With 23 New Faculty, Illinois CS Expands Prowess in Education, Research

9/1/2020 Aaron Seidlitz , Laura Schmitt, & Colin Robertson, Illinois CS

New faculty bolster expertise in topics ranging from online misbehavior to machine learning to broadening participation in computing.

Written by Aaron Seidlitz , Laura Schmitt, & Colin Robertson, Illinois CS

Between January 2020 and August 2021, 23 tremendously talented new faculty members are joining Illinois Computer Science. They’ll help the department to address growing enrollments and a few recent retirements. Beyond the numbers, these faculty have proven track records as dedicated researchers and educators.

This growth offers fresh opportunities to expand possibilities in the classroom, while continuing to break new research ground. These new Illinois CS faculty will provide expertise in topics ranging from online misbehavior to broadening participation in computing, climate forecasting to data-driven agriculture, human-robot interaction to machine learning, and data-oriented computer architecture to cryptography.

Below, learn about each new faculty member’s research experience and what kind of courses will be available to students under their instruction.

Founder Professor Arindam Banerjee
When Arindam Banerjee joins Illinois CS as a Founder Professor in January 2021, he will bring with him a depth of experience in Machine Learning and Data Mining, as well as the application of those subjects to complex real-world problems like climate science, ecology, recommendation systems, finance, medicine, and aviation safety. Since 2005, he has taught and conducted research as a professor at the University of Minnesota, Twin Cities.

Arindam Banerjee
Arindam Banerjee

Banerjee’s work has attracted more than 20 external grants and contracts. Most notably, the National Science Foundation invested nearly $10 million in a project he served as a Co-PI on titled “Understanding Climate Change: A Data Driven Approach” and nearly $2 million in a project he is currently serving as the PI titled “Physics-based Machine Learning for Sub-seasonal Climate Forecasting.” Banerjee has authored dozens of refereed publications, and his work also led to two patents and involvement in a few other key projects with partners like NASA and Oak Ridge National Labs.

Additionally, Banerjee’s research has earned a number of awards and honors, including The George W. Taylor Award for Distinguished Research from the University of Minnesota (2019), an NSF CAREER Award (2010) and eight best paper awards or honorable mentions.

In the classroom at Minnesota, Banerjee introduced and developed the core “Machine Learning” course and has also taught a variety of courses ranging from “Topics in Machine Learning” to “Artificial Intelligence II.” He has advised and mentored several undergraduate, graduate and doctoral students, as well as one postdoctoral fellow.

His dedication to service includes experience serving as an Associated Editor for journals like the IEEE Transactions on Knowledge and Data Engineering, and the Journal of Aerospace Information Systems. In addition to extensive conference service and leadership, Banerjee has also served as a reviewer for the Austrian Science Foundation, and Israeli Science Foundation, NSF, NASA, and Romanian National Research Council.

George Chacko
George Chacko

Research Associate Professor George Chacko
George Chacko
is the Chief Scientific Officer for NETE (NET ESolutions, an NTT Data Company), which drives advancements in health through technological innovation in digital services. He leads NETE Labs, a multi-disciplinary research unit that collaborates with both academia and industry to evaluate new ideas and technologies. While building NETE Labs, Chacko also led NETE’s development of internal portfolio analysis and data infrastructure for the National Institutes of Health (NIH).

Using techniques from scientometrics, Chacko works on problems at the intersecting interests of research funders, research institutions, and industry. Using citation patterns, particularly co-citation and direct citation, he studies novelty, disciplinarity, delayed recognition, diffusion of knowledge from basic to translational research, and the structure of scientific communities. Much of this work relies on modern computing techniques applied to large heterogeneous data.

After postdoctoral work on lymphocyte activation at Washington University School of Medicine and the National Institutes of Health, Chacko joined the Center for Scientific Review (CSR) as a Scientific Review Officer, went on to become Chief of its Bioengineering Sciences and Technology Integrated Review Group, and eventually became Director of CSR’s Office of Planning, Analysis, and Evaluation where he developed his present interests in research informatics, scientometrics, and evaluation for the purpose of studying impact and to support organizational planning.  Before joining NETE in 2015, Chacko spent a year as the Director of Research Information Analytics in the Office of the Vice Chancellor for Research at the University of Illinois at Urbana-Champaign.

Chacko been recognized with two NIH Director's Awards, the CSR Director's Explorer and Architect Awards, and a commendation from the IRS Commissioner. He received a PhD in Biochemistry and Immunology, under the direction of Clark Anderson, at The Ohio State University for studies on the activation of immune cells by Fcγ receptors.

Eshwar Chandrasekharan

Assistant Professor Eshwar Chandrasekharan
Eshwar Chandrasekharan joins Illinois CS from the PhD program in Computer Science at Georgia Institute of Technology, where he devoted his research efforts to combat online misbehavior using human-centered AI. Before his PhD, he obtained a B.Tech and M.Tech in Computer Science from the Indian Institute of Technology Madras.

Chandrasekharan's research interests are at the intersection of Social Computing, Data Science, and Human-centered AI. His research builds a foundation for evaluating and improving approaches to online moderation and for developing new AI-backed sociotechnical systems. Chandrasekharan’s long-term goal is to make the Internet safer and more welcoming, and he currently works toward that end by combining computational techniques and social computing theories.

His research has appeared at high-impact conference venues and has received considerable press coverage—e.g., The New York Times, MIT Technology Review, TechCrunch, The Verge, and MotherBoard. He recently won a Facebook research award for his project on measuring healthy online behavior.

Chandrasekharan has worked with large-scale Internet platforms, including Twitter, Reddit, and Facebook, and his research has impacted their efforts to improve online governance. For example, he developed Crossmod, a new AI-backed moderation system that is currently deployed in an online community with over 14 million subscribers. His research led Reddit to ban many hate groups from the platform. Steve Huffman, Reddit CEO and co-founder, also used Chandrasekharan's work as evidence in his recent testimony before Congress.

As an assistant professor starting this fall, Chandrasekharan will teach CS 598, Antisocial Computing.

Benjamin Cosman
Benjamin Cosman

Teaching Assistant Professor Benjamin Cosman
Benjamin Cosman hails from the CS PhD program at the University of California, San Diego, where his research focused on making it easier to avoid bugs in programming through machine learning and refinement type systems.

A dedicated and enthusiastic educator, Cosman founded UC San Diego’s Splash outreach program in 2016, and participated in many other K-12 outreach activities including mentoring a Girls Who Code club and a Mathcounts team. In 2018, he won the CS department’s annual Doctoral Award for Teaching Excellence.

This fall, Cosman will co-teach CS 173, Discrete Structures.

Payam Delgosha
Payam Delgosha

Research Assistant Professor Payam Delgosha
A research assistant professor, Payam Delgosha recently earned his PhD in electrical engineering and computer science from the University of California, Berkeley. His research interests are broadly in the areas of applied probability, information theory, machine learning, game theory, and quantum information theory.

During his PhD, Delgosha worked on efficient compression algorithms for graphical data. He won the best student paper award at the 2020 IEEE International Symposium on Information Theory for developing a universal low-complexity compression algorithm for sparse marked graphs.

As an Illinois CS faculty member, Delgosha is interested in developing frameworks and algorithms to study problems on large sparse graphs, such as compression and learning. He is teaching CS 498 AML, Applied Machine Learning, in the fall 2020 semester.

Yael Gertner
Yael Gertner

Teaching Assistant Professor Yael Gertner
Yael Gertner received her PhD from the University of Pennsylvania, working on the limits of cryptographic primitives. After that she held a postdoctoral Fellowship in the psychology department at Illinois and was a Beckman Institute Fellow, where she worked on how children acquire language.

She is co-author of two patents—a system and method for encrypting data in pictures and a system and method that employs a multi-user secure scheme utilizing shared keys.

Recently, she played a key role in designing the coursework for the department’s new Illinois Computing Accelerator for Non-Specialists (ICAN) certificate program, which provides non-computer science college graduates with the knowledge and education to succeed in a computing career or graduate CS-degree studies.

As a new teaching assistant professor this fall, she will teach Fundamentals of Algorithms and Excursion into Computing. 

Assistant Professor Saugata Ghose
Saugata Ghose will join Illinois CS in January 2021 following his work as a Systems Scientist at Carnegie Mellon University’s Department of Electrical and Computer Engineering. Prior to this role at Carnegie Mellon, he was a Postdoctoral Research Associate within the same department and earned his Ph.D. in 2014 from Cornell University.

Saugata Ghose
Saugata Ghose

His academic experience keys on several research interests, including:

  • Data-oriented computer architectures, through which he is developing unconventional computer architectures and systems for processing-in-memory and edge computing that target the large amounts of data being generated today. These new systems enable large improvements in the energy efficiency and battery life of computers and consumer devices.  His work focuses on both developing real hardware prototypes of these systems, and on creating software that can allow programmers and users to effortlessly benefit from these improvements.
  • New interfaces between operating systems, computer processors, memory, and storage, which improve performance and efficiency by revisiting and breaking down decades-old assumptions about the boundaries between the components and how they should interact with each other.
  • Architectures for emerging platforms and application domains, such as autonomous vehicles, smart cities, genomics, and miniaturized robotics.

Ghose’s work on four different projects as a Co-PI helped earn research funding, including a grant from the Semiconductor Research Corporation for his work on processing-in-memory. His research has also resulted in awards for Best Paper and Best Poster, as well as a number of peer-reviewed publications, invited papers, and book chapters. He also earned the Wimmer Faculty Fellowship from CMU in April 2019.

At Carnegie Mellon, he served as an instructor for courses on computer systems, digital logic and ECE capstone design, and Ghose designed a new course on the computer architecture of a smartphone.

He has advised or mentored several PhD, Master’s, and undergraduate students over the years. His service and outreach experience includes serving with the SIGMETRICS Committee on Student Engagement, web chair for multiple computer architecture conferences, and Information Director for the MICRO symposium.

Liangyan Gui
Liangyan Gui

Research Assistant Professor Lynna Liangyan Gui
Liangyan Gui will join the Illinois CS faculty in August 2021 as a research assistant professor. Her research focuses on computer vision, deep learning, and robotics.

She is particularly interested in motion prediction for the applications of human-robot interaction, self-driving vehicles, and virtual reality. In addition, she has developed a geometric and statistical framework for shape space analysis and applied it to analyze abnormalities of brain structure in Alzheimer's disease.

At Illinois, Gui is excited to develop autonomous agents that are able to perceive, interpret, and interact with humans and other agents in the dynamic world. She will extend the predictive learning vision to address a broad range of prediction tasks in the wild, such as reasoning about subtle human intention for social interaction. She also plans to leverage prediction to improve the overall perception and planning performance of multi-agent and multi-modal systems.

Gui earned her PhD from Carnegie Mellon University in 2019. She is currently conducting autonomous driving research at Argo AI. Previously, she has spent time at Facebook Reality Labs and Google.

Reyhaneh Jabbarvand
Reyhaneh Jabbarvand

Assistant Professor Reyhaneh Jabbarvand
Reyhaneh Jabbarvand
joins Illinois CS in January 2021 after completing her PhD at the University of California, Irvine. She’s driven to pursue research interests in topics like software engineering, software testing, static program analysis, mobile apps energy and security assessment, search-based software engineering, applied optimization, and applied data science and deep learning.

This dedication has paved the way for 14 conference paper publications and one invited book chapter as lead author. Jabbarvand was also selected to attend Rising Stars in EECS 2019. Prior to that, her work advancing energy testing on Android earned her the 2018 Google PhD fellowship in Programming Technology and Software Engineering. In the same year, she was named a finalist for the Facebook Testing and Verification Challenge.

At the same time, Jabbarvand has cultivated a background in mentoring and teaching. She served as a Teaching Assistant for Sharif University of Technology, where she taught courses like Advanced Computer Security, Advanced Communications Networks, Operating System Concepts, and more. At UCI, she mentored and co-advised research for five graduate students and one undergraduate student – three of which were female students.

Colleen Lewis
Colleen M. Lewis, Associate Professor

 Assistant Professor Colleen Lewis
Colleen Lewis’ research is focused on equitable and effective computer science education. Lewis curates CSTeachingTips.org, a NSF-sponsored project for disseminating effective CS teaching practices.

She joins Illinois CS from Harvey Mudd College, where she was the McGregor-Girand Associate Professor of Computer Science. At the University of California, Berkeley, Lewis completed a PhD in science and mathematics education.

Lewis is active in national efforts to broaden participation in computing (BPC). She is a primary contributor to BPCnet.org, a resource portal for computing departments to broaden participation in computing. She served on an ACM Retention subcommittee and serves on a National Center for Woman and Information Technology (NCWIT) committee to plan educational and community building initiatives among NCWIT’s academic members.

Lewis has received the Undergraduate Mentoring Award from NCWIT and the AnitaB.org Emerging Leader Award for her efforts to broaden participation in computing. This summer, she received a gift from Google to identify the demographic trends and curricular trade-offs in high school CS expansion. With funding from the NSF Computing Innovation Fellows program, she will be working with Dr. Kari George to research effective and inclusive practices for advising PhD students in computing.

In the fall, Lewis will be teaching CS598-CCE, Conceptual Change in Computer Science Education. At Harvey Mudd College, Lewis primarily taught data structures, software engineering, and a course about social justice in STEM.

Charith Mendis
Charith Mendis

Assistant Professor Charith Mendis
Charith Mendis
joins Illinois CS in August 2021 as an assistant professor after finishing his PhD at Massachusetts Institute of Technology. His research interests focus on compilers, program analysis, and machine learning.

The research he conducts leads to a vision he holds for the future in which end-to-end learned compiler optimizations generate state-of-the-art performant code while making the compiler future-proof and easier to maintain. And Mendis has already built upon that vision by creating Ithemal and Vemal. The former is the first learned cost model for basic block throughput estimation. The latter is the first end-to-end learned auto-vectorizer. Both of which, he said, outperforms state-of-the-art heuristics with minimal human intervention.

Mendis’ research led to the 2019 Best Paper award at the ML for Systems workshop, multiple conference publications in both top programming language and machine learning venues, several appearances at invited talks, and service experience on multiple committees. His master’s thesis won the William A. Martin award for best thesis in computer science at MIT in 2015.

Previous teaching experience includes serving as workshop instructor at the University of Moratuwa and as a teaching assistant at MIT for a course titled Performance Engineering of Software Systems. Mendis also supervised the research of undergraduate students and junior PhD students.

Mendis has been announced as a speaker at the upcoming Reflections | Projections conference.

Marco Morales
Marco Morales

Teaching Associate Professor Marco Morales
Marco Morales joins Illinois Computer Science as a teaching associate professor this fall. He has published 20 peer-reviewed papers mostly about motion planning algorithms with applications to robotics. His research interests also include autonomous robots, artificial intelligence, machine learning, computational neuroscience, and computer architecture. 

As an associate professor at Instituto Tecnológico Autónomo de México (ITAM), Morales has supervised the theses of nine MS graduate students and the research projects of 14 undergraduates. He has also led many of these students in their participation in robotics competitions such as the RoboCup, and he was a lecturer at Universidad Nacional Autónoma de México (UNAM).

Morales is a founding member and former president of the Mexican Federation of Robotics (FMR) and a member of the Mexican Academy of Computing. He has served twice as a co-chair of the International Workshop on the Algorithmic Foundations of Robotics (WAFR) and as a co-organizer of robotics competitions including the RoboCup 2012.

This coming academic year, Morales will be teaching CS 498, Applied Machine Learning. He earned his PhD in computer science from Texas A&M University.

Michael Nowak
Michael Nowak

Teaching Assistant Professor Michael Nowak
Michael Nowak earned his doctorate in computer science from Texas A&M University in 2019. In his PhD research, he provided an intuitive and systematic way to characterize the cerebrovasculature system of small murine animal models. 

Specifically, he constructed a whole-brain database of vascular connectivity from the Knife-Edge Scanning Microscope India Ink dataset (Brain Networks Lab, Texas A&M University).  Using this database, scientists are able to study the cerebrovasculature system by issuing text-based queries to extract vessel segments from regions of interest and meeting specific characteristics.

Nowak received a Texas A&M Graduate Teaching Fellowship, which is awarded to exceptional PhD candidates interested in pursuing a career in academia.

As a new teaching assistant professor, Nowak is interested in computer science education, healthcare informatics, and bioinformatics. This fall, he’ll co-teach the Software Design Studio course with professor Michael Woodley.

Yongjoo Park
Yongjoo Park

Assistant Professor Yongjoo Park
Yongjoo Park
joined Illinois CS as an assistant professor following a research fellowship with the University of Michigan – where he also earned his PhD. Over time, he developed research interests in database systems, big data, data analytics and machine learning. These interests lead Park to build systems for interactive-speed data analytics and machine learning, with a special focus on exploiting quality-performance tradeoffs for substantial gains in performance. By combining rigorous statistical theories and large-scale data-intensive systems, he builds fast, quality-guaranteed data analytics and machine learning systems.

His research has made an industry impact, too. By creating VerdictDB, Park introduced the first approximate query processing system that can run on top of any SQL engine. This has now been adopted by several companies, including Walmart and Digital2Go – a Canadian media company.

Park’s academic impact includes five first-authored research papers published in premier database conferences like SIGMOD and VLDB. His work also earned the 2018 ACM SIGMOD Jim Gray Dissertation Award runner-up. He’s also conducted a number of invited talks and workshop presentations.

Previous teaching experience includes being a guest lecturer for courses at the University of Michigan, such as Advanced Database Management Systems and Database Management Systems. Park was also the graduate student instructor for the Web Databases and Information Systems course at the University of Michigan. Finally, he mentored five students at Michigan.

Upon joining Illinois CS, Park will teach the state-of-the-art systems and techniques for managing and understanding large-scale data in modern computing environments, as well as making concrete connections to traditional data models. Specifically, examples include large-scale analytics, stream data processing, event streams, time-series data, documents search, etc.

Gagandeep Singh
Gagandeep Singh

Assistant Professor Gagandeep Singh
In August 2021, Gagandeep Singh will join Illinois CS as an assistant professor following his Ph.D. in Computer Science from ETH Zurich. Singh’s demonstrated research interests range from artificial intelligence to machine learning to programming languages. These subjects are the result of his broader interest in designing novel automated formal reasoning methods for numerical computing systems, such as neural networks and programs.

Singh’s research work has resulted in several papers in top programming languages and machine learning conferences, including four in the past year alone. He has designed two state-of-the-art tools, ELINA and ERAN, for analyzing programs and neural networks respectively. Earlier this year, ERAN verified the highest number of benchmarks of the neural network verification competition organized at the International Conference on Computer-Aided Verification. He has also presented several invited talks on his research at different workshops. His work at ETH earned an award for Best Master’s Thesis in 2014 and Excellence Scholarship in 2012. He also received the President of India gold medal at IIT Patna in 2012 awarded to the best graduating student.

As a teaching assistant, Singh was involved in preparing lectures, exercises, exams, course projects, and grading for eight different courses since 2015. These classes ranged from Reliable and Interpretable Artificial Intelligence to Program Analysis and Synthesis.

Brad Solomon
Brad Solomon

Teaching Assistant Professor Brad Solomon
Brad Solomon joins Illinois CS from Johns Hopkins University, where he held a postdoctoral fellowship in computer science for the past two years. His research interests include developing new algorithms and data structures for the efficient storage, search, and analysis of large-scale datasets, particularly in genomics. 

His PhD, which he earned from Carnegie Mellon University in 2018, provided several algorithmic solutions for enabling sequence search on 100-terabase RNA sequence datasets and demonstrated their use in both general and cancer-specific contexts. 

While at CMU, he received several awards, including a Richard King Mellon Foundation Presidential Fellowship and a T32 training grant award from the National Institutes of Health—an award that prepares qualified predoctoral and/or postdoctoral trainees for careers that have a significant impact on the health-related research needs of our country.

Solomon will be teaching Data Structures (CS 225) and Honors Data Structures this fall. Beyond that, he looks forward to teaching a wide range of introductory courses for both Illinois CS majors and non-majors, as well as advanced topics in algorithms and computational genomics.

Jimeng Sun
Jimeng Sun

Professor Jimeng Sun
By creating artificial intelligence algorithms, systems, and applications, Jimeng Sun seeks to solve important problems related to healthcare. He is particularly focused on deep learning for drug discovery, clinical trial optimization, computational phenotyping, clinical preventative modeling, treatment recommendations, and health monitoring. At Illinois, Sun is developing “Deep Learning for Healthcare,” a course for the online MCS and MCS in Data Science programs.

Prior to joining Illinois, Sun spent six years as an associate professor in the College of Computing at the Georgia Institute of Technology.  He also has industry experience, with six years at IBM’s T.J. Watson Research Center, where he led research on predictive modeling technology for personalized disease risk assessment.

Sun earned a PhD in 2007 and an MS in 2006, both in computer science from Carnegie Mellon University. Before that, he received two degrees in computer science from the Hong Kong University of Science and Technology: an MPhil in 2003 and a BS in 2002.

He is a recipient of an SDM/IBM Early Career Research Award (2017), a Georgia Tech IDEAS Award (2015), a Best Health Connect South Collaboration Award (2015), IBM Research Accomplishment Awards for Intelligent Care Delivery Analysis (2013) and Service Quality Research (2009), and Best Paper Awards at ICDM’08 and SDM’07.  In 2013, he was named an IBM Master Inventor, and, in 2008, he was SIGKDD Dissertation Award Runner-Up.

Deepak Vasisht
Deepak Vasisht

Assistant Professor Deepak Vasisht
Deepak Vasisht
joined Illinois CS after completing his PhD from the Computer Science program at the Massachusetts Institute of Technology. His research interests focus on wireless networks and Internet-of-Things (IoT) systems. Vasisht’s motivation stems from a belief that IoT systems extend computing into the physical world around us, thus impacting medicine, healthcare, homes, industries, cities, and the environment.

While focusing on data-driven agriculture at Microsoft, Vasisht’s dissertation contributed an idea he called FarmBeats. Now publicly available as a Microsoft product offering called Azure Farmbeats, this idea became “an end-to-end data-driven agricultural platform that can collect and analyze data from farms in remote rural areas at a very low cost compared to existing solutions.”

His research has been included in several conference publications, journals or other articles, and in a number of talks. It has contributed to posters and demos, while also providing the basis for several patents. Vasisht earned awards such as the Microsoft Research PhD Fellowship and ACM SIGCOMM Best Paper. He has already served as a journal and external reviewer for outlets like IEEE and ACM, while also participating on the Program Committee for ACM SIGCOMM. This past year, he was also presented with the ACM SIGCOMM Doctoral Dissertation Award.

In the classroom he has been a Teaching Assistant at MIT for the Computer Networks course and at IIT Delhi for the Data Structures course.

Assistant Professor Shenlong Wang
Shenlong Wang
will join Illinois CS as an assistant professor starting in August of 2021, at which time he will have finished his PhD in Computer Science from the University of Toronto. His research focuses on a goal to make robots assist us in our daily lives. To get there, he has worked on building more reliable and generalizable mobile robots, such as self-driving vehicles. His ability to do so stems from knowledge in computer vision, robotics and deep learning to confront problems in 3D perception and reconstruction, localization and mapping, motion estimation, simulation and deep structural learning.

Shenlong Wang
Shenlong Wang

This research focus has led to fellowships with Facebook and Adobe, numerous published papers, appearances for invited talks, conference talks and seminar talks. It has also earned Wang a best thesis award and the fourth rank in the top patent filings all-time leaderboard from Uber.

Professionally he has built upon these interests both in academia and the industry – where he has served as a research scientist, and intern for entities like Uber Advanced Technologies, Snapchat Research, and Microsoft Research Redmond.

Wang has built up an academic background since 2014 as a teaching assistant at the University of Toronto, where he’s taught multiple courses in computer vision and machine learning. He has served as a conference and journal reviewer, and he fulfilled mentorship roles with Women in Computer Vision Workshop and the University of Toronto Undergraduate AI Group.

He plans on teaching courses at both the graduate and undergraduate level in computer vision and robotics.

Yuxiong Wang
Yuxiong Wang

Assistant Professor Yuxiong Wang
In August, Yuxiong Wang joined Illinois CS faculty as an assistant professor, and he brought with him research interests in computer vision, machine learning, and robotics. Specifically, his research delves into reducing the need for human supervision in machine learning. By using computer vision and robotics tasks as illustrative applications, he has developed meta-learning algorithms that enable learning systems to generalize previous experiences to unseen environments and tasks with as little data as possible.

At Illinois, Wang plans to advance research on meta-learning by exploring a principled framework that unifies existing work and guides algorithmic development in different fields, including computer vision, natural language processing, reinforcement learning, and robotics. In addition, he plans to develop embodied lifelong learning agents that operate in dynamic and uncertain environments, actively learn from evolving data streams and tasks, and make intelligent and independent decisions based on insufficient information.

Wang joins Illinois CS after earning his PhD in robotics at Carnegie Mellon University. He’s also served as a postdoctoral fellow at Carnegie Mellon and was a visitor at New York University’s Center for Data Science. He spent time at Facebook AI Research. Wang received an Honorable Mention for the Best Paper Award at ECCV 2020.

Teaching Professor and Director of Onramp Programs Tiffani Williams
As director of Northeastern University’s Align ProgramTiffani Williams helped students who did not have backgrounds in CS find a path to a Master of Computer Science degree.

Tiffani Williams
Tiffani Williams

At Illinois, Williams directs similar efforts to make advanced CS degrees accessible to students who did not study CS as undergraduates. This new bridge program, called the Illinois Computing Accelerator for Non-specialists (iCAN), launched on campus in July; in the future it could potentially expand online and to Chicago, through space at the Discovery Partners Institute. In order to assist people who want to transition to careers in technology, Facebook has provided funding to help establish the new program.

Williams earned a PhD from the University of Central Florida in 2000 after completing a BS from Marquette University in 1994, both in computer science.  Prior to joining Northeastern University in 2017, Williams was an associate professor of Computer Science and Engineering at Texas A&M University, with research focused on computational biology. From 2001 to 2004, she was a postdoctoral fellow at the University of New Mexico.

Her honors include a Radcliffe Institute Fellowship (2004) and an Alfred P. Sloan Foundation Postdoctoral Fellowship in Computational Biology (2002). In 2011, she received the Denice Denton Emerging Leader ABIE Award. At Texas A&M, she won three awards for teaching excellence: the Graduate Faculty Teaching Excellence Award (Department of CSE, 2011), Undergraduate Faculty Teaching Excellence Award (Department of CSE, 2014), and the Distinguished Award in Teaching by the Association of Former Students (College of Engineering, 2016).

Lingming Zhang
Lingming Zhang

Assistant Professor Lingming Zhang
Lingming Zhang
joined Illinois CS after spending the last six years as an assistant professor at The University of Texas at Dallas. Prior to that he earned his PhD in Software Engineering at The University of Texas at Austin.

Zhang's main research interests are in Software Engineering and Programming Languages, with their synergy with Machine Learning and Formal Methods, focusing on building practical software testing and debugging systems to predict, detect, localize, and fix software bugs for different application domains automatically. His research work has been deployed in various real-world software systems, including commercial products from Alibaba and Google.

He is a recipient of an NSF CAREER Award, an NSF CRII Award, multiple best paper awards (including two ACM SIGSOFT Distinguished Paper Awards), a Google Faculty Research Award, a Samsung GRO Award, as well as several other gifts/grants from Alibaba, Amazon, NSF, and NVIDIA.

In his research group, he’s mentored several PhD and MS students. Since the fall of 2014, he has also taught courses ranging from “Software Testing and Validation, and Verification” to “Software Engineering”, ranked within the List of Teachers with “Exceed Expectations” Teaching Performance for three continuous years (2017-2019).

This fall, Zhang will teach CS598, Advanced Software Testing and Debugging.

Assistant Professor Han Zhao
Illinois CS assistant professor Han Zhao earned his PhD from the Machine Learning Department in the School of Computer Science at Carnegie Mellon University in May 2020. He brings with him active research interests in machine learning and artificial intelligence, especially focused on invariant representation learning, tractable probabilistic reasoning, and transfer/multitask learning.

Han Zhao
Han Zhao

Zhao’s research efforts have resulted in several peer-reviewed conference publications, invited talks, journal publications, as well as a patent. He also earned a number of scholarships, including a Google Excellence Scholarship. Professionally, he has served as a reviewer for several leading conferences and journals. 

In the classroom, Zhao served as an instructor at TechX Academy in 2019, where he taught Advanced Introduction to Deep Learning. He also was a teaching assistant at Tsinghua University and the University of Waterloo for Introduction to Information Retrieval and Designing Functional Programs, respectively. At Carnegie Mellon, Zhao was a teaching assistant for three courses: Introduction to Machine Learning, Convex Optimization, and Undergraduate Computational Complexity Theory.

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This story was published September 1, 2020.