, algorithmic game theory, and wireless community networks, focusing on robust and distributed solutions. Her recent publications highlight trends in game theory for wireless networks , distributed coverage algorithms , and optimization for network efficiency , with a strong emphasis on theoretical and practical applications in AI and networking. Scientific Awards: Teaching Excellence Award (NUS, 2024). She has taught courses on Big Data Systems for Data Science and Computational Thinking , bridging academic rigor with industry relevance through her prior experience in risk management, supply chain, and sales at BHP Billiton Marketing Asia.

David Hsuمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Machine Learning
• +۲ مورد دیگر

David Hsu is Provost's Chair Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing, where he founded and directs the NUS Artificial Intelligence Laboratory (NUSAIL) and leads the Smart Systems Institute. His academic leadership includes chairing major conferences such as Robotics: Science & Systems (2015) and IEEE ICRA (2016), alongside editorial roles in IEEE Transactions on Robotics and the Journal of Artificial Intelligence Research. He earned a B.Sc. in Computer Science & Mathematics from the University of British Columbia and a Ph.D. in Computer Science from Stanford University. His research spans robotics, AI, and computational biology, with recent focus on robot planning under uncertainty and human-robot collaboration. Current work integrates machine learning with decision-theoretic planning to enable robust human-robot co-existence in unstructured environments. Analysis of his 2023-2025 publications reveals dominant trends in deformable object manipulation (e.g., clothes handling via semantic keypoints), open-world navigation using scene graphs, and LLM-driven multi-agent reasoning for complex tasks. Key innovations include perspective-aware visual grounding for human-centric interaction and functional object arrangement through compositional generative models, reflecting a strong emphasis on real-world applicability. His scientific contributions have earned prestigious recognition: IJCAI-JAIR Best Paper Prize (2022) for foundational AI research Robotics: Science & Systems Test of Time Award (2021) IEEE Fellowship (2018) for contributions to robotic planning RSS Best Systems Paper Award (2017) RoboCup Best Paper Award at IROS (2015) Humanitarian Robotics Award at ICRA (2015) As director of the Adaptive Computing Laboratory, Hsu drives research on fundamental computational frameworks for human-robot interaction. The lab's work on uncertainty-aware decision-making has secured significant research funding through grants from Singapore's National Research Foundation and industry partnerships with robotics firms. While specific student names aren't publicized, his leadership in the NUSAIL indicates extensive mentorship of doctoral candidates in AI and robotics.

Ioannis Panageasمشاهده پروفایل

استادیار

• Algorithmic Game Theory
• Multi-agent Reinforcement Learning
• Optimization
• +۲ مورد دیگر

Ioannis Panageas is an Assistant Professor in Computer Science at UC Irvine's Donald Bren School, directing the GOALLab. His research develops theory for learning in multi-agent systems, game dynamics, and optimization. Funded by NSF and NRF, he focuses on last-iterate convergence guarantees in games, efficient equilibrium computation, and multi-agent reinforcement learning. Recent Work: Provides first exponential lower bounds for fictitious play in potential games (NeurIPS 2023), efficient Nash equilibrium computation methods (ICLR 2023), and semi-bandit learning dynamics with no-regret guarantees (ICML 2023). Teaching: Offers courses in Algorithmic Game Theory and Optimization for Machine Learning. Currently advising 3 PhD students and 2 MS students.

• Learning Analytics
• Artificial Intelligence in Education
• MOOCs (Massive Open Online Courses)
• +۵ مورد دیگر

Professor George Siemens is a leading academic in the field of learning analytics and AI-driven education, serving as Professor and Director of the Centre for Change and Complexity in Learning at UniSA Education Futures, University of South Australia. His work focuses on advancing educational practices through data analytics, artificial intelligence, and understanding online learning dynamics. His research spans MOOCs, social and emotional learning analytics, and the ethical integration of AI in education. Notable contributions include the development of frameworks like the MOOC Replication Framework (MORF) and the DAIR infrastructure for educational AI research. Key publications include studies on student agency in AI environments, practicum effectiveness in teacher education, and synthetic data fairness in learning analytics. He collaborates internationally, with affiliations previously including the University of Texas Arlington. As a Research Degree Supervisor, he guides students in transformative educational technology research. His work emphasizes actionable intelligence for educators and scalable solutions for lifelong learning in the digital age.