Dr. Miao Yin is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), part of the College of Engineering. He holds a PhD in Computer Engineering from Rutgers University and has industry experience at Google, Amazon Web Services, and Samsung Research America. His research focuses on high-performance and energy-efficient spatial/multimodal intelligence systems, leveraging higher-order tensor decomposition and algorithm-hardware co-optimization. Education: PhD in Computer Engineering, Rutgers University (2023). Research Interests include Spatial Intelligence Systems, Large-Scale Neural Model Optimization, and High-Performance Computing. His lab is equipped with advanced computing resources, including H100 GPU nodes and Ada6000/4090 servers. Awards include the UT System Rising STARs Award ($100k), and multiple travel grants from top conferences. He serves on committees for MLSys, PPoPP, DAC, and ICCD, and reviews for CVPR, NeurIPS, and IEEE journals. Advises PhD students including Sunny Shree and Wei Lin, and collaborates on projects funded by UTA and NSF grants. His lab emphasizes cross-disciplinary collaboration in AI and scientific computing.
Dejan Vukobratovic is a Full Professor in the Department of Power, Electronic and Communication Engineering at the Faculty of Technical Sciences, University of Novi Sad, Serbia. He serves as Editor for IEEE Transactions on Communications (Coding and Information Theory area), ITU Journal on Future and Emerging Technology, and Associate Editor for Frontiers in Communications and Networks, with extensive involvement in international conference organization including upcoming roles as General Chair for IEEE CSCN 2024 and Special Session Chair for IEEE ICMLCN 2025. His research spans Wireless Communications, Machine Learning for Communications, and Internet of Things with emphasis on 5G/B5G networks and AI integration. Current projects include Serbian Science Fund's REWARDING (water quality monitoring using IoT/Edge/Cloud systems) and Horizon Europe's REMARKABLE (rural environmental monitoring in Africa), reflecting his focus on practical applications of massive machine-type communications and large-scale information processing. Dr. Vukobratovic received the 2016 Award for Research Excellence from the Faculty of Technical Sciences. His team's PhD students won the IEEE Vehicular Technology Society UAV Innovation Challenge in 2021, demonstrating applied research impact. He leads the ICONIC research centre (established 2017) coordinating H2020 Twinning project INCOMING with Aalborg University, Chalmers University, and DLR. His advisory roles include serving on PhD committees at Aalborg University and University of Strathclyde, while upcoming 2025 engagements feature keynotes on ML/AI integration at BalkanCom, WISPNET, and ASMS conferences.
Lisimachos Paul Kondis is a Professor at the Department of Computer Science and Engineering, School of Engineering, University of Ioannina (email: lkon@cse.uoi.gr). He has served as a teaching faculty member and administrative staff member in the university's computer science department. Education: Diploma in Electrical Engineering, Aristotle University of Thessaloniki, 1994 M.S. in Electrical and Computer Engineering, Northwestern University, 1996 Ph.D. in Electrical and Computer Engineering, Northwestern University, 1999 Research Interests include communication systems, multimedia processing, and video compression technologies. His teaching responsibilities in 2024/25 cover courses in: Communication Systems (MYY603) Multimedia Processing (MYE025) Video Processing and Compression (D4)
Yue (Stanley) Ling is an Associate Professor in Mechanical and Aerospace Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing. He directs the Computational Thermo-Fluids Laboratory, where his team develops advanced numerical methods for high-fidelity simulation of multiphase flows and heat transfer processes. Research focuses on atomization mechanisms, droplet/bubble dynamics with phase change, and fluid-surface interactions, using adaptive mesh refinement and massively parallel computing. Applications span fuel injection, thermal management, aerodynamics, and environmental processes. Dr. Ling's NSF CAREER Award (2020) supports fundamental research on spray formation mechanisms under turbulent conditions. Education: Ph.D. in Mechanical Engineering, University of Florida (2010) B.S. in Engineering Mechanics, Beihang University (2004) Recent publications develop conservative volume-of-fluid methods for compressible multiphase flows and characterize turbulence-interface interactions. Awards include ASME's Best Video Presentation (2021) and AIAA's Flow Visualization honor (2019). Dr. Ling leads multiple NSF-funded projects and collaborates internationally through the PARIS (Parallel, Robust, Interface Simulator) consortium.
Professor Francis Fatoye is Research Professor of Health Economics and Outcomes at Manchester Metropolitan University's Department of Health Professions, where he serves as Director of the Centre for Health, Psychology and Communities. He co-led the university's REF2021 submission in Allied Health Professions and maintains active research collaborations internationally. Education: PhD in Musculoskeletal and Health Outcomes Assessment, Queen Margaret University MSc Health Economics and Health Policy, University of Birmingham MSc Sports Medicine, University of Nottingham MBA, Ondo State University, Nigeria BSc (Hons) Physiotherapy, University of Ibadan, Nigeria His primary research focuses on health economic evaluations, real-world evidence generation, and musculoskeletal disorder management. Current investigations examine cost-effectiveness of rehabilitation technologies, digital health interventions for chronic pain, and economic models for healthcare resource allocation in low-income settings. Publication analysis reveals strong emphasis on evidence synthesis through systematic reviews and meta-analyses (constituting approximately 40% of recent output), with growing focus on telehealth applications and digital rehabilitation technologies. Research routinely spans multiple healthcare disciplines including physiotherapy, health economics, and public health. Awards and distinctions: Distinguished Alumnus Award, University of Ibadan Inaugural Lecture Honoree, Manchester Metropolitan University Expert Advisory Panel Member for NIHR/HTA programs Professor Fatoye has supervised 19 PhD completions and currently mentors 4 doctoral candidates investigating health economic modeling and rehabilitation outcomes. His research group maintains industry partnerships with Abacus International and Urgo Medical for outcomes assessment studies. Grant funding sources include NIHR, EU Interreg, and Arthritis Research UK. He leads the Health Services and Outcomes Research Group and collaborates with the Centre for Health and Social Evaluation at Teesside University. International activities include adjunct appointments in Kenya and editorial roles for BMC Musculoskeletal Disorders and other journals.
Attilio Fiandrotti is an Associate Professor at the University of Turin, specializing in Computer Science. His academic journey began at Politecnico di Torino, where he earned both his M.Sc. and PhD in Computer Science in 2005 and 2010, respectively. His research spans Video Coding and Video Streaming over Wireless Networks , with a focus on Peer-to-Peer Distribution enhanced by Network Coding . He also contributes to Image Analysis using traditional and deep machine learning techniques, particularly in Lung Nodules Segmentation and Colorectal Polyps Classification . Fiandrotti's work intersects Computer Science and Medical Imaging , addressing challenges in Neural Network Compression and Structured Sparsity . Notable contributions include the UniToChest and UniToPatho datasets, which support research in lung cancer detection and histopathological analysis. He has developed innovative methods like Entropy-based Pruning for transfer learning under resource constraints and the Serene framework for sensitivity-driven neural network regularization. His technical expertise extends to Hardware Architectures and Deep Learning applications in healthcare and signal processing.
Dr. Eric Ameres is a Senior Lecturer and Associate Director of the Graduate School of Arts and Sciences (GSAS) at Rensselaer Polytechnic Institute (RPI). He also serves as the Undergraduate Program Director for GSAS. His affiliations include the Cognitive Science department, Future of Computing Institute (FOCI), Institute for Data Exploration and Analytics (IDEA), and the Experimental Media and Performing Arts Center (EMPAC). Dr. Ameres holds an M.S. and Ph.D. from RPI, where he previously worked as a Senior Research Engineer at EMPAC. His work includes developing The Campfire , an immersive data visualization system, and pioneering video/audio compression technologies (with over a dozen patents held by Google). His industry career involved creating multimedia tools and game development technologies. His research interests span multimedia technology, data visualization, immersive systems, and cognitive science applications. He maintains active affiliations with multiple RPI institutes and continues developing applications for The Campfire as an IDEA affiliate. No scientific awards are explicitly listed in the provided information. Dr. Ameres' contributions include patents and industry-academic collaborations, though no grants or student advising details are provided. He is affiliated with key RPI labs and centers, including EMPAC for experimental media and IDEA for data exploration initiatives.
Nazanin Rahnavard is a Professor in the Department of Electrical and Computer Engineering at the University of Central Florida (UCF), College of Engineering and Computer Science, with a Ph.D. from Georgia Institute of Technology (2007). Her research bridges theoretical signal processing and practical wireless networking applications. Education: Ph.D. in Electrical and Computer Engineering, Georgia Institute of Technology, 2007 Research Focus: She pioneers work in Compressive Sensing for spectrum efficiency, Radio Frequency Cartography for dynamic spectrum access, and Deep Learning applications in high-dimensional wireless data analysis. Her projects address critical challenges in cognitive radio networks and energy-aware sensor systems through tensor frameworks and spin-based device integration. Scientific Recognition: National Science Foundation CAREER award (2011) Outstanding Research Award, Center for Signal and Image Processing, Georgia Tech (2007) UCF CAMP-YES Mentor of the Year Award (2016) Research Leadership: As Principal Investigator, she directs four major funded projects: NSF's Tensor-based Radio Cartography framework, Energy-Aware Spectrally Sparse Signals research, STEM Transfer Students STRONG program, and IARPA's Deep Intermodal Video Analytics. Her mentorship extends through UCF's CAMP-YES program for underrepresented students. Laboratory: She leads the Computational Wireless Networking Lab (CWN Lab) at UCF, evidenced by her research portal cwnlab.eecs.ucf.edu, focusing on next-generation wireless network architectures and AI-driven spectrum management.
Eve Riskin is Dean of Undergraduate Education at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science and the Department of Electrical and Computer Engineering. She holds a PhD in Electrical Engineering from Stanford University (1990) and has a distinguished career spanning academia, research, and leadership in STEM equity. PhD (1990), Stanford University (Electrical Engineering) MS (1986), Stanford University (Operations Research) MS (1985), Stanford University (Electrical Engineering) BS (1984), MIT (Electrical Engineering) Her research focuses on engineering education, accessibility technologies for deaf communities (e.g., MobileASL ), and systemic diversity initiatives. She pioneered the Redshirt in Engineering Model to improve equity and inclusion, and her work addresses mental health, discrimination, and belonging in higher education. Recent projects like GLOBEM explore longitudinal human behavior modeling for educational interventions. Key scientific awards include the NSF Young Investigator Award (1992), Sloan Research Fellowship (1994), and Presidential Mentoring Award (2020). She is a Fellow of IEEE and actively contributes to organizations like ASEE and IEEE . Collaborative research on sign language compression standards Leadership in ADVANCE programs for women in STEM Development of peer coaching networks for faculty growth Analysis of pandemic impacts on vulnerable student populations
Timothy Havens is a Professor at Michigan Technological University in the Department of Computer Science within the College of Computing. He holds the William and Gloria Jackson Professorship and serves as Executive Director of both the Great Lakes Research Center and the Institute of Computing and Cybersystems. Dr. Havens also directs the PRIME Lab and has been recognized for both teaching and research excellence, including the 2014-15 Professor of the Year award from IEEE Eta Kappa Nu and Best Paper Awards at FUZZ-IEEE 2012 and IEEE SMC 2011. Dr. Havens received his Ph.D. in Electrical and Computer Engineering from the University of Missouri, Columbia in 2010. Prior to joining Michigan Tech, he was an NSF/CRA Computing Innovation Postdoctoral Fellow at Michigan State University under Dr. Anil Jain. Before his Ph.D. work, he was an Associate Technical Staff member at MIT Lincoln Laboratory. His educational background includes an M.S. in Electrical Engineering (2000) and a B.S. in Electrical Engineering (1999), both from Michigan Technological University. His research focuses on pattern recognition and machine learning, signal processing, and sensor fusion, with specific expertise in fuzzy integrals, Choquet integration, community detection in networks, and heterogeneous data mining. Dr. Havens has made significant contributions to explainable AI, particularly through the application of fuzzy integrals to deep learning systems. His recent publications demonstrate strong activity in developing novel regularization techniques for fuzzy Choquet integrals, efficient algorithms for community detection, and similarity measures for interval data. Dr. Havens' research has been consistently funded by prestigious organizations including DARPA, NSF, US Navy, Office of Naval Research, National Geospatial-Intelligence Agency, Ford Motor Company, MIT Lincoln Laboratory, and numerous other government and industry partners. His current projects include significant grants for radar systems in the Great Lakes, generative modeling of satellite imagery, and algorithms for autonomous robot systems. Scientific Awards: 2014-15 Professor of the Year by IEEE Eta Kappa Nu, Beta Gamma Chapter Best Paper Award at FUZZ-IEEE 2012 Best Paper Award at IEEE SMC 2011 Best Student Paper Award Finalist (2018) IEEE Franklin V. Taylor Memorial Best Paper Award (2011) Dr. Havens has successfully mentored numerous graduate students, many of whom appear as co-authors on his publications. His research grants demonstrate strong leadership in coordinating multi-institutional collaborations with substantial funding. Beyond his academic work, Dr. Havens is also an active musician, playing bass in several bands including MUFJAC, FLOTUS, Defenestra, Skills of Ortega, Psylocubik, Triptych, Wheels of Fire, and Odibil libidO.
Jing Qin is an Associate Professor in the Department of Mathematics at the University of Kentucky, within the College of Arts & Sciences. His research focuses on Mathematical Optimization, Variational Image Processing, Compressive Sensing, and computational analysis of imaging data. His work addresses challenges in signal recovery, tensor decomposition, hyperspectral imaging, and machine learning applications. Recent publications emphasize advancements in tensor recovery algorithms, hyperspectral band selection, and graph-based signal processing. These contributions highlight innovations in iterative methods, regularization techniques, and deep learning models for structural analysis and robotic systems. Qin’s research bridges theoretical optimization frameworks with practical applications in remote sensing, medical imaging, and computer vision. His articles reflect a strong focus on interdisciplinary solutions, combining mathematical rigor with computational efficiency. Despite no explicitly listed awards or grants, his prolific publication record underscores sustained scholarly impact. Further details are available via his faculty webpage: http://ms.uky.edu/~jqi229/ .
Chris Joslin is a Professor at the School of Computer Science, Carleton University. His office is located in 4302 Canal Building, and he can be reached at Chris.Joslin@carleton.ca. He specializes in interdisciplinary research areas including computer graphics, medical imaging, virtual reality, computer vision, and human-computer interaction. His work bridges theoretical advancements with practical applications in animation, 3D modeling, and medical visualization. Research interests include developing novel techniques for 3D editing, medical image processing, and immersive virtual environments. Notable contributions include advancements in 3D Gaussian splatting, AI-driven MRI analysis, and robust sensor fusion for autonomous systems. His publications span from foundational studies on motion retargeting to applied work in procedural audio generation for soft-body simulations. Recent trends in his articles emphasize integration of deep learning with traditional computer vision tasks, optimization of medical imaging workflows, and enhancing accessibility in virtual reality systems. Despite prolific output, no scientific awards are explicitly mentioned in the provided texts. Advising and grant details remain unspecified, though his involvement in collaborative projects like VPARK and ISIS suggests engagement with interdisciplinary teams. His work is anchored at Carleton’s Herzberg Laboratories, a hub for advanced computational research.
Hadi Ali Akbarpour is an Assistant Professor in the Department of Computer Science at Saint Louis University's School of Science and Engineering . He earned a Ph.D. in Electrical and Computer Engineering from the University of Coimbra, Portugal (2012). Research focuses on Artificial Intelligence and Computer Vision at the intersection of Autonomous Systems , Remote Sensing , and Robotics Key topics: Deep Learning , 3D Reconstruction , Homography Modeling , and Sensor Fusion Recent publications highlight advancements in: Structure-from-Motion for aerial imagery Deep Learning for novel view generation Georegistration accuracy in wide-area contexts 3D Point Cloud integration Agricultural Monitoring via aerial mosaicking Professional accolades include: Best Paper , Presentation , and Challenge Awards Co-chaired the 51st IEEE Applied Imagery Pattern Recognition Workshop Guest Editor for Sensors Journal special edition (2021) His funded projects include roles as Principal Investigator ( $1.1M ), co-PI ( $8M ), and task leader in $6M SBIR projects .
Aruna Balasubramanian is an Associate Professor in the Department of Computer Science at Stony Brook University, where she has served since spring 2015 after completing a post-doctoral fellowship at the University of Washington. Her research focuses on the intersection of networking and systems with emphasis on smartphones, wearable devices, and performance optimization for mobile environments. Dr. Balasubramanian earned her PhD from the University of Massachusetts, Amherst in 2011 and her Masters degree from SUNY Buffalo. Her academic journey includes prestigious fellowships including the Microsoft Research Fellowship (2008-2010) and Computing Innovation Fellowship (2010-2012). Her research program re-architects mobile systems to overcome limitations of wired-environment assumptions, targeting radical performance improvements. Current initiatives include Sustainable NLP (efficient NLP execution on small devices), SBUWear (wearable-enhanced smartphone accessibility), Webgaze (eye-gaze-optimized web loading), and WProfX (bottleneck analysis for mobile page loads). These projects address critical challenges in energy consumption, user experience, and accessibility across mobile and wearable platforms. Recent publications reveal strong trends toward human-centered mobile systems, with significant focus on gaze-based performance optimization, accessibility solutions for visually impaired users, and protocol redesign for emerging applications. Her work consistently bridges networking fundamentals with practical systems implementation, increasingly incorporating NLP efficiency and cross-device interaction paradigms. Her distinguished recognition includes: Applied Networking Research Prize (2015) Sigcomm Dissertation Award Runner Up (2011) UMass Outstanding Dissertation Award (2011) SIGMOBILE Rockstar award (2021) Google Research Scholar Award (2021) NSF CRII award (2016) VMware Early Career Grant (2018) Dr. Balasubramanian has successfully mentored numerous PhD students including Qingqing Cao (University of Washington postdoc), Jian Xu (Oracle), Yi Cao (ByteDance), Conor Kelton (Facebook), and Javad Nejati (Ebay), with current advisees spanning mobile accessibility, NLP efficiency, and network protocol design. Her research program is sustained through competitive funding from NSF (including two 2023 grants), Google (multiple awards since 2016), and VMware. She directs the NetSys (Networked Systems) Lab at Stony Brook, which maintains strong industry partnerships with Google, Microsoft, and Akamai while fostering interdisciplinary collaborations across computer science, HCI, and accessibility research domains. The lab's work on MobileHub (Ubicomp 2015 best paper) and AccessWear (MobiCom 2023) exemplifies its impact on real-world mobile system design.
Anshul Gandhi is an Associate Professor in the Computer Science Department at Stony Brook University , where he leads the PACE Lab (Performance Analysis and Computing Efficiency). He holds an affiliate appointment in the Department of Applied Mathematics & Statistics and focuses on applying analytical tools to optimize computer systems. Education : PhD in Computer Science (2013) from Carnegie Mellon University under Prof. Mor Harchol-Balter; Undergraduate (2007) from IIT Kanpur Research : Sustainable computing, systems for ML, distributed systems efficiency using mathematical tools like Machine Learning, Optimization, Queueing Theory, and Control Theory Teaching : Courses in Data Science statistics (CSE 544/CSE 357) and Sustainable Computing seminars His work addresses system performance modeling , GPU resource allocation , and carbon footprint optimization . Recent projects focus on LLM deployments , edge inference systems , and microservices bottleneck detection . Scientific Awards include: ACM Sigmetrics Rising Star Award (2019) NSF Career Award (2018) Google Faculty Research Award (2015) IBM Faculty Award (2015) Millionaires Club Award (2025) TREES Teaching Excellence Award (2025) He has served as General Co-Chair for ACM Sigmetrics 2025 and Secretary-Treasurer for SIGMETRICS , with research funded by multiple NSF grants , IBM research grants , and SBU OVPR Seed Grants .
