جست‌وجوی استاد و پژوهشگر

Rafael Gómez-Bombarelliمشاهده پروفایل

دانشیار

• Materials Science
• Energy Sustainability
• Healthcare Materials
• +۴ مورد دیگر

Rafael Gómez-Bombarelli is the Paul M. Cook Career Development Professor and Associate Professor of Materials Science and Engineering at MIT. He leads the Learning Matter Lab, focusing on computational materials design. His research integrates atomistic simulations with machine learning to accelerate the discovery of materials for energy, sustainability, and healthcare. Key areas include zeolite synthesis, photoswitchable drugs, and polymer electrolytes. Education: BSc, MSc, and PhD in Chemistry from the University of Salamanca (Spain). Postdoctoral research at Heriot-Watt University (Scotland), Harvard University (Aspuru-Guzik Lab), and industry experience at Kyulux (Japan). Research interests span materials design rules linking atomic-scale properties to macroscopic performance. Recent breakthroughs include custom-designed zeolites for decarbonization and machine learning approaches for photoswitchable drugs. Awards include the Sloan Research Fellowship (2023) and Dreyfus Award (2021). He co-founded Calculario, a materials discovery startup applying quantum chemistry and ML. Active in bridging academia and industry, his work has been featured in MIT Technology Review and Wall Street Journal. Grants and collaborations include Google Faculty Award funding. His lab emphasizes interdisciplinary approaches combining physics-based models with AI, addressing challenges in energy storage, catalysis, and sustainable polymers.

Natarajan Prabhuمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Natarajan Prabhu is a Senior Lecturer on the Educator Track at the Department of Computer Science, National University of Singapore (NUS), Faculty of Computing. He also serves as the Director of the Center for Computing for Social Good and Philanthropy, highlighting his leadership in technology for societal impact. Education: Ph.D. in Computer Science, National University of Singapore M.E. in Computer Science (Gold Medalist, University First Rank) B.Tech. in Information Technology His research focuses on Artificial Intelligence, Machine Learning, Computer Vision, and Multimedia Systems , particularly in multi-camera surveillance, fairness in observation, decision-theoretic coordination, and active perception . His work bridges intelligent systems with ethical considerations in automated surveillance. He teaches foundational courses such as CS2100 (Computer Organisation), CS2103/CS2103T (Software Engineering), and IT1244 (Artificial Intelligence: Technology and Impact). The analysis of his publications reveals a strong thematic focus between 2008 and 2015 on intelligent, decision-theoretic approaches to multi-camera surveillance, emphasizing fairness, scalability, and real-time coordination. His early work on whiteboard documentation also reflects interest in practical, human-centered computing applications. Scientific Awards: Annual Teaching Excellence Award (ATEA) - 2023 Annual Teaching Excellence Award (ATEA) - 2024 Faculty Teaching Excellence Award (FTEA) - 2023 Faculty Teaching Excellence Award (FTEA) - 2024 Natarajan Prabhu is a dedicated educator and researcher with significant contributions to teaching excellence and ethically informed AI systems. His leadership in the Center for Computing for Social Good indicates active engagement in grants and initiatives promoting technology for philanthropy. While specific grants are not listed, his directorship implies oversight of funded projects and research teams. He has mentored research at the doctoral level, as seen in his PhD forum publications. He leads the Center for Computing for Social Good and Philanthropy , which likely involves interdisciplinary teams working on AI for social impact, ethical computing, and community-driven technology solutions.

Bibek Upadhayayمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Cybersecurity
• Deep Learning
• +۳ مورد دیگر

Bibek Upadhayay is an Adjunct Professor in the Department of Electrical & Computer Engineering and Computer Science at the Tagliatela College of Engineering, University of New Haven. His research focuses on AI security, adversarial machine learning, and cybersecurity applications across multiple domains including natural language processing, deep learning models, and social network analysis. He has contributed to developing methods for combating adversarial attacks on large language models (LLMs), enhancing cross-lingual capabilities, and improving detection mechanisms for fake news and human trafficking via automated data fusion. Upadhayay's work spans theoretical advancements like 'Tongue-Tied' attacks on LLM safety and practical applications such as the 'X-Guard' multilingual content moderation system. His recent studies (2023-2025) emphasize vulnerabilities in AI systems through working memory exploitation, multi-language adaptive attacks, and adversarial stimuli targeting brain-computer interfaces. Earlier projects include hybrid deep learning models for fake news detection and OSINT-based approaches against human trafficking. His publications reflect a consistent emphasis on ethical AI applications, cybersecurity innovations, and leveraging machine learning for societal challenges. While no formal student advisees are listed, his collaborative research includes student contributions evident in conference abstracts like the 2022 fake news detection work.

Boyuan Chenمشاهده پروفایل

استادیار

• Robotics
• Perception
• Machine Learning
• +۳ مورد دیگر

Boyuan Chen is an Assistant Professor at Duke University, holding concurrent appointments in the Department of Mechanical Engineering & Materials Science, Department of Electrical and Computer Engineering, and Department of Computer Science within the Pratt School of Engineering. He leads the General Robotics Lab, focusing on advancing embodied intelligence through a full-stack approach encompassing both mechanical design and AI algorithms. His work emphasizes multimodal perception, adaptive systems, and human-AI collaboration. Chen completed his B.S. at Jilin University (2016), M.S. (2018), and Ph.D. (2022) at Columbia University. His research spans robotics, machine learning, and dynamical systems, with notable contributions to self-aware robots, automated scientific discovery, and human-robot interaction. He has been recognized with the ASME Watch List (2025) for pioneering full-stack robotics innovations. Chen teaches extensively across engineering disciplines, including courses in robotics, AI, and mechanical engineering. His lab’s research integrates perception, adaptation, and collaboration to create machines that 'sense, adapt, and connect,' inspired by biological systems. Notable projects include human-shaped agents, self-modeling robots, and AI-driven materials science applications. Chen actively engages in interdisciplinary work, collaborating with academia and industry to bridge robotics and AI with healthcare, materials science, and disaster response. His work frequently appears in top journals/conferences, emphasizing scalable multi-agent systems and embodied AI.

Chiang-Ching Huangمشاهده پروفایل

استاد

• Biostatistics
• Genomics
• Bioinformatics
• +۳ مورد دیگر

Chiang-Ching Huang is a Professor in the Department of Biostatistics at the University of Wisconsin-Milwaukee, College of Public Health . With a PhD in Biostatistics from the University of Michigan and a Master of Science in Statistics from the University of Iowa, he specializes in genomic data analysis, biomarker discovery, and precision medicine applications in cancer and cardiovascular diseases. PhD, Biostatistics, University of Michigan, Ann Arbor MS, Statistics, University of Iowa, Iowa City BS, Applied Mathematics, National Chiao-Tung University, Hsinchu, Taiwan His research focuses on integrating genomic-scale data with phenotypic information to predict disease susceptibility, treatment response, and survival outcomes in cancers like prostate cancer and paediatric brain tumors. He develops advanced analytical techniques for liquid biopsy applications, including cell-free DNA copy number variation analysis and digital PCR-based prognostic models. Recent publications highlight his work on epigenetic modifications in circulating DNA, machine learning approaches to genome-phenome mapping, and multi-gene risk scores for metastatic castration-resistant prostate cancer. His team also explores inflammatory pathways in autoimmune diseases like juvenile dermatomyositis through RNA-Seq and miRNA profiling.

Andrea Kaimمشاهده پروفایل

پژوهشگر

• Mathematical (spatial) optimization
• Landscape ecology
• Ecosystem services
• +۵ مورد دیگر

Dr. Andrea Kaim is a leading researcher at the Helmholtz Centre for Environmental Research (UFZ) , serving as Deputy Head of Department in the Department of Landscape Ecology since 2025 and Head of Junior Research Group AgriScape since 2022. She works at the intersection of mathematical optimization , landscape ecology , and environmental economics , focusing on resolving ecosystem service trade-offs in agricultural landscapes. PhD in Environmental Sciences (2015-2022, UFZ/uni-leipzig) Current habilitation project at Martin-Luther-Universität Halle-Wittenberg Initiator of the AgriScape and SUSALPS projects Her research integrates agent-based modeling with spatial conservation planning to achieve multifunctional agricultural landscapes . She has pioneered methods for combining stakeholder preferences with biophysical optimization through projects like TALE and AgriScape. Andrea actively contributes to environmental policy briefs and public science communication through platforms like Knowledge Before 8 TV segments. Recent publications show a strong focus on grassland management in Germany and the Alps, with particular attention to nitrogen application modeling and recreation-ecosystem service synergies . She develops open-source CoMSES model libraries for transparent ecological modeling and participates in international sustainability networks.

Wei Zhangمشاهده پروفایل

پژوهشگر ارشد

• Ecosystem Service Management
• Biodiversity Conservation
• Natural Resource Governance
• +۷ مورد دیگر

Wei Zhang serves as a Senior Research Fellow and Research Lead on Ecosystem Health and Biodiversity in the Environment and Production Technology Division at the International Food Policy Research Institute (IFPRI). She co-leads the CGIAR Research Initiative on Low-Emission Food Systems (Mitigate+) and leads the Work Package on Living Labs for People (2022-2024). Dr. Zhang's educational background includes a PhD in Agricultural, Food, and Resource Economics from Michigan State University with specialization in Environmental and Resource Economics, an MA in Community Development and Applied Economics from the University of Vermont, and a BA in International Economics from Renmin University of China. Prior to joining IFPRI in 2008, she worked as an Economist in the Environment Department at the World Bank on payment for environmental services. Her research focuses on the intersection between agriculture, development, nature/environment, and climate change. Key interests include the economics and behavior of ecosystem service management and biodiversity conservation, natural resource governance, nature-based/nature-positive solutions, participatory action research, and behavioral experiments for experiential learning, with gender and climate change as cross-cutting issues. She has extensive experience leading multidisciplinary research projects in Africa, South Asia, East and Southeastern Asia, and Latin America. Analysis of her recent publications reveals a strong emphasis on practical applications of ecosystem services research, particularly in developing countries. Her work frequently combines field experiments with policy-relevant analysis, focusing on solutions that balance agricultural productivity with environmental sustainability. The research demonstrates particular strength in using behavioral economics approaches to address natural resource management challenges. NASEM ad-hoc study committee appointment on the status of insects in North America (2025) Dr. Zhang has made significant contributions to capacity strengthening of national partners and has contributed to global research initiatives including TEEB for Agriculture and Food, and working groups such as SESYNC and SNAPP. Her work often involves collaborative research across disciplines and institutions to address complex challenges at the agriculture-environment nexus. She leads several initiatives using experiential learning games as tools for triggering collective change in natural resource management, with applications across multiple continents. Her research on groundwater governance, payment for ecosystem services, and climate-resilient agriculture demonstrates practical applications of her theoretical work.

Käthe Dahlströmمشاهده پروفایل

مدرس ارشد

• Structural Bioinformatics
• Protein Structure-Function Relationship
• Notch Signaling Pathway
• +۵ مورد دیگر

Käthe Dahlström is a Senior Lecturer in the Department of Biochemistry and Cell Biology at Åbo Akademi University's Faculty of Natural Sciences and Engineering. She is affiliated with the InFLAMES flagship (Innovation Ecosystem based on the Immune System) focusing on solutions for health. Her academic work centers on structural biology and bioinformatics approaches to understanding protein structure-function relationships. Dr. Dahlström's research primarily focuses on structural bioinformatics , with particular emphasis on protein 3D structure determination using both computational modeling and crystallographic methods. Her work explores the structural basis for ligand specificities, especially for proteins relevant to human health and disease. Key research areas include Notch signaling pathway components, copper-containing amine oxidases, and molecular recognition mechanisms. Analysis of her publication history shows a consistent research trajectory in structural biology with applications in cancer biology, enzymology, and drug design. Her recent work has increasingly incorporated machine learning approaches for protein structure prediction, reflecting current trends in computational structural biology. She has established productive collaborations, particularly with researcher T.A. Salminen, resulting in multiple high-impact publications. Dr. Dahlström is an active member of the Structural Bioinformatics Laboratory (SBL) where she contributes to advancing methodologies for protein structure determination and analysis. Her research has practical implications for understanding disease mechanisms and developing targeted therapeutic approaches.

• Natural Language Processing
• Computational Linguistics
• Large Language Models
• +۷ مورد دیگر

Yulan He is an active researcher in Natural Language Processing and Computational Linguistics with numerous publications in top-tier conferences including ACL, EMNLP, and COLING from 2023-2025. Their work spans both theoretical advancements in Large Language Model architectures and practical applications in healthcare, social media analysis, and information retrieval. Research interests focus on Large Language Model optimization , including improving faithfulness in rationale generation, enhancing reasoning capabilities, personalizing outputs to user preferences, and optimizing computational efficiency. Significant contributions include frameworks for debiasing opinion summarization, improving depression detection in clinical interviews, and developing methods for Theory-of-Mind reasoning in LLMs. Their work addresses critical challenges in LLM reliability, interpretability, and efficiency. Analysis of recent publications reveals consistent focus on bridging the gap between theoretical LLM capabilities and practical applications , with particular attention to healthcare contexts, social media analysis, and complex reasoning tasks. Their research demonstrates how to make LLMs more reliable, efficient, and aligned with human needs across diverse domains. Scientific contributions include: Novel frameworks for LLM faithfulness and reasoning (Drift, EnigmaToM) Efficient inference methods (SCOPE, PECAN) Bias mitigation techniques (LASS, Rehearse With User) Personalization approaches (PROPER) Healthcare applications (Explainable Depression Detection) As evidenced by senior authorship positions across numerous publications, Yulan He leads research projects and likely supervises graduate students in NLP research. Their work demonstrates strong technical expertise combined with practical problem-solving approaches to real-world NLP challenges.

Vitaly Belikمشاهده پروفایل

استاد

• Deep learning
• Digital epidemiology
• Complex networks
• +۷ مورد دیگر

Prof. Dr. rer. nat. Vitaly Belik is a faculty member at Freie Universität Berlin's Department of Veterinary Medicine , where he leads the Systems Modeling Working Group at the Institute of Veterinary Epidemiology and Biometry. His research spans deep learning , digital epidemiology , complex networks , and biostatistics with applications in veterinary public health and zoonotic disease modeling. His work focuses on: Deep learning for animal behavior recognition and digital pathology Complex network analysis of disease spread and internet media Human-animal mobility patterns in disease transmission Microbiome data analysis and causal inference Project collaborations include: BUA Joint Partnership Funding (2024) for Oder River disaster early warning BMBF funding for KI-based feedback systems in education DAAD partnerships with Australian institutions Current affiliations and activities: Principal investigator in Systems Modeling Working Group Teaching in Master of Science in Statistics program Co-supervisor for multiple PhD candidates Active in international collaborations (Australia, Switzerland, Poland)

Sreyasee Das Bhattacharjeeمشاهده پروفایل

استادیار

• Multimodal Data Analytics
• Affective Computing
• Generative AI
• +۱۰ مورد دیگر

Sreyasee Das Bhattacharjee is an Assistant Professor of Teaching in the Department of Computer Science and Engineering at the University at Buffalo (SUNY Buffalo), within the School of Engineering and Applied Sciences. She leads research in multimodal data analytics, affective computing, and generative AI, with extensive collaborations across institutions like NASA, JPL, Roswell Park, and UCSF. She is actively involved in academic leadership as the MS-AI Program Director at the Institute of AI and Data Science UB and serves on the Faculty Senate. Education: Ph.D., Indian Institute of Technology, Madras M.Tech., Indian Institute of Technology, Delhi M.Sc. in Mathematics, Chennai Mathematical Institute, India Her research focuses on developing AI systems for human-centered applications, particularly in emotion recognition, educational data mining, and immersive technologies. She investigates multimodal generative models, human-machine collaboration, and AI-powered interactive agents, often leveraging wearable sensors and large-scale datasets. Her work bridges computer vision, machine learning, and real-world healthcare and educational challenges. The most recent publications highlight a consistent trajectory in multimodal AI, with emphasis on robust emotion recognition under incomplete data, generative modeling for affective states, and applications in healthcare and education. Key themes include diffusion models, knowledge distillation, explainable AI, and personalized interactive systems, reflecting a strong interdisciplinary approach combining computer science with psychology and medicine. Scientific Awards and Honors: Best Paper Award, IEEE Big Data 2019 and 2017 IBM PhD Fellowship Award 2008 Australian Endeavour Research Fellowship 2007 Fellow, New Faculty Academy, UB 2021 NSF Travel Award 2025 National Scholarship, Govt of India 2000 Dr. Das Bhattacharjee has secured significant external funding, including a $1M NSF grant for a STEM program for refugee and underprivileged students, and leads projects on AI for elder care and plant health monitoring. She mentors a diverse group of graduate and undergraduate students, many of whom have gone on to top institutions and companies like Amazon and CMU. She also serves as Associate Editor for JMIR mHealth and Frontiers in AI, and has held leadership roles in major conferences as Area Chair and Meta Reviewer. She leads the Visual Computing Lab at UB, where her team develops cutting-edge AI systems for multimodal understanding, affective computing, and educational technology. The lab fosters interdisciplinary collaboration and innovation, with ongoing projects in BCI-AI integration, immersive agents in VR, and biosignal-based emotion modeling.

Gabriel W. Hasslerمشاهده پروفایل

استاد

• Big Data
• Biology and Life Sciences
• Coronavirus Disease 2019 (COVID-19)
• +۷ مورد دیگر

Gabriel W. Hassler is an Associate Statistician at the RAND Corporation and a Professor of Policy Analysis at the RAND School of Public Policy. His research focuses on computational Bayesian statistics, scalable statistical models for big data, and their applications in public health and policy. Key areas include viral genetics and clinical outcomes in HIV/AIDS, SARS-CoV-2/COVID-19 evolution, and biological systems analysis. He holds a B.A. in Anthropology and Biology from Washington University in St. Louis and a Ph.D. in Biomathematics from UCLA. His work bridges statistical methodology with real-world challenges, emphasizing scalable solutions for complex datasets. Recent research includes suicide prevention strategies, public health emergency communication systems, and entrepreneurship trends in digital economies. He explores age-specific suicidal ideation in youth welfare systems and evaluates the reach of national emergency alerts. His interdisciplinary approach addresses societal issues through data-driven policy analysis. Notable contributions include modeling misinformation spread via social networks and analyzing challenges faced by Black and female e-commerce entrepreneurs. His work underscores the intersection of statistical innovation, public health, and equitable policy design.

Narcis Fernandez Fuentesمشاهده پروفایل

دانشیار

• Bioinformatics
• Structural Bioinformatics
• Protein Design
• +۵ مورد دیگر

Narcis Fernandez-Fuentes is an Associate Professor at the Institute of Biological, Environmental & Rural Sciences (IBERS) at Aberystwyth University. He was previously a Lecturer at the Leeds Institute of Molecular Medicine (2007-2011) and a Research Associate at Albert Einstein College of Medicine (2004-2007). He holds an M.Sc. in Biotechnology and a Ph.D. in Computational Biology from Universitat Autonoma de Barcelona. His work spans Bioinformatics, Structural Bioinformatics, and Plant Bioinformatics, focusing on protein structure prediction, biomolecular interactions, and computational approaches to plant breeding. Protein design and structure-to-function relationships Peptide-based modulation of protein interactions Genomic analysis of abiotic stress responses in plants SNPs modeling and functional impact assessment Recent articles highlight expertise in computational peptide design for SARS-CoV-2, ulvan lyase characterization, and structural modeling of inflammasomes and protein families. His grants include projects funded by the Welsh Government, BBSRC, and Covestro. Marie Curie Training Fellowship EMBO and FEBS Short Term Fellowships Boehringer Ingelheim Fonds Fellowship

• Stochastic Control
• Probabilistic Modeling
• Decentralized Control Systems
• +۷ مورد دیگر

Dr. Yuyang Zhou is a Lecturer in the School of Computing, Engineering and the Built Environment at Edinburgh Napier University, where he contributes to the Centre for Artificial Intelligence and Robotics. His research focuses on advanced control systems, probabilistic modeling, and intelligent robotics, with active involvement in EU-funded projects such as iMARS (2024–2028). Institution: Edinburgh Napier University School: School of Computing Engineering and the Built Environment Research Centre: Centre for Artificial Intelligence and Robotics Role: Lecturer Email: Y.Zhou@napier.ac.uk Dr. Zhou's research centers on stochastic and nonlinear control systems, with a strong emphasis on decentralized and probabilistic frameworks. His work integrates machine learning, entropy-based monitoring, and message-passing algorithms to enhance system performance under uncertainty. Key domains include smart grids, industrial prediction systems, and robotics. The recent publications highlight a consistent trend in developing innovative control strategies for complex stochastic systems. These include event-triggered relearning, variance and entropy assignment, and fully probabilistic design methods. The research spans applications in industrial automation, energy systems, and future grid technologies, demonstrating a strong interdisciplinary approach combining AI, control theory, and IoT. Dr. Zhou supervises postgraduate research students, including Anna Chernikova (Hybrid and Switched Control Systems) and Fatima Mumtaz (AI-driven smart banking). He collaborates extensively with Prof. Randa Herzallah and is a co-investigator on the European Commission-funded iMARS project, which aims to develop intelligent multi-agent robotic systems for first responders using AI, IoT, and 5G technologies.

Katharina Lückerathمشاهده پروفایل

استاد

• Nuclear Medicine
• Theranostics
• Radioligand Therapy
• +۱۳ مورد دیگر

Prof. Dr. Katharina Lückerath is a leading academic in nuclear medicine and serves as the Head of Preclinical Theranostics at the Clinic for Nuclear Medicine, University Hospital Essen, affiliated with the Faculty of Medicine at the University of Duisburg-Essen. Her research is centered on advancing theranostic approaches in oncology, integrating diagnostic imaging with targeted radionuclide therapies to improve cancer treatment outcomes. Her research interests span nuclear medicine, theranostics, radioligand therapy, molecular imaging, tumor biology, and resistance mechanisms in cancer . She investigates how tumor heterogeneity, metabolism, and microenvironmental factors influence treatment response, aiming to develop more effective and personalized therapies. Her work includes preclinical evaluation of PSMA- and FAPI-targeted agents, combination strategies, and the use of PET/CT and SPECT/CT for biomarker discovery. The recent publications reflect a strong focus on translational oncology , with studies on FAPI- and PSMA-based imaging and therapy, mechanisms of resistance, and novel pretargeting strategies. Her team utilizes advanced preclinical models and imaging technologies to validate new radioligands and optimize dosing and delivery. Collaborative work with international experts underscores the impact of her research in shaping clinical theranostic practices. Prof. Lückerath leads a multidisciplinary research group comprising PhD students, biologists, and radiopharmaceutical scientists, fostering innovation in experimental radiochemistry and preclinical imaging. Her team has access to state-of-the-art facilities including Molecubes PET/SPECT/CT systems and autoradiography tools, enabling high-throughput and high-resolution studies.