Model Selection and Aggregation Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۷۳۶-۷۵۰ از ۱٬۱۴۹ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Suyeon Kangمشاهده پروفایل

استادیار

University of Central FloridaUnited States

Suyeon Kang is an Assistant Professor in the Department of Statistics and Data Science at the University of Central Florida (UCF), within the College of Sciences. Her research focuses on multivariate analysis, causal inference, sensitivity analysis, and mixture models, with applications in disparities research, social sciences, and health sciences. She previously served as a postdoctoral researcher at the University of Florida. Education: Ph.D. in Applied Statistics, University of California, Riverside (2022) M.S. in Statistics, Ewha Womans University (2017) B.S. in Statistics and B.E. in Economics, Ewha Womans University (2014) Research Interests: Her work bridges statistical methodology with interdisciplinary applications, emphasizing causal decomposition analysis, partial identification of causal effects, and methodological advancements in handling unmeasured confounding. She develops statistical software tools (e.g., R packages like MixSemiRob and rrMixture ) to support robust data analysis in complex systems. Publications: Kang’s recent work includes studies on causal decomposition in STEM disparities, sensitivity analysis frameworks, and computational methods for mixture models. Her research often addresses societal challenges through rigorous statistical approaches. Awards/Grants: No awards explicitly listed, but her postdoctoral and faculty roles reflect sustained scholarly engagement. Grants and funding details are not provided in the text.

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Jarek Gryzمشاهده پروفایل

استاد

York UniversityCanada

Professor Jarek Gryz is a faculty member in the Department of Electrical Engineering and Computer Science at York University's Lassonde School of Engineering. He also holds an affiliation with the Department of Philosophy, exploring intersections between AI and cognitive science. His research focuses on database systems, artificial intelligence, data mining, and ethical implications of AI. Gryz has advised multiple PhD graduates and contributes to projects like privacy in AI, explainability challenges, and query optimization. His work spans theoretical foundations and practical implementations, including systems like DB2 and SPARQL query processing. Education: Doctorate in Computer Science (not explicitly stated in texts). Research Interests: Gryz investigates core database technologies (e.g., skyline queries, FP-tree algorithms) alongside emerging areas like AI ethics and privacy. He explores technical challenges in creating moral agents and ensuring transparency in black-box systems. His work bridges computer science with philosophy, addressing societal impacts of AI. Publications: Recent articles highlight advancements in database efficiency, ethical AI frameworks, and constrained clustering. Key themes include optimizing query performance, analyzing maximal vectors, and developing explainable algorithms. Awards: None explicitly mentioned. Advising & Grants: Gryz has supervised students such as Nima Shahbazi and Nikolay Yakovets, contributing to projects like FP-tree bounds and property path query planning. His research aligns with IBM’s DB2 optimizations and forensic data analysis (e.g., 3D crime scene databases). Labs/Teams: Engaged with York’s EECS department and interdisciplinary groups exploring AI ethics and cognitive science applications.

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Vítor Félixمشاهده پروفایل

دانشیار

University of AveiroPortugal

Vítor Félix is an Associate Professor with Aggregation at the University of Aveiro's Department of Chemistry. His academic career spans over three decades, with roles including Visiting Scientist at the University of Reading and collaborations with institutions like the University of Oxford and University of Southampton. His research focuses on supramolecular chemistry, molecular modeling, and anion recognition, leveraging computational tools like quantum calculations and molecular dynamics. Key projects include drug-biological target interactions, halogen bond investigations in interlocked systems, and anion transport mechanisms. His group, the Molecular Modelling Group, is part of the G6 (Virtual Materials and Artificial Intelligence) and L3 (Sustainability) research lines. Notable contributions include studies on halogen-bonding hosts for ion pairs and functionalized hydrogels for bioapplications. Collaborations span international networks in supramolecular chemistry and materials science. Research highlights include experimental and computational studies on anion selectivity, redox-state receptors, and membrane transport systems. Recent work emphasizes drug-like Lego molecules for anion binding and cytotoxicity evaluation. His articles explore themes like chalcogen-bonding rotaxanes, hydrazones in transporters, and bioactive diselenides. Advising and grants are not explicitly detailed, but his work is supported by extensive international partnerships.

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Aravinda Karمشاهده پروفایل

استاد

University of Central FloridaUnited States

Aravinda Kar is a Professor at the University of Central Florida (UCF), affiliated with the College of Optics and Photonics (CREOL), Mechanical and Aerospace Engineering (MAE), and Materials Science and Engineering (MSE). He earned his PhD from the University of Illinois at Urbana-Champaign. His research focuses on laser-assisted manufacturing, semiconductor materials processing, laser beam shaping, and thermal/materials science. Key projects include laser-based material synthesis, doping techniques, and advanced manufacturing diagnostics. Education: PhD in Materials Science from the University of Illinois at Urbana-Champaign. Research Interests Laser-Driven Materials Synthesis: Developing novel nanocomposite films and semiconductor devices via CO₂ laser processing. Semiconductor Fabrication: Innovations in laser doping of silicon carbide (SiC) for mid-infrared optoelectronics. Advanced Manufacturing: Laser additive manufacturing, cladding, and process diagnostics for complex parts. Optical Materials: Enhancing transmittance of TiO₂ thin films via laser sintering. Scientific Contributions Recipient of the Arthur L. Schawlow Award (2021) and Fellowships from National Academy of Inventors (2017) and Laser Institute of America (2000). Edited a tribute journal edition on laser additive manufacturing honoring Prof. Jyoti Mazumder (2023). Guided over 15 PhD students, many of whom won awards at international conferences like ICALEO and IMAPS. Advising & Grants Supervised 15 PhD students and multiple postdoctoral researchers. Research funding includes projects on laser processing, photovoltaic materials, and semiconductor fabrication. Collaborates with industry partners on advanced manufacturing solutions. Labs & Teams Director of the Laser-Advanced Manufacturing, Materials and Micro-Processing (LAMMMP) Lab , which focuses on laser-material interactions, process modeling, and nanomaterial synthesis. Active in interdisciplinary teams addressing biomaterials, energy materials, and optoelectronics.

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Stephen Machadoمشاهده پروفایل

استاد

Oregon State UniversityUnited States

Stephen Machado is a Professor in the Department of Crop and Soil Science at Oregon State University's College of Agricultural Sciences. He leads OSU's long-term research activities at the Columbia Basin Agricultural Research Center (CBARC) in Pendleton, Moro, and Heppner. His primary focus is on dryland cropping systems in semiarid regions of eastern Oregon, aiming to develop sustainable agricultural practices that enhance soil health while maintaining economic viability. His research integrates crop rotations, biochar applications, cover crops, and soil management strategies to address challenges like soil organic matter depletion, drought tolerance, and disease interactions. Key projects include long-term experiments assessing soil carbon dynamics and microbial community responses to agro-management practices. Research trends in his publications emphasize climate-smart agriculture, with a focus on soil carbon sequestration, biochar effects, and cover crop optimization. He explores how agroecosystem management can mitigate climate impacts while improving crop resilience in low rainfall zones. Dr. Machado collaborates with regional farmers and policymakers to translate research into actionable strategies for sustainable dryland farming. His work bridges agronomic practices with environmental stewardship, aiming to balance productivity and ecological health in semiarid agricultural landscapes.

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Victor Hugo Vidal Ribeiroمشاهده پروفایل

استادیار

Oregon State UniversityUnited States

Victor Hugo Vidal Ribeiro is an Assistant Professor at Oregon State University, affiliated with the College of Agricultural Sciences and the Department of Crop and Soil Science. His research focuses on developing science-based solutions for weed management challenges in field crops, with particular expertise in herbicide resistance, herbicide environmental fate, and integrated weed management strategies. He holds a PhD in Agricultural Sciences and has extensive experience in applied research and extension programs addressing agricultural sustainability. His professional accomplishments include leadership in herbicide resistance research, collaborations with farmers and agricultural stakeholders, and contributions to the understanding of weed biology and ecology. He is actively involved in outreach initiatives to disseminate practical solutions to growers. Ribeiro’s research has explored genetic diversity in weed populations, herbicide resistance evolution, and the environmental impacts of pesticides. His recent work emphasizes the genetic basis of resistance in species like Bromus tectorum and Amaranthus hybridus, as well as strategies for managing invasive weeds in dryland wheat and soybean systems. While his primary focus is on agricultural sciences, his interdisciplinary work occasionally extends to energy market design and public health analyses, showcasing his adaptability in addressing complex cross-disciplinary challenges.

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Chunyu Wangمشاهده پروفایل

استاد

Rensselaer Polytechnic InstituteUnited States

Chunyu Wang is a Professor of Biological Sciences and Chemistry and Chemical Biology at Rensselaer Polytechnic Institute (RPI), where he holds a joint appointment in the Center for Biotechnology and Interdisciplinary Studies (CBIS). With an MD from Peking Union Medical College (1996) and a PhD in Molecular Biology and Genetics from Cornell University (2000), followed by postdoctoral training at Columbia University, Dr. Wang has established a distinguished research career spanning nearly two decades at RPI. Dr. Wang's research focuses on the structural mechanisms of Alzheimer's disease and cancer, with particular emphasis on amyloid-beta production, tau-glycan interactions, and ApoE in neurodegeneration, as well as Hedgehog signaling and p53 in oncology. His laboratory employs nuclear magnetic resonance (NMR) spectroscopy as a primary tool to investigate protein structure and dynamics, complemented by biochemical, biophysical, and computational approaches. Recent work has centered on heparan sulfate modifications and their role in disease progression, with promising implications for therapeutic development. Dr. Wang teaches advanced courses in protein structure determination (BCBP 4870/6870) and molecular modeling (BCBP/BIOL 4550/6420), training the next generation of structural biologists. His research program has been continuously funded by NIH, including multiple R01 grants, as well as awards from the Alzheimer's Association, Warren Alpert Foundation, and New York State. In 2024, he was elected as a Fellow of the American Association for the Advancement of Science (AAAS), recognizing his significant contributions to structural biology and neurodegenerative disease research. Selected Scientific Awards: Fellow of the American Association for the Advancement Science (AAAS) (2024) NIA R01 grant for ApoE research featured on RPI News (2024) NIA R01 grant for Tau-glycan interaction in AD (2020) Warren Alpert Foundation grant for Structure-Based Drug Discovery (2019) NIA T32 and R25 grants for Alzheimer's research training (2024) Dr. Wang actively mentors a diverse team of researchers, including postdoctoral associates, graduate students, and undergraduates, fostering an inclusive environment that has produced numerous successful scientists. His laboratory has made significant contributions to understanding the molecular mechanisms of protein aggregation in Alzheimer's disease and the structural basis of Hedgehog signaling in cancer, with work published in high-impact journals including PNAS, Nature Communications, and Angewandte Chemie. Current research directions include developing novel therapeutics targeting protein-glycan interactions and exploring the circadian regulation of heparan sulfate in neurodegenerative processes.

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LIM Ee Pengمشاهده پروفایل

استاد

Singapore Management UniversitySingapore

Professor LIM Ee Peng is a Full-time Faculty member at the School of Computing and Information Systems, Singapore Management University, holding the position of Director for the Artificial Intelligence & Data Science Cluster. He obtained his PhD in Computer Science from the University of Minnesota in 1994. His research focuses on Artificial Intelligence , Data Science , and Healthcare Technology , with notable contributions to multimodal learning systems , large language models , and ESG forecasting . Research Interests: Machine Learning and Deep Learning Natural Language Processing and Dialogue Systems Healthcare Technology and Behavioral Analysis Financial Technology and Risk Management Social Network Analysis and Job Market Modeling Recent work trends emphasize AI ethics (e.g., steering LLMs toward human-aligned behaviors), multimodal fusion (food image analysis, agent-generated content), and applied AI systems (health counseling chatbots, investment management tools). His research often bridges theoretical advancements with real-world applications in healthcare, finance, and education. As a mentor, he has advised students like TAN Shao Wei and led interdisciplinary projects funded by Singapore's National Research Foundation. His lab develops systems like FoodAI (smart food logging), JobSense (career exploration frameworks), and FoodBot (health interventions). Labs/Teams: Leads the Artificial Intelligence & Data Science Cluster at SMU, fostering collaborations between academia and industry partners.

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Stacy Pattersonمشاهده پروفایل

دانشیار

Rensselaer Polytechnic InstituteUnited States

Stacy Patterson is an Associate Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). She holds a B.S. in Computer Science and B.A. in Mathematics from Rutgers University, and an M.S. and Ph.D. in Computer Science from the University of California, Santa Barbara. Her research focuses on distributed machine learning, edge and cloud computing, and networked systems, with an emphasis on privacy-preserving techniques and formal verification of safety-critical systems. Dr. Patterson has received prestigious awards including the Viterbi Postdoctoral Fellowship and the IEEE Control Systems Society Axelby Outstanding Paper Award. Her work bridges theoretical foundations and practical applications, addressing challenges in federated learning, consensus algorithms, and distributed systems resilience. She has contributed to advancements in secure aggregation, privacy-preserving vertical federated learning architectures, and robust network design. Her recent research trends emphasize interdisciplinary solutions for federated learning in dynamic environments, leveraging formal methods to ensure system correctness. Key areas include optimizing communication efficiency, enhancing security in multi-party collaborations, and applying control theory principles to distributed computing systems. Dr. Patterson’s academic journey includes postdoctoral roles at the University of California, Santa Barbara (2009–2011) and the Technion – Israel Institute of Technology (2011–2013). She actively participates in international workshops and conferences, driving forward research in federated learning and edge computing. Her lab focuses on practical implementations of secure distributed algorithms with real-world applications in finance, aerospace, and autonomous systems.

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Haijin Linمشاهده پروفایل

استاد

University of HoustonUnited States

Haijin Lin is a Professor of Accounting at the Bauer College of Business, University of Houston. His research focuses on information economics, regulatory frameworks, managerial incentives, and corporate governance, with notable contributions to accounting theory, quantum information applications, and incentive design. He holds a PhD from Carnegie Mellon University and teaches courses such as Financial Accounting, Cost Accounting, and Management Accounting. Lin's work bridges accounting with interdisciplinary fields like quantum mechanics and topology, as seen in his exploration of entropy in accounting systems and quantum entanglement analogies. His research addresses critical issues in managerial decision-making, audit procurement, and regulatory effectiveness. He has published extensively in top journals including The Accounting Review, Contemporary Accounting Research, and Journal of Accounting Research. Lin serves on the editorial board for Sustainability under the American Accounting Association. His academic contributions span over two decades, with a focus on advancing theoretical frameworks and practical applications in accounting systems, performance evaluation, and institutional governance structures.

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Daniel Paluszczyszynمشاهده پروفایل

مدرس ارشد

De Montfort UniversityUnited Kingdom

Daniel Paluszczyszyn is Senior Lecturer in the School of Engineering and Sustainable Development at De Montfort University, affiliated with the Interdisciplinary Group in Intelligent Transport Systems (DIGITS). His research bridges automotive engineering and infrastructure systems. Current projects focus on sensor modeling for connected autonomous vehicles, developing test scenarios for non-line-of-sight navigation, and container-based software updates for automotive systems. His water systems research includes pump optimization and network simplification tools. Specializes in Bayesian pollution modeling and neural network traffic prediction Developed Water Advisory Demand Evaluation Toolkit for resource planning Leads research in range-extended electric vehicle management systems His publications demonstrate consistent innovation in applying computational methods to transportation and hydraulic engineering challenges.

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Timothy Havensمشاهده پروفایل

استاد

Michigan Technological UniversityUnited States

Timothy Havens is a Professor at Michigan Technological University in the Department of Computer Science within the College of Computing. He holds the William and Gloria Jackson Professorship and serves as Executive Director of both the Great Lakes Research Center and the Institute of Computing and Cybersystems. Dr. Havens also directs the PRIME Lab and has been recognized for both teaching and research excellence, including the 2014-15 Professor of the Year award from IEEE Eta Kappa Nu and Best Paper Awards at FUZZ-IEEE 2012 and IEEE SMC 2011. Dr. Havens received his Ph.D. in Electrical and Computer Engineering from the University of Missouri, Columbia in 2010. Prior to joining Michigan Tech, he was an NSF/CRA Computing Innovation Postdoctoral Fellow at Michigan State University under Dr. Anil Jain. Before his Ph.D. work, he was an Associate Technical Staff member at MIT Lincoln Laboratory. His educational background includes an M.S. in Electrical Engineering (2000) and a B.S. in Electrical Engineering (1999), both from Michigan Technological University. His research focuses on pattern recognition and machine learning, signal processing, and sensor fusion, with specific expertise in fuzzy integrals, Choquet integration, community detection in networks, and heterogeneous data mining. Dr. Havens has made significant contributions to explainable AI, particularly through the application of fuzzy integrals to deep learning systems. His recent publications demonstrate strong activity in developing novel regularization techniques for fuzzy Choquet integrals, efficient algorithms for community detection, and similarity measures for interval data. Dr. Havens' research has been consistently funded by prestigious organizations including DARPA, NSF, US Navy, Office of Naval Research, National Geospatial-Intelligence Agency, Ford Motor Company, MIT Lincoln Laboratory, and numerous other government and industry partners. His current projects include significant grants for radar systems in the Great Lakes, generative modeling of satellite imagery, and algorithms for autonomous robot systems. Scientific Awards: 2014-15 Professor of the Year by IEEE Eta Kappa Nu, Beta Gamma Chapter Best Paper Award at FUZZ-IEEE 2012 Best Paper Award at IEEE SMC 2011 Best Student Paper Award Finalist (2018) IEEE Franklin V. Taylor Memorial Best Paper Award (2011) Dr. Havens has successfully mentored numerous graduate students, many of whom appear as co-authors on his publications. His research grants demonstrate strong leadership in coordinating multi-institutional collaborations with substantial funding. Beyond his academic work, Dr. Havens is also an active musician, playing bass in several bands including MUFJAC, FLOTUS, Defenestra, Skills of Ortega, Psylocubik, Triptych, Wheels of Fire, and Odibil libidO.

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Dariush Hinderbergerمشاهده پروفایل

استاد

Martin Luther University Halle-WittenbergGermany

Dariush Hinderberger is Professor of Physical Chemistry at Martin-Luther University Halle-Wittenberg's Department of Chemistry, specializing in complex self-organizing systems. His technical expertise includes electron paramagnetic resonance (EPR) and infrared spectroscopy. Research encompasses protein hydrogels, thermoresponsive polymers, and biomolecule-detection probes. Recent work explores antibody-polymer interfacial interactions, mitochondria-targeted ROS sensors, and ionic liquids as membrane mimics. He directs the EPR spectroscopy facility.

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Senjuti Basu Royمشاهده پروفایل

دانشیار

New Jersey Institute of Technology (NJIT)United States

Senjuti Basu Roy is an Associate Professor in the Department of Computer Science at New Jersey Institute of Technology (NJIT). She specializes in data mining, machine learning, and healthcare informatics, with a focus on fairness in algorithms, optimization, and interdisciplinary applications in biotechnology and healthcare. Her work integrates AI systems with real-world challenges such as equitable resource allocation, predictive modeling for medical outcomes, and sustainable materials science. Education: Ph.D. in Computer Science, University of Texas at Arlington (2011) M.S. in Computer Science, University of Texas at Arlington (2007) B.Tech. in Computer Science and Engineering, University of Calcutta (2004) Research Interests: Dr. Basu Roy’s research spans algorithmic fairness , top-k query optimization , healthcare data analytics , and crowdsourcing systems . She develops frameworks for equitable data-driven decision-making and explores applications in biomedical engineering, environmental science, and structural safety. Her work bridges theoretical computer science with practical domains like drug discovery and infrastructure resilience. Publications: Her recent work includes advancements in reinforcement learning, fairness-aware preference systems, and computational methods for biomedical problems. Notable contributions include frameworks for feature selection in incomplete datasets and optimization algorithms for maritime logistics. Awards & Grants: She has secured NSF grants including “CAREER: Streamlining Task Deployment on Crowdsourcing Platforms” and “III: Small: Collaborative Research on Derived Attributes.” Her research emphasizes human-AI collaboration and equitable technology design. Advising & Teaching: Teaches courses like Data Mining (CS 634) and Introduction to Data Science (CS 301). Advises on peer-learning methodologies and collaborative task optimization. Her lab focuses on real-world AI applications and interdisciplinary problem-solving.

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Patrice Smithمشاهده پروفایل

استاد

Carleton UniversityCanada

Patrice Smith is a Professor in the Neuroscience department at Carleton University. Their research focuses on molecular mechanisms underlying central nervous system repair and neurodegeneration. Key interests include axon regeneration failure in the mature CNS and mechanisms of neuronal cell death in Parkinson’s disease and traumatic brain injury. Education: Ph.D. from the University of Ottawa, Post-doctoral Fellowship at Harvard University Labs: Smith Lab, investigating cytokine effects on neurite growth and neuroprotective pharmacological agents Selected publications explore calpain-cdk5 pathways in dopaminergic neuron death, BAG5’s role in parkin inhibition, and cdk5’s mediation of neurodegeneration in Parkinson’s models. No scientific awards are explicitly listed, but research contributions are significant in neurodegenerative disease mechanisms.

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فقط ۷۵۰ نتیجه اول نمایش داده می‌شود. برای رسیدن به نتایج دقیق‌تر، جست‌وجو را با فیلترها محدودتر کنید.