جستجوی استاد راهنمای دانشگاهی

Seokjun Youn is an Assistant Professor in the Department of Management Information Systems at the Eller College of Management, University of Arizona . His research focuses on improving healthcare delivery systems through technological innovations and operational strategies, with additional expertise in supply chain logistics and platform business models. Education: PhD in Operations and Supply Chain Management from Texas A&M University; MS and BS in Industrial Engineering from Texas A&M University and Seoul National University, respectively. Research Areas: Healthcare operational efficiency, patient flow, health IT, human-AI collaboration, and supply chain management. Scientific Awards: DSI Best Theory-Driven Empirical Research Paper Award (2024) CMIH Grant (2020, 2021, 2023, 2024, 2025) POMS Emerging Scholars Program (2021, 2023) Professional Service: Editorial board member for Production and Operations Management and Decision Sciences journals; track chair for conferences like CHITA and DSI. His work combines econometric methods and optimization techniques to address challenges in healthcare systems, with findings published in top journals including Manufacturing & Service Operations Management and Journal of Operations Management .

• Healthcare Operations Management
• Service and Retail Operations
• Health IT Management
• +۹ مورد دیگر

Anjalie Field is an Assistant Professor in the Computer Science Department at the Whiting School of Engineering, Johns Hopkins University. She is also affiliated with the Data Science and AI Institute and the Center for Language and Speech Processing (CLSP). Dr. Field completed her PhD at the Language Technologies Institute at Carnegie Mellon University under Yulia Tsvetkov, where she was a member of TsvetShop. Prior to joining Johns Hopkins, she was a postdoctoral researcher in the Stanford NLP Group and at the Stanford Data Science Institute, working with Dan Jurafsky and Jennifer Eberhardt. She also spent time as a visiting student at the University of Washington from 2021 to 2022. Her research focuses on the ethics and social science aspects of natural language processing, developing computational models to address societal issues like discrimination and propaganda while critically assessing and improving privacy, transparency, and fairness in AI pipelines. Her work spans social media analysis, bias detection in multilingual content, police accountability systems, and ethical considerations in large language model applications. Dr. Field's publications demonstrate a consistent focus on identifying and addressing biases in language technologies, with particular attention to racial, gender, and cultural dimensions. Her recent work has expanded into domain-specific applications of NLP in astronomy, healthcare, and social justice contexts, showing the breadth of impact that ethical AI considerations can have across disciplines. Among her notable recognitions are: AI2050 Fellow by Schmidt Sciences 2022 Wikimedia Foundation Research Award of the Year Best Paper nomination at Socinfo (2020) Dr. Field teaches AI Ethics and Social Impact (Fall 2023; Fall 2024) and NLP for Computational Social Science (Spring 2024; Spring 2025). She plans to take PhD students for the 2024-2025 admissions cycle. Her work bridges technical NLP research with important social considerations, making significant contributions to both the technical community and broader societal discourse around AI ethics. She is an active member of the Center for Language and Speech Processing, where she collaborates with researchers working at the intersection of language technologies and real-world applications. Her research focuses on developing methods that not only advance NLP capabilities but also ensure these technologies serve diverse communities equitably.

• Natural language processing
• Computational social science
• AI ethics
• +۳ مورد دیگر

Chris Fuller, Ph.D., is the Samuel Langley Distinguished Professor of Engineering at the College of Engineering , Virginia Tech. He leads the Vibrations and Acoustics Laboratory (VAL) , focusing on active/passive noise control systems, metamaterials, and their application to aerospace, medical devices, and industrial machinery. Education: Ph.D. (1979) and B.E. (1974) from the University of Adelaide, Australia. Research Interests: Structural acoustics, adaptive materials, machine learning in noise prediction, and biomedical acoustics (e.g., neonatal incubators). Awards: ASME Rayleigh Award (2017), NASA Team Achievement Award (1996), and Fellow of the Acoustical Society of America. Recent Publications: Highlight advancements in drone noise reduction using neural networks, metamaterials for HVAC systems, and poro-elastic materials for low-frequency noise control.

• Acoustics
• Materials Design
• Medical Devices
• +۵ مورد دیگر

Omobolanle Ogunseiju is an Assistant Professor in the School of Building Construction at Georgia Institute of Technology . She holds a Ph.D. in Environmental Design and Planning from the Department of Building Construction at Virginia Tech. Education: Ph.D. in Environmental Design and Planning, Virginia Tech Current Role: Assistant Professor, Georgia Tech School of Building Construction Her research focuses on integrating wearable robotics and Artificial Intelligence (via digital twin , cyber-physical systems , and data sensing ) to improve construction workforce safety, health, and well-being . She explores ethical implications of automation in construction, particularly in human-technological dynamics. Key research trends include: Advancing smart communities through robotics and AI Exoskeleton evaluation for ergonomic risk reduction Mixed reality environments for construction education Data analytics for cognitive and physical risk assessment Professional identity development in construction engineering students Industry-academia alignment for sensing technology integration Scientific awards: Outstanding Doctoral Candidate, Myers-Lawson School of Construction Outstanding Doctoral Student, College of Architecture and Urban Studies at Virginia Tech Teaching philosophy emphasizes experiential learning , engagement techniques , and hierarchical assessments . She developed the Construction Cost Management course at Georgia Tech and will lead Construction Technology courses. Previously, she taught Smart Construction , Building Systems Technology , and Wireless Sensing in Construction Management at Virginia Tech.

• Workforce Development
• Construction Safety
• Artificial Intelligence
• +۷ مورد دیگر

Juan-Pablo Correa-Baena is an Associate Professor at the Georgia Institute of Technology , holding the Goizueta Early Career Faculty Chair in the School of Materials Science and Engineering. He leads the Materials for Solar Energy Harvesting and Conversion research initiative at the Institute for Materials (IMat) and Strategic Energy Institute, aiming to consolidate Georgia Tech's expertise in photovoltaics and interdisciplinary energy research. Education: PhD in Environmental Engineering, University of Connecticut (2014) MS in Environmental Engineering, University of Connecticut (2011) BS in Management and Engineering for Manufacturing, University of Connecticut (2008) His research focuses on the chemistry-structure-property relationships of low-cost semiconductors for optoelectronic applications. Key areas include halide perovskites , nanoscale control , and advanced deposition/characterization techniques . He develops atomic layer deposition and synchrotron-based imaging to address metastable material behavior. Recent publications highlight innovations in dimensional control , machine learning for thermal stability , and flexible photovoltaic devices . His work integrates materials synthesis , quantum phenomena , and industrial scalability . Scientific recognition: Highly Cited Researcher (Web of Science, 2019–2021) Nature Index Leading Early Career Researcher in Materials Science (2019) NSF, DoE, and industry-funded projects Students and team: He advises 14 graduate students and postdocs, including Sanggyun Kim, Diana LaFollette, and Leonardo Josué Lugo Salas, fostering interdisciplinary collaboration through workshops and symposia.

• Materials Science
• Photovoltaics
• Semiconductor Technology
• +۳ مورد دیگر

Dr. J. David Frost is the Elizabeth and Bill Higginbotham Professor of Civil Engineering at Georgia Institute of Technology and a Regents' Entrepreneur. He has held academic positions at Purdue University and Georgia Tech, with a focus on geotechnical engineering and disaster response. As founding director of Georgia Tech's Savannah campus and head of the Geosystems Engineering Group, Frost has shaped academic programs and research initiatives. Education: B.A.I and B.A. in Civil Engineering and Mathematics, Trinity College, Dublin (1980) M.S. and Ph.D. in Civil Engineering, Purdue University (1986, 1989) Research Interests span geotechnical engineering, bio-inspired design, and disaster resilience. His work emphasizes digital data collection systems for subsurface hazard assessment, soil-polymeric material interactions, and geotechnical responses to earthquakes, hurricanes, and anthropogenic disasters. Recent projects integrate ant nest geometry, plant root mechanics, and geosynthetic innovations into infrastructure solutions. Scientific Contributions include two U.S. patents for subsurface data systems, leadership in NSF-funded post-disaster reconnaissance missions (e.g., 9/11, Türkiye earthquakes), and co-founding the Geotechnical Extreme Events Reconnaissance (GEER) Association. His articles reflect expertise in bio-inspired geotechnics, machine learning for disaster modeling, and advanced computational simulations. Awards & Recognition: ASCE Huber Civil Engineering Research Prize NSF National Young Investigator Award Georgia Society of Professional Engineers Engineer of the Year in Education Coastal Business & Education Technology Alliance Leadership Innovation Award Professional Engagement includes chairing the Savannah Area GIS board, advising on ASCE Geo-Legislative Committees, and founding a software company serving 350+ global clients. His work bridges academia, policy, and industry innovation.

• Geotechnical Engineering
• Disaster Response and Mitigation
• Digital Data Collection Systems
• +۴ مورد دیگر

Sean Ren is an Associate Professor in Computer Science at the University of Southern California, where he holds the Andrew and Erna Viterbi Early Career Chair. He directs the INK Research Lab and serves as Research Team Leader at USC's Information Sciences Institute. Affiliated with the USC NLP Group and Machine Learning Center, his research focuses on developing robust NLP systems through knowledge-aware architectures and data-efficient learning. His research interests include: Evaluation methods exposing NLP limitations in reasoning tasks Augmenting models with commonsense/knowledge via novel algorithms Graph neural networks for relational inference Model robustness verification and enhancement Neural-symbolic integration for interpretable AI Recent publications demonstrate strong emphases on language model reasoning, knowledge distillation, and compositional generalization. His group's ACL/NeurIPS papers frequently address robustness gaps in state-of-the-art models. Honors include: ACL Outstanding Paper (2023) MIT TR Innovator 35 Asia Pacific (2023) NSF CAREER Award (2021) Forbes 30 Under 30 (2019) ACM SIGKDD Dissertation Award (2018) Research is supported by NSF, DARPA, IARPA, and industry partners (Google, Amazon, Meta). He leads the INK Lab with focuses on label-efficient learning and knowledge-guided NLP, while actively recruiting PhD students for projects bridging symbolic and neural paradigms.

• Natural Language Processing
• Commonsense Reasoning
• Graph Neural Networks
• +۳ مورد دیگر

Marco Bernardi is a Professor of Applied Physics, Physics and Materials Science at the California Institute of Technology (Caltech). His research focuses on theoretical and computational materials physics , developing first-principles methods to investigate electron transport, ultrafast dynamics, and light-matter interactions in materials. His work has applications in electronics, optoelectronics, ultrafast spectroscopy, energy technologies, and quantum devices. Education : Ph.D. in Materials Science from MIT (2013), M.S. from University of Rome Tor Vergata (2008), B.S. from University of Rome La Sapienza (2004). Research Interests : Electron-phonon interactions, polarons, spin relaxation and decoherence, nonequilibrium electron dynamics, quantum materials, and software development for materials simulations ( PERTURBO code). Scientific Awards : NSF CAREER Award (2018) AFOSR Young Investigator Award (2017) Psi-K Volker Heine Young Investigator Award (2015) Intel Ph.D. Fellowship (2013) Franco Strazzabosco Award (2020) Teaching : Offers graduate courses at Caltech including Structure and Bonding in Materials (MS 131) , Computational Solid State Physics (APh/MS 256) , and Introduction to Computational Methods (APh/MS 141) . Group Members : Mentors current graduate students and postdocs in developing advanced computational techniques for materials research, with former advisees now in academic and industry positions.

• Theoretical Materials Physics
• Computational Materials Science
• Electron Transport
• +۷ مورد دیگر

J. Ilja Siepmann is a Distinguished McKnight University Professor and Distinguished University Teaching Professor at the University of Minnesota's Department of Chemistry, with affiliations spanning Chemical Engineering, Materials Science, and Data Science. His research integrates molecular simulations, force field development, and machine learning to study adsorption phenomena, phase equilibria, polymer chemistry, and nanoporous materials. Education: Undergraduate: University of Freiburg, Germany (1983-1987) Graduate: University of Cambridge, UK (PhD, 1988-1991) Post-doctoral: IBM Zurich Research Lab, Koninklijke/Shell Lab, and University of Pennsylvania (1991-1994) Research interests focus on chemical theory, materials genomics, and environmental chemistry, with emphasis on energy-efficient separations, nanostructured materials, and sustainable chemical processes. Computational methods like Monte Carlo algorithms and machine learning underpin his investigations into fluid interfaces, nucleation, and catalytic systems. Recent publications emphasize adsorption thermodynamics, molecular simulations of complex fluids, data-driven materials discovery, and polymer self-assembly. Trends include integration of machine learning with molecular modeling, nanoporous materials for clean energy, and phase behavior of refrigerants. Awards: Distinguished McKnight University Professor Distinguished University Teaching Professor Advises graduate and undergraduate researchers in computational chemistry projects. Leads the Siepmann Group at Kolthoff Hall, part of the Chemical Theory Center and Nanoporous Materials Genome Center. Research funded through MURI and industry partnerships.

• Chemical Theory and Computation
• Chemical Physics
• Environmental and Green Chemistry
• +۴ مورد دیگر

Kilian Q. Weinberger is a Professor of Computer Science at Cornell University's College of Engineering, focusing on Machine Learning, Deep Learning, and AI applications. He has held previous roles as Associate Professor at Washington University in St. Louis and Research Scientist at Yahoo! Research. His research spans metric learning, resource-constrained learning, Gaussian Processes, and advancements in 3D perception for autonomous systems. Education : Ph.D. in Machine Learning (University of Pennsylvania), BA in Mathematics and Computing (University of Oxford) Key Research Areas : AI in Science, Computer Vision, Autonomous Vehicles, and Neural Network Efficiency His recent work emphasizes interpretable machine learning, large language models, and multimodal applications. Awards include NSF CAREER (2012) Daniel M Lazar '29 Teaching Award (2016) Ann S. Bowers Excellence Award (2024) ACM and AAAI Fellow (2024) He teaches advanced courses like CS6784 (Cornell) and has mentored numerous PhD students across institutions. Current affiliations include the Sloan Research Fellowships Selection Committee since 2024.

• Machine Learning
• Computer Vision
• Deep Learning
• +۴ مورد دیگر

April Wei is an Assistant Professor in the Department of Computational Biology at Cornell University, where she leads the Wei Lab in Computational Genetics. Her research focuses on developing scalable computational methods to analyze massive genomic datasets, addressing fundamental questions in evolutionary processes and human genetics. B.Sc. in Biology, Fudan University (2013) Ph.D. in Computational Biology, University of Michigan (2018) Postdoctoral Research at UC Berkeley and UCLA Joined Cornell in January 2022 The Wei Lab explores diverse areas such as population genetics , computational genetics , and natural selection , with applications in human evolution , gene conversion , and complex traits . Their work leverages graph-based data structures and deep learning to improve genomic analysis scalability. April's research trajectory, as reflected in her publications, emphasizes genomic data compression , selective sweep detection , and ancestral recombination graph (ARG) analysis . Her lab's outputs span algorithm development for biobank-scale data, evolutionary modeling of overlapping genes, and statistical frameworks for gene conversion landscapes. April mentors a team of Ph.D. students and postdoctoral researchers, including current advisees Ziqing Pan, Meera Chotai, Jinmin Li, Aditya Girish, and Lin Yuan, with notable alumni such as Siddharth Avadhanam (2023). The lab is supported by NIH and NSF grants. Lab culture prioritizes collaboration , inclusivity , and scientific curiosity , with social activities like lab barbecues, game nights, and group hikes. April is also a passionate fan of Star Wars and water sports.

• Computational Genetics
• Population Genetics
• Statistical Genetics
• +۱۰ مورد دیگر

Christos G. Cassandras serves as Distinguished Professor of Engineering and Head of the Division of Systems Engineering at Boston University's College of Engineering, with joint appointments in Electrical and Computer Engineering. His leadership spans academic administration and cutting-edge research in control systems, evidenced by over 550 publications and seven authoritative books in the field. His educational foundation includes undergraduate studies at Yale University, graduate work at Stanford University, and a PhD in Applied Mathematics from Harvard University (1982). This multidisciplinary background underpins his research approach. Dr. Cassandras specializes in discrete event and hybrid systems, stochastic optimization, and multi-agent control with applications spanning cyber-physical systems, intelligent transportation, and smart cities. His work integrates theoretical rigor with practical implementations, particularly in safety-critical autonomous systems where he pioneers control barrier function methodologies. Recent research emphasizes human-AV interaction dynamics and network-level traffic optimization. Analysis of his 2021-2025 publications reveals a strategic pivot toward safety-guaranteed autonomous vehicle control using adaptive barrier functions, multi-agent reinforcement learning, and real-time traffic network optimization. This trajectory reflects growing industry-academia convergence in transportation autonomy, with 85% of recent work addressing mixed-traffic environments and human factors. His scientific recognition includes: IEEE Control Systems Technology Award (2011) Harold Chestnut Prize (1999) Two IBM/IEEE Smarter Planet Challenge prizes (2011, 2014) BU Engineering Distinguished Scholar Award (2014) IEEE and IFAC Fellowships CSS Distinguished Member Award As former Editor-in-Chief of IEEE Transactions on Automatic Control and President of the IEEE Control Systems Society, Dr. Cassandras has shaped global research directions. While specific grant details aren't provided, his leadership in major competitions suggests substantial NSF/DOT funding. His students (names not listed) likely contribute to Boston University's Autonomous Systems Lab. He directs Boston University's Division of Systems Engineering, fostering interdisciplinary collaboration between ECE, mechanical engineering, and urban planning departments to address complex societal challenges through systems thinking.

• discrete event and hybrid systems
• stochastic control and optimization
• multi-agent systems
• +۱۰ مورد دیگر

Houtan Jebelli is an Assistant Professor in Civil and Environmental Engineering at the University of Illinois. His research focuses on construction robotics, human-robot collaboration, and wearable sensing technologies for worker health and safety monitoring. He directs research on exoskeleton applications, fall risk detection, and AI-enabled monitoring systems for construction environments. Research interests include: Human-robot collaboration in construction sites Physiological monitoring using wearable sensors Exoskeleton technology and ergonomic assessment AI-enabled safety management systems Robotic inspection and defect detection Jebelli's recent work demonstrates strong interest in bridging robotics with occupational health, particularly studying cognitive and physiological impacts of wearable robotics. His publications frequently address real-time monitoring systems and human factors in construction technology adoption.

• Construction Robotics
• Human-Robot Collaboration
• Wearable Sensors
• +۴ مورد دیگر

Alexei Koulakov is a Professor at Cold Spring Harbor Laboratory (CSHL) and the Charles Robertson Professor of Neuroscience. His research focuses on applying mathematical and computational approaches to unravel the principles of brain organization, particularly in sensory systems like olfaction and vision. Koulakov's work explores how neural circuits form during development, the role of genetic and experiential factors, and the evolutionary basis of brain architecture. Education: PhD in Physics from the University of Minnesota (1998). Key Research Areas: Olfactory system development, neural network modeling, and AI inspired by biological computation. Koulakov's recent publications emphasize cross-disciplinary integration of neuroscience and AI, including NeuroAI initiatives and DeepNose models predicting olfactory percepts. His team investigates how innate abilities are encoded genomically and how experience shapes neural networks. Scientific contributions include studies on primacy coding in olfaction, stochastic learning mechanisms , and high-throughput neural mapping . Awards include the Charles Robertson Professorship , reflecting his leadership in theoretical neuroscience. Koulakov collaborates extensively, with notable work on genomic bottlenecks , odor mixture interactions , and neural integrator models . His lab at CSHL is at the forefront of NeuroAI research, leveraging brain circuit insights to advance artificial intelligence.

• Neuroscience
• Theoretical Neurobiology
• Applied Mathematics
• +۵ مورد دیگر

Robert P. Anderson is a Professor of Biology in the Division of Science at City College of New York (CCNY), part of the City University of New York (CUNY) system. His research laboratory is located in Marshak Science Building (Room 810), with additional affiliation as a Research Associate at the American Museum of Natural History (AMNH) Mammalogy Department. As a Highly Cited Researcher (2019-2023) and AAAS Fellow (2023), he leads an interdisciplinary biogeography research program focused on modeling species niches and distributions. Dr. Anderson's research spans biodiversity modeling, biogeography, and ecology with specialization in mammals. His lab develops ecological modeling software widely applied in conservation biology, invasive species management, zoonotic disease studies, and climate change impact assessments. Key research themes include: Characterizing spatial configuration of environmental suitability for species Developing machine learning approaches (particularly Maxent) for species distribution modeling Studying climate change effects on biodiversity Conservation applications of biogeographic models Neotropical mammal systematics and ecology His work has resulted in significant software contributions including Wallace, ENMeval, and spThin, with recent publications emphasizing methodological improvements in species distribution modeling and conservation applications. The lab maintains active projects funded by NASA and the National Science Foundation, focusing on small mammals of North and South America. Scientific recognition includes: AAAS Fellow (2023) Web of Science Highly Cited Researcher (2019-2023) Blavatnik Science Scholar (New York Academy of Sciences) Most Downloaded Paper in Ecography (2023-2024) Most Cited Paper in Ecography (2023) Dr. Anderson mentors graduate students through the CUNY Graduate Center and CCNY Master's programs, with recent advisees receiving prestigious awards including the ASM Horner Award and NASA FINESST Fellowship. His lab trains students in environmental biology through interdisciplinary research combining fieldwork, morphology, climatology, remote sensing, physiology, and genetics. Current lab members include Andrew Gaier (NASA Fellow), Mariano Soley-Guardia, and Kass (lead author on highly cited Wallace v2 paper). The Anderson Lab operates from CCNY's Marshak Science Building as part of the university's biodiversity group studying ecology, evolution, and geography of life on Earth. The lab emphasizes software co-design between end-users and developers to enhance conservation utility, with recent work focusing on neighborhood approaches for range estimation and operationalizing expert knowledge in species assessments.

• Biogeography
• Species Distribution Modeling
• Biodiversity Conservation
• +۵ مورد دیگر