جست‌وجوی استاد و پژوهشگر

• Empirical Corporate Finance
• Banking Regulation
• Climate Finance
• +۳ مورد دیگر

Professor Duc Duy (Louis) Nguyen is Professor in Finance at Durham University Business School. Previously, he was Associate Professor at King's College London and Assistant Professor at University of St Andrews. His research has been featured in Forbes, Harvard Business Review, and BBC. He holds a BSc in Computing from National University of Singapore, MSc in Accounting, Finance, and Management from University of Bristol, and PhD in Finance from University of Edinburgh. Research foci include: Climate risk integration in mortgage markets and corporate finance Impact of social policies (e.g., marriage equality) on credit access Corporate governance and misconduct prevention in banking Local information environments and fraud detection Cross-cultural dimensions of executive decision-making His publication portfolio demonstrates consistent examination of how institutional frameworks (regulation, culture, governance) shape financial behaviors and market outcomes, with recent emphasis on climate finance and social equity impacts. Awards include: David Hume Publication Prize (2015) Semi-finalist, FMA Europe Best Paper Award (2018) Finalist, FMA Asia/Pacific Best Paper Award (2019) Best Registered Report on "Politics and Corporate Power" Vietnam Symposium in Climate Transition Best Paper Award (2024) He currently supervises PhD candidates Bingzhi Zhang and Jing Wei. His externally funded research includes grants from British Academy/Leverhulme Trust and Carnegie UK Trust. He serves as Associate Editor for European Journal of Finance and British Accounting Review. As frequent speaker at central banks and policy forums (Federal Reserve Banks of New York & St. Louis), he translates research insights into regulatory practice and policy development.

Levent Burak Karaمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Computational Design
• +۱۲ مورد دیگر

Levent Burak Kara is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in the Robotics Institute. He is a leading researcher in AI-driven computational design, additive manufacturing, and intelligent engineering systems, leading the Visual Design and Engineering Lab (VDEL) at CMU. Education: B.S., Mechanical Engineering, Middle East Technical University (1998) M.S., Mechanical Engineering, Carnegie Mellon University (2000) Ph.D., Mechanical Engineering, Carnegie Mellon University (2005) His research focuses on integrating machine learning, optimization, and geometric modeling to revolutionize engineering design and manufacturing. Key areas include topology optimization, CAD intelligence, digital twins, generative design, bioengineering, and electronic design automation. His work enables automation of traditionally labor-intensive design processes using deep learning and reinforcement learning. His recent publications reveal a strong trend toward physics-informed surrogate modeling, real-time simulation, manufacturability prediction, and AI-driven automation in mechanical, biomedical, and electronic systems. These works frequently appear in top journals such as Journal of Mechanical Design and Journal of Applied Mechanics , and at premier conferences like NeurIPS and DAC. Scientific Awards: National Science Foundation CAREER Award ASME Design Automation Society Young Investigator Award Google AI for Social Good Impact Scholar Kara advises several Ph.D. students and has secured significant funding from federal agencies such as the NSF and the U.S. Army Research Laboratory, as well as collaborations with industrial leaders including Cadence Design Systems and NVIDIA. His research is also supported by CMU’s NextManufacturing Center and the Critical Technology Initiative. He is actively involved in developing intelligent design systems that leverage AI to automate product design, optimize manufacturing processes, and improve medical diagnostics, particularly in oral cancer screening and organ preservation. His lab, VDEL, is a hub for innovation in AI-enabled engineering.

Benyuan Liuمشاهده پروفایل

استاد

• Data and Computer Communication Networks
• Mobile and Wireless Networks
• Internet Technologies & Applications
• +۳ مورد دیگر

Benyuan Liu is a Professor at the Miner School of Computer and Information Sciences within the Kennedy College of Sciences at the University of Massachusetts Lowell . He serves as Director and Graduate Coordinator for Ph.D. programs, with expertise in Data and Computer Communication Networks, Mobile and Wireless Networks, and Internet Technologies & Applications. Education: B.S., University of Science and Technology of China M.S., Yale University Ph.D., University of Massachusetts Amherst His research focuses on Artificial Intelligence in Medical Imaging , Deep Learning for Endoscopy , and Edge Computing Systems . Recent work includes automated lesion detection, 3D reconstruction from sensor data, and predictive models for financial and reproductive health domains. The 15 most recent publications highlight applications of deep learning in medical diagnostics (thyroid nodules, gastric lesions, dental caries), computer vision (attention mechanisms, transformers), and financial technology (market psychology analysis). Technical themes include mmwave radar processing, diffusion models for synthetic data, and multi-scale feature extraction. Benyuan Liu leads the Computer Networking Lab and CHORDS initiative at UMass Center for Digital Health. His work bridges network optimization with healthcare AI , emphasizing real-time systems and portable diagnostics.

Vincent Fortuinمشاهده پروفایل

استادیار

• Bayesian Deep Learning
• Uncertainty Quantification
• Probabilistic Inference
• +۵ مورد دیگر

Dr. Vincent Fortuin is a tenure-track Assistant Professor at the Technical University of Munich (TUM) and a research group leader at Helmholtz AI in Munich. He leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group and holds multiple prestigious fellowships including the Branco Weiss Fellowship. His academic affiliations include the TUM School of Computation, Information and Technology, the Konrad Zuse School of Excellence in Reliable AI, and the Munich Center for Machine Learning. Dr. Fortuin earned his BSc in Molecular Life Sciences from the University of Hamburg (2012-2015), followed by an MSc in Computational Biology and Bioinformatics from ETH Zürich (2015-2017), where he received the ETH Excellence Scholarship and the Willi Studer Prize. He completed his PhD in Machine Learning at ETH Zürich (2017-2021) under the supervision of Gunnar Rätsch and Andreas Krause, supported by a Swiss Data Science Center PhD Fellowship. Prior to joining TUM, he was a Research Fellow at St. John's College, University of Cambridge (2022-2023). His research focuses on the intersection of Bayesian statistics and deep learning, specifically developing methods for more robust, data-efficient AI systems with reliable uncertainty estimates. His work addresses critical limitations in standard deep learning approaches, particularly their tendency to be overconfident in predictions and require large datasets for training. He investigates better priors and more efficient inference techniques for Bayesian deep learning, deep generative modeling, meta-learning, and PAC-Bayesian theory, with applications in scientific and biomedical domains. Dr. Fortuin's recent publications demonstrate a consistent focus on improving uncertainty quantification in deep learning systems, with increasing emphasis on practical applications in scientific contexts. His work spans from theoretical foundations of Bayesian deep learning to practical implementations in protein design, materials science, and medical applications. A notable trend is his exploration of how to make Bayesian methods more scalable and applicable to modern large-scale AI systems while maintaining theoretical guarantees. Branco Weiss Fellowship (2023) St John's College Research Fellowship (2022) Swiss National Science Foundation Postdoc.Mobility Fellowship (2022) Swiss Data Science Center PhD Fellowship (2018) ETH Excellence Scholarship (2015) Willi Studer Award (2018) Dr. Fortuin actively supervises PhD and Master's students through his ELPIS research group at Helmholtz AI. He serves as a regular reviewer and area chair for major machine learning conferences and is an action editor for TMLR. He co-organizes the Symposium on Advances in Approximate Bayesian Inference (AABI) and the ICBINB initiative, demonstrating his commitment to advancing the field through community building. His research group receives funding from multiple sources including Helmholtz AI, the Branco Weiss Fellowship, and collaborations with international institutions. Dr. Fortuin leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group at Helmholtz AI, which focuses on fundamental machine learning research motivated by real-world scientific problems. The group collaborates extensively with researchers across Helmholtz centers and international institutions, particularly in biomedical applications where reliable uncertainty estimates are crucial.

Karl Griswoldمشاهده پروفایل

استاد

• Protein engineering
• Directed evolution
• Biotherapeutics
• +۲ مورد دیگر

Karl Griswold is a Professor of Engineering at Dartmouth College's Thayer School of Engineering, where he leads the Griswold Research Group focused on protein engineering and biotherapeutics development. His interdisciplinary work spans chemical, biological, and engineering disciplines to address critical challenges in drug-resistant infections and protein therapeutics. Education: BS in Chemistry from Southwest Texas State University (1995) PhD in Chemistry from the University of Texas at Austin (2005) Research Focus: Professor Griswold's laboratory specializes in protein engineering, directed evolution, and biotherapeutics development , creating novel biomolecules with superior functionality compared to natural proteins. The group develops high-throughput screening methods, protein deimmunization strategies, enhanced expression systems, and antibacterial agents targeting drug-resistant infections. Their work has significant translational potential for treating conditions like MRSA and Pseudomonas aeruginosa infections. Scientific Recognition: Wallace H. Coulter Foundation Early Career Translational Research Award in Biomedical Engineering (2008) NIH Biotechnology Training Grant (2000-2003) Royston M. Roberts - Regents Fellowship, University of Texas (1999) DOW Chemical Foundation Scholar, Texas State University (1991-1995) Senior Fellow, National Academy of Inventors Research Leadership: As co-founder and CEO of Stealth Biologics, Professor Griswold translates academic research into commercial applications. His work has secured NIH funding and Dartmouth Innovations Accelerator support, with research featured in Chemical & Engineering News, Vermont Public Radio, and the New Hampshire Union Leader for developing alternatives to traditional antibiotics. Research Environment: The Griswold Research Group maintains extensive collaborations across disciplines including clinical medicine, immunology, structural biology, and chemical engineering. This interdisciplinary approach prepares trainees for careers at the intersection of multiple scientific fields, with research focusing on Biomolecular Antimicrobial Therapies, Deimmunizing Protein Therapeutics, and Gene Library Construction Technologies.

Ying Xuمشاهده پروفایل

استادیار

• Early Education
• Evidence-Based Intervention
• Families and Community
• +۵ مورد دیگر

Ying Xu is an Assistant Professor at the Harvard Graduate School of Education (HGSE), specializing in the design of AI technologies to support children's language, literacy, STEM learning, and well-being. Her research emphasizes creating AI systems that act as interactive learning companions and language partners for children while fostering human-AI collaboration with educators and families. She holds a Ph.D. in Language, Literacy, and Technology from the University of California, Irvine, and previously served as an Assistant Professor at the University of Michigan from 2022 to 2024. Her work focuses on understanding how AI can complement human interactions, particularly through conversational agents integrated into media like books and educational TV programs. Collaborations include partnerships with PBS KIDS, GBH Education, and Sesame Workshop. Funded by organizations such as the National Science Foundation and Schmidt Futures, her research bridges developmental psychology, education, and human-computer interaction. Xu's studies highlight AI's potential for personalized learning while addressing ethical considerations like AI literacy and social impact. Key achievements include numerous best paper awards and recognition as an Early Career Interdisciplinary Scholar by SRCD. Her interdisciplinary approach involves designing technologies that reflect community values and linguistic/cultural diversity, ensuring equitable access to AI-driven educational tools. Xu advocates for balanced AI integration in children's lives, emphasizing the need for transparency about AI's limitations and the importance of maintaining human connections. She explores how AI can empower stakeholders (e.g., educators, parents) to co-create technologies tailored to their needs, ensuring AI serves as a complement—not replacement—for human interaction. Current projects investigate conversational AI's design principles, children's perceptions of AI, and strategies for fostering critical evaluation of AI-generated content. She also examines how AI affects social interactions and developmental processes, advocating for ethical guidelines to maximize benefits while mitigating risks.

• Machine Learning
• Probabilistic Inference
• Decision Making
• +۱ مورد دیگر

Professor Carl Edward Rasmussen is affiliated with the University of Cambridge , where he focuses on Machine Learning , Probabilistic Inference , Decision Making , and Reasoning Under Uncertainty . His work bridges theoretical advancements with practical applications in robotics, control systems, and computational biology. Academic Affiliation: University of Cambridge Academic Role: Professor His research emphasizes scalable Gaussian process methods, Bayesian system identification, and reinforcement learning. Recent projects include transfer learning for antibacterial discovery , graph neural processes for molecular functions , and efficient variational inference techniques . Key themes in his publications highlight uncertainty quantification , model generalization , and nonparametric approaches . Notable Scientific Contributions Advancements in sparse Gaussian process hyperparameter estimation Framework for Bayesian system identification in dynamic systems Hybrid models combining transformers and Gaussian processes

• Documentary Film
• Feature Film
• Installation Art
• +۳ مورد دیگر

Katharina Knust is a filmmaker and artistic staff member at the Lucerne School of Design, Film and Art (HSLU) since 2021. She holds a background in media arts from Bauhaus-Universität Weimar and Concordia University Montreal. Her interdisciplinary work bridges documentary, feature film, and installation art, with notable projects like Letztes Jahr in Utopia (2018), awarded the Basler Filmpreis. Education: Bauhaus-Universität Weimar (Media Arts) Concordia University Montreal (Media Arts) Research focuses on interactive documentary and experimental filmmaking. Her work has screened at festivals like DOK Leipzig and CPH:DOX. She also serves as a guest lecturer in film at the University of Basel and collaborates in residencies/workshops. Notable Achievements: Basler Filmpreis (2018) World premiere at CPH:DOX (2018) Professional Roles: Production Manager for hybrid film-installation projects Assistant Director for boundary-crossing film projects

• Computer Vision
• Autonomous Driving
• Robotics
• +۷ مورد دیگر

Dominik Schörkhuber is a PreDoc Researcher at the Vienna University of Technology (TU Wien) in the Computer Vision department. With a background in Informatics (BSc, Dipl.-Ing.), he focuses on computer vision applications for autonomous driving, robotics, and human-machine interaction. His work spans driver action recognition, pedestrian prediction, and adaptive lighting systems. Current projects: Empathic Vehicle (2024–2026), SyntheticCabin (2021–2025), SmartProtect (2020–2025) Research themes: Video transformers, synthetic data transfer learning, multi-task learning, and sensor-lighting integration Specializes in 3D sensing, nighttime driving analysis, and mobile video creation tools

Xiaofeng Shaoمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Change-Point Analysis
• +۳ مورد دیگر

Xiaofeng Shao is a Professor of Statistics & Data Science at Washington University in St. Louis, with a joint appointment in the Department of Economics. He holds a PhD from the University of Chicago and previously served at the University of Illinois at Urbana-Champaign for 18 years. He is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association. His research focuses on econometrics, time series analysis, change-point detection, high-dimensional statistics, nonparametric methods, and functional data analysis. Recent work emphasizes object-valued time series modeling and machine learning applications in high-dimensional and imaging data. Notable contributions include the dependent wild bootstrap method and self-normalization techniques for time series inference. Key awards include Fellowships from leading statistical societies. His publications span over 20 years, addressing topics like change-point detection in climate projections, statistical methods for COVID-19 infection trends, and high-dimensional dependence testing.

• Wearable Sensors
• Smart Garments
• Sustainable Textiles
• +۵ مورد دیگر

Dr. Shirley Coyle is an Assistant Professor in the School of Electronic Engineering at Dublin City University (DCU) and Programme Chair for the BSc Global Challenges. She holds a BEng in Electronic Engineering from DCU (2000) and a PhD in Biomedical Engineering from NUI Maynooth (2005). Her career includes roles as a Telecoms Engineer at Siemens, Research Fellow at the National Centre for Sensor Research, and Team Leader of Wearable Sensors in the INSIGHT Centre for Data Analytics. She also studied part-time at the Grafton Academy for Fashion Design and later founded a consultancy in wearable technologies. Her research focuses on smart garments, wearable sensors, and sustainable textiles, with applications in healthcare, sports performance, and S.T.E.A.M. integration. Key interests include developing wearable chemical sensors, energy-autonomous sensing systems, and IoT-enabled rehabilitation devices. She has pioneered work on wearable sensors for monitoring chronic diseases, athlete training, and home rehabilitation using VR. Dr. Coyle’s work spans interdisciplinary collaboration, combining biomedical engineering with textile design. Her contributions include innovations in electrospun textiles, self-powered sensors, and sensor integration with microfluidics. She has held leadership roles in DCU’s Governing Authority and promotes STEM education through design-focused initiatives.

Jeff Searlمشاهده پروفایل

استاد

• Speech Production
• Head and Neck Cancer Rehabilitation
• Voice Assessment
• +۳ مورد دیگر

Jeff Searl is a Professor in the Department of Communicative Sciences and Disorders at Michigan State University (MSU), within the College of Communication Arts and Sciences. Previously, he held faculty roles at the University of Kansas Medical Center’s Hearing and Speech Department and the Otolaryngology – Head & Neck Surgery Department. He earned his Ph.D. from the University of Kansas. His research focuses on understanding how chemoradiation and surgery for head and neck cancers affect speech and voice production. The goal is to develop clinical practices that preserve or rehabilitate communication for patients post-treatment. His methods include physiological, acoustic, and auditory-perceptual measurements. Key emphases include optimizing communication while balancing speech intelligibility, physical/cognitive effort, and associated fatigue. Recent work addresses pandemic impacts on laryngectomy patients, clinician practices during crises, and vocal effort measurement across cultures. His lab investigates articulatory pressures, tracheoesophageal speech dynamics, and dysphagia treatment innovations. Though no specific awards are listed, his contributions to voice rehabilitation and clinical methodologies are significant. Dr. Searl advises on telemedicine applications for Parkinson’s voice therapy, group therapy feasibility, and alaryngeal communication modalities. His lab at MSU collaborates on interdisciplinary projects involving speech-language pathologists, engineers, and oncologists to advance communication restoration technologies.

Peter O'Brienمشاهده پروفایل

استاد

• Organic Synthesis
• Medicinal Chemistry
• Suzuki-Miyaura Coupling
• +۲ مورد دیگر

Peter O'Brien is Professor of Chemistry at the University of York, leading research in organic synthesis and medicinal chemistry. His group develops novel methods for stereospecific Negishi/Suzuki-Miyaura cross-coupling reactions and 3D fragment-based drug discovery. Collaborations with AstraZeneca, Diamond X-Chem, and Redbrick Molecular focus on COVID-19 therapeutics and building-block libraries. O'Brien employs high-throughput robotics for reaction optimization and mechanistic studies. His work bridges fundamental organolithium chemistry with applications in synthesizing bioactive heterocycles (piperidines, pyrrolidines). The York 3D Fragment Library, featuring shape-diverse compounds, identified hits against SARS-CoV-2 proteins. Awards include the RSC Stereochemistry Award (2013) and YUSU Teacher of the Year (2019). Selected honors: EPSRC/IAA grants for 3D building blocks commercialization Industrial Fellowships with AstraZeneca (2019-2022) Vice-Chancellor's Teaching Award (2015)

• Software Engineering
• Deep Learning
• Human-Computer Interaction
• +۷ مورد دیگر

Prof. Dr. Chunyang Chen is a full professor in the Department of Computer Science at the Technical University of Munich (TUM) in Heilbronn, Germany, and an adjunct professor at Monash University, Australia. He is a core member of the Munich Data Science Institute, board member of Heilbronn Data Science Center, and fellow at Fortiss. His research spans Software Engineering, Deep Learning, and Human-Computer Interaction (HCI), focusing on AI/ML, NLP, and program analysis applications. PhD from Nanyang Technological University (NTU), Singapore His research explores three primary domains: AI-Driven Mobile App Development : Focused on requirement elicitation, UI design, code generation, and GUI testing using LLMs, NLP, and lightweight program analysis. Software Repository Mining : Involves library/API migration, collaborative editing, query reformulation, and thesaurus construction. Deep Learning Security : Examines robustness and security in mobile apps, including on-device model attacks, backdoor attacks, phishing attacks, and defense mechanisms. His recent publications highlight trends in LLM applications for software testing, bug replay, accessibility, and dataset construction. Awards include Best Paper Honorable Mention at CHI 2024, DECRA, ACM SIGSOFT Early Career Award, multiple Distinguished Paper Awards, and the Chinese Government Award for Outstanding Self-financed Students Abroad. PhD students: Shengcheng Yu, Yanqi Su, Mark Huasong Meng, Wenchao Gu, Zhen Tao, Kai Huang, Sidong Feng, Yujin Huang, Jian Gu Master/Bachelor students: Dustin Minh Duc Vu, Jason Siu, Ruiqi Dong, Minh Nguyen, An-Chieh Chang, Chao Wu, Yujin Huang, Ishan Joshi, Xinyan Li Chunyang Chen has supervised numerous students across institutions including TUM, Monash University, Chinese Academy of Sciences, ANU, and others. His teaching portfolio includes courses on Generative AI, software engineering, and programming foundations. He actively contributes to academic service as a guest editor, program committee member, and reviewer for top-tier conferences and journals.

• Medical Image Analysis
• Machine Learning in Medical Imaging
• Neuroimaging
• +۵ مورد دیگر

Lawrence Staib is Professor of Radiology and Biomedical Imaging, Biomedical Engineering, and Electrical Engineering at Yale University. He serves as Director of Undergraduate Studies in Biomedical Engineering and is a member of Yale's Bioimaging Sciences division, Image Processing & Analysis Group, Yale Biomedical Imaging Institute, and Yale-BI Biomedical Data Science Fellowship program. Dr. Staib earned his A.B. in Physics from Cornell University (1982), followed by a Ph.D. in Engineering and Applied Science from Yale University (1990), and completed a postdoctoral fellowship at Yale School of Medicine (1991). His research focuses on developing advanced medical image analysis methods using machine learning and model-based approaches. Key research areas include neuroimaging applications for autism spectrum disorder classification, cardiac imaging analysis for strain and motion assessment, prostate cancer diagnosis and risk mapping, and innovative techniques for medical image segmentation with limited labeled data. Dr. Staib's work emphasizes uncertainty estimation in deep learning models, multi-modal image registration, and domain adaptation techniques to improve clinical decision support systems. His recent publications demonstrate a strong trend toward developing interpretable AI models for clinical applications, with particular emphasis on fMRI analysis for neurological conditions, cardiac motion analysis, and prostate cancer diagnosis. His work frequently addresses the challenge of limited labeled data in medical imaging through innovative self-supervised, semi-supervised, and few-shot learning approaches. Fellow of the American Institute for Medical and Biological Engineering (AIMBE) (2015) Distinguished Investigator Award from the Academy for Radiology & Biomedical Imaging Research (2017) MICCAI Fellow (2022) Medical Image Analysis Second Best MICCAI Paper Award (2005) ASNR Cum Laude Scientific Exhibit Award (2003) Dr. Staib serves on the editorial board of Medical Image Analysis and as Associate Editor of IEEE Transactions on Biomedical Engineering. His research is supported by NIH grants including the Autism Center of Excellence program. He leads the Image Processing & Analysis Group within Yale's Bioimaging Sciences division, collaborating extensively with James Duncan, John Onofrey, Xenophon Papademetris, and other Yale researchers on applications spanning neuroimaging, cardiology, and oncology. Current projects focus on developing robust AI models for clinical decision support with emphasis on uncertainty quantification and interpretability.