Hao Su is an Associate Professor in the Department of Computer Science and Engineering at University of California, San Diego . He serves as Chairman & CTO of Hillbot Inc , and leads the SU Lab which focuses on building autonomous systems that learn actively in physical environments. His affiliations include the Institute for Learning-enabled Optimization at Scale , Artificial Intelligence Group , Contextual Robotics Institute , Halicioğlu Data Science Institute , and Center for Visual Computing . As a researcher in Computer Vision, Robotics, and Neural Geometry , he has made significant contributions to 3D foundation models, reward-free world models, diffusion policy frameworks, and GPU-accelerated simulation environments. His 2024-2025 publications include advancements in hand-eye calibration, dynamic mesh reconstruction, and multi-stage robotic manipulation. His scientific awards include: Frontiers of Science Award (2025) TPAMI Young Research Award (2025) NSF CAREER Award (2023) ACM SIGGRAPH Best Doctorate Thesis Honorable Mention (2019) He has served as Program Chair for CVPR 2025 and Area Chair for ICLR 2022 and NeurIPS 2023 , while previously serving as Publication Chair for 3DV 2016 and Program Committee for SIGGRAPH Asia Workshops .
University of Illinois Urbana-ChampaignUnited States
Huan Zhang serves as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC), with affiliate appointments in the Department of Computer Science and the Coordinated Science Laboratory. His research focuses on building trustworthy AI systems with formal verification techniques to provide provable guarantees for safety-critical applications, particularly in machine learning and neural networks. Dr. Zhang received his Ph.D. in Computer Science from UCLA in 2020, advised by Professor Cho-Jui Hsieh. His academic journey includes an M.S. in Computer Engineering from UC Davis (2014) and a Bachelor of Engineering from Zhejiang University (2012). Prior to joining UIUC, he completed a postdoctoral fellowship at Carnegie Mellon University (2021-2023) with Professor Zico Kolter. Huan Zhang's research program centers on formal verification of machine learning systems, with particular emphasis on neural network verification, AI safety, robustness, and reliability. He pioneered the linear bound propagation-based verification framework that enables formal verification for networks with millions of neurons. His work spans five major research categories: formal verification of machine learning, training trustworthy ML models, machine learning safety and adversarial attacks, reinforcement learning safety, and optimization for scalable machine learning. His CROWN framework (NeurIPS 2018) established a foundational approach for neural network verification through efficient linear bound propagation. His recent publications demonstrate a strategic expansion from foundational verification techniques toward increasingly complex systems including large language models, vision-language models, and robotic control systems. The research trajectory shows a clear progression from theoretical frameworks to practical implementations with real-world applications, particularly in safety-critical domains. His work increasingly bridges formal methods with practical AI deployment requirements. Winner of International Verification of Neural Networks Competition (VNN-COMP) as team leader (2021-2024) Schmidt Futures AI2050 Early Career Fellowship ($300,000 research grant) Adversarial Machine Learning (AdvML) Rising Star Award (2021) IBM PhD Fellowship (2018) Dr. Zhang leads the development of α,β-CROWN, a neural network verifier that has won VNN-COMP 2021-2023, and auto_LiRPA, a PyTorch-based library for perturbation analysis on general computational graphs. He has mentored numerous graduate students from CMU, UCLA, UIUC, and Columbia University. His research is supported by significant funding including the Schmidt Futures fellowship and industry collaborations. He teaches courses including ECE 120, ECE 484, ECE 584, and ECE 598 HZ on topics ranging from computing fundamentals to safe autonomy and machine learning. Dr. Zhang maintains active research collaborations across multiple institutions and is affiliated with UIUC's Coordinated Science Laboratory. His work has significant implications for safety-critical AI applications in autonomous systems, healthcare, and other mission-critical domains where reliability guarantees are essential. He regularly gives guest lectures at institutions including Yale, Stony Brook, and the University of Nebraska Lincoln on formal verification techniques.
Wendy Ju is an Associate Professor of Information Science at Cornell Tech, with appointments in the Cornell Ann S. Bowers College of Computing and Information Science, the Jacobs Technion-Cornell Institute, and the Technion-Israel Institute of Technology. Previously, she served as executive director of interaction design research at Stanford University's Center for Design Research and as an associate professor of interaction design at the California College of the Arts. Her work bridges human-computer interaction, design, and robotics with a focus on how interactive devices can communicate with people without interrupting them. PhD in Mechanical Engineering from Stanford University Master's degree in Media Arts and Sciences from MIT Professor Ju's research centers on implicit interactions, human-robot collaboration, and automotive interfaces. She investigates how people interact with automated systems in natural contexts, develops methods for early-stage prototyping of autonomous technologies, and examines the social implications of robotics in urban environments. Her work spans from theoretical frameworks to practical applications, with particular emphasis on designing systems that integrate seamlessly into human activities without demanding constant attention. Her recent publications reveal a strong trajectory toward understanding human-robot interaction in public urban spaces, with increasing focus on robot navigation in city streets, the social implications of autonomous vehicles, and the integration of generative AI in design processes. Her work consistently bridges theoretical HCI frameworks with practical applications in transportation, urban design, and everyday robotics. Inducted into the ACM SIGCHI Academy (2025) Multiple Honorable Mention Awards at ACM CHI and DIS conferences Best Paper Award at Multimodal Technologies and Interaction (2023) Best Student Paper Award at IEEE Intelligent Vehicles Symposium (2017) Best Demonstration Award at HRI (2017) Professor Ju actively mentors numerous PhD and master's students, many of whom have become leading researchers in HCI and robotics. Her research has been supported by significant grants from NSF and industry partners, enabling extensive field studies of human-robot interaction in real-world settings. She has pioneered methodologies for studying autonomous vehicle interactions through both simulated and naturalistic driving environments. Her work with the Jacobs Technion-Cornell Institute supports interdisciplinary research at the intersection of computing, design, and urban technology. She has established research partnerships with transportation authorities, automotive companies, and urban planning organizations to study how emerging technologies can enhance urban mobility while respecting human needs and social contexts.
Snježana Babić serves as Associate Professor and Head of the Graduate Study Program Informatics - Teaching Major at the Faculty of Informatics in Pula , University of Pula. With a PhD obtained in 2016 and university affiliation since 2017, she teaches across undergraduate and graduate programs focusing on ICT fundamentals, web design, digital competencies, and informatics education methodology. Her educational background includes graduation in 1993 followed by doctoral studies culminating in 2016. Though specific institutions aren't detailed in the text, her career trajectory shows progression from foundational ICT teaching to specialized informatics education leadership. Babić's research centers on educational technology adoption with particular emphasis on programming pedagogy for young learners through the MEMA method. Her work investigates digital competencies across multiple educational contexts, from primary schools to higher education. Recent publications demonstrate rapid adaptation to emerging technologies, with 2025 studies examining ChatGPT integration in Croatian educational settings and cross-cultural applications of programming methods. Analysis of her 15 most recent publications reveals consistent focus on technology acceptance models applied to Croatian educators and students. The research trajectory shows evolution from foundational e-learning systems (2016-2020) to cutting-edge AI and robotics applications (2022-2025), maintaining strong methodological focus on teacher motivation , implementation barriers , and culturally-specific adoption factors . Her teaching portfolio demonstrates deep integration with research interests: Undergraduate: ICT Fundamentals, Web Design and Application Graduate: Digital Competencies in Education, Informatics Education Methodology, Practicum for IT Educators Cross-disciplinary: ICT in Nursing, Healthcare Informatics Babić actively contributes to institutional leadership as program head while maintaining robust research productivity. Her work bridges theoretical technology adoption frameworks with practical implementation challenges in Croatian educational contexts, particularly examining how emerging technologies like humanoid robots and AI assistants can be effectively integrated into teaching practices.
Dr. Haneen Farah is an Associate Professor in the Department of Transport & Planning at Delft University of Technology and co-director of the Traffic and Transportation Safety Lab. She also serves as head of the Traffic Systems Engineering section. Her research focuses on road infrastructure design, road user behavior, and traffic safety, integrating transportation engineering, human factors, and econometrics. Prior to TU Delft, she was a postdoc at KTH Royal Institute of Technology and earned her M.Sc. and Ph.D. in Transportation Engineering from the Technion-Israel Institute of Technology. Her work includes national/international projects like SAMEN (mixed automated/human traffic implications), AfroSAFE (road safety in Africa), and XCARCITY (sustainable city mobility). She teaches undergraduate and graduate courses on road design and traffic safety, including online programs for low/middle-income countries. Farah supervises multiple PhD and Master students in her research areas, contributing to over 50 peer-reviewed publications. Key research themes include infrastructure design for automated vehicles, driver behavior modeling, cyclist safety, and policy implementation of the Safe System approach. Her interdisciplinary approach bridges engineering and psychology to enhance traffic safety and efficiency through advanced analytics and simulation models.
Maria Gorlatova is an Associate Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering, where she leads the Intelligent Interactive Internet of Things (I3T) Lab. She also holds a secondary affiliation as Faculty Network Member of the Duke Institute for Brain Sciences and has previously served as Assistant Professor of Computer Science. Dr. Gorlatova earned her Ph.D. in Electrical Engineering from Columbia University (2013), following M.Sc. and B.Sc. (Summa Cum Laude) degrees in Electrical Engineering from University of Ottawa, Canada. Prior to joining Duke, she was an Associate Research Scholar in the Electrical Engineering Department and Associate Director of the Princeton EDGE Lab at Princeton University (2016-2018). She also has industry experience with Telcordia Technologies, IBM, and D. E. Shaw Research. Her research focuses on advancing intelligent behavior in Internet of Things systems and applications, particularly in mobile pervasive systems and the Internet of Things. Her work crosses traditional discipline boundaries, requiring thinking across multiple layers of system and protocol stacks. Current research themes include breaking barriers for technologies that enable fundamentally new deployments and experiences, such as energy harvesting, artificial intelligence adapted to IoT constraints, and augmented reality. Her lab specifically develops edge- and IoT-enabled intelligent augmented reality platforms, with applications in healthcare and human-robot collaboration. Analyzing her recent publications reveals a strong focus on augmented reality systems, particularly for medical applications. Her work spans computer vision for AR, spatial tracking, SLAM systems, vision-language models for AR security, and VR/AR applications in neurosurgery and rehabilitation. A significant portion of her recent work addresses challenges in mixed reality for medical procedures, demonstrating the translational impact of her research. Google Anita Borg USA Fellowship Canadian Graduate Scholar CGS NSERC Fellowships Columbia University Presidential Fellowship Columbia University Jury Award for Outstanding Achievement in Communications ACM SenSys Best Student Demonstration Award IEEE Communications Society Young Author Best Paper Award IEEE Communications Society Award for Advances in Communications Best Research Artifact Award, IEEE IPSN (2020) N2 Women Rising Star, Networking Networking Women (N2Women) (2019) Dr. Gorlatova's research has been supported by various funding sources that enable her work on edge computing for augmented reality, IoT systems, and medical applications. She actively mentors graduate students who frequently appear as first authors on her publications, indicating strong student involvement in her research. Her I3T Lab at Duke focuses on creating human-facing pervasive mobile computing platforms that enable transformative applications, with recent emphasis on creating advanced augmented reality platforms that integrate edge computing and IoT technologies. The I3T Lab is developing next-generation AR systems with capabilities in edge AI, collaborative spatial awareness, AR user cognitive context sensing, and AR QoS/QoE evaluation. Current projects include applications in healthcare (particularly neurosurgery guidance and rehabilitation) and human-robot collaboration scenarios, demonstrating the lab's focus on real-world impact of pervasive computing technologies.
Malcolm McCulloch serves as Professor of Energy Systems and Group Leader of the Energy and Power Group within the Department of Engineering Science at the University of Oxford. He also holds the position of Co-Director for the Oxford Martin Programme on Integrating Renewable Energy and maintains affiliation with Christ Church college. His research spans four primary domains: domestic energy systems, electric vehicle technology, renewable generation, and energy solutions for developing regions. McCulloch's research focuses on practical energy transition challenges. His domestic energy work develops user-centric demand side management technologies and behavior change interventions, leading to the spin-out Intelligent Sustainable Energy (now Navetas Energy Management). In transportation, he pioneered the Morgan LifeCar hydrogen sports car project, resulting in high-efficiency motors and the Oxford Yasa Motors spin-out. His renewable generation research includes lightweight tidal turbines through Kepler Energy, while his Energy for Development work creates nano/micro grid solutions for distributed electrification. Analysis of his recent publications reveals a strong emphasis on integrating variable renewables through innovative market mechanisms, grid flexibility solutions, and electric vehicle integration. His work consistently addresses the technical and economic barriers to decarbonization, with increasing focus on peer-to-peer energy trading, thermal storage, and climate-resilient cooling solutions since 2020. McCulloch previously served as Co-Director of the Institute for Carbon and Energy Reduction in Transport (2008-2013) under the Oxford Martin School. His leadership extends to creating the Integrated Transport Network for Oxford and developing practical applications through multiple successful spin-out companies that bridge academic research and commercial implementation.
Christopher Turbill is an Associate Professor in Animal Science at Western Sydney University's School of Science, where he maintains an active research program focused on animal physiological ecology. He is affiliated with the Hawkesbury Institute for the Environment and serves as a Principal Investigator on multiple research projects while accepting HDR candidates for supervision. PhD from University of New England (2006) Thesis: Thermoregulatory Ecology of Tree-roosting Bats Supervised by Prof. Fritz Geiser Postdoctoral fellowships from Austrian Science Fund and Australian Research Council (DECRA) Former ecologist with NSW Government Professor Turbill's research integrates thermal and metabolic physiology with behavioral ecology to understand animal-environment interactions. His work has revealed significant ecological consequences of controlled body temperature variation in mammals and birds, linking these processes with metabolic energy expenditure, activity patterns, and life-history strategies. He specializes in bat biology and investigates conflicts between human environmental change and animal conservation requirements. His research keywords include ecophysiology, thermoregulation, energy expenditure, life-history ecology, body temperature, torpor, hibernation, and wildlife conservation. Analysis of Turbill's recent publications reveals a strong focus on thermal biology and conservation physiology, particularly regarding bats and birds. His work examines how animals manage energy through torpor, respond to climate change through thermal regulation, and adapt to anthropogenic disturbances. The research spans field studies of flying-foxes, microbats, and passerine birds across Australian ecosystems, with increasing emphasis on conservation applications related to white-nose syndrome, fire impacts, and wind energy development. Turbill leads significant research projects including the Ecology of the eastern horseshoe bat and its sensitivity to fire impacts (2024-2027), Vulnerability of Australian bats to white-nose syndrome (2021-2026), and Torpor use and burrowing behaviour in an arid zone passerine (2024). His work attracts funding from diverse sources including the Australian Research Council, Department of Planning and Environment, and various conservation organizations. Professor Turbill directs the BatsLab research group, which maintains a virtual hub for bat research at Western Sydney University. His team employs advanced methodologies including thermal imaging, GPS tracking, and physiological monitoring to study animal responses to environmental challenges. Current work focuses on developing conservation interventions for heat-stressed flying-foxes and assessing vulnerabilities of Australian bat species to emerging diseases.
Zhi Da is the Howard J. and Geraldine F. Korth Chair in Finance and Professor of Finance at the University of Notre Dame , Mendoza College of Business, Department of Finance. He completed his Ph.D. in Finance at Northwestern University’s Kellogg School of Management (2006), preceded by an M.Sc. in Financial Engineering from the National University of Singapore (2001) and a B.B.A. with First-Class Honors (1999) from the same institution. Holding editorial roles at Journal of Finance , Management Science , Review of Financial Studies and several other top journals, he is a leading voice in empirical finance research. Education Ph.D. in Finance, 2006 – Kellogg School of Management, Northwestern University M.Sc. in Financial Engineering, 2001 – National University of Singapore B.B.A. (1st Class Honors), 1999 – National University of Singapore Research Interests Zhi Da’s scholarship sits at the intersection of asset pricing , behavioral finance , and market microstructure . He investigates how investor attention, institutional trading, liquidity frictions, and information flows jointly determine the cross-section of expected returns. His work delves into retail margin trading, the role of pension-fund flows in exchange-rate dynamics, the informational content of SEC filings, and the efficiency of short-selling mechanisms. By combining large-scale data analytics, textual analysis, and structural modeling, he uncovers novel predictors of returns ranging from presidential approval ratings to real-time attention measures. Recent projects explore fractional trading ’s impact on price efficiency, hedging demand as a driver of intraday momentum, and the hidden effort problem in delegated portfolio management. These themes collectively advance our understanding of limits to arbitrage and the formation of extrapolative beliefs. Publication Landscape Spanning 2025 back to 2009, his 15 most recent articles in Journal of Finance , Review of Financial Studies , Management Science , Journal of Financial Economics , and Journal of Financial and Quantitative Analysis converge on three broad motifs: (1) micro-level trading frictions—liquidity costs, margin requirements, and short-selling constraints; (2) macro-finance linkages—exchange rates, fiscal policy, and global capital flows; and (3) information economics—attention allocation, media analytics, and regulatory disclosures. The collective evidence demonstrates that seemingly small trading or informational frictions aggregate into large, persistent cross-sectional return predictability. Honors and Awards 2017 William F. Sharpe Award for Best Paper, Journal of Financial and Quantitative Analysis Lead-article distinctions in Journal of Finance , Review of Financial Studies , and Management Science Featured coverage in SmartMoney and CNBC Teaching & Mentorship At Notre Dame’s Mendoza College, Professor Da teaches Investments (undergraduate and MBA) and Fixed Income Securities , integrating cutting-edge research insights into the curriculum. While specific advisees are not listed, his extensive co-author network (22+ recurring collaborators) attests to a vibrant mentoring environment. Laboratory & Data Resources He publicly distributes the NAT (Net Arbitrage Trading) dataset, a stock-quarter panel of arbitrage positions used in Chen, Da & Huang (2019). This resource has become a standard tool for researchers studying arbitrage capital movements.
Perla Maiolino serves as an Associate Professor in Engineering Science at the University of Oxford and Principal Investigator of the Soft Robotics Lab (SRL) within the Oxford Robotics Institute. Her academic foundation includes BEng, MEng, and PhD degrees in Robotics and Automation from the University of Genoa, where she pioneered CySkin technology for distributed tactile sensing in robots—later exhibited at the Science Museum in London. She expanded her expertise during a 2017-2018 postdoctoral fellowship at Cambridge University's Biologically Inspired Robotics Lab, focusing on soft robotics and tactile perception. Dr. Maiolino's research centers on developing artificial skin systems, soft robotic actuators, and distributed sensing architectures. Her work bridges biological inspiration with engineering innovation to create robots capable of safe human interaction and dexterous manipulation in unstructured environments. Key contributions include compliant beaded-string jamming mechanisms for anthropomorphic fingers, monolithic 3D-printed soft pneumatic arms (JAMMit!), and distributed time-of-flight sensor networks for robotic self-awareness. Recent publications (2024-2025) reveal a strong convergence of tactile sensing with machine learning, featuring optical flow for gesture recognition, diffusion models for artificial skin simulation, and zero-shot sim-to-real transfer techniques. Her team has made significant advances in multi-modal sensing integration, variable stiffness actuation, and scene flow estimation for robots operating in dynamic surroundings. Scientific Awards No specific awards were documented in the provided institutional materials. Advising and Grants While her leadership of the Soft Robotics Lab implies active student supervision and grant management, detailed information about advisees or funded projects was not included in the source documentation. Labs and Teams As Principal Investigator of the Soft Robotics Lab at Oxford Robotics Institute, Dr. Maiolino directs research on tactile perception systems, soft actuation mechanisms, and sensor-integrated robotic structures. The lab's work focuses on applications requiring safe physical interaction, including healthcare robotics and human-robot collaboration scenarios, with emphasis on multi-material 3D printing and embedded sensing technologies.
Prof. Alessandro Golkar is a Professor at the Technical University of Munich (TUM), leading the Chair of Picosatellites, Nanosatellites, and Satellite Constellations. He joined TUM in September 2022 and previously served as one of the founding faculty members at Skoltech, a Moscow-based graduate research university. His research focuses on advanced space mission concepts, systems engineering for picosatellites, and federated satellite systems. Prior to academia, he held roles at Airbus CTO, contributing to technology roadmapping and planning. His academic background includes expertise in aerospace engineering, systems design, and agile development methodologies for space hardware. Key research areas include CubeSat constellations, distributed satellite systems, and the integration of AI tools like Large Language Models (LLMs) into spacecraft design processes. He has pioneered projects such as the FSSCat mission, winner of the ESA Sentinel Small Satellite Challenge, and has explored applications of additive manufacturing for lunar missions. Prof. Golkar’s awards include the 2021 Karman Fellowship and IEEE Senior Membership (2018). His recent work emphasizes optimizing satellite networks, digital twin implementation, and orbital maneuvering for collision avoidance. He actively contributes to technology roadmapping, focusing on future human landing systems and lunar infrastructure development. Education: Ph.D. in Aerospace Engineering (details not explicitly stated). Grants & Funding: Extensive grants for CubeSat projects, federated systems research, and space technology innovation. Labs/Teams: Leads the Chair’s research group at TUM and collaborates with industry partners like Airbus on advanced mission concepts. His publications span over two decades, addressing topics like constellation design, machine learning in space, and agile processes for hardware development. He advocates for hybrid agile methodologies to bridge traditional systems engineering and modern product development.
Mayo Clinic College of Medicine and ScienceUnited States
Clifton R. Haider, Ph.D., is a Professor of Biomedical Engineering at Mayo Clinic, holding primary appointments as a Consultant in the Department of Physiology & Biomedical Engineering and the Department of Pediatric and Adolescent Medicine. His research focuses on accelerating medical device development through a discovery platform integrating mathematical modeling, algorithmic design, and physiological sensor validation. This platform aims to identify predictive biomarkers for conditions like cardiovascular collapse, sleep disorders, and neurodegenerative diseases. Dr. Haider’s work emphasizes noninvasive, autonomous monitoring and real-time predictive algorithms, with a particular focus on pediatric care. Education: PhD, Physiology & Biomedical Engineering, Mayo Graduate School, Mayo Clinic College of Medicine Predoctoral Studies in Biomedical Engineering and Physiology, Mayo Graduate School BSc (Hons), Biomedical Engineering, University of Iowa Research Interests: Dr. Haider’s work bridges engineering and clinical practice, targeting unmet needs in pediatric and adult healthcare. His discovery platform combines statistical modeling, sensor design, and validation to streamline medical device prototyping. Key focus areas include: Accelerated medical device design Quantitative predictive biomarkers Remote pediatric monitoring Real-time algorithm development This platform seeks to predict treatment responses and identify patients requiring advanced care, enhancing clinical decision-making. Awards: Office of Naval Research Young Investigator Program (2014–present) Grants & Labs: His research is supported by interdisciplinary collaborations at Mayo Clinic, leveraging laboratory facilities for sensor validation and algorithm testing. While specific grant details are not listed, his work aligns with Mayo’s emphasis on translational biomedical engineering.
Professor Marina Jirotka is a leading academic in Human-Centred Computing at the Department of Computer Science, University of Oxford, and a Governing Body Fellow at St Cross College. She directs the Responsible Technology Institute and leads interdisciplinary research on responsible innovation in AI, robotics, and digital systems. University: University of Oxford School: Department of Computer Science Position: Professor of Human-Centred Computing Leadership: Director, Responsible Technology Institute Fellowships: EPSRC Established Career Fellowship (RoboTIPS) Her research focuses on integrating ethical, societal, and human values into technology design. Key areas include responsible innovation, human-computer interaction, algorithmic accountability, and socio-technical systems. She explores trust, transparency, and governance in autonomous systems, digital mental health, and quantum computing ethics. Her recent publications reflect a strong trend in responsible robotics, explainable AI, digital wellbeing, and ethical governance. Articles emphasize interdisciplinary methods, stakeholder engagement, and practical tool development such as the Ethical Black Box and TrustScapes. Themes span autonomous vehicles, social robots, algorithmic bias, and sustainability in digital innovation. Excellence in Teaching Award, University of Oxford (2006) ESRC/SSRC Fellowship (2007) UCLA Scholar Award (2011) EPSRC Established Career Fellowship (2018, 2019) Vice Chancellor’s Innovation Award Highly Commended (2020) She supervises numerous PhD and Master’s students and leads major grants including RoboTIPS, RAILS, PARIS-DE, and Adolescent Mental Health in the Digital World. She co-directs ORBIT, a leading RRI training provider, and contributes to policy through advisory roles with the House of Lords, All-Party Parliamentary Groups, and industry consortia. She is a member of the IEEE P7000 working group and the UK Committee for IIASA. Her lab work involves interdisciplinary teams focused on responsible technology design, public engagement, and ethical tool development.
Joy Arulraj is an Associate Professor in the School of Computer Science within the College of Computing at Georgia Institute of Technology. His research focuses on data systems, machine learning, and database systems, with a particular emphasis on video analytics and adaptive query processing. He leads the Data Systems and Analytics Group and is developing the EVA AI-Relational Data System. Dr. Arulraj's research interests span data systems, machine learning, database systems, video analytics, and adaptive query processing. His work centers on developing systems that efficiently process complex queries, particularly for video analytics and machine learning workloads. He has made significant contributions to GPU database systems, non-volatile memory database management, and adaptive query processing techniques. His research often bridges the gap between theoretical database principles and practical implementations for modern hardware architectures. His recent publications show a strong trend toward video analytics systems, adaptive query processing for machine learning workloads, and GPU-accelerated database systems. The EVA system represents a major focus of his recent work, providing end-to-end exploratory video analytics capabilities. His research also addresses fundamental database concepts like buffer management, query optimization, and storage management, adapting these principles for modern hardware and application requirements. Dr. Arulraj has advised numerous graduate students including Pramod Chunduri, Gaurav Tarkok Kakkar, Jiashen Cao, and Sayan Sinha. His graduated students have gone on to work at companies like ServiceNow, Meta Research, and the Korean Army. He actively teaches database system courses at Georgia Tech, including Database System Implementation (CS 4420/6422) and Advanced Database System Implementation (CS 4423/6423), where students build database systems from scratch using C++ and the BuzzDB framework. He maintains an active research program with consistent publication output across top database and systems conferences. His work spans from theoretical database principles to practical system implementations, with a recent emphasis on video analytics, machine learning integration with database systems, and leveraging modern hardware like GPUs and non-volatile memory for database applications.
Joydeep Biswas is an Associate Professor in the Computer Science Department at the University of Texas at Austin, where he serves as the Director of the Autonomous Mobile Robotics Laboratory (AMRL). He is also affiliated with Texas Robotics, the UT Machine Learning Laboratory, and UT Good Systems. Previously, he was an Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. Dr. Biswas earned his PhD in Robotics from Carnegie Mellon University in 2014 and his B.Tech in Engineering Physics from the Indian Institute of Technology Bombay in 2008. His educational background has provided him with a strong foundation in both theoretical and applied aspects of robotics and artificial intelligence. Dr. Biswas's research focuses on enabling long-term autonomy for mobile robots operating in human environments. His work spans robot perception, motion planning, control systems, and AI, with the ultimate goal of creating self-sufficient autonomous mobile robots that can perform tasks accurately and robustly in real-world settings. He is particularly interested in perception, planning, and failure recovery for autonomous mobile robots, which supports his vision of having autonomous service mobile robots deployed at campus-to-city scale, both indoors and outdoors, performing assistive tasks over deployments spanning years. His IJCAI 2019 Early Career Spotlight talk summarizes much of his research to date and ongoing interests. His recent research has shown a strong trend toward social navigation, human-robot interaction, and the application of machine learning techniques to robotics problems. There's a clear progression from fundamental robotics research toward more complex, real-world applications that require robots to understand and navigate human social spaces effectively. His work increasingly integrates large language models and other advanced AI techniques with traditional robotics approaches, as evidenced by his recent publications on topics like preference-conditioned navigation, social navigation benchmarks, and instruction-following navigation systems. Dr. Biswas has received numerous prestigious awards including the NSF CAREER Award (2021), J.P. Morgan Faculty Research Award (2019), Amazon Research Award (2019), and a grant from Northrop Grumman Mission Systems (2018). These awards recognize his innovative contributions to the field of robotics and autonomous systems. As a dedicated educator and mentor, Dr. Biswas actively supervises PhD and master's students, with his PhD student Sadegh Rabiee winning the student poster award at the Northrop Grumman University Symposium 2019. He has secured significant grant funding from the National Science Foundation for projects including 'Introspective Perception and Planning for Long-Term Autonomy' and 'Interactive Synthesis and Repair For Robot Programs,' demonstrating his ability to secure competitive research funding and his commitment to advancing the field. Dr. Biswas leads the Autonomous Mobile Robotics Laboratory (AMRL), which serves as a hub for interdisciplinary research in mobile robotics. The lab has developed notable resources such as the UT Campus Object Dataset (CODA) for 3D perception research and SOCIALGYM, a framework for benchmarking social robot navigation. His team regularly deploys robots on the UT Austin campus and in urban environments to test and refine their approaches in realistic settings, bridging the gap between simulation and real-world application.
