جستجوی استاد راهنمای دانشگاهی

Giancarlo Crocettiمشاهده پروفایل

استادیار

• Data Science
• Distributed Analytics
• Web Data Mining
• +۲ مورد دیگر

Giancarlo Crocetti is an Assistant Professor in the Department of Computer Science, Mathematics and Science at the Collins College of Professional Studies, St. John's University. His teaching focuses on data science, distributed analytics, and web data mining. He holds a Doctor of Professional Studies (D.P.S.) in Applied Computing from Pace University, along with Master's degrees in Data Mining (Central Connecticut State University) and Computer Science (La Sapienza University, Rome). Education: D.P.S., Applied Computing, Pace University M.S., Data Mining, Central Connecticut State University M.S., Computer Science, La Sapienza University - Rome His research interests span cognitive science and semantic representation of knowledge. Crocetti actively contributes to cutting-edge fields like telemedicine analytics, biomarker development for Parkinson's disease, and ensemble models in histological examination. His work bridges computer science with healthcare applications through innovative data mining techniques. Notable research includes developing algorithms for social conversation analysis in NSCLC patients and creating a think tank simulator for virtual expert consultations. His publications demonstrate expertise in both foundational computer science and applied biomedical informatics.

• Applied Linguistics
• Second Language Acquisition (SLA)
• Language Curriculum Development
• +۶ مورد دیگر

Ronald P. Leow is a Professor of Applied Linguistics and Director of Spanish Language Instruction at Georgetown University's Department of Spanish and Portuguese within the College. His expertise spans language curriculum development, instructed second language acquisition (ISLA), psycholinguistics, and computer-assisted language learning (CALL). He holds a Ph.D. in Linguistics from the University of Illinois at Urbana-Champaign. Dr. Leow's research focuses on cognitive processes in L2 learning, written corrective feedback (WCF), and pedagogical applications of SLA theories. He has conducted workshops globally on teacher education and curriculum design. His work bridges SLA theory with classroom practice, emphasizing empirical methodologies like eye-tracking and think-aloud protocols. Key themes in his publications include task-based language teaching, the role of awareness in learning, and the integration of technology in language education. He advocates for curricula that align research findings with practical instructional strategies, promoting depth of processing in L2 acquisition.

Shiwen Maoمشاهده پروفایل

استاد

• Wireless Networks
• Multimedia Communications
• RF Sensing
• +۴ مورد دیگر

Shiwen Mao is a Professor and Earle C. Williams Eminent Scholar Chair at Auburn University's Department of Electrical and Computer Engineering. He serves as Director of the Wireless Engineering Research and Education Center. His education includes a Ph.D. in Electrical Engineering from Polytechnic University, M.S. in Systems Engineering, and dual B.E. degrees in Electronic Engineering and Enterprise Management from Tsinghua University. Research interests span wireless networks, multimedia communications, RF sensing for healthcare IoT, smart grid systems, and machine learning applications. Mao's work focuses on transforming edge intelligence, 6G systems, drone localization, and AI integration for next-generation networks. Recent publications demonstrate strong trends in AI-driven wireless communications, satellite-terrestrial networks, cybersecurity applications of large language models, and generative AI for wireless sensing. Awards and Honors: IEEE Communications Society's Best Paper Award (2021) IEEE Jack Neubauer Memorial Award (2020) Multiple National Science Foundation grants including a $300K award for 6G systems research Best Paper and Best Demo awards from IEEE conferences As Director of the Wireless Engineering Research and Education Center, Mao leads collaborative research involving over 20 faculty members addressing real-world communication challenges. The center develops solutions integrating drone technology, millimeter wave communications, and AI for future wireless systems.

Chengming Zhangمشاهده پروفایل

استادیار

• Efficient machine learning systems
• AI algorithm-hardware Co-design
• Effective efficiency algorithms
• +۵ مورد دیگر

Chengming Zhang is a tenure-track assistant professor in the Computer Science Department at the University of Houston. He recently completed his Ph.D. in Computer Engineering from Indiana University in May 2024, where he was a member of the HiPDAC group working on building efficient and scalable deep learning systems under the advisement of Prof. Dingwen Tao. He has extensive industry research experience with multiple projects at Microsoft Research, Meta Reality Labs, and Argonne National Laboratory. His educational background includes: Ph.D. in Computer Engineering, Indiana University (May 2024) Dr. Zhang's research focuses on creating efficient machine learning systems that can operate effectively across diverse hardware platforms. His work bridges the gap between theoretical algorithms and practical implementation with particular emphasis on efficient machine learning systems for training and inference on parallel, distributed, and heterogeneous hardware; AI algorithm-hardware co-design, particularly for GPU architectures; effective efficiency algorithms including model compression, data efficiency, and parameter-efficient tuning; and large-scale deep learning applications such as Large Language Models, Agents, and Image/Video Generation systems. His publication record demonstrates a consistent focus on optimizing deep learning systems through hardware-aware approaches. The majority of his work centers around making deep learning more efficient through techniques like model compression, hardware-algorithm co-design, and memory optimization. His research spans both theoretical algorithm development and practical system implementation, with publications in top-tier conferences including the International Conference on Supercomputing, PPoPP, and AAAI. A notable trend in his work is the emphasis on practical efficiency - not just theoretical improvements but solutions that deliver real-world performance gains on actual hardware. Dr. Zhang is actively building his research group at the University of Houston and has secured significant computational resources for his lab, including multiple high-performance computing servers worth a total of $130K (two 8-Ada6000 servers and one dual-4090 servers).

Regina Kaplan-Rakowskiمشاهده پروفایل

استادیار

• Immersive Learning Environments
• Artificial Intelligence in Education
• Computer-Assisted Language Learning
• +۵ مورد دیگر

Regina Kaplan-Rakowski is an Assistant Professor in the Department of Learning Technologies at the University of North Texas. Her research focuses on immersive technologies like virtual reality (VR), artificial intelligence (AI), and computer-assisted language learning. She holds a PhD in Curriculum and Instruction from Southern Illinois University (2016), an MA in Foreign Languages (2006), and an MEd in European Studies (2001) from Pedagogical University, Cracow. Her research interests include VR applications in education, emotional responses to technology, and second language acquisition. Notable projects explore VR for social isolation mitigation, AI-mediated language learning, and accessibility for visually impaired learners. She has co-edited multiple books on educational technology, including pandemic-era teaching strategies and AI-driven innovations. Recent publications highlight VR’s impact on language anxiety reduction, AI integration in teacher training, and the effectiveness of immersive technologies in vocabulary and listening comprehension. Her work bridges theoretical insights with practical applications, influencing both classroom practices and technology design.

Mohsen Aminiمشاهده پروفایل

دانشیار

• Cloud Computing
• Artificial Intelligence (AI)
• Domain-Specific Cloud Computing
• +۷ مورد دیگر

Mohsen Amini is an Associate Professor in the Department of Computer Science and Engineering at the University of North Texas (UNT). He previously held tenured positions at the University of Louisiana Lafayette (UL Lafayette) and completed postdoctoral research at the University of Miami and Colorado State University. His research focuses on Cloud 2.0 systems, AI-friendly serverless architectures, and domain-specific computing for Industry 4.0, multimedia, and edge-to-cloud integration. He leads the High Performance Cloud Computing (HPCC) Lab and has attracted over $2.7 million in research funding, including an NSF CAREER Award (2021). His work emphasizes democratizing cloud-native development and enhancing system resilience through heterogeneous computing frameworks. Education: Ph.D. in Computing and Software Engineering, University of Melbourne (2012) M.Sc. in Software Engineering, Ferdowsi University (2006) B.Sc. in Software Engineering, Azad University (2003) Research Interests: Cloud+AI Integration Fluid Computing across Edge-to-Cloud Continuum Domain-Specific Cloud Platforms (e.g., Industry 4.0, Multimedia) Confidential Computing Security Awards: NSF CAREER Award (2021) Best Service Award at IEEE/ACM CCGrid 2023 Francis Patrick Clark/BORSF Endowed Professorship (2021–2024) Grants & Projects: NSF IRES Track 1: Wireless Federated Fog Computing for Remote Industry 4.0 ($400k grant) NSF CAREER: Special-Purpose Cloud for Serverless Multimedia Streaming ($513k grant) SmartSight: AI-Based Platform for Visually Impaired Assistance ($499k grant) Teaching & Advising: Current advising 5 PhD students and leading a team of 25+ researchers Teaches courses on Cloud Computing, Distributed Systems, and Software Engineering Lab & Collaborations: HPCC Lab develops open-source tools like the E2C simulator and OaaS framework. Active collaborations with institutions like IMDEA Networks and industry partners like Hub Enterprise Inc.

Andrew J. Andersonمشاهده پروفایل

استادیار

• Neuroscience
• Neurophysiology
• Cognitive Neuroscience
• +۳ مورد دیگر

Dr. Andrew J. Anderson is an Assistant Professor in the Department of Neurology at the Medical College of Wisconsin. His research focuses on neurophysiological and neuroimaging approaches to understand language processing, cognitive fatigue in aging, and neural networks underlying semantic meaning. He employs techniques such as fMRI, EEG, and structural connectome analysis to explore how the brain encodes and decodes linguistic and experiential information. Dr. Anderson’s work also investigates potential biomarkers for early detection of neurodegenerative diseases like Alzheimer's through pattern analysis of brain activity. Key research areas include: Neurophysiological correlates of speech comprehension and semantic processing Functional connectivity in aging populations, particularly relating to cognitive fatigue Integration of artificial neural networks to model cortical language processing Brain decoding techniques for identifying individual cognitive patterns His publications (2018-2022) emphasize interdisciplinary methods combining neuroscience with computational models, revealing distributed neural networks involved in propositional sentence meaning, and dissociable electrophysiological measures of language processing in aging. While no specific scientific awards are listed, his research contributes to foundational understanding of brain-language interactions and healthy aging processes. Advising and grants: Details not explicitly provided in the text. His affiliations include the Medical College of Wisconsin Neurology department, with potential collaborative ties to affiliated hospitals or research institutes. No lab affiliations are mentioned.

Yang Liمشاهده پروفایل

استادیار

• Computer Architecture
• Machine Learning
• On-Device AI
• +۴ مورد دیگر

Yang Li is an Assistant Professor of Computer Science at Iowa State University, specializing in computer architecture, machine learning, and their intersection. He holds a Ph.D. and M.S. from Carnegie Mellon University (2020), an M.S.E. from the University of Texas at Austin (2013), and a B.E. from Tsinghua University (2011). Prior to academia, he worked as a Senior Research Scientist at Meta, a Research Scientist at Meta, and a Researcher at Microsoft. His research focuses on large language models (LLM) acceleration, on-device AI, cloud infrastructure optimization, and spatiotemporal forecasting. He has contributed to over 20 peer-reviewed publications at top venues like ASPLOS, EMNLP, and ICASSP. Research Interests: Algorithmic and systems-level acceleration of LLMs On-device AI co-design and privacy Cloud memory/power management Graph-based spatiotemporal forecasting Teaching: Taught COMS 6730 (Advanced Topics in ML), COM S 321 (Computer Architecture), and guest-lectured on graph signal processing. Recent teaching scores include 4.75/5.0 (Fall 2024) and 4.67/5.0 (Spring 2025). Awards: IBM Patent Application Award (2021) and multiple patents on power management systems for data centers. Service: Program committee member for DAC 2024, NeurIPS 2024, and ICLR 2025. Reviewer for ACM TACO, IEEE TPAMI, TPDS, and others.

Sheng Liمشاهده پروفایل

استادیار مدعو

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۴ مورد دیگر

Sheng Li is an Adjunct Assistant Professor in the School of Computing at the University of Georgia (UGA). He holds roles such as Graduate Program Faculty and Courtesy Faculty in the Institute of Bioinformatics. Previously, he served as an Assistant Professor at UGA from August 2018 to July 2022. His research focuses on Machine Learning, Computer Vision, Data Mining, Natural Language Processing, Causal Inference, and User Modeling. Li earned his Ph.D. in Computer Engineering from Northeastern University in 2017, following degrees from Nanjing Institute of Post and Telecommunications in China. His work bridges theoretical advancements and practical applications, including AI solutions for healthcare (e.g., medication adherence monitoring), computer vision for wildlife tracking (fish re-identification), and causal inference methodologies. He has received notable awards like the Fred C. Davison Early Career Scholar Award (2022) and the Aharon Katzir Young Investigator Award (2020). His research is funded by grants from agencies such as the US Department of Defense and NIH, supporting projects like Knowledge-Guided Scene Graph Generation and Reasoning for Visual Understanding. Li’s publications span top venues in AI and computer science, addressing challenges in domain adaptation, trustworthy AI, and multimodal learning. He has collaborated on interdisciplinary projects, including bioinformatics and agricultural NLP. His contributions emphasize both technical innovation and societal impact.

Caro Williams-Pierceمشاهده پروفایل

استادیار

• Game-Based Learning
• STEM Education
• Embodied Cognition
• +۵ مورد دیگر

Dr. Caro Williams-Pierce is an Assistant Professor at the University of Maryland's College of Information Studies (iSchool), affiliated with the Youth eXperience (YX) Lab. Her work focuses on designing game-based and digital tools to enhance STEM education, particularly in mathematics. She holds a PhD in Curriculum & Instruction (Mathematics Education) from the University of Wisconsin-Madison and emphasizes embodied constructivist approaches, integrating physical gesture, digital interaction, and language to understand mathematical learning. Her research explores how playful digital environments foster mathematical generalization and problem-solving. Key areas include game design for K-12 education, multimodal teacher training, and hybrid learning spaces. Dr. Williams-Pierce’s projects, such as the 'Read, Watch, Play' initiative, aim to gamify academic courses while maintaining rigorous educational outcomes. Her articles highlight innovations like embodied failure analysis in math learning, collaborative sketching in virtual STEM activities, and the role of gesture in mathematical proof practices. Though not currently accepting PhD students, her lab collaborates on projects like the YX Lab’s youth-centered digital experiences.

Winslow Burlesonمشاهده پروفایل

استاد

• Human-Computer Interaction (HCI)
• Robotics
• Assistive Technology
• +۵ مورد دیگر

Winslow Burleson is a Professor at the University of Arizona's School of Information, specializing in Human-Computer Interaction (HCI), Robotics, and Assistive Technology. His research focuses on developing innovative systems for education, healthcare, and marine engineering. Notable projects include the DRESS system for dementia care, CrowdMuse for collaborative creativity, and underwater robotics interfaces. He has received the James Chen Annual Award for Best Journal Article (2012) and leads interdisciplinary teams in cyberlearning and adaptive tutoring systems. Education background includes advanced studies in computer science and engineering, though specific details are not provided. His work integrates robotics, affective computing, and smart environments to address real-world challenges such as aging, education, and environmental exploration. Research interests span robotics applications in education (e.g., co-robotic learning agents), assistive technologies for independent living, and multimodal learning environments. His contributions include adaptive systems for creative crowdsourcing, emotion-aware tutoring platforms, and underwater human-robot interaction frameworks. Current projects emphasize scalable crowd ideation tools and sensor-driven behavior change interventions. Awards include recognition for impactful journal contributions, while grants fund initiatives like cyberlearning with teachable robots and precision oncology IT tools. Advising and grant activities are central to his academic contributions, though specific student names are not listed. Collaborations involve cross-disciplinary teams in engineering, healthcare, and arts to create impactful technologies. Burleson's lab focuses on developing responsive emotive systems, wearable assistive devices, and immersive multimedia applications. Ongoing work explores scalable solutions for dementia support, marine technology interfaces, and creative human-computer interaction paradigms.

Arman Cohanمشاهده پروفایل

استادیار

• Natural Language Processing
• Language Modeling
• Representation Learning
• +۲ مورد دیگر

Arman Cohan is an Assistant Professor in the Computer Science Department at Yale University, where he leads the Natural Language Processing lab. Previously, he was a Research Scientist at the Allen Institute for AI (AI2) after earning his PhD from Georgetown University. His research spans natural language processing and machine learning, with current focus on: Language modeling architectures and scaling approaches Efficient fine-tuning and adaptation methods Retrieval-augmented generation systems Applications in scientific domains He teaches courses on natural language processing and AI foundation models, covering topics from transformer architectures to safety considerations in large language models.

• Digital Humanities
• Classical Studies
• Ancient Geography
• +۳ مورد دیگر

Tom Elliott serves as Associate Director for Digital Programs and Senior Research Scholar at New York University, alongside his role as Adjunct Assistant Professor. He holds a BS in Computer Science and Classical Studies from Duke University (1989), an MA (1997) and PhD (2004) in Ancient History from UNC Chapel Hill. His career spans software development, military service, and academic leadership in digital humanities. Elliott specializes in digital geography, epigraphy, and ancient administrative studies. He pioneered projects like the Barrington Atlas gazetteer, co-founded the EpiDoc community for digital text encoding, and directed the Ancient World Mapping Center. His current work focuses on the Pleiades gazetteer, integrating archaeological data with open-source digital methodologies. His publications emphasize digital tools' impact on classical scholarship, including seminal works on GIS applications and digital epigraphy. He actively mentors scholars and students in computational methods, shaping the field's trajectory through collaborative initiatives like Papyri.info and IOSPE.

Bo Peiمشاهده پروفایل

استادیار

• Artificial Intelligence in Education
• Educational Data Mining
• Learning Analytics
• +۴ مورد دیگر

Dr. Bo Pei is an Assistant Professor in the Instructional Technology and Learning Design & Technology program at the University of South Florida's College of Education. He leads the VISTA Lab (Visualization, Intelligence, Systems, and Technology in Education) located on the Tampa campus, focusing on interdisciplinary research at the intersection of AI, data visualization, and educational technology. His research explores: AI-driven personalized learning tools and ethical teaching enhancements Educational data mining and learning analytics methodologies Development of trustworthy and explainable AI systems for education Data visualization techniques for actionable learning insights Equitable educational technology design for diverse learners Dr. Pei has secured significant research funding including: NSF ICorps award for AI-Driven Educational Platform Florida High Tech Corridor's Early-Stage Innovation Fund NEW RESEARCHER GRANT for Viz4Edu project Building Partnerships Across Campuses grant He currently advises seven graduate students across PhD and Master's programs in Instructional Technology, Educational Measurement, Computer Science, and related fields. The VISTA Lab team collaborates on projects involving AI tutors, educational dashboards, VR learning environments, and automated literature review systems.

Ruhul Aminمشاهده پروفایل

استادیار

• Artificial Intelligence
• Bioinformatics
• Data Science
• +۵ مورد دیگر

Ruhul Amin is an Assistant Professor at Fordham University's Department of Computer and Information Science, where he explores the intersection of Artificial Intelligence , Data Science , and Public Health . His work spans multiple domains including Bioinformatics , Natural Language Processing , and Computational Social Science . PhD in Computer Science (Stony Brook University, 2019) MS in Computer Science (Stony Brook University, 2015) BSc in Computer Science (Shahjalal University, 2007) His research focuses on developing deep learning algorithms for genome annotation, anomaly detection in high-cardinality spaces, and assistive technologies for visually impaired users. Notably, he designed the Mongol Dip bilingual screen reading software and the Pipilika Bengali text search engine. Recent publications (2025-2024) demonstrate his expanding interests in large language model alignment , sentiment analysis across languages, and distributed reinforcement learning for cybersecurity applications. His work frequently appears at IEEE conferences and in journals like PLOS One. Scientific Recognition BASIS National ICT Award (Bangladesh) Best Poster Awards at IEEE R10 HTC and CEWIT conferences Bangladesh Prime Minister’s Award (2012) mBillionth South Asia Award (2010) He maintains active collaborations with research teams at University of Toronto , University of British Columbia , and Stony Brook University . Contact: moamin@cs.stonybrook.edu