Alberto Quattrini Li is Associate Professor of Computer Science at Dartmouth College, directing the Reality and Robotics Laboratory. His research develops autonomous systems for aquatic environments with applications in environmental monitoring and underwater archaeology. Current NSF-supported projects focus on multi-robot aquatic exploration and archaeological inspection systems. Research thrusts include surface vehicle obstacle avoidance in waterways, low-cost underwater sensing solutions, autonomous underwater construction, and laser-based air-underwater communication. Recent innovations include buoyancy-enabled manipulation systems and multi-sensor SLAM techniques for underwater operations. Quattrini Li advises six PhD students and collaborates with archaeologists through NSF grants. His laboratory prototypes systems using custom surface and underwater robotic platforms. No scientific awards are documented in the source materials.
Dr. Sivakumar Rathinam is a Professor in the Department of Mechanical Engineering at Texas A&M University, affiliated with the College of Engineering and the Computer Science & Engineering department. He holds certifications as a Fellow of ASME (2021) and Senior Member of IEEE (2019). His research focuses on motion planning for autonomous vehicles, collaborative decision-making, combinatorial optimization, and vision-based control systems. He leads the Autonomy Lab, addressing challenges in multi-agent systems, path planning, and rural autonomous vehicle accessibility. Education: Ph.D., Civil Systems Engineering, University of California, Berkeley (2007) M.S., Electrical Engineering & Computer Science, UC Berkeley (2006) M.S., Mechanical Engineering, Texas A&M University (2001) B.Tech., Mechanical Engineering, Indian Institute of Technology Madras (1999) Research Highlights: Dr. Rathinam's work spans autonomous vehicle navigation, UAV coordination, and sensor fusion for adverse conditions. His lab develops algorithms for multi-agent pathfinding and persistent monitoring missions. Recent efforts include rural road detection datasets (R2D2) and thermal/LIDAR sensor fusion for safety in challenging environments. Awards: Outstanding Faculty Contribution Award (2021) Best Paper Runner-Up, ICAPS (2021) Teaching Excellence Award (2012) Labs & Teams: Directs the Autonomy Lab, collaborating with industry partners through the Mechanical Engineering Industry Advisory Council. Active in NSF-funded projects on equitable rural autonomy and multi-UAV recharging frameworks.
Dr. Pauline Hope Cheong is a **President's Professor** at Arizona State University (ASU), holding appointments in the **Hugh Downs School of Human Communication** and affiliated roles across multiple centers including the **Center on Technology, Data and Society** and **Center for Asian Research**. She is a **Senior Global Futures Scientist** and a **Global Futures Scholar**, reflecting her interdisciplinary focus on technology, culture, and society. **Education**: Dr. Cheong earned her Ph.D. from the Annenberg School for Communication and Journalism at the University of Southern California. She has also held a visiting postdoctoral fellowship jointly awarded by the US and UK research councils. **Research Focus**: Her work examines how emerging technologies like AI, robotics, and digital media reshape community interactions, governance, and religious practices. Key areas include smart cities, digital equity, and the intersection of technology with religious authority. Recent projects explore AI governance in nonprofits and faith communities, as well as cross-cultural digital divides. **Awards**: Among her honors are the **Master Teacher Award (Western States Communication Association)**, **Outstanding Faculty Mentor Award (ASU)**, and recognition as **The Great 48 in Arizona** for educational contributions. She is also a **President's Professor**, one of ASU's highest faculty accolades. **Service & Mentorship**: Dr. Cheong chairs doctoral colloquiums for the **Association of Internet Researchers** and **International Society of Media, Religion & Culture**. She co-directed the **Promoting Digital Equity in Tempe** initiative and advises undergraduates, graduate students, and international scholars. Her editorial roles span journals like the *Journal of Communication* and *Western Journal of Communication*. **Teaching**: She teaches courses on intercultural communication, research methods, and honors thesis supervision, emphasizing global and ethical dimensions of technology.
Connor McCann is an Assistant Professor in the Robotics Engineering department at Worcester Polytechnic Institute (WPI). He leads the PRiSM Lab, which focuses on hybrid-stiffness mechanisms blending rigid and soft robotics to enhance robotic capabilities. His work integrates first-principles modeling with application-driven design for areas like robotic grasping, wearable robotics, and bioinspired systems. Education: Ph.D. and M.S. in Mechanical Engineering from Harvard University (2025, 2021), and B.S. in Mechanical Engineering from Yale University (2018). Research interests include the mechanics of rigid-soft material interactions, soft wearable robotics, bioinspired systems, and robotic manipulation. His lab emphasizes translating mechanical principles into functional prototypes for real-world applications. Key research trends in his articles include advancements in wearable robotics for industrial/medical use, aerodynamic control via textile metamaterials, and autonomous robotic exploration using low-cost hardware. His work bridges material science and robotics to create adaptive, high-performance systems. Labs/Teams: Director of the PRiSM Lab at WPI, focused on rigid-soft robotics innovation.
Dr. Fernando E. Casado is a Researcher at the Personal Robotics Lab (PRL) within the Department of Electrical and Electronic Engineering at Imperial College London since March 2023. He holds a BSc in Computer Science (2017, University of Santiago de Compostela) with awards for academic excellence, an MSc in Artificial Intelligence Research (2018, Menéndez Pelayo International University), and a PhD in Computer Science (2022, USC) with Cum Laude distinction for his work on continual federated learning strategies. His research focuses on multi-robot and multi-user machine learning to enhance trustworthy human-robot interaction, addressing challenges such as concept drift, non-stationary data, and personalized robotic behavior. Key contributions include federated learning frameworks for assistive robotics, adaptive algorithms for heterogeneous data, and eye-gaze tracking for trust assessment in HRI. Publications span topics like federated learning, continual learning, and human-robot trust, with applications in assistive devices and smart environments. Awards include the Best Academic Record and Thesis Award (BSc) and the highest honors in his PhD. Casado’s work bridges theoretical machine learning with practical robotics, emphasizing privacy-aware systems and user-centered design. He collaborates internationally, including visits to PRL in 2021, and contributes to advancing adaptive algorithms for real-world robotic applications.
Kang Shin is a Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan's College of Engineering. His research spans multiple domains in computer science and engineering, focusing particularly on automotive security, wireless communications, and distributed systems.
Dr. Maryam Banitalebi Dehkordi is a Senior Lecturer in Robotics and AI at the University of Hertfordshire, UK. She holds a PhD in Perceptual Robotics from Scuola Superiore Sant'Anna (Italy) and a master's in Mechatronics from University Technology Malaysia. Her career spans academia and industry, with roles at institutions like Technical University of Munich and NavVis GmbH, as well as industry projects such as the Innovate UK-funded AgriRobot. Her research focuses on Human-Robot Interaction (HRI) , Explainable Robotics , Assistive Robotics , and autonomous systems . Key projects include DOC (navigation aid for visually impaired individuals) and AgriRobot (precision agricultural robotics). She has expertise in activity recognition via smartphone sensors, navigation systems, and social robotics interfaces. Publications (2012–2025) emphasize explainable AI in robotics, feature selection for activity recognition, and social behavior interpretation. Her work bridges theoretical robotics with real-world applications in healthcare, agriculture, and accessibility. No awards are explicitly listed, but her extensive collaboration network (Italy, Germany, UK) reflects interdisciplinary impact. She advises on robotics projects and has contributed to both academic journals and industry collaborations.
Dr. Thomas E. Ahlering is a Professor and Vice Chairman in the Department of Urology at the University of California, Irvine (UCI) School of Medicine. He is a leading expert in urologic oncology, specializing in robotic radical prostatectomy for prostate cancer, with over 2,500 surgeries performed. He previously served as Chief of Urologic Oncology at City of Hope and Chief of Urology at UC Irvine. His clinical and research work has earned him recognition as one of America’s Best Doctors since 1994 and as a Top 2% Medical Scientist globally. Dr. Ahlering's research interests center on urologic oncology , particularly robotic surgery , functional recovery after prostatectomy , and patient-reported outcomes . He has pioneered techniques in nerve-sparing and urinary anastomosis to improve continence and potency. His work has significantly contributed to the adoption and refinement of da Vinci robotic systems in urologic surgery. His scholarly output includes over 400 publications, with recent work focusing on surgical innovation, international training, patient selection, and long-term oncological outcomes. The articles reflect a strong emphasis on improving surgical precision, minimizing complications, and enhancing quality of life. Themes include technological evolution, global dissemination of robotic techniques, and patient-centered care. Scientific recognition includes: Lifetime Achievement Award in Robotic Surgery (North American Robotic Urology Symposium) Outstanding Faculty Achievement – Lauds and Laurels (UCI Alumni Association) Mentor of the Year (UCI Institute of Clinical and Translational Science) Top 2% of Medical Scientists in the World (Stanford Study) Consistently listed in America’s Best Doctors since 1994 Dr. Ahlering has mentored numerous trainees and residents, contributing significantly to education in urologic oncology. His lab and surgical team, often referred to as "The A-Team," operate at MiVIP Surgical Center and are known for their high coordination, patient-centered approach, and expertise in robotic prostatectomy. He continues to lead innovation in urinary diversion techniques such as the Indiana pouch and ileal neobladder.
Dirk Arnold is a Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. His research focuses on evolutionary computation, optimization, and surrogate modeling, with applications in machine learning and digital media. He is actively involved in teaching and research, contributing to major conferences and journals. PhD, University of Dortmund (2001) MSc, Simon Fraser University (1997) Diplom, University of Dortmund (1995) His research interests lie in evolutionary algorithms, particularly evolution strategies, and their application to noisy and constrained optimization. He investigates constraint handling, surrogate modeling, and parameter adaptation mechanisms. His work bridges theoretical analysis and practical applications in image processing, tone mapping, and robotics. Recent publications show a strong trend toward surrogate-assisted optimization, constrained evolutionary algorithms, and applications in computer vision and graphics. His work is consistently published in top venues such as GECCO, PPSN, and IEEE Transactions on Evolutionary Computation. Scientific Awards: Best Paper Award, Continuous Optimization Track, GECCO 2015 Best Paper Award, ES/EP Track, GECCO 2013 Best Paper Award, PPSN XII 2012 Best Paper Award, ES/EP Track, GECCO 2010 Best Paper Award, PPSN IX 2006 Best Student Paper Award, ACM Symposium on Document Engineering 2014 Arnold has advised numerous students, many of whom are co-authors on his publications. He has received research grants supporting work in evolutionary computation and optimization, though specific grant details are not listed. He is a key member of research clusters in Human-Computer Interaction, Visualization & Graphics, and Algorithms & Bioinformatics. He leads a research group focused on evolutionary algorithms and optimization, collaborating with researchers such as H.-G. Beyer, N. Hansen, and S. Brooks. Future work includes advancing constrained and mixed-integer evolutionary optimization, improving surrogate models, and expanding applications in computer vision and HCI.
Evangelos E. Milios is a Professor in the Faculty of Computer Science at Dalhousie University , Halifax, Nova Scotia. He has been a faculty member since 1998 and leads the MALNIS (Machine Learning and Networked Information Spaces) research group. He is affiliated with the Institute of Big Data Analytics and served as Scientific Director of DeepSense , an innovation hub for ocean data analytics. Education: PhD in Electrical Engineering and Computer Science, MIT (1986) SM & EE, MIT (1983) Dipl. Eng. in Electrical Engineering, NTUA, Greece (1980) His research focuses on visual text analytics, text mining, graph mining, social network analysis, and machine learning . He has made significant contributions to modeling and mining of networked information spaces, with applications in data science and AI. The recent publications reflect a strong trend in data mining, robotics, pattern recognition, and semantic analysis , particularly in log analysis, pose estimation, and information retrieval. His work bridges theoretical algorithms with practical applications in robotics and web technologies. Scientific Awards and Honors: Distinguished Research Professor (2017–2022) Killam Chair in Computer Science (2006–2011) Senior Member, IEEE Professional Engineer, Ontario (1998–2024) He has served in key administrative roles including Associate Dean, Research (2008–2017) and Director of the Graduate Program (1999–2002) . He has supervised numerous graduate students and taught a wide range of courses in AI, machine learning, data science, and networking. His research is supported by major grants and collaborations, including NSERC and industry partnerships. Research Labs and Teams: MALNIS – Focuses on machine learning and networked information spaces. DeepSense – Ocean data analytics and AI innovation. Institute of Big Data Analytics – Cross-disciplinary big data research.
Lukasz (Luke) Ziarek serves as Associate Dean for Academic Affairs and Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. His work bridges theoretical computer science with practical systems engineering, focusing on real-time capabilities in distributed environments and safety-critical applications. His educational foundation includes a PhD in Computer Science from Purdue University (2011) and a BS in Computer Science from the University of Chicago (2003). Ziarek's research centers on formal verification of distributed protocols , real-time systems engineering , and mobile/embedded computing . He pioneers session type theory for IoT security, develops real-time variants of Android (RTDroid) and Standard ML (RTML), and investigates UAV software reliability. His work consistently addresses the tension between theoretical guarantees and practical system constraints in concurrency, timing, and security. Recent publications (2022-2025) reveal three dominant trajectories: formal methods for rate-based session types in IoT protocols, performance analysis of visual SLAM systems for robotics, and security vulnerabilities in embedded platforms like ARM TrustZone. These threads converge on ensuring correctness and timeliness in resource-constrained distributed systems. His scientific accolades include the IEEE Region One Technological Innovation Award (2023) and NSF CAREER Award (2018), reflecting dual excellence in research and education. IEEE Region One Technological Innovation (Academic) Award, 2023 President Emeritus and Mrs. Meyerson Award for Distinguished Undergraduate Teaching and Mentoring, 2022 NSF CAREER Award, 2018 SEAS Early Career Teacher of the Year, 2016 Halstead Award for Outstanding Research in Software Engineering, 2009 Intel Fellowship, 2008 GAANN Fellowship, 2004 Ziarek directs significant research initiatives including a $900K NSF UAV infrastructure project (as PI) and a $1.7M MRI grant for connected vehicle testing. His funding portfolio spans real-time systems, compiler design, and pocket-scale data management, emphasizing collaborative, interdisciplinary approaches to software reliability. CRI:CI-New UAV Infrastructure ($900K, PI 31%) NSF CAREER: Real-time Object-Oriented Systems ($500K, PI 100%) MRI: iCAVE2 Vehicle Testing ($1.7M, co-PI 14%) III: Just-in-Time Data Structures ($499K, co-PI 50%) II-EN: MLton Compiler Research ($606K, PI 63%) He leads an open-source ecosystem including RTDroid (real-time Android), Multi-MLton (parallel SML compiler), and BlueSeal (Android security analyzer), fostering community-driven advances in systems software.
Dr. Shareef Syed is an Assistant Professor in the Department of Surgery at the University of California, San Francisco (UCSF) School of Medicine. He serves as the educational lead for general surgery residents and actively participates in enhancing surgical education curricula. His clinical expertise spans abdominal organ transplantation, pediatric transplant surgery, and advanced laparoscopic procedures for living donor nephrectomy. Syed's research focuses on transplant patient outcomes, novel noninvasive rejection detection methods, and global health collaborations with Costa Rican transplant centers. University of Leicester - MBChB (2008) Royal College of Surgeons - MRCS (2011) University of Southampton - Internal Medicine & Surgery (2010) University of Bristol - Cardiothoracic Surgery (2011) Central Michigan University - General Surgery Residency (2014) UCSF - General Surgery Residency (2016) & Abdominal Transplant Fellowship (2018) His research explores critical areas in transplantation medicine including liver preservation , renal allograft rejection , and Hepatocellular carcinoma outcomes in transplant patients. Recent publications investigate robotic-assisted surgical techniques, machine perfusion applications, and educational methodologies in surgical training. Syed is board-certified in Surgery (2016) and has received teaching awards from UCSF residents. Scientific Awards: Louis E. Zeile Outstanding Resident in Surgery (2014) Lawrence W. Way Critical Thinking Award (2016) Faculty Teaching Award (2019) Professional Memberships: American Society of Transplant Surgeons Royal College of Surgeons The Transplantation Society Americas Hepato-Pancreato-Biliary Association American College of Surgeons American Association for the Study of Liver Diseases American Society of Transplantation As an active clinician-scientist, Dr. Syed contributes to both clinical practice and research methodology development in transplant surgery. His work addresses critical challenges in organ procurement, surgical education, and patient outcome optimization.
Markus Kuhn is a researcher at the University of Cambridge with a diverse academic career spanning over two decades, evidenced by publications from 2003 to 2023. His work bridges educational technology, computer science, and autonomous systems, demonstrating significant interdisciplinary reach across multiple domains. Dr. Kuhn's research has evolved through distinct phases. Initially focused on educational technology (2003-2007), he published extensively on classroom scenarios, collaborative learning, and media integration. His work then broadened to include physics computation (2008) and process support for inquiry learning (2010). More recently (2016-2023), his research has shifted toward autonomous systems, with publications on self-localization technologies and map generation for autonomous vehicles. This publication trajectory reveals a researcher who has successfully adapted his expertise from educational applications to more technical domains while maintaining connections to his foundational work in learning systems. His research demonstrates consistent innovation in applying computational approaches to solve practical problems across different contexts. Dr. Kuhn has maintained long-term collaborations with researchers including Heinz Ulrich Hoppe, Andreas Harrer, and Andreas Lingnau, indicating strong interdisciplinary networks. His work has been published in diverse venues ranging from IEEE Access and Microelectronics Reliability to Research and Practice in Technology Enhanced Learning and International Conferences on Computer-Supported Collaborative Learning.
Niklas Beuter is a Professor of Artificial Intelligence and Data Science at TH Lübeck, Germany, within the Department of Electrical Engineering and Computer Science. He has been in this position since 2023, contributing to both research and education in AI and computer vision. His educational background includes a Diplom in Computer Science with a focus on robotics from Universität Bielefeld, completed with distinction, followed by a Ph.D. from the same institution in 2011. His doctoral research centered on 3D human detection and tracking for mobile robotic platforms, supporting situation awareness in dynamic environments. Beuter's research is deeply rooted in artificial intelligence, computer vision, and robotics , with a focus on dynamic 3D scene analysis, human-robot interaction, and autonomous systems . His work bridges theoretical models with real-world applications, particularly in autonomous driving and intelligent robotic perception. He has led research teams in industry, demonstrating strong leadership in applied AI. The trend in his publications reflects a consistent focus on perception systems for robots and vehicles , evolving from foundational work in 3D reconstruction and gesture-based interaction to advanced topics like pedestrian intent forecasting using deep learning. His contributions span top conferences such as IEEE ICRA, CVPR, and Intelligent Vehicles, indicating sustained engagement with the core AI and robotics communities. While no formal scientific awards are listed in the provided text, his leadership roles and publication record reflect significant professional recognition. He actively supervises student theses and invites students to register for thesis topics via email, indicating an ongoing commitment to mentoring. Although no specific grants are mentioned, his industrial and academic research leadership suggests involvement in funded projects. He is a member of research groups including CoSA and serves as Deputy Head of ISy, showing active participation in institutional research organization. His research has been conducted in collaboration with teams at Universität Bielefeld, Daimler Research Center, and Robert Bosch GmbH, reflecting a strong interdisciplinary and industry-academia network. His work continues to influence both academic research and industrial applications in AI and autonomous systems.
Mathias Pelka is a Professor at the Department of Electrical Engineering and Computer Science at Technische Hochschule Lübeck. His work focuses on intelligent automation technology, smart factory concepts, and Industry 4.0 applications. Education in Informatik (University of Lübeck), Applied Information Technology (Lübeck University of Applied Sciences), and Electrical Engineering (Milwaukee School of Engineering) His research spans localization algorithms, LiDAR-based object detection, neural networks for robotics, and underwater optical distance estimation. Key technologies include sensor fusion, recursive Bayesian filters, and time-based ranging methods. Recent publications highlight trends in autonomous vehicle sensing, indoor positioning systems, and unified location architecture frameworks. His work integrates machine learning, signal processing, and wireless communication for smart infrastructure solutions. Prior roles include software development and Scrum Master positions at Drägerwerk AG & Co. KGaA (2020-2021), software engineering at Ibeo Automotive Systems GmbH (2018-2020), and research associate positions at TH Lübeck (2013-2018).
