جست‌وجوی استاد و پژوهشگر

Xiangyu Zhangمشاهده پروفایل

استاد

• AI Security
• Software Engineering
• Programming Languages and Compilers
• +۵ مورد دیگر

Xiangyu Zhang is the Samuel D. Conte Professor of Computer Science at Purdue University. He specializes in AI security, software analysis, and cyber forensics, with a focus on detecting vulnerabilities in traditional software and AI systems. His research includes dynamic slicing, program profiling, and securing AI models against backdoors. He has led projects funded by DARPA, IARPA, NSF, and industry, securing over $13 million in grants. He has mentored 30+ PhD students and postdocs, many of whom hold academic positions or industry roles. His work has been recognized with awards like the ACM SIGPLAN Distinguished Dissertation Award and NSF Career Award. Education: PhD, Computer Science, University of Arizona (2006) MS & BS, Computer Science, University of Science and Technology of China (2000 & 1998) Research Interests: AI Security: backdoor detection, adversarial attacks, model integrity Program Analysis: dynamic slicing, static/dynamic debugging Cyber Forensics: log analysis, attack investigation Software Reliability: fault localization, vulnerability detection Key Projects: DARPA V-SPELLS: domain-specific program analysis IARPA TrojAI: AI backdoor detection competitions NSF-funded research on model debugging and co-reasoning of software/NL artifacts Awards & Recognition: Top performer in IARPA TrojAI competitions (13/18 rounds) ACM SIGPLAN Distinguished Dissertation Award (2006) NSF Career Award (2009) Ranked 8th globally in systems research productivity (2024) Career Contributions: Secured over $13M in research funding Guided 25 PhDs and 8 postdocs Published 89+ top-tier papers (2014–2024)

Andrew Headمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Programming
• Reading
• +۱۳ مورد دیگر

Andrew Head is an Assistant Professor at the University of Pennsylvania in the Computer and Information Science department. His research focuses on human-computer interaction, programming, and reading, particularly in developing technology for interactive reading and reasoning. He advises PhD students Alyssa Hwang, Hita Kambhamettu, Litao Yan, Jeffrey Tao, and Jessica Shi, and co-leads the Penn Human-Computer Interaction (Penn HCI) group with Danaé Metaxa. His work is published in top venues like ACM CHI, UIST, and ICSE. Research Interests: Andrew's work bridges interactive systems with programming environments, aiming to enhance how scientists and programmers interact with their tools. Key areas include AI-assisted code understanding, math notation accessibility, and medical note interpretation through interactivity. He employs user studies to identify needs and builds interactive systems to address them. Recent Article Trends: His publications emphasize systems-centric HCI approaches, integrating AI into code and document interfaces. Topics span code explanation (e.g., Ivie), property-based testing (e.g., Tyche), math notation augmentation (e.g., FreeForm), and medical informatics (e.g., Explainable Notes). Recent work also explores notebook environments (e.g., Bolt-on, Tyche) and literate programming (e.g., Colaroid). Scientific Awards: Distinguished Paper Award, ICSE 2024 Best Paper Awards at CHI (2024, 2023, 2022, 2019), UIST (2023, 2018), and others. Nominated for Best Paper at CHI 2023 and VL/HCC 2015. Advising & Grants: Andrew advises multiple PhD students and has secured significant grants, including a $1M NSF award for 'Property-based Testing for the People' (2024). His group collaborates with Penn’s MindCORE center and teams like PLClub and PennNLP. Labs & Teams: He co-leads the Penn HCI group, which works closely with other Penn research teams and labs. The group focuses on creating interactive tools that enhance scientific and programming workflows, supported by grants and cross-institutional partnerships.

Stefanie Muellerمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Computational Fabrication
• 3D Printing
• +۷ مورد دیگر

Stefanie Mueller is the TIBCO Career Development Associate Professor at MIT's Electrical Engineering and Computer Science Department, with joint affiliation in Mechanical Engineering. She leads the HCI Engineering Group at the Computer Science and Artificial Intelligence Laboratory (CSAIL), focusing on advancing fabrication techniques through hardware/software innovations that enable novel object interactions. Develops computational fabrication methods combining photochromic dyes, lenticular lenses, birefringent materials, and optical illusions Co-chaired ACM CHI 2023 and ACM UIST 2020 program committees Recipients of 9 MIT EECS Best Undergraduate Researcher Awards among mentees Her research spans four key directions: Appearance-changing Objects: Photo-Chromeleon (ACM UIST 2019), Lenticular Objects (ACM UIST 2021), and Polagons (ACM CHI 2023) demonstrate reprogrammable surfaces through advanced materials and optical engineering. Tracking Systems: InfraredTags (ACM CHI 2022) and G-ID (ACM CHI 2020) enable passive object tracking via infrared markers and slicing artifacts. Embedded Sensing: MechSense (ACM CHI 2023) and Sprayable User Interfaces (ACM CHI 2020) integrate sensing capabilities into complex geometries. Curved Surface Prototyping: FlexBoard (ACM CHI 2023) and CurveBoard (ACM CHI 2020) develop specialized tools for non-planar electronics. Her recent publications focus on functionality segmentation (UIST 2023), fluorescent markers (UIST 2023), and machine-knitted haptics (UIST 2023). These works combine machine learning, material science, and interactive design principles to push fabrication boundaries. Scientific recognition includes: 2022 MIT Technology Review Innovators Under 35 2020 Microsoft Research Faculty Fellowship 2020 Alfred P. Sloan Research Fellowship 2019 ACM UIST Best Paper Award 2019 NSF CAREER Award 2018 MIT EECS Outstanding Educator Award 2017 Forbes 30 Under 30 in Science Mentoring 9 PhD students and over 20 master's students, her lab has produced 20+ publications at top HCI conferences. She redesigned MIT's 6.810 Engineering Interactive Technologies course during the pandemic, maintaining hands-on learning through home electronics kits and Slack-based collaboration.

Jiang Huمشاهده پروفایل

استاد

• VLSI Design Automation
• Hardware Security
• Machine Learning Applications
• +۳ مورد دیگر

Jiang Hu is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, holding the Eric D. Rubin '06 Endowed Professorship. He also serves as Co-Director of Graduate Programs and is affiliated with the Computer Science & Engineering department. His research focuses on VLSI design automation, machine learning applications, and hardware security. He has held roles as editor for IEEE Transactions on CAD and ACM Transactions on Design Automation, and chaired the 2012 ACM International Symposium on Physical Design. Education: B.S. in Optical Engineering (Zhejiang University, 1990), M.S. in Physics (1997), and Ph.D. in Electrical Engineering (University of Minnesota, 2001). He worked at IBM Microelectronics before joining Texas A&M in 2002. Research interests include energy-efficient VLSI circuits, on-chip communication fabrics, analog layout automation, and AI-driven EDA. Recent work emphasizes machine learning for design closure, privacy-preserving frameworks, and systolic array-based architectures. Awards: IEEE Fellow (2016) Humboldt Research Fellowship (2012) Multiple best paper awards at DAC, ICCAD, and ASPDAC Advising and grants: Leads initiatives like the SLICE project, NSF workshops on ML-EDA infrastructure, and serves as Editor-in-Chief of ACM TODAES since 2024. His work bridges academic research and industry applications in EDA and semiconductor design. Labs/Teams: Active contributor to open-source tools like ALIGN for analog layout generation and collaborations on machine learning for EDA commons.

Maizie Zhouمشاهده پروفایل

استادیار

• Computational Genomics
• Bioinformatics
• Computational Neuroscience
• +۶ مورد دیگر

Maizie Zhou is an Assistant Professor in Biomedical Engineering and Computer Science at Vanderbilt University’s School of Engineering. She holds dual PhDs in Computer Science (Stanford University) and Neuroscience (Wake Forest School of Medicine), with additional degrees from Wake Forest University and Huazhong University of Science and Technology. Her research focuses on computational genomics, bioinformatics, and machine learning applied to problems in cancer genomics, single-cell and spatial transcriptomics, and computational neuroscience. She leads the Zhou Lab, which develops algorithms for structural variant detection, neural circuit analysis, and integrative omics approaches. Recent work includes tools like VolcanoSV and stDyer, and she has received grants from NIH, Vanderbilt Brain Institute, and industry partnerships. Key achievements include VUSE Best Paper Awards, Global Engagement Travel Grants, and mentoring students in prestigious programs like the Provost’s Pathbreaking Discovery Award. Her lab also explores the neural underpinnings of cognitive maturation in primates, combining computational and experimental neuroscience. Education: PhDs in Computer Science (Stanford) and Neuroscience (Wake Forest), MS (Computer Science, Wake Forest), BS (Biotechnology, Huazhong). Research interests span computational genomics (e.g., structural variant detection, haplotype phasing), spatial transcriptomics (clustering, integration), and computational neuroscience (neural circuit dynamics, prefrontal cortex plasticity). Her lab’s tools address challenges in precision medicine, cancer genomics, and understanding adolescent brain development. Recent projects include NIH-funded work on spatial transcriptomics and collaborations with Dr. Meltzer’s lab on cancer genomics. Publications highlight advancements in bioinformatics tools and neural mechanisms, with trends toward multi-omics integration and algorithmic innovation in genomics. Awards include the Global Engagement Travel Grant and CCSB Accelerator Fund. Students under her mentorship have excelled in qualifying exams and travel grants, reflecting her impactful training program.

Raouf Boutabaمشاهده پروفایل

استاد

• Network Management
• Systems and Services
• Resource Management in Wired/Wireless Networks
• +۹ مورد دیگر

Raouf Boutaba is a Professor at the University of Waterloo , serving as Director of the David R. Cheriton School of Computer Science since July 2020. He holds prestigious fellowships including FRSC , FIEEE , FIEC , and FCAE . 2024: Inaugural Rogers Chair in Network Automation 2024: Ontario Research Fund–Research Excellence (ORF–RE) $2M grant for next-gen mobile networks 2021: University Professor title, University of Waterloo Research Interests span network automation, resource management in wired/wireless networks, network function virtualization (NFV), software-defined networking (SDN), cloud computing, blockchain, future Internet architecture, and cybersecurity. His work focuses on zero-touch networks, 5G/B5G slicing, and AI-driven orchestration. Scientific Contributions include 15+ recent publications on topics like reinforcement learning for RAN slicing, encrypted traffic classification, quantum network optimization, and self-driving infrastructure. His projects 5G LEAP and 5G ELITE explore network isolation and Open RAN principles. 2024: IFIP/IEEE CNOM Test of Time Paper Award 2024: Graduate Supervision Excellence Award 2021: Kenneth C. Sevcik Outstanding Student Paper Award (advisor) Teaching includes co-developing the NSERC CREATE Network Softwarization program, offering courses like Network Softwarization: Principles and Foundations (Winter 2024) and Technologies and Enablers since 2018. He emphasizes hands-on training in SDN, NFV, Open RAN, and 5G. Students and Collaborations : Supervised PhD students such as Shihabur R. Chowdhury (2021), Nashid Shahriar (2020), and undergrad Leni Aniva (2022 Gov. Gen. Silver Medal ). His team includes researchers working on 5G, blockchain, and AI-driven network management. Professional Leadership : Organized Rogers TEP Workshops (2024-2025), delivered keynotes at IEEE Globecom, ColCom, and BalkanCom, and served on expert panels for AI orchestration and 5G cybersecurity at major symposia.

Alicia Cheمشاهده پروفایل

استادیار

• Neuroscience
• Developmental Neuroscience
• Behavioral Neuroscience
• +۵ مورد دیگر

Alicia Che is an Assistant Professor of Psychiatry at Yale University School of Medicine and serves as Director of Graduate Admissions for the Interdepartmental Neuroscience Program. She joined the Yale Department of Psychiatry in 2021 after completing her postdoctoral fellowship with Dr. Natalia De Marco García at Weill Cornell Medical College and Dr. Gord Fishell at NYU. Her research is conducted through the Che Lab at Yale, where she investigates how early life experiences impact brain circuit assembly and mature function in models of psychiatric illness. Yale School of Medicine, Department of Psychiatry Interdepartmental Neuroscience Program Center for Brain & Mind Health Division of Molecular Psychiatry Wu Tsai Institute Yale Center for the Science of Cannabis and Cannabinoids Dr. Che earned her Ph.D. in Physiology and Neurobiology from the University of Connecticut in 2014, where she worked in the laboratory of Dr. Joseph LoTurco. She received her B.S. with triple majors in Biology, Physics, and Physical Chemistry from Pacific Lutheran University in Washington state in 2009. Her research focuses on understanding developmental trajectories following early life experiences to develop diagnostics and early interventions for psychiatric illnesses. Dr. Che's research examines how sensory inputs, social bonding, stress, and substance exposure impact brain development. She employs a multi-dimensional approach to assess transcriptional, circuit, neuronal activity, and behavioral changes across the entire developmental timeline. Her lab currently focuses on four specific areas: the role of oxytocin in social behavior development, circuit dysfunction in PTSD, early-life cannabinoid exposure effects, and the impact of early life stress on development and adulthood. She utilizes advanced techniques including mouse genetics, slice electrophysiology, and longitudinal in vivo 2-photon imaging on behaving animals. Her most recent publications demonstrate significant contributions to understanding neural circuit development, PTSD mechanisms, and the effects of early life experiences on brain function. Her work spans from molecular neuroscience to behavioral outcomes, with publications in top journals including Nature, Neuron, and Nature Communications. Her research has revealed important insights into how translaminar neuronal activity strengthens cortical columns, how oxytocin facilitates social touch development, and how PTSD affects brain transcriptomics. NARSAD Young Investigator Award (2020) K99/R00 Pathway to Independence Award from NINDS (2019) Dr. Che's work has significant implications for understanding and treating neurodevelopmental disorders, PTSD, and the consequences of early life experiences on mental health. She collaborates extensively with researchers across Yale and beyond, with frequent co-authorship with colleagues including Lin Lin, Alex Kwan, and Christopher Pittenger. Her research program bridges basic neuroscience with clinical applications, aiming to translate findings into potential interventions for psychiatric conditions.

Iain Murrayمشاهده پروفایل

استاد

• Machine Learning
• Probabilistic Modeling
• Density Estimation
• +۳ مورد دیگر

Iain Murray is Professor of Machine Learning and Inference at the School of Informatics, University of Edinburgh. His research focuses on developing flexible probabilistic models applicable across diverse domains including cosmology, neuroscience, perception, speech, sports, and text. Program Chair for ICLR (2018) Publications Chair for ICML (2017, 2018) Area Chair for AISTATS, ICLR, ICML, NeurIPS, and UAI Amazon Scholar (2018-2024), first appointed in Europe Murray's research interests center on probabilistic reasoning using machine learning, with specific expertise in density estimation and Markov chain Monte Carlo methods. His work spans theoretical foundations and practical applications, with significant contributions to neural autoregressive distribution estimation (NADE), real-valued NADE (RNADE), and pseudo-marginal slice sampling techniques. His research has enabled advances in flexible probabilistic modeling across multiple domains. His publications show consistent focus on advancing probabilistic modeling techniques, with recent work emphasizing neural autoregressive models, density estimation methods, and efficient sampling algorithms. The research trajectory demonstrates progression from foundational work on NADE to increasingly sophisticated deep learning approaches for density estimation and inference. Notable Paper Award for NADE work Amazon Scholar (2018-2024) Murray has supervised numerous PhD students who have gone on to prominent positions at Google DeepMind, NYU, stability.ai, and other leading institutions. His teaching responsibilities include the Machine Learning and Pattern Recognition course and project supervision. His research group focuses on developing tractable probabilistic models with applications across multiple scientific domains.

Andrew Headمشاهده پروفایل

استادیار

• Human-computer interaction
• Programming
• Reading
• +۵ مورد دیگر

Andrew Head is an Assistant Professor at the University of Pennsylvania's Department of Computer Science, specializing in Human-Computer Interaction (HCI) and Programming. His work bridges interactive reading , math notation accessibility , and AI-assisted code comprehension . Affiliated with Penn HCI, PLClub, and MindCORE, he co-leads research with Danaé Metaxa and Benjamin Pierce. University of Pennsylvania Assistant Professor, Computer Science Affiliations: Penn HCI, PLClub, MindCORE His research focuses on interactive reading interfaces , AI-powered programming tools , and math notation analysis . Recent projects include: FreeForm : Interactive math notation editor Tyche : Property-based testing tools Explainable Notes : Medical note interpretation systems Publications in CHI , UIST , and ICSE demonstrate his systems-centric approach combining user studies with working prototypes. Notable awards include Best Paper at UIST 2024 and CHI 2022. Advising: Ph.D. Students: Alyssa Hwang, Litao Yan, Hita Kambhamettu, Jeff Tao, Jessica Shi Grants: $1M NSF grant for Property-based Testing Tools (2024) Teaching: Spring 2025: CIS 4120/5120 - Human-Computer Interaction Fall 2024: CIS 7000 - Interactive Reading

Jukka Mannerمشاهده پروفایل

استاد

• Networking
• Wireless and Mobile Networks
• Transport Protocols
• +۷ مورد دیگر

Jukka Manner is a Full Professor (tenured) of Networking Technology at Aalto University's Department of Communications and Networking (Comnet), School of Electrical Engineering. With a career spanning over two decades in internet technologies, he leads research in networking, wireless systems, and energy-efficient ICT solutions. Dr. Manner received his MSc (1999) and PhD (2004) degrees in computer science from the University of Helsinki. His academic journey has been marked by significant contributions to internet standardization through the IETF since 1999, where he served as co-chair of the NSIS working group. Professor Manner's research focuses on networking, software and distributed systems, with particular emphasis on wireless and mobile networks, transport protocols, energy efficient ICT and cyber security. His work bridges theoretical advancements with practical applications, addressing critical challenges in modern communication systems, sustainable networking practices, and security frameworks. His research group has made significant contributions to 5G technologies, UAV communications, and energy-efficient network design. His extensive publication record shows a clear evolution toward sustainability in networking technologies, with recent work focusing on energy efficiency in 5G systems, sustainable web technologies, and the environmental impact of digital infrastructure. The research demonstrates strong interdisciplinary connections between telecommunications engineering, computer science, and environmental science, with particular emphasis on reducing the carbon footprint of digital systems while maintaining performance. Cross of Merit, Signals (2014) Medal for Military Merits for contributions in national defence and C4 (2015) Professor Manner has supervised over 200 MSc theses and more than 20 doctoral dissertations, establishing himself as a dedicated mentor in the field. He has been principal investigator and project manager for over 15 national and international research projects, including serving as Academic Coordinator for the Finnish Future Internet research programme (2008-2012). His leadership extends to conference organization, having served as local co-chair of Sigcomm 2012 in Helsinki, and active participation as a peer reviewer and member of various Technical Program Committees. As an active contributor to internet standardization through the IETF, Professor Manner's work has practical impact on global networking technologies. His research group maintains strong connections with industry partners and participates in shaping future networking standards and practices, particularly in the areas of sustainable networking, 5G evolution, and security frameworks for emerging technologies.

• Photonic Interconnects
• Distributed Machine Learning
• Cloud Computing
• +۳ مورد دیگر

Rachee Singh is an Assistant Professor of Computer Science at Cornell University, leading the sysphotonics research group. She concurrently serves as an Amazon Scholar within the SageMaker Hyperpod teams, specializing in large-scale machine learning infrastructure development for cloud environments. Her research focuses on photonic interconnect systems for server-scale, rack-scale, and long-haul communication networks, targeting performance optimization for distributed machine learning and planet-scale cloud workloads. Key specialties include optical network design, fault-tolerant WAN architectures, and energy-efficient datacenter interconnects, with strong emphasis on practical deployment in real-world systems. Her group bridges theoretical networking principles with applied AI infrastructure challenges. Recent publications demonstrate concentrated innovation in photonic network optimization for ML workloads, particularly in wavelength management, collective communication algorithms, and chip-to-chip photonic fabrics. This work spans optical physics, distributed systems, and machine learning, revealing a trajectory toward sustainable, high-performance AI infrastructure. Scientific recognition includes: Amazon Research Award (2023) Cisco Research Award Dr. Singh actively mentors graduate researchers including Jonathan Aimuyo, Byungsoo Oh, and Arjun Devraj, whose co-authored publications form the core of her group's output. Research funding is secured through competitive grants from the NSF (including a $1M award for chip-to-chip photonic fabrics), SRC/DARPA JUMP 2.0 program, Cisco, and Cornell's Atkinson Center for Sustainability. The sysphotonics group operates as Cornell's hub for photonic network systems research, developing programmable integrated photonics solutions and collaborating with Amazon on SageMaker Hyperpod for next-generation ML infrastructure.

• Networking
• Communications
• Machine Learning
• +۷ مورد دیگر

Christopher G. Brinton is the Elmore Associate Professor of Electrical and Computer Engineering at Purdue University, where he leads the ION research lab. He is affiliated with the Department of Electrical and Computer Engineering in the College of Engineering at Purdue University's West Lafayette campus. Dr. Brinton received his PhD from Princeton University, where he was previously the Associate Director of the EDGE Lab and a Lecturer of Electrical Engineering. His research focuses on the intersection of networking, communications, and machine learning, with particular emphasis on Fog computing systems, the Internet of Things (IoT), NextG Wireless, and social learning networks. His research integrates foundational techniques including convex and non-convex optimization, machine learning, and signal processing to address challenges in networked intelligent systems. The ION lab under his leadership develops both theoretical frameworks and practical implementations for next-generation networking solutions, with strong industry collaborations including Qualcomm, Nokia, Intel, Cisco, Dell, and Ericsson. Recent publications reveal a strong trend toward federated learning, decentralized algorithms, and edge intelligence, with significant contributions to model partitioning, communication-efficient learning, and robust network architectures. His work increasingly bridges traditional communication theory with modern machine learning techniques to solve emerging challenges in distributed networked systems. NSF CAREER Award ONR Young Investigator Program (YIP) Award DARPA Young Faculty Award (YFA) AFOSR Young Investigator Program (YIP) Award Intel Rising Star Faculty Award (RSA) Dr. Brinton teaches several courses including ECE 647: Performance Modeling of Computer Communication Networks, ECE 301: Signals and Systems, and ECE 547: Introduction to Computer Communication Networks. He has co-authored the book 'The Power of Networks: Six Principles That Connect Our Lives' and taught three Massive Open Online Courses (MOOCs) with over 400,000 cumulative students. While not currently actively recruiting students, he remains open to connecting with highly motivated individuals. Dr. Brinton leads the ION (Intelligent Optimization and Networking) research lab, which focuses on creating theoretical foundations and practical implementations for next-generation networked systems. The lab has recently published significant work on 6G taxonomy in collaboration with major industry partners and continues to push boundaries in distributed learning and network optimization.

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
• +۴ مورد دیگر

Alain Durmus is a Professor at École Polytechnique, affiliated with the applied mathematics department (CMAP). His research focuses on computational statistics, machine learning, and stochastic methods, including Monte Carlo algorithms, Bayesian inference, and optimization. He explores topics such as Markov chain Monte Carlo (MCMC), stochastic approximation, and generative models. His work emphasizes theoretical guarantees for algorithms like Langevin Monte Carlo and Hamiltonian Monte Carlo, with applications to high-dimensional Bayesian inference and inverse problems. Key contributions include hypocoercivity analysis of piecewise deterministic MCMC processes, convergence guarantees for stochastic gradient methods, and the development of efficient sampling techniques. He has also contributed to Bayesian imaging and federated learning through works like the QLSD algorithm. Awarded the Best Student Paper Award at ICASSP 2020 for his work on the Sliced-Wasserstein distance. His teaching spans mathematical statistics, stochastic methods, and probability at École Polytechnique and ENS Paris-Saclay. He has also contributed to conferences and workshops on topics ranging from MCMC convergence to optimization in machine learning.

• Optical Communications
• Optoelectronic Circuits
• Optical Switching
• +۷ مورد دیگر

Amalia Miliou is a Professor in the Department of Informatics at Aristotle University of Thessaloniki, where she has served since 1993, progressing through the academic ranks from Lecturer to her current position as Professor since 2022. She holds a PhD in Electrical and Computer Engineering from the University of Florida (1991) with specialization in Optoelectronics, following an MSc in the same field (1988) and a Physics degree from Aristotle University (1985). Her research focuses on optical communications systems, with specific expertise in optoelectronic circuits simulation, optical switching, optical RAM development, converged fiber-wireless technology, 5G networks, and secure optical communications using chaos theory. Over her career, she has supervised numerous graduate students across these research areas, with thesis topics spanning optical memory systems, fiber-wireless integration, chaos-based secure communications, and advanced optical network architectures. Her recent publications (2021-2024) demonstrate a strong focus on next-generation optical networking solutions for 5G/6G applications, including fiber-wireless convergence, optical memory systems for high-speed networks, and innovative approaches to optical signal processing. Her work bridges fundamental photonics research with practical telecommunications applications, particularly in addressing the bandwidth and latency challenges of modern mobile networks. Professor Miliou has served as the Coordinator of the LLP-ERASMUS student exchange program at the Department of Informatics since 1997 and has held various administrative positions including membership in the University Senate and General Assembly. She has led and participated in numerous research projects, most recently focusing on technological improvements for 5G systems through optical-wireless network development (2019-2021), next-generation healthcare applications leveraging 6G networks (2023-2027), and photonic integrated circuits for random access memory (2012-2015).

• Software Engineering
• Refactoring Techniques
• Technical Debt Management
• +۴ مورد دیگر

Christian Newman is an Associate Professor in the Department of Software Engineering at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). He serves as the Graduate Program Director and has expertise in software engineering methodologies, refactoring techniques, and source code analysis. His research focuses on improving code quality, developer practices, and automated documentation. Education: Newman holds a BS, MS, and Ph.D. from Kent State University. His academic background aligns with his current research in software engineering and empirical studies. Research Interests: His work emphasizes identifier naming standards, technical debt management, refactoring strategies, and code reuse. He explores how developers perceive and implement refactoring tools, as well as the role of large language models (LLMs) in programming education and code generation. Publications: Newman's recent work includes studies on identifier semantics, part-of-speech tagging for code analysis, and the performance of LLMs in introductory programming tasks. His research often combines empirical studies with tool development, such as SATDBailiff for technical debt tracking and TSDetect for test smell detection. Teaching & Advising: He teaches courses like SWEN-250 (Personal Software Engineering), SWEN-331 (Engineering Secure Software), and graduate-level thesis supervision. His courses emphasize secure development, software design principles, and team-based projects. Tools & Contributions: Newman has developed tools like srcSlice (static slicing), srcType (type resolution), and SCALAR (identifier analysis). These tools support software evolution, code comprehension, and empirical research in the field.