جست‌وجوی استاد و پژوهشگر

Lingyang Chuمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Computer Science
• +۷ مورد دیگر

Lingyang Chu is an Assistant Professor at McMaster University's Department of Computing and Software, previously serving as a postdoc fellow at Simon Fraser University under Jian Pei. He earned his Ph.D. in Computer Science from the University of Chinese Academy of Sciences. Research interests span data mining , machine learning , and statistics , with focus on trustworthy AI (privacy, interpretability, security, robustness, fairness), federated learning , and graph-based machine learning . His work includes scalable data mining on large graphs and deploying systems like personalized federated learning on Huawei Cloud's Harmony OS devices. Publications emphasize adversarial attacks, medical AI, graph robustness, and federated learning frameworks. His advising record includes 28 mentees across Ph.D., M.Sc., and internship levels. Scientific achievements include Best paper candidate at ICME'13 Best demo award at ICMR'13 Academic service roles include: Program Committee: NeurIPS, SIGKDD, CVPR, ICML, and 12+ other top-tier conferences Journal Reviewer: IEEE TKDE, ACM Transactions on KDD, and 8+ journals Editorial Board: ACM Transactions on KDD (Associate Editor) Grant Reviewer: Hong Kong RGC Labs/teams: Maintained open-source ALID algorithm (VLDB'15) for dominant cluster detection, demonstrating technical leadership in scalable graph mining

Marco Serafiniمشاهده پروفایل

استادیار

• Database Systems
• Distributed Systems
• Data Science
• +۳ مورد دیگر

Marco Serafini is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences (CICS). He leads the DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) and is part of the Center for Data Science. Prior to UMass, Serafini worked as a Senior Scientist at the Qatar Computing Research Institute (QCRI) and held a postdoctoral fellowship at Yahoo! Research in Barcelona. He earned his PhD in Computer Science from TU Darmstadt (Germany), where his thesis was recognized through nominations for best thesis awards across German, Swiss, and Austrian computer science societies. His research focuses on the intersection of database systems, distributed systems, and data science, emphasizing scalable architectures for big data analytics and machine learning. Key areas include computation pushdown in cloud DBMSs, graph neural network training systems, and efficient graph pattern matching. His work addresses challenges in tail latency mitigation, resource optimization, and transparent scaling of ML models. Serafini has contributed to influential systems like Arabesque (for distributed graph mining), E-Store (elastic partitioning), and Aion (event-time stream processing). He has been awarded an NSF CNS Core grant to advance scalable GNN training. His publications span top venues such as ACM SIGOPS, VLDB, and ICDE, reflecting his expertise in both theoretical foundations and practical system implementations. Professional recognition includes thesis nominations from major computer science societies and sustained contributions to open-source projects in distributed computing. Serafini advises students through the DREAM Lab, focusing on preparing the next generation of data systems researchers.

Yongjie Jessica Zhangمشاهده پروفایل

استاد

• Finite Element Method
• Isogeometric Analysis
• Image-Based Geometric Modeling
• +۵ مورد دیگر

Dr. Yongjie Jessica Zhang is a Professor at Carnegie Mellon University, holding appointments in both the Department of Mechanical Engineering and the Department of Biomedical Engineering . She received her B.S. and M.S. in Engineering Mechanics from Tsinghua University, followed by an M.S. in Aerospace Engineering and a Ph.D. in Computational Engineering and Sciences from the University of Texas at Austin. After a postdoctoral fellowship at ICES, she joined CMU in 2007, advancing from assistant to full professor by 2016. Research Interests : Image-based geometric modeling, mesh generation, finite element analysis (FEA), isogeometric analysis, and applications in computational biomedicine, materials science, and computer-assisted surgery. Leadership Roles : Chair of Solid Modeling Association (2019-2020), USACM Executive Committee Member-at-Large (2017-2021), and ELATE Fellow (2017-2018). Her work addresses the critical challenge of automating high-fidelity geometric modeling and mesh generation for complex domains (e.g., human anatomy), which traditionally consumes ~80% of FEA time. Her group develops AI-driven methods for multiscale modeling (molecular to organ), with applications in neuroscience , biomechanics , and 4D printing . Notable awards include the Presidential Early Career Award (PECASE) , NSF CAREER Award , and ASME Van C. Mow Medal (2025) . Dr. Zhang’s publications span over 170 peer-reviewed articles, focusing on truncated hierarchical B-splines , polycube meshing , and neurite transport modeling . She has advised more than 40 students, including PhD candidates and postdoctoral fellows. Her editorial roles include Associate Editor of Computer Aided Geometric Design and editorial board memberships in Computer-Aided Design and Engineering with Computers .

Marzyeh Ghassemiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Healthcare
• +۶ مورد دیگر

Marzyeh Ghassemi is an Associate Professor at MIT in the Department of Electrical Engineering and Computer Science (EECS) and the Institute for Medical Engineering & Science (IMES). She also holds a Canadian CIFAR AI Chair and is a faculty member at the Vector Institute. Her affiliations include the Jameel Clinic and CSAIL. Dr. Ghassemi's work focuses on ethical AI, algorithmic fairness in healthcare, and mitigating bias in medical datasets. She has pioneered projects analyzing racial disparities in end-of-life care, vocal disorder diagnosis using accelerometers, and improving subgroup robustness in machine learning. Education: PhD in Computer Science, MIT MSc in Biomedical Engineering, University of Oxford (Marshall Scholar) B.S. in Computer Science and Electrical Engineering, New Mexico State University (Goldwater Scholar) Research Interests: Her work bridges AI and healthcare, emphasizing fairness, reproducibility, and ethical deployment. Key areas include: - Algorithmic Fairness: Addressing biases in medical imaging and clinical decision systems. - Bias Mitigation: Developing methods to decorrelate sensitive information from predictions. - Health Equity: Studying disparities in end-of-life care and organ procurement. - Generative AI: Applications in mental health and medical imaging. Publications: Her recent work highlights challenges in vision-language models (e.g., handling negation), regulatory gaps in health AI, and bias reduction techniques. These efforts aim to ensure AI systems are both accurate and equitable in clinical settings. Awards: Herman L. F. von Helmholtz Career Development Professorship CIFAR Azrieli Global Scholar MIT Tech Review’s 35 Innovators Under 35 Canada Research Chair Leadership & Impact: She co-chaired the NeurIPS Workshop and founded the Association for Health Learning and Inference. Her projects like Quantifying Racial Disparities in End-of-Life Care reveal systemic biases in ICU treatments. She advocates for transparent AI deployment through frameworks like FUTURE-AI and TRIPOD+. Labs & Teams: Leads MIT’s HealthyML group, collaborating on projects such as voice misuse detection, federated learning for medical imaging, and AI-driven mental health prediction.

Garvesh Raskuttiمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Optimization Theory
• Graphical and Network Modeling
• +۵ مورد دیگر

Garvesh Raskutti is an Associate Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. He holds joint affiliations with the Departments of Computer Science, Electrical and Computer Engineering, and the Wisconsin Institute of Discovery Optimization Group. His research focuses on statistical machine learning, optimization, graphical/network modeling, and information theory, with applications to systems biology and neuroscience. Education: MEng from the University of Melbourne (2008), PhD from UC Berkeley (2012, advised by Martin Wainwright and Bin Yu), and postdoctoral work at SAMSI. Teaching awards include Honored Instructor (2014, 2017) and Madison Teaching and Learning Excellence Fellow (2015). He advises multiple PhD and undergraduate students, including Yuan Li, Hyebin Song, and Lili Zheng. His grants include NGA HM0476-17-1-2003 (Co-PI), ARO W911NF-17-1-0357 (Co-PI), and NSF-DMS 1407028 (Sole PI). Research interests span large-scale statistical inference, computational-statistical trade-offs, and applications in systems biology and neuroscience. His work bridges optimization (e.g., gradient descent, convex regularization), high-dimensional regression, and network analysis. Recent trends in publications emphasize methods for non-convex optimization, sparse models, and network structure learning. Awards include teaching recognition and grants in statistical methodology. Advising spans theoretical and applied projects, with former students like Gunwoong Park (now at University of Korea). Collaborations include interdisciplinary teams in machine learning and signal processing.

• Graph Analytics
• Graph Mining
• Graph Algorithms
• +۳ مورد دیگر

Dr. Lijun Chang is an Associate Professor in the School of Computer Science at the University of Sydney. He holds an ARC Future Fellowship (2019–2022) and an ARC DECRA Fellowship (2015–2017). Previously, he was at the University of New South Wales. His research focuses on graph analytics, mining, algorithms, and network science. He teaches courses like INFO5011 (Competitive Programming), COMP5313 (Large Scale Networks), and COMP9120 (Database Management Systems), and coaches the USYD Programming Competition Teams. Education: B.Eng. in Computer Science & Technology from Renmin University of China; PhD from the Chinese University of Hong Kong. Research highlights include scalable graph processing systems (e.g., ScaleG), densest subgraph detection, and graph similarity search. He leads projects funded by ARC grants such as 'Advanced Search of Cohesive Subgraphs in Big Graphs' (2018) and 'Directionality-Aware Cohesive Subgraph Search' (2022). His work emphasizes efficient algorithms for large-scale networks and graph databases. Awards : ARC Future Fellow, ARC DECRA Fellow Students : Yu KONG, Rashmika MATHTHAKA GAMAGE, Mouyi XU Labs/Teams : Focuses on graph algorithms and systems research, contributing to open-source tools and large-scale network analysis.

Ton Diekerمشاهده پروفایل

دانشیار

• Stochastic Processes
• Operations Research
• Queueing Theory
• +۳ مورد دیگر

Ton Dieker is an Associate Professor in the Department of Industrial Engineering and Operations Research at Columbia University's School of Engineering and Applied Science. He is a DSI Member and affiliated with the Center for Financial and Business Analytics. His research focuses on stochastic models, simulation techniques, and high-dimensional stochastic analysis. Dieker holds a Master’s in Operations Research from Vrije Universiteit Amsterdam (2002) and a PhD in Mathematics from the University of Amsterdam (2006). Dieker’s research explores stochastic processes, queueing theory, and rare-event simulation. He has contributed to methodologies like QPLEX for stochastic systems and advanced techniques in sequential analysis and exact simulation. His work bridges theoretical foundations with computational applications, addressing challenges in large-scale networks and high-dimensional problems. Education: PhD in Mathematics, University of Amsterdam, 2006 Master’s in Operations Research, Vrije Universiteit Amsterdam, 2002 Dieker’s articles emphasize computational modeling, stochastic calculus, and optimization, reflecting his focus on bridging theory and practice. His work often addresses efficiency in simulation, exactness in algorithms, and scalability in complex systems. Awards: Goldstine Fellowship (IBM Research) NSF CAREER Award Erlang Prize (INFORMS) Fouts Family Early Career Professorship (Georgia Tech) He serves on editorial boards for Operations Research and Mathematics of Operations Research . His research also addresses capacity management in stochastic networks and applications in cloud computing and commodity sourcing.

Barzan Mozafariمشاهده پروفایل

دانشیار

• Database Systems
• Data Management
• Machine Learning
• +۳ مورد دیگر

Barzan Mozafari is an Associate Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor, and leads a research group focused on scalable database systems and approximate computing. He holds a PhD from UCLA (2011) and was a Postdoctoral Associate at MIT. His research emphasizes data-intensive systems, combining statistical models, optimization, and machine learning to enhance database performance and predictability. Notable projects include BlinkDB (approximate query processing), DBSeer (database diagnosis), and VerdictDB (platform-independent AQP). He co-founded Keebo, advancing data learning technologies, and contributed to SnappyData (acquired by TIBCO). His work spans technical innovations like CATS/VATS scheduling algorithms (adopted in MySQL/MariaDB) and grants such as ConFlux (NSF-funded supercomputing-big data integration) and a smart black-box for autonomous vehicles. Awards include the NSF CAREER Award and Best Paper recognitions at SIGMOD and EuroSys. Teaching includes EECS 484/584 (Database Management Systems) and advanced topics courses. He advises students like Yongjoo Park (SIGMOD Dissertation Runner-Up), Jiamin Huang, and Boyu Tian.

Scott T. M. Dawsonمشاهده پروفایل

استادیار

• Fluid Mechanics
• Dynamical Systems
• Control Theory
• +۵ مورد دیگر

Scott T. M. Dawson is an Assistant Professor in the Mechanical, Materials, and Aerospace Engineering Department at Illinois Institute of Technology (Illinois Tech). He holds positions in the Armour College of Engineering and leads research at the intersection of fluid mechanics, dynamical systems, control theory, and data science. His work focuses on extracting dynamic models from large datasets to analyze and control turbulent flows and unsteady aerodynamic systems. Education includes a Ph.D. and M.A. from Princeton University (2017, 2013), and B.Eng. and B.S. degrees from Monash University (2010, 2009). Prior to Illinois Tech, he was a postdoctoral scholar at Caltech’s Graduate Aerospace Laboratories under Prof. Beverley McKeon. Research interests emphasize reduced-order modeling, data-driven techniques for fluid flows, and flow control applications. His group’s work is supported by NSF, AFOSR, and DOE grants. Recent projects include sparsity-promoting methods for flow analysis, wavelet-based resolvent analysis, and neural network-driven flow control systems. Publications span over 60 peer-reviewed articles, with a focus on turbulence modeling, transient flow dynamics, and machine learning integration in fluid mechanics. Key contributions include novel algorithms for isolating amplification mechanisms in wall-bounded flows and robust neural network frameworks for closed-loop flow stabilization. Grants and collaborations include multi-year NSF CAREER funding for automated distillation of coherent flow structures. Ongoing efforts explore time-localized spectral methods, nonlinear dimensionality reduction, and hydrogen decarbonization in vehicular systems.

• Social Media Analysis
• Sentiment Analysis
• Computational Journalism
• +۶ مورد دیگر

Fabrício Benevenuto is an Associate Professor in the Computer Science Department at Federal University of Minas Gerais (UFMG), where he conducts interdisciplinary research at the intersection of social media analysis, data science, and computational journalism. His work spans complex networks, machine learning, and natural language processing with strong societal impact. His research focuses on social media dynamics, particularly in Brazilian contexts, with major contributions to hate speech detection, fake news analysis, and political discourse monitoring. He leads large-scale projects against misinformation, including development of systems like WhatsApp Monitor, Media Bias Monitor, and Purple Feed. His work combines technical innovation with real-world applications for election transparency and public discourse integrity. Benevenuto's recent publications demonstrate strong trends in multilingual NLP for social media analysis, with emphasis on Brazilian Portuguese contexts. His team produces both theoretical contributions and practical systems addressing hate speech, misinformation, and media bias. Notable methodological approaches include combining network analysis with linguistic features, developing culturally-aware detection systems, and creating large annotated datasets for understudied languages. CAPES award for best Brazilian computer science thesis (2010) Humboldt Foundation scholarship recipient (2017-2018) Member of TikTok Safety Advisory Council WWW'20 Best Paper Nominee & CNIL-INRIA Privacy Protection Prize winner Multiple best paper awards at CEAS, WBC, and ICWSM conferences Test-of-Time Award at ICWSM'20 Benevenuto actively mentors PhD and MSc students, with numerous advisees securing academic positions at Brazilian universities and research roles at institutions like Max Planck Institute. His projects often receive funding supporting interdisciplinary collaborations across computer science and social sciences. Current work includes large-scale analysis of Telegram political groups, real-time election monitoring systems, and developing culturally-aware NLP tools for Portuguese. He leads research teams working on social media analysis systems with societal impact, particularly focused on Brazilian digital ecosystems. Projects involve cross-institutional collaborations with researchers from MPI-SWS, Max Planck Institute, and various Brazilian universities, emphasizing practical applications for public discourse integrity.

Yuntian Dengمشاهده پروفایل

استادیار

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

Yuntian Deng is an Assistant Professor at the University of Waterloo and a Visiting Professor at NVIDIA. He holds affiliations with Harvard SEAS as an Associate and the Vector Institute as a Faculty Affiliate. He completed his PhD in Computer Science at Harvard under Professors Alexander Rush and Stuart Shieber, followed by a postdoc under Yejin Choi. His research focuses on Natural Language Processing and Machine Learning, with notable contributions in chatbot interaction analysis (WildChat), implicit reasoning models, and markup-to-image generation. He has developed influential tools like OpenNMT and WildVis, and his work has been featured in outlets like the Washington Post and used by OpenAI and Anthropic. Education: PhD in CS (Harvard), Postdoctoral Research (University of Washington). Key achievements include the ACM Gordon Bell Prize for GenSLMs, Best Demo Runner-up at ACL 2017, and Best Paper at DAC 2020. His research emphasizes scalable datasets, efficient reasoning techniques, and real-world applications of AI models. Research interests span NLP, machine learning algorithms, and their applications in areas like dialogue systems, generative models, and ethical AI evaluation. Notable projects include WildChat (1M ChatGPT interactions), implicit chain-of-thought reasoning, and neural steganography for text-based information hiding. His articles explore topics ranging from knowledge distillation to diffusion models, with a focus on bridging theoretical advancements and practical implementations. He actively collaborates with industry partners like NVIDIA and maintains open-source tools to advance AI research accessibility.

Olga Vitekمشاهده پروفایل

استاد

• Statistical Science
• Machine Learning
• Mass Spectrometry
• +۵ مورد دیگر

Olga Vitek is a Professor at Northeastern University's Khoury College of Computer Sciences, with affiliated faculty status in the Department of Chemistry and Chemical Biology. Her research bridges statistical science and machine learning with mass spectrometry-based proteomics and systems biology, focusing on developing open-source software tools like MSstats and Cardinal for quantitative proteomic analyses and imaging. Education: PhD in Statistics (Purdue University), Postdoc at the Ruedi Aebersold Lab (Institute for Systems Biology) Leadership: Director of the Barnett Institute for Chemical and Biological Analysis Her work emphasizes: Statistical experimental design Signal detection in complex mass spectrometry data Causal inference in biomolecular networks Reproducible computational infrastructure Recent publications highlight advancements in quantitative proteomics , mass spectrometry imaging , and causal modeling , with applications spanning cancer research, immunology, and clinical diagnostics. Notable trends include deep learning integration for image analysis and open-source tool development for scalable, transparent workflows. Scientific accolades: Elected Fellow of the American Statistical Association 2021 Gilbert S. Omenn Computational Proteomics Award NSF CAREER award Chan-Zuckerberg Essential Open-source Software award Senior Member, International Society for Computational Biology

Pia Frickerمشاهده پروفایل

دانشیار

• Computational Methodologies
• Urban Design
• Landscape Architecture
• +۸ مورد دیگر

Prof. Pia Fricker is an Associate Professor and Vice Head of the Department of Architecture at Aalto University's School of Arts, Design and Architecture in Finland. She holds the Professorship of Computational Methodologies in Landscape Architecture and Urbanism, directing the Urban Studies and Planning Programme. Her research integrates urban design, landscape architecture, and digital design culture, focusing on data-driven methods, immersive environments, and adaptive urban development. Collaborations include ETH Zurich, Singapore University of Technology and Design, and Hafencity University Hamburg. Key projects include the Metaversity and Future Smart Cities initiatives. Fricker has led over 80 publications and exhibitions globally, including at the Venice Biennale and National Design Centre Singapore. She is an editorial board member for the Journal of Digital Landscape Architecture and peer reviewer for multiple journals. Awards include the Digital Landscape Architecture Award (2018) and DLA Scientific Merit Award (2021). Her teaching emphasizes computational pedagogy and digital innovation in design education. Education: PhD in Architecture (ETH Zurich, 2021) Postgraduate in Didactics (ETH Zurich, 2011) MAS in Computer Aided Architectural Design (ETH Zurich, 2003) MSc Arch in Urban Design & Landscape Architecture (Technical University of Karlsruhe, 2001) Research Interests: Computational design, parametric modeling, mixed reality, climate-adaptive ecosystems, generative AI, and sustainable urban development. Her work bridges emerging technologies with ecological and urban challenges, emphasizing interdisciplinary collaboration. Grants & Projects: Metaversity (2023–2025, Principal Investigator) Future Smart Cities Sasakawa (2023–2024, Principal Investigator) ABRA (2020–2023, Project Member) Awards: DLA Awards (2018, 2021) DLA Review Committee Awards (2020–2022) Exhibition Recognitions (Venice Biennale, National Design Centre Singapore) Labs/Teams: Leads the Urban Studies and Planning Programme and collaborates with interdisciplinary teams on projects like the RAILCORRIDOR Singapore initiative. Active in digital twin development and AI-driven design tools.

Jonathan Hugginsمشاهده پروفایل

استادیار

• Machine Learning
• Bayesian Computation
• Statistical Genetics
• +۳ مورد دیگر

Jonathan Huggins is an Assistant Professor at Boston University, affiliated with the Department of Mathematics & Statistics and the Faculty of Computing & Data Sciences. He holds a Ph.D. in Computer Science from MIT (2018) and a B.A. in Mathematics from Columbia University (2012). His research focuses on developing fast, trustworthy machine learning and Bayesian methods that balance computational efficiency and statistical optimality, with applications in ecological forecasting and genomic data analysis. Education: Ph.D. in Computer Science, Massachusetts Institute of Technology (2018) B.A. in Mathematics, Columbia University (2012) Research Interests: Large-scale machine learning and Bayesian computation Robust statistical inference Applications in genomics and ecological modeling Algorithmic development for scalable inference Key Projects: Stochastic Methods for Data Science: A book on stochastic processes and algorithms VIABEL: A Python package for variational inference and diagnostics ShorTeX: A LaTeX package for mathematical writing Recent Articles: Focus on scalable Bayesian methods, error bounds for iterative algorithms, and mutational signature discovery. His work emphasizes reproducibility and robustness in statistical inference. Awards: Blackwell–Rosenbluth Award (Outstanding Junior Bayesian Researcher) Grants & Funding: Supported by NIH, NSF, and the Department of Defense. Active in advising students across multiple BU programs. Labs/Teams: Affiliated with the BU URBAN Program, Program in Bioinformatics, and Department of Computer Science.

Aurèle Maheoمشاهده پروفایل

پژوهشگر ارشد

• Distributed Systems
• Parallel Computing
• Machine Learning Systems
• +۳ مورد دیگر

Aurèle Maheo is a Research Fellow at the Computer Science Department of Telecom SudParis, affiliated with the Parallel and Distributed Systems (PDS) group. His research focuses on distributed systems, parallel computing, and hardware acceleration, with applications in machine learning and high-performance computing. He has contributed to projects such as SpeedyLoader for optimizing machine learning pipelines and FPGA memory bandwidth improvements. His work bridges theoretical distributed systems principles with practical implementation challenges in large-scale environments. Affiliations: PDS Group, Computer Science Department, Telecom SudParis Research interests include distributed consensus algorithms, system scalability, and hardware-software co-design for accelerating computational workflows. Recent work highlights include presentations on machine learning pipelining techniques and FPGA-based memory optimization strategies.