جست‌وجوی استاد و پژوهشگر

Andrew McCallumمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Natural Language Processing
• +۳ مورد دیگر

Andrew McCallum is a Distinguished Professor and Director of the Center for Data Science at the University of Massachusetts Amherst. He holds a PhD in Computer Science from the University of Rochester (1995) and a BS from Dartmouth College (1989). His research focuses on machine learning, natural language processing, and information extraction, with applications to scientific literature and knowledge base construction. He has pioneered work on conditional random fields and probabilistic databases, and led the development of systems like Rexa, an advanced research paper search engine. Affiliations: Center for Data Science, Center for Intelligent Information Retrieval, Computational Social Science Institute Key Projects: OpenReview.net, Unified Information Extraction, Automated Knowledge Base Construction His work emphasizes extracting actionable knowledge from unstructured text, with contributions to social network analysis, entity resolution, and semi-supervised learning. McCallum has over 300 publications and has received awards including the NSF ITR Grant, IBM Faculty Partnership Awards, and ACM/AAAI Fellowships. He has advised numerous students and served as ICML General Chair (2012). Recent Research Trends: Probabilistic box embeddings, case-based reasoning for knowledge bases, scalable clustering algorithms, and applications in biomedical informatics.

Stratis Ioannidisمشاهده پروفایل

استاد

• Distributed systems
• Networking
• Machine learning
• +۳ مورد دیگر

Stratis Ioannidis is a Professor in the Electrical and Computer Engineering Department at Northeastern University, with a courtesy appointment in the Khoury College of Computer Sciences. His research focuses on distributed systems, networking, machine learning, big data, and privacy. He earned his B.Sc. from the National Technical University of Athens, and M.Sc. and Ph.D. from the University of Toronto. Prior to Northeastern, he worked at Technicolor and Yahoo Labs. Education: B.Sc. in Electrical and Computer Engineering (2002, National Technical University of Athens); M.Sc. and Ph.D. in Computer Science (2004, 2009, University of Toronto). Research interests span machine learning, distributed systems, optimization, and privacy. Key projects include the NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE), and work on federated learning, continual learning, and privacy-preserving algorithms. His research has been supported by NSF, Google, and Facebook grants. Recent publications emphasize federated learning, continual learning, and edge computing. Awards include the NSF CAREER Award, Søren Buus Outstanding Research Award, and multiple best paper awards. He advises numerous PhD students and collaborates across disciplines, including healthcare and wireless networks. Lab activities include SPIRAL and WIoT labs, focusing on machine learning at the edge and network optimization. Grants include the NSF AI Institute and multiple collaborative projects with industry and academia.

Tianzheng Wangمشاهده پروفایل

دانشیار

• Database Systems
• Operating Systems
• Parallel Programming
• +۵ مورد دیگر

Tianzheng Wang is an Associate Professor and Director of the Dual-Degree and Partnerships Programs at the School of Computing Science, Simon Fraser University. His research focuses on database systems, transaction processing, parallel and distributed computing, and embedded systems. He holds a PhD in Computer Science from the University of Toronto (2017) and a BSc in Computing from Hong Kong Polytechnic University (2012). Research Interests: Database systems optimized for modern hardware, parallel programming, synchronization, and distributed architectures. His work emphasizes high-performance transaction processing and efficient indexing techniques, with applications in cloud and embedded systems. Awards: ACM SIGMOD Best Paper Award (2025), IEEE TCSC Early Career Award (2019), and multiple distinguished reviewing recognitions (SIGMOD/VLDB 2021-2024). His research has been integrated into systems like Amazon Redshift and DragonflyDB. Teaching: Leads courses such as CMPT 454 (Database Systems II), CMPT 300 (Operating Systems), and special topics in databases. Actively mentors graduate and undergraduate students in research projects. Labs & Collaborations: Heads the Data-Intensive Systems Lab, part of SFU's Data Science and Systems groups. Collaborates on tools like PiBench for persistent memory benchmarking and contributes to open-source projects like CoroBase and Tabular.

• 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.

Merry Maniمشاهده پروفایل

دانشیار

• Biomedical Imaging
• Signal Processing
• Image Reconstruction
• +۴ مورد دیگر

Dr. Merry Mani is an Associate Professor in Radiology and Imaging Sciences and Biomedical Engineering, specializing in biomedical imaging and signal processing. Her work focuses on advancing MRI-based imaging technologies to study neurological disorders such as Alzheimer's, Autism, and Epilepsy. She holds a Ph.D. in Electrical and Computer Engineering from the University of Rochester (2014) and completed a postdoctoral fellowship at the University of Iowa School of Medicine (2018). Her research combines biophysical modeling with machine learning to explore brain microstructures. Key achievements include the NNARSAD Young Investigator Grant and NIH-funded projects like 'Fast Multi-dimensional Diffusion MRI with Sparse Sampling'. Her lab develops cutting-edge reconstruction methods like qModeL and MUSSELS, prioritizing high spatio-temporal resolution imaging. Major contributions span diffusion MRI acquisition, model-based deep learning, and clinical applications in neurodegenerative diseases. Notable grants include NIH R01EB031169 for Alzheimer’s neurodegeneration studies and projects on rTMS for depression. Her work bridges imaging innovation with clinical impact, aiming to improve diagnosis and treatment through advanced imaging biomarkers.

Jesse Hoeyمشاهده پروفایل

استاد

• Affective Computing
• Health Informatics
• Artificial Intelligence
• +۶ مورد دیگر

Jesse Hoey is a Professor in the David R. Cheriton School of Computer Science at the University of Waterloo and leader of the Computational Health Informatics Lab (CHIL). He serves as a Faculty Affiliate at the Vector Institute and is Editor-in-Chief of the IEEE Transactions on Affective Computing. His research spans affective computing, health informatics, and socially assistive robotics, with a particular focus on developing technologies for elderly care and cognitive assistive applications. Hoey's research interests center around affective intelligence, Bayesian affect control theory (BayesACT), and decision-theoretic planning in uncertain domains. His work integrates social psychology with artificial intelligence to create emotionally aware systems that can interact naturally with humans, particularly those with cognitive impairments such as Alzheimer's disease. He has developed models for social interaction, emotion recognition, and uncertainty management in human-robot collaboration. His recent publications demonstrate a strong trend toward medical applications of AI, particularly in ultrasound analysis and healthcare technology. Many of his papers focus on self-supervised learning techniques for medical imaging and the application of affective computing principles to assistive technologies for dementia care. His work bridges theoretical AI with practical healthcare applications, showing increasing emphasis on real-world implementation. Editor-in-Chief of IEEE Transactions on Affective Computing Hoey has supervised numerous PhD and Master's students through the Computational Health Informatics Lab, with research spanning socially assistive robotics, affective computing, and health informatics. His lab has received funding for projects related to AI for dementia care, smart home technologies, and emotion-aware systems. The CHIL lab collaborates with healthcare institutions including the Toronto Rehabilitation Institute. The Computational Health Informatics Lab (CHIL) focuses on developing intelligent systems that understand and respond to human emotions and social contexts. Current projects include emotionally aligned social robots for dementia care, self-supervised learning for medical ultrasound, and models of social organization as uncertainty management. The lab combines theoretical work in Bayesian modeling with practical applications in healthcare technology.

Irem Dikmenمشاهده پروفایل

استاد

• Construction Project Risk Management
• Climate Risk and Resilience in Built Environment
• Digital Construction Technologies
• +۵ مورد دیگر

Professor Irem Dikmen is a leading academic in Construction Engineering and Management at the University of Reading, where she serves as School Director of Internationalisation in the Chancellor's Building. Her research integrates engineering, management, and information sciences to advance construction project risk management, particularly focusing on climate resilience, digital technologies, and social value in infrastructure systems. PhD, MSc, and BSc in Civil Engineering from Middle East Technical University Her work leverages systems thinking, artificial intelligence, and digital tools to develop decision-support frameworks for megaprojects and climate adaptation. Recent publications highlight innovations in NLP contract analysis, energy performance ontologies, and risk visualization techniques. She supervises students on topics spanning IoT lifecycle management, ESG risks, and NLP defect detection. Key collaborations include the Climate and Finance Research Cluster and Walker Institute , with contributions to digital construction technologies and sustainability risk assessment. Teaching modules include Construction Risk Management, Economics, and Business Organisation.

Elette Boyleمشاهده پروفایل

دانشیار

• Cryptography
• Computer Security
• Coding Theory
• +۷ مورد دیگر

Elette Boyle is an Associate Professor at Reichman University (IDC Herzliya) and a Senior Scientist at NTT Research . She holds a Ph.D. in Mathematics from MIT (advised by Shafi Goldwasser and Yael Tauman Kalai) and an undergraduate degree from Caltech . Education Ph.D. in Mathematics, MIT B.S. in Mathematics, Caltech Her research focuses on cryptographic solutions for secure data processing , particularly in secure multi-party computation , function/homomorphic secret sharing , and distributed point functions . Recent work explores topology-hiding communication , memory checking complexity , and sublinear-communication MPC . Key trends in her publications include: Advancements in Function Secret Sharing for branching programs and sparse vectors. Efficient Secure Multi-Party Computation protocols with preprocessing. Information-theoretic and computational Topology-Hiding Broadcast schemes. Optimized Oblivious Transfer with constant computational overhead. Scientific Awards European Research Council (ERC) Award Israeli Science Foundation (ISF) Grant United States Air Force Office of Scientific Research (AFOSR) Grant Google Research Scholar Award International Association for Cryptologic Research (IACR) Recognition As Director of the Foundations & Applications of Cryptography (FACT) Research Center , she leads collaborative work with institutions like Technion Israel , Cornell University , and NTT Research . Her students include Pierre Meyer (Ph.D.) , Matan Hamilis (Ph.D.) , and D'or Banon (MSc.) .

Peihan Miaoمشاهده پروفایل

استادیار

• Cryptography
• Secure Multi-Party Computation
• Theoretical Computer Science
• +۲ مورد دیگر

Peihan Miao is an Assistant Professor in the Department of Computer Science at Brown University, where she is a member of the Theory Group. Her academic journey began with a BS from the ACM Honors Class at Shanghai Jiao Tong University, followed by a PhD from UC Berkeley in 2019 under the supervision of Sanjam Garg. BS: ACM Honors Class at Shanghai Jiao Tong University PhD: University of California, Berkeley (2019) Dr. Miao's research focuses on cryptography and security, with particular emphasis on bridging the gap between theoretical cryptography and practical applications, especially in the realm of secure multi-party computation. Her work spans both foundational theoretical aspects and applied systems, demonstrating a strong commitment to developing cryptographic techniques that can be implemented in real-world scenarios. She has made significant contributions to private set intersection protocols, secure computation frameworks, and cryptographic primitives that enable privacy-preserving data analysis across various domains including genomics and machine learning. Her publication record reveals a consistent focus on advancing secure computation techniques, with recent work exploring structure-aware private set intersection, updatable cryptographic protocols, and applications of cryptography to emerging domains like federated genomics. Her research demonstrates a progression from theoretical foundations toward practical implementations that address real-world privacy challenges. NSF CAREER Award Meta Research Award Google Research Scholar Award Amazon Research Award Dr. Miao actively mentors PhD students including Xinyi Shi, Phuoc Van Long Pham, and Jifeng Wang, as well as postdoc Gayathri Garimella. She serves on program committees for major cryptography conferences including Crypto, TCC, and Asiacrypt. Her teaching portfolio includes courses on applied cryptography, introduction to cryptography and computer security, and special topics in secure computation, demonstrating her commitment to educating the next generation of cryptographers. She also runs a crypto reading group at Brown University for students interested in cryptography research. As part of Brown's Theory Group, Dr. Miao collaborates with colleagues on advancing the theoretical foundations of computer science while maintaining a strong focus on practical applications of cryptographic techniques.

Pratyush Mishraمشاهده پروفایل

استادیار

• Cryptography
• Computer Security
• zkSNARKs
• +۳ مورد دیگر

Pratyush Mishra is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science, where he is a member of the Security and Privacy Laboratory. His academic career bridges theoretical cryptography with practical implementations in secure computation systems. University of Pennsylvania (Current) UC Berkeley (Alma Mater) Dr. Mishra's research focuses on cryptography and computer security, particularly zkSNARKs, secure computation, and cryptographic proof systems. His work enables efficient privacy-preserving systems through innovations in zero-knowledge proofs and verifiable computation. He is a co-author and co-maintainer of the arkworks ecosystem, which provides foundational libraries for zkSNARK implementations used across industry and academia. His recent publications demonstrate significant contributions to proof accumulation, delegation-friendly zkSNARKs, and scalable zero-knowledge systems. These works appear consistently in top-tier security and cryptography venues including CRYPTO, IEEE S&P, and USENIX Security. ACM SIGSAC Doctoral Dissertation Award Runner-Up (2022) Dr. Mishra serves on prestigious program committees including IEEE S&P (2023-2026), CRYPTO (2023, 2025), and FC (2025). He currently advises five PhD students and teaches advanced cryptography courses covering both theoretical foundations and practical implementations of cryptographic systems. His research has practical impact through his role as founding scientist at Aleo, a startup based on his Zexe research project.

Johan Driesenمشاهده پروفایل

استاد

• Renewable Energy
• Power Electronics
• Electrical Drives
• +۷ مورد دیگر

Johan Driesen is a full Professor at KU Leuven's Faculty of Engineering Sciences, where he serves as Department Head of Electrical Energy Systems and Applications (ELECTA) and Director of the KU Leuven Institute for Energy and Society (KIEM). He also holds leadership positions at EnergyVille as Division Head and Subdivision Head. His work spans both academic and applied research in electrical energy systems. His research interests focus on renewable energy integration, power electronics, electrical drives, electric vehicles, and smart grids. Driesen's work particularly emphasizes distributed generation of electricity, with significant contributions to floating photovoltaics, offshore wind integration, and low-voltage DC systems. His research group actively investigates the optimization of grid integration for renewable energy sources and the development of advanced power electronic converters. Driesen's recent publications reveal a strong emphasis on practical applications of renewable energy systems, with particular focus on reliability analysis of low-voltage DC systems, optimization of PV-battery systems, and integration of electric vehicles with renewable energy sources. His work bridges theoretical analysis with real-world implementation challenges. Laureate prize of the Belgian Royal Society of Electrotechnics (KBVE/SRBE) 'Research and Development' for master theses 1996 2nd place in final round IEEE Region 8 Student Paper Contest 1997 in München, Germany Laureate biannual prize 'R.Sinave' of the Belgian Royal Society of Electrotechnics (KBVE/SRBE) for best PhDs 2002 Driesen has supervised numerous students and researchers, including J. Despeghel who completed work on residential PV-battery system optimization. His current research portfolio includes substantial projects such as Flux50 (working on the future energy system), solar and wind energy in the Belgian marine zone, and smart charging solutions that integrate e-mobility with renewable energy. He serves as promoter or co-promoter on multiple major research initiatives with funding extending through 2028. At EnergyVille, Driesen leads research teams working on cutting-edge energy solutions, with particular focus on DC systems, grid integration challenges, and the development of innovative power electronic solutions for renewable energy applications. His work has significant industrial relevance and practical implementation potential.

Thierry Foucaultمشاهده پروفایل

استاد

• Financial Markets Liquidity
• Market Microstructure
• Big Data in Finance
• +۵ مورد دیگر

Thierry Foucault serves as HEC Foundation Chaired Professor of Finance at HEC Paris, where he conducts influential research at the intersection of financial markets and technology. His work focuses on the determinants of financial markets liquidity and informativeness, the industrial organization of markets, and their effects on the real economy. His primary research interests include Big Data applications in finance, artificial intelligence in trading technologies, over-the-counter (OTC) markets, and how stock markets influence corporate decisions regarding investment and product choices. Foucault's recent work has increasingly examined how technological advancements reshape traditional financial market structures and corporate behavior. Foucault's research portfolio demonstrates a clear progression toward understanding how data abundance and AI transform market dynamics. His publications span theoretical models, empirical investigations, and policy implications, with particular emphasis on how alternative data sources affect traditional expertise in fund management, how algorithmic approaches change market maker behavior, and how investor information horizons influence corporate investment decisions. European Research Council (ERC) Advanced Grant (2021) for a five-year research project on Big Data and AI in financial markets Co-managing editor of the Journal of Financial and Quantitative Analysis (JFQA) since September 1, 2021 As an academic leader, Foucault actively mentors PhD students and collaborates with researchers worldwide. His editorial position at JFQA places him at the forefront of scholarly discourse in quantitative finance. While specific grant details aren't provided in the text, his ERC Advanced Grant represents significant research funding supporting his work on technology's impact on financial markets and corporate investment.

• Machine Learning
• Energy Harvesting
• Low Latency Systems
• +۳ مورد دیگر

Kiwan Maeng is an Assistant Professor in Computer Science and Engineering, focusing on the intersection of machine learning systems, privacy-preserving techniques, and low-latency computing architectures. His research emphasizes algorithm-system co-design for scalable and secure AI implementations. Research Trends : His recent work explores retrieval-augmented generation systems, low-latency diffusion models, privacy-preserving federated learning, and energy-harvesting intermittent computing frameworks. Publications highlight collaborations across machine learning, cryptography, and hardware-software co-design. Key Projects : He leads a 3-year NSF SaTC grant (2024-2027) addressing privacy-preserving data embedding for untrusted ML services, and contributes to serverless video analytics frameworks (SVDE) and VR streaming optimization (PIRATE). Technical Contributions : His scholarship spans 17 conference contributions and 3 journal articles since 2007, with notable work on memory encryption for edge devices, secure MPC-based inference, and sustainable AI systems. Current research focuses on balancing privacy guarantees with model utility while optimizing for environmental efficiency.

• Land Use and Transportation Planning
• Travel Behavior and Built Environment
• Sustainability and Climate Adaptation
• +۵ مورد دیگر

Guang Tian, Ph.D. , is an Assistant Professor of City and Metropolitan Planning at the University of Utah and a faculty member at the Scientific Computing and Imaging Institute . His research bridges land use-transportation planning , travel behavior , and urban data science , with a focus on sustainability , climate adaptation , and equitable transit-oriented development . He previously founded the Center for Equitable Transit-Oriented Communities at the University of New Orleans as an Associate Professor. Education : Ph.D. in City & Metropolitan Planning (University of Utah, 2016) Professional Affiliations : Faculty, Scientific Computing and Imaging Institute (2025–present) His research leverages machine learning and GIS to analyze VMT reduction , active transportation , and the built environment’s impact on mobility . Key findings include the superior performance of random forest models over traditional methods in predicting mode choice and the role of polycentric urban structures in reducing auto dependency. Scientific Awards : Rising Scholar Award (2024, Association of Collegiate Schools of Planning) Grants include funding from the US Department of Transportation for equitable transit communities and multiple Louisiana Transportation Research Center projects on VMT modeling, rail infrastructure, and truck parking efficiency. His teaching centers on GIS applications in urban planning and transportation analysis.

Theo Arentzeمشاهده پروفایل

استاد

• Spatial Choice Behavior
• Decision Support Systems
• Activity-Based Modeling
• +۹ مورد دیگر

Theo Arentze is a Full Professor at Eindhoven University of Technology (TU/e) in the Department of the Built Environment, leading the Real Estate Management and Development group. He is affiliated with EAISI Health and EAISI Mobility research institutes. Education: MSc in Psychology (Cognitive Psychology & AI) from Groningen University, PhD from TU/e Urban Planning Research: Spatial choice behavior, decision support systems, activity-based modeling, agent-based simulation His research integrates bounded rationality into spatial choice models to enhance behavioral realism, with applications in real-estate management , neighborhood development , healthy cities , and hybrid work environments . Recent work includes child-friendly urban planning and energy-efficient housing impacts . Prominent article themes include: Hybrid work location decisions Urban public space affective experiences Child friendliness in residential choices Sustainable energy preferences Spatial decision support systems Social network modeling Scientific recognition includes: Best Poster Award (2025) - Computational Urban Planning Conference Long Paper of Distinction (2021) - Healthy Buildings Europe EuroFM Best Paper Award (2017) Pyke Johnson Award (2016) As an educator, he teaches courses in Urban Planning , Housing Economics , and Quantitative Research Methods . His multidisciplinary team combines expertise from psychology, sociology, and urban economics to create high-quality built environments through behavioral research.