جست‌وجوی استاد و پژوهشگر

Michael O'Boyleمشاهده پروفایل

استاد

• Heterogeneous Code discovery and Optimisation
• Neural Machine Translation and Neural Synthesis
• Deep Neural Network system stack
• +۷ مورد دیگر

Michael O'Boyle is a Professor at the University of Edinburgh's School of Informatics, where he serves as Director of the ARM Research Centre of Excellence and the EPSRC Centre for Doctoral Training in Pervasive Parallelism. Holding an EPSRC Established Career Research Fellowship, he leads pioneering work in compiler technology for heterogeneous architectures, bridging theoretical advances with practical high-performance computing applications. Professor O'Boyle's research spans multiple cutting-edge areas including heterogeneous code discovery and optimization, neural machine translation for program synthesis, deep neural network system stack optimization, software-defined hardware, and compiler/architecture co-design. His approach integrates constraint analysis, program synthesis, and machine learning to address complex challenges in high-performance computing across diverse hardware platforms. His recent publications reveal a strong trend toward integrating machine learning with traditional compiler techniques, particularly in neural program synthesis, tensor optimization, and architecture-aware compilation. This work represents a paradigm shift in compiler design, moving from rule-based systems to learning-based approaches that can automatically adapt to diverse hardware targets. IEEE/ACM CGO 2025 Distinguished Paper Award for 'Tensorize: Fast Synthesis of Tensor Programs from Legacy Code' IEEE/ACM CGO 2024 Test of Time Award ACM GPCE 2023 Best Paper Award for 'C2TACO: Lifting Tensor Code to TACOM' ACM ASPLOS 2021 Distinguished Paper Award IEEE HPCA 2021 Best Paper Award for 'Prodigy: Improving the Memory Latency of Data-Indirect Irregular Workloads' Professor O'Boyle has successfully mentored numerous PhD students who have secured prominent positions in academia (including at Cambridge, Edinburgh, Leeds, and McGill) and industry (including Meta, NVIDIA, Qualcomm, Huawei, and Microsoft). His research is supported by significant funding from EPSRC, ARM, and European projects including Bonseyes and Transmuter, demonstrating strong international recognition and industry impact. He leads the influential Compiler and Architecture Design (CArD) Group at the University of Edinburgh and is a founder of the HiPEAC Network of Excellence, which has grown into a major European initiative connecting researchers and practitioners in high-performance and embedded computing.

Adriana Schulzمشاهده پروفایل

استادیار

• Computational Design
• Computer-Aided Design (CAD)
• Digital Fabrication
• +۴ مورد دیگر

Adriana Schulz is an Assistant Professor in the Department of Computer Science & Engineering at the University of Washington's College of Engineering. She leads a research group focused on computational design, computer-aided design (CAD), and digital fabrication. Her work bridges computer science with practical applications in manufacturing, robotics, and sustainable design. Dr. Schulz received her Ph.D. in Computer Science from MIT in 2018 under the supervision of Professor Wojciech Matusik. Prior to her doctoral studies, she earned a Master's degree in Mathematics from IMPA (Instituto Nacional de Matemática Pura e Aplicada) in Rio de Janeiro, where she worked with Professor Luiz Velho, and a Bachelor's degree in Electronics Engineering from UFRJ (Federal University of Rio de Janeiro). Her research interests center around computational tools that enhance design and manufacturing processes. She develops novel algorithms for CAD systems, computational fabrication techniques, and sustainable design approaches. Her work spans multiple domains including robotics, textiles, electronics, and architecture, with a strong emphasis on creating practical tools that designers and engineers can use in real-world applications. She explores how machine learning, particularly neurosymbolic approaches, can improve design workflows and enable new capabilities in computational design systems. Analysis of her recent publications reveals a strong trend toward more intelligent and user-centered design tools. Her research increasingly integrates machine learning with traditional CAD systems to create more intuitive interfaces, supports sustainable design practices with computational tools, and develops novel fabrication techniques that push the boundaries of what's possible with digital manufacturing. She has made significant contributions to zero-waste fashion design, immersion cooling for high-performance computing, and CAD program understanding through novel representation learning techniques. Innovators Under 35 - MIT Technology Review Bolsa Aluno Nota 10 from FAPERJ Engineer 20000 award Dr. Schulz actively mentors several PhD students and postdoctoral researchers, including Haisen Zhao, Ben Jones, Yuxuan Mei, and others, often in collaboration with colleagues across different departments. Her research has attracted significant media attention, with coverage in major outlets including MIT News, BBC, IEEE Spectrum, Wired, and TechCrunch. Her work on Interactive Robogami was noted as the most read article in the International Journal of Robotics Research in its publication year. She leads a vibrant research group at the University of Washington that focuses on computational design systems, with particular emphasis on creating tools that bridge the gap between digital design and physical fabrication. Her team develops novel algorithms for CAD systems, computational fabrication techniques, and sustainable design approaches that have practical applications across multiple industries.

Zhuoyue Zhaoمشاهده پروفایل

استادیار

• Database systems
• Security
• Query processing
• +۶ مورد دیگر

Zhuoyue Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University at Buffalo, School of Engineering and Applied Sciences. His office is located at 338I Davis Hall, Buffalo, NY 14260, and he can be reached at zzhao35@buffalo.edu or by phone at (716) 645-4735. Dr. Zhao received his PhD in Computer Science from the University of Utah in 2021, where he was advised by Prof. Feifei Li and Prof. Jeff Phillips. Prior to that, he earned his BS in Computer Science from Shanghai Jiao Tong University in 2016, where he was part of the prestigious ACM Class. During his undergraduate studies, he conducted research under Prof. Kenny Zhu and spent Fall 2015 as a research assistant at Hong Kong Polytechnic University supervised by Prof. Eric Lo. Dr. Zhao's research focuses on database management systems, with specific emphasis on traditional and approximate query processing, query optimization, database systems on modern hardware, transaction processing, indexing, and storage. His work bridges theoretical foundations with practical implementations, often resulting in systems that address real-world database challenges. He has made significant contributions to probabilistic query processing, transaction scheduling, and learned indexing techniques. His recent publications demonstrate a clear trajectory toward optimizing database performance in hybrid transactional/analytical processing environments. His research increasingly integrates systems techniques with machine learning approaches, particularly in the area of learned indexes. There's also a strong focus on making database operations more efficient through innovative scheduling mechanisms and query processing techniques that can handle concurrent updates. Google PhD Fellowship (2019-2021) Best Paper Award at SIGMOD 2016 for "Wander Join: Online Aggregation via Random Walks" Best Paper Award at SIGMOD 2025 for "Low-Latency Transaction Scheduling via Userspace Interrupts" Dr. Zhao currently advises several PhD students including Yunnan Yu, Congying Wang, Gaoxiang Liu (co-advised with Prof. Ziming Zhao), and Zhuoran Li. He has successfully guided MS student Nithin Sastry Tellapuri to graduation (Fall 2023), who is now employed at AirPay. His research is supported by significant funding including an NSF CAREER award (#2339596) totaling $599,977 for research on "Speedy and Reliable Approximate Queries in Hybrid Transactional/Analytical Systems" (2024-2029) and an unrestricted Google gift of $30,000 (2021). Dr. Zhao leads the ADBLab research group at UB, where students work on cutting-edge database systems research. His lab focuses on building practical database systems that address real-world challenges in query processing, transaction management, and indexing. The lab maintains strong connections with industry partners and regularly contributes to open-source database projects.

Daniel Abadiمشاهده پروفایل

استاد

• Database system architecture
• Distributed database systems
• Column-oriented databases
• +۵ مورد دیگر

Daniel Abadi is the Darnell-Kanal Professor of Computer Science at the University of Maryland, College Park. He leads the Data Systems Lab at Maryland (DSLAM) and is widely recognized for his groundbreaking contributions to database system architecture and implementation. Previously, he was a faculty member at Yale University where he received the Provost's Teaching Prize. Abadi's research primarily focuses on database system architecture, particularly at the intersection with scalable and distributed systems. He is best known for developing the storage and query execution engines of the C-Store prototype (a column-oriented database system commercialized by Vertica and later acquired by Hewlett-Packard), HadoopDB research (commercialized by Hadapt and acquired by Teradata), and deterministic distributed transactional systems like Calvin (currently being commercialized by Fauna). His work bridges theoretical innovation with practical industrial impact. Analysis of his recent publications reveals a consistent trajectory toward solving fundamental challenges in distributed database systems. His research has evolved from foundational work on column-stores and hybrid database architectures to cutting-edge innovations in geo-replicated transactions, concurrency control mechanisms, and the integration of machine learning with database systems. The trend shows increasing focus on practical implementations that address real-world scalability and performance challenges in large-scale data processing environments. ACM Fellow Churchill Scholarship recipient NSF CAREER Award winner Sloan Research Fellowship recipient VLDB Best Paper Award winner Two VLDB Test of Time Awards (for C-Store and HadoopDB) 2008 SIGMOD Jim Gray Doctoral Dissertation Award 2013-2014 Yale Provost's Teaching Prize 2013 VLDB Early Career Researcher Award Professor Abadi has successfully mentored several PhD students, most notably Alexander Thomson and Jose Falerio, both of whom won the prestigious SIGMOD Jim Gray Doctoral Dissertation Award for their work under his supervision. His research has been generously supported by multiple NSF grants including BIGDATA awards and other funding mechanisms that have enabled significant advances in database technology. He actively collaborates with industry partners, with several of his research projects leading directly to commercial products. At the University of Maryland, Abadi directs the Data Systems Lab at Maryland (DSLAM), which focuses on developing innovative database technologies that address contemporary challenges in data management. The lab's research spans distributed transaction processing, database architecture, and the integration of database systems with emerging computing paradigms. Notable projects include SLOG (Serializable, Low-latency, Geo-replicated Transactions), which eliminates traditional tradeoffs in distributed database design, and ongoing work in deterministic database systems that provide strong consistency guarantees without sacrificing performance.

• Digital Archaeology
• Computational Archaeology
• Machine Learning in Archaeology
• +۱۳ مورد دیگر

Karsten Lambers is Professor of Digital and Computational Archaeology at the Faculty of Archaeology, Leiden University, where he leads research and teaching in the application of computational methods to archaeological data. His work integrates machine learning, remote sensing, text mining, and citizen science to advance archaeological prospection and heritage management. He is affiliated with the Department of Archaeological Sciences and plays key roles in research groups and university-wide initiatives such as SAILS and ARCHON. His research interests span Digital Archaeology , Machine Learning in Archaeology , Remote Sensing , Geoarchaeology , and Human-Environment Interaction . He investigates how computational tools can extract meaningful archaeological information from large datasets, including LiDAR imagery and excavation reports. His fieldwork spans Central Europe and Latin America, with a focus on prehistoric landscapes and cultural heritage. The analysis of his recent publications reveals a strong trend toward automated detection using deep learning (e.g., R-CNN, WODAN), named entity recognition in archaeological texts (e.g., ArcheoBERTje), and citizen science integration for data validation. His work bridges archaeology with computer science, geomatics, and environmental science, emphasizing interdisciplinary collaboration and methodological rigor. His scientific awards include: Best Thesis Award (University of Zurich, 2005) EUROPA NOSTRA Award (2020, 2022) Membership in the German Archaeological Institute (since 2022) Lambers actively supervises students and leads major research projects such as ABMA, EXALT, and Heritage Quest. He has secured substantial research funding and collaborates widely with computer scientists, geophysicists, and palaeoecologists. His teaching includes digital methods, modeling, and simulation, often linked to ongoing research. He has also contributed to open educational resources and digital textbooks in archaeology. He leads or participates in several research labs and teams, including the Digital Archaeology Research Group (which he chairs), the Heritage Quest citizen science project, and interdisciplinary teams focusing on alpine terraces and Iraqi prospection. His work emphasizes the integration of digital tools into practical archaeological workflows, advocating for complementary human-computer strategies.

Zongyi Liمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Scientific Computing
• Partial Differential Equations
• +۵ مورد دیگر

Zongyi Li is a Research Fellow at Massachusetts Institute of Technology , hosted by Kaiming He. They are currently pursuing a Ph.D. in Computing and Mathematical Sciences at Caltech (2019-2025), mentored by Anima Anandkumar and Andrew Stuart. Ph.D. candidate: Computing and Mathematical Sciences, Caltech (2019-2025) B.Sc. in Computer Science and Mathematics with a Jazz minor from Washington University in St. Louis (2015-2019) They focus on Neural Operators for learning solution operators in Partial Differential Equations (PDEs) , particularly in fluid mechanics and earth science . Their work models physical simulations with chaotic behaviors and complex geometries, showing applications in weather forecasting , carbon storage , and aerodynamics simulation . Publications emphasize resolution-invariant models , chaotic systems , and zero-shot super-resolution capabilities. Their research combines Fourier analysis , graph networks , and physics-informed loss functions to achieve state-of-the-art performance in PDE solving with up to 1000x speedup over traditional solvers. Fellowships: Kortschak Scholarship PIMCO Fellowship Amazon AI4Science Fellowship Nvidia Fellowship MIT Novo Nordisk AI Fellowship Code & Open-Source: Co-developer of the NeuralOperator library Implementations for Fourier Neural Operators , Graph Neural Operators , and Tensorized Neural Operators Media Recognition: Quanta Magazine MIT Tech Review NVIDIA Features Towards Data Science

Danica Kragicمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Danica Kragic is a Professor of Computer Science at the School of Electrical Engineering and Computer Science at the Royal Institute of Technology (KTH) in Stockholm, Sweden. She serves as the Director of the Centre for Autonomous Systems and leads the Robotics, Perception and Learning Lab at KTH. Her research focuses on advancing robotics capabilities through computer vision and machine learning approaches. MSc in Mechanical Engineering from the Technical University of Rijeka, Croatia (1995) PhD in Computer Science from KTH (2001) Professor Kragic's research primarily centers on robotics, computer vision, and machine learning, with particular emphasis on robotic manipulation, grasp planning, and human-robot interaction. Her work bridges theoretical foundations with practical applications, exploring how robots can understand and interact with objects in complex environments. She investigates how visual and tactile sensing can be integrated to improve robotic perception and manipulation capabilities, with applications ranging from industrial automation to assistive robotics. Her recent publications demonstrate a strong focus on advanced grasp planning techniques, tactile sensing for manipulation, and mathematical representations for robotic control. Kragic's research shows increasing integration of machine learning approaches with traditional robotics frameworks, particularly in the areas of grasp synthesis, object recognition, and human-robot collaboration. Her work spans theoretical contributions in mathematical representations of grasps to practical implementations of robotic systems capable of adapting to novel objects and situations. 2007 IEEE Robotics and Automation Society Early Academic Career Award IEEE Fellow ERC Starting Grant (2012) Member of The Royal Swedish Academy of Sciences Member of The Royal Swedish Academy of Engineering Sciences Honorary Doctorate from Lappeenranta University of Technology Professor Kragic's research has been supported by major funding bodies including the EU, Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research, and Swedish Research Council. While specific student names aren't listed in the provided information, her publication record suggests extensive mentorship of PhD students and postdoctoral researchers in robotics and computer vision. Her lab, the Robotics, Perception and Learning Lab, serves as a hub for interdisciplinary research connecting computer science, engineering, and cognitive science perspectives on robotic systems. As Director of the Centre for Autonomous Systems at KTH, Kragic oversees a major research initiative focused on advancing autonomous technologies. Her Robotics, Perception and Learning Lab brings together researchers working on visual perception, machine learning, and robotic manipulation, with particular emphasis on developing systems that can understand and interact with objects in unstructured environments. The lab's work spans theoretical foundations of robotic manipulation to practical implementations of systems capable of learning from experience.

• Power Electronics
• Renewable Energy Systems
• Electrical Machine Drives
• +۳ مورد دیگر

Dr. Xinan Zhang is an Associate Professor in the School of Engineering at The University of Western Australia (UWA), specializing in Electrical, Electronic, and Computer Engineering. He holds a BEng from Fudan University (2008) and a PhD from Nanyang Technological University (2014). Before joining UWA in 2019, he held roles as a Lecturer and Research Fellow in Singapore and Australia. His research focuses on power electronics, electrical machine drives, and renewable energy, with over 60 top-tier publications. He is the Portfolio Lead for Industry Engagement in UWA's School of Engineering and co-leads the Power and Clean Energy (PACE) research group. Education: BEng in Electrical Engineering, Fudan University (2004–2008) PhD in Electrical Engineering, Nanyang Technological University (2010–2014) Research Interests: Dr. Zhang’s work spans power electronics, renewable energy systems, energy storage, and smart grid technologies. He emphasizes practical applications, such as battery management systems for vanadium redox flow batteries and adaptive control strategies for microgrids. His contributions address challenges in energy efficiency, grid stability, and sustainable power solutions. Articles & Trends: Recent publications focus on advanced control algorithms for inverters, battery modeling, and renewable energy integration. His work combines data-driven methods with traditional control theory to enhance system efficiency and reliability. Notable areas include DC microgrid control, vanadium redox flow battery optimization, and model predictive control for power electronics. Awards: Listed in Stanford University’s Top 2% Scientists (2020–2022) Grants & Collaborations: He leads or co-leads projects funded by the Australian government and industry partners, including the GenX Betavoltaic Battery Pilot Manufacturing Process and Mine Electrification . These projects aim to advance clean energy technologies and industrial applications. Labs & Teams: As co-lead of the PACE group, he fosters interdisciplinary collaboration to tackle global energy challenges, aligning with UN Sustainable Development Goals for affordable and clean energy (SDG 7).

Alessandro Golkarمشاهده پروفایل

استاد

• Space Systems Engineering
• CubeSat and Nanosatellite Technology
• Satellite Constellations
• +۵ مورد دیگر

Prof. Alessandro Golkar is a Professor at the Technical University of Munich (TUM), leading the Chair of Picosatellites, Nanosatellites, and Satellite Constellations. He joined TUM in September 2022 and previously served as one of the founding faculty members at Skoltech, a Moscow-based graduate research university. His research focuses on advanced space mission concepts, systems engineering for picosatellites, and federated satellite systems. Prior to academia, he held roles at Airbus CTO, contributing to technology roadmapping and planning. His academic background includes expertise in aerospace engineering, systems design, and agile development methodologies for space hardware. Key research areas include CubeSat constellations, distributed satellite systems, and the integration of AI tools like Large Language Models (LLMs) into spacecraft design processes. He has pioneered projects such as the FSSCat mission, winner of the ESA Sentinel Small Satellite Challenge, and has explored applications of additive manufacturing for lunar missions. Prof. Golkar’s awards include the 2021 Karman Fellowship and IEEE Senior Membership (2018). His recent work emphasizes optimizing satellite networks, digital twin implementation, and orbital maneuvering for collision avoidance. He actively contributes to technology roadmapping, focusing on future human landing systems and lunar infrastructure development. Education: Ph.D. in Aerospace Engineering (details not explicitly stated). Grants & Funding: Extensive grants for CubeSat projects, federated systems research, and space technology innovation. Labs/Teams: Leads the Chair’s research group at TUM and collaborates with industry partners like Airbus on advanced mission concepts. His publications span over two decades, addressing topics like constellation design, machine learning in space, and agile processes for hardware development. He advocates for hybrid agile methodologies to bridge traditional systems engineering and modern product development.

Amir Ali Ahmadiمشاهده پروفایل

استاد

• Optimization Theory
• Control Theory
• Dynamical Systems
• +۷ مورد دیگر

Amir Ali Ahmadi is a Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE), with affiliations across multiple disciplines including PACM, Computer Science, Mechanical & Aerospace Engineering, Electrical Engineering, and the Center for Statistics and Machine Learning. He serves as Director of Princeton's Optimization and Quantitative Decision Science Certificate Program and has taken temporary roles at Citadel GQS (2021-2022) and Google Brain (2020-2021). His research bridges optimization theory , dynamical systems , and control theory , focusing on scalable algorithms for complex problems in robotics, autonomous systems, and machine learning. He has pioneered DSOS/SDSOS relaxations as alternatives to traditional sum-of-squares methods, enabling faster solutions through linear/second-order cone programming. Recent publications explore: Higher-order Newton methods for socially responsible investment Data-efficient learning of dynamical systems Computational complexity of local minima Robust-to-dynamics optimization frameworks Award highlights include: 2024 Egon Balas Prize in Optimization 2024 Princeton Engineering Council Teaching Award 2023 Distinguished Teaching Award (Princeton SEAS) 2019 NSF CAREER Award 2017 DARPA Young Faculty Award 2017 Sloan Fellowship in Computer Science He advises prominent researchers like Georgina Hall (Tucker Prize finalist) and Bachir El Khadir (Goldstine Fellow), and leads the Princeton Optimization Seminar and MURI project on Control-Oriented Learning on the Fly.

Heather Batteyمشاهده پروفایل

استاد

• Statistical Theory
• High-Dimensional Inference
• Sparsity in Statistics
• +۳ مورد دیگر

Heather Battey is a Professor in the Department of Mathematics at Imperial College London's Faculty of Natural Sciences. Her work bridges foundational statistical theory with practical scientific applications, focusing on parametrization effects, sparsity, and high-dimensional inference. Education PhD, University of Cambridge (2008-2011) Research Interests Battey's research examines how model structure and parametrization influence inferential procedures, particularly in high-dimensional settings. She investigates the equivalence between sparsity and reparametrization, and challenges traditional Fisherian statistical abstractions through modern practices. Her publications reveal a pattern of innovation in high-dimensional regression, covariance matrix analysis, and statistical methodology for complex data. Collaborations span disciplines including machine learning, economics, and biomedical research. Scientific Awards Fellow of the Institute of Mathematical Statistics (2023) EPSRC Early Career Research Fellowship (2020-2026) EPSRC Postdoctoral Research Fellowship (2017-2020) Advising and Grants Battey supervises PhD students Charlotte Edgar, Jakub Rybak, and Rebecca Lewis, with informal guidance to Henrique Hoeltgebaum. Over 15 pre-doctoral researchers have been mentored in topics ranging from support vector machines to spatial point processes. Current funding includes an EPSRC grant for theoretical foundations of inference with nuisance parameters and prior support for covariance matrix inference.

Joydeep Biswasمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Long-term autonomy
• +۵ مورد دیگر

Joydeep Biswas is an Associate Professor in the Computer Science Department at the University of Texas at Austin, where he serves as the Director of the Autonomous Mobile Robotics Laboratory (AMRL). He is also affiliated with Texas Robotics, the UT Machine Learning Laboratory, and UT Good Systems. Previously, he was an Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. Dr. Biswas earned his PhD in Robotics from Carnegie Mellon University in 2014 and his B.Tech in Engineering Physics from the Indian Institute of Technology Bombay in 2008. His educational background has provided him with a strong foundation in both theoretical and applied aspects of robotics and artificial intelligence. Dr. Biswas's research focuses on enabling long-term autonomy for mobile robots operating in human environments. His work spans robot perception, motion planning, control systems, and AI, with the ultimate goal of creating self-sufficient autonomous mobile robots that can perform tasks accurately and robustly in real-world settings. He is particularly interested in perception, planning, and failure recovery for autonomous mobile robots, which supports his vision of having autonomous service mobile robots deployed at campus-to-city scale, both indoors and outdoors, performing assistive tasks over deployments spanning years. His IJCAI 2019 Early Career Spotlight talk summarizes much of his research to date and ongoing interests. His recent research has shown a strong trend toward social navigation, human-robot interaction, and the application of machine learning techniques to robotics problems. There's a clear progression from fundamental robotics research toward more complex, real-world applications that require robots to understand and navigate human social spaces effectively. His work increasingly integrates large language models and other advanced AI techniques with traditional robotics approaches, as evidenced by his recent publications on topics like preference-conditioned navigation, social navigation benchmarks, and instruction-following navigation systems. Dr. Biswas has received numerous prestigious awards including the NSF CAREER Award (2021), J.P. Morgan Faculty Research Award (2019), Amazon Research Award (2019), and a grant from Northrop Grumman Mission Systems (2018). These awards recognize his innovative contributions to the field of robotics and autonomous systems. As a dedicated educator and mentor, Dr. Biswas actively supervises PhD and master's students, with his PhD student Sadegh Rabiee winning the student poster award at the Northrop Grumman University Symposium 2019. He has secured significant grant funding from the National Science Foundation for projects including 'Introspective Perception and Planning for Long-Term Autonomy' and 'Interactive Synthesis and Repair For Robot Programs,' demonstrating his ability to secure competitive research funding and his commitment to advancing the field. Dr. Biswas leads the Autonomous Mobile Robotics Laboratory (AMRL), which serves as a hub for interdisciplinary research in mobile robotics. The lab has developed notable resources such as the UT Campus Object Dataset (CODA) for 3D perception research and SOCIALGYM, a framework for benchmarking social robot navigation. His team regularly deploys robots on the UT Austin campus and in urban environments to test and refine their approaches in realistic settings, bridging the gap between simulation and real-world application.

Veronika Eyringمشاهده پروفایل

استاد

• Climate science
• Climate modelling
• Machine learning for climate models
• +۷ مورد دیگر

Veronika Eyring serves as Head of the Earth System Model Evaluation and Analysis Department at the German Aerospace Center (DLR) Institute of Atmospheric Physics and Professor of Climate Modelling at the University of Bremen. She holds dual appointments at these leading institutions, directing cutting-edge research at the intersection of climate science and artificial intelligence. Education: 2008: Habilitation in Environmental Physics at the University of Bremen 1999: PhD in Physics from the University of Bremen 1994: Diploma in Physics from the University of Erlangen Professor Eyring's research program focuses on improving climate models and projections through innovative integration of machine learning techniques and spaceborne Earth observations. Her work spans process-oriented modeling, development of observationally-based performance metrics, and understanding systematic biases in climate models. She has pioneered approaches to weighting model projections based on their performance using machine learning, significantly advancing the field of climate model evaluation. Her research has critical applications across multiple sectors including aeronautics, space research, transportation, and energy systems. Analysis of her recent publications reveals a clear trajectory toward deeper integration of machine learning with traditional climate modeling approaches. Her work has increasingly focused on developing community tools like the Earth System Model Evaluation Tool (ESMValTool) and leading major international initiatives such as the USMILE project (Understanding and Modelling the Earth System with Machine Learning). The publications span climate science, machine learning, Earth system modeling, and remote sensing, with specific emphasis on climate model evaluation, parameterization techniques, and improved climate projections. Scientific Awards: AGU Ambassador Award (2024) TUM Distinguished Affiliated Professor (2024) Gottfried Wilhelm Leibniz Prize (2021) ERC Synergy Grant (2019) Thomson Reuters Highly Cited Researcher (2016-2021) Top female researchers award, Helmholtz-Society (2015) Professor Eyring actively supervises a large research group comprising PhD students working on ML-based sea ice parameterizations, causal model evaluation for air-sea interactions, and machine learning-based detection of droughts in climate projections. She leads the prestigious ERC Synergy Grant USMILE and secured significant funding through the DFG Gottfried Wilhelm Leibniz Prize. Her research group at DLR includes multiple postdocs, research scientists, and software engineers working collaboratively on climate informatics projects. Professor Eyring leads the Earth System Model Evaluation and Analysis Department at DLR, which encompasses research groups focused on CMIP model evaluation, ESMValTool development, and machine learning applications in climate science. She founded and supervises the 'Climate Informatics' Group at the DLR Institute for Data Science in Jena. Her department maintains strong international collaborations, particularly with the National Center for Atmospheric Research (NCAR) in Boulder, Colorado, where she serves as an Affiliate Scientist.

Charles Rahalمشاهده پروفایل

دانشیار

• Social Science Methodology
• Applied Social Data Science
• High-dimensional Econometrics
• +۸ مورد دیگر

Charles Rahal is an Associate Professor in Data Science and Informatics at the University of Oxford, with additional affiliations as an Associate Member of Nuffield College and Researcher at the Gradel Institute, New College. He serves as a Co-Investigator at the ESRC Centre for Care and sits on the Steering Group of Reproducible Research Oxford. His academic career includes previous roles as a Senior Departmental Research Lecturer at the Leverhulme Centre for Demographic Science and a British Academy Postdoctoral Fellow. Dr. Rahal completed his PhD in 2016 and has established himself as a prominent social science methodologist and applied social data scientist with expertise in high-dimensional econometrics. His research spans multiple domains, focusing particularly on unique Big Data origination processes and their relationship to social inequality, mobility, and stratification. He is deeply engaged in machine learning methods, civic technology, spatial and time series econometrics, model uncertainty, and scientometrics. His recent publications reveal a strong trend toward computational social science, with significant contributions to understanding prediction limits, pandemic impacts, healthcare systems, and environmental sustainability. The articles demonstrate his interdisciplinary approach, bridging traditional social science with cutting-edge computational methods, particularly in the analysis of large-scale datasets and development of novel metrics like the InterModel Vigorish for model comparison. Dr. Rahal is actively involved in teaching and mentoring, co-convening courses in Demographic Analysis, Life Course Research, and the Oxford Partner site of the Summer Institute in Computational Social Sciences. He has developed workshops on machine learning, command line interfaces, and LaTeX, reflecting his commitment to methodological training in social science. He leads the Metrics and Models lab and maintains several open-source projects including the GWAS Diversity Monitor and RobustiPy. His editorial roles include Associate Editor-in-Chief at the Journal of Social Computing and Associate Editor at ACM Transactions on Social Computing, highlighting his influence in shaping computational social science methodology.

• Recommender Systems
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Julian McAuley is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego's Jacobs School of Engineering. His research spans recommender systems, machine learning, natural language processing, music information retrieval, and multimodal learning. He maintains an active research group with numerous PhD students and postdocs working on cutting-edge AI problems. His research interests focus on developing advanced algorithms for personalized recommendation systems, with particular emphasis on sequential recommendation, multimodal learning, and integrating large language models with traditional recommendation approaches. His work bridges the gap between theoretical machine learning and practical applications across multiple domains including e-commerce, music, and healthcare. McAuley has published extensively in top-tier conferences including NeurIPS, ICML, KDD, SIGIR, and ACL, with his most recent work exploring the intersection of large language models and recommendation systems. His publications reveal a strong trend toward multimodal approaches that combine text, vision, and audio for more comprehensive understanding and recommendation. He has received significant research funding from major technology companies including Google, Amazon, Facebook, Adobe, and Samsung, as well as government agencies like the National Science Foundation and Department of Defense. His work has practical applications across multiple industries, with a focus on improving user experience through better personalization. McAuley advises numerous PhD students who have gone on to successful careers at leading technology companies and academic institutions. His former students include Wang-Cheng Kang and Jianmo Ni at Google DeepMind, Chris Donahue and Zachary Lipton as assistant professors at CMU, and Ruining He at Google Deepmind.