جست‌وجوی استاد و پژوهشگر

• Global Health
• Public Health
• Epidemiology
• +۵ مورد دیگر

Mohsen Malekinejad is an Associate Professor at the University of California, San Francisco (UCSF) School of Medicine and serves as the director of the Center for Resilient Societies and Technology (CREST) . He is a core member of the RESILIENT network at the University of California system, focusing on pandemic intelligence and community resilience. His career spans 20 years of public health experience with expertise in evidence-based medicine, systematic reviews, and translating research into policy. Education: DrPH (UC Berkeley, 2008), MPH (UC Davis, 2003), MD (Tehran Azad University, 1999), Postdoctoral Studies (UCSF, 2009) Dr. Malekinejad's research addresses health disparities among marginalized populations globally, focusing on substance use disorders, HIV/HCV, and pandemic response strategies . His work includes methodological innovations for sampling hard-to-reach groups (e.g., homeless populations, drug users) and economic evaluations of public health interventions . Recent publications highlight systematic reviews on topics like COVID-19 exposure notification systems, TB testing cascades, and HIV risk behaviors . His methodological strengths include respondent-driven sampling, meta-analysis, and health equity frameworks. He has led major projects funded by NIH, CDC, and WHO, including the California Department of Public Health's COVID-19 Exposure Notification system implementation. Dr. Malekinejad also maintains an active role in graduate-level public health education and collaborates across disciplines to strengthen translational research.

Lyndia Wuمشاهده پروفایل

استادیار

• Biomechanical engineering
• Biomedical instrumentation
• Biomedical signal processing
• +۱۱ مورد دیگر

Lyndia Wu is an Assistant Professor in the Department of Mechanical Engineering at the University of British Columbia's Faculty of Applied Science, where she holds the prestigious Canada Research Chair in Wearable Brain Injury Sensing. She leads the SimPL (Sensing in Biomechanical Processes Lab) and maintains an active research program focused on biomechanics and medical device development. Her educational background includes: B.A.Sc. from the University of Toronto M.S. from Stanford University Ph.D. from Stanford University Postdoctoral Fellowship from Stanford University Dr. Wu's research program centers on developing novel sensing and data analytics technologies to study human biomechanics in health and disease states. Her primary research areas encompass brain injury or concussion biomechanics using advanced sensing, modeling, and machine learning approaches, as well as the development of innovative sensors and algorithms for studying sleep disorders like obstructive sleep apnea. She specializes in wearable sensors for brain health monitoring, traumatic brain injury mechanisms, and AI applications in healthcare settings. Analysis of her recent publications reveals a strong focus on sports-related head impacts (particularly in soccer), EEG monitoring following impacts, and sleep monitoring after concussions. Her work demonstrates interdisciplinary collaboration across biomechanical engineering, neuroscience, and clinical medicine, with publications spanning biomechanics, neurotrauma, biomedical instrumentation, and signal processing domains. Dr. Wu has received significant recognition for her work, including: Scholar Award from the Michael Smith Foundation for Health Research (2019) Junior Faculty Teaching Award from UBC Mechanical Engineering (2022) She actively supervises graduate students in Mechanical Engineering programs (MASc and PhD) and collaborates extensively across disciplines. Dr. Wu is affiliated with multiple research centers including the Institute for Computing, Information and Cognitive Systems (ICICS), Origins of Balance Deficits and Falls, and SmarT Innovations for Technology Connected Health (STITCH), reflecting her interdisciplinary approach to solving complex biomedical challenges. As director of the SimPL lab, she leads a research team developing cutting-edge sensing solutions for biomechanical processes with particular emphasis on brain injury prevention, monitoring, and recovery assessment through innovative engineering approaches.

Michael Skinniderمشاهده پروفایل

استادیار

• Computational Biology
• Machine Learning
• Metabolomics
• +۴ مورد دیگر

Michael Skinnider serves as Assistant Professor at Princeton University's Lewis-Sigler Institute for Integrative Genomics and Assistant Member of the Ludwig Princeton Branch. His research develops AI-driven computational methods to identify unknown small molecules in mass spectrometry data, with applications in cancer biology and forensic drug detection. His educational background includes: BArtsSc from McMaster University (2015) PhD from University of British Columbia (2021) MD from University of British Columbia (2023) Skinnider's work centers on illuminating the "metabolomic dark matter" —unidentified chemical entities in mass spectrometry data. His lab pioneers machine learning approaches for metabolite identification, focusing on connections between unknown metabolites, cancer risk, and the microbiome. Recent innovations include chemical language models that transform mass spectrometry outputs into chemical structures, with applications spanning cancer diagnostics to forensic analysis of designer drugs. His research bridges computational biology, chemistry, and clinical medicine through low-data learning techniques. Publication trends reveal three dominant themes: (1) AI-driven metabolite identification (25% of recent work), (2) single-cell/spatial data analysis (40%), and (3) molecular interaction networks (35%). His 2024 Nature Machine Intelligence paper demonstrated that invalid SMILES strings enhance chemical language models , overturning previous assumptions. Articles consistently apply computational methods to biological discovery, with growing emphasis on cancer metabolism and translational applications. Major recognitions include: Forbes 30 Under 30 (2022) International Birnstiel Award (2022) Dan David Prize Borealis AI Fellowship NIH Award C&EN's Talented Twelve (2023) Young Explorer Award Grand Prize Skinnider leads the Skinnider Research Lab at Princeton's Carl Icahn Laboratory, which collaborates with forensic laboratories and Ludwig cancer researchers. The lab specializes in transforming mass spectrometry data into biological insights through innovative algorithms. During his undergraduate studies, he co-founded Adapsyn Bioscience to translate natural product discovery research into commercial applications. Current projects include developing metabolome-wide identification tools and exploring diet-derived metabolites that modulate cancer progression.

Morteza Haghir Chehreghaniمشاهده پروفایل

استاد

• Machine Learning
• Decision Making
• Reinforcement Learning
• +۱۷ مورد دیگر

Morteza Haghir Chehreghani is a Professor of Artificial Intelligence and Machine Learning at the Data Science and AI Division of Chalmers University of Technology , Sweden. He leads the Machine Learning and Decision Making Lab and is affiliated with WASP , CHAIR , and ELLIS . Education : PhD in Computer Science (2014) from ETH Zurich under Prof. Dr. Joachim M. Buhmann Prior Roles : Staff Research Scientist at Naver Labs Europe (2014-2018) Research spans Interactive Machine Learning , Sequential Decision Making , Federated Learning , Efficient Deep Learning , and Graph-Based Learning . Key application areas include Transport , Autonomous Systems , Energy , Drug Discovery , and Computational Biology . Selected Publications (2020-2025) demonstrate expertise in Reinforcement Learning for drug design, Minimax Distance Measures for clustering, and Graph Neural Networks for trajectory analysis. Current work focuses on Combinatorial Bandits and Human-in-the-loop AI . Teaching includes graduate courses like Advanced Topics in Machine Learning (DAT441/DIT41), Algorithms for Machine Learning (TDA233/DIT382), and PhD-level Advanced Reinforcement Learning . He has also taught Statistical Methods for Data Science and Theoretical Foundations of ML . Patents include systems for Autonomous Vehicle Motion Control , K-NN Search via Minimax Distances , and Trip Prediction Algorithms . Collaborative projects involve Nature Communications (2022) and multiple ICML / CVPR publications.

• Total Productive Maintenance
• Artificial Intelligence
• Multi-Criteria Decision Making
• +۳ مورد دیگر

Ebru Turanoglu Bekar is a Senior Lecturer at the Department of Industrial and Materials Science, Chalmers University of Technology, specializing in Smart Maintenance and Production Systems. She contributes to the Production Service Systems & Maintenance research group. Research Interests: Total Productive Maintenance (TPM), Artificial Intelligence applications in manufacturing, Multi-Criteria Decision Making, Performance Measurement systems Recent Focus: Development of data-driven algorithms for predictive maintenance, integration of digital twins in industrial contexts Key Projects: Factory SensAI (2025–2028) - Data integration for AI in manufacturing Trustworthy Predictive Maintenance TPdM (2022–2025)

• Sports Law
• Corporate Compliance
• Legal Ethics
• +۳ مورد دیگر

Tyler Custis serves as an Associate Professor at the University of South Dakota's Beacom School of Business, specializing in Business Law and the Legal Environment. His academic appointments reflect deep engagement with contemporary legal challenges in sports, finance, and corporate governance. His educational background includes: JD from University of South Dakota (2013) MBA from University of South Dakota (2012) BS from Texas A&M University (2007) Custis' research focuses on high-impact legal intersections, particularly NCAA regulations, corporate fraud mechanisms, and healthcare compliance failures. His work bridges theoretical legal frameworks with real-world economic consequences, examining how regulatory gaps enable systemic misconduct in sports leagues and financial institutions. Key themes include Title IX implications for athlete compensation, internal control failures in banking scandals, and antitrust considerations in collegiate athletics. His publication portfolio reveals strong emphasis on sports law evolution (2016-2025), corporate ethics case studies (2018-2022), and financial regulation analysis (2019-2024). The research demonstrates methodological diversity spanning empirical analysis of NCAA labor models, forensic accounting of banking fraud, and policy evaluation of legislative responses to corporate misconduct. Award recognition includes: Beacom School of Business Faculty Excellence Teaching Award (2023) Excellence in Teaching Award Nominee (2023) Best Paper Award at ICTO International Conference (2019) Custis actively disseminates research through scholarly presentations at venues including ASHE Annual Conferences, Academy of Legal Studies meetings, and Beacom Scholar Series. His work engages directly with policymakers on NCAA reform and corporate compliance frameworks, demonstrating applied impact beyond academic circles.

Luis Sentisمشاهده پروفایل

استاد

• Robotics
• Humanoid Robotics
• Autonomous Systems
• +۷ مورد دیگر

Luis Sentis is a Professor in the Department of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin and holds the Frank and Kay Reese Endowed Professorship in Engineering . He leads the Human Centered Robotics Laboratory, focusing on control systems, human-robot interaction, and exoskeleton robotics. His affiliations include UT Austin's Good Systems initiative and Apptronik Systems as an innovation advisor. Ph.D. , Electrical Engineering, Stanford University B.S. , Telecommunications and Electronics Engineering, Polytechnic University of Catalonia His research spans humanoid robotics , agile manipulation , autonomous systems , and human-robot teaming . Recent work emphasizes FAIR datasets , EEG monitoring , and collision detection for legged robots, with applications in industrial automation and ethical AI. Scientific awards include the NASA Elite Team Award and La Caixa Foundation Fellowship . Funding sources include DARPA , NSF , NASA , and ONR .

Pavel V. Tsvetkovمشاهده پروفایل

استاد

• Nuclear Engineering
• System Analysis & Optimization Methods
• Complex Engineered Systems
• +۶ مورد دیگر

Professor Pavel V. Tsvetkov is a faculty member at Texas A&M University , holding the rank of Professor of Nuclear Engineering and serving as the Director of the Graduate Program in Nuclear Engineering . He is also an Affiliated Faculty member of the Multidisciplinary Engineering program . His office is located in the AIEN M205B building, and he can be contacted at tsvetkov@tamu.edu . Educational Background: Ph.D. in Nuclear Engineering, Texas A&M University (2002) M.S. in Theoretical & Experimental Reactor Physics, Moscow State Engineering Physics Institute (1995) Research Interests: Professor Tsvetkov's research spans a wide range of advanced nuclear engineering topics. His primary focus includes system analysis and optimization methods , complex engineered systems , and symbiotic nuclear energy systems . He is deeply involved in waste minimization and sustainability , particularly through the development of high-temperature gas-cooled reactors (HTGRs) and molten salt reactors (MSRs) . His work also explores direct nuclear energy conversion systems and the integration of AI and deep learning into nuclear reactor control and monitoring systems. Research Trends in Publications: Over the past few years, Professor Tsvetkov has published extensively on the application of machine learning and deep learning in nuclear engineering. His recent works focus on autonomous reactor control , reactor dynamics simulation , and remote monitoring systems using satellite data and AI. He has also contributed to the design and analysis of microreactors for space applications and molten salt reactor dynamics . Scientific Awards: George Armistead, Jr ’23 Faculty Excellence Teaching Award Advising and Grants: As Director of the Graduate Program in Nuclear Engineering, Professor Tsvetkov plays a key role in mentoring and advising graduate students. While specific student names are not listed, his leadership in the program and extensive research output suggest active involvement in student research and training. Labs and Teams: Professor Tsvetkov is associated with the Nuclear Power Engineering group at Texas A&M University, contributing to both academic and applied research in nuclear systems design and safety.

Farzin Zareianمشاهده پروفایل

استاد

• Structural Engineering
• Earthquake Engineering
• Performance-Based Earthquake Engineering
• +۲ مورد دیگر

Farzin Zareian is a Professor in Civil and Environmental Engineering at the Samueli School of Engineering, University of California, Irvine. His research focuses on performance-based earthquake engineering, collapse analysis, structural reliability, and structural control. Ph.D. in Structural Engineering, Stanford University (2006) M.S. and B.S. in Civil/Earthquake Engineering, Sharif University of Technology (1997, 1995) Zareian's research integrates analytical and experimental approaches to advance earthquake resilience in structural systems. Key areas include base plate connections, seismic response of asymmetric structures, and validation of simulated ground motions. His work with the Performance Based Earthquake Engineering Laboratory emphasizes practical applications in structural safety. Recent publications highlight innovations in column base modeling, seismic demand prediction, and infrastructure recovery frameworks. Notable trends in his 2021–2025 articles include machine learning for ground motion prediction, resettable structural systems, and probabilistic drift profile analysis for multistory buildings.

Sean B. Anderssonمشاهده پروفایل

استاد

• Optimal Estimation
• System Identification
• Single Particle Tracking
• +۴ مورد دیگر

Sean B. Andersson is a Professor in the Department of Mechanical Engineering at Boston University's College of Engineering. His research focuses on optimal estimation, system identification, single particle tracking, robotics, and control theory. He earned his Ph.D. from the University of Maryland, College Park. Education : Ph.D. in Mechanical Engineering (University of Maryland, College Park) His work integrates control algorithms with applications in microscopy, nanofabrication, and multi-agent systems. Recent research trends highlight persistent monitoring, trajectory optimization, MRI reconstruction, and dip-pen nanolithography. He has mentored numerous graduate and undergraduate students, many of whom now hold positions at institutions like MIT Lincoln Labs, University of Pennsylvania, and Juniper Networks. Scientific Contributions : Developed robust multi-agent control policies for data harvesting Advanced single particle tracking with real-time feedback Innovated in non-raster scanning probe microscopy Optimized sensor scheduling via minimax and semidefinite programming His lab team combines theoretical and applied research in robotics and control systems, with alumni contributing to academia, industry, and research labs globally.

Bei Jiangمشاهده پروفایل

دانشیار

• Biostatistics
• Functional Data Analysis
• Statistical Machine Learning
• +۶ مورد دیگر

Dr. Bei Jiang is an Associate Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta , Canada. She holds the Canada CIFAR AI Chair and is affiliated with the Alberta Machine Intelligence Institute (Amii) . Her academic journey includes a PhD in Biostatistics (2014) from the University of Michigan, MS (2008) and BS (2004) from the University of Alberta and Beijing University of Technology, respectively. Current Positions : 2021–Present (Associate Professor), 2022–Present (CIFAR AI Chair) Past Appointments : Assistant Professor (2015–2021), Postdoctoral Fellow at Columbia University (2014–2015), Research Assistant at University of Michigan (2009–2013) Research Interests : Dr. Jiang specializes in methods for joint modeling of longitudinal and health outcome data , Bayesian hierarchical modeling , functional and imaging data analysis , and statistical machine learning . Her work integrates kernel machine regression , differential privacy , and synthetic data generation to address challenges in heterogeneous health data and neuroimaging. Scientific Awards : Highlights include the 2015 SAMSI New Research Fellow , multiple Rackham Conference Travel Awards (2013, 2012), and prestigious NSERC scholarships (2009–2012). She has also received the J Gordin Kaplan Graduate Award (2008) and Statistical Society of Canada Travel Award (2008). Grants : $375,000 (CIFAR AI Chairs, 2022–2027), $480,000 (MITACS Accelerate, 2022–2025), and $210,000 (Canadian Statistical Sciences Institute, 2022–2025) Students and Postdocs : She mentors numerous PhD , MSc , and Postdoctoral Fellows , including Junxi Zhang (2023–Present), Enze Shi (2022–Present), and former advisees like Wenxing Guo (now Lecturer at University of Essex) and Yafei Wang (Assistant Professor at University of Alberta).

Lianne Lefsrudمشاهده پروفایل

دانشیار

• Risk Management
• Sustainability
• Organizational Theory
• +۵ مورد دیگر

Lianne Lefsrud serves as Associate Professor and Risk, Innovation, and Sustainability Chair (RISC) in the Department of Chemical and Materials Engineering at the University of Alberta's Faculty of Engineering. Her interdisciplinary research bridges engineering, social sciences, and policy to transform risk management practices across energy, mining, construction, and railroading industries, directly influencing regulations, building codes, and industry operations for sustainable development. Her academic credentials include: BSc in Civil Engineering (Cooperative Program), University of Alberta (1994) MSc in Interdisciplinary Civil & Environmental Engineering and Sociology, University of Alberta (1996) PhD in Strategic Management and Organization, Alberta School of Business (2014) Dr. Lefsrud's research centers on risk management frameworks for sustainability challenges. She examines hazard identification, social license to operate, and technology adoption drivers in high-hazard industries, with emphasis on prospective risk assessment (e.g., hydrogen infrastructure design) and retrospective analysis (e.g., microplastic pollution impacts). Her work integrates circular economy principles into energy systems while addressing unintended consequences across UN Sustainable Development Goals. Recent publications (2024-2025) demonstrate heavy focus on machine learning applications for rail and construction safety, hydrogen infrastructure risk analysis, and science denial mitigation. Key patterns show cross-industry adaptation of AI for incident prediction, regulatory gap analysis for emerging energy systems, and socio-technical approaches to reconcile sustainability goals with operational realities. Scientific recognition includes: Erb Post-Doctoral Fellowship (University of Michigan) Dow Sustainability Research Fellowship (Ross School of Business) Dr. Lefsrud mentors graduate students through industry-integrated projects like her Sustainable Design course where teams generated patents and city solutions. Her research secures Alberta Innovates funding with 1:4 industrial-to-federal matching, collaborating with Suncor, Transport Canada, and Canadian Standards Association. Grants target practical implementations including railcar inspection systems and hydrogen safety protocols. She co-founded Insight Risk Systems and leads the Lefsrud Lab, prioritizing inclusive teams with under-represented groups (women, Indigenous, LGBTQ2S+, neurodiverse) to tackle 'wicked problems' in sustainability. The lab leverages interdisciplinary partnerships across engineering, computer science, psychology, and environmental sociology for real-world risk management solutions.

Zukui Liمشاهده پروفایل

استاد

• Mathematical Optimization
• Machine Learning
• Process Systems Engineering
• +۴ مورد دیگر

Zukui Li is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta's Faculty of Engineering, where he leads a research group focused on mathematical optimization, machine learning, and process systems engineering. His work spans oil sands extraction, steel production, biomedical applications, and advanced optimization methods. Education: Ph.D. in Chemical Engineering, Rutgers University (2010) M.Sc. in Control Theory and Control Engineering, University of Science and Technology of China (2005) B.Sc. in Automatic Control, University of Science and Technology of China (2002) Postdoctoral Training: Princeton University (2010-2012) Research Focus: Dr. Li's research integrates mathematical optimization and machine learning for complex process systems. His primary areas include: Advanced optimization techniques (robust, stochastic, and distributionally robust optimization) Machine learning applications in process monitoring and biomedical systems Industrial applications in energy, manufacturing, and resource extraction Specific innovations include physics-informed ML for anemia treatment, adaptive optimization for steel production, and distributionally robust methods for uncertainty management. Publication Trends (2019-2023): Recent articles demonstrate a strong focus on uncertainty-aware optimization methods, with increasing integration of machine learning techniques. Dominant themes include distributionally robust optimization, adaptive decision-making under uncertainty, neural network approximations for complex constraints, and applications in industrial process control and biomedical systems. Theoretical advancements are consistently coupled with practical implementations in energy and manufacturing sectors. Research Group: Leads an active team developing optimization frameworks and machine learning solutions for process engineering challenges. Group website: Dr. Zukui Li's Research Group

• Graph Theory
• Graph Databases
• Knowledge Graphs
• +۵ مورد دیگر

Carlos R. Rivero is an Associate Professor in the Department of Computer Science at the Rochester Institute of Technology (RIT), located within the Golisano College of Computing and Information Sciences. His primary research focuses on graph theory applications in knowledge graphs, graph databases, and computer-aided program comprehension. He holds a PhD from the University of Seville (Spain), completed in 2012, with postdoctoral work at the University of Idaho (USA). His teaching responsibilities include courses such as Principles of Data Management, Data Mining, and Big Data exploration. Rivero has advised numerous PhD and Master’s students, contributing to research projects in link prediction, knowledge graph completion, and educational technology. He actively serves on program committees for conferences like The Web Conference and SIGKDD, and has reviewed for journals including the VLDB Journal and Communications of the ACM. His research emphasizes evaluating knowledge graph embeddings, improving link prediction methodologies, and developing tools for educational feedback in programming. He has contributed to projects like AYNEXT, which streamlines link prediction evaluation, and CAFE, a neighborhood-aware knowledge graph completion tool. Rivero’s work bridges theoretical advancements with practical applications in education and industry. Notable contributions include frameworks for automated feedback in programming courses and methodologies for assessing inference patterns in knowledge graphs. His grants and service roles reflect a commitment to advancing computational methods and fostering academic collaboration in data science and education.

Charlotte Williamsمشاهده پروفایل

استاد

• Sustainable Polymer Chemistry
• Catalyst Development
• Renewable Resources Utilization
• +۴ مورد دیگر

Professor Charlotte Williams is a leading academic in sustainable materials science at the University of Oxford. Her research focuses on developing catalysts to transform renewable resources and CO₂ into polymers, aiming to replace petrochemicals in scalable materials production. She collaborates with Unilever on the Clean Future initiative, pioneering recyclable and biodegradable polymers for consumer products. Her work was recognized with the 2021 Unilever Clean Future Supplier Brilliance Award, jointly awarded with Professors Rosseinsky and Cooper. Williams’ research spans heterodinuclear catalyst design, ring-opening copolymerization, and chemical recycling of polymers. She leads a multidisciplinary team including scientists, engineers, and policy experts to decarbonize chemical supply chains, contributing to the UK’s net-zero goals. Key collaborations include the EPSRC Prosperity Partnership with the Universities of Liverpool and Oxford, focusing on low-carbon laundry detergent materials. Education: Not explicitly detailed in text. Grants: EPSRC Prosperity Partnership (2021–2026), Unilever-funded projects. Labs/Teams: Collaborations with Liverpool University and industry partners like Unilever. Future Work: Expanding applications of CO₂-derived polymers in consumer goods and advancing recyclable materials. Her publications emphasize catalyst innovation, sustainable polymer synthesis, and materials lifecycle analysis, reflecting her commitment to bridging academic research with industrial sustainability challenges.