جست‌وجوی استاد و پژوهشگر

Alan Tsangمشاهده پروفایل

استادیار

• Artificial Intelligence
• Multi-agent Systems
• Game Theory
• +۷ مورد دیگر

Alan Tsang is an Assistant Professor at Carleton University's School of Computer Science. He specializes in Multi-agent Systems and Computational Social Choice, focusing on strategic interactions in social networks. Previously, he was a Post-Doctoral Researcher at the National University of Singapore and earned his PhD from the Cheriton School of Computer Science at the University of Waterloo under Dr. Kate Larson. His research integrates game theory and agent-based simulations to address fairness, voting mechanisms, and social network dynamics. He is affiliated with Carleton's Institute for Data Science and the Department of Human-Computer Interaction (HCI), and serves as Information Officer of ACM SIGAI. His educational background includes a Master of Mathematics in Graph Theory and a Bachelor of Mathematics in Bioinformatics, both from the University of Waterloo. His work explores interdisciplinary applications, such as modeling pandemic impacts and ethical computing challenges. He teaches courses like Multiagent Systems and Computing, Society, and Ethics, and actively mentors students in graduate and undergraduate research programs. Key research areas include algorithmic fairness, social choice mechanisms, and agent-based modeling of societal systems. He leads the Games, Agents, and Incentives Workshop and collaborates on projects analyzing voting behavior in homophilic networks and fair allocation strategies.

Carson Leungمشاهده پروفایل

استاد

• Big Data Science
• Data Mining
• Machine Learning
• +۵ مورد دیگر

Dr. Carson Kai-Sang Leung is a Full Professor in Computer Science at the University of Manitoba's Faculty of Science. He founded and directs the Database & Data Mining Lab. His research focuses on big data science, data mining, machine learning, health informatics, and visual analytics. He holds SMIEEE and SMACM fellowships, reflecting his contributions to the field. Education: B.Sc., M.Sc., and Ph.D. from the University of British Columbia (UBC). Research interests include human-centered exploratory data mining, image databases, and scalable algorithms. He emphasizes user-driven constraints in mining processes and has developed techniques like the segment support map and OSSM for optimized frequency counting. His work on subimage queries in large image databases addresses real-world challenges in visual data retrieval. Publications span data mining, healthcare analytics, and transportation systems. His lab collaborates on projects like visual analytics for motor vehicle accidents and environmental data science for smart cities. He is affiliated with institutions such as the Institute of Industrial Mathematical Sciences (IIMS) and TRLabs. Key awards: Senior Member of IEEE (SMIEEE) and Senior Member of ACM (SMACM).

• Computational Neuroscience
• Neural Networks
• Machine Learning

Christodoulou Chris is a Professor at the Department of Computer Science, University of Cyprus. He joined in 2005 and holds a Visiting Research Fellowship at Birkbeck College, University of London. His educational background includes a BEng in Electronic Engineering from Queen Mary and Westfield College (1991), a PhD in Neural Networks from King's College London (1997), and a BA in German from Birkbeck College (2008). His research focuses on Computational Neuroscience, Neural Networks, and Machine Learning, with specific interests in neural coding, self-control modeling, computational neuronal modeling, multi-agent reinforcement learning, and practical machine learning applications. Recent publications (2013-2025) demonstrate interdisciplinary approaches combining neuroscience, computer science, and optimization techniques, with emerging emphasis on protein structure prediction and biomedical applications. Scientific awards include: Best Paper Award, ICSR 2015 Best Paper Award, MODELS 2008 Doctoral Symposium He leads the Computational Intelligence and Neuroscience (CIN) research group and has secured funding from European projects (SocioCoast, CYberSafety, TAMIT). He mentors students through Google Summer of Code and collaborates internationally.

Michael A. Benderمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Cache and I/O-Efficient Computing
• +۷ مورد دیگر

Michael A. Bender is the John L. Hennessy Chaired Professor of Computer Science at Stony Brook University. His work spans both theoretical and applied domains in algorithms, data structures, and storage systems. He co-founded Tokutek, Inc., a database company acquired by Percona in 2015, and has held visiting positions at MIT and King's College London. Education: PhD in Computer Science from Harvard University (1998), DEA and Magistère in Computer Science from École Normale Supérieure de Lyon (1993), BA in Applied Mathematics from Harvard (1992). His research focuses on cache-oblivious algorithms , I/O-efficient computing , parallel systems , and scheduling . His work addresses scalability in large-scale data management and computational geometry, with applications in bioinformatics and robotics. The 15 most recent publications highlight advancements in list labeling , hash table design , graph algorithms , and memory optimization . These papers reflect his commitment to solving practical problems with rigorous algorithmic approaches. Awards include: PODS Best Paper Award (2024) ASPLOS Distinguished Paper Award (2023) USENIX FAST Best Paper Award (2016) Chancellor's Award for Excellence in Teaching (2015) R&D 100 Award (2006) He has led the Stony Brook Computer Science Honors Program and advised graduate students in algorithmic research. His grants portfolio includes 39 funded projects, emphasizing algorithm engineering and systems optimization.

Dominik Kempaمشاهده پروفایل

استادیار

• String algorithms
• Data compression
• Compressed data structures
• +۳ مورد دیگر

Dominik Kempa is an Assistant Professor in the Department of Computer Science at Stony Brook University, NY. He previously held postdoctoral positions at Johns Hopkins University (2021), UC Berkeley (2020), and the University of Warwick (2019). His PhD (2015) and MS (Computer Science) are from the University of Helsinki, while his BS (Mathematics) and MS (Computer Science) are from Jagiellonian University, Kraków. Research focuses on string algorithms, data compression, compressed data structures, bioinformatics, and parallel/external-memory algorithms. Notable contributions include compressed indexing techniques for large-scale genomic data and theoretical advancements in LZ77 parsing and suffix array construction. He leads projects like the NSF-funded CAREER initiative on scalable compressed sequence indexing. Key awards include the NSF CAREER Award (2024), Junior Researcher Award, and Outstanding Doctoral Dissertation Award. His work bridges theoretical foundations and practical implementations, with over 30 publications in top venues like STOC, FOCS, and SODA. Education: PhD Computer Science (2015, University of Helsinki), MS Computer Science & BS Mathematics (Jagiellonian University) Selected Awards: NSF CAREER, Junior Researcher Award Professional Activities: Program committee member for STACS, SODA, DCC, and multiple conferences since 2020 Research highlights include resolving the Burrows-Wheeler Transform conjecture (2020) and developing sublinear-time LZ77 factorization (2024).

Van Anh Huynh-Thuمشاهده پروفایل

استادیار

• Machine Learning
• Bioinformatics
• Gene Regulatory Network Inference
• +۳ مورد دیگر

Vân Anh Huynh-Thu is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Liège (Belgium). Her research focuses on improving machine learning techniques with an emphasis on model interpretability. She is based at B28: Systems and Modeling in Quartier Polytech, Allée de la Découverte 10, 4000 Liège, Belgium. Her primary research interests span Machine Learning , Bioinformatics , and Gene Regulatory Network Inference . Dr. Huynh-Thu has developed several influential methods including GENIE3, dynGENIE3, and Jump3 for inferring gene regulatory networks from expression data. Her work bridges the gap between machine learning theory and biological applications, particularly in understanding complex disease mechanisms through computational approaches. She has made significant contributions to interpretable machine learning models that maintain high predictive accuracy while providing insights into feature importance and model behavior. Her research demonstrates a progression from purely computational methods toward translational applications in medical research. Analysis of her recent publications reveals a clear trajectory from foundational work on gene regulatory network inference toward broader applications in medical research, particularly in Crohn's disease. Her research increasingly integrates machine learning with clinical applications, demonstrating a shift from purely computational methods to translational research with direct medical implications. The consistent emphasis across her work is on interpretability, rigorous validation, and the application of tree-based methods to complex biological systems, with a growing focus on proteomics and biomarker discovery for inflammatory bowel diseases. Dr. Huynh-Thu maintains an active GitHub presence with implementations of her methods, demonstrating her commitment to open science and reproducibility. Her software repositories have garnered significant attention from the research community, with GENIE3 alone having 88 stars and 37 forks on GitHub. She has developed multiple implementations of her algorithms in Python, MATLAB, and R, making them accessible to researchers across different computational environments. Her work has been influential in the DREAM challenges, where GENIE3 was the best performer in two network inference competitions.

Osama Mahmoudمشاهده پروفایل

مدرس

• Predictive Modelling
• Health Data Science
• Machine Learning
• +۶ مورد دیگر

Dr. Osama Mahmoud is a Lecturer (Assistant Professor) in Data Science and Statistics at the University of Essex, affiliated with the School of Mathematics, Statistics and Actuarial Science (SMSAS) and the Department of Mathematical Sciences. He holds dual roles as the Director of the BSc Data Science and Analytics programme and Deputy Director of Research at SMSAS. His academic journey includes a PhD in Statistics from the University of Essex and an Honorary Senior Researcher position at the University of Bristol Medical School (2020–2024). Dr. Mahmoud's research focuses on Predictive Modelling, Health Data Science, Machine Learning, Bio and Medical Statistics, and Explainable AI. He pioneered the Slope-Hunter method (Nature Communications, 2022) for bias correction in genome-wide studies and developed tools like the Proportion Overlapping Score (POS) and open-source packages on CRAN/GitHub. His work bridges statistical methodology with applications in healthcare, environmental epidemiology, and industrial collaboration. Key contributions include studies on sleep-breast cancer mortality linkages (2025), lung function-cardiovascular risk relationships (2024), and smoking's role in depression recovery (2022). He has secured grants supporting collaborations with UNDP, Rolls Royce, and Recruitment Smart. Teaching innovations include pioneering programming and text analytics modules. Education: PhD in Statistics, University of Essex Affiliations: UK Reproducibility Network (UKRN) Institutional Lead, British Data Science Society Member Grants: Projects in Data Science, theoretical/applied Statistics, and industrial partnerships Tools: ESKNN, OTE, propOverlap packages on CRAN His work emphasizes reproducibility, with over 50 peer-reviewed publications and international teaching engagements at ICTP (Italy), EuADS (Luxembourg), and UK institutions.

Aakriti Upadhyayمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Motion Planning
• Topological Data Analysis
• +۳ مورد دیگر

Aakriti Upadhyay is a Research Fellow at the Colorado School of Mines (CSM), working in the Dynamic Automata Lab (DyALab) under Dr. Neil T. Dantam. She holds a Ph.D. in Computer Science from the University at Albany, SUNY, where she was part of the Robotics Algorithms and Computable Systems (RACS) lab under Dr. Chinwe P. Ekenna. Her research integrates Computational Geometry, Applied Mathematics, Machine Learning, and Topological Data Analysis (TDA) to advance motion planning for robotics and computational biology applications. Her work focuses on optimizing robot motion paths using topological methods, with extensions to identifying protein interaction sites. Key areas include robust path planning, fault tolerance in robotic systems, and topological modeling of biomolecular surfaces. Her research trends emphasize interdisciplinary applications of TDA, bridging robotics and computational biology. Collaborations include developing tools for Intrinsically Disordered Proteins (IDPs) analysis and enhancing path planning algorithms through geometric and topological insights. Aakriti has contributed to academic initiatives like the Student Activities Committee and advocated for women in engineering during the pandemic. Her work reflects a commitment to advancing both technical and community-oriented academic goals.

Burak Ermanمشاهده پروفایل

استاد

• Statistical Mechanics
• Protein Dynamics
• Drug Design
• +۳ مورد دیگر

Professor Burak Erman is a distinguished academic currently serving as a Professor of Science and Engineering at Koç University. He holds a Ph.D. from Istanbul Technical University (1974) and has held faculty positions at Robert College School of Engineering (1969–1971), Boğaziçi University (1971–1998), and Sabancı University (1998–2002). His research focuses on applying statistical mechanics to predict protein function, drug design, and protein-drug interactions. Erman has authored over 200 scientific papers, two books, and two edited volumes. Affiliations: Turkish Academy of Sciences, TÜBİTAK Science Board, and editorial boards of Computational Polymer Science and Polymer Gels and Networks . Collaborations: Max-Planck Institute, ESPCI Paris, and Cincinnati University. His research interests include protein dynamics, entropy changes in mutations, and computational methods for drug design. Key contributions include the Gaussian Network Model for protein behavior analysis and studies on KRAS mutations in cancer. Erman has received prestigious awards like the 2007 American Chemical Society Whitby Award. Awards: 1991 Simavi Science Award, 1991 TÜBİTAK Science Award, 2007 ACS Whitby Award. Labs/Teams: Erman Research Group focuses on protein dynamics and computational biology. Grants and advising details are not explicitly mentioned, but his extensive publications and collaborations highlight significant contributions to interdisciplinary research in biophysics and molecular biology.

Chao Liمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence in Healthcare
• Medical Imaging
• Precision Medicine
• +۳ مورد دیگر

Chao Li is a Principal Research Fellow at the University of Cambridge's Faculty of Mathematics, specializing in AI-driven healthcare solutions. His research focuses on precision medicine through image-based AI and multi-omics integration, with applications in neurological disease modeling, surgical oncology, and clinical AI safety. He leads the Centre for Mathematical Imaging in Healthcare , advancing translational AI for personalized medicine. Research Themes: Image-based AI for precision mental health AI in surgical and interventional oncology Multi-omics approaches for disease characterization Evaluation of AI innovations for clinical translation Publications highlight advancements in multimodal fusion for diagnostics, histology-molecular marker integration, and neuroimaging analytics for mental health. His work bridges computational mathematics with clinical challenges, emphasizing real-world healthcare impact. Labs/Teams: Active member of the Centre for Mathematical Imaging in Healthcare , collaborating across departments to develop AI tools for clinical deployment.

Laks V.S. Lakshmananمشاهده پروفایل

استاد

• Data Management
• Graph Computing
• Machine Learning
• +۵ مورد دیگر

Laks V.S. Lakshmanan is a Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. His research focuses on data management, graph computing, machine learning, and algorithms, with notable contributions to dense subgraph discovery, influence maximization, and healthcare informatics. He teaches advanced courses on databases and data management, including CPSC 404 (Advanced Relational Databases) and CPSC 534L (Topics in Data Management). His awards include the ACM SIGMOD Research Highlight Award, the IEEE Data Science Best Paper Award, and recognition as an ACM Distinguished Scientist (2016). His work bridges theoretical algorithm design with practical applications in social networks, bioinformatics, and healthcare. Key research themes include optimizing graph algorithms for large-scale data, combating misinformation through network analysis, and developing efficient methods for subgraph enumeration and influence propagation. His recent publications explore topics like clinical event prediction (TRACE), cost-effective LLM selection (ThriftLLM), and cross-modal consistency in AI systems. Education: Details not explicitly provided in sources. Grants & Funding: Recipient of NSERC Discovery Accelerator Supplements. Labs/Teams: Engaged in UBC's data management research groups and collaborative initiatives with industry partners.

• Chemical Engineering
• Materials Science
• Nanotechnology
• +۳ مورد دیگر

Prof. Robert Grass is a Lecturer at the Department of Chemistry and Applied Biosciences at ETH Zurich, affiliated with the Institute for Chemical and Bioengineering Sciences. His research focuses on innovative applications of nanotechnology, DNA-based storage systems, and sustainable catalytic processes for CO2 valorization. Grass has pioneered silica-encapsulated DNA technologies for traceability in healthcare, environmental monitoring, and anti-counterfeiting measures. His work bridges chemical engineering with information technology, addressing challenges in long-term data preservation and molecular-level security. Current projects include developing compostable DNA storage materials and designing catalysts for methanol synthesis from CO2, contributing to both environmental sustainability and energy systems. Grass's interdisciplinary approach integrates nanomaterials design, enzymatic processes, and machine learning to advance next-generation storage and sensing technologies. Research Interests: Development of DNA-based storage systems with error-correction mechanisms Nanoparticle engineering for medical and environmental applications Catalytic materials for CO2 conversion and green chemistry Bio-inspired security systems using molecular randomness Sustainable materials for long-term data preservation His recent work highlights advancements in silica-encapsulated DNA tracers for tracking pathogen transmission dynamics, as well as low-nuclearity catalysts enabling efficient methanol synthesis from CO2. Grass actively explores the intersection of nanotechnology and digital information, including cryptographic applications leveraging DNA's inherent complexity.

Andreas Milias Argeitisمشاهده پروفایل

دانشیار

• Systems Biology
• Biochemistry & Molecular Biology
• Mathematics, Interdisciplinary Applications
• +۶ مورد دیگر

Andreas Milias Argeitis is an Associate Professor in the Faculty of Science and Engineering at the University of Groningen. He leads the Milias-Argeitis Lab within the Molecular Systems Biology research unit at the Groningen Biomolecular Sciences & Biotechnology Institute (GBB). His research focuses on integrating experimental and computational approaches to understand cellular processes, particularly the coordination between cell growth, division, and metabolic dynamics in budding yeast. Key research areas include systems biology, TOR signaling pathways, cell cycle regulation, and the application of machine learning and mathematical modeling. His lab develops advanced tools like optogenetic control systems and deep learning algorithms for cell segmentation and tracking. Recent work has revealed metabolic oscillations linked to the cell cycle and explored the role of proteins like Sch9 in TORC1-dependent signaling. Notable achievements include an NWO Vidi Grant (2018) and an ENW Science-M Grant (2023) for studying how growth drives the cell division cycle. He has over 40 peer-reviewed publications, including work in Nature Communications , Nature Metabolism , and Journal of Cell Science . His research bridges fundamental biology with technological innovations in single-cell analysis and synthetic biology. Lab activities include CRISPR/Cas9 genome editing protocols, fluorescent protein maturation studies, and the development of photo-switchable enzymes. Collaborations span biochemistry, mathematics, and engineering, reflecting his interdisciplinary approach to unraveling cellular mechanisms.

• Human-AI Value Alignment
• Ethical Machine Learning
• Algorithmic Fairness
• +۵ مورد دیگر

Neil S. Gaikwad is an Assistant Professor at the University of North Carolina, Chapel Hill, in the School of Data Science and Society and Department of Computer Science. He holds a PhD in Society-Centered AI from MIT and has affiliations with the MIT Dalai Lama Center for Ethics and UNC’s Parr Center for Ethics. His research focuses on Human-AI Alignment, ethical machine learning, and policy design for global equity, particularly in underserved populations. His work integrates ethical principles into AI systems to address societal disparities, climate change, and algorithmic fairness. Dr. Gaikwad has led projects such as the AI and Policy research program, developed systems like Daemo and EteRNA, and contributed to high-impact publications in AAAI, ACM CSCW, and PNAS. He has mentored over 30 students, many of whom have secured fellowships and advanced AI fairness research. Awards include MIT’s Karl Taylor Compton Prize and Facebook Research Fellowship. His teaching spans courses on AI ethics, policy, and societal impact at MIT, CMU, and UNC, with student evaluations praising his mentorship and course design. He has advised global institutions like the UN and EU on AI policy, and his work bridges academia, industry, and policy through initiatives like Data-driven Humanitarian Mapping. His art installations, such as Beyond Boundaries , explore environmental and ethical themes, reflecting his interdisciplinary approach to science and society.

Susanna Röblitzمشاهده پروفایل

استاد

• Systems Biology
• Computational Pharmacology
• Mathematical Modeling
• +۴ مورد دیگر

Susanna Röblitz is a Professor at the Department of Informatics within the Computational Biology Unit at the University of Bergen. Her research focuses on mathematical modeling of high-dimensional dynamical systems in life sciences, including molecular conformation dynamics, systems biology, and pharmacology. She specializes in rare event sampling, meshfree methods, clustering algorithms, and uncertainty quantification, with a particular emphasis on oscillatory systems such as hormonal rhythms. Her teaching includes courses such as BINF100 Introduction to Bioinformatics and BINF305 Systems Biology . Her research has been supported by grants from the Research Council of Norway and Nordforsk, including projects like Markov State Models for Cellular Phenotype Switching and RAS-TOOLS . Key research areas include hormonal regulation (e.g., menstrual cycle modeling), gene regulatory networks, and applications in pharmacokinetics and aquaculture. Recent work highlights include the development of Markov state models for gene networks and spectral clustering techniques for analyzing complex biological systems. She has supervised students such as Anna Kristina Fredheim Grandma, whose master’s thesis focused on pharmacokinetic modeling in personalized dosing. Her interdisciplinary collaborations span computational biology, veterinary medicine, and clinical pharmacology.