جست‌وجوی استاد و پژوهشگر

Ramon Weberمشاهده پروفایل

استادیار

• Computational design
• sustainability
• structural modeling
• +۲ مورد دیگر

Ramon Weber is an Assistant Professor of Architecture at the University of California, Berkeley, where he investigates computational design methods and simulation tools for sustainable architecture with emphasis on embodied carbon reduction. His work bridges architectural design, structural engineering, and environmental science to develop low-carbon building frameworks. His educational background includes: PhD in Architecture – Building Technology from the Massachusetts Institute of Technology MSc (with distinction) in Integrative Technologies and Architectural Design Research from the University of Stuttgart MS in Media Arts and Sciences from the Massachusetts Institute of Technology BSc in Architecture from ETH Zürich (Swiss Federal Institute of Technology) Dr. Weber's research integrates computational design, structural modeling, and generative systems to optimize building sustainability. He develops automation tools for carbon-efficient design processes, focusing on material efficiency, space utilization, and renewable energy integration while maintaining structural integrity and architectural quality. His publication record (2019-2024) reveals a consistent trajectory toward decarbonizing construction through computational methods. Key themes include robotic timber fabrication, solar-integrated building systems, embodied carbon estimation algorithms, and spatial optimization techniques—all demonstrating cross-disciplinary innovation between architecture, engineering, and environmental science. Scientific recognition includes: MIT Presidential Fellowship Scholar of the Norman Foster Foundation and the Rolex Institute, Madrid, Spain No information regarding student advising or research grants was provided in source materials. Dr. Weber's professional trajectory includes research at MIT's Mediated Matter Group and practice at Zaha Hadid Architects' ZHA|CODE research group, with work exhibited at SFMOMA, Cooper Hewitt Design Triennial, and Ars Electronica.

Ergun Aklemanمشاهده پروفایل

استاد

• Shape Modeling
• Geometric Data Structures
• Non-Photorealistic Rendering
• +۶ مورد دیگر

Ergun Akleman is a Professor at Texas A&M University, affiliated with the College of Performance, Visualization & Fine Arts, Department of Visualization. He holds degrees from Istanbul Technical University (B.S. Electrical Engineering, 1981) and Georgia Institute of Technology (M.S. and Ph.D. in Electrical Engineering, 1986 and 1992). His research focuses on shape modeling, geometric data structures, non-photorealistic rendering, volume modeling, and 3D printing innovations. His work explores cutting-edge topics such as topologically interlocked structures, space-filling geometries, and cooperative robotics in additive manufacturing. Recent studies include frameworks for collision-free 3D printing and theoretical advancements in computational design. His articles often bridge computational geometry with real-world applications in materials science and manufacturing. No scientific awards are explicitly mentioned in the provided texts. His research has implications for architecture, material science, and digital fabrication. He has contributed to projects like SafeZone and VoroNoodles , emphasizing interdisciplinary approaches to geometric challenges.

• Artificial Intelligence
• Machine Learning
• Multimodal Models
• +۴ مورد دیگر

Dr. Silvio Savarese is an Adjunct Professor in the Department of Computer Science at Stanford University. His research focuses on advancing artificial intelligence, particularly in multimodal models, autonomous agents, and large language models (LLMs). He has contributed to foundational work in vision-language models like BLIP3 and XGen, as well as frameworks for training action models and optimizing AI agents. His projects emphasize practical applications such as CRM systems, code generation, and ethical AI. Current interests include scalable reasoning in LLMs, efficient model architectures, and multimodal pretraining for 3D understanding. Key research directions include developing unified multimodal models capable of handling vision, language, and point cloud data, as seen in projects like ULIP and Merlion. He explores agent systems through benchmarks like Behavior-1K and BOLAA, aiming to improve real-world interaction capabilities. Recent work addresses challenges in data generation (Text2Data), agent optimization (PRACT), and safety through methods like LZ Penalty for text repetition control. His contributions span open-source libraries (LAVIS, OMNIXAI) and datasets (DialogStudio, Eye-BEHAVIOR), emphasizing reproducibility and community impact. While no formal awards are listed, his extensive publication record and industry collaborations highlight academic and industrial relevance. Current projects include high-fidelity video synthesis (xGen-videosyn-1), time series forecasting (Moirai-MoE), and lightweight agent frameworks (AgentLite).

Jacelyn Rice-Boayueمشاهده پروفایل

استادیار

• Sustainable Water Resource Management
• Environmental Justice
• Geospatial Analytics
• +۵ مورد دیگر

Jacelyn Rice-Boayue is an Assistant Professor in the Department of Civil, Construction and Environmental Engineering at North Carolina State University. Her research focuses on sustainable water resource management, environmental justice, and geospatial analytics. She investigates water quality, environmental health contaminants, and antibiotic resistance in wastewater-impacted systems, often collaborating with community stakeholders through participatory GIS approaches. Her work integrates engineering, hydrology, and social sciences to address complex human-water system challenges. Recent research includes developing methodologies to identify antibiotic pollution vulnerabilities and modeling endocrine disruptor exposures during low-flow conditions. The group’s studies often emphasize cross-cultural perspectives and community engagement in water policy decisions. Key contributions span antibiotic resistance hazards, wastewater treatment optimization, and public acceptance of water reuse practices. Articles frequently analyze watershed-scale risks and propose decision-support frameworks for environmental monitoring. Despite no explicit awards listed, her active publication record reflects sustained academic engagement in high-impact areas like environmental justice and water security. Dr. Rice-Boayue’s research group works closely with stakeholders in cities like Charlotte to co-design solutions addressing environmental inequities. Current projects include spatial decision systems for pandemic wastewater surveillance and antibiotic pollution mitigation strategies for vulnerable watersheds.

Jose Florez-Arangoمشاهده پروفایل

دانشیار

• Health Informatics
• Human-Computer Interaction
• Human Factors Engineering
• +۴ مورد دیگر

Jose Florez-Arango is a Colombian physician and Associate Professor at the Graduate School of Medical Sciences, Weill Cornell Medicine, with over 20 years of experience as a clinician, educator, and researcher. His work focuses on human factors and technology-human interaction to improve healthcare outcomes in resource-constrained environments, positioning him as a thought leader in Latin American telemedicine. His research spans Health Informatics, Human-Computer Interaction, and Human Factors Engineering, emphasizing minimal-code solutions for clinical decision support, knowledge transfer, and workload reduction. Key areas include telemedicine implementation, visual knowledge representation, personalized health tools, and curricular innovation for 21st-century healthcare providers, utilizing methodologies like community-based research and participatory design. Publications reveal a trend toward immersive simulations (VR for obstetrics training), NLP-driven maternal health analytics, mobile mental health screening in global contexts (e.g., Fiji), and XML-based decision support systems—all targeting low-resource settings and provider-patient experience enhancement. Scientific distinctions include: ADVANCE Administrative Fellow – Texas A&M University (2021-2022) MEEl Fellow – COM (2021) Schull Institute Scholar (2009) MFA Scholarship – UTHSC (2007) Fulbright Scholar (2005-2009) Dr. Florez-Arango mentors students within Population Health Sciences but specific advisees are unlisted; his current augmented reality mental health project implies grant activity though no funding details are provided. He leads initiatives in rapid-deployment health tech, collaborating with interdisciplinary teams on tools for disaster management and prehospital care. Active lab work includes developing dynamic electronic patient authoring systems and visual knowledge representation frameworks, with a strong emphasis on field evaluation in Latin America and Pacific Island nations to ensure real-world applicability.

Jeffrey Derbyمشاهده پروفایل

استاد مدعو

• Signal Processing
• Telecommunications
• Digital Communications
• +۳ مورد دیگر

Jeffrey Derby serves as Adjunct Professor of Electrical and Computer Engineering at Duke University, a position he has held since 1984 while concurrently working as Senior Technical Staff Member in IBM's PowerPC Technology Development area at Research Triangle Park. His dual academic-industry career spans decades with significant contributions to signal processing subsystems for telecommunications applications. Dr. Derby earned his Ph.D. in electrical engineering from Columbia University in 1975, following which he spent seven years at Bell Laboratories before joining IBM in 1982. His research focuses on analog and digital signal processing with direct applications in telecommunications, particularly in digital communications and speech processing systems. Key contributions include low-power programmable signal processors, pole-zero approximation techniques, and multirate subband coding methodologies as evidenced by his publications spanning 16 years. Dr. Derby's publication record from 1987-2003 demonstrates consistent innovation in signal processing hardware design, with increasing emphasis on power efficiency and high-performance architectures for communication systems. His work bridges theoretical signal processing concepts with practical telecommunications implementations. No scientific awards, fellowships, or medals are documented in the source material. At Duke, Dr. Derby teaches ECE 590: Advanced Topics in Electrical and Computer Engineering, sharing his industry expertise with graduate students. While his IBM role involves active patent development (22 US patents as inventor/co-inventor), the text provides no details about research grants or student advising activities. Within IBM Microelectronics Division, Dr. Derby contributes to the PowerPC Technology Development team, focusing on analog/digital signal processing subsystems for next-generation telecommunications infrastructure.

Raman Aravamudhanمشاهده پروفایل

دانشیار مهمان

• Model Driven Development
• Low-Code
• No-Code
• +۳ مورد دیگر

Raman Aravamudhan is a Visiting Associate Professor at the University of Iowa. His research focuses on Model Driven Development, Low-Code/No-Code systems, Verification and Validation, Databases, and Software Engineering. He is affiliated with the university's academic community and can be contacted via email at raman-aravamudhan@uiowa.edu , located in Office 201C MacLean Hall, Iowa City, IA 52242. Dr. Aravamudhan's work emphasizes innovative approaches to software development frameworks and methodologies, particularly in reducing barriers to entry through low-code/no-code solutions while maintaining robust verification processes.

• Nuclear Engineering
• Reactor Physics
• Fuel Cycle Modeling
• +۳ مورد دیگر

Madicken Munk is an Adjunct Assistant Professor in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois' College of Engineering. She specializes in nuclear energy systems, reactor modeling, and computational tools for fuel cycle analysis. Her work emphasizes open-source software development, particularly with the yt platform for data visualization and analysis. Research interests include reactor physics, advanced reactor design (e.g., pebble-bed fluoride salt-cooled reactors), and hybrid computational methods for radiation transport. She has contributed to projects such as the Mark-I PB-FHR reactor and HALEU fuel cycle dynamics. Recent work focuses on yt 4.0 enhancements and sensitivity analysis of nuclear fuel transitions. Publications span reactor simulation, data visualization, and planetary geochronology instrumentation. Collaborations include FHR test reactor design and open-source tool development for scientific workflows. No scientific awards are listed, but her contributions to computational frameworks and reactor modeling are notable. Teaching includes courses on nuclear energy systems (NPRE 247) and reactor theory (NPRE 555). Active in FHR development and computational infrastructure for nuclear engineering applications.

Peter W. Lenzمشاهده پروفایل

عضو هیئت علمی

• Auditory Perception
• Speech Intelligibility
• Psychoacoustics
• +۷ مورد دیگر

Peter W. Lenz is a Teaching Associate Professor in the Department of Psychology at the University of Wisconsin-Milwaukee, part of the College of Letters and Science. He is actively involved in teaching and research, with a focus on auditory perception and speech intelligibility. Research Interests: Dr. Lenz's research investigates how high-amplitude speech signals can suffer from intelligibility rollover, particularly affecting users of hearing assistive devices. His work explores how interpolated noise and subcritical speech bands can preserve or enhance speech clarity at high volumes. This research has implications for hearing aid design and auditory neuroscience, particularly in understanding neural firing saturation and lateral inhibition in the auditory pathway. His work bridges experimental psychology, acoustics, and assistive technology development. Recent Publications Trends: His publications from 2011 to 2023 show a consistent focus on speech band filtering, noise interpolation, and intelligibility preservation at high intensities. The research employs psychoacoustic experiments and acoustical modeling, often published in the Journal of the Acoustical Society of America. The work increasingly addresses neural mechanisms underlying perception, suggesting a deepening integration of cognitive and physiological models. Teaching: Dr. Lenz teaches foundational and advanced courses including Psych 101 (Introduction to Psychology), Psych 325 (Research Methods), Psych 503 (Perception), and Psych 623 (Perceptual Processes), contributing significantly to the psychology curriculum. Education: Ph.D. in Psychology, University of Wisconsin-Milwaukee, 2005 Advising and Grants: While no formal students or grant funding are mentioned in the text, Dr. Lenz leads the Auditory Perception Laboratory at UWM, suggesting involvement in mentoring undergraduate and graduate researchers. His sustained publication record indicates ongoing research activity, potentially supported by internal or external funding. Laboratory: He conducts research through the Auditory Perception Laboratory at UWM, where experimental studies on speech processing and perception are carried out.

Sachin Kattiمشاهده پروفایل

استاد مدعو

• Wireless Communication
• Machine Learning
• Sensor Networks
• +۵ مورد دیگر

Sachin Katti is an Adjunct Professor in the Department of Electrical Engineering at Stanford University. His research focuses on wireless communication systems, machine learning integration in sensors and networks, and edge computing. He explores full-duplex radios, decentralized cellular networks, and innovative applications of RF and IoT technologies. Katti has contributed to projects like Backscatter communication, PIM video coding, and ML-EXray for edge model visibility. His work bridges theoretical advancements with practical implementations in areas like network optimization, sensor transparency, and autonomous systems. Education: Not explicitly stated in the provided text. Research Interests: Sachin Katti’s work spans wireless communication protocols, machine learning for edge devices, and decentralized network architectures. He investigates how to improve efficiency, reliability, and security in systems ranging from full-duplex radios to IoT-enabled sensor networks. His projects often address real-world challenges like energy efficiency, trust-free connectivity, and intelligent data processing. Recent efforts include trust-free cellular networks (d-Cellular), ML-driven video compression (PIM), and federated learning for autonomous driving. Articles Trends: His recent publications emphasize blending machine learning with communication systems (e.g., ML sensors, co-design for cloud robotics) and advancing RF technologies (backscatter networks, ISM band utilization). There’s also a focus on edge computing challenges, such as visibility into deployed ML models and network offloading policies for robotics. Awards: No scientific awards explicitly mentioned in the provided text. Advising & Grants: No student advisees or grant details explicitly listed in the text. His contributions are primarily through research publications and systems development. Labs/Teams: Involved in collaborative projects like Backscatter Communication, Full Duplex Radios, and SoftRAN (Software-Defined Radio Access Networks). His work often leverages Stanford’s infrastructure for testing dense WiFi networks (e.g., BeHop testbed) and RF localization systems (SpotFi).

Amir Sadovnikمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Deep Learning
• +۱۱ مورد دیگر

Amir Sadovnik is an Assistant Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. He holds a PhD in Electrical and Computer Engineering from Cornell University and a BEng from The Cooper Union. Previously, he served as an Assistant Professor at Lafayette College, where he focused on undergraduate education and curriculum innovation. His educational background includes: PhD in Electrical and Computer Engineering, Cornell University, 2014 BEng in Electrical and Computer Engineering, The Cooper Union, 2009 Amir Sadovnik's research lies at the intersection of computer vision, machine learning, and human-computer interaction, with a strong focus on human-centered problems. His work explores how humans perceive and interact with visual content, leading to research in emotion recognition, face similarity, fashion compatibility, blur detection, and reinforcement learning. He leverages deep neural networks to tackle subjective tasks that are challenging for machines but intuitive for humans. His research also extends into bioinformatics, where he applies machine learning to genome analysis and sequence similarity. The 15 most recent publications reflect a consistent trend in using deep learning for perception-driven tasks. These include emotion prediction, face and object similarity, adversarial robustness, and biomedical data analysis. His work often integrates attention mechanisms, U-Net architectures, and reinforcement learning frameworks, demonstrating a multidisciplinary approach across computer vision, signal processing, and computational biology. While no formal scientific awards are listed in the provided texts, his involvement in grant-funded projects, such as the NSF-funded 'CS for Appalachia' initiative, highlights recognition of his educational and research contributions. Amir Sadovnik is actively engaged in teaching and mentoring. He has taught graduate and undergraduate courses including ECE 517 (Reinforcement Learning), ECE 574 (Computer Vision), and COSC 525 (Deep Learning). He has also led curriculum redesign efforts to make computer science more inclusive, particularly through integrating digital humanities into introductory programming courses. His educational grants and projects emphasize interdisciplinary learning and real-world data applications. He is involved in a research-practice partnership (NSF Grant No. 1923509) aimed at bringing computer science education to K–12 schools in East Tennessee. He leads research projects such as Easy Visual Question Answering with Video and Counting, and developed the educational tool Code-A-Story to integrate computer science with literacy for elementary students. These projects reflect his commitment to both cutting-edge research and accessible, impactful education.

Erin Dreelinمشاهده پروفایل

استادیار

• Stormwater Management
• Aquatic Ecology
• Water Quality
• +۵ مورد دیگر

Erin Dreelin serves as Assistant Professor in the Department of Fisheries and Wildlife within Michigan State University's College of Agriculture and Natural Resources, while also holding leadership roles as Associate Director of the MSU Water Alliance and Program Coordinator for the WaterCube National Research Traineeship (NRT). Her work centers on facilitating cross-disciplinary water research collaborations and implementing ecosystem protection strategies through stakeholder engagement with local governments and communities. Her academic credentials include a Ph.D. in Ecology from the University of Georgia, an M.S. in Biology from Old Dominion University, and a B.S. in Biology from Villanova University. Dreelin's research focuses on sustainable stormwater management practices to protect aquatic ecosystems, with emphasis on urbanization impacts, stream ecology, and water quality preservation. She actively develops innovative low-impact development techniques and Smart Growth policies, working directly with municipal authorities to revise building codes and land development practices. Her approach integrates scientific rigor with practical outreach to maintain ecosystem services while accommodating human development needs. Recent publications demonstrate her shift toward advanced water quality monitoring methodologies, particularly rapid detection systems for public health applications. The 2024 MiNET study validates statewide wastewater surveillance for infectious disease tracking, while the 2022 qPCR research establishes faster beach safety protocols, both showing significant improvements over traditional culture-based methods through real-time data applications. No specific scientific awards are documented in the source materials. She directs multiple major grants including the NSF-funded WaterCube NRT (2023-2028) for big data water science, Michigan EGLE's MINET Beach Support project (2022-2026), and EPA-sponsored research on drinking water disinfection optimization (2023-2026). Her grant portfolio emphasizes translating research into actionable public health solutions through partnerships with state and federal agencies. Dreelin coordinates the MiNET network involving 19 laboratories, 29 health departments, 6 Native American tribes, and 60 wastewater treatment plants monitoring 214 sites across Michigan. This infrastructure supports the MSU Global Water Initiative and WaterCube NRT program, establishing statewide capacity for environmental health monitoring and rapid response to emerging pathogen threats.

Arthur Leskمشاهده پروفایل

استاد

• Genomics
• Structural Biology
• Protein Structure and Evolution
• +۳ مورد دیگر

Arthur Lesk is a Professor of Biology and Biochemistry and Molecular Biology at Pennsylvania State University, affiliated with the BMMB Graduate Program and Bioinformatics initiatives. His research focuses on genomics, protein structure evolution, and biological network analysis, with notable contributions to mammoth genome sequencing and alternative splicing studies. Collaborations include work with Profs. Miller and Schuster on comparative genomics of mammoths and elephants. Lesk has authored influential textbooks like Introduction to Protein Science and pioneered methods for protein folding pattern classification. His lab explores structural bioinformatics, regulatory network motifs, and systems-level biological organization, bridging computational and experimental approaches. Research Interests: Genomics: Comparative analysis of protein-coding genes, alternative splicing consequences, and evolutionary pressures Structural Biology: Protein folding patterns, evolutionary conservation, and functional implications of structural changes Biological Networks: Regulatory network motif connectivity, transcription pathways, and hierarchical network assembly principles Key Projects: Decoding mammoth/elephant genome differences to study protein evolution Developing MUSTANG structural alignment algorithms Mapping network motif combinations in yeast regulatory systems Advising & Grants: While no specific students are listed, Lesk’s work has been supported by grants enabling large-scale genomic projects and computational infrastructure development. His lab contributes to the UCSC Genome Browser and structural bioinformatics tools. Labs/Teams: Active in cross-disciplinary collaborations within PSU’s Biochemistry department and international genome sequencing consortia.

Jacob Andreasمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Jacob Andreas is an Associate Professor of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), affiliated with the MIT-IBM Watson AI Lab and the School of Engineering. His research focuses on advancing artificial intelligence, machine learning, and natural language processing, with particular emphasis on neurosymbolic methods, language model reasoning, and interdisciplinary applications such as AI governance and bioacoustic analysis of sperm whale communication. He explores the intersection of language models and robotics, developing techniques for scalable system-level reasoning, code generation, and efficient computation allocation. His work also addresses societal challenges, including mitigating misinformation through generative AI and informing policy through white papers on AI governance. Andreas’s contributions span foundational ML theory (e.g., scaling laws) and applied systems (e.g., WhaleLM for bioacoustic analysis). He collaborates across disciplines, integrating cognitive science insights into model design and advocating for ethically aligned AI development. His research has been recognized in MIT’s EECS Faculty Awards and contributes to MIT’s broader mission of impactful technological innovation. While no specific awards are listed, his active role in high-impact projects underscores his academic leadership. His advising and grant work drive collaborative research in AI, with a focus on training next-generation researchers through the MIT-IBM Watson AI Lab and interdisciplinary initiatives.

Thomas Senftleمشاهده پروفایل

دانشیار

• Computational Catalysis
• Energy Conversion
• Heterogeneous Catalysis
• +۳ مورد دیگر

Thomas Senftle is an Associate Professor in the Department of Chemical and Biomolecular Engineering at Rice University. He joined Rice as an Assistant Professor in 2017. His research focuses on computational modeling of catalytic systems for energy conversion and environmental remediation, with an emphasis on multi-component catalysts at quantum and classical scales. He earned a B.S. in Chemical Engineering from the University of Notre Dame (2010) and a Ph.D. from Penn State (2015), followed by postdoctoral research at Princeton on semiconductor-based photoelectrochemical systems. His research integrates density functional theory (DFT) and microkinetic modeling to design catalysts for low-carbon technologies, including CO₂ electroreduction and PFAS degradation. Key areas include: (1) photocatalytic electrode interfaces, (2) metal-support interactions in heterogeneous catalysts, and (3) accelerated simulation methods for reactive systems. He leads the Senftle Group, which develops open-source computational tools and collaborates on environmental applications like PFOA degradation over boron nitride photocatalysts. Publications span catalytic mechanisms, materials design, and environmental remediation strategies. His work bridges theory and experiment, with contributions to machine learning for catalyst discovery and feature selection methods (e.g., iBART algorithm). Active collaborations include projects on proton sponge promotion for CO₂ reduction and iron catalyst dynamics during CO₂ hydrogenation. Professional activities include editorial roles, conference presentations, and data science applications in catalysis. His group maintains an active GitHub repository for simulation codes and collaborates with industry on scalable catalytic solutions.