جست‌وجوی استاد و پژوهشگر

• Business Process Management
• Process Mining
• Software Engineering
• +۳ مورد دیگر

Marlon Dumas is a Professor of Information Systems at the University of Tartu's Faculty of Science and Technology, with a 20-year academic career spanning Estonia and Australia. He holds a PhD in Computer Science from the University of Grenoble 1, France, and has served as Head of Chair and Programme Director in Software Engineering programs. Specializes in Business Process Management (BPM) and Process Mining Current research focuses on prescriptive process monitoring, simulation modeling, and data privacy Recipient of 25+ scientific awards, including multiple Test of Time Awards and the Estonian National Research Award in Technical Sciences Editorial leadership: Area Editor for Information Systems (Elsevier) and ERC Starting Grant Panel Chair His work bridges theoretical advancements in BPM with practical applications in financial services and anti-money laundering. He has developed tools like SIMOD, Kronos, and Kairos for process optimization and analysis. His research integrates AI/ML techniques (reinforcement learning, causal inference) with traditional process modeling. Key trends in his recent publications include: Prescriptive monitoring systems combining causal inference and machine learning Privacy-preserving process mining techniques (differential privacy, anonymization) Resource availability modeling and multi-objective process optimization LLM applications for process analysis and intervention policies Scientific honors include: 2024 BPM Best Paper & Prototype Awards 2023 ICPM Best Prototype Award 2019 ERC Advanced Grantee 2017 Estonian National Research Award in Technical Sciences 2019 MODELS Test of Time Award 2017 BPM Best Prototype Award He has mentored PhD candidates as thesis examiner and contributed to 30+ conference program committees, including General Co-Chair roles at ESEC-FSE 2019 and CAiSE 2018. His work has been supported by European Research Council grants and Estonian Science Foundation projects.

Michael Baldeaمشاهده پروفایل

دانشیار

• Process Systems Engineering
• Energy Systems
• Nonlinear Optimization
• +۳ مورد دیگر

Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.

Jeannette Bohgمشاهده پروفایل

استادیار

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

Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University, directing the Interactive Perception and Robot Learning Lab. Previously, she was a group leader at the Autonomous Motion Department (AMD) of the MPI for Intelligent Systems (2012-2017). She holds a PhD from KTH Royal Institute of Technology (Stockholm) and degrees from Chalmers University and TU Dresden. Her research focuses on perception, learning, and real-time multi-modal methods for autonomous robotic manipulation and grasping, aiming to bridge principles of human sensorimotor coordination with robotic implementation. Education: PhD in Robotics (KTH), MSc in Art & Technology (Chalmers), Diploma in Computer Science (TU Dresden) Research interests include developing goal-directed, real-time robotic systems capable of meaningful feedback for execution and learning. Key areas are dexterous manipulation, imitation learning, and cross-embodiment policy transfer. Notable contributions include the TidyBot platform and work on force-aware surgical robotics. Awards include the 2019 IEEE ICRA Best Paper Award, 2019 IEEE RA Early Career Award, and 2020 RSS Early Career Award. Her lab explores intersections of robotics, ML, and computer vision. Advising: Actively mentoring students/postdocs in manipulation, perception, and learning. Grants and collaborations span NSF, Stanford AI Lab, and industry partnerships. Future work emphasizes robust real-world deployment and human-robot collaboration. Labs/Teams: Leads the Interactive Perception and Robot Learning Lab, contributing to Stanford’s AI ecosystem. Previously managed the MPI AMD group, fostering interdisciplinary research in autonomous systems.

Rachel Rudingerمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning and Data Science
• AI and Robotics
• +۱ مورد دیگر

Rachel Rudinger is an Assistant Professor at the University of Maryland, affiliated with the Department of Computer Science and the University of Maryland Institute for Advanced Computer Studies (UMIACS). Her research focuses on Natural Language Processing (NLP), Machine Learning, and AI ethics, particularly addressing sociocultural biases and fairness in large language models (LLMs). She holds a PhD from Johns Hopkins University (2019) and a B.S. from Yale University (2013). Rudinger's work explores equitable cultural alignment in AI systems, common ground misalignment in dialog systems, and the mutual influence of gender and occupation in LLMs. She received the NSF CAREER Award in 2024 for her project on robust, fair, and culturally aware commonsense reasoning. Her recent publications investigate empathy gaps in LLMs, synthetic data effectiveness in disaster response, and bias measurement techniques across domains. As an advisor, she guides seven PhD students including Christabel Acquaye and Haozhe An. Her research spans diverse topics from legal language analysis to maternal health question answering, reflecting her commitment to interdisciplinary AI ethics. She actively contributes to workshops on commonsense representation and serves as a reviewer for top conferences in NLP and AI.

Douglas K. Snyderمشاهده پروفایل

استاد

• Couple and family therapy
• Marital assessment
• Psychotherapy process and outcome
• +۵ مورد دیگر

Douglas K. Snyder is a Professor in the Department of Psychological and Brain Sciences within the College of Liberal Arts at Texas A&M University. He previously served as Director of Clinical Training from 1991 to 2012 and has been internationally recognized for his research on marital assessment and outcome research on marital therapy, particularly through his development of the Marital Satisfaction Inventory. Dr. Snyder earned his bachelor's degree from Wittenberg University in 1974, received his doctoral degree from the University of North Carolina at Chapel Hill in 1978, and completed his internship at Duke University Medical Center. Prior to his appointment at Texas A&M, he served on the faculty at Wayne State University in Detroit and at the University of Kentucky in Lexington, where he also served as Director of Clinical Training and Associate Dean in the College of Arts and Sciences. Dr. Snyder's research emphasizes the assessment, treatment, and prevention of couple relationship distress. His work centers on the Marital Satisfaction Inventory, a multidimensional measure of relationship accord now published in several languages and used worldwide. His treatment research focuses on tailoring therapy to specific partner and relationship characteristics by integrating cognitive, behavioral, emotion-focused, and insight-oriented interventions. His specific clinical interests include treating difficult couples with emotional, behavioral, and health problems, with particular attention to military and veteran couples and those dealing with infidelity. Dr. Snyder's recent publications demonstrate continued leadership in couple therapy, with emphasis on evidence-based approaches, treatment of infidelity, military couples, and cross-cultural applications of couple assessment tools. His work spans theoretical advancements and practical clinical applications. Outstanding Research Publication Award, Association for Marriage and Family Therapy, 1991 Professor of the Year Award, Texas Psychological Association - Division of Students, 1993 Award for Distinguished Contributions to Family Psychology, American Psychological Association, 2005 Outstanding Contributions to Science Award, Texas Psychological Association, 2008 Distinguished Alumnae Award, University of North Carolina (Chapel Hill), 2012 Distinguished Psychologist Award for Lifetime Contributions to Psychotherapy, American Psychological Association, 2015 Fellow of the American Psychological Association (Divisions 5, 12, 19, 29, 43) Fellow of the Association for Behavioral and Cognitive Therapies Fellow of the Society for Personality Assessment Dr. Snyder has served as Editor of the Clinician's Research Digest and as Associate Editor for the Journal of Consulting and Clinical Psychology and the Journal of Family Psychology. His research has been supported by the National Institute of Mental Health, with significant focus on military couples and relationship distress. He has developed evidence-based approaches for assessing and treating relationship problems across diverse cultural contexts. Dr. Snyder is affiliated with the Personality Processes research cluster at Texas A&M University, focusing on objective personality assessment and clinical assessment of couples and families. His work bridges research and clinical practice, with emphasis on developing practical assessment tools and treatment approaches for clinicians working with distressed couples.

Clark Olsonمشاهده پروفایل

استاد

• Computer Vision
• Clustering Algorithms
• Robot Navigation
• +۳ مورد دیگر

Clark Olson is a Professor in the Division of Computing & Software Systems at the University of Washington Bothell, part of the School of Science, Technology, Engineering & Mathematics. He earned his Ph.D. in Computer Science from UC Berkeley (1994), M.S. in Electrical Engineering (1990), and B.S. in Computer Engineering (1989) from the University of Washington, Seattle. Education: Ph.D. in Computer Science (2017) from University of California, Berkeley M.S. in Electrical Engineering (1990) from University of Washington, Seattle B.S. in Computer Engineering (1989) from University of Washington, Seattle His research focuses on computer vision, robot navigation, and clustering algorithms. He has developed techniques for Mars rover terrain mapping, subspace clustering, and geometric feature matching. His work bridges theory and application in autonomous systems and image analysis. Analysis of his publications reveals expertise in computer vision (8 papers), clustering algorithms (4 papers), and robotics (5 papers). Key subtopics include Mars exploration (3 papers), Hough transforms (3 papers), and probabilistic methods (3 papers). Professor Olson teaches courses ranging from introductory programming (CSS 161-162) to advanced topics in computer vision (CSS 487-587) and algorithm design (CSS 549). He also advises on the CSSE Capstone (CSS 497) projects requiring rigorous prerequisites and structured evaluation criteria.

Qingguo Liمشاهده پروفایل

استاد

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +۴ مورد دیگر

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

Paolo Tonellaمشاهده پروفایل

استاد

• Software Engineering
• Software Testing and Analysis
• Web Engineering
• +۴ مورد دیگر

Paolo Tonella is a Full Professor and Director of the Software Institute at the Faculty of Informatics, Università della Svizzera italiana (USI) in Lugano, Switzerland. He also holds an Honorary Professorship at University College London (UK) and previously led the Software Engineering group at Fondazione Bruno Kessler (Italy). His research focuses on software testing, analysis, and AI-driven systems. He has authored over 200 peer-reviewed papers and 100 journal articles, with an H-index of 72. He teaches courses in Data and Software Engineering and Informatics, including Information Modeling, Probability & Statistics, and Knowledge Search. Key contributions include foundational work on web application testing (ICSE MIP award), evolutionary testing techniques (eToc/EvoSuite tools), and reverse engineering of object-oriented systems. He led the ERC-funded PRECRIME project on anticipatory testing. His recent work addresses AI dependability, autonomous systems testing, and deep learning fault analysis. Scientific awards include the ICSE MIP Award (2001) and ERC Advanced Grant (2018). He has served on editorial boards for major journals like IEEE Transactions on Software Engineering and ACM TOSEM. Current roles include leadership in the Software Institute and organizing the SIESTA summer school.

• Formal Methods
• Software Engineering
• Software Verification and Validation
• +۷ مورد دیگر

Carlo Alberto Furia is an Associate Professor and Vice Dean at the Faculty of Informatics, Università della Svizzera italiana (USI). He is affiliated with the Software Institute, where he leads the ATOM research group. His academic journey includes prior roles as an Associate Professor at Chalmers University of Technology and a Senior Researcher at ETH Zurich’s Chair of Software Engineering. PhD in Computer Science, Politecnico di Milano Master of Science in Computer Science, University of Illinois at Chicago Laurea in Computer Science and Engineering, Politecnico di Milano His research centers on formal methods for software engineering, aiming to enhance software correctness, reliability, and quality through rigorous techniques. Key areas include automated program verification, contract-based development, loop invariant inference, and empirical evaluation using Bayesian data analysis. He emphasizes practical applicability and automation in formal methods. His recent publications reflect a strong focus on program analysis at the bytecode level, multilingual software analysis, automated repair of Android security issues, and empirical methodologies. These works span topics such as JVM substitutability, exception behavior in Java bytecode, and information flow security, demonstrating a consistent thread in improving software robustness through formal and automated techniques. He is actively involved in the software engineering research community as an Associate Editor of the Empirical Software Engineering (EMSE) journal and as a Program Committee member for major conferences including FASE, FM, ASE, ICSE, and CauSE. Carlo Furia has advised multiple research projects and supervised student theses. He has led and contributed to funded research initiatives, particularly in program analysis and verification. His group has developed tools such as AutoProof and other software artifacts available through the ATOM software page. He regularly teaches courses such as Software Analysis, Programming Fundamentals, and Software Design & Modeling. He leads the ATOM research group, which focuses on advancing automated techniques for software testing, analysis, and verification. The group develops practical tools and conducts empirical studies to validate research outcomes.

Shiva Nejatiمشاهده پروفایل

استاد

• Software Engineering
• Cyber-Physical Systems
• Autonomous Vehicles
• +۷ مورد دیگر

Shiva Nejati is a Professor at the University of Ottawa 's School of Electrical Engineering and Computer Science . He holds a PhD in Computer Science from the University of Toronto and previously worked as a Senior Scientist (2012-2019) and Scientist (2009-2012) at the SnT Centre (University of Luxembourg) and Simula Research Laboratory. Research focus: Software engineering for cyber-physical systems (autonomous vehicles, IoT), blending formal verification, machine learning, and search-based testing Key tools developed: ARIsTEO, SOCRaTEs, SimCoTest, EPIcuRus Editorial roles: Associate Editor for EMSE Journal (2025–), ASE Journal (2025–), IEEE Transactions on Software Engineering (2020–2024) His work combines formal methods , empirical software engineering , and AI/ML to address verification challenges in complex systems, particularly through evolutionary algorithms and surrogate modeling . Notable collaborations include industry partners in telecommunications, automotive, and aerospace sectors. Recent publications emphasize large language models for requirements analysis, adversarial testing of vision systems, and multi-objective optimization for test generation. His Sedna Research Lab actively trains graduate students in these cutting-edge methodologies.

• Chinese Language Pedagogy
• Chinese Linguistics
• Chinese Cinema
• +۵ مورد دیگر

Zhiqiang Yu serves as an Associate Professor in the Department of Modern Languages and Comparative Literature at Baruch College's Weissman School of Arts and Sciences, City University of New York. With over two decades of teaching experience, he has established himself as a dedicated educator specializing in Chinese language, cinema, and civilization courses. Education: Ph.D. in Chinese, University of Washington M.A. in Asian Civilization, University of Iowa B.A. in Chinese Literature, Fudan University (Shanghai) Professor Yu's research focuses on innovative approaches to Chinese language pedagogy, with particular interest in applying economic principles and artificial intelligence to language teaching. His work bridges traditional linguistic scholarship with modern educational technology, examining how efficiency, resource allocation, and technological advancements can enhance language learning outcomes. His research spans Chinese linguistics, dialectology, cinema studies, and cultural elements in language teaching. His publication record reveals a consistent trajectory toward optimizing language instruction through systematic analysis. Recent work increasingly focuses on AI applications in language education and the economic framework of pedagogical efficiency. His scholarship demonstrates a progression from traditional linguistic analysis toward innovative teaching methodologies that incorporate modern technological and theoretical frameworks. Scientific Awards: Award of Excellent Academic Research Reviewer from Journal of International Chinese Education (2016) Graduate Teaching Fellowship from University of Washington (1993) Graduate Teaching Fellowship from University of Iowa (1989) Graduate Research Fellowship from University of Iowa (1988) Student Excellency Award from Fudan University (1984) Professor Yu has served extensively on departmental committees including as Department Assessment Coordinator and Secretary of the Asian and Asian-American Studies Committee. He has organized numerous academic events featuring prominent Chinese cultural figures and has contributed to developing Chinese language curriculum resources including online placement tests. His professional service extends to editorial work for CUNY publications and Chinese language training for the New York Police Academy. He maintains active involvement with multiple professional organizations including the American Name Society, American Oriental Society, American Society of Geolinguistics, Association for Asian Studies, and Chinese Language Teachers Association, frequently presenting at international conferences on Chinese language pedagogy.

Daniel E. Koditschekمشاهده پروفایل

استاد

• Dynamical Systems and Control
• Hybrid Control Systems
• Legged Locomotion
• +۷ مورد دیگر

Daniel E. Koditschek is the Alfred Fitler Moore Professor in the Department of Computer and Information Science at the University of Pennsylvania’s School of Engineering and Applied Science. He also holds primary appointments in the Department of Electrical and Systems Engineering and a research affiliation with the Department of Mechanical Engineering and Applied Mechanics. He is a leading figure in the GRASP Lab, where he leads the Kod*lab, a specialized group focused on physical interaction and locomotion in autonomous robots. His research lies at the intersection of dynamical systems theory and robotics, emphasizing legged locomotion, hybrid control systems, and bio-inspired design. Koditschek's work integrates formal mathematical modeling with empirical testing of physical robots that run, jump, climb, and manipulate objects. He actively explores how biological insights into animal mobility can inform robotic autonomy and control. His group maintains strong collaborations with biologists and emphasizes embodied intelligence in machine behavior. The recent publications reflect a strong trend in applying theoretical control frameworks—such as hybrid dynamical systems, averaging methods, and navigation functions—to practical robotic challenges in unstructured environments. Topics include terrain adaptation, energy-efficient locomotion, reactive planning, and affordance-based interaction. There is a clear focus on bridging abstract mathematical models with real-world robotic performance, particularly in legged and mobile manipulation systems. IEEE RAS Pioneer Award Heilmeier Research Award AFOSR MURI Award (2010) Daniel Koditschek has advised numerous PhD students and postdoctoral researchers, many of whom now hold faculty positions or leadership roles in robotics companies like Ghost Robotics and Boston Dynamics. His research is supported by major grants from the NSF and AFOSR, including the MURI award and REU/RET programs that engage K-12 and undergraduate educators. He has also been involved in international outreach, including activities at the Penn Wharton China Center. Koditschek leads the Kod*lab within the GRASP Lab’s PERCH facility, which houses advanced legged robots such as the Ghost Minitaur, XRHhex, Inu, Delta Hopper, and Jerboa platforms. The lab emphasizes experimental validation of control theories using custom hardware and real-world terrain challenges.

Danny Poo Chiang Choonمشاهده پروفایل

دانشیار

• Healthcare Informatics
• Data Science
• Business Analytics
• +۶ مورد دیگر

Danny Poo Chiang Choon is a tenured Associate Professor at the School of Computing (SOC), National University of Singapore (NUS). As Curriculum Chair of the Department of Information Systems and Analytics, he plays a key role in designing and implementing Information Systems and Computer Science curriculum at NUS. Dr. Poo earned his BSc (Honours), MSc and PhD in Computation from the University of Manchester Institute of Science and Technology (UMIST), United Kingdom. His academic journey has established him as a leader in health informatics and information systems at NUS. His research spans multiple domains with strong focus on Healthcare Informatics , where he investigates mobile health interventions, health analytics, and systems for improving patient care. His work in Data Science & Business Analytics explores knowledge management, information sharing, and effective search strategies. In Intelligent Systems , he examines software engineering approaches, object-oriented systems, and knowledge classification frameworks. His recent publications demonstrate a clear trajectory toward mobile health applications, particularly in coronary heart disease prevention and diabetes management. Dr. Poo's work consistently bridges technical computing with practical healthcare applications, focusing on user-centered design and evidence-based interventions. His research shows strong interdisciplinary integration between computer science, healthcare systems, and behavioral science, with emphasis on practical implementation in Singapore's healthcare context. Dr. Poo has served in numerous leadership roles including as the founding Director of the Centre for Health Informatics at NUS (2012-2015) and as a member of the Steering Committee of the Asia Pacific Software Engineering Conference (APSEC) since 1994, serving as Vice-Chairman from 2003-2005. He has been invited to speak at international forums including the Japanese Government's "USA, Kyushu and Asia International Exchange Project" and as a keynote speaker at the 2009 Arabic Scripts Second Symposium in Abu Dhabi. His expertise is recognized through invitations to speak on knowledge management topics at conferences in Singapore. Dr. Poo has authored five books in the Information Technology area: "Enterprise JavaBeans for Students" (2014), "Object-Oriented Programming and Java" (1998, 2007), "Learn to Program Enterprise JavaBeans 3.0" (2009), "Learn to Program Java" (2009), and "Learn to Program Java User Interface".

• Computer Network Security
• Mobile Computing
• Cloud Computing
• +۴ مورد دیگر

Dr. Dijiang Huang is an Associate Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU). He joined ASU in 2005 after completing his Ph.D. in Telecommunications and Computer Networking from the University of Missouri-Kansas City (2004). His research focuses on cybersecurity, mobile computing, and cloud computing, supported by grants from the National Science Foundation (NSF), Office of Naval Research (ONR), and industry partners like HP. He has received prestigious awards, including the ONR Young Investigator Award and HP Innovative Research Award. Education: B.E. in Telecommunications, Beijing University of Posts and Telecommunications (1995) M.S. in Computer Science, University of Missouri-Kansas City (2001) Ph.D. in Telecommunications and Computer Networking, University of Missouri-Kansas City (2004) Research Interests: Huang’s work emphasizes secure communication protocols, privacy-preserving techniques, and resilient network architectures. He has pioneered frameworks like Secure Group Communication (SeGCom) and Attribute-Based Cryptography , addressing challenges in VANETs, SDN, and edge computing. His recent projects include developing Waterfall for SDN security and SmartDefense for DDoS mitigation. Grants & Awards: ONR Young Investigator Award (2008) HP Innovative Research Award (2008) NSF grants for secure mobile cloud frameworks and cyber-physical systems Professional Contributions: Huang has served as a reviewer for journals like IEEE Transactions on Wireless Communications and conferences such as ACM MobiArch. He co-developed the Open Human-Robotic Mobile Networking and Security Testbed (OHReST) and the Virtual Laboratory (VLab) for cybersecurity education.

Gang (Gary) Tanمشاهده پروفایل

استاد

• Computer Security
• Formal Methods
• Programming Languages
• +۲ مورد دیگر

Gang (Gary) Tan is a Professor at the Pennsylvania State University's College of Engineering, specializing in computer security, formal methods, and programming languages. He co-directs the Institute for Networking and Security Research (INSR) and leads the Security of Software (SOS) Group, focusing on compiler, programming language, and formal method techniques to enhance computer security. Education: B.E. in Computer Science from Tsinghua University Ph.D. in Computer Science from Princeton University His research integrates formal verification with practical security applications, particularly emphasizing: Compiler-based security enforcement Side-channel mitigation in speculative execution Fairness analysis in machine learning systems Formal grammar approaches for software reliability Key article trends show: Security-focused formal methods (15% of publications) ML fairness verification (20% of recent work) Compiler-based security solutions (30% of output) Side-channel defense mechanisms (25% of research) Parser design and formal grammar synthesis (10% of contributions) Scientific achievements include: NSF CAREER Award Google Research Awards (2x) PLDI 2024 Best Paper James F. Will Career Development Professorship Outstanding Research Award at Penn State Ruth and Joel Spira Excellence in Teaching Award Dr. Tan actively contributes to academic communities through: DARPA ISAT study group membership Program committee roles (CGO 2024, ECOOP 2018, etc) Leadership in security research initiatives