جست‌وجوی استاد و پژوهشگر

• Theoretical Computer Science
• Statistics
• High-Dimensional Estimation
• +۷ مورد دیگر

Tselil Schramm is an Assistant Professor in the Department of Statistics at Stanford University, with courtesy appointments in Computer Science and Mathematics. She is actively engaged in research and teaching in theoretical computer science and statistics. Department: Department of Statistics School: School of Humanities and Sciences University: Stanford University Office: CoDa E254 Email: tselil@stanford.edu She earned her PhD from UC Berkeley under Prasad Raghavendra and Satish Rao, followed by postdoctoral work at Harvard and MIT with Boaz Barak, Jon Kelner, Ankur Moitra, and Pablo Parrilo. Her research lies at the intersection of theoretical computer science and statistics, focusing on high-dimensional estimation, information-computation tradeoffs, sum-of-squares algorithms, and random graph theory. She develops algorithms for statistical problems and investigates the boundaries between what is statistically possible and what is computationally feasible. Her recent publications span topics including the overlap-gap property, discrepancy algorithms, robust message passing, semidefinite programming, spectral clustering, and random geometric graphs, appearing in top venues such as STOC, FOCS, COLT, NeurIPS, and The Annals of Statistics. She teaches a range of courses, including Introduction to Statistics (STATS 60), Theory of Statistics II (STATS 300B), and Machine Learning Theory (STATS 214 / CS 228M), reflecting her expertise in both foundational and advanced statistical theory. Runner-up for Best Paper at COLT 2021 Invited to STOC 2022 special issue of SICOMP Invited to SODA 2016 special issue of ACM Transactions on Algorithms Invited to CCC 2019 special issue of Theory of Computing Tselil Schramm advises and collaborates with numerous students and researchers, including Shuangping Li, Misha Ivkov, and Siqi Liu. She has been involved in multiple research grants and projects, particularly in the areas of high-dimensional inference and algorithmic robustness. Her work often bridges theoretical guarantees with practical algorithmic design. She is affiliated with Stanford’s theoretical computer science and statistics research groups, contributing to a vibrant academic environment. Her future work is expected to further explore the limits of efficient computation in statistical settings, with potential applications in machine learning, signal processing, and network analysis.

Javed I. Khanمشاهده پروفایل

استاد

• Computer Networking
• Cyber Infrastructure
• Active and Programmable Networking
• +۱۴ مورد دیگر

Javed I. Khan is a Professor and Chair of the Department of Computer Science at Kent State University . He leads cutting-edge research in networking, cyber infrastructure, and perceptual systems, with funding from DARPA, NSF, NASA, AFRL, and the World Bank. He is the director of the Media Communications and Networking Research Laboratory (MEDIANET) and actively contributes to international education networks. Ph.D., University of Hawaii at Manoa B.Sc., Bangladesh University of Engineering and Technology (BUET) His research spans active and programmable networking , peer-to-peer systems , semantic design , multimedia communication , and medical imaging . He has pioneered work in holographic associative memory and complex system visualization . His interdisciplinary work integrates computer science with human-centered design and global development. The recent publications reflect a strong trend in decentralized systems , next-generation architectures , and intelligent networking . Themes include P2P computing, dynamic routing, content-based search, and perceptual engineering, indicating a forward-looking research vision in network evolution and human-machine symbiosis. Fulbright Senior Specialist on High Performance Education Networking Open Grants Fellow, East West Center, Hawaii Dr. Khan has advised national and international bodies including the World Bank on research and education networks (RENs) for sustainable development. His lab has secured grants from DARPA , NSF , NASA , and AFRL , supporting innovative projects in space communication, multisensory fusion, and digital library systems. He leads the MEDIANET Lab , which houses the Internet Teaching Lab (ITL) and Student Research Portal . The lab fosters collaboration through the MEDIANET Collaboration Server and supports projects in active networking, P2P computing, and perceptual media.

Michael Whiteمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Generation
• Spoken Language Dialogue Systems
• +۳ مورد دیگر

Michael White is a Professor in the Department of Linguistics at The Ohio State University, specializing in computational linguistics, natural language generation, and spoken language dialogue systems. He holds a Ph.D. in Computer Science from the University of Pennsylvania and has collaborated with interdisciplinary teams in both academia and industry, including roles at CoGenTex, Inc., and visiting positions at Facebook/Meta and Australian universities. His research emphasizes controllable and ethical AI, with a focus on neural language models and their applications in healthcare and conversational systems. Education: Ph.D. in Computer Science, University of Pennsylvania. Professional Background: Former Research Fellow at the University of Edinburgh School of Informatics, and founder of CoGenTex, Inc. Collaborations include researchers in Computer Science and Linguistics at Ohio State, such as Eric Fosler-Lussier and William Schuler. Research Interests: Neural language generation controllability, ethical AI alignment, discourse processing, and conversational systems. Recent projects include developing virtual patient simulators for medical education and exploring knowledge distillation to mitigate AI-generated harm. Articles Trends: Focused on advancing neural NLG techniques, improving dialogue system safety, and leveraging large language models for domain-specific tasks (e.g., healthcare, KBQA). Recent work addresses transparency, data efficiency, and ethical implications in AI. Scientific Awards: Lumley Interdisciplinary Research Award (2023), PRE Accelerator Award. Grants: Supported by interdisciplinary initiatives such as the virtual patient project collaboration with Eric Fosler-Lussier and Laura Wagner. Advising and Grants: Mentors students in computational linguistics and leads projects funded by Ohio State’s interdisciplinary programs. Current work involves neuro-symbolic approaches for patient preparation assistants and data-efficient conversational AI. Labs/Teams: Collaborates with the Ohio State Computational Linguistics Group and industry partners. Active in organizing workshops on discourse-driven NLG and ethical AI challenges.

Hwa Changمشاهده پروفایل

دانشیار

• Wireless Communications
• e-care
• Computer Networking
• +۵ مورد دیگر

Hwa Chang is a tenured Associate Professor in the Department of Electrical & Computer Engineering at Tufts University , where he has been a faculty member since 1987. He directs the Tufts Wireless Lab and oversees the Computer Engineering Program . His research focuses on wireless communications, sensor networks, distributed computing, and engineering education. Education: Ph.D. in Electrical and Computer Engineering (Drexel University, 1987), M.S. in Computer Science (Montana State University, 1983), B.S. in Engineering Science (National Cheng Kung University, 1977). Research Interests: Wireless Sensor Networks (WSNs), energy-efficient routing, mobile ad hoc networks, grid computing, and network security. Notable projects include the Wireless Grid initiative and WISeNET , exploring resource-sharing in dynamic environments. Awards: Outstanding Alumni Award (National Cheng-Kung University, 2005) Excellent Leadership and Service Award (New England Association of Chinese Professionals, 2005) $600K NSF grant for Virtual Markets research (2002-2004) Advising & Grants: Advised ~150 master students and 160 senior design projects Ph.D. graduates include Amlir Davis (2013), Na Wang (2009) Recipient of grants from BBN, NSF, and industry partnerships like A$W Technology Corp. Professional Activities: Leadership roles in New England Association of Chinese Professionals, IEEE, and global science organizations. Editor-in-chief for multiple conference proceedings.

• Biometric Systems
• Cryptography
• Information Security
• +۳ مورد دیگر

Amina Bassit is a Researcher in the Department of Computer Science and Engineering at Michigan State University's College of Engineering. Her work focuses on secure biometric systems, cryptographic protocols, and information security. She contributes to advancing privacy-preserving technologies in authentication and identification applications. Her research emphasizes encrypted biometric recognition, homomorphic encryption, and adversarial attack mitigation. Key areas include template protection, secure search algorithms, and compliance with international standards like ISO/IEC 24745. She also explores practical implementations of biometric systems for real-world applications such as digital travel credentials. Recent contributions include studies on multiplication-free encryption methods for efficiency, remote cancelable biometric systems, and analyzing adversarial transferability across machine learning models. Her work bridges theoretical advancements with practical deployment challenges in cybersecurity and AI security domains. Though no specific awards or grants are listed, her prolific publication record (2021-2025) reflects sustained engagement in high-impact research. She collaborates on interdisciplinary projects at the intersection of computer science, cryptography, and applied machine learning.

Varghese Jacobمشاهده پروفایل

استاد

• Information Systems
• Management Information Systems
• Operations Research
• +۱۱ مورد دیگر

Varghese Jacob is a Professor of Management Information Systems and holds the Ashbel Smith Professor and Lars Magnus Ericsson Chair titles at the Naveen Jindal School of Management, University of Texas at Dallas. He has served in key administrative roles including Senior Associate Dean since 2002 and previously as Associate Dean (1999–2002). Prior to joining UTD, he was an Associate Professor at The Ohio State University's College of Business. Ph.D. in Management, Purdue University (1986) M.S. in Physics, Indian Institute of Technology (Delhi) (1980) His research spans Information Systems, Operations Research, and Artificial Intelligence , focusing on data mining, dynamic pricing, software requirements, and group decision support. He explores how data reduction techniques, neural networks, and incentive structures impact business systems. His work integrates technical modeling with behavioral and strategic considerations in IT adoption and e-commerce. His recent publications highlight consistent contributions to top-tier journals such as Management Science , Information Systems Research , and European Journal of Operational Research . The research trends show a strong focus on data quality, transaction analysis, IT strategy, and open-source collaboration , with methodological diversity spanning empirical studies, optimization models, and experimental designs. Ashbel Smith Professor Lars Magnus Ericsson Chair Dr. Jacob has co-chaired four Ph.D. dissertations and served on multiple dissertation committees. He has secured research funding from organizations such as JETRO, Ameritech Foundation, and internal university grants, supporting work in e-commerce, genetic algorithms, and telecommunications. His leadership in accreditation (AACSB) and academic policy at UTD underscores his institutional impact. He has been actively involved in executive education, organizing and teaching programs on object-oriented systems, internet business, and client-server computing. His professional affiliations include the Association for Information Systems (AIS), INFORMS, IEEE Computer Society, and ACM, reflecting his interdisciplinary engagement.

Bin Renمشاهده پروفایل

استادیار

• Parallel Computing
• High-Performance Computing
• Compiler Techniques
• +۷ مورد دیگر

Bin Ren is an Assistant Professor in the Department of Computer Science at the College of William & Mary, where he has been a faculty member since Fall 2016. He holds a Ph.D. in Computer Science and Engineering from The Ohio State University (2014) and was a postdoctoral research associate at Pacific Northwest National Laboratory from 2014 to 2016. Research Interests: His work centers on high-performance computing, compiler techniques, and machine learning systems, with a focus on enabling real-time and energy-efficient deep neural network execution on mobile and edge devices. He explores compiler optimizations, DNN pruning, neural architecture search, and GPU memory management to improve system performance and efficiency. Publication Trends: His recent publications (2023–2025) reveal a strong focus on compiler-aware deep learning systems, mobile and edge AI, and performance optimization across heterogeneous platforms. Key themes include DNN acceleration, memory efficiency, real-time inference, and hardware-software co-design. His work frequently appears in top-tier venues such as ASPLOS, SC, CVPR, and PLDI. Scientific Awards: NSF CAREER Award, 2021 Best Paper Award, SC 2020 Best Student Paper Nomination, SC 2020 Jeffress Trust Award, 2020 ISLPED Design Contest First Place, 2020 Student Cluster Reproducibility Challenge Paper, SC 2019 Best Paper Award, CGO 2013 SIGPLAN Research Highlights, 2013 Advising and Grants: Bin Ren has advised numerous Ph.D. and master’s students, many of whom have co-authored influential papers. His research has been supported by competitive grants, including the NSF CAREER Award. He actively mentors students in areas of parallel computing, compiler design, and machine learning systems. He has also received funding from the Jeffress Trust Awards and other sources to support interdisciplinary research. Professional Service: He has served in leadership roles such as Program Co-Chair for PPoPP'25 and HIPS'21, Track Co-Chair for ICPP'24 and HiPC'24, and Artifact Evaluation Co-Chair for PPoPP'24 and ALENEX'25. He is a frequent reviewer for top journals and conferences including TPDS, TACO, NeurIPS, and SC. Teaching: He teaches courses such as CS304 (Computer Organization) and CS642 (Compiler Techniques for High Performance Computing), contributing to both undergraduate and graduate education in systems and programming. Lab and Team: His research group focuses on system-software co-design for efficient AI deployment. Collaborators include researchers from institutions like Pacific Northwest National Laboratory and The Ohio State University. His team works on real-world applications in healthcare, autonomous systems, and scientific computing.

Ian Laneمشاهده پروفایل

دانشیار

• Natural Language Processing
• Speech Recognition
• Machine Translation
• +۵ مورد دیگر

Ian Lane is an Associate Professor in the Computer Science and Engineering Department at the University of California, Santa Cruz's Baskin Engineering school, serving as Program Director for the Natural Language Processing Professional Master's Degree Program. He joined UCSC in Fall 2022 after an extensive career spanning academia and industry. His research centers on computational systems that understand spoken human language, spanning speech recognition, transcription, meaning interpretation, and contextually appropriate responses. Key research areas include: Natural Language Processing for real-world applications Conversational AI systems development Speech-to-speech translation technologies Multimodal interaction (audio-visual integration) Language technologies that learn through real-world interaction His recent publications demonstrate strong focus on hallucination detection in LLMs, tabular data understanding, explainable AI, and robust speech recognition systems. Current work emphasizes "in the wild" language technologies that adapt through user interaction. Dr. Lane has received recognition through impactful industry applications including Jibbigo (the first mobile speech translation app) and military translation systems deployed in Iraq and Afghanistan. He actively mentors students and collaborates across UCSC's Silicon Valley Campus programs including Games and Playable Media and Human-Computer Interaction. His vision includes integrating NLP with virtual environments for language learning and skill acquisition.

Yiwei Wangمشاهده پروفایل

استادیار

• Natural Language Processing
• Large Language Models
• Graph Machine Learning
• +۳ مورد دیگر

Yiwei Wang is an Assistant Professor at the Department of Computer Science, University of California, Merced, leading the UC Merced NLP Lab . He holds a Ph.D. from National University of Singapore (2023), M.Phil from Hong Kong University of Science and Technology (2019), and B.S. from Southeast University (2017). His research focuses on natural language processing , large language models , and graph machine learning , with emphasis on trustworthy AI systems.

• Artificial Intelligence
• Natural Language Processing
• Knowledge Management
• +۷ مورد دیگر

Jorjeta Jetcheva is an Assistant Professor in the Computer Engineering Department at San José State University, part of the Charles W. Davidson College of Engineering. She brings extensive industry experience from leadership roles at Accenture, Fujitsu Laboratories of America, Itron, and Firetide, where she led innovations in AI, smart grids, and wireless networking. Her research focuses on Artificial Intelligence-based Personal Assistants, Natural Language Processing, and Knowledge Management . Her work bridges human-centered AI and enterprise applications, with emphasis on intelligent agents, knowledge platforms, and ethical AI systems. She explores how AI can enhance user productivity, decision-making, and system security across domains such as healthcare, energy, and customer service. The recent publications highlight a strong trend in Natural Language Processing applied to real-world problems like fake health news detection, agent assist systems, and knowledge graph integration. Her research also spans Smart Grid analytics, cybersecurity, and AI ethics , reflecting a multidisciplinary approach grounded in both theoretical rigor and practical deployment. Scientific Awards: Best Application Paper Award at IEEE Big Data Service 2022 Best Paper Award at SmartGridComm 2014 Top 10 Finalist, Fujitsu Next Generation Product Idea Contest 2016 ('Robo Butler') CSU STEM-NET Faculty Fellow (2023) Advising and Grants: Professor Jetcheva is the Principal Investigator of a $2.5 million NSF S-STEM grant titled 'Empowering Students to Succeed in Engineering and Computer Science'. She actively mentors graduate students, including Master’s student Garima Chaphekar, with whom she co-authored an award-winning paper on fake health news detection. She serves on the SJSU AI Advisory Committee and leads the AI Research Cluster, fostering interdisciplinary collaboration and student success. Labs and Teams: She leads the AI Research Cluster at SJSU, promoting innovation in artificial intelligence across departments. Her work involves close collaboration with students, industry partners, and interdisciplinary teams focused on deploying scalable, ethical AI solutions.

Mary Gibbsمشاهده پروفایل

مدرس

• Information Systems
• Data Analysis
• Business Intelligence
• +۳ مورد دیگر

Mary Gibbs serves as a Lecturer in the Department of Information Systems at the Orfalea College of Business, California Polytechnic State University (Cal Poly), while concurrently working as an Information Technology consultant in the Office of the Registrar. Her dual roles focus on applications research, automation, and technical data analysis for campus-wide initiatives including process optimization, class availability enhancement, and 4-year graduation rate improvement. Her educational background includes an MBA from Cal Poly's Orfalea College of Business, during which she earned membership in the Beta Gamma Sigma and Golden Key honors societies. Professional expertise spans database administration, business intelligence, and software development lifecycle management. Gibbs' research interests center on practical applications of information systems in higher education administration, with emphasis on Oracle Campus Solutions query development, data-driven decision support systems, and automation of institutional reporting processes. Her work directly impacts operational efficiency in academic resource allocation and student success metrics. Professional recognition includes: Beta Gamma Sigma honors society membership Golden Key honors society membership With over a decade of institutional experience at Cal Poly, Gibbs leverages extensive industry background from technology firms ranging from startups to corporations like Microsoft. Her pre-academic career encompassed Quality Assurance Management, Test Engineering, and Database Administration on projects including Windows OS development, medical robotics, and Web 2.0 platforms. Current technical work involves Oracle Campus Solutions query development to support data analysis for campus strategic initiatives.

• Machine Learning
• Explainable AI
• Fairness in AI
• +۳ مورد دیگر

Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University, Director of the Stanford AI Lab (SAIL), and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI). He also serves as Chief Scientist at Visual Layer and Virtue AI, and is a Member of the National Academy of Engineering. His research centers on Machine Learning Methods, Explainability, Fairness & Ethics of AI, and Machine Learning Systems. He develops interpretable and reliable models, addresses algorithmic fairness, and builds efficient large-scale ML systems through frameworks like XGBoost. His work bridges theoretical rigor with real-world applications in healthcare and human-centered AI. His recent publications (2023–2025) demonstrate leadership in generative AI evaluation, model reliability, and ethical frameworks. Key trends include developing live benchmarks for research synthesis, on-device calibration techniques, multi-objective optimization with constraints, and societal impact assessment tools—showcasing a trajectory from foundational ML systems to responsible AI deployment. Honors include: Member of the National Academy of Engineering Details about his advising and grant activities were not provided in source materials, though his leadership roles indicate extensive mentorship and funding oversight. As Director of SAIL, he shapes one of the world’s premier AI research centers, while his HAI fellowship drives interdisciplinary initiatives ensuring AI advances human welfare. His industry roles at Visual Layer and Virtue AI translate academic research into practical AI solutions.

Bo Zengمشاهده پروفایل

دانشیار

• Optimization
• Robust Optimization
• Stochastic Programming
• +۷ مورد دیگر

Bo Zeng is an Associate Professor in the Swanson School of Engineering at the University of Pittsburgh. His research focuses on developing and utilizing optimization and analytics tools to address challenges in real systems, particularly in discrete optimization models with uncertainties, game theory models, and advanced data analysis and computing methods. His work is extensively applied in engineering, healthcare, and management systems. Dr. Zeng earned his PhD from Purdue University in 2007 and his BS from Xian Jiaotong University in 1998. His research interests span optimization, robust optimization, stochastic programming, power systems, demand response, renewable energy integration, high performance computing, game theory, mixed integer programming, and multilevel optimization. Analysis of Dr. Zeng's recent publications reveals a strong focus on power systems and energy applications, with significant contributions to microgrid planning, demand response modeling, renewable energy integration, and robust optimization techniques. His work demonstrates a consistent pattern of applying advanced mathematical optimization methods to solve complex problems in energy systems, with increasing attention to uncertainty modeling and risk management in power grid operations. Dr. Zeng has collaborated extensively with researchers across multiple institutions, particularly in China, reflecting the global nature of energy research and the international collaboration needed to address complex energy challenges. His publications span high-impact journals in power systems, optimization, and interdisciplinary energy research.

Luwen Huangfuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Business Analytics
• Public Health
• +۴ مورد دیگر

Luwen (Vivian) Huangfu serves as an Assistant Professor in the Management Information Systems Department at San Diego State University's Fowler College of Business. Holding a PhD from the University of Arizona (2019), she has published over 30 peer-reviewed articles in premier AI venues including IEEE Transactions and ACM conferences, accumulating 300+ citations. Her research bridges artificial intelligence with business analytics, public health, and transportation systems. Her educational foundation includes: PhD in Management Information Systems, University of Arizona (2019) Dr. Huangfu's research pioneers few-shot learning and generalized zero-shot recognition with applications spanning pavement distress detection , mental health intervention , and code search optimization . She develops novel architectures for multi-label image classification and document clustering using large language models, with significant contributions to weakly supervised learning and metric network design. Her work consistently addresses real-world challenges in transportation infrastructure, cybersecurity, and healthcare analytics. Recent publications (2023-2025) reveal a strategic focus on efficient deep learning for resource-constrained environments, particularly in transportation and medical imaging. She integrates spatial contextual awareness in multiple instance learning while advancing prompt-based refinement for long-tailed distributions. Her growing emphasis on LLM-driven security intelligence and social media mental health analysis demonstrates cross-domain impact. Her scientific accolades include: Corporation for Education Network Initiatives in California (CENIC) Award (2024) NIH-supported Summer Institute 2024/2025 Cohort NSF-supported University of Maryland Travel Award (2024) Department of Energy (DOE) Award (2022, 2023) NSF-DOE-Jointly-Supported Travel Award (2023) Management Information Systems Quarterly (MISQ) Scholarly Development Academy (2022) 24 total awards including multiple NIH/NSF grants and teaching fellowships As a principal investigator, she has secured funding from DOE, NIH, and NSA while serving as advisor for undergraduate and graduate research scholarships. Her service includes NSF ACCESS Program advisory, NIH/NSF grant review panels, and editorial roles for IEEE Transactions and Journal of Medical Internet Research. She actively mentors students through SDSU scholarship committees and DEI initiatives while maintaining rigorous peer-review commitments across 15+ journals and conferences.

Kamel Lahouelمشاهده پروفایل

استادیار

• Machine Learning
• Cancer Early Detection
• Cell-free DNA Analysis
• +۴ مورد دیگر

Kamel Lahouel is an Assistant Professor at the Early Detection and Prevention Division of the Translational Genomics Research Institute (TGen) . He joined TGen in February 2022 and focuses on mathematical and statistical models applied to cancer biology. Education : Ph.D. in Applied Mathematics and Statistics from Johns Hopkins University (2018). Dr. Lahouel specializes in machine learning and stochastic processes to analyze cell-free DNA for cancer early detection and minimal residual disease testing (MRD) . His methodological work includes branching processes , Markov chains , and non-parametric statistics for tasks like stochastic optimization and multiple hypothesis testing . His research also explores pattern recognition in latent dynamical systems . Recent publications highlight his development of generative models for tumorigenesis timelines (2020), supervised mutational signatures in cancer (2021), and data-driven blood testing combined with PET-CT (2020). His work bridges applied mathematics with clinical oncology , emphasizing computational approaches for early cancer detection.