جستجوی استاد راهنمای دانشگاهی

Francis Y. Yanمشاهده پروفایل

استادیار

• Networked systems
• Cloud infrastructure & resource management
• Machine learning for systems and networks
• +۳ مورد دیگر

Francis Y. Yan is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC), holding an affiliate appointment in Electrical & Computer Engineering within the Grainger College of Engineering. He leads the Illinois Networked Systems and AI (NSAI) research group, focusing on building intelligent networked systems that are safe, robust, and performance-optimized through practical machine learning integration. Prior to joining UIUC in January 2025, he served as a Senior Researcher at Microsoft Research Redmond under Victor Bahl. His educational background includes: Ph.D. in Computer Science from Stanford University (2020), advised by Keith Winstein and Philip Levis B.S. in Computer Science (Yao Class) and B.A. in Economics from Tsinghua University (2015) Additional undergraduate studies at MIT Yan's research adopts a holistic approach to practical machine learning for networked systems, emphasizing judicious application rather than indiscriminate use. He builds real-world systems and research platforms to lay ML foundations, devises deployable algorithms using domain insights, and validates performance through extensive empirical evidence. His work consistently addresses operator concerns regarding ML deployment—focusing on safety, robustness, generalization, and efficiency—while strategically combining ML with classical networking and systems techniques. Analysis of his 15 most recent publications (2023-2025) reveals dominant themes in resource allocation for microservices (DeDe, Autothrottle), real-time video optimization (Mowgli, GRACE), and LLM-driven network algorithm design. His work bridges theoretical advances with industrial deployment, evidenced by platforms like Puffer (400,000+ users) and OpenNetLab that have become community standards for validating congestion control algorithms. His research has been recognized with top honors: USENIX NSDI Outstanding Paper Award (2024) for Autothrottle APNet Best Paper Award (2022) IRTF Applied Networking Research Prize (2021) USENIX NSDI Community Award (2020) USENIX ATC Best Paper Award (2018) for Pantheon Yan actively recruits master's and undergraduate researchers for his NSAI group, prioritizing self-motivated students for projects in networked systems and AI. His research is supported by industry collaborations (notably Microsoft) and manifests in deployable platforms like Puffer—which has enabled award-winning research at NSDI and SIGCOMM—and OpenNetLab for real-time communications. His work directly impacts production systems including Microsoft Teams and Bing. He founded and directs the Illinois Networked Systems and AI (NSAI) research group, which operates critical infrastructure including Puffer (a live TV service and research platform) and OpenNetLab. These platforms facilitate community-wide validation of novel algorithms, with Puffer alone supporting multiple best-paper awards at top conferences. Current workstreams span cloud resource management (Teal, Autothrottle, DeDe), low-latency video (Puffer, Tambur, Mowgli), and LLM-augmented systems (Nada, Designing Network Algorithms via LLMs).

Nam Lingمشاهده پروفایل

استاد

• Video Coding and Compression
• Deep Learning Approaches for Video/Image Coding
• Deep Learning Approaches for Video/Image Processing
• +۳ مورد دیگر

Nam Ling is a Ph.D. holder and IEEE/IET Fellow currently serving as the Wilmot J. Nicholson Family Chair Professor in the Department of Computer Science and Engineering at Santa Clara University's School of Engineering. His career spans academic leadership roles, including Chair of the Department (2010-2023) and Associate Dean for the School of Engineering (2002-2010). He has held visiting/chair professorships at Tianjin University, Xi’an University of Posts & Telecommunications, Lanzhou University, Fuzhou University, Shanghai Jiao Tong University, National Dong Hwa University, and National University of Singapore. B.Eng. in Electrical Engineering from National University of Singapore M.S. and Ph.D. in Computer Engineering from University of Louisiana, Lafayette His research focuses on video/image compression, deep learning for coding/processing, and machine-centric media coding. He has authored 280+ publications, contributed to seven international standards, and secured 20+ U.S./European/PCT patents. He pioneered DNN approaches to video coding, VVC/HEVC optimization, and compressive sensing methods. As an academic leader, he chaired IEEE Hot Chips, ICME, VCIP, and U-Media conferences, served as IEEE/APSIPA Distinguished Lecturer, and held editorial roles for IEEE TCAS I, J-STSP, and Springer journals. His accolades include six Santa Clara University awards, three IEEE Umedia Best Paper Awards, and recognition as IEEE ICCE Best Paper winner in 2003. Honors & Awards: IEEE Fellow (2008-present) IET Fellow (2011-present) AAIA Fellow (2022-present) IEEE ICCE Best Paper Award (2003) IEEE Umedia Best Paper Awards (2016, 2017, 2019) University/School-level awards at Santa Clara University

Hongxin Huمشاهده پروفایل

استاد

• Network Security
• Machine Learning for Security
• IoT Security
• +۴ مورد دیگر

Hongxin Hu is a Professor and Associate Chair in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York (SUNY). His research spans security, networking, and machine learning, with publications across top conferences including security (S&P, CCS, USENIX Security, and NDSS), networking (SIGCOMM and NSDI), machine learning (NeurIPS, ICML, and EMNLP), and human-computer interaction (CHI and CSCW). His work has been funded by NSF (SaTC, CNS, IIS, OAC, SOC), USDOT, VMware, Amazon, Google, and Dell. Dr. Hu earned his PhD in Computer Science and Engineering from Arizona State University in 2012. His academic journey has led him to become a prominent researcher in cybersecurity with a strong publication record and significant research impact. Dr. Hu's research interests encompass a wide range of topics at the intersection of security, networking, and artificial intelligence. His work focuses on Emerging Network Technologies and Security (5G/Future-G, NFV, SDN, Edge computing), Machine Learning for Security and Privacy , Security and Privacy in IoT and Cyber-Physical Systems , and AI for Social Good (addressing online abuse, unsafe children's games, and cyberbullying). His interdisciplinary approach has enabled him to tackle complex security challenges through innovative solutions that combine networking expertise with machine learning techniques. His recent publications demonstrate a strong trend toward applying large language models and advanced machine learning techniques to security challenges, particularly in content moderation, vulnerability detection, and privacy protection. The research spans multiple domains including voice assistant security, IoT security, network security, and social media safety, showing a consistent pattern of addressing real-world security problems with cutting-edge technical approaches. IEEE Big Data Security Senior Research Award (2025) ACM SACMAT Test-of-Time Award (2024) NSF CAREER Award (2019) Multiple Best Paper Awards from ACM ASIACCS (2022), ACSAC (2020), IEEE ICC (2020), and ACM SIGCSE (2018) Amazon Faculty Research Award (2022) First Place Award in ACM SIGCOMM 2018 Student Research Competition Dr. Hu has successfully advised multiple PhD students, including Nishant Vishwamitra who joined UT San Antonio as a tenure-track Assistant Professor. His research has been generously funded by major agencies and industry partners. As an active member of the academic community, he serves as Associate Editor for IEEE Transactions on Dependable and Secure Computing and Computers & Security, and has held numerous leadership roles in major security conferences including TPC Co-Chair for ASONAM 2025 and IWSPA 2024/2025. Dr. Hu leads a vibrant research group that has produced significant contributions in network security function virtualization, intrusion detection systems, and privacy-preserving technologies. Current projects include developing LLM-assisted vulnerability detection systems, defenses against jailbreak attacks on large language models, and security mechanisms for emerging networking technologies. His team's work on IoT security, voice assistant applications, and online content moderation has received wide recognition and press coverage.

Alan Bovikمشاهده پروفایل

استاد

• Image and Video Processing
• Digital Television
• Digital Cinema
• +۵ مورد دیگر

Alan Bovik is a Professor at The University of Texas at Austin, holding the prestigious Cockrell Family Endowed Regents Chair in Engineering. He serves as Director of the Laboratory for Image and Video Engineering (LIVE) and maintains dual faculty appointments in the Department of Electrical and Computer Engineering and the Institute for Neuroscience. His extensive contributions have established him as a leading authority in visual information processing with global impact. Dr. Bovik's research spans multiple domains with a primary focus on image and video processing, digital television and digital cinema, computational vision, and visual perception. His work has fundamentally advanced the understanding of human visual perception, leading to practical applications in video quality assessment and image processing systems. With over 800 technical publications cited more than 75,000 times and an H-index above 100, his research has had extraordinary impact across academia and industry. He is recognized as a Highly-Cited Researcher by Clarivate Analytics, placing him among the most influential researchers globally. Analysis of Dr. Bovik's recent publications reveals a strong emphasis on video quality assessment for emerging applications like user-generated content, high-motion streaming, and adaptive video delivery. His work seamlessly integrates deep learning approaches with traditional signal processing techniques to develop perceptually accurate models. There's a clear trend toward addressing practical challenges in video streaming quality, compression artifacts, and the unique characteristics of modern video content. His research bridges theoretical foundations with real-world applications, making significant contributions to both academic understanding and industry standards. Dr. Bovik's exceptional contributions have been recognized with numerous prestigious awards: IEEE Fourier Award (2019) for seminal contributions to perception-based image and video processing Edwin H. Land Medal (2017) from The Optical Society Primetime Emmy Award for Outstanding Achievement in Engineering Development (2015) Norbert Wiener Society Award (2013) Claude Shannon / Harry Nyquist Technical Achievement Award (2005) Multiple best paper awards from IEEE, EURASIP, and Picture Coding Symposium As an educator and mentor, Dr. Bovik has guided numerous students through his leadership at LIVE. His professional service includes founding and serving as Editor-in-Chief of the IEEE Transactions on Image Processing (1996-2002) and chairing the inaugural IEEE International Conference on Image Processing in 1994. His industry impact is substantial, evidenced by his Primetime Emmy Award and frequent consultation with major institutions. Dr. Bovik is also a registered Professional Engineer in Texas, demonstrating his practical engineering expertise alongside theoretical contributions. Dr. Bovik leads the Laboratory for Image and Video Engineering (LIVE), which maintains strong affiliations with multiple research centers including the Wireless Networking and Communications Group (WNCG), Center for Perceptual Systems, Telecommunications and Signal Processing Research Center, and Institute for Computational Engineering and Sciences. These interdisciplinary connections enable research that bridges engineering, neuroscience, and computer science to advance our understanding of visual perception and processing.

Jasmine Gnanaduraiمشاهده پروفایل

دانشیار

• Machine Learning
• Wireless Sensor Networks
• Cloud Computing
• +۷ مورد دیگر

Jasmine Gnanadurai serves as an Associate Professor in the Department of Electrical Engineering & Computer Science at George Fox University's College of Engineering. She teaches a range of courses including Introduction to Computer Science, Software Engineering, Servant Engineering, and Senior Design. Her office is located in Wood-Mar 222, where she holds regular office hours for student consultation. Dr. Gnanadurai's educational background includes: Ph.D. in Computer Science (2017) from Anna University, Chennai, India M.Phil (2006) from Bharathidasan University, Tiruchirappalli, India M.C.A. (2000) from Bharathidasan University, Tiruchirappalli, India B.Sc. in Computer Science (1997) from Bharathidasan University, Tiruchirappalli, India Dr. Gnanadurai's research spans multiple cutting-edge domains in computer science and engineering. Her work in wireless sensor networks has focused on optimization algorithms for zone-based networks, particularly using techniques like DBSCAN clustering and particle swarm optimization. She has made significant contributions to cloud computing architecture, especially in resource allocation and load balancing for peer-to-peer networks. Her expertise extends to machine learning applications across diverse fields including agriculture productivity, student feedback analysis, and crowd behavior recognition. Her recent publications demonstrate a strong interdisciplinary approach, bridging computer science with practical applications in healthcare, education, smart cities, and transportation. She has published extensively on deep learning techniques for EMG-based hand gesture recognition, blockchain applications for electric vehicle charging infrastructure, and immersive technologies for educational settings. These works reflect her commitment to addressing real-world challenges through technological innovation. Dr. Gnanadurai actively contributes to the engineering education mission at George Fox University through multiple channels. She teaches foundational computer science courses, advanced software engineering, and participates in the university's distinctive Servant Engineering program where students develop solutions to humanitarian needs. She also guides senior engineering students through their capstone Senior Design projects, which partner with industry sponsors to solve real-world problems. As part of the College of Engineering, Dr. Gnanadurai works within interdisciplinary teams that collaborate on projects addressing humanitarian needs through the Servant Engineering program. This program connects students with community partners to develop engineering solutions that serve vulnerable populations, reflecting the university's Christian mission of service. Her involvement in Senior Design connects students with industry partners including Xerox, Intel, Garmin, and numerous regional companies, providing students with valuable real-world experience.

• Machine Learning
• Computer Vision
• Data Compression
• +۳ مورد دیگر

Don Adjeroh is a Professor and Associate Chair in the Lane Department of Computer Science and Electrical Engineering at West Virginia University (WVU). He serves as Graduate Coordinator for CS programs and has led multiple NSF-sponsored projects including Bridges in Digital Health and Multi-Scale Integrative Approach to Digital Health . His research focuses span Machine Learning , Bioinformatics , Computational Biology , and Data Compression . NSF CAREER Award recipient Collaborated with institutions like University of Central Florida and University of Canterbury Led workshops on Plant Image Analysis and Long Non-Coding RNA research His academic contributions include co-authoring the book The Burrows Wheeler Transform: Data Compression, Suffix Arrays, and Pattern Matching (Springer, 2008) and developing software tools like BSMP and PSA for data compression and protein family modeling. He teaches graduate-level courses in String Algorithms and Information Theory , with former projects involving undergraduate and graduate students in DNA microarray image processing and 3D video compression research. Key Scientific Awards : NSF CAREER Award NASA-WV Space Consortium funding WV-EPSCOR grants NSF-CITeR support DOD-ONR/DHS/DOJ/NIJ/NHPRC sponsorships

Igor dos Santos Montagnerمشاهده پروفایل

استاد آموزشی

• Computer Education
• Computing Education Research
• Programming Pedagogy
• +۳ مورد دیگر

Igor dos Santos Montagner is a Teaching Professor in the Department of Computer Science and Engineering at Insper Institute of Education and Research, where he has worked for seven years. He holds a Bachelor's (2010) and Doctorate (2017) in Computer Science from the University of São Paulo, with a doctoral internship at INSA-Rouen, France. His educational background includes undergraduate and doctoral studies at the University of São Paulo under supervision of Professors Roberto Hirata Jr and Nina Hirata, with doctoral work conducted in collaboration with Professor Stéphane Canu at INSA-Rouen. Montagner's research focuses primarily on Computing Education Research, with specific interests in student-centered teaching methodologies and their impact on developing professional skills beyond technical content. He also investigates the use of Free Software in academic environments and its educational benefits. His work combines theoretical educational frameworks with practical classroom implementations, particularly in programming education and computer science fundamentals. His publication record shows a clear trajectory from his doctoral work in image processing and computer vision toward his current focus on computing education. His recent articles demonstrate increasing specialization in programming pedagogy, particularly around teaching the C programming language, mastery-oriented grading systems, and integrating open source contributions into academic coursework. The publications reflect strong international collaboration, particularly with the University of Illinois at Urbana-Champaign. Montagner actively contributes to the academic community through conference presentations at venues like SIGCSE and through his involvement in developing innovative educational approaches. His work emphasizes practical implementation and measurable educational outcomes. He advises on curriculum development and has created electives such as the Open Development course, which teaches students through real open source contributions. His projects include 'Frequent testing and feedback' in partnership with UIUC researchers, and 'Learning the C programming language' with colleagues at Insper. These projects focus on optimizing student effort and improving learning experiences in programming education. Montagner maintains an active academic blog discussing both professional and personal perspectives on technology and education, and has contributed to open source projects including ElementaryOS during the pandemic period.

Krishnan Anandمشاهده پروفایل

استاد

• Operations Management
• Supply Chain Management
• Economics of Information Systems
• +۴ مورد دیگر

Krishnan Anand is a David Eccles Professor and Faculty Fellow in the Department of Operations and Information Systems at the University of Utah’s David Eccles School of Business. He holds tenure and has a distinguished academic career, previously teaching at Wharton and Kellogg. With a B.Tech from IIT Madras, an M.S. from the University of Rochester, and a Ph.D. from Stanford University, Anand specializes in Operations/Supply Chain Management and the economics of Information Systems. His research focuses on ethics, sustainability, supply chain contracts, and the interplay of inventories, information, and incentives. He has received multiple teaching awards, including the Brady Superior Teaching Award and the Daniels Fund Leadership in Ethics Education Award, and pioneered the Global Supply Chain Management course for undergraduates and MBAs. Anand’s research has been recognized with prestigious awards, including the Management Science Best Paper Award (2010–2012) and a best paper award at the Conference on Information Systems and Technology (2016). His work bridges theoretical rigor with practical insights, addressing challenges in IT outsourcing ethics, sustainable operations, and supply chain dynamics. He has delivered invited talks at major business schools and conferences, emphasizing both academic and practitioner audiences. Professionally, Anand transitioned from software entrepreneurship to academia, bringing industry perspectives to his teaching and research. His current roles include advising doctoral students and consulting on academic projects. His publications span topics from AI-driven agriculture systems to cybersecurity and predictive maintenance, reflecting his interdisciplinary approach to solving real-world operational and technological challenges.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۸ مورد دیگر

Rohini Srihari is a Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York, where she also serves as Associate Chair. Additionally, she holds an Adjunct Professor position in the Department of Linguistics. Her academic journey includes a PhD in Computer Science from the University at Buffalo (1992) and a B. Math degree from the University of Waterloo (Canada). Dr. Srihari's research spans multiple cutting-edge areas in artificial intelligence, with a strong focus on Natural Language Processing, Information Retrieval, and Text Mining. Her work encompasses both theoretical foundations and practical applications, particularly in Conversational AI systems, disinformation detection, and social unrest prediction. She has pioneered approaches in multilingual text mining for less commonly taught languages and developed innovative methods for social media analysis. Her recent research emphasizes AI for Social Good, addressing critical societal challenges through technology. Her publication record shows a clear evolution from foundational work in multimedia information retrieval and document analysis toward increasingly socially impactful applications. The recent publications demonstrate a strong focus on using AI to address misinformation, predict civil unrest, and develop empathetic conversational agents. Her work increasingly integrates multiple data sources and leverages state-of-the-art deep learning techniques while maintaining a focus on real-world applicability and social impact. Awards & Recognition: Bronze prize in the Alexa Prize Socialbot Grand Challenge 4 (2021) Two US patents in text mining and image analysis Extensive publication record with over 100 papers and an h-index of 40 Dr. Srihari has secured significant research funding from major agencies including the National Science Foundation (NSF), DARPA, and IARPA. She has directed multiple research projects focused on AI applications for social good, including an NSF-funded project on Purposeful Conversational Agents based on Hierarchical Knowledge Graphs. She supervises students across both Computer Science and Linguistics departments, fostering interdisciplinary research. Her research group, focused on Conversational AI for Social Good, addresses critical issues like combating disinformation and assisting the disabled through trustworthy socialbots. She also directs the Center of Excellence for Document Analysis and Recognition (CEDAR) and has led interdisciplinary student teams in major competitions. Her industry experience includes founding and directing technology startups focused on big data analytics, two of which were acquired by larger media analytics companies.

Hana Vrzakovaمشاهده پروفایل

پژوهشگر ارشد

• Eye-Tracking Analysis
• Machine Learning
• Multimodal Modeling
• +۵ مورد دیگر

Hana Vrzakova is a Postdoctoral Research Associate at the University of Colorado and holds leadership roles at the University of Eastern Finland (UEF) and Microsurgery Center KYS. Her research focuses on eye-tracking technologies, machine learning applications in human-computer interaction, and medical informatics. She investigates multimodal modeling for surgical training, cognitive workload assessment, and social media behavior analysis. Key roles include Head of Medical ICT Development at KYS and Head of Research at UEF’s EMC. Her work bridges computer science with clinical applications, particularly in microsurgery and neurosurgery training. She has pioneered tools like the Web-Tracking Plugin (WTP) for real-time AOI annotations and contributed to spectral video integration in medical imaging. Research interests span gaze-based intent prediction, affect recognition in code reviews, and collaborative problem-solving dynamics. She has received awards such as the ACM LAK2020 Best Paper Award and the CHI Doctoral Consortium Award, reflecting her impactful contributions to learning analytics and human-computer interaction. Her publications emphasize interdisciplinary approaches, combining machine learning with medical device development, surgical education, and stress biomarker modeling. Current projects include HSI standards for neurosurgery and VR-based anatomical training systems.

Joseph P. Havlicekمشاهده پروفایل

استاد

• Signal Processing
• Image Processing
• Video Processing
• +۷ مورد دیگر

Dr. Joseph P. Havlicek is the Gerald Tuma Presidential Professor in the Gallogly College of Engineering at the University of Oklahoma, where he has served since 1997. He directs the OU Center for Intelligent Transportation Systems and is a full member of the OU Institute for Biomedical Engineering, Science, and Technology. His extensive career spans both academic and professional realms, with significant contributions to signal processing, image analysis, and transportation systems. His educational background includes: BS in Electrical Engineering from Virginia Tech (1986) MS in Electrical Engineering from Virginia Tech (1988) PhD in Electrical and Computer Engineering from the University of Texas at Austin (1996) Dr. Havlicek's research spans multiple domains including signal, image, and video processing; modulation domain signal processing; target tracking; medical imaging; and intelligent transportation systems. His work bridges theoretical signal processing with practical applications in healthcare and transportation. He has pioneered techniques in AM-FM signal and image modeling, uncertainty measures, and developed innovative approaches for medical image analysis and infrared target tracking. Analysis of his recent publications reveals a strong interdisciplinary trend, with significant work at the intersection of signal processing and medical imaging, particularly in bone marrow assessment and cancer treatment monitoring using PET/CT imaging. His computer vision research focuses on infrared object detection and tracking, with recent work on dual-band infrared systems improving small object detection. The publications also demonstrate his continued theoretical contributions to signal processing, particularly in modulation domain techniques and entropy-based uncertainty measures. His scientific achievements have been recognized with numerous awards: University of Oklahoma Outstanding Faculty Advisor Award (2006) University of Oklahoma College of Engineering Brandon H. Griffith Faculty Award (2003) University of Oklahoma IEEE Favorite Instructor Award (1998, 2000) Department of the Navy Award of Merit for Group Achievement (1990) University of Texas Engineering Foundation Award for Exemplary Engineering Teaching (1992) Dr. Havlicek has been exceptionally successful in securing research funding, serving as PI or co-PI on more than 100 externally funded grants and contracts totaling over $25 million. His professional service includes editorial positions with IEEE Transactions on Image Processing and IEEE Transactions on Industrial Informatics, as well as leadership roles in major conferences including ICIP and ICASSP. He has chaired both graduate and undergraduate studies committees in the ECE department. He leads the OU Center for Intelligent Transportation Systems, which develops advanced technologies for traffic management and traveler information systems. His biomedical engineering work through the OU Institute focuses on medical image analysis, particularly for cancer treatment monitoring and bone marrow assessment. His research teams regularly collaborate with medical institutions to translate signal processing techniques into clinical applications.

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.

Yuksel Celikمشاهده پروفایل

مدرس پاره‌وقت

• Artificial Intelligence
• Cyber Security
• Optimization Algorithms
• +۳ مورد دیگر

Dr. Yuksel Celik is an Adjunct Lecturer at the University at Albany's Massry School of Business and an Associate Professor at Karabuk University (Turkey). He holds a PhD in Computer Engineering from Selcuk University (2013) and completed postdoctoral research in Information Security and Digital Forensics at SUNY Albany. Research Focus: His work spans artificial intelligence, cybersecurity, and optimization algorithms, with applications in healthcare, genomics, and agriculture. Key methodologies include deep learning, bio-inspired computing, and computer vision for solving complex real-world problems. Publication Trends: Recent articles (2023-2025) demonstrate strong emphasis on hybrid AI models, security applications, agricultural technology, and medical diagnostics. Common themes include optimization techniques, anomaly detection, and multimodal learning approaches. Awards & Honors: Senior Member of IEEE Academic Contributions: Actively mentors graduate students and leads research projects funded by grants including the EU Erasmus+ program. Teaches courses in cybersecurity, data structures, and mobile application development.

Kyungim Baekمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Bioinformatics
• +۳ مورد دیگر

Kyungim Baek is an Associate Professor in the Department of Information and Computer Sciences at the University of Hawai'i at Manoa, where she maintains an active research program and teaching schedule through Spring 2023. She serves as co-PI of the Bioinformatics Laboratory (BiL) and previously held roles as Associate Director of the Bioinformatics Core for the Pacific Center for Emerging Infectious Diseases Research (2010-2013). Ph.D. in Computer Science, Colorado State University (2002) M.Sc. in Computer Science, Sogang University (1996) B.Sc. in Mathematics, Sogang University (1994) Her research spans computer vision, machine learning, and bioinformatics, with significant contributions in plant phenotyping systems, protein structure prediction, and neural circuit modeling. Current work focuses on deep learning applications for plant identification (WTPlant system) and UAV autonomy, building on earlier work in visual perception modeling and genomic sequence analysis. Publications show strong interdisciplinary trends, with bioinformatics dominating 2010-2015 output (protein phosphorylation prediction, ncRNA discovery) and computer vision applications increasing post-2015 (plant phenotyping, UAV systems). Recent work demonstrates convergence of computer vision and agricultural technology through deep learning approaches for field applications. Best Cognitive Vision Paper Award (2003 International Conference on Computer Vision Systems) She has supervised numerous graduate research projects through ICS 699/700/800 courses and mentored capstone projects. Current teaching includes ICS 235 (Machine Learning), ICS 434 (Data Science), and ICS 483 (Computer Vision), maintaining a consistent teaching load since 2005 with over 15 distinct course offerings. Baek leads the Bioinformatics Laboratory (BiL) research group, collaborating with the Pacific Center for Emerging Infectious Diseases Research. Her work integrates computer vision techniques with biological applications, particularly in plant identification systems and genomic analysis tools.

Chuck Andersonمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Reinforcement Learning
• +۹ مورد دیگر

Chuck Anderson is a Professor in the Department of Computer Science at Colorado State University (CSU), with joint appointments in the Molecular, Cellular, and Integrative Neuroscience Program, the School of Biomedical Engineering, the Graduate Degree Program in Ecology, and the Online Systems Engineering Program. His research focuses on machine learning, deep learning, and reinforcement learning, with applications to brain-computer interfaces, high-dimensional data, health, environment, and energy. Research Interests: His work spans algorithm development for classification, modeling, and control, with a strong emphasis on understanding learned representations. He applies these methods to neuroscience, climate science, programming, and accessibility technologies, demonstrating a highly interdisciplinary approach. His research integrates insights from computer science, neuroscience, and engineering to solve complex real-world problems. Publication Trends: His recent and advised publications reveal a sustained focus on reinforcement learning, deep neural networks, and brain-computer interfaces. There is a growing trend toward diffusion models, ensemble methods, and applications in climate and healthcare, indicating forward-looking and impactful research directions. Scientific Awards: CSU Online Outstanding Distance Educator Award (2024) Best Overall Paper Award, IJCNN 2015 Advising and Grants: He has advised over 15 Ph.D. students, with recent graduates working on reinforcement learning, neuroimaging, climate AI, and program synthesis. While specific grants are not listed, his sustained output and advising suggest consistent research funding. He also leads an AI consulting company, Pattern Exploration, focusing on custom AI solutions for health, environment, and energy. Labs and Teams: Though not explicitly named, his work implies leadership in a machine learning research group at CSU, likely involving students and collaborators across neuroscience, biomedical engineering, and climate science. His involvement in online AI programs and consulting indicates active outreach and applied research teams.