جستجوی استاد راهنمای دانشگاهی

Maria Chikinaمشاهده پروفایل

استادیار

• Computational Biology
• Genomics
• Machine Learning
• +۷ مورد دیگر

Maria Chikina is an Assistant Professor at the University of Pittsburgh School of Medicine's Department of Computational and Systems Biology. She holds a PhD in Molecular Biology from Princeton University. Her research focuses on developing computational methods to analyze large-scale genomic datasets, bridging statistical rigor with biological insights to overcome experimental biases. Key research areas include latent variable modeling (e.g., PLIER, CellCODE), interpretable neural networks for sequence-to-function modeling, evolutionary rate analysis (RERconverge), and applications in tumor immunology, exercise genomics, and infectious disease (e.g., SARS-CoV-2). Her lab has developed tools like InstaPrism, NIFA, and L0 segmentation for data-driven biological discovery. Her work spans collaborations with institutions like UPMC (on tumor microenvironment) and the Molecular Transducers of Physical Activity Consortium (MoTraPAC). Notable projects include analyzing convergent evolution in marine mammals and subterranean species, and developing epigenetic biomarkers for disease states through the ECHO program. Lab members include PhD students (Rezwan Hosseini, Tugrul Balci) and postdocs (Tina Subic, Anish Sevekari). Past students Wynn Meyer now leads a group at Lehigh University. Her group emphasizes open-source tools (GitHub repository ChikinaLab) and interdisciplinary approaches to systems biology challenges.

Charles Baden-Fullerمشاهده پروفایل

استاد

• Strategy
• Business Models
• Innovation
• +۷ مورد دیگر

Charles Baden-Fuller is the Centenary Professor of Strategy and leader of the Strategy Group at Bayes Business School , City St George’s, University of London . He is concurrently a Senior Fellow at the Wharton School, University of Pennsylvania . Recognised among the world’s top strategy scholars, he has directed major multi-institutional research initiatives and served as Editor-in-Chief of Long Range Planning (1999-2010). Education & Qualifications BA (Oxon) – University of Oxford MA Economics – Cornell University PhD – London School of Economics Research Interests Charles’s work lies at the intersection of strategic management , business model innovation and digital transformation . His early research explained how mature firms can be rejuvenated and how alliances create competitive advantage. More recently he has advanced the concept of business models , investigating what they are, how they evolve, and how managerial cognition shapes their deployment in increasingly digitalised environments. Empirical settings span biotechnology, financial services, automotive software ecosystems and creative industries. Publications & Impact With over 80 refereed journal articles and five influential books—including the seminal Rejuvenating the Mature Business (Harvard Business Press)—his scholarship has shaped both academic theory and managerial practice. Recent articles (2021-2025) focus on digital platform alliances, entrepreneurial networking logics, and AI-enabled business model design. Honours & Awards Fellow of the Strategic Management Society (SMS Fellow, 2009) Fellow of the British Academy of Management Fellow of the British Academy Fellow of AACSB Doctoral Supervision & Research Funding He has supervised to completion more than six PhD students, many now faculty at leading universities. His research has attracted over £4 million in grants from the European Union , UK ESRC , EPSRC and the Mack Institute-Wharton , supporting multi-university teams at Bayes, Sussex, LSE, CREATE-Glasgow, Grenoble EM and Wharton. Professional & Outreach Roles Beyond academia, Charles serves as director or strategic advisor to several high-technology start-ups and as trustee of a major charity. He is a frequent keynote speaker for industry and policy audiences, and his research findings have been featured in the Financial Times , Management Today and other leading media.

Onur Varolمشاهده پروفایل

استادیار

• Computational Social Science
• Network Science
• Machine Learning
• +۴ مورد دیگر

Onur Varol is an Assistant Professor at Sabanci University's Computer Science Department and leads the VIRAL Lab, which focuses on computational social science, network science, and machine learning. He has affiliations with the Center of Excellence for Data Analytics. His research spans social bot detection, misinformation analysis, and online behavior modeling.

• Geotechnical Engineering
• Offshore Engineering
• Civil Engineering
• +۷ مورد دیگر

Amin Barari is an Adjunct Professor at the School of Engineering, RMIT University, Australia. His research focuses on geotechnical and offshore engineering, particularly in foundation systems for offshore wind turbines, soil-structure interaction, and seismic liquefaction mitigation. He has extensive experience in experimental and numerical analysis of pile foundations, bucket foundations, and caisson structures. His work integrates advanced computational methods (e.g., machine learning, finite element modeling) to predict foundation behavior under extreme conditions. Research interests include: offshore wind energy foundations, soil liquefaction, cyclic stability diagrams, and probabilistic hazard assessment frameworks. He has supervised multiple PhD/Masters projects on topics like resilient foundations in calcareous deposits and pile foundation dynamics in expansive soils. His publications span over 147 research outputs, emphasizing geotechnical challenges in coastal and offshore environments. Dr. Barari collaborates with international institutions and has expertise in experimental testing (e.g., large-scale load testing, centrifuge modeling) and advanced AI-driven frameworks for geohazard prediction. His work contributes to sustainable infrastructure design and risk mitigation strategies for renewable energy systems.

Bryan Trippمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Deep Learning
• Robotics
• +۱ مورد دیگر

Bryan Tripp is an Associate Professor at the University of Waterloo, specializing in computational neuroscience, deep learning, robotics, and medical AI. He leads the BRAIN Lab, which focuses on developing neural system models that interact with the physical world through robots. His research integrates neurobiological models with advanced machine learning techniques to study visuomotor processes and robotic applications. Tripp teaches courses such as Computational Neuroscience (SYDE 552), Deep Learning (SYDE 577), and Biomedical Engineering Design Workshops (BME 461/462). His lab has achieved milestones including the OREO robotic head, the first spiking neural network model for complex action planning, and comprehensive datasets for robotic grasping. His recent work emphasizes Medical AI applications, with graduate positions available. The BRAIN Lab is affiliated with the Centre for Theoretical Neuroscience and Waterloo.AI, contributing to interdisciplinary AI research initiatives.

Alfred Chongمشاهده پروفایل

دانشیار

• Actuarial Science
• Financial Mathematics
• Quantitative Risk Management
• +۵ مورد دیگر

Dr. Alfred Chong is an Associate Professor in the Department of Actuarial Mathematics and Statistics at Heriot-Watt University (HWU). Previously, he served as an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC) and co-founded the Illinois Risk Lab. His research focuses on Actuarial Science, Financial Mathematics, and Quantitative Risk Management, addressing emerging risks like cyber, pandemic, and climate risks, leveraging machine learning, optimization, and stochastic control. He holds a PhD from The University of Hong Kong and King's College London, and is an Associate of the Society of Actuaries. Chong actively contributes to academic governance, including roles in the EPSRC Mathematical Sciences Early Career Forum and the Maxwell Institute's Data and Decisions research theme. Education: PhD in Actuarial Science, University of Hong Kong & King's College London Research Interests: Chong explores risk sharing mechanisms, forward preferences in insurance, and mitigation strategies for large-scale risks. His work integrates data analytics and machine learning to solve decision-making challenges, such as cybersecurity risk assessment, pandemic resource allocation, and climate risk modeling. Recent projects include incident-specific cyber insurance design and delegated investment strategies for retirement savings. Awards: Michael V. Colla Prize for Mathematics Related to Medicine (2022) Best of 2020 in the Annual Meeting of the Casualty Actuarial Society (2021) Advising & Grants: Chong supervises PhD students in holistic risk management, forward preferences, and reinforcement learning applications. He has secured grants supporting interdisciplinary research in risk modeling and insurance innovation. Labs & Teams: Co-founder of the Illinois Risk Lab (UIUC), now leading research at HWU's Actuarial Mathematics & Statistics department. Engaged with the International Centre for Mathematical Sciences for knowledge exchange initiatives.

Cameron Muscoمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Machine Learning
• Numerical Linear Algebra
• +۳ مورد دیگر

Cameron Musco is an Assistant Professor in the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst. He is affiliated with the Theory Group and conducts research at the intersection of theoretical computer science, numerical linear algebra, and machine learning. His work focuses on randomized algorithms, streaming, and distributed computation, with applications in data science. Education: PhD in Computer Science, MIT (2019) BS in Computer Science and Applied Mathematics, Yale University Research Interests: Musco's research emphasizes algorithm design for large-scale data analysis, including fast randomized methods for linear algebraic problems. He explores topics such as low-memory computation, matrix approximations, and graph algorithms, driven by applications in machine learning and distributed systems. Publications: His recent work spans advancements in hierarchical matrix approximation, graph-based nearest neighbor search, and fair resource allocation, reflecting expertise in both theoretical foundations and practical algorithmic innovation. Awards: He has received an NSF Career Award, Google Research Scholar Award, and recognition for anti-racism leadership. He actively reviews for top conferences in theoretical computer science and machine learning. Advising & Grants: Musco advises multiple PhD students and has grants from NSF and Google. His lab collaborates on projects like low-rank matrix approximation and causal discovery. Labs/Teams: He is part of the Theoretical Computer Science Group and the Center for Data Science at UMass.

Manolis Savvaمشاهده پروفایل

دانشیار

• Computer Graphics
• Computer Vision
• 3D Scene Analysis
• +۴ مورد دیگر

Manolis Savva is an Associate Professor in the School of Computing Science at Simon Fraser University and holds the Canada Research Chair in Computer Graphics. He specializes in 3D scene analysis, generative methods for 3D content, and computer graphics for AI. His research bridges computer graphics, vision, and robotics. Education: Ph.D. (Computer Science, Stanford University, 2016), MS (Computer Science, Stanford, 2012), B.A. (Physics & Computer Science, Cornell, 2009). Research Interests: Human-centric 3D scene analysis, generative 3D content creation, AI-driven rendering, and applications in robotics. Key projects include Habitat (Embodied AI platform), ScanNet , and ShapeNet datasets. Recent Articles: Focus on articulated object modeling, 3D scene synthesis, and AI-driven visualization. Notable work includes SceneMotifCoder (generating object arrangements) and R3DS (panoramic scene understanding). Awards: CHCCS Early Career Award (2022), ICLR 2023 Outstanding Paper Award, ICCV 2019 Best Paper Nomination, and SGP 2020 Dataset Award (ScanNet). Lab/Teams: Leads research groups in 3DLG (3D Learning and Graphics) and GrUVi (Graphics and Vision). Collaborates on projects like AI Habitat and HomeRobot .

Nishant Gargمشاهده پروفایل

استادیار

• Construction Materials
• Sustainable Infrastructure
• Environmental Engineering
• +۳ مورد دیگر

Nishant Garg is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign. His research focuses on sustainable construction materials, particularly cement-based systems, leveraging advanced characterization techniques such as X-ray scattering, neutron diffraction, and Raman imaging. His work addresses environmental sustainability through innovations in low-carbon materials, waste utilization, and durability enhancement. Education: Ph.D. in Nanoscience, Aarhus University (2015) M.S. in Civil Engineering Materials, Iowa State University (2012) B.E. and Diploma in Civil Engineering, Thapar Institute of Engineering & Technology and Chandigarh College of Eng. & Tech. (2010, 2007) Research Interests: Sustainable cement chemistry, material characterization, carbonation processes, and development of eco-friendly construction materials. His lab, the Garg Group, emphasizes multi-scale analysis (nano-to-macro) to bridge fundamental science and practical applications. Key Contributions: Innovations include the UR2 test for cement reactivity, SorpVision for automated sorptivity assessment, and VR tools for materials education. These advances aim to reduce costs, improve material performance, and promote circular economy practices. Awards and Roles: Dean’s Award for Excellence in Research (2025) Member, Transportation Research Board AKM 50 Committee (2025–Present) Recipient of American Ceramic Society’s Stephen Brunauer Award (2021) Advising and Grants: Actively recruiting MS/Ph.D. students. Leads initiatives on low-carbon concrete, funded by NSF and industry collaborations. Serves on CEE advisory committees and graduate admissions. Labs/Teams: The Garg Group integrates interdisciplinary approaches, collaborating with materials scientists, engineers, and data scientists to tackle global infrastructure challenges.

Min Chenمشاهده پروفایل

استادیار

• Terrestrial Ecosystem Modeling
• Remote Sensing Applications
• Human-Earth System Interactions
• +۳ مورد دیگر

Min Chen is an Assistant Professor in the Department of Forest and Wildlife Ecology at the University of Wisconsin–Madison, affiliated with the Russell Labs. His research focuses on terrestrial ecosystem modeling, remote sensing applications, and human-Earth system interactions. He holds a PhD in Earth & Atmospheric Sciences from Purdue University, an MS in Remote Sensing and GIS from Beijing Normal University, and a BS in Computer Science from Beijing Normal University. His postdoctoral work included roles at the Carnegie Institution for Science and Harvard University. Research interests include forest carbon dynamics, methane emissions from wetlands, wildfire risk analysis, and the integration of remote sensing with Earth system models. His work emphasizes advancing methods for global-scale environmental monitoring using satellite and drone technologies. Notable contributions include studies on forest edge dynamics, vegetation-climate feedbacks, and the application of machine learning in ecological modeling. Recent publications highlight advancements in leaf trait prediction using transfer learning, global wetland methane flux modeling, and the impacts of climate change on land-use patterns. His lab develops innovative approaches to track terrestrial carbon cycles and assess human-driven environmental changes. Ongoing projects explore urban land expansion effects on carbon balances and phenological shifts under global change scenarios.

Cecilia Boschiniمشاهده پروفایل

پژوهشگر

• Cryptography
• Post-Quantum Cryptography
• Lattice-Based Cryptography
• +۳ مورد دیگر

Cecilia Boschini is a Researcher affiliated with the Institute for Theoretische Informatik (Theoretical Computer Science) at ETH Zürich. Her work focuses on advanced cryptographic systems, particularly in post-quantum cryptography, lattice-based protocols, and privacy-preserving technologies. She contributes to developing secure digital signature schemes, threshold cryptography, and efficient cryptographic algorithms resistant to quantum computing threats. Her research bridges theoretical foundations with practical applications in cybersecurity and distributed systems. Key contributions include innovations in fail-stop signatures, two-round threshold signatures (Ringtail), and lattice-based multi-signature systems (MuSig-L). Boschini’s work emphasizes balancing security with efficiency, often addressing challenges in mobile device security and memory encryption. She has published extensively in top-tier venues, with a focus on cryptographic protocols that enhance privacy and data integrity without compromising usability.

Gabriel A. Silvaمشاهده پروفایل

استاد

• Neuroscience
• Neural Computing
• Quantum Algorithms
• +۷ مورد دیگر

Gabriel A. Silva is a Professor in the Shu Chien-Gene Lay Department of Bioengineering at UC San Diego’s Jacobs School of Engineering, with a joint appointment as Assistant Professor in Ophthalmology. His research bridges neuroscience, theoretical physics, and applied mathematics to explore how the brain encodes and processes information, leveraging quantum logic and algorithms for advanced neural modeling. University: University of California, San Diego School: Jacobs School of Engineering Department: Shu Chien-Gene Lay Department of Bioengineering Academic Rank: Professor Joint Appointment: Assistant Professor in Ophthalmology Research Interests: Silva focuses on neural computation at cellular and network scales, aiming to abstract biological mechanisms into mathematical models that emulate brain-like processing. His work has implications for understanding neurological disorders, developing neural engineering nanotechnologies, and advancing AI systems through emergent complexity. Recent Article Trends: His publications span quantum-enhanced neural modeling, EEG-based disease detection, nonlinear dynamics in brain networks, and interdisciplinary applications of graph theory. Emerging themes include the integration of category theory for network analysis and AI optimization via emergence-promoting schemes. Labs & Teams: Affiliated with UC San Diego’s Institute of Engineering in Medicine, Silva leads research at the intersection of bioengineering, ophthalmology, and neural systems, fostering collaborations with neuroscience and quantum computing domains.

Yan Chenمشاهده پروفایل

استاد

• Cybersecurity
• Networking
• Distributed Systems Security
• +۵ مورد دیگر

Yan Chen is a Professor of Computer Science at Northwestern University's Robert R. McCormick School of Engineering and Applied Science. He leads the Northwestern Lab for Internet and Security Technology (LIST) and the Center for Ultra-scale Computing and Information Security. His research focuses on cybersecurity, network measurement, and distributed systems security. Chen holds a Ph.D. from UC Berkeley (2003), M.S. from SUNY Stony Brook, and B.E. from Zhejiang University. Research interests include securing networking systems, intrusion detection, cloud-native platforms, and mobile security. Notable awards include the DOE Early CAREER Award (2005), Air Force Young Investigator Award (2007), and ACM ASPLOS'18 Most Influential Paper Award. His work has been cited over 17,000 times with an h-index of 62 (2024). Key contributions include the LIST lab's advancements in APT detection, provenance tracking in microservices, and security frameworks like FlowCog. He advises numerous Ph.D. students and has graduated over 20 researchers now in academia and industry.

Asuman Ozdaglarمشاهده پروفایل

استاد

• Optimization
• Machine Learning
• Game Theory
• +۷ مورد دیگر

Asuman Ozdaglar is the MathWorks Professor of Electrical Engineering and Computer Science and Department Head of EECS at MIT. She also serves as Deputy Dean of Academics for the MIT Stephen A. Schwarzman College of Computing. Her research focuses on large-scale networked systems, including optimization, game theory, social networks, and distributed algorithms. Education: BS in Electrical and Electronics Engineering from Middle East Technical University (1996), SM (1998) and PhD (2003) in Electrical Engineering and Computer Science from MIT. Research emphasizes nonlinear optimization, machine learning, and network economics. She leads work on robust algorithms, misinformation dynamics, and networked systems. Affiliated with the Laboratory for Information and Decision Systems (LIDS) and the Operations Research Center (ORC). Her contributions span theoretical and applied domains, including distributed optimization methods, social network analysis, and privacy-preserving data mechanisms. Active in shaping academic policy through her roles in the College of Computing.

Ali Ghodsiمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۳ مورد دیگر

Ali Ghodsi is a Professor at the University of Waterloo and Director of the Data Science Lab, with affiliations at the Vector Institute. His research spans machine learning, deep learning, and artificial intelligence, with applications in natural language processing, bioinformatics, and computer vision. His group develops theoretical frameworks and algorithms for analyzing large-scale datasets, focusing on neural network architectures, knowledge distillation, and model efficiency. Current projects include deep learning for identity control, computational antibody design, and generative AI/large language models. Ghodsi has authored influential tutorials on diffusion models, graph neural networks, and large language models. Notable research contributions include computational methods for de novo peptide sequencing from mass spectrometry data, green simulation-assisted reinforcement learning, and efficient natural language processing models. His lab maintains collaborations with industry partners including Google, Amazon, and Roche.