جست‌وجوی استاد و پژوهشگر

• Causal Machine Learning
• Topological Data Analysis
• Riemannian Optimization
• +۵ مورد دیگر

Qian Li is a Lecturer in Computing at the School of Electrical Engineering, Computing and Mathematical Sciences (EECMS) at Curtin University, Australia. She holds a Ph.D. from the Chinese Academy of Sciences and M.Sc. degrees from Shandong University and the University of Luxembourg. Her research focuses on causal machine learning, topological data analysis, and optimal transport, with applications in computer vision, data science, and recommendation systems. She has published over 50 articles in top-tier venues like IEEE Transactions and ACM conferences. Education Ph.D., Chinese Academy of Science (CAS) MSc (Research), Shandong University MSc (Research), University of Luxembourg Research Interests Dr. Li explores causal reasoning for machine learning, leveraging mathematical tools like Riemannian geometry and optimal transport to address challenges in robustness and interpretability. Her work spans causal inference, counterfactual fairness, and explainable AI, with applications in healthcare, energy, and commerce. Recent projects include causal-based recommendation systems and topological data analysis techniques. Key Achievements Secured a $120k grant from China's National Natural Science Foundation (2020-2024). Lead researcher on AI-driven solar energy storage projects with UNSW and Providence Asset Group. Recipient of prestigious scholarships including Chinese National Graduate Scholarship (2016, top 1%). Grants & Students Current Ph.D. students include Xiangmeng Wang and Tri Dung Duong. She has supervised graduates like Yangyang Shu (Adelaide University Research Associate) and Jun Yin (UTS). Labs & Teams Leads research in causal AI and topological data analysis, collaborating with institutions like UTS and the University of Melbourne.

Hari Sundarمشاهده پروفایل

دانشیار

• Parallel Algorithms
• High Performance Computing
• Computational Sciences
• +۳ مورد دیگر

Hari Sundar is an Associate Professor in the Department of Computer Science at Tufts University, holding the Ada Lovelace Associate Professorship. Previously, he served as an Associate Professor at the Kahlert School of Computing, University of Utah. His research focuses on developing parallel algorithms for computational sciences and high-performance computing, addressing challenges in biosciences, geophysics, computational fluid dynamics, and computational relativity. He leads efforts in adaptive mesh refinement, geometric multigrid methods, and scalable scientific computing frameworks like Dendro-GR for numerical relativity. Education: Ph.D. in Computer Science from the University of Pennsylvania (2009), and a Bachelor of Engineering from the University of Delhi (2000). Postdoctoral work at the Oden Institute, University of Texas at Austin. Research Interests: Parallel algorithms, high-performance computing architectures, computational relativity (binary black hole simulations), multiphase flow modeling, and domain-specific languages for scientific computing. His work emphasizes scalability and efficiency on modern supercomputers. Key Contributions: Development of the Dendro-GR platform for gravitational wave simulations, scalable PDE solvers, and GPU-optimized algorithms for phonon transport and genomic sequence alignment. His recent work includes advancements in gravitational waveform modeling for LISA space missions and thermodynamically consistent two-phase flow simulations. Grants & Collaborations: Active in NSF-funded projects on computational relativity, multiphase flow algorithms, and scalable PDE solvers. Collaborates across disciplines in astrophysics, materials science, and bioinformatics.

Marco Donatoمشاهده پروفایل

استادیار

• Hardware for Machine Learning
• Non-Volatile Memories
• SoC Design
• +۵ مورد دیگر

Marco Donato is an Assistant Professor in both the Department of Electrical and Computer Engineering and the Department of Computer Science at Tufts University. He leads the TECS Lab (Testchip, Embedded Computing Systems) focused on hardware design for emerging applications. Prior to joining Tufts, he was a postdoctoral fellow at Harvard University's John A. Paulson School of Engineering and Applied Sciences. Dr. Donato received his academic training from prestigious institutions: Ph.D. in Electrical Sciences and Computer Engineering from Brown University (2016) M.Sc. in Electrical Engineering from Università di Roma La Sapienza, Rome, Italy (2010) B.Sc. in Electrical Engineering from Università di Roma La Sapienza, Rome, Italy (2008) Dr. Donato's research primarily focuses on designing reliable and energy-efficient hardware systems leveraging emerging technologies. His work centers on co-design methodologies for building specialized architectures for machine learning applications that utilize dense, fault-prone embedded non-volatile memories. He investigates noise modeling and reliability aspects of next-generation memory technologies, with particular emphasis on how these can be effectively integrated into system-on-chip (SoC) designs for edge computing and IoT applications. His research bridges the gap between circuit-level design and system-level architecture to create holistic solutions for hardware acceleration of machine learning workloads. Analysis of Dr. Donato's publication record reveals a strong focus on hardware acceleration for machine learning, particularly through innovative memory system designs. His work spans multiple domains including non-volatile memory technologies, energy-efficient circuit design, and flexible SoC architectures. A notable trend is his exploration of how emerging memory technologies can be leveraged to create more efficient implementations of deep neural networks, with particular attention to the trade-offs between reliability, density, and energy consumption. His research often involves full-stack approaches that consider everything from device physics to system architecture. Dr. Donato is actively involved in mentoring and has indicated he is "looking for Ph.D. students." His work has been supported by significant research grants that have enabled the fabrication of multiple test chips, as evidenced by his extensive publication record in top-tier venues including IEEE Journal of Solid-State Circuits, ISSCC, and MICRO. He leads the TECS Lab at Tufts University, which focuses on testchip development, embedded computing systems, and hardware acceleration. The lab appears to maintain connections with researchers at Harvard University and other institutions, reflecting Dr. Donato's collaborative approach to research. The lab's work emphasizes practical, real-world implementations of novel hardware concepts through actual silicon fabrication, which is relatively rare in academic settings.

Florian Buettnerمشاهده پروفایل

استاد

• Bioinformatics
• Machine Learning
• Multi-omics Data Integration
• +۲ مورد دیگر

Florian Buettner is Professor for Bioinformatics in Oncology at Goethe University Frankfurt, with affiliations at the German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ). His research integrates multi-omics data with machine learning for cancer research. Research focuses on: Multi-omics bioinformatics AI for precision oncology Probabilistic modeling Single-cell analysis Uncertainty quantification Buettner received an ERC Consolidator Grant to develop trustworthy AI models for cancer diagnosis. His methodological innovations include techniques for single-cell RNA sequencing analysis and model calibration.

Latifur Khanمشاهده پروفایل

استاد

• Data Mining
• Cybersecurity
• Machine Learning
• +۲ مورد دیگر

Dr. Latifur Khan is a Professor in the Department of Computer Science at the University of Texas at Dallas' Erik Jonsson School of Engineering and Computer Science. He directs the Database and Data Mining Laboratory and conducts research in data mining, cybersecurity, and semantic web technologies. Research domains include: Large language models for threat detection Fairness in machine learning Vulnerability analysis in software systems Graph-based information retrieval Recent publications focus on AI security applications in transportation systems, political conflict analysis using NLP, and federated learning for IoT security. His work consistently bridges theoretical algorithms with practical cybersecurity implementations. Research grants include funding from NSF, NASA, Raytheon, Nokia, and SUN Microsystems. Teaching includes courses in Plant Breeding (PBG 450/550) and Breeding Clonal Crops (PBG 551).

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۴ مورد دیگر

Dr. Milos Hauskrecht is a Professor of Computer Science at the University of Pittsburgh's School of Computing and Information. He holds a PhD from MIT (1997) and an M.Sc. from Slovak Technical University (1988). His research focuses on AI, machine learning, and data mining, with applications in medicine and finance. He leads projects in real-time clinical monitoring, anomaly detection, and time-series analysis of EHR data. He has advised numerous PhD and MS students, including notable alumni now at Amazon, DeepMind, and Microsoft. Research interests include reasoning under uncertainty, optimization, and AI-driven medical decision support. Current grants include NIH funding for AI in renal therapy and clinical monitoring. He has published widely in top venues like ICML, NeurIPS, and journals such as Artificial Intelligence in Medicine. His work on conditional outlier detection earned the Homer Warner Award (AMIA 2010). He teaches machine learning and advises on interdisciplinary AI projects.

Chiara Sabattiمشاهده پروفایل

استاد

• Statistical Genetics
• Computational Biology
• Bayesian Statistics
• +۲ مورد دیگر

Chiara Sabatti is a Professor of Biomedical Data Science and Statistics at Stanford University, with affiliations to the Stanford Center for Computational, Evolutionary and Human Genomics (CEHG), Bio-X, and the Stanford Cancer Institute. She serves as Associate Director for Stanford Data Science and has led the development of the Data Science Major curriculum since 2012. Research Focus: Statistical models for high-throughput genomics data, causal inference in genetic studies, false discovery rate control, and knockoff methods for variable selection. Key Leadership: Associate Chair for Education and Training (2020-present), Vice Chair of Biomedical Data Science (2018-2019). Her work bridges statistical genetics with data science education, emphasizing robustness and interpretability in scientific findings. Recent publications highlight innovations in genome-wide association studies (GWAS), causal variant localization, and cost-effective sequencing techniques for underrepresented populations. Current projects include developing knockoff-based methods to address population structure and multi-resolution hypothesis testing. Scientific Awards: Institute of Mathematical Statistics (IMS) Fellow (2022) NSF CAREER Award (2003-2008) She mentors doctoral and graduate students in Biomedical Data Science, collaborates with the Data Studio on interdisciplinary projects, and actively recruits curious researchers to her lab. Her outreach efforts focus on expanding data science education and increasing research participation from underrepresented groups.

Giulia Totiمشاهده پروفایل

استادیار

• Computer Science Education
• Ethics in Data Science
• Machine Learning
• +۳ مورد دیگر

Giulia Toti is an Assistant Professor of Teaching in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. Her work focuses on computer science education, equity in curriculum design, and fostering inclusive learning environments. She teaches courses such as Applied Machine Learning (CPSC 330), Fairness, Accountability, Transparency, and Ethics (FATE) in Data Science (DSCI 430), and Computers and Society (CPSC 430). Her research explores diversity initiatives in CS education, mastery learning frameworks, and equitable grading practices. Notable contributions include studies on pandemic-era remote teaching impacts and the development of Agora, a tool for enhancing large-classroom engagement. Toti has received UBC Faculty Teaching Awards for her pedagogical innovations. Her interdisciplinary work spans machine learning applications in industry and healthcare, including semantic search systems for clinical data (SemEHR) and predictive analytics for energy production. She is affiliated with the ACE Lab and actively contributes to curriculum reforms addressing DEI (Diversity, Equity, Inclusion) challenges in STEM education. Grants/Awards: Faculty Teaching Awards Labs/Teams: ACE Lab (Advancing Computing Education) Advising: No explicit advisee listings found, but contributes to pedagogical research impacting teaching practices.

Haiyan Huangمشاهده پروفایل

استاد

• Computational Biology
• Applied Statistics
• High Dimensional Data Analysis
• +۳ مورد دیگر

Haiyan Huang is a Professor and Chair of the Department of Statistics at the University of California, Berkeley. She is affiliated with the Center for Computational Biology and the Graduate Group in Biostatistics. Her research focuses on computational biology, applied statistics, and high-dimensional genomic data analysis, with an emphasis on network modeling and translational bioinformatics. Education details are not explicitly listed, but her academic background includes a PhD in Statistics. She has advised numerous graduate students and postdocs, contributing to impactful research in statistical methods for genomics and bioinformatics. Her work spans statistical methodology development, including sparse canonical correlation analysis, biclustering, and single-cell RNA sequencing analysis. Notable projects include predicting tumor heterogeneity, designing random heteropolymers, and linking protein expression to cell motility. Publications highlight her contributions to gene network inference, reproducibility in high-throughput experiments, and integrating multi-platform genomic data. Awards include recognition by Faculty of 1000 Biology for her 2010 PNAS work. Huang teaches advanced courses in computational biology, statistical theory, and consulting. Her lab collaborates with experts in bioengineering, chemistry, and pediatrics, fostering interdisciplinary research. Current projects include precision medicine, deep learning applications, and probabilistic modeling of biological systems. Her team uses variational inference, hidden Markov models, and machine learning to address challenges in single-cell analysis and pharmacogenomics. The lab’s work has been published in top journals like Nature, PNAS, and Bioinformatics, reflecting her leadership in computational genomics.

• Political Science
• Development Studies
• Comparative Politics
• +۷ مورد دیگر

Michelle D'Arcy is an Associate Professor in the Department of Political Science at Trinity College Dublin. Her research focuses on state capacity, property rights, taxation, and development in Africa and early modern Europe. She leads projects such as 'Credible Enforcement before Credible Commitment' and 'Devolution and Local Democracy in Kenya,' funded by the Swedish Research Council and the Swedish International Centre for Local Democracy. Her work explores institutional sequencing, democratic governance, and the impact of historical state structures on modern policies. Education: Details not explicitly provided in the text. Research Interests: Dr. D'Arcy investigates how historical institutional frameworks shape contemporary political and economic outcomes. Key themes include state-building, taxation systems, devolution processes, and gender dynamics in state formation. Her work bridges comparative politics, development studies, and historical analysis. Publications: Her recent work emphasizes cadastres' role in economic growth, gender in early modern state formation, and taxation in sub-Saharan Africa. These studies highlight interdisciplinary approaches, combining historical data with contemporary case studies. Awards: Trinity Creative and Visual Arts Fund Award (2020) Equality Fund Award (2018) Political Science Association of Ireland Prize for Excellence in Teaching and Learning (2016) Grants: Swedish Research Council (2014-2018), Swedish International Centre for Local Democracy (2013-2015) Dr. D'Arcy collaborates on initiatives to improve local democracy and accountability in sub-Saharan Africa, particularly in Kenya. Her research underscores the interconnectedness of historical governance structures and modern socio-economic challenges.

Ziquan Liuمشاهده پروفایل

مدرس

• Reliable Machine Learning
• Uncertainty Quantification
• Computer Vision
• +۴ مورد دیگر

Dr. Ziquan Liu is a Lecturer (Teaching & Research) at Queen Mary University of London's School of Electronic Engineering and Computer Science, affiliated with the Centre for Multimodal AI. He holds a PhD from City University of Hong Kong (2023) and dual B.Sc./B.Eng. degrees from Beihang University (2017). His research focuses on trustworthy machine learning, adversarial robustness, and uncertainty quantification in foundation models. He has served as a reviewer for top conferences like NeurIPS, ICLR, and CVPR, earning an Outstanding Reviewer Award in 2021. His teaching includes modules on machine learning for visual data analysis and principles of machine learning. He supervises PhD students in AI safety and reliability, with notable work on conformal prediction, adversarial attacks, and multimodal learning. His research outputs span top venues such as ICML, CVPR, and NeurIPS, addressing challenges in algorithmic fairness, model certification, and cross-modal alignment.

Yang Shenمشاهده پروفایل

دانشیار

• Algorithms for modeling biological molecules
• Optimization and machine learning
• Systems and control
• +۵ مورد دیگر

Yang Shen is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, affiliated with the Department of Computer Science and Engineering and the Institute of Biosciences and Technology. He holds a B.E. in Automation from the University of Science and Technology of China (2002) and a Ph.D. in Systems Engineering from Boston University (2008). His research focuses on algorithms for modeling biological molecules, systems, and data, with applications in protein docking, drug design, systems biology, and omics. He has received prestigious awards such as the NSF CAREER Award (2020) and MIRA Award (2017). His work integrates machine learning, optimization, and graph theory to address challenges in computational biology. Notable contributions include generative AI for protein design, interpretable models for compound-protein affinity prediction, and Bayesian active learning for protein docking. Shen has advised numerous students, including Yuning You, Mostafa Karimi, and Arghamitra Talukder, who have received awards like the Chevron Scholarship and NSF Graduate Fellowships. His lab actively collaborates on projects in drug discovery, synthetic biology, and precision medicine. Shen has led funded projects totaling over $3.5 million from NIH and NSF, exploring topics like molecular mechanisms of cancer mutations and AI-driven drug design. He serves on editorial boards for journals like the Journal of Biological Systems and has organized workshops such as the International Workshop on Biomedical Informatics with Optimization and Machine Learning (BOOM). His research bridges computational methods and biological systems, advancing both theory and practical applications in healthcare and biotechnology.

Chao Tianمشاهده پروفایل

دانشیار

• Information Theory
• Distributed Data Storage
• Coding Theory
• +۶ مورد دیگر

Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.

Subodha Kumarمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Blockchain
• +۱۱ مورد دیگر

Subodha Kumar is the Paul R. Anderson Distinguished Chair Professor of Statistics, Operations, and Data Science at Temple University’s Fox School of Business. He also serves as Founding Director of the Center for Business Analytics and Disruptive Technologies and holds a secondary appointment in Information Systems. With prior faculty roles at the University of Washington and Texas A&M University, he has been recognized with prestigious awards including the INFORMS ISS Distinguished Fellow Award (2023) and POMS Fellow (2019). His research focuses on artificial intelligence, machine learning, blockchain, fintech, and cybersecurity, with over 240 publications in top journals and two books. He actively contributes to academic leadership as Deputy Editor of the Production and Operations Management Journal and Founding Executive Editor of Management and Business Review. Research Interests: Artificial Intelligence Machine Learning Blockchain Fintech Healthcare Analytics Social Media Analytics Supply Chain Analytics Awards: INFORMS ISS Distinguished Fellow Award (2023) POMS Fellow (2019) Changjiang Scholars Chair Professorship (China) Namesake Award: The Subodha Kumar and Geoffrey G. Parker Award for Digital Transformation Research Grants & Advising: Secured NIH grants, advises PhD students in Operations & Supply Chain Management, and oversees the Center for Business Analytics. His editorial roles include multiple top journals and conference leadership (e.g., POMS Annual Conference 2018). Labs/Teams: Leads the Center for Business Analytics and Disruptive Technologies, fostering innovation in analytics and tech-driven business strategies.

Abdulkadir Çelikkanatمشاهده پروفایل

استادیار

• Genome Representation Learning
• Graph Representation Learning
• Machine Learning
• +۵ مورد دیگر

Abdulkadir Çelikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark, where he is part of the DKW (Data Science and Knowledge) research group. His research focuses on genome representation learning, graph representation learning, and machine learning applications in bioinformatics and network science. Research Interests: His work lies at the intersection of artificial intelligence and biological data analysis, with a strong emphasis on scalable methods for genome and metagenome representation using k-mer profiles, as well as modeling dynamic and complex networks. He develops novel machine learning models to capture the structure and evolution of graphs over time. Recent Research Trends: His recent publications, appearing in top-tier venues like NeurIPS, AAAI, and AISTATS, demonstrate a consistent focus on improving scalability and effectiveness in representation learning. Key themes include revisiting traditional k-mer methods for modern deep learning, modeling citation dynamics, and developing continuous-time node embedding techniques. His work bridges theoretical advances with practical applications in genomics and network analysis. Scientific Awards: Best Paper Award, TGL Workshop @ NeurIPS 2023 Top Reviewer, LoG 2024 Conference Advising and Grants: While current advisees are not listed, he is actively leading research projects as evidenced by his recent publications and project organization (e.g., Nordic ProbAI summer school). His work is supported through institutional affiliations and likely competitive research funding, given the high-impact venues of his publications. Labs and Teams: He is affiliated with the DKW group at Aalborg University. Previously, he was part of the Inria OPIS team and the Centre for Visual Computing during his Ph.D., and worked in the Section for Cognitive Systems at DTU Compute as a postdoctoral researcher.