جست‌وجوی استاد و پژوهشگر

Zijian Shaoمشاهده پروفایل

پژوهشگر ارشد

• Electromagnetics
• Antenna Design
• Radio Frequency Engineering
• +۵ مورد دیگر

Zijian Shao is a Postdoctoral Research Associate at Princeton University's School of Engineering and Applied Science, affiliated with the Department of Electrical Engineering. His work focuses on advanced antenna design, electromagnetic modeling, and machine learning applications in RF/mmWave systems for 5G/6G telecommunications. Advisor: Kaushik Sengupta Email: zs9193@princeton.edu Office: Engineering Quadrangle Atrium Shao's research explores the intersection of machine learning and electromagnetic design , particularly for next-generation wireless communication. He specializes in antenna miniaturization , MIMO decoupling , and metasurface-enabled beamforming , with applications in sub-terahertz circuits and integrated sensing systems. His recent publications demonstrate expertise in deep learning-assisted inverse design of multi-port RF systems and spoof surface plasmon polariton-based antenna optimization . While no formal awards are listed, his work contributes to advancing compact, high-efficiency antenna arrays for 5G/6G networks.

• Digestive physiology
• Wildlife biology
• Allometry
• +۳ مورد دیگر

Marcus Clauss is a Research Professor at the University of Zurich, leading the Clinic for Zoo Animals, Exotic Pets, and Wildlife. He holds a Dr. med. vet. habil (Habilitation) from the University of Munich and certifications from the European College of Veterinary and Comparative Nutrition and the Bayerische Tierärztekammer. His expertise spans digestive physiology, wildlife biology, and allometry, with a focus on zoo animal nutrition, wild herbivore ecology, and zoogeochemical processes. Education: Dr. med. vet. habil (Habilitation), University of Munich (2007) Diplomate of the European College of Veterinary and Comparative Nutrition (2006) Certified Veterinary Specialist in Animal Nutrition and Dietetics (2005) Doctor med. vet., University of Munich (2000) MSc Wild Animal Health, Zoological Society of London (1998) Veterinarian, University of Munich (1997) Research Interests: Clauss' work integrates physiology, ecology, and conservation. Key areas include: Comparative digestive physiology across vertebrates Nutritional adaptations in zoo and free-ranging herbivores Zoogeochemical studies of mineral retention and excretion Ethology and behavioral management of captive animals Evolutionary links between anatomy and environmental adaptation Articles Trends: Recent publications (2023-2025) emphasize zoo animal nutrition, digestive physiology of exotic species, and conservation-relevant studies like rhinoceros health monitoring. He frequently collaborates on interdisciplinary projects combining physiology, ecology, and veterinary science. Labs/Teams: Leads research groups at the University of Zurich focusing on zoo animal health, wildlife nutrition, and evolutionary physiology. Collaborates internationally with institutions like the Leibniz Institute for Zoo and Wildlife Research and the Zoological Society of London.

Lu Shijianمشاهده پروفایل

دانشیار

• Computer Vision
• Deep Learning
• Image and Video Analytics
• +۱۴ مورد دیگر

Lu Shijian is an Associate Professor (tenured) at the School of Computer Science and Engineering , Nanyang Technological University (NTU) , Singapore. He holds a PhD in Electrical and Computer Engineering from the National University of Singapore and leads the Visual Intelligence Lab (VILab) , focusing on humanlike visual perception, understanding, and creation. University: Nanyang Technological University School: School of Computer Science and Engineering Academic Rank: Associate Professor (tenured) Email: Shijian.Lu@ntu.edu.sg Office: N4-02C-101, NTU, Singapore His research spans computer vision, deep learning, image and video analytics, visual intelligence, and machine learning , with key topics including scene text detection, unsupervised domain adaptation, image synthesis, satellite image analytics, and facial expression recognition. His work integrates supervised, semi-supervised, and self-supervised learning across 2D images, 3D point clouds, and multi-spectral data. The recent publications highlight a strong trend in domain adaptation, generative modeling, 3D vision, and multimodal AI . His lab produces high-impact work accepted at top venues like CVPR, ICCV, ECCV, NeurIPS, and TPAMI, with applications in autonomous systems, image editing, and robust AI. Top winner of ICFHR2014 Competition on Word Recognition from Historical Documents Top winner of ICDAR 2013 Robust Reading Competition (scene text segmentation) Top winner of ICDAR 2013 Document Image Binarization Contest (DIBCO 2013) Top winner of H-DIBCO 2010 – Handwritten Document Image Binarization Competition Top winner of ICDAR 2009 Document Image Binarization Contest (DIBCO 2009) Lu advises several PhD students and serves as an Associate Editor for Pattern Recognition and Neurocomputing . He has held leadership roles in top conferences as Senior Program Committee member (IJCAI, AAAI) and Area Chair (ICDAR, WACV). His lab, the Visual Intelligence Lab , is actively recruiting PhD students and conducting cutting-edge research in visual intelligence, with recent work on 3D Gaussian splatting, backdoor attacks, and vision-language models.

• Databases
• Programming Languages
• Compilers
• +۷ مورد دیگر

Amir Shaikhha is an Associate Professor (Reader) in the School of Informatics at the University of Edinburgh. He was previously an Assistant Professor (Lecturer) at the same institution from 2020 to 2024 and a Departmental Lecturer at the University of Oxford until August 2020. He is also a Junior Research Fellow at University College, Oxford. His academic journey began with a Ph.D. from EPFL in 2018, where he was awarded the Google Ph.D. Fellowship in structured data analysis and a Ph.D. thesis distinction. His research centers on the design and implementation of data-analytics systems, drawing upon techniques from databases, programming languages, compilers, and machine learning. He develops high-performance systems such as SDQL.py, StructTensor, and VecHT, focusing on the compilation of data science workloads and optimization of tensor operations. His work bridges the gap between high-level abstractions and efficient execution, particularly in sparse and probabilistic computing domains. The recent publications highlight a strong trend in compiler-driven optimizations for data-intensive applications, including automatic differentiation, loop fusion, probabilistic programming, and domain-specific language (DSL) restaging. His research integrates machine learning for systems decisions and emphasizes reproducibility and performance. He has published consistently in top venues like PLDI, OOPSLA, SIGMOD, and CGO, reflecting sustained impact in programming languages and database systems. Dahl-Nygaard Junior Prize, 2025 Google Research Scholar Award, 2025 Most Influential Paper Award, GPCE 2024 Best Paper Award, GPCE 2017 Most Reproducible Paper Award, SIGMOD 2017 Google Ph.D. Fellowship, 2017 Amir Shaikhha has advised PhD students including Hesam Shahrokhi and has been nominated for Best Supervisor of the Year at the University of Edinburgh. He leads research projects that have received recognition and support through awards and grants, including the Google Research Scholar Award. He actively serves the community through program committees (e.g., GPCE, DBPL, DRAGSTERS), editorial roles, and peer review for premier journals. His leadership in organizing workshops and conferences underscores his role as a central figure in the programming languages and databases research communities. He leads a research group focused on compiler and database systems, with recent open-source releases such as StructTensor and VecHT. His team collaborates with researchers from institutions like MIT, EPFL, and TU Berlin, and he co-chairs workshops like Sparse@PLDI and DRAGSTERS. His lab emphasizes innovation in how data-intensive programs are compiled and executed efficiently across modern hardware.

• Computer Vision
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at University of Maryland, College Park, with a joint appointment in the Institute of Advanced Computer Studies (UMIACS). Previously, he served as an Assistant Professor at the same institution from August 2018 to June 2024, and spent one year as a Visiting Research Scientist at Google Research from September 2017 to August 2018. His educational background includes: PhD in Robotics and Artificial Intelligence from Carnegie Mellon University (2017), advised by Abhinav Gupta, with thesis titled 'Discovering and Leveraging Visual Structure for Large-scale Recognition' MS in Artificial Intelligence from Carnegie Mellon University (2011), supervised by Alyosha Efros and Martial Hebert BTech in Computer Science and Engineering from Jaypee Institute of Information Technology (2010) Professor Shrivastava's research focuses on computer vision and machine learning, with particular expertise in object detection, image recognition, and neural representations. His work bridges theoretical advances with practical applications, exploring how visual systems can discover and leverage structure in large-scale recognition problems. He has made significant contributions to understanding the role of supervision in vision transformers, developing novel approaches for object-state composition recognition, and creating efficient neural representations for videos and 3D scenes. His research often addresses fundamental challenges in visual recognition, including handling novelty in open-world environments and improving the efficiency of visual systems. An analysis of his recent publications reveals a strong emphasis on neural representations, particularly for dynamic content like videos and 3D scenes. His work demonstrates increasing sophistication in handling open-world vision problems, with research spanning object discovery, localization, and representation learning. The publications show a clear progression toward more efficient and scalable models, with recent work focusing on model compression, sparse representations, and addressing the challenges of working with limited annotations. His scientific contributions have been recognized with several prestigious awards: Best Paper Award (Applications) at IEEE Winter Conference on Applications of Computer Vision (2020) Microsoft Research PhD Fellowship (2014-2016) Best Student Paper Award at IEEE Winter Conference on Applications of Computer Vision (2014) Outstanding Reviewer Award at IEEE CVPR (2015) Professor Shrivastava has successfully mentored numerous graduate students, many of whom have become prominent researchers in computer vision. His Amazon Research Awards (2020 and 2023) have supported innovative projects including 'The pursuit of knowledge: discovering and localizing new concepts using dual memory' and 'Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos.' He has served as Area Chair for major conferences including ICCV, CVPR, and AAAI, demonstrating his leadership in the computer vision community. His research has attracted significant funding from both academic and industry sources, supporting his exploration of fundamental questions in visual recognition and representation learning.

• Ultrafast Laser Physics
• Optics and Photonics
• Laser-Matter Interaction in Extreme Conditions
• +۷ مورد دیگر

Dr. Ludovic Rapp is a Senior Research Fellow at the Research School of Physics , Australian National University (ANU) , and leads the High-Power Laser group at the Laser Physics Centre (LPC) . His expertise spans ultrafast laser interaction with matter , beam shaping , and laser-induced microexplosions for synthesizing super-dense material phases , including novel silicon allotropes. He is also the Laser Safety Officer for the Research School of Physics.

Aliaksandr Bandarenkaمشاهده پروفایل

استاد

• Electrocatalysis
• Energy Conversion
• Energy Storage
• +۵ مورد دیگر

Prof. Dr. Aliaksandr Bandarenka is a Professor at the Technical University of Munich (TUM) in the TUM School of Natural Sciences , leading the Assistant Professorship of Physics of Energy Conversion and Storage . His research focuses on electrochemical surface science and energy materials development. Education: PhD in Chemistry from Belarusian State University (2005) Key Collaborations: Ruhr University Bochum, University of Twente, Technical University of Denmark Prof. Bandarenka's research explores: Design of electrocatalytic materials via bottom-up approaches Characterization of electrified interfaces Development of sustainable energy conversion/storage systems Surface structure-activity relationships in catalysis Recent article trends (2024) include: ORR electrocatalyst optimization using ZIF-8 templating Advanced impedance spectroscopy for battery/electrolyzer diagnostics Mesoporous oxide materials for energy applications Surface structure effects on double layer capacitance Scientific Recognition: Ernst Haage-Prize (2016) Hans-Jürgen Engell Award (2013) He teaches graduate courses on: Electrified interfaces Energy materials science Electrocatalysis fundamentals Hands-on experiments in battery technology

Raymond J. Mooneyمشاهده پروفایل

استاد

• Artificial Intelligence
• Bioinformatics and Computational Biology
• Machine Learning
• +۱۴ مورد دیگر

Raymond J. Mooney is a Professor in the Department of Computer Science at the University of Texas at Austin, where he has been a faculty member since 1987. He is the Director of the UT Artificial Intelligence Laboratory and affiliated with multiple research groups including the Machine Learning Research Group, UT Computational Linguistics Lab, and the UT Center for Computational Biology and Bioinformatics. He holds a B.S., M.S., and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign, where his thesis was supervised by Gerald DeJong. His research spans diverse areas in artificial intelligence, machine learning, and natural language processing: Natural Language Learning Connecting Language and Perception Statistical Relational Learning Information Extraction Transfer and Active Learning Abductive Reasoning Text Mining and Clustering Recommender Systems Knowledge-Base Refinement Recent publications highlight trends in grounded language processing, human-robot interaction, and multimodal reasoning. He has been recognized with prestigious fellowships including ACL (2014), ACM (2010), and AAAI (2005). Scientific awards: Fellow of the Association for Computational Linguistics (2014) Fellow of the Association for Computing Machinery (2010) Fellow of the American Association for Artificial Intelligence (2005) Classic Paper Award (2019) Best Paper Awards (2007, 2004, 1996) He teaches graduate courses like CS 371R: Information Retrieval and Web Search (Fall 2025) and CS 395T: Grounded Natural Language Processing (Spring 2025). His research labs include: UT Artificial Intelligence Laboratory Machine Learning Research Group UT Computational Linguistics Lab UT Center for Computational Biology and Bioinformatics

Gedas Bertasiusمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Video Understanding
• +۵ مورد دیگر

Gedas Bertasius is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. Previously, he served as a postdoctoral researcher at Meta AI (Facebook AI) and earned his PhD in Computer Science from the University of Pennsylvania. His academic journey began with a bachelor’s degree in Computer Science from Dartmouth College. Dr. Bertasius specializes in computer vision and machine learning with specific interests in: Video understanding First-person vision (egocentric vision) Human behavior modeling Multimodal deep learning Transfer learning Computer vision for sports analytics Video+robotics integration His research produces practical frameworks like Video ReCap for hierarchical captioning of long videos, SiLVR for language-based video reasoning, and BASKET for fine-grained skill estimation. He focuses on developing models that can process videos across multiple temporal granularities while maintaining computational efficiency. Key research themes in his work include: Recursive video processing architectures Space-time attention mechanisms Generative video modeling LLM integration with vision systems 3D-aware representation learning Continual learning for video QA He has received notable recognition, including: CVPR 2024 Egocentric Vision (EgoVis) Distinguished Paper Award CVPR 2020 Best Paper Award Nomination First Place at CVPR 2025 Multi-Discipline Lecture Understanding Workshop Dr. Bertasius collaborates with prominent researchers like Mohit Bansal and Lorenzo Torresani . His recent publications demonstrate expertise in advancing video-language models, with applications in semantic alignment, temporal grounding, and cross-modal reasoning. For detailed information about his research, publications, and ongoing projects, please visit his official website .

Riccardo Raheliمشاهده پروفایل

استاد

• Telecommunications
• Digital Signal Processing
• Wireless Communications
• +۵ مورد دیگر

Riccardo Raheli is a Full Professor at the University of Parma , Department of Engineering and Architecture, with a career spanning over three decades in Information and Communication Technologies (ICT). He has served as Chair of the Councils for Telecommunications and Communication Engineering programs, and as representative of the University of Parma in CNIT and its Members' Assembly. Education: Laurea in Electronic Engineering (University of Pisa, 1983), M.Sc. in Electrical and Computer Engineering (University of Massachusetts, 1986), Postgraduate Diploma (Scuola Superiore Sant'Anna, 1987) Key Roles: President of Degree Councils (2002-2018), CNIT Committee Member (2000-2005), Editorial Board member for IEEE Transactions, Springer and MDPI journals His research bridges telecommunications , digital signal processing , and healthcare applications , producing extensive international publications and industrial patents. He has co-authored monographs including Detection Algorithms for Wireless Communications (Wiley, 2004) and LDPC Coded Modulations (Springer, 2009). Recent article trends show interdisciplinary work in automotive stress monitoring (IoT/Matlab-based systems), video processing for healthcare (neonatal seizures, respiratory monitoring), and acoustic field control (microphone virtualization, personal sound zones). His work spans machine learning applications in automotive systems, stochastic acoustic modeling , and power-line communications . Scientific Leadership : Co-Chair for IEEE conferences (ICC 2010, GLOBECOM 2011, ISPLC 2020) Editorial roles in 7+ international journals Grants & Collaborations : Led industrial patents in communications systems Coordinated CNIT Technical Reports series (2025) He teaches Wireless Communications and Digital Signals Laboratory , emphasizing Matlab/Simulink proficiency. His laboratory sessions focus on practical implementation of signal processing algorithms, requiring full software installation on personal devices.

Lan Guanمشاهده پروفایل

استاد

• Membrane transport
• Membrane proteins
• Solute transport mechanisms
• +۶ مورد دیگر

Lan Guan is a Professor at Texas Tech University Health Sciences Center in the Department of Cell Physiology and Molecular Biophysics within the School of Medicine. He also serves as Co-Director of the Center for Membrane Protein Research. His research focuses on membrane proteins, which constitute approximately 30% of all eukaryotic proteins and play crucial roles in many aspects of cell function. Dr. Guan's research seeks to understand the mechanisms of solute transport and lay the foundation for advances in disease treatment and human health. He employs an integrated approach including cryo-EM single-particle analysis, X-ray crystallography, ligand binding, molecular dynamics simulations, thermodynamics, genetic engineering, novel amphiphiles, and many other biochemical & biophysical analyses. His current research focuses on cation-coupled bacterial and human transporters. Dr. Guan is currently supported by an NIGMS MIRA R35 Award (2024). His publication record demonstrates expertise in membrane protein structure and function, particularly with melibiose transporters (MelB) and their mechanisms. His work spans structural biology, biochemistry, and biophysics, with significant contributions to understanding membrane transport mechanisms. His research has led to important insights into membrane protein structure-function relationships, particularly in sugar transporters. Dr. Guan's work has implications for understanding fundamental biological processes and potential therapeutic applications related to membrane transport. NIGMS MIRA R35 Award 2024 Dr. Guan actively collaborates with researchers across disciplines and institutions, as evidenced by his extensive publication record with numerous co-authors. His work bridges structural biology, biochemistry, and biophysics to advance our understanding of membrane protein function. He is affiliated with the Center for Membrane Protein Research, where he contributes to advancing methodologies for studying these challenging but critically important biological molecules. His work on novel amphiphiles and detergent design has helped overcome technical barriers in membrane protein research.

John Harry Evansمشاهده پروفایل

استاد

• Economic Accounting
• Incentive and Reporting Models
• Managerial and Tax Reporting
• +۲ مورد دیگر

John Harry Evans is the III Katz Alumni Professor of Accounting at the Department of Accounting, Katz School of Business, University of Pittsburgh. His research focuses on economic perspectives of accounting phenomena, analytical models of incentives, experimental studies in managerial and tax reporting, and empirical investigations of healthcare incentives. He teaches courses in Financial Accounting, Financial Statement Analysis, Managerial Accounting, and Strategic Cost Management. Research spans incentive design, corporate governance, healthcare accounting, and empirical methodologies. Recent publications address CEO turnover, physician compensation, tax reporting ethics, and organizational incentive structures. Awarded multiple honors including the Outstanding Management Accounting Paper Award (2012), Provost Award for Excellence in Mentoring (2011), and multiple Best Paper Awards across journals. Active in professional service as a financial consultant to the Department of Defense and expert witness in legal cases. Designed financial analysis training programs for healthcare managers at University of Pittsburgh Physicians and Highmark Insurance Company.

Yair Weissمشاهده پروفایل

استاد

• Human and machine vision
• Machine Learning
• Bayesian methods
• +۳ مورد دیگر

Prof. Yair Weiss is a faculty member at the School of Computer Science and Engineering, The Hebrew University of Jerusalem . He holds a PhD in Brain and Cognitive Sciences from MIT and an MSC in Applied Mathematics from Tel-Aviv University. Education: MSc in Applied Mathematics, Tel-Aviv University (1993) PhD in Brain and Cognitive Sciences, MIT (1998) His research focuses on Human and Machine Vision , Machine Learning , Bayesian Methods , and Neural Computation . Recent work explores adversarial examples, generative models, and robustness in neural networks. Recent publications highlight trends in: Understanding neural network representations Advancements in GANs and adversarial training Image restoration and translation techniques Perceptual distance modeling Bayesian approaches to computer vision Mathematical analysis of deep learning architectures

• Polymer Chemistry
• Nanomedicine
• Sustainable Materials
• +۷ مورد دیگر

Dr. Friederike Adams is an Independent Research Group Leader at the University of Stuttgart and University of Tübingen, focusing on Precision Polymers for Pharmaceutics . Her work bridges polymer chemistry and nanomedicine, emphasizing sustainable materials for drug delivery systems. Education: PhD in Chemistry (2019, TU Munich), M.Sc. in Chemistry (2015, TU Munich), B.Sc. in Chemistry (2013, TU Munich) Awards: No explicit awards listed Research: Specializes in living-type polymerizations, catalyst design, and post-polymerization functionalization for drug and RNA delivery . Publications: 15+ works on sustainable polyesters, metal-catalyzed polymerization, and nanocarrier systems. Students: Mentors 10+ PhD, master’s, and bachelor’s students, including Lea-Sophie Hornberger and Philipp Weingarten . Collaboration: Joint research group with the Schnichels Lab at the Eye Hospital Tübingen. Funded by BMBF and Baden-Württemberg Ministry of Science under Germany’s Excellence Strategy.

• Carbohydrate Chemistry
• Chemical Glycobiology
• Glycomedicine
• +۱۱ مورد دیگر

Dr. Martin Anthony Fascione is a Reader in Chemistry at the University of York, where he leads the Fascione Lab within the Department of Chemistry. His research focuses on the interface between chemistry and biology, particularly in the field of chemical glycobiology. He has established himself as a leading researcher in carbohydrate chemistry with expertise in synthetic methods and biological applications. Dr. Fascione received his Ph.D. from the University of Leeds in 2009 under the supervision of W. Bruce Turnbull, followed by a Marie Curie International Outgoing Fellowship at the University of British Columbia with Prof. Steve Withers and the University of York with Prof. Gideon Davies. Since August 2014, he has been at the York Structural Biology Laboratory, progressing from Lecturer to his current position as Reader. His research centers on complex sugars (glycans) and their roles in biological processes and disease, with particular emphasis on sialic acid-like molecules critical for bacterial pathogens. The Fascione group develops chemical tools to study and perturb glycan activity in vivo using synthetic and enzymatic carbohydrate chemistry, organocatalysis, enzymology, and molecular biology. Key research areas include pseudaminic acid biosynthesis, protein bioconjugation, and glycoconjugate development for therapeutic applications. Analysis of Dr. Fascione's recent publications reveals a strong focus on bacterial glycans, particularly pseudaminic acid, and their role in pathogenesis. His work combines synthetic chemistry with biological applications, developing novel methods for protein modification, carbohydrate synthesis, and glycan-based therapeutics. Major themes include bioorthogonal chemistry, enzymatic synthesis of complex carbohydrates, and the development of glycoconjugates for therapeutic and diagnostic applications. Dr. Fascione has received significant recognition for his work, including: Marie Curie International Outgoing Fellowship (2012-2014) ERC Consolidator Grant (2022) As an active researcher, Dr. Fascione supervises PhD students and collaborates extensively with researchers in the UK and internationally. His laboratory is involved in multiple research projects focused on chemical glycobiology for infectious disease research and therapeutic development. The Fascione Lab has established itself as a leading center for research at the chemistry-biology interface, with particular expertise in carbohydrate-active enzymes and glycan-based tools for biological investigation.